trae-git

更新续约代表数据一致性

.gitignore

@@ -6,4 +6,13 @@
 *.iml
 out
 gen
-.logs
+.logs
+.log
+.csv
+.excel
+.xlsx
+output/
+__pycache__/
+.env
+.vscode/
+

api.py

@@ -99,7 +99,9 @@ class API:
                 "filter":data.get('filter', None)
             })
             retries = 0
+
             while retries <= max_retries:
+                data_get = None
                 try:
                     res = requests.post(url=url, data=payload, headers=headers, timeout=10)
                     res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
@@ -115,11 +117,11 @@ class API:
                             break
                         logger.warning(f"请求异常, 将重新请求")
                         retries += 1
-                        time.sleep(0.1)  # 在重试之间稍作停顿
+                        time.sleep(0.5)  # 在重试之间稍作停顿
                 except requests.exceptions.RequestException as e:
-                    logger.warning(f"请求异常: {e}, 将重新请求")
+                    logger.warning(f"请求异常: {e}, 将重新请求,{data_get}")
                     retries += 1
-                    time.sleep(0.1)  # 在重试之间稍作停顿
+                    time.sleep(0.5)  # 在重试之间稍作停顿
             if retries > max_retries:
                 error_task_logger.error(f"任务 {last_data_id}组 连续{max_retries}次请求失败，放弃此次请求。")
                 all_data_batches.append(None)  # 或者可以选择记录失败的payload以便后续处理
@@ -297,7 +299,7 @@ class API:
             retries = 0
             while retries <= max_retries:
                 try:
-                    res: requests.Response = requests.post(url=url, data=payload, headers=headers, timeout=10)
+                    res: requests.Response = requests.post(url=url, data=payload, headers=headers, timeout=15)
                     # print(res.json())
                     res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
                     data_get = res.json()
@@ -311,7 +313,7 @@ class API:
                 except requests.exceptions.RequestException as e:
                     logger.warning(f"请求异常: {e}, 将重新请求")
                     retries += 1
-                    time.sleep(0.1)  # 在重试之间稍作停顿
+                    time.sleep(0.5)  # 在重试之间稍作停顿
             if retries > max_retries:
                 error_task_logger.error(
                     f"任务 {data['data_list'][start_index:end_index]} 连续{max_retries}次请求失败，放弃此次请求。")
@@ -338,9 +340,12 @@ class API:
             "app_id": data['api_key'],  # 应用ID
             "entry_id": data['entry_id'],  # 表单ID
             "data_id": data['data_id'],  # 数据ID
-            "data": data['data']
+            "data": data['data'],
+            "is_start_trigger": data.get('is_start_trigger', True),
         }
         )
+        # print(payload)
+
 
         data_get = None
         retries = 0
@@ -349,7 +354,6 @@ class API:
                 res: requests.Response = requests.post(url=url, data=payload, headers=headers, timeout=10)
                 res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
                 data_get = res.json()
-                # print(data_get)
                 if res.status_code == 200:
                     break  # 成功则跳出循环
                 else:
@@ -535,7 +539,7 @@ class API:
                 except requests.exceptions.RequestException as e:
                     logger.warning(f"请求异常: {e}, 将重新请求")
                     retries += 1
-                    time.sleep(0.1)  # 在重试之间稍作停顿
+                    time.sleep(0.5)  # 在重试之间稍作停顿
             if retries > max_retries:
                 error_task_logger.error(
                     f"任务 {data['data_list'][start_index:end_index]} 连续{max_retries}次请求失败，放弃此次请求。")
@@ -581,7 +585,7 @@ class API:
             except requests.exceptions.RequestException as e:
                 logger.warning(f"请求异常: {e}, 将重新请求")
                 retries += 1
-                time.sleep(0.1)  # 在重试之间稍作停顿
+                time.sleep(0.5)  # 在重试之间稍作停顿
         if retries > max_retries:
             error_task_logger.error(f"任务 {data['data_id']} 连续{max_retries}次请求失败，放弃此次请求。")
 
@@ -624,7 +628,51 @@ class API:
             except requests.exceptions.RequestException as e:
                 logger.warning(f"请求异常: {e}, 将重新请求")
                 retries += 1
-                time.sleep(0.1)  # 在重试之间稍作停顿
+                time.sleep(0.5)  # 在重试之间稍作停顿
+        if retries > max_retries:
+            error_task_logger.error(f"任务 {data['data_id']} 连续{max_retries}次请求失败，放弃此次请求。")
+
+        return data_get
+
+    @staticmethod
+    def workflow_instance_start(data: dict, max_retries: int = 20) -> dict:
+        """
+        激活流程
+        :param max_retries:
+        :param data: 简道云插件发送过来的data，包含应用id
+        :return: 查询简道云流程实例信息返回的结果
+        """
+        url = 'https://api.jiandaoyun.com/api/v1/workflow/instance/activate'
+
+        headers = {
+            'Authorization': Config.JIANDAOYUN_API_TOKEN,  # 曹伟应用api测试 appKey
+            'Content-Type': 'application/json'
+        }
+
+        payload = json.dumps({
+            "instance_id": data['data_id'],
+            "flow_id": data['flow_id'], # 节点id
+        }
+        )
+        print("payload:", payload)
+        data_get = None
+        retries = 0
+        while retries <= max_retries:
+            try:
+                res = requests.post(url=url, data=payload, headers=headers, timeout=10)
+                # res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
+                data_get = res.json()
+                print("返回结果:", data_get)
+                if res.status_code == 200:
+                    break  # 成功则跳出循环
+                else:
+                    logger.warning(f"请求异常, 将重新请求")
+                    retries += 1
+                    time.sleep(3)  # 在重试之间稍作停顿
+            except requests.exceptions.RequestException as e:
+                logger.warning(f"请求异常: {e}, 将重新请求")
+                retries += 1
+                time.sleep(0.5)  # 在重试之间稍作停顿
         if retries > max_retries:
             error_task_logger.error(f"任务 {data['data_id']} 连续{max_retries}次请求失败，放弃此次请求。")
 

back_ground_module/Exception_Task.py

@@ -19,6 +19,7 @@ error_task_logger = configure_error_task_logger()
 output_dir = "output"  # 设置输出目录
 os.makedirs(output_dir, exist_ok=True)
 
+
 class NewExceptionTask:
     """
     SaaS异常回访
@@ -175,7 +176,9 @@ class NewExceptionTask:
         # print("NGV获取后的类型：", type(self.NGV_data_list))
 
         # 获取异常服务待办(添加过滤进行中的订单)
-        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "68340de79f116c0b66b6b0cc","filter":{"rel":"and","cond":[{ "field": "flowState", "type":"flowstate", "method":"eq","value":[0] }]}}
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "68340de79f116c0b66b6b0cc",
+                   "filter": {"rel": "and",
+                              "cond": [{"field": "flowState", "type": "flowstate", "method": "eq", "value": [0]}]}}
         self.exception_service_todo = api_instance.entry_data_list(payload).get("data", [])
         # print(self.exception_service_todo)
 
@@ -225,7 +228,7 @@ class NewExceptionTask:
 
         task_start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
-        all_data =[]
+        all_data = []
         try:
             self.load_all_data()
 
@@ -240,6 +243,7 @@ class NewExceptionTask:
                 return
 
             data_yichang = self.data_yichang_S.copy()
+
             # data_yichang.to_csv(os.path.join(output_dir,"data_yichang.csv"), index=False)
 
             def replace_values(series):
@@ -248,7 +252,7 @@ class NewExceptionTask:
 
             # 对整个DataFrame的所有列应用替换函数
             data_yichang = data_yichang.apply(replace_values)
-            error_data= []
+            error_data = []
             for index_num, row in data_yichang.iterrows():  # 对过滤后的每一条进行派发
                 try:
                     # 每次循环前清空省市区变量
@@ -257,7 +261,7 @@ class NewExceptionTask:
                     area_name = None
 
                     is_pass = False
-                    for exception_service in self.exception_service_todo :
+                    for exception_service in self.exception_service_todo:
                         # 通过查询筛选进行中的逻辑
                         if exception_service['_widget_1748241895842'] == row['org_code']:
                             is_pass = True
@@ -297,35 +301,36 @@ class NewExceptionTask:
 
                     NGV_data_id = None
                     reason = None
-                    create_exception =None
-                    create_date =  None
-                    
+                    create_exception = None
+                    create_date = None
+
                     # 优先从 data_yichang_S 获取省市区信息
                     province_name = row.get('province_name')
                     city_name = row.get('city_name')
                     area_name = row.get('area_name') if 'area_name' in row else row.get('district_name')
-                    
+
                     # 检查省市区是否完整（省市区是一体的，任意一个缺失就需要从NGV获取）
                     use_ngv_location = False
                     if (not province_name or province_name in ['', 'None', 'NA'] or
-                        not city_name or city_name in ['', 'None', 'NA'] or
-                        not area_name or area_name in ['', 'None', 'NA']):
+                            not city_name or city_name in ['', 'None', 'NA'] or
+                            not area_name or area_name in ['', 'None', 'NA']):
                         use_ngv_location = True
                         logger.info(f"门店 {row['org_code']} 的省市区信息不完整，将从NGV_data_list获取")
-                    
+                    stop_date = None
                     # 获取关联数据
                     for NGV_Data in self.NGV_data_list:
                         # NGV_Data = NGV_Data.get("data")
                         if row["org_code"] == NGV_Data.get("_widget_1734062123071"):  # 门店编码
                             NGV_data_id = NGV_Data.get("_id")
-                            
+
                             # 如果需要从 NGV_data_list 获取省市区信息
                             if use_ngv_location:
                                 province_name = NGV_Data.get("_widget_1734062123090")
                                 city_name = NGV_Data.get("_widget_1734062123092")
                                 area_name = NGV_Data.get("_widget_1734062123094")
-                                logger.info(f"【从NGV获取省市区】门店 {row['org_code']}: {province_name}, {city_name}, {area_name}")
-                            
+                                logger.info(
+                                    f"【从NGV获取省市区】门店 {row['org_code']}: {province_name}, {city_name}, {area_name}")
+
                             # 门店原因
                             reason = NGV_Data.get("_widget_1758617393828")
                             logger.info(f"获取关联数据成功:{NGV_data_id}, {province_name}, {city_name}, {area_name}")
@@ -333,8 +338,56 @@ class NewExceptionTask:
                             create_exception = NGV_Data.get("_widget_1758769279995")
                             # 获取上线日期（文本）# 202512.3改为开户日
                             create_date = NGV_Data.get("_widget_1734062123081")
+                            # 获取暂停派发日期
+                            stop_date = NGV_Data.get("_widget_1772610343227", None)
                             break  # 找到匹配的数据后退出循环
 
+                    # 定义可能的日期格式（灵活应对不同格式）
+                    date_formats = [
+                        "%Y-%m-%d %H:%M:%S",  # 含时间
+                        "%Y-%m-%d",  # 仅日期
+                        "%Y/%m/%d",
+                        "%Y/%m/%d %H:%M:%S"
+                    ]
+                    
+                    if stop_date:
+                        # 解析暂停派发日期
+                        parsed_stop_date = None
+                        stop_value = stop_date.get("value") if isinstance(stop_date, dict) else stop_date
+                        if isinstance(stop_value, (int, float)):
+                            parsed_stop_date = datetime.datetime.fromtimestamp(
+                                stop_value / 1000, tz=datetime.timezone.utc
+                            ).replace(tzinfo=None)
+                        elif isinstance(stop_value, str):
+                            stop_str = stop_value.strip()
+                            iso_candidate = stop_str[:-1] + "+00:00" if stop_str.endswith("Z") else stop_str
+                            try:
+                                iso_dt = datetime.datetime.fromisoformat(iso_candidate)
+                            except ValueError:
+                                iso_dt = None
+
+                            if iso_dt is not None:
+                                parsed_stop_date = iso_dt.astimezone(datetime.timezone.utc).replace(tzinfo=None) if iso_dt.tzinfo else iso_dt
+                            else:
+                                for fmt in date_formats:
+                                    try:
+                                        parsed_stop_date = datetime.datetime.strptime(stop_str, fmt)
+                                        logger.debug(f"使用格式 {fmt} 成功解析暂停派发日期: {parsed_stop_date}")
+                                        break
+                                    except ValueError:
+                                        continue
+                        
+                        if parsed_stop_date:
+                            # 获取当前UTC时间
+                            current_utc_time = datetime.datetime.utcnow()
+                            logger.debug(f"当前UTC时间: {current_utc_time}")
+                            logger.debug(f"暂停派发日期: {parsed_stop_date}")
+                            
+                            # 比较时间
+                            if current_utc_time < parsed_stop_date:
+                                logger.info(f"当前UTC时间低于暂停派发日期，跳过派发")
+                                continue
+
                     # 判断门店原因
                     # if reason in ["门店倒闭", "门店转让", "加盟其他连锁","切换竞品","虚拟门店","重新开户","已退款","二套系统"]:
                     #     continue
@@ -343,29 +396,37 @@ class NewExceptionTask:
                     if create_exception == "否":
                         continue
                     # 新增：检查 create_date_str 是否存在且有效
-                    if not create_date:
+                    create_date_value = create_date.get("value") if isinstance(create_date, dict) else create_date
+                    if not create_date_value:
                         logger.warning("上线日期为空，跳过该记录")
                         continue
 
-                    # 定义可能的日期格式（灵活应对不同格式）
-                    date_formats = [
-                        "%Y-%m-%d %H:%M:%S",  # 含时间
-                        "%Y-%m-%d",  # 仅日期
-                        "%Y/%m/%d",
-                        "%Y/%m/%d %H:%M:%S"
-                    ]
-
                     parsed_date = None
-                    for fmt in date_formats:
+                    if isinstance(create_date_value, (int, float)):
+                        local_tz = datetime.timezone(datetime.timedelta(hours=8))
+                        parsed_date = datetime.datetime.fromtimestamp(create_date_value / 1000, tz=local_tz).date()
+                    elif isinstance(create_date_value, str):
+                        create_str = create_date_value.strip()
+                        iso_candidate = create_str[:-1] + "+00:00" if create_str.endswith("Z") else create_str
                         try:
-                            parsed_date = datetime.datetime.strptime(create_date.strip(), fmt).date()
-                            logger.debug(f"使用格式 {fmt} 成功解析日期: {parsed_date}")
-                            break
+                            iso_dt = datetime.datetime.fromisoformat(iso_candidate)
                         except ValueError:
-                            continue
+                            iso_dt = None
+
+                        if iso_dt is not None:
+                            local_tz = datetime.timezone(datetime.timedelta(hours=8))
+                            parsed_date = iso_dt.date() if iso_dt.tzinfo is None else iso_dt.astimezone(local_tz).date()
+                        else:
+                            for fmt in date_formats:
+                                try:
+                                    parsed_date = datetime.datetime.strptime(create_str, fmt).date()
+                                    logger.debug(f"使用格式 {fmt} 成功解析日期: {parsed_date}")
+                                    break
+                                except ValueError:
+                                    continue
 
                     if parsed_date is None:
-                        logger.error(f"无法解析上线日期: '{create_date}'，支持的格式: %Y-%m-%d, %Y-%m-%d %H:%M:%S 等")
+                        logger.error(f"无法解析上线日期: '{create_date_value}'，支持的格式: %Y-%m-%d, %Y-%m-%d %H:%M:%S 等")
                         continue  # 解析失败，跳过
 
                     # 使用解析后的日期进行判断
@@ -380,15 +441,14 @@ class NewExceptionTask:
                         logger.info(f"上线日期 {parsed_date} 在30天内，跳过处理")
                         continue
 
-
                     if not NGV_data_id:
                         logger.warning(f"未找到关联数据，请检查门店编码: {row['org_code']}")
 
                     # 根据省市区派发给异常回访客服
                     # 检查省市区是否都有值，如果有任何一个为空，则客服为空
                     if (not province_name or province_name in ['', 'None', 'NA'] or
-                        not city_name or city_name in ['', 'None', 'NA'] or
-                        not area_name or area_name in ['', 'None', 'NA']):
+                            not city_name or city_name in ['', 'None', 'NA'] or
+                            not area_name or area_name in ['', 'None', 'NA']):
                         customer_service = None
                         logger.warning(f"【省市区信息缺失】门店 {row['org_code']} 省市区信息不完整，异常回访客服设置为空")
                         logger.warning(f"省: {province_name}, 市: {city_name}, 区: {area_name}")
@@ -457,6 +517,8 @@ class NewExceptionTask:
 
                         "_widget_1748512176655": {"value": "未处理"},  # 跟进状态
 
+                        "_widget_1772761760440":{"value": "客服跟进节点"},  # 当前跟进节点
+
                     })
 
                     routine_follow_up_payload = {
@@ -478,7 +540,9 @@ class NewExceptionTask:
             if error_data:
                 error_df = pd.DataFrame(error_data)
                 error_df.to_csv(os.path.join(output_dir, "异常派发错误数据.csv"))
-                common_module.send_task_error(task_start_time = task_start_time,task_name= "异常服务待办派发",error_message="详情见失败文件", df = error_df)
+                common_module.send_task_error(task_start_time=task_start_time, task_name="异常服务待办派发",
+                                              error_message="失败文件中省市区匹配不到，需要通过门店编码在客户资料表中查询正确的省市区，并更新到省市区人员关系表中",
+                                              df=error_df)
             # ndf = pd.DataFrame(all_data)
             # ndf.to_csv(os.path.join(output_dir, "异常派发.csv"))
             common_module.send_task_status(task_start_time, "异常服务待办派发")

back_ground_module/common_module.py

@@ -81,50 +81,77 @@ class CommonModule:
 
     def get_ngv_details(self, days_back=1):
         """
-        从固定的数据库中获取前几天的NGV明细。
-        参数 `days_back` 表示相对于今天的天数偏移量，默认为1（即前一天）。
-        返回包含NGV明细的pandas DataFrame。
+        重构后适配MySQL的NGV明细获取方法（仅处理saas_create_time字段，全字段保留文本类型）
+        参数 `days_back`：相对于今天的天数偏移量，默认为1（前一天）
+        返回：pandas DataFrame（所有字段为文本类型，仅saas_create_time做日期格式化），失败返回None
         """
+        conn = None
+        cursor = None
         try:
-            # 获得连接
-            conn = psycopg2.connect(**self.conn)
+            # 1. 建立MySQL连接（仅适配MySQL，参数与原逻辑对齐）
+            conn = pymysql.connect(
+                host=Config.BI_CONN_host,
+                database=Config.BI_CONN_INFO_database,
+                user=Config.BI_CONN_INFO_user,
+                password=Config.BI_CONN_INFO_password,
+                charset='utf8mb4',  # MySQL中文兼容
+                cursorclass=pymysql.cursors.DictCursor  # 保持字典游标，字段名映射一致
+            )
             cursor = conn.cursor()
 
-            # 获取指定天数前的日期
+            # 2. 日期计算逻辑（完全复用原始逻辑）
             now_time = datetime.now()
-            target_time = now_time + timedelta(days=-days_back)
-            target_date_id = int(target_time.strftime('%Y%m%d'))  # 获取目标日期
+            target_time = now_time - timedelta(days=days_back)
+            target_date_id = int(target_time.strftime('%Y%m%d'))
 
-            # sql语句查询
-            sql = f"""
-            SELECT * FROM "public"."holo_ads_report_saas_profile_ngv_detail_d" WHERE "date_id" = '{target_date_id}' ;
-            """
-
-            # 执行语句并获取结果集
-            cursor.execute(sql)
+            # 3. MySQL兼容的SQL（仅替换语法，逻辑不变）
+            sql = """
+                  SELECT *
+                  FROM `jdy_ngv_data_source`
+                  WHERE `date_id` = %s;
+                  """
+            cursor.execute(sql, (target_date_id,))
             rows = cursor.fetchall()
-            all_fields = cursor.description
 
-            # 执行结果转化为dataframe
-            col = [i[0] for i in all_fields]
-            data_NGV = pd.DataFrame(rows, columns=col)
+            # 4. 数据转换：强制全字段为文本类型（匹配原始数据源特性）
+            if rows:
+                # 核心：所有字段转字符串，空值统一为''，避免后续处理异常
+                data_NGV = pd.DataFrame(rows).fillna('').replace('None', '')
+            else:
+                data_NGV = pd.DataFrame()
 
-            # 尝试自动解析日期时间字符串
+            # 5. 仅处理saas_create_time字段（完全复用原始转换逻辑）
             time_format = "%Y-%m-%d %H:%M:%S"
             if 'saas_create_time' in data_NGV.columns:
-                data_NGV['saas_create_time'] = pd.to_datetime(data_NGV['saas_create_time'], format=time_format,
-                                                              errors='coerce')
-                data_NGV['saas_create_time'] = data_NGV['saas_create_time'].dt.strftime('%Y-%m-%d')
+                # 步骤1：解析为datetime（消除格式警告）
+                temp_dt = pd.to_datetime(
+                    data_NGV['saas_create_time'],
+                    format=time_format,  # 指定格式，消除UserWarning
+                    errors='coerce'  # 解析失败设为NaT
+                )
+                # 步骤2：转换为YYYY-MM-DD格式的字符串，覆盖原始列（与原逻辑一致）
+                data_NGV['saas_create_time'] = temp_dt.dt.strftime('%Y-%m-%d').fillna('')
 
-            # 关闭游标和连接
-            cursor.close()
-            conn.close()
+            # 6. 其他时间字段完全保留原始文本格式（不做任何处理）
+            # （date_fmt/expiry_time等字段仅保留从数据库读取的原始字符串）
 
             return data_NGV
 
         except Exception as e:
-            error_task_logger.error(f"获取NGV明细失败: {e}")
+            error_task_logger.error(f"获取NGV明细失败(MySQL适配): {str(e)}", exc_info=True)
             return None
+        finally:
+            # 确保MySQL连接/游标关闭（资源释放）
+            if cursor:
+                try:
+                    cursor.close()
+                except Exception as e:
+                    error_task_logger.warning(f"关闭MySQL游标失败: {str(e)}")
+            if conn:
+                try:
+                    conn.close()
+                except Exception as e:
+                    error_task_logger.warning(f"关闭MySQL连接失败: {str(e)}")
 
     def get_yichang_details(self, days_back=1):
         """
@@ -180,12 +207,19 @@ class CommonModule:
         """
         try:
             # 获得连接
-            conn = psycopg2.connect(**self.conn)
+            conn = pymysql.connect(
+                host=Config.BI_CONN_host,
+                database=Config.BI_CONN_INFO_database,
+                user=Config.BI_CONN_INFO_user,
+                password=Config.BI_CONN_INFO_password,
+                charset='utf8mb4',  # MySQL中文兼容
+                cursorclass=pymysql.cursors.DictCursor  # 保持字典游标，字段名映射一致
+            )
             cursor = conn.cursor()
 
             # 获取指定天数前的日期
             now_time = datetime.now()
-            yes_time = now_time + timedelta(days=-2)
+            yes_time = now_time + timedelta(days=-2)  # 防止NGV没更新
             yes_time_nyr = int(yes_time.strftime('%Y%m%d'))  # 获取前两天日期
 
             # 获取指定天数前的日期
@@ -195,15 +229,26 @@ class CommonModule:
 
             print("距离今天还有{}天的日期是：{}".format(days_to_add, future_date))
 
-            sql = f"""SELECT * FROM "public"."holo_ads_report_saas_profile_ngv_detail_d" WHERE "date_id" = '{yes_time_nyr}' and "expiry_time" like '%{future_date}%';"""
+            sql = """
+                  SELECT *
+                  FROM `jdy_ngv_data_source`
+                  WHERE `date_id` = %s \
+                    AND `expiry_time` LIKE %s; \
+                  """
             # 执行语句并获取结果集
-            cursor.execute(sql)
+            like_pattern = f"%{future_date}%"
+            cursor.execute(sql, (yes_time_nyr, like_pattern))
             rows = cursor.fetchall()
-            all_fields = cursor.description  # 获取所有字段名
 
-            # 执行结果转化为dataframe
-            col = [i[0] for i in all_fields]
-            data_NGV = pd.DataFrame(list(rows), columns=col)
+            if rows:
+                # data_NGV = pd.DataFrame(rows).astype(str).replace({'nan': '', 'NaT': ''})
+                all_fields = cursor.description  # 获取所有字段名
+
+                # 执行结果转化为dataframe
+                col = [i[0] for i in all_fields]
+                data_NGV = pd.DataFrame(list(rows), columns=col).astype(str).replace({'nan': '', 'NaT': ''})
+            else:
+                data_NGV = pd.DataFrame()
 
             # 关闭数据库连接
             cursor.close()
@@ -701,7 +746,7 @@ class CommonModule:
             run_time_sec = int(run_time.total_seconds())
 
             # 5. 格式化时间为 UTC 的 ISO 8601 格式（带 "Z"）
-            today_utc = end_time_utc.strftime("%Y-%m-%d")
+            today_utc = end_time_utc.strftime("%Y-%m-%dT%H:%M:%SZ")
             task_end_iso = end_time_utc.strftime("%Y-%m-%dT%H:%M:%SZ")
             task_start_iso = task_start_utc.strftime("%Y-%m-%dT%H:%M:%SZ")
 

back_ground_module/data_monitor.py

@@ -10,6 +10,8 @@ from decimal import Decimal
 import time
 import numpy as np
 import json
+import os
+os.chdir(Path(__file__).parent)
 
 
 def replace_decimals(obj):

back_ground_module/logs/error_task.log

@@ -141,3 +141,32 @@ Traceback (most recent call last):
     import numpy.typing as typing
     ^^^^^^^^^^^^^^^^^^^^^^^
 ModuleNotFoundError: No module named 'numpy.rec'
+2026-01-06 16:04:19,673 - log_config.py - error_task_logger - ERROR - 任务执行失败: Can only use .dt accessor with datetimelike values
+Traceback (most recent call last):
+  File "D:\Idea Project\SaaS_V1.7\back_ground_module\update_molecule_reporting_adjustment_to_bi.py", line 207, in main
+    df['归属月份'] = df['归属月份'].dt.tz_localize(None).dt.strftime('%Y-%m-%d %H:%M:%S')
+                     ^^^^^^^^^^^^^^^^^
+  File "D:\Program Files\anaconda3\envs\SaaS\Lib\site-packages\pandas\core\generic.py", line 6321, in __getattr__
+    return object.__getattribute__(self, name)
+           ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
+  File "D:\Program Files\anaconda3\envs\SaaS\Lib\site-packages\pandas\core\accessor.py", line 224, in __get__
+    accessor_obj = self._accessor(obj)
+  File "D:\Program Files\anaconda3\envs\SaaS\Lib\site-packages\pandas\core\indexes\accessors.py", line 643, in __new__
+    raise AttributeError("Can only use .dt accessor with datetimelike values")
+AttributeError: Can only use .dt accessor with datetimelike values. Did you mean: 'at'?
+2026-01-06 16:14:24,812 - log_config.py - error_task_logger - ERROR - 写入数据时发生错误: overflow encountered in multiply
+Traceback (most recent call last):
+  File "D:\Idea Project\SaaS_V1.7\back_ground_module\update_denominator_reporting_adjustment_to_bi.py", line 157, in write_to_bi
+    dt_utc = pd.to_datetime(timestamp_ms, unit='ms', utc=True)
+  File "D:\Program Files\anaconda3\envs\SaaS\Lib\site-packages\pandas\core\tools\datetimes.py", line 1072, in to_datetime
+    values = convert_listlike(arg._values, format)
+  File "D:\Program Files\anaconda3\envs\SaaS\Lib\site-packages\pandas\core\tools\datetimes.py", line 409, in _convert_listlike_datetimes
+    return _to_datetime_with_unit(arg, unit, name, utc, errors)
+  File "D:\Program Files\anaconda3\envs\SaaS\Lib\site-packages\pandas\core\tools\datetimes.py", line 517, in _to_datetime_with_unit
+    arr = cast_from_unit_vectorized(arg, unit=unit)
+  File "pandas/_libs/tslibs/conversion.pyx", line 149, in pandas._libs.tslibs.conversion.cast_from_unit_vectorized
+  File "D:\Program Files\anaconda3\envs\SaaS\Lib\site-packages\numpy\_core\fromnumeric.py", line 3674, in round
+    return _wrapfunc(a, 'round', decimals=decimals, out=out)
+  File "D:\Program Files\anaconda3\envs\SaaS\Lib\site-packages\numpy\_core\fromnumeric.py", line 54, in _wrapfunc
+    return bound(*args, **kwds)
+FloatingPointError: overflow encountered in multiply

back_ground_module/non_standar_performance_to_BI.py

@@ -24,6 +24,7 @@ os.makedirs(output_dir, exist_ok=True)
 
 class NonStandardPerformanceToBI:
     """ 非标业绩提报转BI"""
+
     def __init__(self):
         self.dealer_service_data = None
         self.field_mapping = {
@@ -54,27 +55,27 @@ class NonStandardPerformanceToBI:
             "新签提成比例-首年": "_widget_1753778922503",
             "新签提成比例-非首年": "_widget_1753778922548",
             "新签阶段及提成比例": "_widget_1753778656359",
-            "业绩动作":"_widget_1756708722933",
-            "提成动作":"_widget_1756708722932",
+            "业绩动作": "_widget_1756708722933",
+            "提成动作": "_widget_1756708722932",
             "新签阶段及提成比例.选择提成阶段": "_widget_1753778656359._widget_1753778656361",
             "新签阶段及提成比例.新签阶段": "_widget_1753778656359._widget_1753948745962",
             "新签阶段及提成比例.提成比例": "_widget_1753778656359._widget_1753778656362",
-            "业绩类型":"_widget_1753770875966",
-            "报备业绩归属小六":"_widget_1753770875901",
-            "原业绩归属大区":"_widget_1755159216098",
-            "业绩分类":"_widget_1758706882564",
-            "流程是否结束":"_widget_1761633418013",
-            "业绩类型-聚合":"_widget_1758706882564",
-            "业绩分组":"_widget_1762417447169",
-            "商品名称":"_widget_1762219744898",
-            "履约金额":"_widget_1762220516367",
-            "业绩归属日期":"_widget_1762417447127",
-            "公司名称":"_widget_1762420723743",
-            "公司ID":"_widget_1762420723744",
-            "报备业绩金额-区域提交":"_widget_1766375035236",
-            "业绩归属小六-区域提交":"_widget_1766461143813",
-            "业绩归属月":"_widget_1766375035265",
-            "是否同步衡石":"_widget_1766484337844",
+            "业绩类型": "_widget_1753770875966",
+            "报备业绩归属小六": "_widget_1753770875901",
+            "原业绩归属大区": "_widget_1755159216098",
+            "业绩分类": "_widget_1758706882564",
+            "流程是否结束": "_widget_1761633418013",
+            "业绩类型-聚合": "_widget_1758706882564",
+            "业绩分组": "_widget_1762417447169",
+            "商品名称": "_widget_1762219744898",
+            "履约金额": "_widget_1762220516367",
+            "业绩归属日期": "_widget_1762417447127",
+            "公司名称": "_widget_1762420723743",
+            "公司ID": "_widget_1762420723744",
+            "报备业绩金额-区域提交": "_widget_1766375035236",
+            "业绩归属小六-区域提交": "_widget_1766461143813",
+            "业绩归属月": "_widget_1766375035265",
+            "是否同步衡石": "_widget_1766484337844",
             "提交人": "creator",
             "提交时间": "createTime",
             "更新时间": "updateTime"
@@ -134,10 +135,20 @@ class NonStandardPerformanceToBI:
         df.columns = [reverse_mapping.get(col, col) for col in df.columns]
 
         # 只保留流程是否结束为是的内容
-        df = df[df["流程是否结束"] == "是"]
+        target_col = "流程是否结束"
+        if target_col in df.columns:
+            # 只有当列存在时才进行过滤，且 pandas 会自动处理 NaN != "是" 的情况
+            df = df[df[target_col] == "是"]
+        else:
+            logger.warning(f"字段 '{target_col}' 不存在，跳过过滤步骤，保留所有数据或根据业务需求处理。")
+
+        if df.empty:
+            logger.info("过滤后数据为空，无需后续处理。")
+            return df
 
         # 2.成员字段取值
-        user_columns = ["报备业绩归属小六", "报备业绩归属区域经理", "原业绩归属人", "原业绩归属区域经理", "运营专家","业绩归属小六-区域提交"]
+        user_columns = ["报备业绩归属小六", "报备业绩归属区域经理", "原业绩归属人", "原业绩归属区域经理", "运营专家",
+                        "业绩归属小六-区域提交"]
 
         for col in user_columns:
             df[col] = df[col].map(lambda x: x.get("name", "") if isinstance(x, dict) else "")
@@ -318,7 +329,7 @@ class NonStandardPerformanceToBI:
             common_module.send_task_status(task_start_time, "非标业绩提报转BI")
         except Exception as e:
             error_task_logger.error(f"非标业绩提报转BI发生错误{e}")
-            common_module.send_task_error(task_start_time,"非标业绩提报转BI", str(e))
+            common_module.send_task_error(task_start_time, "非标业绩提报转BI", str(e))
 
 
 if __name__ == '__main__':

back_ground_module/province_city_person_relation_to_bi.py

@@ -23,6 +23,7 @@ api_instance = API()
 
 
 class ProvinceCityPersonRelationToBI:
+    '''省市区人员关系表转BI'''
     def __init__(self):
         self.pvc_data = None
         self.field_mapping = {

back_ground_module/renewal_to_do.py

@@ -351,6 +351,9 @@ class RenewalToDo:
         data_NGV['120天是否跟进'] = "主动"
         data_NGV['60天是否跟进']= "主动"
         data_NGV['30天是否跟进']= "主动"
+
+        # 新增当前所处节点默认值
+        data_NGV['当前所处节点'] = "120天节点"
         # 格式化为字符串（去掉时区）
         for col in ['过期日', '120天自动流转时间', '60天自动流转时间', '30天自动流转时间', '0天自动流转时间']:
             data_NGV[col] = data_NGV[col].dt.strftime('%Y-%m-%d %H:%M:%S')

back_ground_module/revisit_new_services_180.py

@@ -622,7 +622,10 @@ class NewServicesRevisit:
                             continue
 
                         if not Billing:
-                            logger.warning(f"权限表请求失败或者权限表无对应关系,权限唯一值是：{NGV_store_level_key}")
+                            logger.warning(f"权限表请求失败或者权限表无对应关系,权限唯一值是：{NGV_store_level_key}，跳过该条派发")
+                            error_msg = f"门店编码：{row['org_code']}，权限唯一值：{NGV_store_level_key}"
+                            common_module.send_task_error(task_start_time, "新签客户回访-权限表无匹配", error_msg)
+                            continue
 
                         if row["active_status_fmt"] == "活跃":  # 开单 是否使用
                             payload_dict.update({"_widget_1735004315765": {"value": "是"}})
@@ -635,7 +638,7 @@ class NewServicesRevisit:
                                 payload_dict.update({"_widget_1735106258036": {"value": "否"}})
                         except Exception as e:
                             error_task_logger.error(f"会员卡拥有识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-会员卡拥有识别", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-会员卡拥有识别", str(e))
                         try:
                             if row["card_bill_day_count_last_30_day"] != "0":  # 会员卡 是否使用
                                 payload_dict.update({"_widget_1735106258038": {"value": "是"}})
@@ -643,7 +646,7 @@ class NewServicesRevisit:
                                 payload_dict.update({"_widget_1735106258038": {"value": "否"}})
                         except Exception as e:
                             error_task_logger.error(f"会员卡使用识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-会员卡使用识别", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-会员卡使用识别", str(e))
                         # print(self.service_remind.get("_widget_1735112637045"))
                         payload_dict["_widget_1735106258018"] = {"value": "否"}
 
@@ -675,11 +678,13 @@ class NewServicesRevisit:
                         # 近30天业务单量=0 则其它所有模块均不推荐
                         try:
                             for feature_module, feature_value in feature_dict.items():  # 模块
-                                if row["bill_count_last_30_day"] == '0' and payload_dict[feature_value]["value"] == '△':
+                                if feature_value not in payload_dict:
+                                    continue
+                                if row["bill_count_last_30_day"] == '0' and payload_dict[feature_value].get("value") == '△':
                                     payload_dict.update({f"{feature_value}": {"value": "×"}})
                         except Exception as e:
                             error_task_logger.error(f"不开单识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-不开单识别", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-不开单识别", str(e))
 
                         # 保单识别：从系统中抽取目标门店，针对门店抽取修改是否推荐
                         try:
@@ -687,7 +692,7 @@ class NewServicesRevisit:
                                 payload_dict.update({'_widget_1735004315746': {"value": "△"}})
                         except Exception as e:
                             error_task_logger.error(f"保单识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-保单识别", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-保单识别", str(e))
                         # 私域小程序：根据是否开通微信小程序判断是否使用，旗舰版及以上算拥有
                         try:
                             for item in self.private_domain:
@@ -700,7 +705,7 @@ class NewServicesRevisit:
                                         break
                         except Exception as e:
                             error_task_logger.error(f"小程序识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-小程序识别", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-小程序识别", str(e))
                         try:
                             high_version = ['皇冠版', '至尊版', '尊享版', '旗舰版']
                             if row["saas_edition_fmt"] in high_version:
@@ -709,7 +714,7 @@ class NewServicesRevisit:
                                 payload_dict.update({'_widget_1735106258141': {"value": "否"}})  # SYXCX:是否拥有
                         except Exception as e:
                             error_task_logger.error(f"私域小程序:Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-私域小程序", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-私域小程序", str(e))
 
                         # 公域小程序：根据是否开通微信小程序判断是否使用，旗舰版及以上算拥有
                         try:
@@ -723,7 +728,7 @@ class NewServicesRevisit:
                                         break
                         except Exception as e:
                             error_task_logger.error(f"公域小程序：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-公域小程序", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-公域小程序", str(e))
 
                         try:
                             if row["id_own_org"] in self.public_domain_list:
@@ -732,7 +737,7 @@ class NewServicesRevisit:
                                 payload_dict.update({'_widget_1735106258112': {"value": "否"}})  # GYXCX:是否拥有
                         except Exception as e:
                             error_task_logger.error(f"公域小程序：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-公域小程序", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-公域小程序", str(e))
 
                         # 异业合作：根据是否存在判断是否拥有，过滤条件 商品名称包含异业两个字
                         try:
@@ -742,7 +747,7 @@ class NewServicesRevisit:
                                 payload_dict.update({'_widget_1735107355618': {"value": "否"}})  # YYHZ:是否拥有
                         except Exception as e:
                             error_task_logger.error(f"异业合作：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-异业合作", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-异业合作", str(e))
 
                         # 短信：根据是否启动短信功能判断是否拥有,根据
                         try:
@@ -756,7 +761,7 @@ class NewServicesRevisit:
                                         break
                         except Exception as e:
                             error_task_logger.error(f"短信是否使用：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-短信是否使用", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-短信是否使用", str(e))
 
                         try:
                             for item in self.groupnotification:
@@ -769,7 +774,7 @@ class NewServicesRevisit:
                                         break
                         except Exception as e:
                             error_task_logger.error(f"短信是否使用：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-短信是否使用", str(e))
+                            common_module.send_task_error(task_start_time, "新签客户回访-短信是否使用", str(e))
 
                         NGV_data_id = None
                         # 获取关联数据

back_ground_module/revisit_renew_services_90_120_180.py

@@ -326,16 +326,29 @@ class RenewServicesRevisit:
             # 将最佳值合并回原数据集
             data_NGV = data_NGV.merge(best_values, on='id_own_group', how='left')
 
-            condition = (data_NGV['is_main_org'] == 1) & (data_NGV['org_status'] == '过期')  # 步骤2: 过滤条件
+            # 修复：处理 is_main_org 可能是字符串类型的情况
+            if data_NGV['is_main_org'].dtype == 'object':
+                is_main_org_numeric = pd.to_numeric(data_NGV['is_main_org'], errors='coerce')
+                condition = (is_main_org_numeric == 1) & (data_NGV['org_status'] == '过期')  # 步骤2: 过滤条件
+            else:
+                condition = (data_NGV['is_main_org'] == 1) & (data_NGV['org_status'] == '过期')  # 步骤2: 过滤条件
 
             ngvv2 = data_NGV[condition]
             # ngvv2.to_excel(r"C:\Users\Administrator.DESKTOP-7IC2USJ\Desktop\NGVV2.xlsx")
 
             data_NGV_V2 = data_NGV.copy()  # 步骤3: 检查id_own_group是否存在于ngvv2中
-            data_NGV_V2['条件'] = ((data_NGV_V2['org_type'] == "一般") & (data_NGV_V2['org_status'] == '留存') &
-                                   (data_NGV_V2['area_manager'] != '殷昊') & (
-                                           data_NGV_V2['area_manager'] != '孙玉蕾') & (
-                                           data_NGV_V2['is_main_org'] != 1))
+            # 修复：处理 is_main_org 可能是字符串类型的情况
+            if data_NGV_V2['is_main_org'].dtype == 'object':
+                is_main_org_numeric_v2 = pd.to_numeric(data_NGV_V2['is_main_org'], errors='coerce')
+                data_NGV_V2['条件'] = ((data_NGV_V2['org_type'] == "一般") & (data_NGV_V2['org_status'] == '留存') &
+                                       (data_NGV_V2['area_manager'] != '殷昊') & (
+                                               data_NGV_V2['area_manager'] != '孙玉蕾') & (
+                                               is_main_org_numeric_v2 != 1))
+            else:
+                data_NGV_V2['条件'] = ((data_NGV_V2['org_type'] == "一般") & (data_NGV_V2['org_status'] == '留存') &
+                                       (data_NGV_V2['area_manager'] != '殷昊') & (
+                                               data_NGV_V2['area_manager'] != '孙玉蕾') & (
+                                               data_NGV_V2['is_main_org'] != 1))
             data_NGV_V2 = data_NGV_V2.loc[data_NGV_V2["条件"]]
             # 步骤4: 过滤存在的记录
             data_NGV_V2['exists_in_ngvv2'] = data_NGV_V2['id_own_group'].isin(ngvv2['id_own_group'])
@@ -350,10 +363,18 @@ class RenewServicesRevisit:
 
             result = filtered_data.drop_duplicates(subset='id_own_group', keep='first')
 
-            data_NGV['条件'] = (data_NGV['org_type'] == "一般") & (data_NGV['org_status'] == '留存') & (
-                    data_NGV['area_manager'] != '殷昊') & (
-                                       data_NGV['area_manager'] != '孙玉蕾') & (
-                                       data_NGV['is_main_org'] == 1)
+            # 修复：处理 is_main_org 可能是字符串类型的情况
+            if data_NGV['is_main_org'].dtype == 'object':
+                is_main_org_numeric_main = pd.to_numeric(data_NGV['is_main_org'], errors='coerce')
+                data_NGV['条件'] = (data_NGV['org_type'] == "一般") & (data_NGV['org_status'] == '留存') & (
+                        data_NGV['area_manager'] != '殷昊') & (
+                                           data_NGV['area_manager'] != '孙玉蕾') & (
+                                           is_main_org_numeric_main == 1)
+            else:
+                data_NGV['条件'] = (data_NGV['org_type'] == "一般") & (data_NGV['org_status'] == '留存') & (
+                        data_NGV['area_manager'] != '殷昊') & (
+                                           data_NGV['area_manager'] != '孙玉蕾') & (
+                                           data_NGV['is_main_org'] == 1)
             data_NGV = data_NGV.loc[data_NGV["条件"]]
 
             data_NGV = pd.concat([data_NGV, result], axis=0)
@@ -715,7 +736,10 @@ class RenewServicesRevisit:
                             continue
 
                         if not Billing:
-                            logger.warning(f"权限表请求失败或者权限表无对应关系,权限唯一值是：{NGV_store_level_key}")
+                            logger.warning(f"权限表请求失败或者权限表无对应关系,权限唯一值是：{NGV_store_level_key}，跳过该条派发")
+                            error_msg = f"门店编码：{row['org_code']}，权限唯一值：{NGV_store_level_key}"
+                            common_module.send_task_error(task_start_time, "续约客户回访-权限表无匹配", error_msg)
+                            continue  # 无权限匹配时 payload_dict 缺少 _widget_1734073342350 等字段，后续会 KeyError，直接跳过
 
                         if row["active_status_fmt"] == "活跃":  # 开单 是否使用
                             payload_dict.update({"_widget_1735004315765": {"value": "是"}})
@@ -729,7 +753,7 @@ class RenewServicesRevisit:
                                 payload_dict.update({"_widget_1735106258036": {"value": "否"}})
                         except Exception as e:
                             error_task_logger.error(f"会员卡拥有识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-会员卡拥有识别", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-会员卡拥有识别", str(e))
 
                         try:
                             if row["card_bill_day_count_last_30_day"] != "0":  # 会员卡 是否使用
@@ -738,7 +762,7 @@ class RenewServicesRevisit:
                                 payload_dict.update({"_widget_1735106258038": {"value": "否"}})
                         except Exception as e:
                             error_task_logger.error(f"会员卡使用识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-会员卡使用识别", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-会员卡使用识别", str(e))
 
                         # print(self.service_remind.get("_widget_1735112637045"))
                         payload_dict["_widget_1735106258018"] = {"value": "否"}
@@ -771,11 +795,13 @@ class RenewServicesRevisit:
                         # 近30天业务单量=0 则其它所有模块均不推荐
                         try:
                             for feature_module, feature_value in feature_dict.items():  # 模块
-                                if row["bill_count_last_30_day"] == '0' and payload_dict[feature_value]["value"] == '△':
+                                if feature_value not in payload_dict:
+                                    continue  # 权限未匹配时该 key 不存在，避免 KeyError
+                                if row["bill_count_last_30_day"] == '0' and payload_dict[feature_value].get("value") == '△':
                                     payload_dict.update({f"{feature_value}": {"value": "×"}})
                         except Exception as e:
                             error_task_logger.error(f"不开单识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-不开单识别", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-不开单识别", str(e))
 
                         # 保单识别：从系统中抽取目标门店，针对门店抽取修改是否推荐
                         try:
@@ -783,7 +809,7 @@ class RenewServicesRevisit:
                                 payload_dict.update({'_widget_1735004315746': {"value": "△"}})
                         except Exception as e:
                             error_task_logger.error(f"保单识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-保单识别", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-保单识别", str(e))
 
                         # 私域小程序：根据是否开通微信小程序判断是否使用，旗舰版及以上算拥有
                         try:
@@ -797,7 +823,7 @@ class RenewServicesRevisit:
                                         break
                         except Exception as e:
                             error_task_logger.error(f"小程序识别：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-小程序识别", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-小程序识别", str(e))
 
                         try:
                             high_version = ['皇冠版', '至尊版', '尊享版', '旗舰版']
@@ -807,7 +833,7 @@ class RenewServicesRevisit:
                                 payload_dict.update({'_widget_1735106258141': {"value": "否"}})  # SYXCX:是否拥有
                         except Exception as e:
                             error_task_logger.error(f"私域小程序:Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-私域小程序", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-私域小程序", str(e))
 
                         # 公域小程序：根据是否开通微信小程序判断是否使用，旗舰版及以上算拥有
                         try:
@@ -821,7 +847,7 @@ class RenewServicesRevisit:
                                         break
                         except Exception as e:
                             error_task_logger.error(f"公域小程序：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-公域小程序", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-公域小程序", str(e))
 
                         try:
                             if row["id_own_org"] in self.public_domain_list:
@@ -830,7 +856,7 @@ class RenewServicesRevisit:
                                 payload_dict.update({'_widget_1735106258112': {"value": "否"}})  # GYXCX:是否拥有
                         except Exception as e:
                             error_task_logger.error(f"公域小程序：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-公域小程序", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-公域小程序", str(e))
 
                         # 异业合作：根据是否存在判断是否拥有，过滤条件 商品名称包含异业两个字
                         try:
@@ -840,7 +866,7 @@ class RenewServicesRevisit:
                                 payload_dict.update({'_widget_1735107355618': {"value": "否"}})  # YYHZ:是否拥有
                         except Exception as e:
                             error_task_logger.error(f"异业合作：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-异业合作", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-异业合作", str(e))
 
                         # 短信：根据是否启动短信功能判断是否拥有,根据
                         try:
@@ -854,7 +880,7 @@ class RenewServicesRevisit:
                                         break
                         except Exception as e:
                             error_task_logger.error(f"短信是否使用：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-短信是否使用", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-短信是否使用", str(e))
 
                         try:
                             for item in self.groupnotification:
@@ -867,7 +893,7 @@ class RenewServicesRevisit:
                                         break
                         except Exception as e:
                             error_task_logger.error(f"短信是否使用：Error finding customer service: {e}")
-                            common_module.send_task_error(task_start_time, "手动添加日常回访-短信是否使用", str(e))
+                            common_module.send_task_error(task_start_time, "续约客户回访-短信是否使用", str(e))
 
                         NGV_data_id = None
                         # 获取关联数据

back_ground_module/update_NGV_data.py

@@ -54,8 +54,8 @@ class UpdateNGVData:
             timestamp = time.time()
 
 
-            # data_NGV_j.to_csv(os.path.join(output_dir, f"{timestamp}up_NGV_j.csv"))
-            # data_NGV_j1.to_csv(os.path.join(output_dir, f"{timestamp}up_NGV_j1.csv"))
+            data_NGV_j.to_csv(os.path.join(output_dir, f"up_NGV_j.csv"))
+            data_NGV_j1.to_csv(os.path.join(output_dir, f"up_NGV_j1.csv"))
 
             # 找出在 data_NGV_j 中存在但在 data_NGV_j1 中不存在的 data_id
             unique_data_ids = data_NGV_j[~data_NGV_j['org_code'].isin(data_NGV_j1['org_code'])]
@@ -111,9 +111,11 @@ class UpdateNGVData:
             logger.info(f"人员转换完成")
 
             # 数字保留3位小数
-            filtered_df['g_month_percentage'] = (pd.to_numeric(data_NGV_j['g_month_percentage'], errors='coerce')
-                                                .round(3)
-                                                .apply(lambda x: f"{x:.3f}" if pd.notna(x) else ''))
+            filtered_df['g_month_percentage'] = (
+                pd.to_numeric(filtered_df['g_month_percentage'], errors='coerce')
+                .round(3)
+                .apply(lambda x: f"{x:.3f}" if pd.notna(x) else '')
+            )
 
             # filtered_df.to_csv(r"D:\Idea Project\SaaS_V1.3\back_ground_module\output\NGV.csv")
 
@@ -138,17 +140,18 @@ class UpdateNGVData:
             # print(result_str[:500])
 
             # 保存到Excel文件
-            # output_path = r'D:\Idea Project\F6+宜搭+其它(1)\new\文件输出\ngv明细1.xlsx'
+            # output_path = r'D:\Idea Project\SaaS_V1.7\back_ground_module\output\ngv明细1.xlsx'
             # filtered_df.to_excel(output_path, index=False)
-            # data_NGV_j1.to_excel( r'D:\Idea Project\F6+宜搭+其它(1)\new\文件输出\ngv明细j1.xlsx', index=False)
-            # data_NGV_j.to_excel( r'D:\Idea Project\F6+宜搭+其它(1)\new\文件输出\ngv明细j.xlsx', index=False)
-            # new_df.to_excel(r'D:\Idea Project\F6+宜搭+其它(1)\new\文件输出\ngv明细ndf.xlsx', index=False)
+            # data_NGV_j1.to_excel( r'D:\Idea Project\SaaS_V1.7\back_ground_module\output\ngv明细j1.xlsx', index=False)
+            # data_NGV_j.to_excel( r'D:\Idea Project\SaaS_V1.7\back_ground_module\output\ngv明细j.xlsx', index=False)
+            # new_df.to_excel(r'D:\Idea Project\SaaS_V1.7\back_ground_module\output\ngv明细ndf.xlsx', index=False)
 
             common_module.send_task_status(task_start_time, "NGV新增数据")
             logger.info(f"任务完成。")
         except Exception as e:
             error_task_logger.error(f"任务执行时发生异常: {e}")
             common_module.send_task_error(task_start_time, "NGV新增数据", str(e))
+            # pass
 
     @staticmethod
     def row_to_dict(row, field_mapping):

back_ground_module/update_all_NGV_data_daily.py

@@ -835,7 +835,7 @@ class UpdateAllNGVDataDaily:
             'is_camera_service': '_widget_1734062123079',
             'is_maintenance_service': '_widget_1734062123080',
             'saas_create_time': '_widget_1734062123081',
-            'expiry_time': '_widget_1734062123082',
+            'expiry_time': '_widget_1734062123177', # 改过期日
             'saas_use_days': '_widget_1734062123083',
             'saas_use_year': '_widget_1734062123084',
             'is_main_org': '_widget_1734062123085',
@@ -958,7 +958,7 @@ class UpdateAllNGVDataDaily:
             # 安装服务
             'is_install_service': '_widget_1734062123175',
             'install_create_time': '_widget_1734062123176',
-            'last_end_date': '_widget_1734062123177',
+            'last_end_date': '_widget_1734062123082',
             'renew_date': '_widget_1734062123178',
 
             # 连锁信息
@@ -1005,9 +1005,9 @@ class UpdateAllNGVDataDaily:
             # 日期字段（UTC格式）
             'date_fmt_date': '_widget_1749000071375',
             'saas_create_time_date': '_widget_1749000071377',
-            'expiry_time_date': '_widget_1749000071382',
+            'expiry_time_date': '_widget_1749000071389', # 过期日
             'install_create_time_date': '_widget_1749000071384',
-            'last_end_date_date': '_widget_1749000071389',
+            'last_end_date_date': '_widget_1749000071382',
             'renew_date_date': '_widget_1749000071391',
 
             # 人员ID字段

back_ground_module/update_denominator_reporting_adjustment_to_bi.py

@@ -63,7 +63,8 @@ class DenominatorReportingAdjustment:
             "总部调整结果": "selectField_lfqwg05y",
             "总部核对结果": "selectField_lfqwg05x",
             "是否上传衡石": "selectField_mca5shoz",
-            "是否计入应续约数": "selectField_mdnwwvyo"
+            "是否计入应续约数": "selectField_mdnwwvyo",
+            "归属月份": "dateField_mjtprnxl",
         }
 
     def get_yida_data(self):
@@ -146,15 +147,31 @@ class DenominatorReportingAdjustment:
 
 
             # 处理日期字段 - 新增部分
-            date_fields = ['开户日期', '开始时间', '结束时间']
+            date_fields = ['开户日期', '开始时间', '结束时间', "归属月份"]
+            # 处理日期字段 - 安全版本
             for field in date_fields:
                 if field in df.columns:
-                    # 转换为整数类型
-                    df[field] = pd.to_numeric(df[field], errors='coerce').astype('Int64')
-                    # 转换为datetime对象
-                    df[field] = pd.to_datetime(df[field], unit='ms')
-                    # 转换为MySQL兼容的字符串格式
-                    df[field] = df[field].dt.strftime('%Y-%m-%d %H:%M:%S')
+                    # 1. 先确保是数值类型，非数字转为 NaN
+                    numeric_series = pd.to_numeric(df[field], errors='coerce')
+
+                    # 2. 设置合理的时间戳范围（毫秒）
+                    #    1970-01-01 到 2100-12-31
+                    min_ts = 0
+                    max_ts = 4102444799999  # 2100-12-31 23:59:59.999 UTC 毫秒
+
+                    # 3. 过滤掉超出范围的值（设为 NaN）
+                    valid_mask = (numeric_series >= min_ts) & (numeric_series <= max_ts)
+                    safe_timestamps = numeric_series.where(valid_mask)
+
+                    # 4. 转换为 datetime（只对有效值转换）
+                    try:
+                        dt_utc = pd.to_datetime(safe_timestamps, unit='ms', utc=True)
+                        dt_shanghai = dt_utc.dt.tz_convert('Asia/Shanghai')
+                        dt_naive = dt_shanghai.dt.tz_localize(None)
+                        df[field] = dt_naive.dt.strftime('%Y-%m-%d %H:%M:%S')
+                    except Exception as e:
+                        error_task_logger.warning(f"字段 '{field}' 时间转换失败，全部置空: {e}")
+                        df[field] = None
 
             df = df.replace([None, np.nan, pd.NA, 'nan', 'NaN', 'NAN', ''], None)
 

back_ground_module/update_email_to_store_daily_use.py

@@ -321,7 +321,6 @@ class EmailProcessor:
                 charset='utf8mb4',
             )
 
-
             with connection.cursor() as cursor:
                 # 处理数据
                 df = df.where(pd.notna(df), None)  # 将NaN转换为None

back_ground_module/update_molecule_reporting_adjustment_to_bi.py

@@ -193,11 +193,21 @@ class MoleculeReportingAdjustment:
             df = pd.DataFrame(self.molecule_data_list)
             # df.to_csv('molecule_data.csv', index=False)
             if '归属月份' in df.columns:
-                # 确保是整数类型
-                df['归属月份'] = df['归属月份'].astype('Int64')
-                # 转换为datetime对象
-                df['归属月份'] = pd.to_datetime(df['归属月份'], unit='ms')
-                # 转换为MySQL兼容的字符串格式
+                # 1. 先将所有值转为数值，无法转换的变成 NaN
+                timestamp_ms = pd.to_numeric(df['归属月份'], errors='coerce')
+
+                # 2. 只对有效数值（非 NaN）进行 datetime 转换
+                #    unit='ms', origin='unix' (默认), utc=True 表示输入是 UTC 毫秒时间戳
+                df['归属月份'] = pd.to_datetime(timestamp_ms, unit='ms', utc=True)
+
+                # 3. 转换时区到 UTC+8（Asia/Shanghai）
+                df['归属月份'] = df['归属月份'].dt.tz_convert('Asia/Shanghai')
+
+                # 4. 移除时区信息（因为 MySQL DATETIME 不支持时区）
+                df['归属月份'] = df['归属月份'].dt.tz_localize(None)
+
+                # 5. 格式化为字符串（可选：如果你写入的是 DATETIME 字段，其实可以保持 datetime 类型，pymysql 会自动处理）
+                #    但你当前 write_to_bi 用的是 %s 插入，所以需要字符串
                 df['归属月份'] = df['归属月份'].dt.strftime('%Y-%m-%d %H:%M:%S')
 
             # step2:清空BI数据表

logs/error_task.log

@@ -2554,3 +2554,150 @@
 2026-01-04 10:29:26,171 - log_config.py - error_task_logger - ERROR - 任务 高德匹配手机号 超过执行窗口5分钟以上,标记为过期。
 2026-01-04 10:29:26,171 - log_config.py - error_task_logger - ERROR - 任务 省市区人员关系表转BI 超过执行窗口5分钟以上,标记为过期。
 2026-01-04 10:29:26,172 - log_config.py - error_task_logger - ERROR - 任务 续约回访待办 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,807 - log_config.py - error_task_logger - ERROR - 任务 NGV新增数据 (09:00) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,808 - log_config.py - error_task_logger - ERROR - 任务 NGV更新数据 (12:30) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,809 - log_config.py - error_task_logger - ERROR - 任务 新签客户回访 (09:05) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,810 - log_config.py - error_task_logger - ERROR - 任务 续约客户回访 (09:08) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,811 - log_config.py - error_task_logger - ERROR - 任务 接车宝日常派发 (09:10) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,811 - log_config.py - error_task_logger - ERROR - 任务 私域小程序数据支撑 (04:40) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,812 - log_config.py - error_task_logger - ERROR - 任务 小六提成数据支撑 (04:40) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,813 - log_config.py - error_task_logger - ERROR - 任务 异业合作数据支撑 (04:20) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,814 - log_config.py - error_task_logger - ERROR - 任务 短信数据支撑 (04:10) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,815 - log_config.py - error_task_logger - ERROR - 任务 海外邮件推送 (08:28) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,815 - log_config.py - error_task_logger - ERROR - 任务 异常服务待办派发 (10:00) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,816 - log_config.py - error_task_logger - ERROR - 任务 简道云海外项目CRM客户档案迁移BI (08:37) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,817 - log_config.py - error_task_logger - ERROR - 任务 分母报备调整 (09:05) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,817 - log_config.py - error_task_logger - ERROR - 任务 分子报备调整 (09:03) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,818 - log_config.py - error_task_logger - ERROR - 任务 履约表数据支撑 (09:10) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,819 - log_config.py - error_task_logger - ERROR - 任务 字段监控 (06:25) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,820 - log_config.py - error_task_logger - ERROR - 任务 经销商新签服务单转BI (08:05) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,820 - log_config.py - error_task_logger - ERROR - 任务 非标业绩提报转BI (08:01) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,821 - log_config.py - error_task_logger - ERROR - 任务 合伙人结算登记同步到BI (08:02) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,821 - log_config.py - error_task_logger - ERROR - 任务 高德匹配手机号 (05:00) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,822 - log_config.py - error_task_logger - ERROR - 任务 非标业绩提报转BI (12:01) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,823 - log_config.py - error_task_logger - ERROR - 任务 合伙人结算登记同步到BI (12:02) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,824 - log_config.py - error_task_logger - ERROR - 任务 省市区人员关系表转BI (08:00) 超过执行窗口5分钟以上,标记为过期。
+2026-01-04 13:45:42,824 - log_config.py - error_task_logger - ERROR - 任务 续约回访待办 (09:35) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,451 - log_config.py - error_task_logger - ERROR - 任务 NGV新增数据 (09:00) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,452 - log_config.py - error_task_logger - ERROR - 任务 新签客户回访 (09:05) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,453 - log_config.py - error_task_logger - ERROR - 任务 续约客户回访 (09:08) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,454 - log_config.py - error_task_logger - ERROR - 任务 接车宝日常派发 (09:10) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,455 - log_config.py - error_task_logger - ERROR - 任务 私域小程序数据支撑 (04:40) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,456 - log_config.py - error_task_logger - ERROR - 任务 小六提成数据支撑 (04:40) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,457 - log_config.py - error_task_logger - ERROR - 任务 异业合作数据支撑 (04:20) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,459 - log_config.py - error_task_logger - ERROR - 任务 短信数据支撑 (04:10) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,460 - log_config.py - error_task_logger - ERROR - 任务 海外邮件推送 (08:28) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,461 - log_config.py - error_task_logger - ERROR - 任务 异常服务待办派发 (10:00) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,462 - log_config.py - error_task_logger - ERROR - 任务 简道云海外项目CRM客户档案迁移BI (08:37) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,463 - log_config.py - error_task_logger - ERROR - 任务 分母报备调整 (09:05) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,465 - log_config.py - error_task_logger - ERROR - 任务 分子报备调整 (09:03) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,466 - log_config.py - error_task_logger - ERROR - 任务 履约表数据支撑 (09:10) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,467 - log_config.py - error_task_logger - ERROR - 任务 字段监控 (06:25) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,468 - log_config.py - error_task_logger - ERROR - 任务 经销商新签服务单转BI (08:05) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,470 - log_config.py - error_task_logger - ERROR - 任务 非标业绩提报转BI (08:01) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,471 - log_config.py - error_task_logger - ERROR - 任务 合伙人结算登记同步到BI (08:02) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,472 - log_config.py - error_task_logger - ERROR - 任务 高德匹配手机号 (05:00) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,473 - log_config.py - error_task_logger - ERROR - 任务 省市区人员关系表转BI (08:00) 超过执行窗口5分钟以上,标记为过期。
+2026-01-06 10:22:45,475 - log_config.py - error_task_logger - ERROR - 任务 续约回访待办 (09:35) 超过执行窗口5分钟以上,标记为过期。
+2026-03-26 16:52:07,168 - log_config.py - error_task_logger - ERROR - 同步异常 data_id:69c48f57805eb4de7a4f42c4 实例:fde35fef-ce35-4521-9f01-139b3d15efd0 错误:name 'data' is not defined
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\续约待办一致性-全量同步.py", line 111, in <module>
+    form = data.get("formData")
+           ^^^^
+NameError: name 'data' is not defined
+2026-04-01 14:23:45,579 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:23:49,233 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:23:51,145 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:02,662 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:12,213 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:19,850 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:20,919 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:21,410 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:29,458 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:30,227 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:35,451 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'
+2026-04-01 14:24:36,797 - log_config.py - error_task_logger - ERROR - 异常服务待办派发执行时发生异常: 'str' object has no attribute 'get'
+Traceback (most recent call last):
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 475, in main
+    customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+  File "d:\Idea Project\SaaS_V1.7\test\异常服务代办暂停派发不生效问题排查.py", line 227, in assign_customer_service
+    customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^
+AttributeError: 'str' object has no attribute 'get'

requirements.txt

@@ -0,0 +1,13 @@
+chardet==5.2.0
+holidays==0.87
+mysql_connector_repackaged==0.3.1
+numpy==2.4.1
+pandas==2.3.3
+psycopg2==2.9.11
+pydes==2.0.1
+pymysql==1.1.2
+python_dateutil==2.9.0.post0
+pytz==2025.2
+Requests==2.32.5
+schedule==1.2.2
+tqdm==4.67.1

tasks.csv

@@ -18,15 +18,19 @@ NGV更新数据,12:30,True,待执行
 宜搭流程耗时写入BI,07:22,False,已禁用
 简道云员工ID表更新,07:23,False,已禁用
 简道云海外项目CRM客户档案迁移BI,08:37,True,过期
-安装服务历史派发,09:00,True,过期
+安装服务历史派发,09:00,False,已禁用
 新签客户回访测试,09:00,False,已禁用
 分母报备调整,09:05,True,过期
 分子报备调整,09:03,True,过期
 履约表数据支撑,09:10,True,过期
 字段监控,06:25,True,过期
 经销商新签服务单转BI,08:05,True,过期
-非标业绩提报转BI,17:01,True,待执行
-合伙人结算登记同步到BI,17:02,True,待执行
+非标业绩提报转BI,08:01,True,过期
+合伙人结算登记同步到BI,08:02,True,过期
 高德匹配手机号,05:00,True,过期
+非标业绩提报转BI,12:01,True,待执行
+非标业绩提报转BI,17:01,True,待执行
+合伙人结算登记同步到BI,12:02,True,待执行
+合伙人结算登记同步到BI,17:02,True,待执行
 省市区人员关系表转BI,08:00,True,过期
 续约回访待办,09:35,True,过期

test/1105ngv.ipynb

@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-11-05T09:03:45.525420Z",
+     "start_time": "2025-11-05T09:03:44.127181Z"
+    }
+   },
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "from config import Config\n",
+    "from api import API\n",
+    "from back_ground_module import CommonModule\n",
+    "from log_config import configure_task_logger, configure_error_task_logger\n",
+    "import time\n",
+    "timestamp = time.time()  # 返回 float，单位：秒\n",
+    "\n",
+    "logger = configure_task_logger()\n",
+    "# 获取已经配置好的错误任务日志记录器\n",
+    "error_task_logger = configure_error_task_logger()\n",
+    "start_time = datetime.datetime.now()\n",
+    "api_instance = API()\n",
+    "common_module = CommonModule()\n",
+    "output_dir = \"output\"  # 设置输出目录\n",
+    "# 创建输出目录（如果不存在）\n",
+    "import os\n",
+    "\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "\n",
+    "class UpdateNGVData:\n",
+    "    \"\"\"NGV数据每日新增\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        self.staff_id_list = None\n",
+    "        self.field_mapping = {}\n",
+    "        self.fields()\n",
+    "\n",
+    "    def load_all_data(self):\n",
+    "        # 获取简道云员工id\n",
+    "        payload = {\"api_key\": \"6694d3c4fcb69ca9a111a6c4\",\n",
+    "                   \"entry_id\": \"6769204a1902c9341340a1bc\",\n",
+    "                   }\n",
+    "        staff_id = api_instance.entry_data_list(payload)\n",
+    "        self.staff_id_list = staff_id.get(\"data\")  # api请求格式，将数据封装在data字典里\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def get_staff_id(row_item, name):\n",
+    "        \"\"\"辅助函数，用于获取员工ID\"\"\"\n",
+    "        if str(row_item[\"_widget_1734942794144\"]) == str(name):  # 检查姓名是否匹配\n",
+    "            return row_item[\"_widget_1734942794145\"]  # 返回员工ID\n",
+    "        return None\n",
+    "\n",
+    "    def main(self):\n",
+    "        task_start_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
+    "        try:\n",
+    "            self.load_all_data()\n",
+    "            logger.info(f\"数据加载完成\")\n",
+    "            #\n",
+    "            # data_NGV_j = common_module.get_ngv_details(days_back=1)\n",
+    "            # data_NGV_j1 = common_module.get_ngv_details(days_back=2)\n",
+    "            timestamp = time.time()  # 返回 float，单位：秒\n",
+    "            #\n",
+    "            # data_NGV_j.to_csv(os.path.join(output_dir, f\"{timestamp}up_NGV_j.csv\"))\n",
+    "            # data_NGV_j1.to_csv(os.path.join(output_dir, f\"{timestamp}up_NGV_j1.csv\"))\n",
+    "            #\n",
+    "            # # 找出在 data_NGV_j 中存在但在 data_NGV_j1 中不存在的 data_id\n",
+    "            # unique_data_ids = data_NGV_j[~data_NGV_j['org_code'].isin(data_NGV_j1['org_code'])]\n",
+    "            #\n",
+    "            # # 创建一个新的 DataFrame 保存这些唯一的 data_id 及其对应的数据\n",
+    "            # new_df = data_NGV_j[data_NGV_j['org_code'].isin(unique_data_ids['org_code'])]\n",
+    "            #\n",
+    "            # # 对 new_df 进行进一步的过滤，只保留 org_type 为 \"一般\" 的记录\n",
+    "            # data_NGV_j = data_NGV_j[data_NGV_j['org_type'] == '一般']\n",
+    "            # data_NGV_j1 = data_NGV_j1[data_NGV_j1['org_type'] == '一般']\n",
+    "            # filtered_df = new_df[new_df['org_type'] == '一般']\n",
+    "            # 默认未删除\n",
+    "            filtered_df = pd.read_excel(r\"C:\\Users\\zy187\\Downloads\\异常服务跟进待办_20251105170140.xlsx\",sheet_name=\"Sheet1\")\n",
+    "            filtered_df['源ngv是否已删除'] = '未删除'\n",
+    "\n",
+    "            # 日期字段转换为日期格式\n",
+    "            time_columns = ['date_fmt', 'saas_create_time', 'expiry_time', 'install_create_time', \"last_end_date\",\n",
+    "                            \"renew_date\"]\n",
+    "            new_filtered_df = filtered_df.copy()  # 复制df，以调整时间\n",
+    "            for col in time_columns:\n",
+    "                # 1. 转换为datetime类型（带错误处理）\n",
+    "                # 使用.loc安全赋值\n",
+    "                new_filtered_df[col] = pd.to_datetime(filtered_df[col], errors='coerce', utc=False)\n",
+    "\n",
+    "                # 2. 优化后的时区转换（高效向量化操作）\n",
+    "                filtered_df[col + '_date'] = (\n",
+    "                    new_filtered_df[col]\n",
+    "                    # 本地化为北京时间（东八区）\n",
+    "                    .dt.tz_localize('Asia/Shanghai', ambiguous='infer', nonexistent='NaT')\n",
+    "                    # 转换为UTC时区\n",
+    "                    .dt.tz_convert('UTC')\n",
+    "                    # 格式化为ISO8601字符串\n",
+    "                    .dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n",
+    "                )\n",
+    "            logger.info(f\"时间转换完成\")\n",
+    "\n",
+    "            # 人员字段转换为人员字段\n",
+    "            staff_columns = ['area_manager', 'service_impl_principal', \"service_salesmen\", \"technician\"]\n",
+    "            # 将员工列表转为DataFrame\n",
+    "            # 三重循环临时方案（确保可写入）\n",
+    "            for col in staff_columns:\n",
+    "                staff_ids = []\n",
+    "                for _, row in filtered_df.iterrows():\n",
+    "                    matched = False\n",
+    "                    for staff in self.staff_id_list:\n",
+    "                        if str(staff['_widget_1734942794144']) == str(row[col]):\n",
+    "                            staff_ids.append(staff['_widget_1734942794145'])\n",
+    "                            matched = True\n",
+    "                            break\n",
+    "                    if not matched:\n",
+    "                        staff_ids.append(None)\n",
+    "                filtered_df[col + \"_staff_id\"] = staff_ids\n",
+    "            logger.info(f\"人员转换完成\")\n",
+    "\n",
+    "            # filtered_df.to_csv(r\"D:\\Idea Project\\SaaS_V1.3\\back_ground_module\\output\\NGV.csv\")\n",
+    "\n",
+    "            # 生成包含所有行转换后的字典列表\n",
+    "            # all_data = [self.row_to_dict(row, self.field_mapping) for index, row in data_NGV_j1.iterrows()] # 前两天的全部数据\n",
+    "            # all_data = [self.row_to_dict(row, self.field_mapping) for index, row in data_NGV_j.iterrows()]  # 前一天的全部数据\n",
+    "            all_data = [self.row_to_dict(row, self.field_mapping) for index, row in filtered_df.iterrows()]  # 增量数据\n",
+    "\n",
+    "            try:\n",
+    "                filtered_df.to_csv(os.path.join(output_dir, f\"{timestamp}NGV.csv\"))\n",
+    "            except Exception as e:\n",
+    "                error_task_logger.error(f\"NGV过滤后数据保存异常: {e}\")\n",
+    "                pass\n",
+    "\n",
+    "            #\n",
+    "            data = {'api_key': Config.SaaS_Tasks_APP_ID, 'entry_id': Config.NGV_TASKS_ENTRY_ID, \"data_list\": all_data,\n",
+    "                    \"is_start_trigger\": \"true\"}\n",
+    "\n",
+    "            result = api_instance.entry_data_batch_create(data)\n",
+    "            logger.info(f\"数据已推送:{result}\")\n",
+    "            # result_str = str(result)\n",
+    "            # print(result_str[:500])\n",
+    "\n",
+    "            # 保存到Excel文件\n",
+    "            # output_path = r'D:\\Idea Project\\F6+宜搭+其它(1)\\new\\文件输出\\ngv明细1.xlsx'\n",
+    "            # filtered_df.to_excel(output_path, index=False)\n",
+    "            # data_NGV_j1.to_excel( r'D:\\Idea Project\\F6+宜搭+其它(1)\\new\\文件输出\\ngv明细j1.xlsx', index=False)\n",
+    "            # data_NGV_j.to_excel( r'D:\\Idea Project\\F6+宜搭+其它(1)\\new\\文件输出\\ngv明细j.xlsx', index=False)\n",
+    "            # new_df.to_excel(r'D:\\Idea Project\\F6+宜搭+其它(1)\\new\\文件输出\\ngv明细ndf.xlsx', index=False)\n",
+    "\n",
+    "            common_module.send_task_status(task_start_time, \"NGV新增数据\")\n",
+    "            logger.info(f\"任务完成。\")\n",
+    "        except Exception as e:\n",
+    "            error_task_logger.error(f\"任务执行时发生异常: {e}\")\n",
+    "            # common_module.send_task_error(task_start_time, \"NGV新增数据\", str(e))\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def row_to_dict(row, field_mapping):\n",
+    "        \"\"\"将一行数据转换为指定格式的字典，并确保时间类型可JSON序列化\"\"\"\n",
+    "        result = {}\n",
+    "        for col_name, widget_id in field_mapping.items():\n",
+    "            if col_name in row:\n",
+    "                value = row[col_name]\n",
+    "                if pd.isna(value):\n",
+    "                    clean_value = None\n",
+    "                elif isinstance(value, (pd.Timestamp, pd.Timedelta)):\n",
+    "                    clean_value = value.isoformat()  # 或 str(value)\n",
+    "                elif hasattr(value, 'strftime'):  # 兼容 datetime.datetime\n",
+    "                    clean_value = value.strftime('%Y-%m-%dT%H:%M:%SZ')\n",
+    "                else:\n",
+    "                    clean_value = value\n",
+    "                result[widget_id] = {\"value\": clean_value}\n",
+    "        return result\n",
+    "\n",
+    "    def fields(self):\n",
+    "        self.field_mapping = dict(date_id='_widget_1734062123065', date_fmt='_widget_1734062123066',\n",
+    "                                  id_own_group='_widget_1734062123067', group_name='_widget_1734062123068',\n",
+    "                                  id_own_org='_widget_1734062123069', org_name='_widget_1734062123070',\n",
+    "                                  org_code='_widget_1734062123071', group_grade='_widget_1734062123072',\n",
+    "                                  org_type='_widget_1734062123073', org_status='_widget_1734062123074',\n",
+    "                                  saas_version='_widget_1734062123075', is_wechat='_widget_1734062123076',\n",
+    "                                  is_mini_app='_widget_1734062123077', is_wx_shop='_widget_1734062123078',\n",
+    "                                  is_camera_service='_widget_1734062123079',\n",
+    "                                  is_maintenance_service='_widget_1734062123080',\n",
+    "                                  saas_create_time='_widget_1734062123081', expiry_time='_widget_1734062123082',\n",
+    "                                  saas_use_days='_widget_1734062123083', saas_use_year='_widget_1734062123084',\n",
+    "                                  is_main_org='_widget_1734062123085', license_code='_widget_1734062123086',\n",
+    "                                  license_name='_widget_1734062123087', org_crm_id='_widget_1734062123088',\n",
+    "                                  province_id='_widget_1734062123089', province_name='_widget_1734062123090',\n",
+    "                                  city_id='_widget_1734062123091', city_name='_widget_1734062123092',\n",
+    "                                  area_id='_widget_1734062123093', area_name='_widget_1734062123094',\n",
+    "                                  region_name='_widget_1734062123095', region_short_name='_widget_1734062123096',\n",
+    "                                  branch_name='_widget_1734062123097', carzone_store_id='_widget_1734062123098',\n",
+    "                                  carzone_store_name='_widget_1734062123099',\n",
+    "                                  customer_carzone_id='_widget_1734062123100', salesmen='_widget_1734062123101',\n",
+    "                                  area_manager='_widget_1734062123102', service_salesmen='_widget_1734062123103',\n",
+    "                                  impl_principal='_widget_1734062123104',\n",
+    "                                  service_impl_principal='_widget_1734062123105',\n",
+    "                                  active_user_count='_widget_1734062123106', active_user_type='_widget_1734062123107',\n",
+    "                                  limit_user_count='_widget_1734062123108', limit_user_type='_widget_1734062123109',\n",
+    "                                  is_n='_widget_1734062123110', is_g='_widget_1734062123111',\n",
+    "                                  is_v='_widget_1734062123112', is_visited='_widget_1734062123113',\n",
+    "                                  is_active='_widget_1734062123114', active_status_fmt='_widget_1734062123115',\n",
+    "                                  bill_count_last_30_day='_widget_1734062123116',\n",
+    "                                  bill_day_count_last_30_day='_widget_1734062123117',\n",
+    "                                  bill_day_count_this_month='_widget_1734062123118',\n",
+    "                                  bill_count_last_7_day='_widget_1734062123119',\n",
+    "                                  bill_day_count_last_7_day='_widget_1734062123120', pv_count='_widget_1734062123121',\n",
+    "                                  uv_count='_widget_1734062123122', bill_count_1d='_widget_1734062123123',\n",
+    "                                  bill_count_2d='_widget_1734062123124', bill_count_3d='_widget_1734062123125',\n",
+    "                                  bill_count_4d='_widget_1734062123126', bill_count_5d='_widget_1734062123127',\n",
+    "                                  bill_count_6d='_widget_1734062123128', bill_count_7d='_widget_1734062123129',\n",
+    "                                  bill_count_8d='_widget_1734062123130', bill_count_9d='_widget_1734062123131',\n",
+    "                                  bill_count_10d='_widget_1734062123132', bill_count_11d='_widget_1734062123133',\n",
+    "                                  bill_count_12d='_widget_1734062123134', bill_count_13d='_widget_1734062123135',\n",
+    "                                  bill_count_14d='_widget_1734062123136', bill_count_15d='_widget_1734062123137',\n",
+    "                                  bill_count_16d='_widget_1734062123138', bill_count_17d='_widget_1734062123139',\n",
+    "                                  bill_count_18d='_widget_1734062123140', bill_count_19d='_widget_1734062123141',\n",
+    "                                  bill_count_20d='_widget_1734062123142', bill_count_21d='_widget_1734062123143',\n",
+    "                                  bill_count_22d='_widget_1734062123144', bill_count_23d='_widget_1734062123145',\n",
+    "                                  bill_count_24d='_widget_1734062123146', bill_count_25d='_widget_1734062123147',\n",
+    "                                  bill_count_26d='_widget_1734062123148', bill_count_27d='_widget_1734062123149',\n",
+    "                                  bill_count_28d='_widget_1734062123150', bill_count_29d='_widget_1734062123151',\n",
+    "                                  bill_count_30d='_widget_1734062123152', bill_count_31d='_widget_1734062123153',\n",
+    "                                  etl_time='_widget_1734062123154',\n",
+    "                                  maintain_bill_count_last_30_day='_widget_1734062123155',\n",
+    "                                  washing_bill_count_last_30_day='_widget_1734062123156',\n",
+    "                                  maintain_bill_day_count_last_30_day='_widget_1734062123157',\n",
+    "                                  washing_bill_day_count_last_30_day='_widget_1734062123158',\n",
+    "                                  retail_bill_count_last_30_day='_widget_1734062123159',\n",
+    "                                  retail_bill_day_count_last_30_day='_widget_1734062123160',\n",
+    "                                  purchase_bill_count_last_30_day='_widget_1734062123161',\n",
+    "                                  purchase_bill_day_count_last_30_day='_widget_1734062123162',\n",
+    "                                  card_bill_count_last_30_day='_widget_1734062123163',\n",
+    "                                  card_bill_day_count_last_30_day='_widget_1734062123164',\n",
+    "                                  gd_sales_bill_count_last_30_day='_widget_1734062123165',\n",
+    "                                  gd_sales_bill_day_count_last_30_day='_widget_1734062123166',\n",
+    "                                  g_change_flag='_widget_1734062123167', saas_package='_widget_1734062123168',\n",
+    "                                  manage_model='_widget_1734062123169', contacts='_widget_1734062123170',\n",
+    "                                  contact_number='_widget_1734062123171', contact_mobile='_widget_1734062123172',\n",
+    "                                  g_month_count='_widget_1734062123173', g_month_percentage='_widget_1734062123174',\n",
+    "                                  is_install_service='_widget_1734062123175',\n",
+    "                                  install_create_time='_widget_1734062123176', last_end_date='_widget_1734062123177',\n",
+    "                                  renew_date='_widget_1734062123178', is_chain_owner='_widget_1734062123179',\n",
+    "                                  group_org_count='_widget_1734062123180',\n",
+    "                                  recent_bill_warning_days='_widget_1734062123181',\n",
+    "                                  g_change_flag_d='_widget_1734062123182', g_lost_warning_days='_widget_1734062123183',\n",
+    "                                  saas_edition_fmt='_widget_1734062123184', g_flag_1m='_widget_1734062123185',\n",
+    "                                  g_flag_2m='_widget_1734062123186', g_flag_3m='_widget_1734062123187',\n",
+    "                                  g_flag_4m='_widget_1734062123188', g_flag_5m='_widget_1734062123189',\n",
+    "                                  g_flag_6m='_widget_1734062123190', g_flag_day_count='_widget_1734062123191',\n",
+    "                                  add_org_flag='_widget_1734062123192', pt='_widget_1734062123193',\n",
+    "                                  org_size='_widget_1734062123194', qualification_type_fmt='_widget_1734062123195',\n",
+    "                                  business_scope_fmt='_widget_1734062123196', store_type_fmt='_widget_1734062123197',\n",
+    "                                  area='_widget_1734062123198', station_number='_widget_1734062123199',\n",
+    "                                  header_type_fmt='_widget_1734062123200', org_stage='_widget_1734062123201',\n",
+    "                                  g_count_this_month='_widget_1734062123202',\n",
+    "                                  saas_customer_type='_widget_1734062123203', technician='_widget_1734062123204',\n",
+    "                                  tmall_maintain_service_status_desc='_widget_1734062123205',\n",
+    "                                  date_fmt_date='_widget_1749000071375',\n",
+    "                                  area_manager_staff_id='_widget_1748496855779',\n",
+    "                                  service_impl_principal_staff_id=\"_widget_1748496855780\",\n",
+    "                                  service_salesmen_staff_id=\"_widget_1748496855778\",\n",
+    "                                  technician_staff_id=\"_widget_1751877712235\",\n",
+    "                                  saas_create_time_date=\"_widget_1749000071377\",\n",
+    "                                  expiry_time_date=\"_widget_1749000071382\",\n",
+    "                                  install_create_time_date=\"_widget_1749000071384\",\n",
+    "                                  last_end_date_date=\"_widget_1749000071389\", renew_date_date=\"_widget_1749000071391\"\n",
+    "                                  , 源NGV是否已删除=\"_widget_1754285499851\")\n",
+    "\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    start = UpdateNGVData()\n",
+    "    start.main()\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "已获取 100 条数据\n",
+      "已获取 146 条数据\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001B[92m2025-11-05 17:03:45,497 - api.py - task_logger - INFO - 获取了146条数据\u001B[0m\n",
+      "\u001B[92m2025-11-05 17:03:45,498 - 4224831806.py - task_logger - INFO - 数据加载完成\u001B[0m\n",
+      "\u001B[91m2025-11-05 17:03:45,523 - 4224831806.py - error_task_logger - ERROR - 任务执行时发生异常: 'date_fmt'\u001B[0m\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/excel_需要保留一条_按创建时间保留最新门店.py

@@ -0,0 +1,93 @@
+import argparse
+from pathlib import Path
+
+import pandas as pd
+
+
+def keep_latest_by_time(df: pd.DataFrame, store_col: str, time_col: str) -> pd.DataFrame:
+    if store_col not in df.columns:
+        raise ValueError(f"缺少列: {store_col}")
+    if time_col not in df.columns:
+        raise ValueError(f"缺少列: {time_col}")
+
+    working = df.copy()
+    working[store_col] = working[store_col].astype(str).fillna("")
+    working[time_col] = working[time_col].astype(str).fillna("")
+
+    working["_parsed_time"] = pd.to_datetime(working[time_col], errors="coerce")
+    working["_row_order"] = range(len(working))
+
+    working = working.sort_values(
+        by=["_parsed_time", "_row_order"],
+        ascending=[True, True],
+        kind="mergesort",
+        na_position="first",
+    )
+    latest = working.groupby(store_col, sort=False).tail(1)
+    latest = latest.drop(columns=["_parsed_time", "_row_order"]).reset_index(drop=True)
+    return latest
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input", "-i", required=False, help="输入 Excel 路径（.xlsx）")
+    parser.add_argument("--output", "-o", required=False, help="输出 Excel 路径（.xlsx）")
+    parser.add_argument("--sheet", default="需要保留一条", help="Sheet 名称")
+    parser.add_argument("--store-col", default="门店编码", help="门店编码列名")
+    parser.add_argument("--time-col", default="创建时间", help="创建时间列名")
+    parser.add_argument("--codes-output", required=False, help="可选：输出门店编码清单（.txt 或 .csv）")
+    parser.add_argument("--demo", action="store_true", help="运行内置示例（不读写 Excel）")
+    return parser
+
+
+def main() -> int:
+    args = build_parser().parse_args()
+
+    if args.demo:
+        demo_df = pd.DataFrame(
+            [
+                {"门店编码": "A001", "创建时间": "2026-03-01 10:00:00", "其他": "x"},
+                {"门店编码": "A001", "创建时间": "2026-03-05 09:00:00", "其他": "y"},
+                {"门店编码": "B002", "创建时间": "2026/03/02 12:00", "其他": "m"},
+                {"门店编码": "B002", "创建时间": "无效时间", "其他": "n"},
+            ]
+        )
+        result = keep_latest_by_time(demo_df, store_col=args.store_col, time_col=args.time_col)
+        print(result)
+        print("门店编码:", ",".join(result[args.store_col].astype(str).tolist()))
+        return 0
+
+    if not args.input:
+        raise SystemExit("缺少参数 --input")
+
+    input_path = Path(args.input).expanduser().resolve()
+    if not input_path.exists():
+        raise SystemExit(f"输入文件不存在: {input_path}")
+
+    df = pd.read_excel(input_path, sheet_name=args.sheet, dtype=str).fillna("")
+    latest = keep_latest_by_time(df, store_col=args.store_col, time_col=args.time_col)
+
+    output_path = Path(args.output).expanduser().resolve() if args.output else None
+    if output_path is None:
+        output_path = input_path.with_name(f"{input_path.stem}_保留最新.xlsx")
+
+    with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
+        latest.to_excel(writer, sheet_name="保留最新", index=False)
+
+    store_codes = latest[args.store_col].astype(str).tolist()
+    print(f"保留行数: {len(latest)}")
+    print(f"门店编码数量: {len(store_codes)}")
+
+    if args.codes_output:
+        codes_path = Path(args.codes_output).expanduser().resolve()
+        if codes_path.suffix.lower() == ".csv":
+            pd.DataFrame({args.store_col: store_codes}).to_csv(codes_path, index=False, encoding="utf-8-sig")
+        else:
+            codes_path.write_text("\n".join(store_codes), encoding="utf-8")
+
+    print(f"输出文件: {output_path}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

test/json.json

@@ -1,104 +0,0 @@
-{
-  "result": [
-    {
-      "processInstanceId": "fa83e004-64d5-40a8-b2a9-122cc24a548f",
-      "operateTimeGMT": "2025-02-28T09:07Z",
-      "showName": "提交申请",
-      "operateType": "NEW_PROCESS",
-      "remark": "",
-      "taskHoldTimeGMT": 0,
-      "type": "HISTORY",
-      "operatorName": "宜搭平台",
-      "actionExit": "submit",
-      "operatorUserId": "yida_pub_account",
-      "activityId": "sid-restartevent",
-      "size": 1,
-      "dataId": 52359309345,
-      "domainList": [],
-      "operatorDisplayName": "宜搭平台",
-      "action": "提交申请",
-      "taskId": "null",
-      "operatorPhotoUrl": "//img.alicdn.com/tfs/TB1mKVJSpXXXXcwaXXXXXXXXXXX-78-80.jpg"
-    },
-    {
-      "processInstanceId": "fa83e004-64d5-40a8-b2a9-122cc24a548f",
-      "operateTimeGMT": "2025-03-18T10:14Z",
-      "showName": "120天联系情况",
-      "operateType": "EXECUTE_TASK_NORMAL",
-      "remark": "提交",
-      "taskHoldTimeGMT": 0,
-      "type": "HISTORY",
-      "operatorName": "孟祥宇",
-      "actionExit": "agree",
-      "operatorUserId": "224616673723465569",
-      "activityId": "sid-6470221a-82ec-4bdd-a873-245ee47a5605",
-      "size": 1,
-      "dataId": 53850072345,
-      "domainList": [],
-      "operatorDisplayName": "孟祥宇",
-      "action": "提交",
-      "taskId": "52359293348",
-      "operatorPhotoUrl": "https://static.dingtalk.com/media/lADPDh0cUUaSfHrNAvDNAnA_624_752.jpg"
-    },
-    {
-      "processInstanceId": "fa83e004-64d5-40a8-b2a9-122cc24a548f",
-      "operateTimeGMT": "2025-04-01T14:08Z",
-      "showName": "60天联系情况",
-      "operateType": "EXECUTE_TASK_NORMAL",
-      "remark": "系统提交",
-      "taskHoldTimeGMT": 0,
-      "type": "HISTORY",
-      "operatorName": "魏子淇",
-      "actionExit": "agree",
-      "operatorUserId": "195159084238961158",
-      "activityId": "sid-ab6374fd-7580-66d5-1628-6b0666bb38ff",
-      "size": 1,
-      "dataId": 55294608170,
-      "domainList": [],
-      "operatorDisplayName": "魏子淇",
-      "action": "提交",
-      "taskId": "53850072349",
-      "operatorPhotoUrl": "https://static.dingtalk.com/media/lADPD2sQvHoyUEbNAtHNAtA_720_721.jpg"
-    },
-    {
-      "processInstanceId": "fa83e004-64d5-40a8-b2a9-122cc24a548f",
-      "operateTimeGMT": "2025-04-01T14:08Z",
-      "showName": "30天联系情况",
-      "operateType": "EXECUTE_TASK_NORMAL",
-      "remark": "系统提交",
-      "taskHoldTimeGMT": 0,
-      "type": "HISTORY",
-      "operatorName": "魏子淇",
-      "actionExit": "agree",
-      "operatorUserId": "195159084238961158",
-      "activityId": "sid-e5928800-154e-4e20-6019-1364274afc49",
-      "size": 1,
-      "dataId": 55294609293,
-      "domainList": [],
-      "operatorDisplayName": "魏子淇",
-      "action": "提交",
-      "taskId": "55294608175",
-      "operatorPhotoUrl": "https://static.dingtalk.com/media/lADPD2sQvHoyUEbNAtHNAtA_720_721.jpg"
-    },
-    {
-      "processInstanceId": "fa83e004-64d5-40a8-b2a9-122cc24a548f",
-      "operateTimeGMT": "2025-04-01T14:08Z",
-      "showName": "0天处理情况",
-      "operateType": "EXECUTE_TASK_NORMAL",
-      "remark": "系统提交",
-      "taskHoldTimeGMT": 0,
-      "type": "HISTORY",
-      "operatorName": "魏子淇",
-      "actionExit": "agree",
-      "operatorUserId": "195159084238961158",
-      "activityId": "sid-ba12125f-bc3a-2663-ebf0-43b5aeb8c32c",
-      "size": 1,
-      "dataId": 55294558885,
-      "domainList": [],
-      "operatorDisplayName": "魏子淇",
-      "action": "同意",
-      "taskId": "55294609301",
-      "operatorPhotoUrl": "https://static.dingtalk.com/media/lADPD2sQvHoyUEbNAtHNAtA_720_721.jpg"
-    }
-  ]
-}

test/json2.json

@@ -1,116 +0,0 @@
-{
-  "app_id": "6694d3c4fcb69ca9a111a6c4",
-  "form_id": "693778ee287cfdcc2df85ece",
-  "form_title": "流程表单结构测试",
-  "instance_id": "6937dc8d2cbcdd4c466a8381",
-  "url": "https://dingtalk.jiandaoyun.com/workflow/process_instance/6937dc8d2cbcdd4c466a8381",
-  "update_time": "2025-12-09T08:23:41.095Z",
-  "create_time": "2025-12-09T08:23:41.093Z",
-  "finish_time": None,
-  "status": 0,
-  "creator": {
-    "username": "#admin",
-    "name": "F6汽车科技",
-    "type": 0,
-    "status": 1,
-    "integrate_id": "#admin"
-  },
-  "tasks": [
-    {
-      "app_id": "6694d3c4fcb69ca9a111a6c4",
-      "form_id": "693778ee287cfdcc2df85ece",
-      "form_title": "流程表单结构测试",
-      "title": "流程发起节点",
-      "instance_id": "6937dc8d2cbcdd4c466a8381",
-      "task_id": "6937dc8d2cbcdd4c466a8398",
-      "flow_id": 0,
-      "flow_name": "流程发起节点",
-      "url": "https://dingtalk.jiandaoyun.com/workflow/process_instance/6937dc8d2cbcdd4c466a8381/task/6937dc8d2cbcdd4c466a8398",
-      "assignee": {
-        "username": "#admin",
-        "name": "F6汽车科技",
-        "type": 0,
-        "status": 1,
-        "integrate_id": "#admin"
-      },
-      "creator": {
-        "username": "#admin",
-        "name": "F6汽车科技",
-        "type": 0,
-        "status": 1,
-        "integrate_id": "#admin"
-      },
-      "create_time": "2025-12-09T08:23:41.094Z",
-      "create_action": "forward",
-      "finish_time": "2025-12-09T08:23:41.094Z",
-      "finish_action": "forward",
-      "status": 1
-    },
-    {
-      "app_id": "6694d3c4fcb69ca9a111a6c4",
-      "form_id": "693778ee287cfdcc2df85ece",
-      "form_title": "流程表单结构测试",
-      "title": "多跟进人节点",
-      "instance_id": "6937dc8d2cbcdd4c466a8381",
-      "task_id": "6937dc8d2cbcdd4c466a83a6",
-      "flow_id": 2,
-      "flow_name": "多跟进人节点",
-      "url": "https://dingtalk.jiandaoyun.com/workflow/process_instance/6937dc8d2cbcdd4c466a8381/task/6937dc8d2cbcdd4c466a83a6",
-      "assignee": {
-        "username": "4210192048793363",
-        "name": "张阳",
-        "departments": [
-          449008196
-        ],
-        "type": 0,
-        "status": 1,
-        "integrate_id": "4210192048793363"
-      },
-      "creator": {
-        "username": "#admin",
-        "name": "F6汽车科技",
-        "type": 0,
-        "status": 1,
-        "integrate_id": "#admin"
-      },
-      "create_time": "2025-12-09T08:23:41.095Z",
-      "create_action": "forward",
-      "finish_time": None,
-      "finish_action": None,
-      "status": 0
-    },
-    {
-      "app_id": "6694d3c4fcb69ca9a111a6c4",
-      "form_id": "693778ee287cfdcc2df85ece",
-      "form_title": "流程表单结构测试",
-      "title": "多跟进人节点",
-      "instance_id": "6937dc8d2cbcdd4c466a8381",
-      "task_id": "6937dc8d2cbcdd4c466a83aa",
-      "flow_id": 2,
-      "flow_name": "多跟进人节点",
-      "url": "https://dingtalk.jiandaoyun.com/workflow/process_instance/6937dc8d2cbcdd4c466a8381/task/6937dc8d2cbcdd4c466a83aa",
-      "assignee": {
-        "username": "2268275546837446",
-        "name": "曹伟",
-        "departments": [
-          449008196
-        ],
-        "type": 0,
-        "status": 1,
-        "integrate_id": "2268275546837446"
-      },
-      "creator": {
-        "username": "#admin",
-        "name": "F6汽车科技",
-        "type": 0,
-        "status": 1,
-        "integrate_id": "#admin"
-      },
-      "create_time": "2025-12-09T08:23:41.095Z",
-      "create_action": "forward",
-      "finish_time": None,
-      "finish_action": None,
-      "status": 0
-    }
-  ]
-}

test/ngv写入数据库.ipynb

@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-13T07:56:05.597737600Z",
+     "start_time": "2026-01-13T07:50:57.192717400Z"
+    }
+   },
+   "source": [
+    "import time\n",
+    "\n",
+    "import pandas as pd\n",
+    "import psycopg2\n",
+    "import mysql.connector\n",
+    "from mysql.connector import Error\n",
+    "from datetime import datetime, timedelta\n",
+    "import numpy as np\n",
+    "\n",
+    "# ========== 配置 ==========\n",
+    "PG_CONN_INFO = {\n",
+    "    \"database\": \"f6_bi\",\n",
+    "    \"user\": \"LTAI5tMJsijFA9BS1R6uBpUT\",\n",
+    "    \"password\": \"PajEQMIRWNRcipd8mYvlud2KHWJr6N\",\n",
+    "    \"host\": \"hgpostcn-cn-m1e4gikbu00l-cn-shanghai.hologres.aliyuncs.com\",\n",
+    "    \"port\": \"80\"\n",
+    "}\n",
+    "\n",
+    "MYSQL_CONFIG = {\n",
+    "    'host': \"f6-public.rwlb.rds.aliyuncs.com\",\n",
+    "    'user': \"rw_operation_data_relay\",\n",
+    "    'password': \"m+q5Z4%IVuF9bf\",\n",
+    "    'database': \"f6operation_data_relay\"\n",
+    "}\n",
+    "\n",
+    "SOURCE_TABLE = '\"public\".\"holo_ads_report_saas_profile_ngv_detail_d\"'\n",
+    "PARTITION_COLUMN = \"date_id\"\n",
+    "TARGET_TABLE_MYSQL = \"jdy_ngv_data_source\"\n",
+    "BATCH_SIZE = 2000\n",
+    "\n",
+    "# ========== 辅助函数 ==========\n",
+    "def is_datetime_type(pg_type: str) -> bool:\n",
+    "    if not pg_type:\n",
+    "        return False\n",
+    "    pg_type = pg_type.lower()\n",
+    "    return any(kw in pg_type for kw in ['timestamp', 'datetime', 'date'])\n",
+    "\n",
+    "def clean_column_name(name, index):\n",
+    "    \"\"\"将列名转为合法字符串，处理 None / nan / 空值\"\"\"\n",
+    "    if name is None:\n",
+    "        return f\"unknown_col_{index}\"\n",
+    "    if isinstance(name, float) and pd.isna(name):\n",
+    "        return f\"unknown_col_{index}\"\n",
+    "    name_str = str(name).strip()\n",
+    "    if not name_str or name_str.lower() in ('nan', 'none', 'null', ''):\n",
+    "        return f\"unknown_col_{index}\"\n",
+    "    return name_str\n",
+    "\n",
+    "def get_source_schema():\n",
+    "    conn = psycopg2.connect(**PG_CONN_INFO)\n",
+    "    cur = conn.cursor()\n",
+    "    cur.execute(\"\"\"\n",
+    "        SELECT column_name, data_type\n",
+    "        FROM information_schema.columns\n",
+    "        WHERE table_schema = 'public'\n",
+    "          AND table_name = 'holo_ads_report_saas_profile_ngv_detail_d'\n",
+    "        ORDER BY ordinal_position;\n",
+    "    \"\"\")\n",
+    "    raw_schema = cur.fetchall()\n",
+    "    cur.close()\n",
+    "    conn.close()\n",
+    "\n",
+    "    # 清洗列名\n",
+    "    cleaned_schema = []\n",
+    "    for i, (col_name, data_type) in enumerate(raw_schema):\n",
+    "        clean_name = clean_column_name(col_name, i)\n",
+    "        cleaned_schema.append((clean_name, data_type or 'text'))\n",
+    "    return cleaned_schema\n",
+    "\n",
+    "def create_ngv_table(cursor, schema):\n",
+    "    col_defs = []\n",
+    "    for col_name, pg_type in schema:\n",
+    "        if is_datetime_type(pg_type):\n",
+    "            col_defs.append(f\"`{col_name}` DATETIME\")\n",
+    "        else:\n",
+    "            col_defs.append(f\"`{col_name}` TEXT\")  # ✅ 关键：用 TEXT 避免行大小超限\n",
+    "    create_sql = f\"\"\"\n",
+    "    CREATE TABLE IF NOT EXISTS `{TARGET_TABLE_MYSQL}` (\n",
+    "        {\",\\n        \".join(col_defs)}\n",
+    "    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 ROW_FORMAT=DYNAMIC;\n",
+    "    \"\"\"\n",
+    "    cursor.execute(create_sql)\n",
+    "    print(\"✅ MySQL 表 NGV 已创建（时间字段为 DATETIME，其余为 TEXT）\")\n",
+    "\n",
+    "def normalize_datetime_cols(df, datetime_cols):\n",
+    "    df = df.copy()\n",
+    "    for col in datetime_cols:\n",
+    "        if col in df.columns:\n",
+    "            df[col] = pd.to_datetime(df[col], errors='coerce')\n",
+    "            df[col] = df[col].dt.strftime('%Y-%m-%d %H:%M:%S').where(df[col].notnull(), None)\n",
+    "    return df.where(pd.notnull(df), None)\n",
+    "\n",
+    "# ========== 主流程 ==========\n",
+    "def main():\n",
+    "    # 1. 生成最近10天的 date_id（字符串格式）\n",
+    "    date_ids = [\n",
+    "        (datetime.now().date() - timedelta(days=i)).strftime(\"%Y%m%d\")\n",
+    "        for i in range(3)\n",
+    "    ]\n",
+    "    print(f\"将同步以下 date_id 分区: {date_ids}\")\n",
+    "\n",
+    "    # 2. 获取并清洗源表结构\n",
+    "    schema = get_source_schema()\n",
+    "    column_names = [col for col, _ in schema]\n",
+    "    datetime_cols = [col for col, typ in schema if is_datetime_type(typ)]\n",
+    "\n",
+    "    print(f\"检测到 {len(column_names)} 个字段，其中时间字段: {datetime_cols[:3]}...\")\n",
+    "\n",
+    "    # 3. 连接 MySQL\n",
+    "    mysql_conn = mysql.connector.connect(**MYSQL_CONFIG)\n",
+    "    mysql_cursor = mysql_conn.cursor()\n",
+    "\n",
+    "    try:\n",
+    "        # 4. 创建目标表\n",
+    "        create_ngv_table(mysql_cursor, schema)\n",
+    "\n",
+    "        # 5. 构造插入 SQL\n",
+    "        placeholders = \", \".join([\"%s\"] * len(column_names))\n",
+    "        cols_str = \", \".join([f\"`{c}`\" for c in column_names])\n",
+    "        insert_sql = f\"INSERT INTO `{TARGET_TABLE_MYSQL}` ({cols_str}) VALUES ({placeholders})\"\n",
+    "\n",
+    "        # 6. 清空目标表\n",
+    "        mysql_cursor.execute(f\"TRUNCATE TABLE `{TARGET_TABLE_MYSQL}`;\")\n",
+    "        print(\"🗑️ 已清空 NGV 表\")\n",
+    "\n",
+    "        # 7. 按 date_id 分批处理\n",
+    "        # -- 新增：固定列名用于 SELECT --\n",
+    "        fixed_columns = [col for col, _ in schema]\n",
+    "        quoted_fixed_columns = \", \".join([f'\"{c}\"' for c in fixed_columns])\n",
+    "\n",
+    "        # 动态选择排序字段（必须在 fixed_columns 中）\n",
+    "        exclude_cols = {PARTITION_COLUMN} | set(datetime_cols)\n",
+    "        candidates = [col for col in fixed_columns if col not in exclude_cols]\n",
+    "        order_col = f'\"{candidates[0]}\"' if candidates else f'\"{PARTITION_COLUMN}\"'\n",
+    "\n",
+    "        if \"org_code\" not in column_names:\n",
+    "            raise ValueError(\"❌ 源表中未找到唯一字段 'org_code'\")\n",
+    "\n",
+    "        for date_id in date_ids:\n",
+    "            print(f\"\\n>>> 处理 date_id = {date_id}\")\n",
+    "            last_org_code = None  # 游标：上一批最大的 org_code\n",
+    "            i = 1\n",
+    "\n",
+    "            while True:\n",
+    "                time.sleep(3)\n",
+    "                pg_conn = psycopg2.connect(**PG_CONN_INFO)\n",
+    "                pg_cur = pg_conn.cursor()\n",
+    "\n",
+    "                # 构造 WHERE 条件\n",
+    "                if last_org_code is None:\n",
+    "                    where_clause = f'\"{PARTITION_COLUMN}\" = %s'\n",
+    "                    params = (date_id,)\n",
+    "                else:\n",
+    "                    where_clause = f'\"{PARTITION_COLUMN}\" = %s AND \"org_code\" > %s'\n",
+    "                    params = (date_id, last_org_code)\n",
+    "\n",
+    "                sql = f\"\"\"\n",
+    "                    SELECT {quoted_fixed_columns}\n",
+    "                    FROM {SOURCE_TABLE}\n",
+    "                    WHERE {where_clause}\n",
+    "                    ORDER BY \"org_code\"\n",
+    "                    LIMIT {BATCH_SIZE};\n",
+    "                \"\"\"\n",
+    "                pg_cur.execute(sql, params)\n",
+    "                rows = pg_cur.fetchall()\n",
+    "                pg_cur.close()\n",
+    "                pg_conn.close()\n",
+    "\n",
+    "                if not rows:\n",
+    "                    break\n",
+    "\n",
+    "                df_batch = pd.DataFrame(rows, columns=fixed_columns)\n",
+    "                df_batch = normalize_datetime_cols(df_batch, datetime_cols)\n",
+    "                df_batch.to_csv(f\"输出查看{i}.csv\", index=False)\n",
+    "                i += 1\n",
+    "\n",
+    "                # 更新游标：取本批最后一条的 org_code\n",
+    "                last_org_code = df_batch.iloc[-1][\"org_code\"]\n",
+    "\n",
+    "                # 清洗并插入 MySQL\n",
+    "                def sanitize_row(row):\n",
+    "                    return tuple(\n",
+    "                        None if (isinstance(x, float) and pd.isna(x)) or pd.isna(x) else x\n",
+    "                        for x in row\n",
+    "                    )\n",
+    "\n",
+    "                data_tuples = [sanitize_row(row) for row in df_batch.values]\n",
+    "                mysql_cursor.executemany(insert_sql, data_tuples)\n",
+    "                mysql_conn.commit()\n",
+    "\n",
+    "                inserted = len(data_tuples)\n",
+    "                print(f\"  date_id={date_id} 已插入 {inserted} 行 (last_org_code={last_org_code})\")\n",
+    "\n",
+    "            print(f\"✅ date_id={date_id} 同步完成\")\n",
+    "\n",
+    "        print(f\"\\n🎉 同步完成！数据已写入 MySQL 表 `{TARGET_TABLE_MYSQL}`\")\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ 错误: {e}\")\n",
+    "        mysql_conn.rollback()\n",
+    "    finally:\n",
+    "        mysql_cursor.close()\n",
+    "        mysql_conn.close()\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "将同步以下 date_id 分区: ['20260113', '20260112', '20260111']\n",
+      "检测到 141 个字段，其中时间字段: []...\n",
+      "✅ MySQL 表 NGV 已创建（时间字段为 DATETIME，其余为 TEXT）\n",
+      "🗑️ 已清空 NGV 表\n",
+      "\n",
+      ">>> 处理 date_id = 20260113\n",
+      "✅ date_id=20260113 同步完成\n",
+      "\n",
+      ">>> 处理 date_id = 20260112\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS201812070004175)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS201903250025112)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS201907240033962)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS201910150040257)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS201912160046642)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202003230057861)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202006080088028)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202010040108419)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202104090119587)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202106210130145)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202108300139429)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202112050146822)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202204250175005)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202209300189703)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202303230219406)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202308210240694)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202402260259380)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202406260273963)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202411040284912)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202503300294788)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202507150304118)\n",
+      "  date_id=20260112 已插入 2000 行 (last_org_code=CHS202511120312981)\n",
+      "  date_id=20260112 已插入 1278 行 (last_org_code=TQB201509180060)\n",
+      "✅ date_id=20260112 同步完成\n",
+      "\n",
+      ">>> 处理 date_id = 20260111\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS201812070004175)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS201903250025112)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS201907240033962)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS201910150040257)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS201912160046642)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202003230057861)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202006080088042)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202010050108424)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202104090119621)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202106210130146)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202108300139429)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202112050146822)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202204250175005)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202209300189703)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202303230219406)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202308210240694)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202402260259380)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202406260273963)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202411040284912)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202503300294779)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202507150304098)\n",
+      "  date_id=20260111 已插入 2000 行 (last_org_code=CHS202511120312975)\n",
+      "  date_id=20260111 已插入 1259 行 (last_org_code=TQB201509180060)\n",
+      "✅ date_id=20260111 同步完成\n",
+      "\n",
+      "🎉 同步完成！数据已写入 MySQL 表 `jdy_ngv_data_source`\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/sampleCloud/续约代办宜搭同步简道云-数据一致性.ipynb

@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## 全量同步",
+   "id": "69bf37484b68b727"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    ""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/yida_xuyuedaiban_paifa.py

@@ -401,6 +401,7 @@ try:
                 ts = int(round(t * 1000))
                 randint = random.randint(100000000, 999999999)
                 req = res_new['result'] + "|" + formData['textField_kuntp6fk'] + "|" + formData['textField_kuntp6fl']+ "|" + formData['employeeField_kykw5ege'] + "_" + str(ts) + "_" + str(randint)
+                # 实例ID|门ID|服务单号|专属运营顾问
                 str_en = des_encrypt(req)
                 print(str_en.decode('utf-8'))
                 req_new = str_en.decode('utf-8')

test/商机问题表删除.ipynb

@@ -0,0 +1,39 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_excel(fr\"C:\\Users\\hp_z66\\Downloads\\商机问题跟进表_20260331114857.xlsx\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/商机问题跟进表.ipynb

@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## 重复派发",
+   "id": "2d5eea6406e5bd27"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    ""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/宜搭续约待办转换简道云数据源.py

@@ -0,0 +1,524 @@
+import os
+import pandas as pd
+import json
+import ast
+import re
+from datetime import datetime
+from chardet import detect
+import unicodedata
+
+
+# ==============================
+# 核心修复：处理数组类型数据的工具函数
+# ==============================
+def is_empty_value(value):
+    """判断值是否为空（支持数组、字符串、数值等所有类型）"""
+    if pd.isna(value):
+        return True
+    if value is None:
+        return True
+    if isinstance(value, str) and value.strip() == '':
+        return True
+    if isinstance(value, (list, tuple, set)) and len(value) == 0:
+        return True
+    return False
+
+
+def clean_special_characters(value):
+    """清理特殊字符（支持数组类型，避免歧义错误）"""
+    # 处理空值
+    if is_empty_value(value):
+        return value
+
+    # 处理数组类型（如 ['a', 'b']）
+    if isinstance(value, (list, tuple)):
+        cleaned_list = []
+        for item in value:
+            if isinstance(item, str):
+                cleaned_list.append(_clean_single_string(item))
+            else:
+                cleaned_list.append(item)
+        return cleaned_list
+
+    # 处理字符串类型
+    elif isinstance(value, str):
+        return _clean_single_string(value)
+
+    # 其他类型（数值、布尔等）直接返回
+    else:
+        return value
+
+
+def _clean_single_string(text):
+    """清理单个字符串的特殊字符（内部调用）"""
+    try:
+        # 移除控制字符（保留换行、制表符）
+        cleaned = ''.join(
+            char for char in text
+            if unicodedata.category(char)[0] != 'C' or char in '\n\t'
+        )
+        # 移除特定无法编码的字符（如右至左标记）
+        special_chars = ['\u202d', '\u202c', '\u202a', '\u202b', '\u200b']
+        for char in special_chars:
+            cleaned = cleaned.replace(char, '')
+        # 替换全角空格为半角空格
+        cleaned = cleaned.replace('\u3000', ' ')
+        return cleaned
+    except:
+        return text
+
+
+def get_safe_encoding(file_path=None):
+    """获取安全的编码格式（优先UTF-8-SIG）"""
+    if file_path and os.path.exists(file_path):
+        with open(file_path, 'rb') as f:
+            raw_data = f.read(10000)
+            result = detect(raw_data)
+            detected = result['encoding']
+            if detected in ['utf-8', 'utf-8-sig', 'gbk', 'gb2312']:
+                return 'utf-8-sig'
+    return 'utf-8-sig'
+
+
+# ==============================
+# 第一步：生成 expanded_yd_data.csv（彻底修复数组问题）
+# ==============================
+def generate_expanded_csv(output_dir):
+    """生成展开后的源文件（支持数组类型数据）"""
+    os.makedirs(output_dir, exist_ok=True)
+    converted_csv_path = os.path.join(output_dir, "converted_yd_data.csv")
+
+    if not os.path.exists(converted_csv_path):
+        print(f"❌ 错误：converted_yd_data.csv 不存在，路径：{converted_csv_path}")
+        return None
+
+    # 读取文件（兼容不同编码）
+    encoding = get_safe_encoding(converted_csv_path)
+    try:
+        df = pd.read_csv(converted_csv_path, encoding=encoding)
+        print(f"✅ 成功读取 converted_yd_data.csv（编码：{encoding}），数据规模：{df.shape[0]}行 × {df.shape[1]}列")
+    except Exception as e:
+        print(f"❌ 读取 converted_yd_data.csv 失败：{str(e)}")
+        return None
+
+    if 'data' not in df.columns:
+        print(f"❌ 错误：converted_yd_data.csv 中缺少 'data' 列")
+        return None
+
+    # 清理非data列的特殊字符（仅处理字符串列）
+    non_data_cols = [col for col in df.columns if col != 'data']
+    for col in non_data_cols:
+        if df[col].dtype == 'object':  # 仅处理字符串类型列
+            df[col] = df[col].apply(clean_special_characters)
+
+    # 检查并解析 data 列（核心：处理数组类型）
+    sample = df['data'].dropna().iloc[0] if not df['data'].dropna().empty else ""
+    print("Sample of 'data' column (after cleaning):")
+    print(repr(str(sample))[:200])  # 转为字符串避免数组打印过长
+
+    if isinstance(sample, str):
+        print("Detected string format, parsing with ast.literal_eval...")
+
+        def safe_literal_eval(x):
+            if is_empty_value(x):
+                return {}
+            try:
+                # 先清理字符串，再解析
+                cleaned_x = clean_special_characters(x) if isinstance(x, str) else str(x)
+                parsed = ast.literal_eval(cleaned_x)
+                # 解析后再次清理（处理数组中的特殊字符）
+                return clean_special_characters(parsed)
+            except (ValueError, SyntaxError, TypeError) as e:
+                print(f"Parse error on: {repr(str(x))[:100]}... Error: {str(e)[:50]}")
+                return {}
+
+        df['data'] = df['data'].apply(safe_literal_eval)
+
+    # 展开 data 列（处理数组类型，转为字符串存储）
+    def flatten_expanded_data(expanded_df):
+        """展平数据，将数组转为字符串"""
+        for col in expanded_df.columns:
+            if expanded_df[col].dtype == 'object':
+                expanded_df[col] = expanded_df[col].apply(
+                    lambda x: ','.join(map(str, x)) if isinstance(x, (list, tuple)) else x
+                )
+        return expanded_df
+
+    expanded = pd.json_normalize(df['data'])
+    expanded = flatten_expanded_data(expanded)  # 数组转字符串（如 ['a','b'] → "a,b"）
+
+    # 清理展开后的数据
+    for col in expanded.columns:
+        expanded[col] = expanded[col].apply(clean_special_characters)
+
+    # 合并数据
+    other_cols = df.drop(columns=['data'])
+    new_df = pd.concat([other_cols.reset_index(drop=True), expanded.reset_index(drop=True)], axis=1)
+
+    # 数据过滤（修复数组转字符串后的判断逻辑）
+    if 'instanceStatus' in new_df.columns:
+        # 确保过滤条件处理字符串
+        new_df['instanceStatus'] = new_df['instanceStatus'].astype(str)
+        new_df = new_df[new_df["instanceStatus"].str.strip() == "RUNNING"]
+        print(f"✅ 过滤后（instanceStatus=RUNNING）：{new_df.shape[0]}行")
+    else:
+        print(f"⚠️ 警告：缺少 'instanceStatus' 列，跳过状态过滤")
+
+    col = "textField_kto3q3ev"
+    if col in new_df.columns:
+        # 处理数组转字符串后的空值判断
+        new_df[col] = new_df[col].apply(
+            lambda x: ','.join(map(str, x)) if isinstance(x, (list, tuple)) else x
+        ).astype(str)
+        mask2 = (new_df[col].str.strip() == "") | (new_df[col].str.strip() == "nan")
+        new_df = new_df[mask2]
+        print(f"✅ 过滤后（{col}为空）：{new_df.shape[0]}行")
+    else:
+        print(f"⚠️ 警告：缺少 '{col}' 列，跳过订单编码过滤")
+
+    # 保存文件（UTF-8-SIG编码）
+    expanded_csv_path = os.path.join(output_dir, "expanded_yd_data.csv")
+    try:
+        new_df.to_csv(
+            expanded_csv_path,
+            index=False,
+            encoding='utf-8-sig',
+            na_rep='',
+            errors='replace'
+        )
+        print(f"✅ Expanded data saved to: {expanded_csv_path}（编码：utf-8-sig）")
+        return expanded_csv_path
+    except Exception as e:
+        print(f"❌ 保存 expanded_yd_data.csv 失败：{str(e)}")
+        return None
+
+
+# ==============================
+# 第二步：格式转换核心函数（兼容数组处理结果）
+# ==============================
+def convert_to_data_ngv_format(
+        source_csv_path,
+        target_csv_path,
+        main_output_path,
+        additional_output_path,
+        doc_output_path
+):
+    print("\n=== 开始格式转换（目标格式：data_NGV.csv）===")
+    try:
+        # 读取目标文件
+        if not os.path.exists(target_csv_path):
+            raise FileNotFoundError(f"目标文件不存在：{target_csv_path}")
+
+        # 兼容目标文件的编码
+        try:
+            df_target = pd.read_csv(target_csv_path, encoding='utf-8-sig')
+            target_encoding = 'utf-8-sig'
+        except:
+            df_target = pd.read_csv(target_csv_path, encoding='gbk')
+            target_encoding = 'gbk'
+
+        print(f"✅ 成功读取目标文件：{target_csv_path}（编码：{target_encoding}）")
+        print(f"   数据规模：{df_target.shape[0]}行 × {df_target.shape[1]}列")
+
+        # 读取源文件（已处理数组问题）
+        df_source = pd.read_csv(source_csv_path, encoding='utf-8-sig')
+        print(f"✅ 成功读取源文件：{source_csv_path}（编码：utf-8-sig）")
+        print(f"   数据规模：{df_source.shape[0]}行 × {df_source.shape[1]}列")
+
+    except Exception as e:
+        print(f"❌ 文件读取失败：{str(e)}")
+        return False
+
+    # 字段映射（适配 data_NGV 格式）
+    print("\n=== 建立字段映射关系 ===")
+    field_mapping = {
+        'createTimeGMT': 'saas_create_time',
+        'modifiedTimeGMT': 'etl_time',
+        'title': 'org_remark',
+        'instanceStatus': 'active_status_fmt',
+        'processInstanceId': 'id_own_org',
+        'actionExecutor': 'technician',
+        'originator': 'salesmen',
+        'textField_kuj8nx00': 'province_name',
+        'textField_kuj8nx01': 'city_name'
+    }
+
+    # 筛选有效映射
+    valid_mapping = {}
+    for source_field, target_field in field_mapping.items():
+        if target_field in df_target.columns and source_field in df_source.columns:
+            valid_mapping[source_field] = target_field
+            print(f"  {source_field} → {target_field}")
+
+    if len(valid_mapping) == 0:
+        print(f"⚠️ 警告：未找到有效字段映射，将仅填充默认值")
+
+    # 字段分类
+    target_columns = set(df_target.columns)
+    source_columns = set(df_source.columns)
+    additional_columns = list(source_columns - target_columns - set(valid_mapping.keys()))
+    print(f"\n=== 字段统计 ===")
+    print(f"  目标文件总字段数：{len(target_columns)}")
+    print(f"  源文件总字段数：{len(source_columns)}")
+    print(f"  有效映射字段数：{len(valid_mapping)}")
+    print(f"  额外字段数（放入单独文件）：{len(additional_columns)}")
+
+    # 创建结果数据结构
+    df_main = df_target.iloc[0:0].copy()
+    df_main.insert(0, 'main_file_id', '')  # 关联ID列
+    df_additional = pd.DataFrame(columns=['main_file_id'] + additional_columns)
+
+    # 数据处理工具函数（兼容字符串化的数组）
+    def extract_org_info(title_text):
+        if is_empty_value(title_text):
+            return '', ''
+        title_str = str(title_text).strip()
+        org_name_match = re.search(r'门店名称：([^,，\n]+)', title_str)
+        org_code_match = re.search(r'门店编码：([^,，\n]+)', title_str)
+        org_name = org_name_match.group(1).strip() if org_name_match else ''
+        org_code = org_code_match.group(1).strip() if org_code_match else ''
+        return org_name, org_code
+
+    def extract_technician(executor_text):
+        if is_empty_value(executor_text):
+            return ''
+        executor_str = str(executor_text).strip()
+        # 匹配字符串化的数组中的中文姓名（如 "{'nameInChinese':'何钊'}"）
+        name_match = re.search(r"'nameInChinese':\s*'([^']+)'", executor_str)
+        return name_match.group(1).strip() if name_match else ''
+
+    def convert_gmt_time(gmt_str):
+        if is_empty_value(gmt_str):
+            return None
+        try:
+            time_str = str(gmt_str).replace('T', ' ').replace('Z', '').strip()
+            return pd.to_datetime(time_str)
+        except:
+            return None
+
+    # 逐行处理数据
+    print(f"\n=== 开始数据处理（共{len(df_source)}行） ===")
+    for idx, source_row in df_source.iterrows():
+        main_file_id = f"REC-{idx:06d}"
+
+        # 处理主文件数据
+        main_row = pd.Series(index=df_main.columns, dtype='object')
+        main_row['main_file_id'] = main_file_id
+
+        # 填充映射字段（处理字符串化的数组）
+        for source_field, target_field in valid_mapping.items():
+            value = source_row[source_field]
+            if not is_empty_value(value):
+                # 将字符串化的数组转为普通字符串（如 "a,b" → "a,b"）
+                if isinstance(value, (list, tuple)):
+                    main_row[target_field] = ','.join(map(str, value))
+                else:
+                    main_row[target_field] = str(value).strip()
+            else:
+                main_row[target_field] = ''
+
+        # 处理关键业务字段
+        create_time = convert_gmt_time(source_row.get('createTimeGMT'))
+        if create_time:
+            main_row['date_fmt'] = create_time.strftime('%Y/%m/%d')
+            main_row['date_id'] = int(create_time.strftime('%Y%m%d'))
+            main_row['pt'] = main_row['date_id']
+
+        # 提取门店信息
+        org_name, org_code = extract_org_info(source_row.get('title'))
+        if org_name:
+            main_row['org_name'] = org_name
+            main_row['group_name'] = org_name
+        if org_code:
+            main_row['org_code'] = org_code
+
+        # 提取处理人
+        technician = extract_technician(source_row.get('actionExecutor'))
+        if technician:
+            main_row['technician'] = technician
+
+        # 填充默认值
+        default_values = {
+            'org_type': '一般',
+            'org_status': '留存',
+            'group_grade': '普通客户（VIP）',
+            'is_active': 1,
+            'active_status_fmt': '活跃',
+            'province_name': main_row.get('province_name', '未知'),
+            'city_name': main_row.get('city_name', '未知'),
+            'area_name': '未知',
+            'is_wechat': 0,
+            'is_mini_app': 0,
+            'id_own_group': 0,
+            'org_code': main_row.get('org_code', '')
+        }
+
+        for col, default_val in default_values.items():
+            if col in main_row.index and is_empty_value(main_row[col]):
+                main_row[col] = default_val
+
+        df_main.loc[idx] = main_row
+
+        # 处理额外字段文件
+        additional_row = pd.Series(index=df_additional.columns, dtype='object')
+        additional_row['main_file_id'] = main_file_id
+
+        for col in additional_columns:
+            if col in source_row.index:
+                value = source_row[col]
+                if not is_empty_value(value):
+                    # 统一转为字符串存储（兼容数组）
+                    if isinstance(value, (list, tuple)):
+                        additional_row[col] = ','.join(map(str, value))
+                    else:
+                        additional_row[col] = str(value).strip()
+                else:
+                    additional_row[col] = ''
+
+        df_additional.loc[idx] = additional_row
+
+        # 进度提示（大数据量优化）
+        if (idx + 1) % 500 == 0 or (idx + 1) == len(df_source):
+            print(f"  已处理 {idx + 1}/{len(df_source)} 行")
+
+    # 数据类型优化
+    print(f"\n=== 优化数据类型 ===")
+    numeric_cols = ['date_id', 'pt', 'is_active', 'is_wechat', 'is_mini_app',
+                    'id_own_group', 'active_user_count', 'limit_user_count']
+
+    for col in numeric_cols:
+        if col in df_main.columns:
+            # 处理字符串格式的数值
+            df_main[col] = pd.to_numeric(
+                df_main[col].astype(str).str.replace(',', ''),  # 移除数组转字符串的逗号
+                errors='coerce'
+            ).fillna(0).astype(int)
+
+    # 填充空值
+    df_main = df_main.fillna('')
+    df_additional = df_additional.fillna('')
+
+    # 保存文件
+    print(f"\n=== 保存结果文件 ===")
+    try:
+        # 保存主文件（匹配 data_NGV 格式）
+        df_main.to_csv(
+            main_output_path,
+            index=False,
+            encoding='utf-8-sig',
+            na_rep='',
+            errors='replace'
+        )
+        print(f"✅ 主文件保存成功: {main_output_path}（编码：utf-8-sig）")
+
+        # 保存额外字段文件
+        df_additional.to_csv(
+            additional_output_path,
+            index=False,
+            encoding='utf-8-sig',
+            na_rep='',
+            errors='replace'
+        )
+        print(f"✅ 额外字段文件保存成功: {additional_output_path}（编码：utf-8-sig）")
+
+        # 生成说明文档
+        generate_relation_doc(doc_output_path, main_output_path, additional_output_path,
+                              len(target_columns), len(source_columns), len(valid_mapping))
+        print(f"✅ 关联说明文档保存成功: {doc_output_path}")
+
+        return True
+
+    except Exception as e:
+        print(f"❌ 文件保存失败: {str(e)}")
+        return False
+
+
+# ==============================
+# 辅助函数：生成关联说明文档
+# ==============================
+def generate_relation_doc(doc_path, main_file, additional_file, target_col_count, source_col_count, mapping_count):
+    doc_content = f"""# 数据格式转换结果说明
+## 目标格式文件：data_NGV.csv
+
+### 一、文件概述
+1. **主文件（匹配目标格式）**
+   - 文件名：{os.path.basename(main_file)}
+   - 格式来源：完全匹配 data_NGV.csv 结构
+   - 数据规模：{pd.read_csv(main_file, encoding='utf-8-sig').shape[0]}行 × {target_col_count + 1}列
+   - 编码格式：UTF-8-SIG（兼容所有字符和数组数据）
+
+2. **额外字段文件**
+   - 文件名：{os.path.basename(additional_file)}
+   - 包含内容：源文件中 data_NGV.csv 没有的字段
+   - 数据规模：{pd.read_csv(additional_file, encoding='utf-8-sig').shape[0]}行 × {pd.read_csv(additional_file, encoding='utf-8-sig').shape[1]}列
+   - 编码格式：UTF-8-SIG
+
+### 二、关键处理说明
+1. **数组数据处理**：源文件中的数组（如 ['a','b']）已转为字符串（"a,b"）存储，避免格式错误
+2. **特殊字符清理**：自动移除无法编码的控制字符（如右至左标记 \u202d）
+3. **编码统一**：所有文件使用 UTF-8-SIG 编码，兼容中文和特殊字符
+
+### 三、关联方法
+1. **关联字段**：`main_file_id`（格式：REC-000000）
+2. **Excel导入**：数据→自文本/CSV→选择文件→编码选择"UTF-8"→完成
+
+### 四、使用建议
+1. 主文件可直接用于业务系统，与 data_NGV.csv 格式完全兼容
+2. 额外字段文件用于原始数据追溯，通过 main_file_id 关联
+3. 数组转字符串后的数据可通过 Excel 的"文本分列"功能恢复为数组
+"""
+    with open(doc_path, 'w', encoding='utf-8') as f:
+        f.write(doc_content)
+
+
+# ==============================
+# 主执行函数
+# ==============================
+def main():
+    # 配置文件路径（请根据您的实际位置修改！）
+    base_dir = "D:\\Idea Project\\SaaS_V1.7\\test\\output"
+    target_csv_path = "D:\\Idea Project\\SaaS_V1.7\\test\\output\\data_NGV.csv"
+
+    # 生成 expanded_yd_data.csv（解决数组问题）
+    expanded_csv_path = generate_expanded_csv(base_dir)
+    if not expanded_csv_path:
+        print("❌ 生成 expanded_yd_data.csv 失败，终止转换")
+        return
+
+    # 配置输出路径
+    main_output_path = os.path.join(base_dir, "主文件_匹配data_NGV格式.csv")
+    additional_output_path = os.path.join(base_dir, "额外字段文件_源文件特有列.csv")
+    doc_output_path = os.path.join(base_dir, "格式转换说明.md")
+
+    # 执行转换
+    success = convert_to_data_ngv_format(
+        source_csv_path=expanded_csv_path,
+        target_csv_path=target_csv_path,
+        main_output_path=main_output_path,
+        additional_output_path=additional_output_path,
+        doc_output_path=doc_output_path
+    )
+
+    if success:
+        print(f"\n🎉 格式转换全部完成！")
+        print(f"📁 输出目录：{base_dir}")
+        print(f"🔔 重要：Excel导入时需选择'UTF-8'编码，数组数据已转为逗号分隔字符串")
+    else:
+        print(f"\n❌ 格式转换失败，请查看日志信息排查问题")
+
+
+# ==============================
+# 执行入口
+# ==============================
+if __name__ == "__main__":
+    # API模块导入（不影响核心功能）
+    try:
+        from yd_api import YDAPI
+        from api import API
+
+        print("✅ 成功导入 API 模块")
+    except ImportError:
+        print("⚠️ 警告：未找到 yd_api 或 api 模块，跳过API初始化（不影响格式转换）")
+
+    # 执行主流程
+    main()

test/宜搭续约待办转简道云源数据新.py

@@ -0,0 +1,241 @@
+import os
+import sys
+import pandas as pd
+from datetime import datetime
+
+# 获取上级目录并加入路径
+nb_path = os.path.abspath('')
+parent_dir = os.path.dirname(nb_path)
+sys.path.append(parent_dir)
+
+from back_ground_module import CommonModule
+from log_config import configure_task_logger, configure_error_task_logger
+from yd_api import YDAPI
+from api import API
+from tqdm import tqdm
+
+logger = configure_task_logger()
+error_task_logger = configure_error_task_logger()
+api_instance = API()
+yd_api_instance = YDAPI()
+common_module = CommonModule()
+output_dir = "output"
+os.makedirs(output_dir, exist_ok=True)
+
+# 加载数据
+# df = pd.read_csv(r"D:\Idea Project\SaaS_V1.7\test\output\expanded_yd_data.csv",encoding="gbk").astype(str)
+df = pd.read_excel(r"C:\Users\hp_z66\OneDrive\Desktop\门店分析新.xlsx",sheet_name="新建").astype(str)
+df2 = pd.read_excel(
+    r"D:\Idea Project\SaaS_V1.7\test\output\续约服务流程_20260324165743.xlsx"
+).fillna('').astype(str)
+# 从df中获取流程编码获取流程详细信息 # 测试注释
+token = yd_api_instance.generateToken()
+FORMID = "FORM-PE866MD1MJMU0WGLYRFLYEN5YN9L1I55Z7ZUK22"
+appType = "APP_UYZ0KG6L0CCNV80GZ66O"
+systemToken = "XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2"
+
+all_instance_data = []
+for index, row in tqdm(df.iterrows(), total=len(df)):
+    instance_id = row["实例ID"]
+    instance_info = yd_api_instance.processes_instancesInfos(token, instance_id, appType, systemToken)
+    data = instance_info.get("data")
+    if data:
+        # 提取 formData 中的字段并合并到外层
+        form_data = data.get("formData", {})
+        if isinstance(form_data, dict):
+            data.update(form_data)
+        
+        # 手动注入实例 ID，确保映射能找到
+        data["实例ID"] = instance_id
+        all_instance_data.append(data)
+
+ndf = pd.DataFrame(all_instance_data)
+ndf.to_csv(r"D:\Idea Project\SaaS_V1.7\\test\output\yd_process_details.csv", index=False)
+
+# 读取宜搭流程详情（已提前导出）
+ndf = pd.read_csv(r"D:\Idea Project\SaaS_V1.7\test\output\yd_process_details.csv")
+
+# 简道云字段中文名 → 字段ID 映射
+jdy_map = {
+    "门店编码": "_widget_1764820541661",
+    "120天是否跟进": "_widget_1764820541628",
+    "120天处理人": "_widget_1764820541634",
+    "120天跟进时间": "_widget_1765352838631",
+    "60天是否跟进": "_widget_1764820541630",
+    "60天处理人": "_widget_1764820541635",
+    "60天跟进时间": "_widget_1765352838632",
+    "30天是否跟进": "_widget_1764820541632",
+    "30天处理人": "_widget_1764820541636",
+    "30天跟进时间": "_widget_1765352838633",
+    "是否联系上": "_widget_1764820541638",
+    "现阶段问题": "_widget_1764820541641",
+    "联系情况及问题说明": "_widget_1764820541653",
+    "潜在商机": "_widget_1764820541657",
+    "商机详情": "_widget_1764820541659",
+    "不续约原因": "_widget_1764820541700",
+    "产品问题": "_widget_1764820541707",
+    "服务问题": "_widget_1764820541709",
+    "门店问题": "_widget_1764820541711",
+    "价格问题": "_widget_1764820541713",
+    "不续约具体情况说明": "_widget_1764820541702",
+    "宜搭实例ID": "_widget_1774339442956",
+}
+
+# 宜搭字段ID → 简道云中文名 映射
+yd_field_id_to_jdy_chinese = {
+    "textField_ksydghqw": "门店编码",
+    "radioField_kuntp6fm": "120天是否跟进",
+    "textField_livc8bjj": "120天处理人",
+    "dateField_lifr1fdv": "120天跟进时间",
+    "radioField_kurxyhvp": "60天是否跟进",
+    "textField_livc8bjl": "60天处理人",
+    "dateField_lifr1fdx": "60天跟进时间",
+    "radioField_kurxyhvq": "30天是否跟进",
+    "textField_livc8bjm": "30天处理人",
+    "dateField_lifr1fdy": "30天跟进时间",
+    "radioField_l85ppdia": "是否联系上",
+    "radioField_r3yeqvd": "现阶段问题",
+    "textAreaField_972lhkt": "联系情况及问题说明",
+    "radioField_ljqi5we3": "潜在商机",
+    "textareaField_liviovx0": "商机详情",
+    "selectField_l31clxfy": "不续约原因",
+    "selectField_l31clxfz": "产品问题",
+    "selectField_l31clxg0": "服务问题",
+    "selectField_l31clxg1": "门店问题",
+    "selectField_l31clxg2": "价格问题",
+    "textareaField_l31clxg4": "不续约具体情况说明",
+    "radioField_l85ppdie": "续约意愿",
+    "实例ID":"宜搭实例ID"
+}
+
+# 值映射（用于标准化选项值）
+value_mapping = {
+    "现阶段问题": {"暂时没有问题": "暂时无问题"},
+    "不续约原因": {"产品原因": "产品问题", "门店原因": "门店问题"},
+    "服务问题": {
+        "联系不上小六": "联系不上运营顾问",
+        "小六态度问题": "运营顾问态度问题",
+        "小六业务不专业": "运营顾问业务不专业",
+        "小六离职未能获取不续约原因": "运营顾问离职未能获取不续约原因"
+    },
+    "120天是否跟进": {"小六": "主动", "系统": "自动"},
+    "60天是否跟进": {"小六": "主动", "系统": "自动"},
+    "30天是否跟进": {"小六": "主动", "系统": "自动"},
+}
+
+# ========================
+# 1. 获取员工姓名 → ID 映射
+# ========================
+payload_staff = {
+    "api_key": "6694d3c4fcb69ca9a111a6c4",  # 注意：应为 app_id，不是 api_key（根据你实际接口调整）
+    "entry_id": "6769204a1902c9341340a1bc",
+}
+staff_resp = api_instance.entry_data_list(payload_staff)
+staff_id_list = staff_resp.get("data", [])
+
+# 构建映射字典：姓名 -> 员工ID
+name_to_staff_id = {}
+for item in staff_id_list:
+    name = item.get("_widget_1734942794144", "").strip()
+    staff_id = item.get("_widget_1734942794145", "").strip()
+    if name and staff_id:
+        name_to_staff_id[name] = staff_id
+
+logger.info(f"加载 {len(name_to_staff_id)} 名员工信息")
+
+# ========================
+# 2. 定义哪些字段是“人员字段”（需替换为ID）
+# ========================
+STAFF_COLUMNS_CHINESE = {
+    "120天处理人",
+    "60天处理人",
+    "30天处理人",
+    "运营顾问",
+    "运营专家",
+    "区域客服",
+}
+# 构建门店编码 → data_id 映射
+df2["门店编码_clean"] = df2["门店编码"].astype(str).str.strip().replace('nan', '')
+jdy_store_map = df2.set_index("门店编码_clean")["data_id"].to_dict()
+
+date_fields_chinese = {"120天跟进时间", "60天跟进时间", "30天跟进时间"}
+
+update_records = []
+
+for idx, row in ndf.iterrows():
+    yd_store_code = str(row.get("textField_ksydghqw", "")).strip()
+    if not yd_store_code or yd_store_code == "nan":
+        continue
+    jdy_id = jdy_store_map.get(yd_store_code)
+    if not jdy_id:
+        continue
+
+    # 构造 data 字段：每个字段必须是 { "value": ... }
+    data_dict = {}
+
+    for yd_field_id, jdy_chinese in yd_field_id_to_jdy_chinese.items():
+        raw_val = row.get(yd_field_id, "")
+        if pd.isna(raw_val) or str(raw_val).strip().lower() in {"", "nan", "-", "无", "null"}:
+            continue
+
+        # 处理日期字段
+        if jdy_chinese in date_fields_chinese:
+            try:
+                if isinstance(raw_val, (int, float)) or (
+                        isinstance(raw_val, str) and raw_val.replace('.', '', 1).isdigit()):
+                    ts = float(raw_val)
+                    if ts < 1e12:
+                        ts *= 1000
+                    final_value = int(ts)
+                else:
+                    dt = pd.to_datetime([str(raw_val)], errors='coerce')[0]
+                    if pd.isna(dt):
+                        raise ValueError("Invalid date")
+                    if dt.tz is None:
+                        dt = dt.tz_localize('Asia/Shanghai')
+                    final_value = int(dt.tz_convert('UTC').timestamp() * 1000)
+                if not (1577836800000 <= final_value <= 1900000000000):
+                    continue
+            except Exception as e:
+                logger.error(f"日期转换失败 [{jdy_chinese}]: {raw_val}, {e}")
+                continue
+        else:
+            str_val = str(raw_val)
+            final_value = value_mapping.get(jdy_chinese, {}).get(str_val, str_val)
+
+            # 如果是人员字段，尝试替换为员工ID
+            if jdy_chinese in STAFF_COLUMNS_CHINESE:
+                staff_id = name_to_staff_id.get(str_val)
+                if staff_id:
+                    final_value = staff_id
+                else:
+                    logger.warning(f"未找到员工ID，保留原姓名 [{jdy_chinese}]: {str_val}")
+
+        jdy_field_id = jdy_map.get(jdy_chinese)
+        if jdy_field_id:
+            data_dict[jdy_field_id] = {"value": final_value}
+
+    if data_dict:
+        update_records.append({
+            "data_id": jdy_id,
+            "data": data_dict
+        })
+
+# 批量发送更新请求
+logger.info(f"共构造 {len(update_records)} 条更新记录")
+APP_ID = "675b900991ad2491c69389ca"
+# ENTRY_ID = "6965eec36b73376aa0b5bff8"
+ENTRY_ID = "6931063d64187eaf6b927557"
+
+for record in tqdm(update_records):
+    payload = {
+        "api_key": APP_ID,
+        "entry_id": ENTRY_ID,
+        # "transaction_id": str(uuid.uuid4()),  # 推荐：保证幂等
+        "data_id": record["data_id"],
+        "data": record["data"],
+        "is_start_trigger": False
+    }
+
+    res = api_instance.entry_data_update(payload)
+    # print(res)

test/宜搭获取续约待办数据.py

@@ -0,0 +1,200 @@
+import os
+from datetime import datetime, timezone, timedelta
+import pandas as pd
+from holidays.countries import saint_martin as record
+from tqdm import tqdm
+import json
+from yd_api import YDAPI
+from api import API
+import time
+
+output_dir = "output"
+os.makedirs(output_dir, exist_ok=True)
+
+api_instance = API()
+yd_api_instance = YDAPI()
+
+def generate_monthly_ranges(start: str, end: str):
+    """
+    生成按自然月划分的时间段列表（左闭右开）
+    例如: [('2025-11-01T00:00:00Z', '2025-12-01T00:00:00Z'), ...]
+    """
+    start_dt = datetime.fromisoformat(start.replace("Z", "+00:00"))
+    end_dt = datetime.fromisoformat(end.replace("Z", "+00:00"))
+
+    ranges = []
+    current = start_dt
+
+    while current < end_dt:
+        # 下一个月的第一天
+        if current.month == 12:
+            next_month = current.replace(year=current.year + 1, month=1, day=1)
+        else:
+            next_month = current.replace(month=current.month + 1, day=1)
+        # 不超过 end_dt
+        segment_end = min(next_month, end_dt)
+        ranges.append((
+            current.strftime("%Y-%m-%dT00:00:00Z"),
+            segment_end.strftime("%Y-%m-%dT00:00:00Z")
+        ))
+        current = next_month
+
+    return ranges
+
+class GetYDData:
+
+    def __init__(self):
+        self.FORMID = "FORM-PE866MD1MJMU0WGLYRFLYEN5YN9L1I55Z7ZUK22"
+        self.appType = "APP_UYZ0KG6L0CCNV80GZ66O"
+        self.systemToken = "XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2"
+
+        # 第一段：2025-01-01 到 2025-11-01
+        first_segment = ("2025-01-01T00:00:00Z", "2025-02-01T00:00:00Z")
+
+        # 第二段：2025-11-01 到当前时间（按月拆分）
+        now_utc_str = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+        monthly_segments = generate_monthly_ranges("2025-02-01T00:00:00Z", now_utc_str)
+
+        # 合并所有时间段
+        self.time_ranges = [first_segment] + monthly_segments
+
+        print("📅 计划拉取以下时间段：")
+        for i, (s, e) in enumerate(self.time_ranges, 1):
+            print(f"  {i}. {s} → {e}")
+
+    def build_value_to_label_map(self, form_structure):
+        value_to_label_map = {}
+        fields = form_structure.get("result", [])
+        for field in fields:
+            field_id = field.get("fieldId")
+            component = field.get("componentName")
+            props = field.get("props", {})
+            data_source = props.get("dataSource", [])
+
+            if component in ["SelectField", "RadioField"] and data_source:
+                option_map = {}
+                for opt in data_source:
+                    val = opt.get("value")
+                    if val is None:
+                        continue
+                    text_obj = opt.get("text", {})
+                    if isinstance(text_obj, dict):
+                        zh_text = text_obj.get("zh_CN")
+                        if zh_text is None and "value" in text_obj:
+                            raw = text_obj["value"]
+                            if isinstance(raw, str) and raw.startswith('"') and raw.endswith('"'):
+                                zh_text = raw[1:-1]
+                            else:
+                                zh_text = str(val)
+                        elif zh_text is None:
+                            zh_text = str(val)
+                    else:
+                        zh_text = str(text_obj)
+                    option_map[str(val)] = zh_text
+                if option_map:
+                    value_to_label_map[field_id] = option_map
+        return value_to_label_map
+
+    def convert_record_values(self, record, value_map):
+        converted = {}
+        for key, val in record.items():
+            if key in value_map and val is not None:
+                str_val = str(val)
+                converted[key] = value_map[key].get(str_val, val)
+            else:
+                converted[key] = val
+        return converted
+
+    def fetch_records_in_range(self, token, start_time, end_time):
+        """拉取指定时间范围内的所有记录"""
+        try:
+            first_page = yd_api_instance.read_processes_instances(
+                token=token,
+                formUuid=self.FORMID,
+                page=1,
+                n=100,
+                appType=self.appType,
+                systemToken=self.systemToken,
+                instanceStatus="",
+                modifiedFromTimeGMT=start_time,
+                modifiedToTimeGMT=end_time,
+            )
+        except Exception as e:
+            print(f"❌ 首页请求失败 ({start_time} ～ {end_time}): {e}")
+            return []
+
+        total_count = first_page.get("totalCount", 0)
+        total_pages = (total_count // 100) + (1 if total_count % 100 else 0)
+        print(f"📊 [{start_time[:10]} ～ {end_time[:10]}] 总记录数: {total_count}, 共 {total_pages} 页")
+
+        all_records = []
+        if total_count > 0:
+            all_records.extend(first_page.get("data", []))
+            for page in tqdm(range(2, total_pages + 1), desc=f"{start_time[:7]}"):
+                try:
+                    resp = yd_api_instance.read_processes_instances(
+                        token=token,
+                        formUuid=self.FORMID,
+                        page=page,
+                        n=100,
+                        appType=self.appType,
+                        systemToken=self.systemToken,
+                        instanceStatus="",
+                        modifiedFromTimeGMT=start_time,
+                        modifiedToTimeGMT=end_time,
+                    )
+                    page_data = resp.get("data", [])
+                    all_records.extend(page_data)
+                    time.sleep(0.15)  # 稍微增加间隔，更安全
+                except Exception as e:
+                    print(f"⚠️ 第 {page} 页失败 ({start_time[:10]}): {e}")
+                    continue
+        return all_records
+
+    def main(self):
+        # Step 1: 获取表单结构
+        token = yd_api_instance.generateToken()
+        form_struct = yd_api_instance.get_form_structures(
+            token=token,
+            formUuid=self.FORMID
+        )
+        value_map = self.build_value_to_label_map(form_struct)
+        print("\n✅ 表单选项映射构建完成")
+
+        # Step 2: 按时间段拉取
+        all_records = []
+        all_records_detils = []
+        for start_time, end_time in self.time_ranges:
+            print(f"\n⏳ 拉取: {start_time} → {end_time}")
+            records = self.fetch_records_in_range(token, start_time, end_time)
+            all_records.extend(records)
+            try:
+                record_data = record.get("data", [])
+                all_records_detils.extend(record_data)
+            except Exception as e:
+                continue
+
+        print(f"\n📥 总共获取 {len(all_records)} 条流程实例")
+
+        # # Step 3: 转换 formData
+        converted_records = []
+        for inst in all_records:
+            form_data = inst.get("formData", {})
+            converted = self.convert_record_values(form_data, value_map)
+            converted_records.append(converted)
+
+        # Step 4: 保存
+        if all_records:
+            df = pd.DataFrame(all_records)
+            output_path = os.path.join(output_dir, "converted_yd_data.csv")
+            df.to_csv(output_path, index=False)
+
+            df1 = pd.DataFrame(all_records_detils)
+            output_path1 = os.path.join(output_dir, "converted_yd_data_detail.csv")
+            df1.to_csv(output_path1, index=False)
+            print(f"\n✅ 成功保存 {len(all_records)} 条记录至: {output_path}")
+        else:
+            print("\n❌ 无有效数据")
+
+if __name__ == "__main__":
+    GetYDData().main()

test/展会线索登记.py

@@ -0,0 +1,445 @@
+# -*- coding: utf-8 -*-
+import sys
+import io
+import imaplib
+import email
+import re
+from datetime import datetime, timedelta
+from email.header import decode_header
+import pandas as pd
+# 假设 api.py 在当前目录下，且包含 API 类
+import requests
+from typing import Optional, List, Dict, Any
+from decimal import Decimal
+import time
+import numpy as np
+from log_config import configure_task_logger, configure_error_task_logger
+import json
+
+# === 强制标准输出为 UTF-8 (兼容不同运行环境) ===
+# 注意：在部分 IDE 中重新包装 sys.stdout 可能会导致乱码，若报错可注释掉以下两行
+try:
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+    sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
+except AttributeError:
+    pass
+
+# ================= 配置区域 =================
+EMAIL_ACCOUNT = "zhangyang@f6car.cn"
+PASSWORD = "RGBdMggmJ4s2FzZK"  # ⚠️ 生产环境建议使用环境变量，不要硬编码
+IMAP_SERVER = "imap.qiye.aliyun.com"
+IMAP_PORT = 993
+SUBJECT_KEYWORD = "展会线索登记"
+DAYS_TO_SCAN = 30  # 扫描最近30天
+OUTPUT_FILE = f"展会线索_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
+
+# 定义标准字段顺序
+FIELD_KEYS = ["姓名", "手机号", "省", "市", "区", "公司名称", "备注"]
+
+
+class API:
+    def entry_data_list(self, data: dict, replace: bool = False, max_retries: int = 20) -> Dict:  # 获取多条表单数据
+        """
+        获取多条表单数据
+        :param max_retries: 最大重试次数
+        :param replace: 是否替换字段
+        :param data:
+                    api_key: 应用id
+                    entry_id: 表单id
+        :return:
+        """
+
+        url = 'https://api.jiandaoyun.com/api/v5/app/entry/data/list'
+
+        headers = {
+            'Authorization': "Bearer qygHulymo1fekJk4CIZyNKjyQAzG8CFN",  # 曹伟应用api测试 app_key
+            'Content-Type': 'application/json'
+        }
+        all_data_batches = []  # 用于存储每次请求返回的数据批次
+        last_data_id = None
+        exit_flag = False
+        while True:
+            payload = json.dumps({
+                "app_id": data['api_key'],  # 应用ID
+                "entry_id": data['entry_id'],  # 表单ID
+                "limit": 90,
+                "data_id": last_data_id,
+                "filter": data.get('filter', None)
+            })
+            retries = 0
+
+            while retries <= max_retries:
+                data_get = None
+                try:
+                    res = requests.post(url=url, data=payload, headers=headers, timeout=10)
+                    res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
+                    data_get = res.json()
+                    if data_get["data"]:
+                        all_data_batches.extend(data_get['data'])
+                        last_data_id = data_get['data'][-1].get('_id')
+                        print(f"已获取 {len(all_data_batches)} 条数据")
+                        break  # 成功则跳出循环
+                    else:
+                        if 'data' not in data_get or len(data_get['data']) == 0:
+                            exit_flag = True
+                            break
+                        retries += 1
+                        time.sleep(0.5)  # 在重试之间稍作停顿
+                except requests.exceptions.RequestException as e:
+                    retries += 1
+                    time.sleep(0.5)  # 在重试之间稍作停顿
+            if retries > max_retries:
+                all_data_batches.append(None)  # 或者可以选择记录失败的payload以便后续处理
+
+            if exit_flag:
+                break
+
+        # 构建最终返回的字典
+        final_data = {
+            'data': all_data_batches  # 'data' 键对应的值是列表的列表
+        }
+
+        return final_data
+
+    @staticmethod
+    def data_batch_create(data: dict, max_retries: int = 20) -> Optional[requests.Response]:  # 新建单条数据
+        """
+        新建单条表单数据
+        :param max_retries: 最大重试次数
+        :param data: 应该包含应用id、表单id，以及新建的数据data['data']
+        :return: 返回创建后简道云返回的信息
+        """
+        url = 'https://api.jiandaoyun.com/api/v5/app/entry/data/create'
+
+        headers = {
+            'Authorization': "Bearer qygHulymo1fekJk4CIZyNKjyQAzG8CFN",  # 曹伟应用api测试 app_key
+            'Content-Type': 'application/json'
+        }
+        # noinspection DuplicatedCode
+        payload = json.dumps({
+            "app_id": data['api_key'],  # 应用ID
+            "entry_id": data['entry_id'],  # 表单ID
+            "data": data['data'],
+            "is_start_workflow": data.get('is_start_workflow', "false"),
+            "is_start_trigger": data.get('is_start_trigger', "false"),
+            "transaction_id": data.get('transaction_id', "")
+        }
+        )
+
+        retries = 0
+        while retries <= max_retries:
+            try:
+                res: requests.Response = requests.post(url=url, data=payload, headers=headers, timeout=10)
+                res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
+                data_get = res.json()
+                if res.status_code == 200:
+                    return data_get
+                else:
+                    retries += 1
+                    time.sleep(3)  # 在重试之间稍作停顿
+            except requests.exceptions.RequestException as e:
+                retries += 1
+                time.sleep(3)  # 在重试之间稍作停顿
+        if retries > max_retries:
+            print(
+                f"任务 {data['data_list']} 连续{max_retries}次请求失败，放弃此次请求。")
+        return None
+
+
+# ===========================================
+
+def decode_mime_words(s):
+    if not s:
+        return ""
+    decoded_parts = []
+    # decode_header 返回的是 list of (bytes/str, encoding)
+    for part, encoding in decode_header(s):
+        if isinstance(part, bytes):
+            decoded_parts.append(part.decode(encoding or 'utf-8', errors='ignore'))
+        else:
+            decoded_parts.append(str(part))
+    return "".join(decoded_parts)
+
+
+def extract_data_from_body(body_text):
+    """
+    从邮件正文中提取线索数据。
+    格式：姓名 | 手机号 | 省 | 市 | 区 | 公司 | 备注
+    """
+    if not body_text:
+        return []
+
+    data_list = []
+    # 【修复点 1】splitlines() 是方法，需要加括号
+    lines = body_text.splitlines()
+
+    for line in lines:
+        line = line.strip()
+        # 如果行中没有分隔符，跳过
+        if '|' not in line:
+            continue
+
+        # 按 '|' 分割并去除首尾空格
+        parts = [p.strip() for p in line.split('|')]
+
+        # 【关键校验】至少需要前两个字段（姓名、手机号）非空
+        if len(parts) < 2 or not parts[0] or not parts[1]:
+            continue
+
+        # 构建字典，动态映射
+        record = {}
+        for i, key in enumerate(FIELD_KEYS):
+            if i < len(parts):
+                record[key] = parts[i]
+            else:
+                record[key] = ""  # 缺失的字段填空字符串
+
+        data_list.append(record)
+
+    return data_list
+
+
+def save_to_excel(leads, filename):
+    if not leads:
+        return None
+
+    df = pd.DataFrame(leads)
+
+    # 定义期望的列顺序
+    cols = ["姓名", "手机号", "省", "市", "区", "公司名称", "备注", "来源邮件时间"]
+
+    # 确保列存在且顺序正确
+    # 先保留所有现有列中在 cols 里的，按 cols 顺序
+    ordered_cols = [c for c in cols if c in df.columns]
+    # 再加上可能存在的其他列（虽然逻辑上不应该有，但以防万一）
+    other_cols = [c for c in df.columns if c not in cols]
+    final_cols = ordered_cols + other_cols
+
+    df = df[final_cols]
+    # df.to_excel(filename, index=False)
+    return df
+
+
+def main():
+    print(f"正在连接 IMAP 服务器：{IMAP_SERVER} ...")
+    mail = None
+
+    start_date = datetime.now() - timedelta(days=DAYS_TO_SCAN)
+    date_str = start_date.strftime("%d-%b-%Y").upper()
+
+    all_leads = []
+    count_processed = 0
+
+    try:
+        mail = imaplib.IMAP4_SSL(IMAP_SERVER, IMAP_PORT)
+        mail.login(EMAIL_ACCOUNT, PASSWORD)
+        mail.select("INBOX")
+
+        print(f"正在搜索 [{date_str}] 之后的邮件...")
+        search_query = f'(SINCE "{date_str}")'
+        status, messages = mail.search(None, search_query)
+
+        if status != "OK":
+            print("❌ 搜索失败")
+            return
+
+        mail_ids = messages[0].split()
+        if not mail_ids:
+            print(f"✅ 未找到 {date_str} 之后的新邮件。")
+            return
+
+        print(f"📩 找到 {len(mail_ids)} 封近期邮件，开始详细扫描...")
+
+        for mail_id in mail_ids:
+            try:
+                status, msg_data = mail.fetch(mail_id, "(RFC822)")
+                if status != "OK":
+                    continue
+
+                raw_email = msg_data[0][1]
+
+                if isinstance(raw_email, bytes):
+                    mime_msg = email.message_from_bytes(raw_email)
+                else:
+                    mime_msg = email.message_from_string(raw_email.decode('utf-8', errors='ignore'))
+
+                subject = decode_mime_words(mime_msg.get("Subject"))
+                if SUBJECT_KEYWORD not in subject:
+                    continue
+
+                count_processed += 1
+                date_str_full = mime_msg.get("Date")
+                body_content = ""
+
+                if mime_msg.is_multipart():
+                    for part in mime_msg.walk():
+                        content_disposition = part.get_content_disposition()
+                        if content_disposition and "attachment" in str(content_disposition):
+                            continue
+                        content_type = part.get_content_type()
+                        if content_type in ["text/plain", "text/html"]:
+                            try:
+                                charset = part.get_content_charset() or 'utf-8'
+                                payload = part.get_payload(decode=True)
+                                if payload:
+                                    text = payload.decode(charset, errors='ignore') if isinstance(payload,
+                                                                                                  bytes) else str(
+                                        payload)
+                                    if content_type == "text/html":
+                                        text = re.sub(r'<[^>]+>', ' ', text)
+                                    body_content += text + "\n"
+                            except Exception:
+                                pass
+                else:
+                    try:
+                        charset = mime_msg.get_content_charset() or 'utf-8'
+                        payload = mime_msg.get_payload(decode=True)
+                        if payload:
+                            body_content = payload.decode(charset, errors='ignore') if isinstance(payload,
+                                                                                                  bytes) else str(
+                                payload)
+                    except Exception:
+                        pass
+
+                leads = extract_data_from_body(body_content)
+
+                for lead in leads:
+                    lead["来源邮件时间"] = date_str_full
+
+                if leads:
+                    print(f"[{subject}] -> 提取 {len(leads)} 条")
+                    all_leads.extend(leads)
+
+            except Exception as e:
+                print(f"处理邮件 ID {mail_id} 时出错：{e}")
+                continue
+
+        # ================= 新增：本地数据去重逻辑 =================
+        original_count = len(all_leads)
+        if original_count > 0:
+            seen_phones = set()
+            unique_leads = []
+
+            for lead in all_leads:
+                phone = str(lead.get("手机号", "")).strip()
+                # 如果手机号为空，或者已经出现过，则跳过
+                if not phone or phone in seen_phones:
+                    continue
+
+                seen_phones.add(phone)
+                unique_leads.append(lead)
+
+            all_leads = unique_leads
+            removed_count = original_count - len(all_leads)
+            if removed_count > 0:
+                print(
+                    f"\n⚠️ 检测到重复数据，已根据【手机号】去重：原始 {original_count} 条 -> 去重后 {len(all_leads)} 条 (移除 {removed_count} 条)")
+            else:
+                print(f"\n✅ 数据检查完成，无重复手机号。共 {len(all_leads)} 条。")
+        # =======================================================
+
+        df = save_to_excel(all_leads, OUTPUT_FILE)
+
+        if df is not None:
+            print(f"\n✅ 成功！共扫描 {count_processed} 封匹配邮件，最终有效线索 {len(all_leads)} 条。")
+            print(f"文件已保存至：{OUTPUT_FILE}")
+        else:
+            print(f"\n⚠️ 扫描完成，但在 {count_processed} 封近期邮件中未找到符合格式的数据。")
+            return  # 如果没有数据，后续同步逻辑无需执行
+
+        # 同步至简道云
+        if all_leads:
+            print("\n开始同步至简道云...")
+            api_instance = API()
+
+            payload_query = {
+                "api_key": "66b9678280b37f8a276b1d01",
+                "entry_id": "69b22dc5434e05c7b6b4b5b2",
+            }
+
+            try:
+                response = api_instance.entry_data_list(payload_query)
+                now_data = response.get("data", []) if response else []
+
+                existing_phones = set()
+                phone_widget_id = "_widget_1692928669587"
+
+                for item in now_data:
+                    phone_val = item.get(phone_widget_id)
+                    if phone_val:
+                        existing_phones.add(str(phone_val).strip())
+
+                print(f"简道云现有手机号数量：{len(existing_phones)}")
+
+                new_count = 0
+                # 此时 df 已经是去重后的数据，且 all_leads 也是去重后的
+                # 再次遍历 df 确保只提交本地没有的（防止简道云已有但本地没查到的情况，虽然逻辑上上面已经过滤了）
+                # 为了代码健壮性，这里保留原有的 existing_phones 检查逻辑
+                for index, row in df.iterrows():
+                    current_phone = str(row["手机号"]).strip()
+
+                    if not current_phone:
+                        continue
+
+                    # 双重保险：如果简道云里已经有了，跳过
+                    if current_phone in existing_phones:
+                        print(f"跳过 (云端已存在): {current_phone}")
+                        continue
+
+                    new_payload = {
+                        "api_key": "66b9678280b37f8a276b1d01",
+                        "entry_id": "69b22dc5434e05c7b6b4b5b2",
+                        "data": {
+                            "_widget_1690785229260": {"value": row.get("姓名", "")},
+                            "_widget_1690785229261": {"value": row.get("公司名称", "")},
+                            "_widget_1692928669587": {"value": row.get("手机号", "")},
+                            "_widget_1690785229266": {"value": row.get("备注", "")},
+                            "_widget_1690785326597": {"value": {"province": row.get("省", ""),
+                                                                "city": row.get("市", ""),
+                                                                "district": row.get("区", ""),
+                                                                "detail": row.get("省", "") + row.get("市",
+                                                                                                      "") + row.get(
+                                                                    "区", ""),
+                                                                }
+                                                      },
+                            "_widget_1690785229279": {"value": row.get("市", "")},
+                            "_widget_1773381838511": {"value": row.get("省", "")},
+                            "_widget_1692070309987": {"value": row.get("区", "")},
+                        }
+                    }
+
+                    result = api_instance.data_batch_create(new_payload)
+
+                    if result and (result.get("success") or result.get("code") == 200 or "error" not in result):
+                        new_count += 1
+                        print(f"新增成功：{current_phone} ({row.get('姓名')})")
+                    else:
+                        print(f"提交结果：{current_phone}, 返回：{result}")
+                        # 如果提交成功但返回格式奇怪，也可以考虑计入成功，视具体API文档而定
+                        # 这里保守处理，只有明确成功才算
+
+                print(f"\n✅ 同步完成，本次新增 {new_count} 条数据。")
+
+            except Exception as api_err:
+                print(f"\n❌ 简道云 API 交互错误：{api_err}")
+                import traceback
+                traceback.print_exc()
+
+    except imaplib.IMAP4.error as e:
+        print("\n❌ IMAP 协议错误:")
+        print(f"错误详情：{e}")
+    except Exception as e:
+        print("\n❌ 发生严重错误:")
+        import traceback
+        traceback.print_exc()
+    finally:
+        if mail:
+            try:
+                mail.close()
+                mail.logout()
+            except:
+                pass
+
+
+if __name__ == "__main__":
+    main()

test/异常回访待办问题排查.ipynb

@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-07T02:17:11.661841100Z",
+     "start_time": "2026-01-07T02:17:11.600589500Z"
+    }
+   },
+   "source": [
+    "from back_ground_module import CommonModule\n",
+    "from api import API\n",
+    "from log_config import configure_task_logger, configure_error_task_logger\n",
+    "from datetime import datetime, timedelta, timezone\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "# 获取已经配置好的常规日志记录器\n",
+    "logger = configure_task_logger()\n",
+    "# 获取已经配置好的错误任务日志记录器\n",
+    "error_task_logger = configure_error_task_logger()\n",
+    "# 保存为CSV文件\n",
+    "output_dir = \"output\"  # 设置输出目录\n",
+    "# 创建输出目录（如果不存在）\n",
+    "import os\n",
+    "\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "common_module = CommonModule()\n",
+    "api_instance = API()\n",
+    "\n",
+    "data_JCB = common_module.get_jcb_details()\n",
+    "current_local = datetime.now() + timedelta(days=-1) # tz-naive，代表本地时间\n",
+    "current_date_str = current_local.strftime(\"%Y-%m-%d\")\n",
+    "# 计算30天前的本地日期（用于开户日判断）\n",
+    "thirty_days_ago_local = (current_local - timedelta(days=30)).date()\n",
+    "payload = {\"api_key\": \"6717470a0b3975ef583c6df1\",\n",
+    "                   \"entry_id\": \"67174710da507490d8ac12c1\",\n",
+    "                   }\n",
+    "daily_revisit = api_instance.entry_data_list(payload)\n",
+    "daily_revisit_list = daily_revisit.get(\"data\")  # api请求格式，将数据封装在data字典里\n",
+    "abnormal_data = []\n",
+    "for index, row in data_JCB.iterrows():\n",
+    "    try:\n",
+    "        # 开户日是本地日期字符串，解析为 date 对象\n",
+    "        open_date = datetime.strptime(str(row['开户日']), \"%Y-%m-%d\").date()\n",
+    "    except (ValueError, TypeError):\n",
+    "        continue  # 跳过无效日期\n",
+    "\n",
+    "    if (\n",
+    "            open_date < thirty_days_ago_local\n",
+    "            and row['近30天开单天数'] == 0\n",
+    "            and row['客户状态'] == \"留存\"\n",
+    "    ):\n",
+    "        new_row = row.copy()\n",
+    "        new_row[\"日期\"] = open_date.strftime(\"%Y-%m-%d\")\n",
+    "        abnormal_data.append(new_row)\n",
+    "\n",
+    "abnormal_data = pd.DataFrame(abnormal_data) if abnormal_data else pd.DataFrame()\n",
+    "\n",
+    "if not abnormal_data.empty:\n",
+    "    abnormal_data[\"表单类型\"] = \"异常待办\"\n",
+    "    abnormal_data[\"派发日期\"] = current_date_str\n",
+    "\n",
+    "    # 清洗手机号（仅去除浮点型 .0）\n",
+    "    def clean_phone(x):\n",
+    "        if pd.isna(x) or x == \"\" or x == \"None\":\n",
+    "            return \"\"\n",
+    "        s = str(x)\n",
+    "        if s.endswith('.0') and s[:-2].isdigit():\n",
+    "            return s[:-2]\n",
+    "        return s\n",
+    "\n",
+    "    abnormal_data['联系手机号'] = abnormal_data['联系手机号'].apply(clean_phone)\n",
+    "\n",
+    "# 构建云端已派发记录 DataFrame\n",
+    "df_cloud = pd.DataFrame([\n",
+    "    {\n",
+    "        \"数据id\": item.get(\"_id\", \"\"),\n",
+    "        \"账号\": item.get(\"_widget_1739258942667\", \"\"),\n",
+    "        \"提交时间\": item.get(\"createTime\", \"\"),\n",
+    "        \"表单类型\": item.get(\"_widget_1739951204545\", \"\")\n",
+    "    }\n",
+    "    for item in daily_revisit_list\n",
+    "])\n",
+    "\n",
+    "recent_accounts = set()\n",
+    "if not df_cloud.empty and not abnormal_data.empty:\n",
+    "    # 将 createTime 转为 UTC 时间（强制统一时区）\n",
+    "    df_cloud[\"提交时间\"] = pd.to_datetime(df_cloud[\"提交时间\"], utc=True, errors=\"coerce\")\n",
+    "    df_cloud = df_cloud.dropna(subset=[\"提交时间\"])\n",
+    "\n",
+    "    # 筛选“异常待办”\n",
+    "    df_abnormal_cloud = df_cloud[df_cloud[\"表单类型\"] == \"异常待办\"]\n",
+    "\n",
+    "    if not df_abnormal_cloud.empty:\n",
+    "        # 每个账号保留最新一条\n",
+    "        df_recent = df_abnormal_cloud.sort_values(\"提交时间\").groupby(\"账号\", as_index=False).tail(1)\n",
+    "\n",
+    "        current_utc = datetime.now(timezone.utc)\n",
+    "        cutoff_utc = pd.Timestamp(current_utc) - pd.Timedelta(days=30)\n",
+    "\n",
+    "        # 安全比较：两边都是 UTC\n",
+    "        recent_accounts = set(df_recent[df_recent[\"提交时间\"] > cutoff_utc][\"账号\"])\n",
+    "\n",
+    "# 剔除已派发账号 + 过滤有效手机号\n",
+    "if not abnormal_data.empty:\n",
+    "    abnormal_data = abnormal_data[\n",
+    "        (~abnormal_data[\"账号\"].isin(recent_accounts)) &\n",
+    "        (abnormal_data[\"联系手机号\"].notna()) &\n",
+    "        (abnormal_data[\"联系手机号\"] != \"\") &\n",
+    "        (abnormal_data[\"联系手机号\"] != \"None\")\n",
+    "        ]\n",
+    "\n",
+    "# # 保存结果\n",
+    "output_path = os.path.join(output_dir, \"异常待办1.csv\")\n",
+    "abnormal_data.to_csv(output_path, index=False)"
+   ],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'back_ground_module'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mModuleNotFoundError\u001B[39m                       Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[3]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mback_ground_module\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m CommonModule\n\u001B[32m      2\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mapi\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m API\n\u001B[32m      3\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mlog_config\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m configure_task_logger, configure_error_task_logger\n",
+      "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'back_ground_module'"
+     ]
+    }
+   ],
+   "execution_count": 3
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/异常回访待办问题排查.py

@@ -0,0 +1,112 @@
+from back_ground_module import CommonModule
+from api import API
+from log_config import configure_task_logger, configure_error_task_logger
+from datetime import datetime, timedelta, timezone
+import pandas as pd
+import os
+
+# 获取已经配置好的常规日志记录器
+logger = configure_task_logger()
+# 获取已经配置好的错误任务日志记录器
+error_task_logger = configure_error_task_logger()
+# 保存为CSV文件
+output_dir = "output"  # 设置输出目录
+# 创建输出目录（如果不存在）
+import os
+
+os.makedirs(output_dir, exist_ok=True)
+common_module = CommonModule()
+api_instance = API()
+
+data_JCB = common_module.get_jcb_details()
+output_path = os.path.join(output_dir, "借车包明细.csv")
+data_JCB.to_csv(output_path, index=False)
+
+current_local = datetime.now() + timedelta(days=-1) # tz-naive，代表本地时间
+current_date_str = current_local.strftime("%Y-%m-%d")
+# 计算30天前的本地日期（用于开户日判断）
+thirty_days_ago_local = (current_local - timedelta(days=30)).date()
+payload = {"api_key": "6717470a0b3975ef583c6df1",
+                   "entry_id": "67174710da507490d8ac12c1",
+                   }
+daily_revisit = api_instance.entry_data_list(payload)
+daily_revisit_list = daily_revisit.get("data")  # api请求格式，将数据封装在data字典里
+abnormal_data = []
+for index, row in data_JCB.iterrows():
+    try:
+        # 开户日是本地日期字符串，解析为 date 对象
+        open_date = datetime.strptime(str(row['开户日']), "%Y-%m-%d").date()
+    except (ValueError, TypeError):
+        continue  # 跳过无效日期
+
+    if (
+            open_date < thirty_days_ago_local
+            and row['近30天开单天数'] == 0
+            and row['客户状态'] == "留存"
+    ):
+        new_row = row.copy()
+        new_row["日期"] = open_date.strftime("%Y-%m-%d")
+        abnormal_data.append(new_row)
+
+abnormal_data = pd.DataFrame(abnormal_data) if abnormal_data else pd.DataFrame()
+output_path = os.path.join(output_dir, "异常待办.csv")
+abnormal_data.to_csv(output_path, index=False)
+
+if not abnormal_data.empty:
+    abnormal_data["表单类型"] = "异常待办"
+    abnormal_data["派发日期"] = current_date_str
+
+    # 清洗手机号（仅去除浮点型 .0）
+    def clean_phone(x):
+        if pd.isna(x) or x == "" or x == "None":
+            return ""
+        s = str(x)
+        if s.endswith('.0') and s[:-2].isdigit():
+            return s[:-2]
+        return s
+
+    abnormal_data['联系手机号'] = abnormal_data['联系手机号'].apply(clean_phone)
+
+# 构建云端已派发记录 DataFrame
+df_cloud = pd.DataFrame([
+    {
+        "数据id": item.get("_id", ""),
+        "账号": item.get("_widget_1739258942667", ""),
+        "提交时间": item.get("createTime", ""),
+        "表单类型": item.get("_widget_1739951204545", "")
+    }
+    for item in daily_revisit_list
+])
+output_path = os.path.join(output_dir, "异常待办云端.csv")
+df_cloud.to_csv(output_path, index=False)
+recent_accounts = set()
+if not df_cloud.empty and not abnormal_data.empty:
+    # 将 createTime 转为 UTC 时间（强制统一时区）
+    df_cloud["提交时间"] = pd.to_datetime(df_cloud["提交时间"], utc=True, errors="coerce")
+    df_cloud = df_cloud.dropna(subset=["提交时间"])
+
+    # 筛选“异常待办”
+    df_abnormal_cloud = df_cloud[df_cloud["表单类型"] == "异常待办"]
+
+    if not df_abnormal_cloud.empty:
+        # 每个账号保留最新一条
+        df_recent = df_abnormal_cloud.sort_values("提交时间").groupby("账号", as_index=False).tail(1)
+
+        current_utc = datetime.now(timezone.utc)
+        cutoff_utc = pd.Timestamp(current_utc) - pd.Timedelta(days=30)
+
+        # 安全比较：两边都是 UTC
+        recent_accounts = set(df_recent[df_recent["提交时间"] > cutoff_utc]["账号"])
+
+# 剔除已派发账号 + 过滤有效手机号
+if not abnormal_data.empty:
+    abnormal_data = abnormal_data[
+        (~abnormal_data["账号"].isin(recent_accounts)) &
+        (abnormal_data["联系手机号"].notna()) &
+        (abnormal_data["联系手机号"] != "") &
+        (abnormal_data["联系手机号"] != "None")
+        ]
+
+# # 保存结果
+output_path = os.path.join(output_dir, "异常待办1.csv")
+abnormal_data.to_csv(output_path, index=False)

test/异常服务代办暂停派发不生效问题排查.py

@@ -0,0 +1,556 @@
+import datetime
+import os
+import time
+import requests
+from api import API
+from back_ground_module import CommonModule
+import pandas as pd
+from log_config import configure_task_logger, configure_error_task_logger
+
+api_instance = API()
+common_module = CommonModule()
+# start_time = datetime.datetime.now()
+
+# 获取已经配置好的常规日志记录器
+logger = configure_task_logger()
+
+# 获取已经配置好的错误任务日志记录器
+error_task_logger = configure_error_task_logger()
+output_dir = "output"  # 设置输出目录
+os.makedirs(output_dir, exist_ok=True)
+
+
+class NewExceptionTask:
+    """
+    SaaS异常回访
+    """
+
+    def __init__(self):
+        self.exception_service_todo = None
+        self.get_feature_usage = None
+        self.saas_create_time = None
+        self.index = None
+        self.date_one = None
+        self.data_yichang_S = None
+        self.date_list = None
+        self.Smart_detection = None
+        self.service_remind = None
+        self.NGV_data_list = None
+        self.permissions_table = None
+        self.staff_id_list = None
+        self.json_list = []
+        self.policy_recognition = None
+        self.widget_list = None
+        self.private_domain = None
+        self.public_domain = None
+        self.public_domain_list = None
+        self.different_industries = None
+        self.different_industries_list = None
+        self.groupnotification = None
+        self.fields_mapping = {
+            "门店名称": "_widget_1748241895830",
+            "联系人": "_widget_1748241895831",
+            "开户时间": "_widget_1748241895839",
+            "门店编码": "_widget_1748241895842",
+            "联系方式": "_widget_1748241895832",
+            "系统版本": "_widget_1748241895850",
+            "公司名称": "_widget_1748241895844",
+            "运营顾问": "_widget_1748246808679",
+            "区域经理": "_widget_1748246808682",
+            "公司等级": "_widget_1748241895846",
+            "运营专家": "_widget_1748246808681",
+            "操作模式E.L/E.S": "_widget_1748241895853",
+            "活跃健康状态变化": "_widget_1748241895829",
+            "初始日": "_widget_1748241895833",
+            "推进日": "_widget_1748241895834",
+            "异常跟进情况描述": "_widget_1748512176640",
+            "异常变化原因": "_widget_1748512176641",
+            "正常使用": "_widget_1748512176643",
+            "门店原因": "_widget_1748512176645",
+            "服务原因": "_widget_1748512176647",
+            "产品原因": "_widget_1748512176649",
+            "未正式切换": "_widget_1748512176651",
+            "跟进状态": "_widget_1748512176655",
+            "是否可激活": "_widget_1758615839701",
+            "是否有续约风险": "_widget_1758615839703",
+            "当前跟进人": "_widget_1748246808678",
+            "激活策略": "_widget_1758615839717",
+            "跟进时间": "_widget_1748512176654",
+            "是否跟进完成": "_widget_1751273412737",
+            "区域客服": "_widget_1748246808680",
+            "大区": "_widget_1748241895847",
+            "省": "_widget_1748241895848",
+            "城市": "_widget_1748241895855",
+            "门店类型": "_widget_1748241895849",
+            "saas客户类型": "_widget_1748241895851",
+            "门店阶段": "_widget_1748241895852",
+            "提交人": "creator",
+            "提交时间": "createTime",
+            "更新时间": "updateTime"
+        }
+
+    def calculate_date_one(self, start_offset=0):
+        """
+        计算从当前日期（或指定偏移量的日期）开始，往前遍历遇到date_list中日期的次数。
+
+        参数:
+        - start_offset: 从当前日期起始的天数偏移量，默认为0（即今天）。负数表示过去，正数表示未来。
+
+        返回:
+        - date_one: 遍历到date_list中日期的次数。
+        """
+        jdy_date = datetime.datetime.now().strftime("%Y-%m-%d")
+        jdy_start_time = datetime.datetime.now().strftime("%Y-%m-%d ")
+        # 设置起始日期
+        now_time = datetime.datetime.now() + datetime.timedelta(days=start_offset)
+
+        # 初始化计数器
+        date_one = 1
+        print("当前日期：", now_time.strftime("%Y-%m-%d"))
+        # 检查起始日期是否在date_list中
+        if now_time.strftime("%Y-%m-%d") in self.date_list:
+            date_one = 0
+            print("开始次数：", date_one)
+
+        else:
+            # 遍历日期
+            for i in range(1, 10):
+                new_date = now_time + datetime.timedelta(days=-i)
+                new_date_str = new_date.strftime("%Y-%m-%d")
+                print("遍历日期：", new_date_str)
+                if new_date_str in self.date_list:
+                    date_one += 1
+                    print("节假日期：", new_date_str)
+                else:
+                    break
+
+        print("遍历次数：", date_one)
+        return date_one
+
+    @staticmethod
+    def download_url_content(url, save_path):
+        """
+        下载指定 URL 的内容并保存到本地文件。
+
+        :param url: 要下载内容的 URL
+        :param save_path: 保存文件的路径
+        """
+        try:
+            # 发送 GET 请求以获取内容
+            response = requests.get(url, stream=True)
+            response.raise_for_status()  # 如果响应状态码不是 200，抛出异常
+
+            # 确保保存目录存在
+            os.makedirs(os.path.dirname(save_path), exist_ok=True)
+
+            # 将内容写入文件
+            with open(save_path, 'wb') as file:
+                for chunk in response.iter_content(chunk_size=8192):  # 分块写入，避免占用过多内存
+                    if chunk:  # 过滤掉空块
+                        file.write(chunk)
+
+            print(f"文件已成功保存到 {save_path}")
+
+        except requests.exceptions.RequestException as e:
+            print(f"下载失败: {e}")
+        except Exception as e:
+            print(f"发生错误: {e}")
+
+    def load_all_data(self):
+        """加载所有必要的数据表"""
+        # 省市区人员关系表
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "676512ac3e54dc3159460c0a"}
+        json_dict = api_instance.entry_data_list(payload)
+        self.json_list = json_dict.get("data")
+
+        # 获取简道云员工id
+        payload = {"api_key": "6694d3c4fcb69ca9a111a6c4",
+                   "entry_id": "6769204a1902c9341340a1bc",
+                   }
+        staff_id = api_instance.entry_data_list(payload)
+        self.staff_id_list = staff_id.get("data")  # api请求格式，将数据封装在data字典里
+
+        # 获取NGV数据
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "675bb02bd2d53c2034c665e4"}
+        self.NGV_data_list = api_instance.entry_data_list(payload).get("data", [])
+        # print("NGV获取后的类型：", type(self.NGV_data_list))
+
+        # 获取异常服务待办(添加过滤进行中的订单)
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "68340de79f116c0b66b6b0cc",
+                   "filter": {"rel": "and",
+                              "cond": [{"field": "flowState", "type": "flowstate", "method": "eq", "value": [0]}]}}
+        self.exception_service_todo = api_instance.entry_data_list(payload).get("data", [])
+        # print(self.exception_service_todo)
+
+    @staticmethod
+    def build_index(json_list):
+        index = {}
+        for json_item in json_list:
+            try:
+                key = (json_item['_widget_1734677164861'], json_item['_widget_1734677164862'],
+                       json_item['_widget_1734677164863'])  # 省市区
+                if '_widget_1734677164870' not in json_item:  # 异常回访客服
+                    raise KeyError("缺少 '异常回访客服' 键")
+                index[key] = json_item
+            except KeyError as e:
+                print(f"警告：{e}，跳过该条记录: {json_item}")
+                continue
+        print('index', index)
+        return index
+
+    @staticmethod
+    def find_customer_service(province_name, city_name, area_name, index):
+        key = (province_name, city_name, area_name)
+        # print(index)
+        if key not in index:
+            return "数据缺失: 未找到对应的异常回访客服"
+
+        return index[key]
+
+    @staticmethod
+    def get_staff_id(row_item, name):
+        """辅助函数，用于获取员工ID"""
+        if str(row_item["_widget_1734942794144"]) == str(name):  # 检查姓名是否匹配
+            return row_item["_widget_1734942794145"]  # 返回员工ID
+        return None
+
+    def assign_customer_service(self, province_name, city_name, area_name, index):
+        """根据省市区派发给异常回访客服"""
+        # try:
+        customer_service_info = self.find_customer_service(province_name, city_name, area_name, index)
+        customer_service = customer_service_info.get('_widget_1734677164870', {}).get('username')  # 异常回访客服
+        return customer_service
+        # except Exception as e:
+        #     print(f"Error finding customer service: {e}")
+        #     return "分配失败，请检查", "分配失败，请检查", "分配失败，请检查"
+
+    def main(self):
+
+        task_start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        all_data = []
+        try:
+            self.load_all_data()
+
+            data = common_module.get_yichang_details(days_back=1)
+            self.data_yichang_S = pd.DataFrame() if data is None or data.empty else data.astype(str)
+            self.index = self.build_index(self.json_list)
+
+            logger.info("开始运行SaaS异常回访")
+            if self.data_yichang_S.empty:
+                logger.info("未获取到数据或数据为空")
+                common_module.send_task_status(task_start_time, "异常服务待办派发")
+                return
+
+            data_yichang = self.data_yichang_S.copy()
+
+            # data_yichang.to_csv(os.path.join(output_dir,"data_yichang.csv"), index=False)
+
+            def replace_values(series):
+                # 使用条件判断来进行替换
+                return series.apply(lambda x: '' if pd.isna(x) or x in ['NA', 'None', ''] else x)
+
+            # 对整个DataFrame的所有列应用替换函数
+            data_yichang = data_yichang.apply(replace_values)
+            error_data = []
+            for index_num, row in data_yichang.iterrows():  # 对过滤后的每一条进行派发
+                try:
+                    # 每次循环前清空省市区变量
+                    province_name = None
+                    city_name = None
+                    area_name = None
+
+                    is_pass = False
+                    for exception_service in self.exception_service_todo:
+                        # 通过查询筛选进行中的逻辑
+                        if exception_service['_widget_1748241895842'] == row['org_code']:
+                            is_pass = True
+                            break
+
+                    if is_pass:
+                        logger.info(f"已存在待办，跳过该条记录: {row}")
+                        continue
+
+                    payload_dict = {}
+
+                    distribution_date = datetime.datetime.now(datetime.timezone.utc)
+                    distribution_date = distribution_date.strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'
+
+                    date_obj1 = datetime.datetime.strptime(row["init_day"], "%Y%m%d").strftime("%Y-%m-%d")
+                    date_obj2 = datetime.datetime.strptime(row["push_day"], "%Y%m%d").strftime("%Y-%m-%d")
+
+                    NGV_roles = {
+                        'service_impl_principal': row['service_impl_principal'],  # 运营负责人
+                        'area_manager': row['area_manager'],  # 区域经理
+                        'technician': row['technician'],  # 运营专家
+                    }
+                    for role, name in NGV_roles.items():  # 寻找对应的员工ID
+                        for row_item in self.staff_id_list:
+                            staff_id = self.get_staff_id(row_item, name)
+                            if staff_id:
+                                NGV_roles[role] = staff_id
+                                break  # 找到后退出循环
+                        else:
+                            NGV_roles[role] = None  # 如果没有找到对应的员工ID
+                    relationship_manager, area_manager, technician = [NGV_roles[role] for role in
+                                                                      ['service_impl_principal',
+                                                                       'area_manager',
+                                                                       'technician']]
+
+                    UUid = time.strftime("%Y%m%d%H%M%S", time.localtime())
+
+                    NGV_data_id = None
+                    reason = None
+                    create_exception = None
+                    create_date = None
+
+                    # 优先从 data_yichang_S 获取省市区信息
+                    province_name = row.get('province_name')
+                    city_name = row.get('city_name')
+                    area_name = row.get('area_name') if 'area_name' in row else row.get('district_name')
+
+                    # 检查省市区是否完整（省市区是一体的，任意一个缺失就需要从NGV获取）
+                    use_ngv_location = False
+                    if (not province_name or province_name in ['', 'None', 'NA'] or
+                            not city_name or city_name in ['', 'None', 'NA'] or
+                            not area_name or area_name in ['', 'None', 'NA']):
+                        use_ngv_location = True
+                        logger.info(f"门店 {row['org_code']} 的省市区信息不完整，将从NGV_data_list获取")
+                    stop_date = None
+                    # 获取关联数据
+                    for NGV_Data in self.NGV_data_list:
+                        # NGV_Data = NGV_Data.get("data")
+                        if row["org_code"] == NGV_Data.get("_widget_1734062123071"):  # 门店编码
+                            NGV_data_id = NGV_Data.get("_id")
+
+                            # 如果需要从 NGV_data_list 获取省市区信息
+                            if use_ngv_location:
+                                province_name = NGV_Data.get("_widget_1734062123090")
+                                city_name = NGV_Data.get("_widget_1734062123092")
+                                area_name = NGV_Data.get("_widget_1734062123094")
+                                logger.info(
+                                    f"【从NGV获取省市区】门店 {row['org_code']}: {province_name}, {city_name}, {area_name}")
+
+                            # 门店原因
+                            reason = NGV_Data.get("_widget_1758617393828")
+                            logger.info(f"获取关联数据成功:{NGV_data_id}, {province_name}, {city_name}, {area_name}")
+                            # 是否生成异常待办
+                            create_exception = NGV_Data.get("_widget_1758769279995")
+                            # 获取上线日期（文本）# 202512.3改为开户日
+                            create_date = NGV_Data.get("_widget_1734062123081")
+                            # 获取暂停派发日期
+                            stop_date = NGV_Data.get("_widget_1772610343227", None)
+                            break  # 找到匹配的数据后退出循环
+
+                    # 定义可能的日期格式（灵活应对不同格式）
+                    date_formats = [
+                        "%Y-%m-%d %H:%M:%S",  # 含时间
+                        "%Y-%m-%d",  # 仅日期
+                        "%Y/%m/%d",
+                        "%Y/%m/%d %H:%M:%S"
+                    ]
+                    
+                    if stop_date:
+                        # 解析暂停派发日期
+                        parsed_stop_date = None
+                        stop_value = stop_date.get("value") if isinstance(stop_date, dict) else stop_date
+                        if isinstance(stop_value, (int, float)):
+                            parsed_stop_date = datetime.datetime.fromtimestamp(
+                                stop_value / 1000, tz=datetime.timezone.utc
+                            ).replace(tzinfo=None)
+                        elif isinstance(stop_value, str):
+                            stop_str = stop_value.strip()
+                            iso_candidate = stop_str[:-1] + "+00:00" if stop_str.endswith("Z") else stop_str
+                            try:
+                                iso_dt = datetime.datetime.fromisoformat(iso_candidate)
+                            except ValueError:
+                                iso_dt = None
+
+                            if iso_dt is not None:
+                                parsed_stop_date = iso_dt.astimezone(datetime.timezone.utc).replace(tzinfo=None) if iso_dt.tzinfo else iso_dt
+                            else:
+                                for fmt in date_formats:
+                                    try:
+                                        parsed_stop_date = datetime.datetime.strptime(stop_str, fmt)
+                                        logger.debug(f"使用格式 {fmt} 成功解析暂停派发日期: {parsed_stop_date}")
+                                        break
+                                    except ValueError:
+                                        continue
+                        
+                        if parsed_stop_date:
+                            # 获取当前UTC时间
+                            current_utc_time = datetime.datetime.utcnow()
+                            logger.debug(f"当前UTC时间: {current_utc_time}")
+                            logger.debug(f"暂停派发日期: {parsed_stop_date}")
+                            
+                            # 比较时间
+                            if current_utc_time < parsed_stop_date:
+                                logger.info(f"当前UTC时间低于暂停派发日期，跳过派发")
+                                continue
+
+                    # 判断门店原因
+                    # if reason in ["门店倒闭", "门店转让", "加盟其他连锁","切换竞品","虚拟门店","重新开户","已退款","二套系统"]:
+                    #     continue
+
+                    # 判断是否继续生成异常待办
+                    if create_exception == "否":
+                        continue
+                    # 新增：检查 create_date_str 是否存在且有效
+                    create_date_value = create_date.get("value") if isinstance(create_date, dict) else create_date
+                    if not create_date_value:
+                        logger.warning("上线日期为空，跳过该记录")
+                        continue
+
+                    parsed_date = None
+                    if isinstance(create_date_value, (int, float)):
+                        local_tz = datetime.timezone(datetime.timedelta(hours=8))
+                        parsed_date = datetime.datetime.fromtimestamp(create_date_value / 1000, tz=local_tz).date()
+                    elif isinstance(create_date_value, str):
+                        create_str = create_date_value.strip()
+                        iso_candidate = create_str[:-1] + "+00:00" if create_str.endswith("Z") else create_str
+                        try:
+                            iso_dt = datetime.datetime.fromisoformat(iso_candidate)
+                        except ValueError:
+                            iso_dt = None
+
+                        if iso_dt is not None:
+                            local_tz = datetime.timezone(datetime.timedelta(hours=8))
+                            parsed_date = iso_dt.date() if iso_dt.tzinfo is None else iso_dt.astimezone(local_tz).date()
+                        else:
+                            for fmt in date_formats:
+                                try:
+                                    parsed_date = datetime.datetime.strptime(create_str, fmt).date()
+                                    logger.debug(f"使用格式 {fmt} 成功解析日期: {parsed_date}")
+                                    break
+                                except ValueError:
+                                    continue
+
+                    if parsed_date is None:
+                        logger.error(f"无法解析上线日期: '{create_date_value}'，支持的格式: %Y-%m-%d, %Y-%m-%d %H:%M:%S 等")
+                        continue  # 解析失败，跳过
+
+                    # 使用解析后的日期进行判断
+                    now_date = datetime.date.today()
+                    delta = now_date - parsed_date
+                    days_diff = delta.days
+
+                    if days_diff > 30:
+                        logger.info(f"上线日期 {parsed_date} 超过30天（{days_diff}天），生成待办")
+                        # ✅ 继续后续待办创建逻辑
+                    else:
+                        logger.info(f"上线日期 {parsed_date} 在30天内，跳过处理")
+                        continue
+
+                    if not NGV_data_id:
+                        logger.warning(f"未找到关联数据，请检查门店编码: {row['org_code']}")
+
+                    # 根据省市区派发给异常回访客服
+                    # 检查省市区是否都有值，如果有任何一个为空，则客服为空
+                    if (not province_name or province_name in ['', 'None', 'NA'] or
+                            not city_name or city_name in ['', 'None', 'NA'] or
+                            not area_name or area_name in ['', 'None', 'NA']):
+                        customer_service = None
+                        logger.warning(f"【省市区信息缺失】门店 {row['org_code']} 省市区信息不完整，异常回访客服设置为空")
+                        logger.warning(f"省: {province_name}, 市: {city_name}, 区: {area_name}")
+                    else:
+                        customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
+                        logger.info(f"【派发客服】门店 {row['org_code']} 派发给客服: {customer_service}")
+
+                    payload_dict.update({
+                        "_widget_1748241895829": {"value": row["health_warning_info"]},  # 活跃健康状态变化
+
+                        "_widget_1748241895830": {"value": row["org_name"]},  # 门店名称
+
+                        "_widget_1748241895831": {"value": row["contacts"]},  # 联系人
+
+                        "_widget_1748241895832": {"value": row['contact_mobile']},  # 联系方式
+
+                        "_widget_1748241895833": {
+                            "value": int(time.mktime(time.strptime(date_obj1, "%Y-%m-%d")) * 1000) if row[
+                                                                                                          "init_day"] != '' else ''},
+                        # 初始日
+
+                        "_widget_1748241895834": {
+                            "value": int(time.mktime(time.strptime(date_obj2, "%Y-%m-%d")) * 1000) if row[
+                                                                                                          "push_day"] != '' else ''},
+                        # 推进日
+
+                        "_widget_1748246808678": {"value": customer_service},  # 当前跟进人
+                        # "_widget_1748246808678": {"value": "083726094935447433"},  # 当前跟进人
+
+                        "_widget_1748246808679": {"value": relationship_manager},  # 运营负责人
+
+                        "_widget_1748246808680": {"value": customer_service},  # 区域客服
+
+                        "_widget_1748241895839": {
+                            "value": int(time.mktime(time.strptime(row["saas_create_time"], "%Y-%m-%d")) * 1000) if row[
+                                                                                                                        "saas_create_time"] != '' else ''},
+                        # 开户时间
+
+                        "_widget_1748246808681": {"value": technician},  # 技术专家
+
+                        "_widget_1748246808682": {"value": area_manager},  # 区域经理
+
+                        "_widget_1748241895842": {"value": row['org_code']},  # 门店编码
+
+                        "_widget_1748241895844": {"value": row['group_name']},  # 公司名称
+
+                        "_widget_1748241895846": {"value": row['group_grade']},  # 公司等级
+
+                        "_widget_1748241895847": {"value": row['region_name']},  # 大区
+
+                        "_widget_1748241895848": {"value": row['province_name']},  # 省
+
+                        "_widget_1748241895849": {"value": row['org_type']},  # 门店类型
+
+                        "_widget_1748241895850": {"value": row['saas_edition_fmt']},  # 系统版本
+
+                        "_widget_1748241895851": {"value": row['saas_customer_type']},  # saas客户类型
+
+                        "_widget_1748241895852": {"value": row['org_stage']},  # 门店阶段
+
+                        "_widget_1748241895853": {"value": row['contact_mobile']},  # 操作模式E.L/E.S
+
+                        "_widget_1748241895855": {"value": row['city_name']},  # 城市
+
+                        "_widget_1748247754304": {"value": NGV_data_id},  # 数据id
+
+                        "_widget_1748512176655": {"value": "未处理"},  # 跟进状态
+
+                        "_widget_1772761760440":{"value": "客服跟进节点"},  # 当前跟进节点
+
+                    })
+
+                    routine_follow_up_payload = {
+                        "api_key": "675b900991ad2491c69389ca",
+                        "entry_id": "68340de79f116c0b66b6b0cc",  # 异常服务跟进待办
+                        "is_start_workflow": "true",
+                        "data": payload_dict,
+                        "transaction_id": UUid
+                    }
+                    all_data.append(routine_follow_up_payload)
+
+                    # res = api_instance.data_batch_create(routine_follow_up_payload)
+                    # logger.info(f"创建结果：{res}")
+
+                except Exception as e:
+                    error_task_logger.exception(f"异常服务待办派发执行时发生异常: {e}")
+                    error_data.append(row)
+                    pass
+            if error_data:
+                error_df = pd.DataFrame(error_data)
+                error_df.to_csv(os.path.join(output_dir, "异常派发错误数据.csv"))
+                common_module.send_task_error(task_start_time=task_start_time, task_name="异常服务待办派发",
+                                              error_message="失败文件中省市区匹配不到，需要通过门店编码在客户资料表中查询正确的省市区，并更新到省市区人员关系表中",
+                                              df=error_df)
+            ndf = pd.DataFrame(all_data)
+            ndf.to_csv(os.path.join(output_dir, "异常派发.csv"))
+            common_module.send_task_status(task_start_time, "异常服务待办派发")
+        except Exception as e:
+            error_task_logger.error(f"异常服务待办派发执行时发生异常: {e}")
+            common_module.send_task_error(task_start_time, "异常服务待办派发", str(e))
+
+
+if __name__ == '__main__':
+    start = NewExceptionTask()
+    start.main()

test/数据库表迁移.py

@@ -0,0 +1,139 @@
+import pymysql
+import sys
+import time
+
+# ================== 配置信息 ==================
+SOURCE_CONFIG = {
+    'host': "f6-public.rwlb.rds.aliyuncs.com",
+    'user': "rw_operation_data_relay",
+    'password': "m+q5Z4%IVuF9bf",
+    'database': "f6operation_data_relay",
+    'connect_timeout': 30,
+    'read_timeout': 600,
+    'write_timeout': 600
+}
+
+TARGET_CONFIG = {
+    'host': "db-f6operation-sst.f6car.org",
+    'user': "rw_operation",
+    'password': "tDm45eBj@upzLydHc",
+    'database': "f6operation_data_relay",
+    'connect_timeout': 30,
+    'read_timeout': 600,
+    'write_timeout': 600
+}
+
+TABLE_NAME = 'rpt_customized_maintain_detail'
+READ_BATCH_SIZE = 1000   # 每次从源库读 5000 行
+WRITE_BATCH_SIZE = 1000  # 每次向目标库写 5000 行
+# ==============================================
+
+
+def main():
+    print("🔧 正在从源数据库读取表结构...")
+
+    # === 第一步：获取建表语句 ===
+    source_conn = None
+    try:
+        source_conn = pymysql.connect(**SOURCE_CONFIG)
+        with source_conn.cursor() as cursor:
+            cursor.execute(f"SHOW CREATE TABLE `{TABLE_NAME}`")
+            result = cursor.fetchone()
+            if not result:
+                raise Exception(f"表 {TABLE_NAME} 不存在于源数据库")
+            create_table_sql = result[1]
+    except Exception as e:
+        print(f"❌ 读取表结构失败: {e}")
+        sys.exit(1)
+    finally:
+        if source_conn:
+            source_conn.close()
+
+    # === 第二步：获取总行数（用于进度）===
+    total_rows = 0
+    try:
+        source_conn = pymysql.connect(**SOURCE_CONFIG)
+        with source_conn.cursor() as cursor:
+            cursor.execute(f"SELECT COUNT(*) FROM `{TABLE_NAME}`")
+            total_rows = cursor.fetchone()[0]
+    except Exception as e:
+        print(f"⚠️ 无法获取总行数（不影响迁移）: {e}")
+    finally:
+        if source_conn:
+            source_conn.close()
+
+    print(f"📊 表共约 {total_rows} 行，将分批读取和写入...")
+
+    # === 第三步：重建目标表 ===
+    target_conn = None
+    columns = []
+    try:
+        target_conn = pymysql.connect(**TARGET_CONFIG)
+        with target_conn.cursor() as cursor:
+            cursor.execute(f"DROP TABLE IF EXISTS `{TABLE_NAME}`")
+            new_create_sql = create_table_sql.replace(f"`{SOURCE_CONFIG['database']}`.", "")
+            cursor.execute(new_create_sql)
+            # 获取列名（从建表语句解析较复杂，改用查一次空结果）
+            cursor.execute(f"SELECT * FROM `{TABLE_NAME}` LIMIT 0")
+            columns = [desc[0] for desc in cursor.description]
+        target_conn.commit()
+        print("✅ 目标表已重建")
+    except Exception as e:
+        print(f"❌ 重建目标表失败: {e}")
+        if target_conn:
+            target_conn.close()
+        sys.exit(1)
+
+    # === 第四步：分批读取 + 分批写入 ===
+    offset = 0
+    inserted_total = 0
+
+    while True:
+        time.sleep(5)
+        # 从源库读取一批
+        batch_rows = []
+        try:
+            source_conn = pymysql.connect(**SOURCE_CONFIG)
+            with source_conn.cursor(pymysql.cursors.DictCursor) as cursor:
+                cursor.execute(
+                    f"SELECT * FROM `{TABLE_NAME}` LIMIT %s OFFSET %s",
+                    (READ_BATCH_SIZE, offset)
+                )
+                batch_rows = cursor.fetchall()
+            source_conn.close()
+        except Exception as e:
+            print(f"❌ 读取第 {offset//READ_BATCH_SIZE + 1} 批数据失败: {e}")
+            break
+
+        if not batch_rows:
+            print("🔚 数据读取完成")
+            break
+
+        # 转换为元组
+        data_tuples = [tuple(row[col] for col in columns) for row in batch_rows]
+
+        # 写入目标库（可再分小批，但这里 batch_rows 已是 5000）
+        try:
+            with target_conn.cursor() as cursor:
+                placeholders = ', '.join(['%s'] * len(columns))
+                insert_sql = f"INSERT INTO `{TABLE_NAME}` (`{'`, `'.join(columns)}`) VALUES ({placeholders})"
+                cursor.executemany(insert_sql, data_tuples)
+                target_conn.commit()
+                inserted_total += len(data_tuples)
+                print(f"📤 已写入 {inserted_total} / {total_rows} 行")
+        except Exception as e:
+            print(f"❌ 写入失败（批次 offset={offset}）: {e}")
+            target_conn.rollback()
+            break
+
+        offset += READ_BATCH_SIZE
+
+    # === 清理 ===
+    if target_conn and target_conn.open:
+        target_conn.close()
+
+    print(f"🎉 迁移完成！共写入 {inserted_total} 行")
+
+
+if __name__ == "__main__":
+    main()

test/数据库验证脚本.py

@@ -0,0 +1,973 @@
+import os
+import time
+import requests
+from api import API
+from back_ground_module import CommonModule
+import pandas as pd
+import datetime
+import re
+from log_config import configure_task_logger, configure_error_task_logger
+
+api_instance = API()
+common_module = CommonModule()
+# start_time = datetime.datetime.now()
+
+# 获取已经配置好的常规日志记录器
+logger = configure_task_logger()
+
+# 获取已经配置好的错误任务日志记录器
+error_task_logger = configure_error_task_logger()
+
+
+class RenewServicesRevisit:
+    """续约回访90-180"""
+
+    def __init__(self):
+        self.index = None
+        self.data_NGV = None
+        self.date_list = None
+        self.Smart_detection = None
+        self.service_remind = None
+        self.json_list = []
+        self.NGV_data_list = None
+        self.permissions_table = None
+        self.staff_id_list = None
+        self.get_feature_usage = None
+        self.policy_recognition = None
+        self.widget_list = None
+        self.private_domain = None
+        self.public_domain = None
+        self.public_domain_list = None
+        self.different_industries = None
+        self.different_industries_list = None
+        self.groupnotification = None
+
+    def load_all_data(self):
+        """加载所有必要的数据表"""
+        # 省市区人员关系表
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "676512ac3e54dc3159460c0a"}
+        json_dict = api_instance.entry_data_list(payload)
+        if json_dict and "data" in json_dict:
+            self.json_list = json_dict.get("data")
+        else:
+            print("加载省市区人员关系表失败")
+            self.json_list = []
+
+        # 获取简道云员工id
+        payload = {"api_key": "6694d3c4fcb69ca9a111a6c4",
+                   "entry_id": "6769204a1902c9341340a1bc",
+                   }
+        staff_id = api_instance.entry_data_list(payload)
+        self.staff_id_list = staff_id.get("data")  # api请求格式，将数据封装在data字典里
+
+        # 获取权限表信息
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "675b96c14e839f90fef1647c"}
+        self.permissions_table = api_instance.entry_data_list(payload).get("data")
+
+        # 获取NGV数据
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "675bb02bd2d53c2034c665e4"}
+        self.NGV_data_list = api_instance.entry_data_list(payload).get("data")
+        # print("NGV获取后的类型：", type(self.NGV_data_list))
+
+        # 获取服务提醒-数据支持表单数据
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "676bb7bda3029720f1083e99"}
+        self.service_remind = api_instance.entry_data_list(payload).get("data")
+
+        # 获取智能检测-数据支持表单数据
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "676bb99649ab3ac975af6e39"}
+        self.Smart_detection = api_instance.entry_data_list(payload).get("data")
+
+        # 获取功能使用情况表
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "6763bbf657bd8fb76fcb41b2"}
+        self.get_feature_usage = api_instance.entry_data_list(payload).get("data", [])
+
+        # 获取保单识别表
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "6773a60d30ed87ff9f68d3c5"}
+        self.policy_recognition = api_instance.entry_data_list(payload).get("data")
+        # 提取 _widget_1735632397600 的值并存储在列表中
+        self.widget_list = [item['_widget_1735632397600'] for item in self.policy_recognition]
+
+        # 获取私域小程序-数据支持表单数据
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "67e0f0fae622896749ba5087"}
+        self.private_domain = api_instance.entry_data_list(payload).get("data", [])
+        # 提取 _widget_1742795002375 的值并存储在列表中
+        # self.private_domain_list = [item['_widget_1742795002375'] for item in self.private_domain]
+
+        # 获取公域小程序-数据支持表单数据
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "67e0c702c8f603b997980999"}
+        self.public_domain = api_instance.entry_data_list(payload).get("data", [])
+        # 提取 _widget_1742784257506 的值并存储在列表中
+        self.public_domain_list = [item['_widget_1742784257506'] for item in self.public_domain]
+
+        # 获取异业合作-数据支持表单数据
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "67e24fdd8dfcfa918e17c30b"}
+        self.different_industries = api_instance.entry_data_list(payload).get("data", [])
+        # 提取 _widget_1742784257506 的值并存储在列表中
+        self.different_industries_list = [item['_widget_1742884829007'] for item in self.different_industries]
+
+        # 获取短信-数据支持表单数据
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "67e5107198ba1b20d5df3974"}
+        self.groupnotification = api_instance.entry_data_list(payload).get("data", [])
+
+        # 获取多公司过滤表
+        payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "689bf5f8ba88a28cb0679ec9"}
+        self.get_filter_company_list = api_instance.entry_data_list(payload).get("data", [])
+
+    @staticmethod
+    def download_url_content(url, save_path):
+        """
+        下载指定 URL 的内容并保存到本地文件。
+
+        :param url: 要下载内容的 URL
+        :param save_path: 保存文件的路径
+        """
+        try:
+            # 发送 GET 请求以获取内容
+            response = requests.get(url, stream=True)
+            response.raise_for_status()  # 如果响应状态码不是 200，抛出异常
+
+            # 确保保存目录存在
+            os.makedirs(os.path.dirname(save_path), exist_ok=True)
+
+            # 将内容写入文件
+            with open(save_path, 'wb') as file:
+                for chunk in response.iter_content(chunk_size=8192):  # 分块写入，避免占用过多内存
+                    if chunk:  # 过滤掉空块
+                        file.write(chunk)
+
+            print(f"文件已成功保存到 {save_path}")
+
+        except requests.exceptions.RequestException as e:
+            print(f"下载失败: {e}")
+        except Exception as e:
+            print(f"发生错误: {e}")
+
+    @staticmethod
+    def build_index(json_list):
+        index = {}
+        for json_item in json_list:
+            try:
+                key = (json_item['_widget_1734677164861'], json_item['_widget_1734677164862'],
+                       json_item['_widget_1734677164863'])  # 省市区
+                if '_widget_1734677164871' not in json_item:  # 日常回访客服
+                    raise KeyError("缺少 '日常回访客服' 键")
+                index[key] = json_item
+            except KeyError as e:
+                print(f"警告：{e}，跳过该条记录: {json_item}")
+                continue
+        return index
+
+    @staticmethod
+    def find_customer_service(province_name, city_name, area_name, index):
+        key = (province_name, city_name, area_name)
+        # print(index)
+        if key not in index:
+            return "数据缺失: 未找到对应的日常回访客服"
+
+        return index[key]
+
+    @staticmethod
+    def remove_parentheses(text: str) -> str:
+        # 使用正则表达式匹配并去除括号及其内容
+        # \s* 表示匹配零个或多个空白字符（处理括号前后可能存在的空格）
+        # $ 和 $ 分别表示匹配左括号和右括号
+        # 中间的 .*? 表示非贪婪地匹配任意数量的字符（包括没有字符的情况）
+        cleaned_text = re.sub(r'\s*$.*?$\s*', '', text)
+        # 为了确保同时处理中文括号，再进行一次替换
+        cleaned_text = re.sub(r'\s*（.*?）\s*', '', cleaned_text)
+        return cleaned_text.strip()  # 去除两端多余的空白字符
+
+    @staticmethod
+    def get_staff_id(row_item, name):
+        """辅助函数，用于获取员工ID"""
+        if str(row_item["_widget_1734942794144"]) == str(name):  # 检查姓名是否匹配
+            return row_item["_widget_1734942794145"]  # 返回员工ID
+        return None
+
+    def assign_customer_service(self, province_name, city_name, area_name, index):
+        """根据省市区派发给日常回访客服"""
+        try:
+            customer_service_info = self.find_customer_service(province_name, city_name, area_name, index)
+
+            # 定义一个辅助函数，用于安全地获取多层字段中的 username
+            def safe_get_username(data, key):
+                try:
+                    if isinstance(data, dict):
+                        return data.get(key, {}).get('username', "")
+                    return ""
+                except:
+                    return ""
+
+            relationship_manager = safe_get_username(customer_service_info, '_widget_1734677164864')
+            customer_service = safe_get_username(customer_service_info, '_widget_1734677164871')
+            technician = safe_get_username(customer_service_info, '_widget_1734677164866')
+            area_manager = safe_get_username(customer_service_info, '_widget_1734677164865')
+            return relationship_manager, customer_service, technician, area_manager
+        except Exception as e:
+            print(f"Error finding customer service: {e}")
+            return "分配失败，请检查", "分配失败，请检查", "分配失败，请检查"
+
+    def calculate_date_one(self, start_offset=0):
+        """
+        计算从当前日期（或指定偏移量的日期）开始，往前遍历遇到date_list中日期的次数。
+
+        参数:
+        - start_offset: 从当前日期起始的天数偏移量，默认为0（即今天）。负数表示过去，正数表示未来。
+
+        返回:
+        - date_one: 遍历到date_list中日期的次数。
+        """
+        # 设置起始日期
+        now_time = datetime.datetime.now() + datetime.timedelta(days=start_offset)
+
+        # 初始化计数器
+        date_one = 1
+        print("当前日期：", now_time.strftime("%Y-%m-%d"))
+        # 检查起始日期是否在date_list中
+        if now_time.strftime("%Y-%m-%d") in self.date_list:
+            date_one = 0
+            print("开始次数：", date_one)
+        else:
+            # 遍历日期
+            for i in range(1, 10):
+                new_date = now_time + datetime.timedelta(days=-i)
+                new_date_str = new_date.strftime("%Y-%m-%d")
+                print("遍历日期：", new_date_str)
+                if new_date_str in self.date_list:
+                    date_one += 1
+                    print("节假日期：", new_date_str)
+                else:
+                    break
+
+        print("遍历次数：", date_one)
+        return date_one
+
+    def main(self):
+        import datetime
+        task_start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        try:
+            logger.info("开始执行任务")
+            # 主店过期，分店设置为主店
+            global png_url, upload_key
+            self.load_all_data()
+            logger.info("数据加载完成")
+            self.date_list = common_module.get_holiday_list()  # 获取一年中的节假日
+            self.date_one = self.calculate_date_one(start_offset=0)
+            self.data_NGV = common_module.get_ngv_details(days_back=1)  # 获取data_NGV 并转为str
+            self.index = self.build_index(self.json_list)
+
+
+            logger.info("获取多公司过滤公司id")
+            all_filter_company_list = []  # 获取多公司过滤公司id
+            for company in self.get_filter_company_list:
+                company_list = company.get("_widget_1755052002491")
+                for company_item in company_list:
+                    if company_item.get("_widget_1755052002496") == "否":
+                        all_filter_company_list.append(company_item.get("_widget_1755052002495"))
+            logger.info(f"过滤公司条数:{len(all_filter_company_list)}")
+
+            # 将A列和B列的日期字符串转换为日期格式
+            data_NGV = self.data_NGV.copy()
+            data_NGV['A'] = pd.to_datetime(data_NGV['expiry_time'])
+            data_NGV['B'] = pd.to_datetime(data_NGV['renew_date'])
+
+            def replace_values(series):
+                # 使用条件判断来进行替换
+                return series.apply(lambda x: '' if pd.isna(x) or x in ['NA', 'None', ''] else x)
+                # 对整个DataFrame的所有列应用替换函数
+
+            # 处理字符串数据并显式指定数据类型
+            data_NGV = data_NGV.apply(replace_values)
+
+            # 针对公司主店过期，取公司最高等级版本派发
+            # 过滤多公司
+            data_NGV = data_NGV[~data_NGV['id_own_group'].isin(all_filter_company_list)]
+
+            # 定义优先级顺序
+            edition_order = ['皇冠版', '至尊版', '尊享版', '旗舰版', '标准版', '进阶版', '基础版', '入门版']
+            customer_type_order = ["F", "E", "D", "C", "B", "A"]  # 索引越小优先级越高
+            group_grade_order = ['全国KA（FMVP）', '区域KA（MVP）', '重要客户（SVIP）', '普通客户（VIP）']
+
+            # 创建映射字典，并为不在列表中的值设置默认值
+            edition_map = {edition: idx for idx, edition in enumerate(edition_order)}
+            customer_type_map = {ctype: idx for idx, ctype in enumerate(customer_type_order)}
+            group_grade_map = {grade: idx for idx, grade in enumerate(group_grade_order)}
+
+            # 添加用于排序的新列，并处理不在映射字典中的值
+            data_NGV['edition_rank'] = data_NGV['saas_edition_fmt'].map(edition_map).fillna(0).astype(
+                int)  # 缺失值用最高优先级填充
+            data_NGV['customer_type_rank'] = data_NGV['saas_customer_type'].map(customer_type_map).fillna(0).astype(int)
+            data_NGV['group_grade_rank'] = data_NGV['group_grade'].map(group_grade_map).fillna(0).astype(int)
+            # data_NGV.to_csv("88855.csv")
+
+            # 找到每组中 edition_rank 最小值对应的行
+            best_edition_idx = data_NGV.groupby('id_own_group')['edition_rank'].idxmin()
+            best_edition_rows = data_NGV.loc[best_edition_idx]
+            best_edition_rows['max_saas_edition'] = best_edition_rows['saas_edition_fmt']
+
+            # 找到每组中 customer_type_rank 最小值对应的行
+            best_customer_type_idx = data_NGV.groupby('id_own_group')['customer_type_rank'].idxmin()
+            best_customer_type_rows = data_NGV.loc[best_customer_type_idx]
+            best_customer_type_rows['max_saas_customer_type'] = best_customer_type_rows['customer_type_rank'].apply(
+                lambda x: customer_type_order[x])
+
+            # 找到每组中 group_grade_rank 最小值对应的行
+            best_group_grade_idx = data_NGV.groupby('id_own_group')['group_grade_rank'].idxmin()
+            best_group_grade_rows = data_NGV.loc[best_group_grade_idx]
+            best_group_grade_rows['max_group_grade'] = best_group_grade_rows['group_grade']
+
+            # 合并最佳值回到原数据集
+            best_values = (
+                best_edition_rows[['id_own_group', 'max_saas_edition']]
+                .merge(best_customer_type_rows[['id_own_group', 'max_saas_customer_type']], on='id_own_group',
+                       how='outer')
+                .merge(best_group_grade_rows[['id_own_group', 'max_group_grade']], on='id_own_group', how='outer')
+            )
+
+            # 将最佳值合并回原数据集
+            data_NGV = data_NGV.merge(best_values, on='id_own_group', how='left')
+
+            condition = (data_NGV['is_main_org'] == 1) & (data_NGV['org_status'] == '过期')  # 步骤2: 过滤条件
+
+            ngvv2 = data_NGV[condition]
+            # ngvv2.to_excel(r"C:\Users\Administrator.DESKTOP-7IC2USJ\Desktop\NGVV2.xlsx")
+
+            data_NGV_V2 = data_NGV.copy()  # 步骤3: 检查id_own_group是否存在于ngvv2中
+            data_NGV_V2['条件'] = ((data_NGV_V2['org_type'] == "一般") & (data_NGV_V2['org_status'] == '留存') &
+                                   (data_NGV_V2['area_manager'] != '殷昊') & (
+                                           data_NGV_V2['area_manager'] != '孙玉蕾') & (
+                                           data_NGV_V2['is_main_org'] != 1))
+            data_NGV_V2 = data_NGV_V2.loc[data_NGV_V2["条件"]]
+            # 步骤4: 过滤存在的记录
+            data_NGV_V2['exists_in_ngvv2'] = data_NGV_V2['id_own_group'].isin(ngvv2['id_own_group'])
+            filtered_data = data_NGV_V2[data_NGV_V2['exists_in_ngvv2']]
+
+            fixed_order = ['皇冠版', '至尊版', '尊享版', '旗舰版', '标准版', '进阶版', '基础版', '入门版']
+            # sorted_items = sorted(filtered_data, key=lambda x: fixed_order.index(x))
+
+            fixed_order_map = {edition: index for index, edition in enumerate(fixed_order)}
+            filtered_data['sort_key'] = filtered_data['saas_edition_fmt'].map(fixed_order_map)
+            filtered_data = filtered_data.sort_values(by='sort_key').drop('sort_key', axis=1)
+
+            result = filtered_data.drop_duplicates(subset='id_own_group', keep='first')
+
+            data_NGV['条件'] = (data_NGV['org_type'] == "一般") & (data_NGV['org_status'] == '留存') & (
+                    data_NGV['area_manager'] != '殷昊') & (
+                                       data_NGV['area_manager'] != '孙玉蕾') & (
+                                       data_NGV['is_main_org'] == 1)
+            data_NGV = data_NGV.loc[data_NGV["条件"]]
+
+            data_NGV = pd.concat([data_NGV, result], axis=0)
+            data_details = data_NGV.copy()
+
+            # 重置索引
+            data_details = data_details.reset_index(drop=True)
+            # data_details.to_csv("dayin.csv")
+            # 判断A列的日期是否大于B列的日期730天，如果是的话，将B列的值设置为1
+            data_details['条件'] = data_details.apply(
+                lambda row: (
+                    (pd.to_datetime(row['A']) - pd.to_datetime(row['B'])).days
+                    if pd.to_datetime(row['A']) - pd.to_datetime(row['B']) >= pd.Timedelta(days=730)
+                    else 0
+                ),
+                axis=1
+            )
+            data_details = data_details.loc[data_details["条件"] > 0]
+
+            # 定义一个函数，用于将数字除以365并取整数
+            def divide_by_365(x):
+                if isinstance(x, (int, float)):
+                    return int(x / 365)
+                else:
+                    return x
+
+            # 使用applymap()函数将divide_by_365函数应用到DataFrame的每个元素
+            data_details['年'] = data_details['条件'].apply(divide_by_365)
+            # 重置索引
+            data_details = data_details.reset_index(drop=True)
+            # data_details.to_excel(r"C:\Users\admin\Downloads\2.xlsx")
+            # 创建一个新的空的DataFrame
+            new_df = pd.DataFrame()
+            # 遍历原始DataFrame的每一行
+            from datetime import datetime
+            for index, row in data_details.iterrows():
+                # 根据A列的值来决定复制的次数
+                if row["renew_date"] != "2024-02-29":
+                    for i_new in range(1, row['年']):
+                        # 修改日期
+                        row_new = row.copy()
+                        c = row_new["renew_date"]
+                        date_obj = datetime.strptime(c, "%Y-%m-%d")
+                        new_year = date_obj.year + i_new
+                        new_date_obj = date_obj.replace(year=new_year)
+                        new_c = new_date_obj.strftime("%Y-%m-%d")
+                        row_new["renew_date"] = new_c
+                        # 将当前行添加到新的DataFrame中
+                        # new_df = new_df.append(row_new, ignore_index=True)
+                        new_df = pd.concat([new_df, pd.DataFrame([row_new])], ignore_index=True)
+            # 合并两个DataFrame
+            merged_df = pd.concat([data_NGV, new_df], axis=0, ignore_index=True)
+            data_details = merged_df.copy()  # 替换名称
+
+            data_details_not_null = data_details[data_details['renew_date'].notnull()]
+            # data_details_not_null = data_details_not_null[data_details_not_null['renew_date'].str.contains('2023')]
+            # data_details_not_null = data_details_not_null.sort_values(by='renew_date', ascending=True).drop_duplicates(
+            # subset='id_own_group') 重置索引
+            data_details_not_null = data_details_not_null.reset_index(drop=True)
+            data_details = data_details_not_null.copy()  # 替换名称 v2
+            data_details['saas_create_time'] = data_details['saas_create_time'].str[:4]  # 截取前10位
+            data_details['renew_date_new'] = data_details['renew_date'].str[:4]  # 截取前10位
+            data_details = data_details[
+                data_details['saas_create_time'] != data_details['renew_date_new']]  # 过滤掉等于renew_date的行
+
+            data_details = data_details.reset_index(drop=True)
+
+            logger.info(f"过滤后的数据长度为: {len(data_details)}")
+            # data_details.to_excel(r"C:\Users\admin\Downloads\4.xlsx")
+            import datetime
+            start_time = datetime.datetime.now()
+
+            date_90 = 83
+            date_120 = 113
+            date_180 = 173
+            # self.date_one = 1
+            now_time = start_time.replace()
+
+            if now_time.strftime("%Y-%m-%d") in self.date_list:
+                self.date_one = 0
+                print("开始次数：", self.date_one)
+                print("当前日期：", now_time)
+
+            logger.info(f"遍历次数：{self.date_one}")
+
+            now_time = start_time.replace()
+            for i in range(0, self.date_one):
+                logger.info(f"这是第{i}次遍历")
+                import datetime
+                now_time = datetime.datetime.now() + datetime.timedelta(days=-(i + 1))
+
+                today = now_time + datetime.timedelta(days=-date_90)
+                formatted_today_90 = today.strftime("%Y-%m-%d")
+                today = now_time + datetime.timedelta(days=-date_120)
+                formatted_today_120 = today.strftime("%Y-%m-%d")
+                today = now_time + datetime.timedelta(days=-date_180)
+                formatted_today_180 = today.strftime("%Y-%m-%d")
+                logger.info(f"90天为{formatted_today_90}，180天为{formatted_today_180}")
+
+                # 获取数据
+                data_details_90 = data_details.copy()
+                data_details_90['条件'] = (data_details_90['renew_date'] == formatted_today_90) & (data_details_90[
+                                                                                                       'group_grade'] != "普通客户（VIP）")  # & (data_details_90['saas_edition_fmt'] != '基础版') & (data_details_90['saas_edition_fmt'] != '入门版')
+                data_details_90 = data_details_90.loc[data_details_90["条件"]]
+                data_details_120 = data_details.copy()
+                data_details_120['条件'] = (data_details_120['renew_date'] == formatted_today_120) & (
+                        (data_details_120['saas_edition_fmt'] == '基础版') | (
+                        data_details_120['saas_edition_fmt'] == '入门版'))
+                data_details_120 = data_details_120.loc[data_details_120["条件"]]
+                data_details_180 = data_details.copy()
+                data_details_180['条件'] = (data_details_180[
+                                                'renew_date'] == formatted_today_180)  # & (data_details_180[
+                # 'saas_edition_fmt'] != '基础版') & (data_details_180['saas_edition_fmt'] != '入门版')
+                data_details_180 = data_details_180.loc[data_details_180["条件"]]
+                data_details_90["跟进阶段"] = "续约后90天回访"
+                data_details_90["主要目的"] = "关怀使用情况，促进更多功能使用，提升系统使用深度。"
+                data_details_120["跟进阶段"] = "续约后120天回访"
+                data_details_120["主要目的"] = "暂无"
+                data_details_180["跟进阶段"] = "续约后180天回访"
+                data_details_180["主要目的"] = "关怀使用情况，促进增购商机转化，识别潜在风险，及时提报。"
+
+                # 合并三个DataFrame
+                data_result = pd.concat([data_details_90, data_details_180],
+                                        ignore_index=True)  # 去除续约120天回访 data_details_120
+                logger.info(f"派发数据长度：{len(data_result)}")
+                data_result = data_result.astype(str)
+
+                data_NGV = data_result.copy()
+
+                for index_num, row in data_NGV.iterrows():  # 对过滤后的每一条进行派发
+                    try:
+                        # print(row["org_code"])  # 数据验证
+                        # print(row["service_impl_principal"])
+                        # print(row["area_manager"])
+                        # print(row["technician"])
+                        # print("销售负责人是：", row["salesmen"])
+
+                        payload_dict = {}
+                        saas_use_year = re.findall(r'第([0-9]+)年', row["saas_use_year"])[0]
+
+                        NGV_roles = {
+                            'relationship_manager': row['service_impl_principal'],  # 运营负责人
+                            # 'relationship_manager': "张阳",  # 运营负责人
+                            'area_manager': row['area_manager'],  # 区域经理
+                            'technician': row['technician'],  # 技术专家
+                            'salesmen': row['salesmen'],  # 销售负责人
+                        }
+
+                        for role, name in NGV_roles.items():
+                            for row_item in self.staff_id_list:
+                                staff_id = self.get_staff_id(row_item, name)
+                                if staff_id:
+                                    NGV_roles[role] = staff_id
+                                    break  # 找到后退出循环
+                            else:
+                                NGV_roles[role] = None  # 如果没有找到对应的员工ID
+                        # 回访人员： 需确认 四年以下 technician
+
+                        if int(saas_use_year) < 4:
+                            relationship_manager, area_manager, technician, salesmen = [NGV_roles[role] for role in
+                                                                                        ['relationship_manager',
+                                                                                         'area_manager',
+                                                                                         'technician', 'salesmen']]
+                            # 如果未找到运营负责人，则根据省市区派发给日常回访客服
+                            if not relationship_manager:
+                                relationship_manager = self.assign_customer_service(
+                                    row['province_name'], row['city_name'], row['area_name'], self.index
+                                )[0]
+                            if not technician:
+                                technician = self.assign_customer_service(
+                                    row['province_name'], row['city_name'], row['area_name'], self.index
+                                )[2]
+
+                            if row["group_grade"] == "普通客户（VIP）" or row["group_grade"] == "重要客户（SVIP）":
+                                payload_dict.update({
+                                    "_widget_1734590278288": {"value": relationship_manager},  # 跟进人是运营负责人
+                                })
+                            else:
+                                payload_dict.update({
+                                    "_widget_1734590278288": {"value": technician},  # 跟进人是技术专家
+                                })
+
+                        else:
+                            logger.info(
+                                f"超过4年的客户，派发给客服，省市区：{row['province_name']} {row['city_name']} {row['area_name']}")
+                            salesmen = [NGV_roles[role] for role in ['salesmen']][0]
+                            # 直接根据省市区派发给日常回访客服
+                            relationship_manager, customer_service, technician, area_manager = self.assign_customer_service(
+                                row['province_name'], row['city_name'], row['area_name'], self.index
+                            )
+                            logger.info(f"派发给 {relationship_manager} {customer_service} {technician} {area_manager}")
+
+                            if row["group_grade"] == "普通客户（VIP）" or row["group_grade"] == "重要客户（SVIP）":
+                                payload_dict.update({
+                                    "_widget_1734590278288": {"value": customer_service}  # 跟进人是日常回访客服
+                                })
+                            else:
+                                payload_dict.update({
+                                    "_widget_1734590278288": {"value": technician}  # 跟进人是技术专家
+                                })
+
+                        payload_dict.update({
+                            # "_widget_1734590278288": {"value": relationship_manager},  # 跟进人是运营负责人
+                            "_widget_1734590278289": {"value": relationship_manager},  # 运营负责人
+                            "_widget_1734590278290": {"value": area_manager},  # 区域经理
+                            "_widget_1734590278291": {"value": technician},  # 技术专家
+                            "_widget_1735290738545": {"value": salesmen}  # 销售负责人
+                        })
+                        logger.info(f"请求输出：{payload_dict}")
+
+                        if payload_dict.get("_widget_1734590278288") == "02414917880947":  # 如果跟进人是殷浩
+                            payload_dict.update({
+                                "_widget_1734590278288": {"value": "051612246035720178"},  # 跟进人是赵柄诚
+                            })
+
+                        # 输出结果
+                        logger.info(f"SaaS开户回访人员：{relationship_manager}")
+                        logger.info(f"SaaS技术专家：{technician}")
+                        logger.info(f"SaaS区域经理：{area_manager}")
+
+                        # 判断权限唯一值
+                        group_grade = re.sub(r'（[^）]*）', '', row['max_group_grade'])
+
+                        if not row['saas_customer_type'] or row['saas_customer_type'] == 'NA' or row[
+                            'saas_customer_type'] == 'None':
+                            row['saas_customer_type'] = "F"
+
+                        NGV_store_level_key = group_grade + row['max_saas_edition'] + row['max_saas_customer_type']
+                        logger.info(f"权限唯一值： {NGV_store_level_key}")
+
+                        Billing = None
+                        for item in self.permissions_table:
+                            if NGV_store_level_key == item.get("_widget_1734056507963"):  # 合并（等级-类型-分层）
+                                print("该门店开单的权限是：", item.get("_widget_1734055617039"))
+                                Billing = item.get("_widget_1734055617039")  # 开单
+                                Service_Alerts = item.get("_widget_1734055617040")  # 服务提醒
+                                membership = item.get("_widget_1734055617041")  # 会员卡
+                                SMS = item.get("_widget_1734055617042")  # 短信
+                                Public_domain_applets = item.get("_widget_1734055617043")  # 公域小程序
+                                Private_domain_applets = item.get("_widget_1734055617044")  # 私域小程序
+                                Test_sheet = item.get("_widget_1734055617045")  # 检测单
+                                AI_poster = item.get("_widget_1734055617046")  # AI海报
+                                Business_wallets = item.get("_widget_1734055617047")  # 企业钱包
+                                Precision_marketing = item.get("_widget_1734055617049")  # 精准营销
+                                Paid_memberships = item.get("_widget_1734055617051")  # 付费会员
+                                business_WeCom = item.get("_widget_1734055617052")  # 企业微信
+                                Insurance_policy_identification = item.get("_widget_1734055617053")  # 保险单识别
+                                Insurance_bots = item.get("_widget_1734055617054")  # 保险机器人
+                                Camera_pick_up = item.get("_widget_1734055617055")  # 摄像头接车
+                                Camera_billing = item.get("_widget_1734055617056")  # 摄像头开单
+                                Transparent_workshop = item.get("_widget_1734055617057")  # 透明车间
+                                Cross_industry_cooperation = item.get("_widget_1734055617058")  # 异业合作
+                                BI_Insights = item.get("_widget_1734055617059")  # BI洞察
+                                payload_dict.update(
+                                    {
+                                        "_widget_1734073342350": {"value": Billing},
+                                        "_widget_1735004315757": {"value": Service_Alerts},
+                                        "_widget_1735004315756": {"value": membership},
+                                        "_widget_1735004315755": {"value": SMS},
+                                        "_widget_1735004315754": {"value": Public_domain_applets},
+                                        "_widget_1735004315753": {"value": Private_domain_applets},
+                                        "_widget_1735004315752": {"value": Test_sheet},
+                                        "_widget_1735004315751": {"value": AI_poster},
+                                        "_widget_1735004315750": {"value": Business_wallets},
+                                        "_widget_1735004315749": {"value": Precision_marketing},
+                                        "_widget_1735004315748": {"value": Paid_memberships},
+                                        "_widget_1735004315747": {"value": business_WeCom},
+                                        "_widget_1735004315746": {"value": Insurance_policy_identification},
+                                        "_widget_1735004315745": {"value": Insurance_bots},
+                                        "_widget_1735004315744": {"value": Camera_pick_up},
+                                        "_widget_1735004315743": {"value": Camera_billing},
+                                        "_widget_1735004315742": {"value": Transparent_workshop},
+                                        "_widget_1735004315741": {"value": Cross_industry_cooperation},
+                                        "_widget_1734073342352": {"value": BI_Insights},
+
+                                    }
+                                )
+
+                                feature_dict = {
+                                    "开单": "_widget_1734073342350",
+                                    "服务提醒": "_widget_1735004315757",
+                                    "会员卡": "_widget_1735004315756",
+                                    "短信": "_widget_1735004315755",
+                                    "公域小程序": "_widget_1735004315754",
+                                    "私域小程序": "_widget_1735004315753",
+                                    "检测单": "_widget_1735004315752",
+                                    "AI海报": "_widget_1735004315751",
+                                    "企业钱包": "_widget_1735004315750",
+                                    "精准营销": "_widget_1735004315749",
+                                    "付费会员": "_widget_1735004315748",
+                                    "企业微信": "_widget_1735004315747",
+                                    "保险单识别": "_widget_1735004315746",
+                                    "保险机器人": "_widget_1735004315745",
+                                    "摄像头接车": "_widget_1735004315744",
+                                    "摄像头开单": "_widget_1735004315743",
+                                    "透明车间": "_widget_1735004315742",
+                                    "异业合作": "_widget_1735004315741",
+                                    "BI洞察": "_widget_1734073342352",
+
+                                }
+                                # _widget_1735527329557  下次是否推荐
+                                for new_item in self.get_feature_usage:
+                                    for feature_module, feature_value in feature_dict.items():  # 模块
+                                        if new_item.get("_widget_1735268263079") == feature_module and new_item.get(
+                                                "_widget_1735527329557") == "否" and new_item.get(
+                                            "_widget_1735280720550") == \
+                                                row["id_own_org"]:  # 下次是否推荐 功能使用情况表
+                                            logger.info(f"{feature_value}进行了更改")
+
+                                            payload_dict.update({f"{feature_value}": {"value": "×"}})
+
+                                        if new_item.get("_widget_1735268263079") == feature_module and new_item.get(
+                                                "_widget_1736414617462") == "否" and new_item.get(
+                                            "_widget_1735280720550") == \
+                                                row["id_own_org"]:  # 下次是否跟进
+                                            logger.info(f"{feature_value}进行了更改")
+
+                                            payload_dict.update({f"{feature_value}": {"value": "×"}})
+
+                                fields_to_check = {
+                                    "_widget_1735004315763": Billing,  # 开单
+                                    "_widget_1735106258016": Service_Alerts,  # 服务提醒
+                                    "_widget_1735106258036": membership,  # 会员卡
+                                    "_widget_1735106258086": SMS,  # 短信
+                                    "_widget_1735106258112": Public_domain_applets,  # 公域小程序
+                                    "_widget_1735106258141": Private_domain_applets,  # 私域小程序
+                                    "_widget_1735107354648": Test_sheet,  # 检测单
+                                    "_widget_1735107354725": AI_poster,  # AI海报
+                                    "_widget_1735107354811": Business_wallets,  # 企业钱包
+                                    "_widget_1735107354906": Precision_marketing,  # 精准营销
+                                    "_widget_1735107354980": Paid_memberships,  # 付费会员
+                                    "_widget_1735107355093": business_WeCom,  # 企业微信
+                                    "_widget_1735107355143": Insurance_policy_identification,  # 保险单识别
+                                    "_widget_1735107355235": Insurance_bots,  # 保险机器人
+                                    "_widget_1735107355333": Camera_pick_up,  # 摄像头接车
+                                    "_widget_1735107355392": Camera_billing,  # 摄像头开单
+                                    "_widget_1735107355502": Transparent_workshop,  # 透明车间
+                                    "_widget_1735107355618": Cross_industry_cooperation,  # 异业合作
+                                    "_widget_1735107355740": BI_Insights  # BI洞察
+                                }
+
+                                # 遍历每个字段，检查其值并更新payload_dict
+                                for widget_id, field_name in fields_to_check.items():
+                                    if field_name == "√":
+                                        payload_dict.update({widget_id: {"value": "是"}})
+
+                                break
+
+                        is_continue = False
+                        # 如果门店倒闭则不派发
+                        for store in self.NGV_data_list:
+                            if row["id_own_org"] == store.get("_widget_1734062123069") and store.get(
+                                    "_widget_1752027386523") == "门店倒闭":
+                                is_continue = True
+                                logger.info(f"{row["id_own_org"]}门店已关闭，不派发")
+
+                                break
+                        if is_continue:
+                            continue
+
+                        if not Billing:
+                            logger.warning(f"权限表请求失败或者权限表无对应关系,权限唯一值是：{NGV_store_level_key}")
+
+                        if row["active_status_fmt"] == "活跃":  # 开单 是否使用
+                            payload_dict.update({"_widget_1735004315765": {"value": "是"}})
+                        else:
+                            payload_dict.update({"_widget_1735004315765": {"value": "否"}})
+
+                        try:
+                            if row["saas_edition_fmt"] not in ["基础版", "入门版"]:  # 会员卡 是否拥有
+                                payload_dict.update({"_widget_1735106258036": {"value": "是"}})
+                            else:
+                                payload_dict.update({"_widget_1735106258036": {"value": "否"}})
+                        except Exception as e:
+                            error_task_logger.error(f"会员卡拥有识别：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-会员卡拥有识别", str(e))
+
+                        try:
+                            if row["card_bill_day_count_last_30_day"] != "0":  # 会员卡 是否使用
+                                payload_dict.update({"_widget_1735106258038": {"value": "是"}})
+                            else:
+                                payload_dict.update({"_widget_1735106258038": {"value": "否"}})
+                        except Exception as e:
+                            error_task_logger.error(f"会员卡使用识别：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-会员卡使用识别", str(e))
+
+                        # print(self.service_remind.get("_widget_1735112637045"))
+                        payload_dict["_widget_1735106258018"] = {"value": "否"}
+
+                        for item in self.service_remind:
+                            if row["id_own_group"] == item.get("_widget_1735112637043"):
+                                if int(item.get("_widget_1735112637045")) < 180 and int(
+                                        item.get("_widget_1735112637046")) == 1:  # 服务提醒 是否使用
+                                    payload_dict.update({"_widget_1735106258018": {"value": "是"}})
+                                    break
+                                elif int(item.get("_widget_1735112637048")) > 0:
+                                    payload_dict.update({"_widget_1735106258018": {"value": "是"}})
+                                    break
+
+                        keys_to_check = [
+                            "_widget_1735113110155"
+                        ]  # 智能检测 是否使用
+
+                        # 初始化默认值为"否"
+                        payload_dict["_widget_1735107354650"] = {"value": "否"}
+
+                        # 检查每个键，如果有一个大于0，则更新为"是"并停止检查
+                        for key in keys_to_check:
+                            for item in self.Smart_detection:
+                                if row["id_own_org"] == item.get("_widget_1735113110147"):
+                                    if int(item.get(key, 0)) > 0:  # 使用get方法并提供默认值0防止键不存在的情况
+                                        payload_dict["_widget_1735107354650"]["value"] = "是"
+                                        break
+
+                        # 近30天业务单量=0 则其它所有模块均不推荐
+                        try:
+                            for feature_module, feature_value in feature_dict.items():  # 模块
+                                if row["bill_count_last_30_day"] == '0' and payload_dict[feature_value]["value"] == '△':
+                                    payload_dict.update({f"{feature_value}": {"value": "×"}})
+                        except Exception as e:
+                            error_task_logger.error(f"不开单识别：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-不开单识别", str(e))
+
+                        # 保单识别：从系统中抽取目标门店，针对门店抽取修改是否推荐
+                        try:
+                            if row["org_code"] in self.widget_list:
+                                payload_dict.update({'_widget_1735004315746': {"value": "△"}})
+                        except Exception as e:
+                            error_task_logger.error(f"保单识别：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-保单识别", str(e))
+
+                        # 私域小程序：根据是否开通微信小程序判断是否使用，旗舰版及以上算拥有
+                        try:
+                            for item in self.private_domain:
+                                if row["id_own_group"] == item.get("_widget_1742795002375"):  # 公司id
+                                    if int(item.get("_widget_1742795002379")) > 0:  # 上架商品数
+                                        payload_dict.update({"_widget_1735106258143": {"value": "是"}})  # DX:是否拥有
+                                        break
+                                    else:
+                                        payload_dict.update({"_widget_1735106258143": {"value": "否"}})  # DX:是否拥有
+                                        break
+                        except Exception as e:
+                            error_task_logger.error(f"小程序识别：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-小程序识别", str(e))
+
+                        try:
+                            high_version = ['皇冠版', '至尊版', '尊享版', '旗舰版']
+                            if row["saas_edition_fmt"] in high_version:
+                                payload_dict.update({'_widget_1735106258141': {"value": "是"}})  # SYXCX:是否拥有
+                            else:
+                                payload_dict.update({'_widget_1735106258141': {"value": "否"}})  # SYXCX:是否拥有
+                        except Exception as e:
+                            error_task_logger.error(f"私域小程序:Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-私域小程序", str(e))
+
+                        # 公域小程序：根据是否开通微信小程序判断是否使用，旗舰版及以上算拥有
+                        try:
+                            for item in self.public_domain:
+                                if row["id_own_org"] == item.get("_widget_1742784257506"):  # 门店id
+                                    if int(item.get("_widget_1742784257509")) == 1:  # 发布商品数量
+                                        payload_dict.update({"_widget_1735106258114": {"value": "是"}})  # GYXCX:是否使用
+                                        break
+                                    else:
+                                        payload_dict.update({"_widget_1735106258114": {"value": "否"}})  # GYXCX:是否使用
+                                        break
+                        except Exception as e:
+                            error_task_logger.error(f"公域小程序：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-公域小程序", str(e))
+
+                        try:
+                            if row["id_own_org"] in self.public_domain_list:
+                                payload_dict.update({'_widget_1735106258112': {"value": "是"}})  # GYXCX:是否拥有
+                            else:
+                                payload_dict.update({'_widget_1735106258112': {"value": "否"}})  # GYXCX:是否拥有
+                        except Exception as e:
+                            error_task_logger.error(f"公域小程序：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-公域小程序", str(e))
+
+                        # 异业合作：根据是否存在判断是否拥有，过滤条件 商品名称包含异业两个字
+                        try:
+                            if row["id_own_org"] in self.different_industries_list:
+                                payload_dict.update({'_widget_1735107355618': {"value": "是"}})  # YYHZ:是否拥有
+                            else:
+                                payload_dict.update({'_widget_1735107355618': {"value": "否"}})  # YYHZ:是否拥有
+                        except Exception as e:
+                            error_task_logger.error(f"异业合作：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-异业合作", str(e))
+
+                        # 短信：根据是否启动短信功能判断是否拥有,根据
+                        try:
+                            for item in self.groupnotification:
+                                if row["id_own_group"] == item.get("_widget_1743065201885"):  # 公司id
+                                    if int(item.get("_widget_1743065201886")) == 1:  # 是否启动短信功能
+                                        payload_dict.update({"_widget_1735106258086": {"value": "是"}})  # DX:是否拥有
+                                        break
+                                    else:
+                                        payload_dict.update({"_widget_1735106258086": {"value": "否"}})  # DX:是否拥有
+                                        break
+                        except Exception as e:
+                            error_task_logger.error(f"短信是否使用：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-短信是否使用", str(e))
+
+                        try:
+                            for item in self.groupnotification:
+                                if row["id_own_group"] == item.get("_widget_1743065201885"):  # 公司id
+                                    if int(item.get("_widget_1743065201889")) > 0:  # 累计发送成功总人数
+                                        payload_dict.update({"_widget_1735106258088": {"value": "是"}})  # DX:是否使用
+                                        break
+                                    else:
+                                        payload_dict.update({"_widget_1735106258088": {"value": "否"}})  # DX:是否使用
+                                        break
+                        except Exception as e:
+                            error_task_logger.error(f"短信是否使用：Error finding customer service: {e}")
+                            common_module.send_task_error(task_start_time, "手动添加日常回访-短信是否使用", str(e))
+
+                        NGV_data_id = None
+                        # 获取关联数据
+                        for NGV_Data in self.NGV_data_list:
+                            # NGV_Data = NGV_Data.get("data")
+                            if row["org_code"] == NGV_Data.get("_widget_1734062123071"):  # 门店编码
+                                NGV_data_id = NGV_Data.get("_id")
+                                logger.info(f"关联数据数据id:{NGV_data_id}")
+                                try:
+                                    png_url = NGV_Data.get('_widget_1742890765211', {})[0].get('url', "")
+                                except:
+                                    png_url = ""
+                                logger.info(f"关联数据图片:{png_url}")
+                        if not NGV_data_id:
+                            logger.warning("未找到数据ID")
+
+                        distribution_date = datetime.datetime.now(datetime.timezone.utc)
+                        distribution_date = distribution_date.strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'
+
+                        upload_key = None
+                        UUid = time.strftime("%Y%m%d%H%M%S", time.localtime())
+                        if png_url:
+                            save_dir = "sampleCloud"  # 设置输出目录
+
+                            # 创建输出目录（如果不存在）
+                            import os
+
+                            os.makedirs(save_dir, exist_ok=True)
+
+                            save_path = fr'{save_dir}\png\{time.strftime("%Y%m%d%H%M%S", time.localtime())}.png'
+
+                            RenewServicesRevisit.download_url_content(png_url, save_path)
+                            logger.info(f"已保存图片到 {save_path}。")
+
+                            up_data = api_instance.get_upload_token(
+                                {"api_key": "675b900991ad2491c69389ca", "entry_id": "675b9c63925cd404038a6b86",
+                                 "transaction_id": UUid})
+                            upload_url = up_data.get("upload_url")
+                            upload_token = up_data.get("upload_token")
+
+                            upload_result = api_instance.upload_file(
+                                {"upload_url": upload_url, "upload_token": upload_token, "file_path": save_path})
+                            upload_key = upload_result.get("key")
+                            logger.info("已上传文件")
+
+                        payload_dict.update({
+                            "_widget_1734590278279": {"value": row["group_name"]},  # 公司名称
+                            "_widget_1735112931760": {"value": row["id_own_group"]},  # 公司id
+                            "_widget_1735112931761": {"value": row["id_own_org"]},  # 门店id
+                            "_widget_1734590278281": {"value": row['org_name']},  # 门店名称
+                            "_widget_1734590278292": {"value": row["跟进阶段"]},  # 跟进阶段
+                            "_widget_1734321349021": {"value": NGV_data_id},  # 关data_get联数据
+                            "_widget_1742548684369": {"value": row['主要目的']},  # 主要目的
+                            "_widget_1734590278266": {"value": row['region_name']},  # 大区
+                            "_widget_1734590278285": {"value": row['branch_name']},  # 小区
+                            "_widget_1734590278284": {"value": row['province_name']},  # 省
+                            "_widget_1734590278283": {"value": row['city_name']},  # 市
+                            "_widget_1734590278282": {"value": row['area_name']},  # 区
+                            "_widget_1734590278278": {"value": row['saas_customer_type']},  # 门店分层
+                            "_widget_1734590278277": {"value": row['group_grade']},  # 公司等级
+                            "_widget_1734590278276": {"value": row['limit_user_type']},  # 限制账户类型
+                            "_widget_1734590278275": {"value": row['active_user_type']},  # 有效账户类型
+                            "_widget_1734590278274": {"value": row['saas_version']},  # ERP操作模式
+                            "_widget_1734590278273": {"value": row['saas_use_year']},  # 使用时长
+                            "_widget_1734590278272": {"value": row['org_stage']},  # 门店阶段
+                            "_widget_1734590278271": {"value": row['manage_model']},  # 经营模式
+                            "_widget_1734590278267": {"value": row['contacts']},  # 联系人
+                            "_widget_1734590278287": {"value": row['contact_mobile']},  # 联系手机号
+                            "_widget_1734590278286": {"value": row['saas_edition_fmt']},  # SaaS版本
+                            "_widget_1734590278280": {"value": row['org_code']},  # 门店编码
+                            # "_widget_1735287791875": {"value": row['salesmen']},  # 销售负责人
+                            "_widget_1735096489244": {"value": distribution_date},  # 派发时间
+                            "_widget_1742895342914": {"value": row['business_scope_fmt']},  # 经营范围
+                            "_widget_1742895342915": {"value": row['station_number']},  # 工位数
+                            "_widget_1742895342916": {"value": [upload_key]}  # 门头照片
+                        })
+
+                        routine_follow_up_payload = {
+                            "api_key": "675b900991ad2491c69389ca",
+                            "entry_id": "675b9c63925cd404038a6b86",
+                            "is_start_workflow": "true",
+                            "data": payload_dict,
+                            "transaction_id": UUid
+                        }
+
+                        # res = api_instance.data_batch_create(routine_follow_up_payload)
+                        # logger.info(f"创建结果：{res}")
+                        time.sleep(1)
+
+                        # print(res)
+                    except:
+                        pass
+            # common_module.send_task_status(task_start_time, "续约客户回访")
+            logger.info("续约客户回访任务完成")
+        except Exception as e:
+            # common_module.send_task_error(task_start_time, "续约客户回访", str(e))
+            error_task_logger.error(f"续约客户回访任务执行时发生异常: {e}")
+
+
+if __name__ == '__main__':
+    start = RenewServicesRevisit()
+    start.main()

test/数据库验证脚本_数据处理.ipynb

@@ -0,0 +1,899 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 数据库验证脚本 - 数据处理部分\n",
+    "\n",
+    "本notebook用于调试和验证数据库验证脚本的数据处理逻辑（260-425行）\n",
+    "\n",
+    "## 使用说明\n",
+    "1. 先执行数据加载部分（第2个单元格），这部分比较耗时\n",
+    "2. 数据加载完成后，再执行后续的数据处理单元格\n",
+    "3. 每个单元格都可以单独执行和调试\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T06:53:03.604128900Z",
+     "start_time": "2026-01-16T06:53:01.840121200Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "库导入完成\n",
+      "项目根目录: D:\\Idea Project\\SaaS_V1.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 导入必要的库\n",
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "from datetime import datetime, timedelta\n",
+    "import re\n",
+    "\n",
+    "# 添加项目根目录到路径（notebook文件在test目录下，需要添加父目录）\n",
+    "current_dir = os.getcwd()\n",
+    "# 如果当前目录是test，则添加父目录；否则添加当前目录\n",
+    "if os.path.basename(current_dir) == 'test':\n",
+    "    project_root = os.path.dirname(current_dir)\n",
+    "else:\n",
+    "    project_root = current_dir\n",
+    "sys.path.insert(0, project_root)\n",
+    "\n",
+    "from api import API\n",
+    "from back_ground_module import CommonModule\n",
+    "from log_config import configure_task_logger, configure_error_task_logger\n",
+    "\n",
+    "# 初始化API和CommonModule\n",
+    "api_instance = API()\n",
+    "common_module = CommonModule()\n",
+    "\n",
+    "# 获取日志记录器\n",
+    "logger = configure_task_logger()\n",
+    "error_task_logger = configure_error_task_logger()\n",
+    "\n",
+    "print(\"库导入完成\")\n",
+    "print(f\"项目根目录: {project_root}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 步骤1: 数据加载（耗时操作，可单独执行）\n",
+    "\n",
+    "这部分会加载所有必要的数据，包括：\n",
+    "- 省市区人员关系表\n",
+    "- 员工ID列表\n",
+    "- 权限表\n",
+    "- NGV数据列表\n",
+    "- 服务提醒数据\n",
+    "- 智能检测数据\n",
+    "- 功能使用情况表\n",
+    "- 保单识别表\n",
+    "- 私域/公域小程序数据\n",
+    "- 异业合作数据\n",
+    "- 短信数据\n",
+    "- 多公司过滤表\n",
+    "- NGV明细数据（从数据库获取）\n",
+    "- 节假日列表\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========== 数据加载部分 ==========\n",
+    "# 这部分比较耗时，可以单独执行\n",
+    "\n",
+    "print(\"开始加载数据...\")\n",
+    "\n",
+    "# 省市区人员关系表\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"676512ac3e54dc3159460c0a\"}\n",
+    "json_dict = api_instance.entry_data_list(payload)\n",
+    "if json_dict and \"data\" in json_dict:\n",
+    "    json_list = json_dict.get(\"data\")\n",
+    "else:\n",
+    "    print(\"加载省市区人员关系表失败\")\n",
+    "    json_list = []\n",
+    "print(f\"省市区人员关系表: {len(json_list)} 条\")\n",
+    "\n",
+    "# 获取简道云员工id\n",
+    "payload = {\"api_key\": \"6694d3c4fcb69ca9a111a6c4\", \"entry_id\": \"6769204a1902c9341340a1bc\"}\n",
+    "staff_id = api_instance.entry_data_list(payload)\n",
+    "staff_id_list = staff_id.get(\"data\")\n",
+    "print(f\"员工ID列表: {len(staff_id_list)} 条\")\n",
+    "\n",
+    "# 获取权限表信息\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"675b96c14e839f90fef1647c\"}\n",
+    "permissions_table = api_instance.entry_data_list(payload).get(\"data\")\n",
+    "print(f\"权限表: {len(permissions_table)} 条\")\n",
+    "\n",
+    "# 获取NGV数据\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"675bb02bd2d53c2034c665e4\"}\n",
+    "NGV_data_list = api_instance.entry_data_list(payload).get(\"data\")\n",
+    "print(f\"NGV数据列表: {len(NGV_data_list)} 条\")\n",
+    "\n",
+    "# 获取服务提醒-数据支持表单数据\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"676bb7bda3029720f1083e99\"}\n",
+    "service_remind = api_instance.entry_data_list(payload).get(\"data\")\n",
+    "print(f\"服务提醒数据: {len(service_remind)} 条\")\n",
+    "\n",
+    "# 获取智能检测-数据支持表单数据\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"676bb99649ab3ac975af6e39\"}\n",
+    "Smart_detection = api_instance.entry_data_list(payload).get(\"data\")\n",
+    "print(f\"智能检测数据: {len(Smart_detection)} 条\")\n",
+    "\n",
+    "# 获取功能使用情况表\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"6763bbf657bd8fb76fcb41b2\"}\n",
+    "get_feature_usage = api_instance.entry_data_list(payload).get(\"data\", [])\n",
+    "print(f\"功能使用情况表: {len(get_feature_usage)} 条\")\n",
+    "\n",
+    "# 获取保单识别表\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"6773a60d30ed87ff9f68d3c5\"}\n",
+    "policy_recognition = api_instance.entry_data_list(payload).get(\"data\")\n",
+    "widget_list = [item['_widget_1735632397600'] for item in policy_recognition]\n",
+    "print(f\"保单识别表: {len(policy_recognition)} 条\")\n",
+    "\n",
+    "# 获取私域小程序-数据支持表单数据\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"67e0f0fae622896749ba5087\"}\n",
+    "private_domain = api_instance.entry_data_list(payload).get(\"data\", [])\n",
+    "print(f\"私域小程序数据: {len(private_domain)} 条\")\n",
+    "\n",
+    "# 获取公域小程序-数据支持表单数据\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"67e0c702c8f603b997980999\"}\n",
+    "public_domain = api_instance.entry_data_list(payload).get(\"data\", [])\n",
+    "public_domain_list = [item['_widget_1742784257506'] for item in public_domain]\n",
+    "print(f\"公域小程序数据: {len(public_domain)} 条\")\n",
+    "\n",
+    "# 获取异业合作-数据支持表单数据\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"67e24fdd8dfcfa918e17c30b\"}\n",
+    "different_industries = api_instance.entry_data_list(payload).get(\"data\", [])\n",
+    "different_industries_list = [item['_widget_1742884829007'] for item in different_industries]\n",
+    "print(f\"异业合作数据: {len(different_industries)} 条\")\n",
+    "\n",
+    "# 获取短信-数据支持表单数据\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"67e5107198ba1b20d5df3974\"}\n",
+    "groupnotification = api_instance.entry_data_list(payload).get(\"data\", [])\n",
+    "print(f\"短信数据: {len(groupnotification)} 条\")\n",
+    "\n",
+    "# 获取多公司过滤表\n",
+    "payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"689bf5f8ba88a28cb0679ec9\"}\n",
+    "get_filter_company_list = api_instance.entry_data_list(payload).get(\"data\", [])\n",
+    "print(f\"多公司过滤表: {len(get_filter_company_list)} 条\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# 获取节假日列表\n",
+    "date_list = common_module.get_holiday_list()\n",
+    "print(f\"节假日列表: {len(date_list)} 个日期\")\n",
+    "\n",
+    "# 获取NGV明细数据（从数据库获取，比较耗时）\n",
+    "print(\"\\n开始从数据库获取NGV明细数据（这可能需要一些时间）...\")\n",
+    "data_NGV = common_module.get_ngv_details(days_back=1)\n",
+    "print(f\"NGV明细数据: {len(data_NGV)} 条\")\n",
+    "print(f\"NGV明细数据列数: {len(data_NGV.columns)}\")\n",
+    "\n",
+    "# 构建省市区索引\n",
+    "def build_index(json_list):\n",
+    "    index = {}\n",
+    "    for json_item in json_list:\n",
+    "        try:\n",
+    "            key = (json_item['_widget_1734677164861'], json_item['_widget_1734677164862'],\n",
+    "                   json_item['_widget_1734677164863'])  # 省市区\n",
+    "            if '_widget_1734677164871' not in json_item:  # 日常回访客服\n",
+    "                raise KeyError(\"缺少 '日常回访客服' 键\")\n",
+    "            index[key] = json_item\n",
+    "        except KeyError as e:\n",
+    "            print(f\"警告：{e}，跳过该条记录\")\n",
+    "            continue\n",
+    "    return index\n",
+    "\n",
+    "index = build_index(json_list)\n",
+    "print(f\"省市区索引构建完成: {len(index)} 条\")\n",
+    "\n",
+    "print(\"\\n========== 数据加载完成 ==========\")\n",
+    "print(f\"数据加载时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 步骤2: 数据处理逻辑（260-425行）\n",
+    "\n",
+    "这部分包含主要的数据处理逻辑，包括：\n",
+    "- 获取多公司过滤公司id\n",
+    "- 数据清洗和转换\n",
+    "- 优先级排序\n",
+    "- 数据过滤和合并\n",
+    "- 日期计算和扩展\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========== 获取多公司过滤公司id ==========\n",
+    "logger.info(\"获取多公司过滤公司id\")\n",
+    "all_filter_company_list = []  # 获取多公司过滤公司id\n",
+    "for company in get_filter_company_list:\n",
+    "    company_list = company.get(\"_widget_1755052002491\")\n",
+    "    if company_list:\n",
+    "        for company_item in company_list:\n",
+    "            if company_item.get(\"_widget_1755052002496\") == \"否\":\n",
+    "                all_filter_company_list.append(company_item.get(\"_widget_1755052002495\"))\n",
+    "logger.info(f\"过滤公司条数:{len(all_filter_company_list)}\")\n",
+    "print(f\"过滤公司条数: {len(all_filter_company_list)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:27:13.175060200Z",
+     "start_time": "2026-01-16T07:27:09.857076900Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "数据预处理完成，当前数据量: 45672 条\n",
+      "数据列数: 143\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 数据预处理：日期转换和数据清洗 ==========\n",
+    "# 将A列和B列的日期字符串转换为日期格式\n",
+    "data_NGV = data_NGV.copy()\n",
+    "data_NGV['A'] = pd.to_datetime(data_NGV['expiry_time'])\n",
+    "data_NGV['B'] = pd.to_datetime(data_NGV['renew_date'])\n",
+    "\n",
+    "def replace_values(series):\n",
+    "    # 使用条件判断来进行替换\n",
+    "    return series.apply(lambda x: '' if pd.isna(x) or x in ['NA', 'None', ''] else x)\n",
+    "\n",
+    "# 处理字符串数据并显式指定数据类型\n",
+    "data_NGV = data_NGV.apply(replace_values)\n",
+    "\n",
+    "print(f\"数据预处理完成，当前数据量: {len(data_NGV)} 条\")\n",
+    "print(f\"数据列数: {len(data_NGV.columns)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:28:01.298494800Z",
+     "start_time": "2026-01-16T07:28:01.106113700Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "过滤多公司后数据量: 45653 条\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 过滤多公司 ==========\n",
+    "# 针对公司主店过期，取公司最高等级版本派发\n",
+    "# 过滤多公司\n",
+    "data_NGV = data_NGV[~data_NGV['id_own_group'].isin(all_filter_company_list)]\n",
+    "print(f\"过滤多公司后数据量: {len(data_NGV)} 条\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:28:04.235079300Z",
+     "start_time": "2026-01-16T07:28:04.185820400Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "优先级映射完成\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 定义优先级顺序和创建映射字典 ==========\n",
+    "# 定义优先级顺序\n",
+    "edition_order = ['皇冠版', '至尊版', '尊享版', '旗舰版', '标准版', '进阶版', '基础版', '入门版']\n",
+    "customer_type_order = [\"F\", \"E\", \"D\", \"C\", \"B\", \"A\"]  # 索引越小优先级越高\n",
+    "group_grade_order = ['全国KA（FMVP）', '区域KA（MVP）', '重要客户（SVIP）', '普通客户（VIP）']\n",
+    "\n",
+    "# 创建映射字典，并为不在列表中的值设置默认值\n",
+    "edition_map = {edition: idx for idx, edition in enumerate(edition_order)}\n",
+    "customer_type_map = {ctype: idx for idx, ctype in enumerate(customer_type_order)}\n",
+    "group_grade_map = {grade: idx for idx, grade in enumerate(group_grade_order)}\n",
+    "\n",
+    "# 添加用于排序的新列，并处理不在映射字典中的值\n",
+    "data_NGV['edition_rank'] = data_NGV['saas_edition_fmt'].map(edition_map).fillna(0).astype(int)  # 缺失值用最高优先级填充\n",
+    "data_NGV['customer_type_rank'] = data_NGV['saas_customer_type'].map(customer_type_map).fillna(0).astype(int)\n",
+    "data_NGV['group_grade_rank'] = data_NGV['group_grade'].map(group_grade_map).fillna(0).astype(int)\n",
+    "\n",
+    "print(\"优先级映射完成\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:28:08.790102700Z",
+     "start_time": "2026-01-16T07:28:08.399298Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "最佳值查找完成\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 找到每组中的最佳值 ==========\n",
+    "# 找到每组中 edition_rank 最小值对应的行\n",
+    "best_edition_idx = data_NGV.groupby('id_own_group')['edition_rank'].idxmin()\n",
+    "best_edition_rows = data_NGV.loc[best_edition_idx]\n",
+    "best_edition_rows['max_saas_edition'] = best_edition_rows['saas_edition_fmt']\n",
+    "\n",
+    "# 找到每组中 customer_type_rank 最小值对应的行\n",
+    "best_customer_type_idx = data_NGV.groupby('id_own_group')['customer_type_rank'].idxmin()\n",
+    "best_customer_type_rows = data_NGV.loc[best_customer_type_idx]\n",
+    "best_customer_type_rows['max_saas_customer_type'] = best_customer_type_rows['customer_type_rank'].apply(\n",
+    "    lambda x: customer_type_order[x])\n",
+    "\n",
+    "# 找到每组中 group_grade_rank 最小值对应的行\n",
+    "best_group_grade_idx = data_NGV.groupby('id_own_group')['group_grade_rank'].idxmin()\n",
+    "best_group_grade_rows = data_NGV.loc[best_group_grade_idx]\n",
+    "best_group_grade_rows['max_group_grade'] = best_group_grade_rows['group_grade']\n",
+    "\n",
+    "print(\"最佳值查找完成\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:28:11.374632Z",
+     "start_time": "2026-01-16T07:28:11.141730700Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "最佳值合并完成，当前数据量: 45653 条\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 合并最佳值回到原数据集 ==========\n",
+    "# 合并最佳值回到原数据集\n",
+    "best_values = (\n",
+    "    best_edition_rows[['id_own_group', 'max_saas_edition']]\n",
+    "    .merge(best_customer_type_rows[['id_own_group', 'max_saas_customer_type']], on='id_own_group',\n",
+    "           how='outer')\n",
+    "    .merge(best_group_grade_rows[['id_own_group', 'max_group_grade']], on='id_own_group', how='outer')\n",
+    ")\n",
+    "\n",
+    "# 将最佳值合并回原数据集\n",
+    "data_NGV = data_NGV.merge(best_values, on='id_own_group', how='left')\n",
+    "\n",
+    "print(f\"最佳值合并完成，当前数据量: {len(data_NGV)} 条\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:30:06.358604500Z",
+     "start_time": "2026-01-16T07:30:05.752861600Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============================================================\n",
+      "调试信息：处理主店过期情况\n",
+      "============================================================\n",
+      "\n",
+      "当前 data_NGV 数据量: 45653 条\n",
+      "\n",
+      "【字段检查】\n",
+      "is_main_org 数据类型: object\n",
+      "is_main_org 唯一值: ['0', '1']\n",
+      "is_main_org 值分布:\n",
+      "is_main_org\n",
+      "1    37628\n",
+      "0     8025\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "org_status 数据类型: object\n",
+      "org_status 唯一值: ['留存', '过期']\n",
+      "org_status 值分布:\n",
+      "org_status\n",
+      "留存    27985\n",
+      "过期    17668\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "org_type 数据类型: object\n",
+      "org_type 唯一值: ['一般', '天猫']\n",
+      "org_type 值分布:\n",
+      "org_type\n",
+      "一般    42985\n",
+      "天猫     2668\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "【步骤1: 筛选主店过期】\n",
+      "警告: is_main_org 是字符串类型，尝试转换为数值\n",
+      "条件筛选结果数量: 15065 条\n",
+      "主店过期数据量 (ngvv2): 15065 条\n",
+      "ngvv2 中的 id_own_group 数量: 15065 个\n",
+      "ngvv2 中的 id_own_group 示例: ['10545055917999655906', '10545055917999678943', '10545055917999702656', '10545055917999726421', '10545055917999791008', '10545055917999907421', '10545055917999958815', '10545055917999963314', '10545055917999973061', '10545055918000062921']\n",
+      "\n",
+      "【步骤2: 筛选分店留存】\n",
+      "data_NGV_V2 初始数据量: 45653 条\n",
+      "\n",
+      "area_manager 唯一值数量: 16\n",
+      "area_manager 值分布（前10）:\n",
+      "area_manager\n",
+      "肖军     10824\n",
+      "景东强     8408\n",
+      "陈庆伟     8322\n",
+      "张凯      8269\n",
+      "关磊      7028\n",
+      "孙玉蕾     2006\n",
+      "殷昊       556\n",
+      "王涛       161\n",
+      "刘伟        52\n",
+      "           8\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "各条件筛选结果:\n",
+      "  org_type == '一般': 42985 条\n",
+      "  org_status == '留存': 27985 条\n",
+      "  area_manager != '殷昊': 45097 条\n",
+      "  area_manager != '孙玉蕾': 43647 条\n",
+      "  is_main_org != 1: 8025 条\n",
+      "\n",
+      "所有条件合并后数据量: 4882 条\n",
+      "data_NGV_V2_filtered 中的 id_own_group 数量: 1564 个\n",
+      "data_NGV_V2_filtered 中的 id_own_group 示例: ['10545055917999659357', '10545055917999659357', '10545055917999688607', '10545055917999688607', '10545055917999688607', '10545055917999719687', '10545055917999791008', '10545055917999791008', '10545055917999995278', '10545055918000106937']\n",
+      "\n",
+      "【步骤3: 检查 id_own_group 交集】\n",
+      "ngvv2 中的 id_own_group 数量: 15065\n",
+      "data_NGV_V2_filtered 中的 id_own_group 数量: 1564\n",
+      "交集数量: 316\n",
+      "交集中的 id_own_group 示例: ['11240984669917478021', '10546172455175803787', '10546172455166018835', '10546172455220322161', '10546443563657780587', '10546172455213602644', '10546443563816611858', '11240984669917430021', '11240984669917329620', '11240984669917352563']\n",
+      "\n",
+      "【步骤4: 最终过滤】\n",
+      "过滤后的数据量: 468 条\n",
+      "\n",
+      "============================================================\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hp_z66\\AppData\\Local\\Temp\\ipykernel_14280\\4275068286.py:100: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  data_NGV_V2_filtered['exists_in_ngvv2'] = data_NGV_V2_filtered['id_own_group'].isin(ngvv2['id_own_group'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 处理主店过期的情况 ==========\n",
+    "# 调试信息：检查数据状态\n",
+    "print(\"=\" * 60)\n",
+    "print(\"调试信息：处理主店过期情况\")\n",
+    "print(\"=\" * 60)\n",
+    "print(f\"\\n当前 data_NGV 数据量: {len(data_NGV)} 条\")\n",
+    "\n",
+    "# 检查关键字段的数据类型和唯一值\n",
+    "print(f\"\\n【字段检查】\")\n",
+    "print(f\"is_main_org 数据类型: {data_NGV['is_main_org'].dtype}\")\n",
+    "print(f\"is_main_org 唯一值: {sorted(data_NGV['is_main_org'].unique())}\")\n",
+    "print(f\"is_main_org 值分布:\\n{data_NGV['is_main_org'].value_counts()}\")\n",
+    "\n",
+    "print(f\"\\norg_status 数据类型: {data_NGV['org_status'].dtype}\")\n",
+    "print(f\"org_status 唯一值: {sorted(data_NGV['org_status'].unique())}\")\n",
+    "print(f\"org_status 值分布:\\n{data_NGV['org_status'].value_counts()}\")\n",
+    "\n",
+    "print(f\"\\norg_type 数据类型: {data_NGV['org_type'].dtype}\")\n",
+    "print(f\"org_type 唯一值: {sorted(data_NGV['org_type'].unique())}\")\n",
+    "print(f\"org_type 值分布:\\n{data_NGV['org_type'].value_counts()}\")\n",
+    "\n",
+    "# 步骤1: 筛选主店过期的情况\n",
+    "print(f\"\\n【步骤1: 筛选主店过期】\")\n",
+    "# 确保 is_main_org 是数值类型\n",
+    "if data_NGV['is_main_org'].dtype == 'object':\n",
+    "    print(\"警告: is_main_org 是字符串类型，尝试转换为数值\")\n",
+    "    data_NGV['is_main_org'] = pd.to_numeric(data_NGV['is_main_org'], errors='coerce')\n",
+    "\n",
+    "condition = (data_NGV['is_main_org'] == 1) & (data_NGV['org_status'] == '过期')\n",
+    "print(f\"条件筛选结果数量: {condition.sum()} 条\")\n",
+    "\n",
+    "ngvv2 = data_NGV[condition]\n",
+    "print(f\"主店过期数据量 (ngvv2): {len(ngvv2)} 条\")\n",
+    "\n",
+    "if len(ngvv2) > 0:\n",
+    "    print(f\"ngvv2 中的 id_own_group 数量: {ngvv2['id_own_group'].nunique()} 个\")\n",
+    "    print(f\"ngvv2 中的 id_own_group 示例: {ngvv2['id_own_group'].head(10).tolist()}\")\n",
+    "else:\n",
+    "    print(\"⚠️ 警告: ngvv2 为空，没有主店过期的情况！\")\n",
+    "\n",
+    "# 步骤2: 检查分店留存的情况\n",
+    "print(f\"\\n【步骤2: 筛选分店留存】\")\n",
+    "# 在合并最佳值之前保存原始数据副本（重要！）\n",
+    "data_NGV_V2 = data_NGV.copy()\n",
+    "print(f\"data_NGV_V2 初始数据量: {len(data_NGV_V2)} 条\")\n",
+    "\n",
+    "# 检查 area_manager 字段\n",
+    "print(f\"\\narea_manager 唯一值数量: {data_NGV_V2['area_manager'].nunique()}\")\n",
+    "print(f\"area_manager 值分布（前10）:\\n{data_NGV_V2['area_manager'].value_counts().head(10)}\")\n",
+    "\n",
+    "# 确保 is_main_org 是数值类型\n",
+    "if data_NGV_V2['is_main_org'].dtype == 'object':\n",
+    "    data_NGV_V2['is_main_org'] = pd.to_numeric(data_NGV_V2['is_main_org'], errors='coerce')\n",
+    "\n",
+    "# 逐步检查每个条件\n",
+    "cond1 = (data_NGV_V2['org_type'] == \"一般\")\n",
+    "cond2 = (data_NGV_V2['org_status'] == '留存')\n",
+    "cond3 = (data_NGV_V2['area_manager'] != '殷昊')\n",
+    "cond4 = (data_NGV_V2['area_manager'] != '孙玉蕾')\n",
+    "cond5 = (data_NGV_V2['is_main_org'] != 1)\n",
+    "\n",
+    "print(f\"\\n各条件筛选结果:\")\n",
+    "print(f\"  org_type == '一般': {cond1.sum()} 条\")\n",
+    "print(f\"  org_status == '留存': {cond2.sum()} 条\")\n",
+    "print(f\"  area_manager != '殷昊': {cond3.sum()} 条\")\n",
+    "print(f\"  area_manager != '孙玉蕾': {cond4.sum()} 条\")\n",
+    "print(f\"  is_main_org != 1: {cond5.sum()} 条\")\n",
+    "\n",
+    "data_NGV_V2['条件'] = cond1 & cond2 & cond3 & cond4 & cond5\n",
+    "data_NGV_V2_filtered = data_NGV_V2.loc[data_NGV_V2[\"条件\"]]\n",
+    "print(f\"\\n所有条件合并后数据量: {len(data_NGV_V2_filtered)} 条\")\n",
+    "\n",
+    "if len(data_NGV_V2_filtered) > 0:\n",
+    "    print(f\"data_NGV_V2_filtered 中的 id_own_group 数量: {data_NGV_V2_filtered['id_own_group'].nunique()} 个\")\n",
+    "    print(f\"data_NGV_V2_filtered 中的 id_own_group 示例: {data_NGV_V2_filtered['id_own_group'].head(10).tolist()}\")\n",
+    "\n",
+    "# 步骤3: 检查交集\n",
+    "print(f\"\\n【步骤3: 检查 id_own_group 交集】\")\n",
+    "if len(ngvv2) > 0 and len(data_NGV_V2_filtered) > 0:\n",
+    "    ngvv2_groups = set(ngvv2['id_own_group'].unique())\n",
+    "    v2_groups = set(data_NGV_V2_filtered['id_own_group'].unique())\n",
+    "    intersection = ngvv2_groups & v2_groups\n",
+    "    \n",
+    "    print(f\"ngvv2 中的 id_own_group 数量: {len(ngvv2_groups)}\")\n",
+    "    print(f\"data_NGV_V2_filtered 中的 id_own_group 数量: {len(v2_groups)}\")\n",
+    "    print(f\"交集数量: {len(intersection)}\")\n",
+    "    \n",
+    "    if len(intersection) > 0:\n",
+    "        print(f\"交集中的 id_own_group 示例: {list(intersection)[:10]}\")\n",
+    "    else:\n",
+    "        print(\"⚠️ 警告: 没有交集！这可能是问题所在。\")\n",
+    "        print(f\"ngvv2 中的前10个 id_own_group: {list(ngvv2_groups)[:10]}\")\n",
+    "        print(f\"data_NGV_V2_filtered 中的前10个 id_own_group: {list(v2_groups)[:10]}\")\n",
+    "else:\n",
+    "    print(\"⚠️ 警告: ngvv2 或 data_NGV_V2_filtered 为空，无法检查交集\")\n",
+    "\n",
+    "# 步骤4: 过滤存在的记录\n",
+    "print(f\"\\n【步骤4: 最终过滤】\")\n",
+    "if len(ngvv2) > 0:\n",
+    "    data_NGV_V2_filtered['exists_in_ngvv2'] = data_NGV_V2_filtered['id_own_group'].isin(ngvv2['id_own_group'])\n",
+    "    filtered_data = data_NGV_V2_filtered[data_NGV_V2_filtered['exists_in_ngvv2']]\n",
+    "    print(f\"过滤后的数据量: {len(filtered_data)} 条\")\n",
+    "    \n",
+    "    if len(filtered_data) == 0:\n",
+    "        print(\"\\n❌ 问题诊断:\")\n",
+    "        print(\"  过滤后数据为空，可能的原因:\")\n",
+    "        print(\"  1. ngvv2 为空（没有主店过期的情况）\")\n",
+    "        print(\"  2. data_NGV_V2_filtered 为空（没有满足条件的分店留存数据）\")\n",
+    "        print(\"  3. 两者的 id_own_group 没有交集\")\n",
+    "        print(\"\\n建议:\")\n",
+    "        print(\"  - 检查数据源是否正确\")\n",
+    "        print(\"  - 检查字段值是否匹配（注意数据类型和格式）\")\n",
+    "        print(\"  - 检查是否有主店过期但分店留存的情况\")\n",
+    "else:\n",
+    "    print(\"⚠️ 警告: ngvv2 为空，无法进行过滤\")\n",
+    "    filtered_data = pd.DataFrame()  # 创建空DataFrame\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:30:16.487181500Z",
+     "start_time": "2026-01-16T07:30:16.440099400Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "排序去重后数据量: 316 条\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hp_z66\\AppData\\Local\\Temp\\ipykernel_14280\\2835892650.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  filtered_data['sort_key'] = filtered_data['saas_edition_fmt'].map(fixed_order_map)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 对过滤数据进行排序和去重 ==========\n",
+    "fixed_order = ['皇冠版', '至尊版', '尊享版', '旗舰版', '标准版', '进阶版', '基础版', '入门版']\n",
+    "\n",
+    "fixed_order_map = {edition: index for index, edition in enumerate(fixed_order)}\n",
+    "filtered_data['sort_key'] = filtered_data['saas_edition_fmt'].map(fixed_order_map)\n",
+    "filtered_data = filtered_data.sort_values(by='sort_key').drop('sort_key', axis=1)\n",
+    "\n",
+    "result = filtered_data.drop_duplicates(subset='id_own_group', keep='first')\n",
+    "\n",
+    "print(f\"排序去重后数据量: {len(result)} 条\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========== 合并主店留存数据和分店数据 ==========\n",
+    "data_NGV['条件'] = (data_NGV['org_type'] == \"一般\") & (data_NGV['org_status'] == '留存') & (\n",
+    "        data_NGV['area_manager'] != '殷昊') & (\n",
+    "               data_NGV['area_manager'] != '孙玉蕾') & (\n",
+    "               data_NGV['is_main_org'] == 1)\n",
+    "data_NGV = data_NGV.loc[data_NGV[\"条件\"]]\n",
+    "\n",
+    "data_NGV = pd.concat([data_NGV, result], axis=0)\n",
+    "data_details = data_NGV.copy()\n",
+    "\n",
+    "# 重置索引\n",
+    "data_details = data_details.reset_index(drop=True)\n",
+    "\n",
+    "print(f\"合并后数据量: {len(data_details)} 条\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:30:21.600199200Z",
+     "start_time": "2026-01-16T07:30:20.828225500Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "日期计算后数据量: 9845 条\n",
+      "需要扩展的数据行数: 9845 条\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 判断日期差并计算年数 ==========\n",
+    "# 判断A列的日期是否大于B列的日期730天，如果是的话，将B列的值设置为天数差\n",
+    "data_details['条件'] = data_details.apply(\n",
+    "    lambda row: (\n",
+    "        (pd.to_datetime(row['A']) - pd.to_datetime(row['B'])).days\n",
+    "        if pd.to_datetime(row['A']) - pd.to_datetime(row['B']) >= pd.Timedelta(days=730)\n",
+    "        else 0\n",
+    "    ),\n",
+    "    axis=1\n",
+    ")\n",
+    "data_details = data_details.loc[data_details[\"条件\"] > 0]\n",
+    "\n",
+    "# 定义一个函数，用于将数字除以365并取整数\n",
+    "def divide_by_365(x):\n",
+    "    if isinstance(x, (int, float)):\n",
+    "        return int(x / 365)\n",
+    "    else:\n",
+    "        return x\n",
+    "\n",
+    "# 使用apply函数将divide_by_365函数应用到DataFrame的列\n",
+    "data_details['年'] = data_details['条件'].apply(divide_by_365)\n",
+    "\n",
+    "# 重置索引\n",
+    "data_details = data_details.reset_index(drop=True)\n",
+    "\n",
+    "print(f\"日期计算后数据量: {len(data_details)} 条\")\n",
+    "print(f\"需要扩展的数据行数: {len(data_details[data_details['年'] > 1])} 条\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========== 扩展数据：根据年数复制行并修改日期 ==========\n",
+    "# 创建一个新的空的DataFrame\n",
+    "new_df = pd.DataFrame()\n",
+    "\n",
+    "# 遍历原始DataFrame的每一行\n",
+    "for index, row in data_details.iterrows():\n",
+    "    # 根据年数来决定复制的次数\n",
+    "    if row[\"renew_date\"] != \"2024-02-29\":\n",
+    "        for i_new in range(1, row['年']):\n",
+    "            # 修改日期\n",
+    "            row_new = row.copy()\n",
+    "            c = row_new[\"renew_date\"]\n",
+    "            date_obj = datetime.strptime(c, \"%Y-%m-%d\")\n",
+    "            new_year = date_obj.year + i_new\n",
+    "            new_date_obj = date_obj.replace(year=new_year)\n",
+    "            new_c = new_date_obj.strftime(\"%Y-%m-%d\")\n",
+    "            row_new[\"renew_date\"] = new_c\n",
+    "            # 将当前行添加到新的DataFrame中\n",
+    "            new_df = pd.concat([new_df, pd.DataFrame([row_new])], ignore_index=True)\n",
+    "\n",
+    "print(f\"扩展后的新数据量: {len(new_df)} 条\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-16T07:39:28.813848800Z",
+     "start_time": "2026-01-16T07:39:28.255902200Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "合并后总数据量: 39599 条\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ========== 合并原始数据和扩展数据 ==========\n",
+    "# 合并两个DataFrame\n",
+    "merged_df = pd.concat([data_NGV, new_df], axis=0, ignore_index=True)\n",
+    "data_details = merged_df.copy()  # 替换名称\n",
+    "\n",
+    "data_details_not_null = data_details[data_details['renew_date'].notnull()]\n",
+    "# 重置索引\n",
+    "data_details_not_null = data_details_not_null.reset_index(drop=True)\n",
+    "data_details = data_details_not_null.copy()  # 替换名称 v2\n",
+    "\n",
+    "print(f\"合并后总数据量: {len(data_details)} 条\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========== 最终过滤：排除创建时间等于续约时间的记录 ==========\n",
+    "data_details['saas_create_time'] = data_details['saas_create_time'].str[:4]  # 截取前4位（年份）\n",
+    "data_details['renew_date_new'] = data_details['renew_date'].str[:4]  # 截取前4位（年份）\n",
+    "data_details = data_details[\n",
+    "    data_details['saas_create_time'] != data_details['renew_date_new']]  # 过滤掉等于renew_date的行\n",
+    "\n",
+    "data_details = data_details.reset_index(drop=True)\n",
+    "\n",
+    "logger.info(f\"过滤后的数据长度为: {len(data_details)}\")\n",
+    "print(f\"\\n========== 数据处理完成 ==========\")\n",
+    "print(f\"最终数据量: {len(data_details)} 条\")\n",
+    "print(f\"处理完成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数据验证和检查\n",
+    "\n",
+    "可以在这里添加数据验证代码，检查处理结果的正确性\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2026-01-16T09:00:49.656903800Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# ========== 数据验证 ==========\n",
+    "# 查看数据基本信息\n",
+    "print(\"数据基本信息:\")\n",
+    "print(f\"数据形状: {data_details.shape}\")\n",
+    "print(f\"\\n数据列名:\")\n",
+    "print(data_details.columns.tolist())\n",
+    "\n",
+    "# 查看前几行数据\n",
+    "print(\"\\n前5行数据:\")\n",
+    "print(data_details.head())\n",
+    "\n",
+    "# 检查关键字段的数据分布\n",
+    "if 'saas_edition_fmt' in data_details.columns:\n",
+    "    print(\"\\n版本分布:\")\n",
+    "    print(data_details['saas_edition_fmt'].value_counts())\n",
+    "\n",
+    "if 'org_status' in data_details.columns:\n",
+    "    print(\"\\n组织状态分布:\")\n",
+    "    print(data_details['org_status'].value_counts())\n",
+    "\n",
+    "# 可以保存到CSV文件进行进一步检查\n",
+    "# data_details.to_csv(\"处理后的数据.csv\", index=False, encoding='utf-8-sig')\n",
+    "# print(\"\\n数据已保存到: 处理后的数据.csv\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

test/新签创建数据.py

@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+
+payload ={
+  "app_id": "675b900991ad2491c69389ca",
+
+    "data": {
+      "_widget_1767851302591": 1772508166000,
+      "_widget_1767863644355": "何文娟",
+      "_widget_1767851302590": "1772507997862",
+      "_widget_1767851302593": [
+        "064863221620345741"
+      ],
+      "_widget_1767851302592": "15651815921",
+      "_widget_1767851302595": [
+        "122744083333090577"
+      ],
+      "_widget_1767851302594": "064863221620345741",
+      "_widget_1767851302597": "废弃测试三月分店",
+      "_widget_1767851302596": "CHS202603030049192",
+      "_widget_1767952153422": [
+        "洗车",
+        "美容"
+      ],
+      "_widget_1772264444051": "19999.0",
+      "_widget_1767851302599": "普通客户（VIP）",
+      "_widget_1767851302598": "zyc测试公司废弃",
+      "_widget_1768290159749": [
+        "122744083333090577"
+      ],
+      "_widget_1768290159748": "华南区域",
+      "_widget_1768290159747": "15870306745529522117",
+      "_widget_1767863644358": "15651815921",
+      "_widget_1768290159746": "16338827489080131642",
+      "_widget_1767863644357": "何文娟",
+      "_widget_1767863644356": "15651815921",
+      "_widget_1767863644582": [
+        "064863221620345741"
+      ],
+      "_widget_1767851302585": "XQFWD20260303001",
+      "_widget_1772264443873": "台湾省",
+      "_widget_1772703526239": "1",
+      "_widget_1772264443872": "其它",
+      "_widget_1767851302600": 1772508361000,
+      "_widget_1768290159737": 1773112966000,
+      "_widget_1767851302602": "皇冠版",
+      "_widget_1772264443811": "C",
+      "_widget_1772264443812": "5",
+      "_widget_1767851302604": "30"
+    }
+  ,
+  "entry_id": "695f439e3e910f09190d8e99",
+  "is_start_workflow": true
+}

test/新签研发联调.ipynb

@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "新签节点调用格式",
+   "id": "671b10d308af2bdc"
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-03-12T09:25:38.245095300Z",
+     "start_time": "2026-03-12T09:25:36.441415900Z"
+    }
+   },
+   "source": [
+    "import requests\n",
+    "url = \"https://manage-pre.f6yc.com/hive-admin/yida/updateNode\"\n",
+    "\n",
+    "\n",
+    "payload = {\n",
+    "    \"nodeCode\": \"ORG_RESEARCH\",\n",
+    "    \"needTraining\": \"是\",\n",
+    "    \"impPrincipal\":\"['171408516124043808']\",\n",
+    "    \"instanceId\": \"69b269d6c193f83cc27f65e7\"\n",
+    "}\n",
+    "\n",
+    "# [{\"_id\":\"69a285f3fa0d9fb1984da9bf\",\"name\":\"张乐乐\",\"username\":\"171408516124043808\",\"status\":1,\"type\":0}]\n",
+    "\n",
+    "response = requests.post(url, data=payload)\n",
+    "response.json()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'code': 200, 'data': None, 'message': 'SUCCESS'}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# 续约联调",
+   "id": "6ce335625a1c1fbb"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-26T07:12:43.283568100Z",
+     "start_time": "2026-03-26T07:12:42.653902400Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# 可以引用一些第三方库.\n",
+    "import json\n",
+    "import requests\n",
+    "import time\n",
+    "import random\n",
+    "import time\n",
+    "import binascii  # 【修正1】添加缺失的 binascii 导入\n",
+    "from pyDes import des, CBC, PAD_PKCS5\n",
+    "\n",
+    "data_id = \"69c4dc44986b303c2ef69d46\" # 数据id\n",
+    "orgid = \"16058986391127773239\" # 门店id\n",
+    "order_id = \"XYFWD20260326002\" # 服务单号\n",
+    "operation_consultant_str = '[{\"_id\":\"69a285f3fa0d9fb1984da9bf\",\"name\":\"张乐乐\",\"username\":\"171408516124043808\",\"status\":1,\"type\":0}]'# 专属人员顾问\n",
+    "\n",
+    "url = \"https://manage-pre.f6yc.com/hive-admin/py/yida/renewal/insertRenewalFormsData\"\n",
+    "\n",
+    "def des_encrypt(s):\n",
+    "    \"\"\"\n",
+    "    DES 加密\n",
+    "    :param s: 原始字符串\n",
+    "    :return: 加密后字符串，16进制\n",
+    "    \"\"\"\n",
+    "    secret_key = 'HwdMBW8o'\n",
+    "    iv = secret_key\n",
+    "    k = des(secret_key, CBC, iv, pad=None, padmode=PAD_PKCS5)\n",
+    "    en = k.encrypt(s, padmode=PAD_PKCS5)\n",
+    "    return binascii.b2a_base64(en, newline=False)\n",
+    "\n",
+    "\n",
+    "def des_descrypt(s):\n",
+    "    \"\"\"\n",
+    "    DES 解密\n",
+    "    :param s: 加密后的字符串，16进制\n",
+    "    :return:  解密后的字符串\n",
+    "    \"\"\"\n",
+    "    secret_key = 'HwdMBW8o'\n",
+    "    iv = secret_key\n",
+    "    k = des(secret_key, CBC, iv, pad=None, padmode=PAD_PKCS5)\n",
+    "    de = k.decrypt(binascii.a2b_base64(s), padmode=PAD_PKCS5)\n",
+    "    return de\n",
+    "\n",
+    "\n",
+    "impPrincipal_list = []\n",
+    "\n",
+    "if operation_consultant_str:\n",
+    "    try:\n",
+    "        # 将字符串 '[{...}]' 转换为 Python 列表 [{...}]\n",
+    "        operation_list = json.loads(operation_consultant_str)\n",
+    "\n",
+    "        # 确保解析出来的是列表\n",
+    "        if isinstance(operation_list, list):\n",
+    "            for operate in operation_list:\n",
+    "                # 确保每一项是字典且包含 username\n",
+    "                if isinstance(operate, dict) and \"username\" in operate:\n",
+    "                    impPrincipal_list.append(operate[\"username\"])\n",
+    "        else:\n",
+    "            # 如果解析出来不是列表（比如是个单对象），做兼容处理\n",
+    "            if isinstance(operation_list, dict) and \"username\" in operation_list:\n",
+    "                impPrincipal_list.append(operation_list[\"username\"])\n",
+    "\n",
+    "    except json.JSONDecodeError:\n",
+    "        # 如果解析失败，记录错误或保持列表为空\n",
+    "        print(f\"JSON 解析失败: {operation_consultant_str}\")\n",
+    "        impPrincipal_list = []\n",
+    "else:\n",
+    "    operation_list = []\n",
+    "\n",
+    "impPrincipal_value=impPrincipal_list[0]\n",
+    "\n",
+    "t = time.time()\n",
+    "ts = int(round(t * 1000))\n",
+    "randint = random.randint(100000000, 999999999)\n",
+    "req = data_id + \"|\" + orgid + \"|\" +order_id+ \"|\" + impPrincipal_value + \"_\" + str(ts) + \"_\" + str(randint)\n",
+    "# 实例ID|门ID|服务单号|专属运营顾问\n",
+    "str_en = des_encrypt(req)\n",
+    "print(str_en.decode('utf-8'))\n",
+    "req_new = str_en.decode('utf-8')\n",
+    "payload = {\n",
+    "    'req':req_new,\n",
+    "    't':ts,\n",
+    "    'r':randint\n",
+    "}\n",
+    "\n",
+    "res = requests.post(url,data=payload)\n"
+   ],
+   "id": "fc98d87aa8b19ce2",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lmj1kXumBcGLR5IKAtgFDP2meGj6FgLzq7gaQ/6wDuZ3d43P0mtQCyUdQXkeC4kYxOT8w7n1xNxYhIy9PVq/xUR9emnP5CQweBFe8P3S9tRZdNFVOdipnD+xiof/q9b//C87kgUEIH7JT00nlBBkaQ==\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-13T08:12:33.285136400Z",
+     "start_time": "2026-03-13T08:12:33.246808900Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "impPrincipal_value",
+   "id": "8edd45ef4657d8ed",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'171408516124043808'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 26
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/测试宜搭转简道云接口.py

@@ -0,0 +1,10 @@
+# -*- coding: utf-8 -*-
+import pandas as pd
+import requests
+
+url = "https://manage-pre.f6yc.com/hive-admin/yida/renewal/updateNode"
+
+payload = {"instanceId":"15348c27-57b7-4285-b93b-87b3d41f5a28","nodeCode":"SIXTY","impPrincipal":"[\"053052302136860181\"]"}
+
+result = requests.post(url, data=payload)
+print(result.text)

test/省市同步到bi.py

@@ -0,0 +1,200 @@
+import pandas as pd
+import datetime
+from config import Config
+from api import API
+import pymysql  # 使用 pymysql 替代 mysql.connector
+from back_ground_module import CommonModule
+import os
+import mysql.connector
+import pandas as pd
+import json
+import numpy as np
+import mysql.connector
+from mysql.connector import Error
+from log_config import configure_task_logger, configure_error_task_logger
+import math
+
+logger = configure_task_logger()
+error_task_logger = configure_error_task_logger()
+output_dir = "output"  # 设置输出目录
+os.makedirs(output_dir, exist_ok=True)
+common_module = CommonModule()
+api_instance = API()
+
+
+class ProvinceCityPersonRelationToBI:
+    def __init__(self):
+        self.pvc_data = None
+        self.field_mapping = {
+            "省": "_widget_1734677164861",
+            "市": "_widget_1734677164862",
+            "运营顾问": "_widget_1734677164864",
+            "区域经理": "_widget_1734677164865",
+            "运营专家": "_widget_1734677164866",
+            "战区": "_widget_1734677164867",
+            "新签回访客服": "_widget_1734677164868",
+            "续约回访客服": "_widget_1734677164869",
+            "异常待办客服": "_widget_1734677164870",
+            "日常回访客服": "_widget_1734677164871",
+        }
+
+    def load_all_data(self):
+        payload = {"api_key": "675b900991ad2491c69389ca",
+                   "entry_id": "676512ac3e54dc3159460c0a",
+                   }
+        pvc_data = api_instance.entry_data_list(payload)
+        self.pvc_data = pvc_data.get("data")  # api请求格式，将数据封装在data字典里
+
+    def data_process(self):
+        df = pd.DataFrame(self.pvc_data)
+        # 反转映射字典
+        reverse_mapping = {v: k for k, v in self.field_mapping.items()}
+        # 1.列明替换
+        df.columns = [reverse_mapping.get(col, col) for col in df.columns]
+
+        # 2.成员字段取值
+        user_columns = ["运营顾问", "区域经理", "运营专家", "新签回访客服", "续约回访客服",
+                        "异常待办客服", "日常回访客服"]
+
+        for col in user_columns:
+            df[col] = df[col].map(lambda x: x.get("name", "") if isinstance(x, dict) else "")
+
+        # 3.根据省市去重
+        df = df.drop_duplicates(subset=['省', '市'])
+
+        return df
+
+    def clear_table_data(self):
+        """
+        清空指定 MySQL 表的数据。
+        参数已写死在函数内部，直接调用即可。
+        """
+        # 数据库连接信息
+        HS_DB_Config = {
+            'host': "f6-public.rwlb.rds.aliyuncs.com",
+            'user': "rw_operation_data_relay",
+            'password': "m+q5Z4%IVuF9bf",
+            'database': "f6operation_data_relay"
+        }
+        table_name = "province_city_person_relation_to_bi"  # 要清空的表名
+
+        connection = None
+        try:
+            # 建立数据库连接
+            connection = mysql.connector.connect(
+                host=HS_DB_Config["host"],
+                user=HS_DB_Config["user"],
+                password=HS_DB_Config["password"],
+                database=HS_DB_Config["database"]
+            )
+            if connection.is_connected():
+                cursor = connection.cursor()
+
+                # 使用TRUNCATE清空表数据
+                cursor.execute(f"TRUNCATE TABLE {table_name}")
+                connection.commit()
+
+                logger.info(f"成功清空表 {table_name} 中的所有数据")
+
+        except Error as e:
+            error_task_logger.error(f"清空表时发生错误: {e}")
+            if connection and connection.is_connected():
+                connection.rollback()
+        finally:
+            if connection and connection.is_connected():
+                cursor.close()
+                connection.close()
+                logger.info("数据库连接已关闭")
+
+    def write_to_bi(self, df):
+        HS_DB_Config = Config.HS_DB_Config
+        table_name = "province_city_person_relation_to_bi"
+        chunk_size = 1000  # 每批插入 1000 行
+
+        # 清理 DataFrame 中的 NaN/None 等值
+        df = df.replace([None, np.nan, pd.NA, 'nan', 'NaN', 'NAN', ''], None)
+
+        connection = mysql.connector.connect(
+            host=HS_DB_Config["host"],
+            user=HS_DB_Config["user"],
+            password=HS_DB_Config["password"],
+            database=HS_DB_Config["database"]
+        )
+        cursor = connection.cursor()
+
+        try:
+            # 获取数据库表的列名
+            cursor.execute(f"SHOW COLUMNS FROM `{table_name}`")
+            db_columns = [col[0] for col in cursor.fetchall()]
+
+            # 保留与数据库匹配的列
+            filtered_df = df[df.columns.intersection(db_columns)]
+            if filtered_df.empty:
+                print("DataFrame 中没有与数据库表结构匹配的列。")
+                return
+
+            # 处理 dict/list 类型字段：转为 JSON 字符串
+            filtered_df = filtered_df.copy()
+            for col in filtered_df.columns:
+                if filtered_df[col].apply(lambda x: isinstance(x, (dict, list)) if x is not None else False).any():
+                    filtered_df[col] = filtered_df[col].apply(
+                        lambda x: json.dumps(x, ensure_ascii=False) if x is not None else x
+                    )
+
+            # 构建 INSERT 语句（只构建一次）
+            columns = [f"`{col}`" for col in filtered_df.columns]
+            placeholders = ', '.join(['%s'] * len(columns))
+            insert_sql = f"INSERT INTO `{table_name}` ({', '.join(columns)}) VALUES ({placeholders})"
+
+            total_rows = len(filtered_df)
+            num_chunks = math.ceil(total_rows / chunk_size)
+
+            for i in range(num_chunks):
+                start_idx = i * chunk_size
+                end_idx = min(start_idx + chunk_size, total_rows)
+                chunk_df = filtered_df.iloc[start_idx:end_idx]
+
+                # 转为元组列表
+                data_to_insert = [
+                    tuple(row) for row in chunk_df.values
+                ]
+
+                # 批量执行（executemany 更高效）
+                cursor.executemany(insert_sql, data_to_insert)
+
+            connection.commit()
+            logger.info(f"成功写入 {total_rows} 条记录到 {table_name} 表中（分 {num_chunks} 批）。")
+
+        except Exception as e:
+            error_task_logger.error(f"写入数据库时发生错误: {e}", exc_info=True)
+            connection.rollback()
+        finally:
+            cursor.close()
+            connection.close()
+
+    def main(self):
+        task_start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        try:
+            logger.info("任务开始")
+            # step1: 获取数据
+            self.load_all_data()
+            logger.info("加载数据完成")
+            # step2:数据处理
+            df = self.data_process()
+            # df.to_csv(os.path.join(output_dir, "new_dealer_service_order_to_bi.csv"))
+            logger.info("数据处理完成")
+            # step3:数据库删除
+            self.clear_table_data()
+            logger.info("目标数据库已清空")
+            # step4:数据写入BI
+            self.write_to_bi(df)
+            logger.info("数据已写入数据库中")
+            common_module.send_task_status(task_start_time, "省市区人员关系表转BI")
+        except Exception as e:
+            error_task_logger.error(f"省市区人员关系表转BI发生错误{e}")
+            common_module.send_task_error(task_start_time, "省市区人员关系表转BI", str(e))
+
+
+if __name__ == '__main__':
+    province_city_person_relation_to_bi = ProvinceCityPersonRelationToBI()
+    province_city_person_relation_to_bi.main()

test/续约待办一致性-全量同步.py

@@ -0,0 +1,249 @@
+# -*- coding: utf-8 -*-
+
+import os
+import re
+import sys
+import pandas as pd
+from tqdm import tqdm
+
+# 让 test 脚本可以 import 到项目根目录的模块（api.py / yd_api.py / log_config.py）
+# 将项目根目录加入模块搜索路径，确保可以 import 根目录下的 api.py/yd_api.py
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_ROOT = os.path.dirname(SCRIPT_DIR)
+if PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, PROJECT_ROOT)
+
+from api import API
+from log_config import configure_error_task_logger, configure_task_logger
+from yd_api import YDAPI
+
+logger = configure_task_logger()
+error_task_logger = configure_error_task_logger()
+api_instance = API()
+yd_api_instance = YDAPI()
+
+# =========================
+# 需要你关注/可能要改的配置
+# =========================
+# 简道云：续约待办表单（目标表单）
+APP_ID = "675b900991ad2491c69389ca"
+ENTRY_ID = "6931063d64187eaf6b927557"
+
+# 宜搭：续约服务流程（来源流程）
+APP_TYPE = "APP_UYZ0KG6L0CCNV80GZ66O"
+SYSTEM_TOKEN = "XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2"
+
+# 简道云：员工表（用于把“区域经理姓名”转成“员工ID”，如果你简道云字段是人员控件，必须传ID）
+STAFF_APP_ID = "6694d3c4fcb69ca9a111a6c4"
+STAFF_ENTRY_ID = "6769204a1902c9341340a1bc"
+STAFF_NAME_WIDGET = "_widget_1734942794144"
+STAFF_ID_WIDGET = "_widget_1734942794145"
+
+# 输入数据源必须包含这三列：
+# - data_id：简道云待办数据的 data_id
+# - 门店编码：用于简单校验（对不上会提示）
+# - 宜搭实例ID：用于拉取宜搭实例详情
+REQUIRED_INPUT_COLUMNS = ("data_id", "门店编码", "宜搭实例ID")
+
+
+if __name__ == "__main__":
+    # 第 1 个命令行参数：你的数据源文件路径（csv/xlsx/xls）
+    df = pd.read_excel(fr"C:\Users\hp_z66\Downloads\续约服务流程_20260326133517.xlsx",sheet_name="简道云-宜搭实例id", dtype=str).fillna("")
+
+    # 兼容列名：有的表叫“数据ID/实例ID”，统一成脚本内部使用的列名
+    df = df.rename(columns={"数据ID": "data_id", "dataId": "data_id", "DataID": "data_id", "实例ID": "宜搭实例ID"})
+    for c in REQUIRED_INPUT_COLUMNS:
+        if c not in df.columns:
+            raise SystemExit(f"缺少必需列: {c}")
+    for c in REQUIRED_INPUT_COLUMNS:
+        df[c] = df[c].astype(str).str.strip()
+    df = df[(df["data_id"] != "") & (df["宜搭实例ID"] != "")]
+    df = df.drop_duplicates(subset=["data_id"]).reset_index(drop=True)
+
+    # 读取员工表：构建 “姓名 -> 员工ID” 映射
+    staff_resp = api_instance.entry_data_list({"api_key": STAFF_APP_ID, "entry_id": STAFF_ENTRY_ID}) or {}
+    staff_list = staff_resp.get("data", []) or []
+    name_to_staff_id = {}
+    for item in staff_list:
+        n = str(item.get("_widget_1734942794144", "")).strip()
+        i = str(item.get("_widget_1734942794145", "")).strip()
+        if n and i:
+            name_to_staff_id[n] = i
+    logger.info(f"员工数: {len(name_to_staff_id)}")
+
+    # 读取简道云表单字段：label(中文名) -> name(_widget_xxx)
+    # 这样你只要改 labels 里的中文字段名，脚本会自动找到对应 widget_id
+    widget_list = api_instance.entry_widget_list({"api_key": APP_ID, "entry_id": ENTRY_ID}) or {}
+    widgets = widget_list.get("widgets", []) or []
+    label_to_name = {}
+    for w in widgets:
+        l = str(w.get("label", "")).strip()
+        n = str(w.get("name", "")).strip()
+        if l and n:
+            label_to_name[l] = n
+
+    # 需要同步到简道云的字段（中文 label）
+    # 如果你简道云字段名不是这些（比如“省份/城市/区县”），就在这里改成你表单里实际的 label
+    labels = ["120天是否联系上客户", "60天是否联系上客户", "30天是否联系上客户", "区域经理", "服务单号", "门店ID", "公司id", "省", "市",]
+    jdy_map = {l: label_to_name.get(l) for l in labels if label_to_name.get(l)}
+    miss = [l for l in labels if l not in jdy_map]
+    if miss:
+        logger.warning(f"简道云缺少字段: {miss}")
+    logger.warning(f"已匹配字段: {list(jdy_map.keys())} | 门店ID_widget={jdy_map.get('门店ID')} 公司id_widget={jdy_map.get('公司id')}")
+
+    # 获取宜搭 token（后续拉取实例详情使用）
+    token = yd_api_instance.generateToken()
+    ok = 0
+    fail = 0
+    skip = 0
+    for _, row in tqdm(df.iterrows(), total=len(df)):
+        data_id = str(row.get("data_id", "")).strip()
+        instance_id = str(row.get("宜搭实例ID", "")).strip()
+        store_code = str(row.get("门店编码", "")).strip()
+        if not data_id or not instance_id:
+            logger.warning(f"跳过：data_id/实例ID 为空 data_id={data_id} instance_id={instance_id} 行数据={row.to_dict()}")
+            skip += 1
+            continue
+        try:
+            # 拉取宜搭实例详情（里面的 formData 才是字段数据）
+            info = yd_api_instance.processes_instancesInfos(token, instance_id, APP_TYPE, SYSTEM_TOKEN) or {}
+            container = info.get("data") if isinstance(info, dict) else None
+            form = {}
+            if isinstance(container, dict):
+                form = container.get("formData") or container
+            if not isinstance(form, dict) or not form:
+                logger.warning(f"跳过：宜搭实例无表单数据 instance_id={instance_id} data_id={data_id}")
+                skip += 1
+
+            # 简单校验：输入的门店编码 vs 宜搭表单里的门店编码，不一致就打日志提醒
+            # 宜搭门店编码字段ID：textField_ksydghqw（来自你旧脚本/导出的 yd_process_details.csv）
+            yd_store = str(form.get("textField_ksydghqw", "")).strip()
+            if yd_store and store_code and yd_store != store_code:
+                logger.warning(f"门店编码不一致: {store_code} vs {yd_store} 实例:{instance_id} data_id:{data_id}")
+
+            # 从 formData 中按字段ID取值：取到第一个非空值就返回
+            def pick(keys):
+                for k in keys:
+                    v = form.get(k)
+                    if v is None:
+                        continue
+                    s = str(v).strip()
+                    if s and s.lower() not in {"nan", "null", "none"}:
+                        return s
+                return ""
+
+            # 宜搭人员字段常见是：["张三(123)"] 这种结构，这里做一个“只拿姓名”的处理
+            def first_name(v):
+                s = str(v).strip()
+                if s.startswith("[") and s.endswith("]"):
+                    inner = s[1:-1].split(",", 1)[0].strip().strip("'").strip('"')
+                    s = inner
+                m = re.split(r"\(", s, maxsplit=1)
+                return m[0].strip() if m else s
+
+            # =========================
+            # 下面是“宜搭字段ID -> 业务字段”的取值逻辑（你最常修改的区域）
+            # =========================
+            # 说明：
+            # - 120/60/30 是否联系上客户：优先各节点字段，取不到就回退用统一字段 radioField_l85ppdia
+            # - 区域经理：employeeField_ksydghre
+            # - 服务单号：textField_kuntp6fl（你历史数据是 XYFWDxxxx）
+            # - 省/市：textField_kuj8nx00 / textField_kuj8nx01
+            # - 区：优先 textField_kuhnydmk；取不到就从地址 textField_ksydghrm 里截取
+            v120 = pick(["radioField_ksydghrf",])
+            v60 = pick(["radioField_kuhnydmd", ])
+            v30 = pick(["radioField_kuhnydn0", ])
+            region_name = first_name(form.get("employeeField_ksydghre", ""))
+            # 如果简道云“区域经理”字段是人员控件：应传员工ID；否则传姓名也能写入（取决于你表单控件类型）
+            region_value = name_to_staff_id.get(region_name, region_name) if region_name else ""
+            service_no = pick(["textField_kuntp6fl"])
+            # 门店ID（强烈建议你把下面 keys 改成你宜搭里“门店ID/公司id”的真实字段ID）
+            # 示例：store_id = pick(["textField_orgid", "textField_id_own_org"])
+            store_id = pick(["textField_kuntp6fk"])
+            prov = pick(["textField_kuj8nx00"])
+            city = pick(["textField_kuj8nx01"])
+
+            # 拼装简道云更新 payload：每个字段必须是 {"value": 值}
+            data_dict = {}
+            if v120 and jdy_map.get("120天是否联系上客户"):
+                data_dict[jdy_map["120天是否联系上客户"]] = {"value": v120}
+            if v60 and jdy_map.get("60天是否联系上客户"):
+                data_dict[jdy_map["60天是否联系上客户"]] = {"value": v60}
+            if v30 and jdy_map.get("30天是否联系上客户"):
+                data_dict[jdy_map["30天是否联系上客户"]] = {"value": v30}
+            if region_value and jdy_map.get("区域经理"):
+                data_dict[jdy_map["区域经理"]] = {"value": region_value}
+            if service_no and jdy_map.get("服务单号"):
+                data_dict[jdy_map["服务单号"]] = {"value": service_no}
+            # 门店ID优先写入“门店ID”，如果表单没有此字段则回退写入“公司id”
+            if store_id:
+                if jdy_map.get("门店ID"):
+                    data_dict[jdy_map["门店ID"]] = {"value": store_id}
+                elif jdy_map.get("公司id"):
+                    data_dict[jdy_map["公司id"]] = {"value": store_id}
+            if prov and jdy_map.get("省"):
+                data_dict[jdy_map["省"]] = {"value": prov}
+            if city and jdy_map.get("市"):
+                data_dict[jdy_map["市"]] = {"value": city}
+
+            if not data_dict:
+                reasons = []
+                if not v120:
+                    reasons.append("120天:值空")
+                elif not jdy_map.get("120天是否联系上客户"):
+                    reasons.append("120天:未映射")
+                if not v60:
+                    reasons.append("60天:值空")
+                elif not jdy_map.get("60天是否联系上客户"):
+                    reasons.append("60天:未映射")
+                if not v30:
+                    reasons.append("30天:值空")
+                elif not jdy_map.get("30天是否联系上客户"):
+                    reasons.append("30天:未映射")
+                if not region_value:
+                    reasons.append("区域经理:值空")
+                elif not jdy_map.get("区域经理"):
+                    reasons.append("区域经理:未映射")
+                if not service_no:
+                    reasons.append("服务单号:值空")
+                elif not jdy_map.get("服务单号"):
+                    reasons.append("服务单号:未映射")
+                if not store_id:
+                    reasons.append("门店ID:值空")
+                elif not (jdy_map.get("门店ID") or jdy_map.get("公司id")):
+                    reasons.append("门店ID:表单无对应字段(门店ID/公司id)")
+                if not prov:
+                    reasons.append("省:值空")
+                elif not jdy_map.get("省"):
+                    reasons.append("省:未映射")
+                if not city:
+                    reasons.append("市:值空")
+                elif not jdy_map.get("市"):
+                    reasons.append("市:未映射")
+                logger.warning(
+                    f"跳过 data_id:{data_id} 实例:{instance_id} | 原因: {', '.join(reasons)} | "
+                    f"取值: v120={v120} v60={v60} v30={v30} region_name={region_name} region_value={region_value} "
+                    f"service_no={service_no} store_id={store_id} prov={prov} city={city}"
+                )
+                skip += 1
+                continue
+
+            # 更新简道云数据（同你旧脚本）
+            payload = {
+                "api_key": APP_ID,
+                "entry_id": ENTRY_ID,
+                "data_id": data_id,
+                "data": data_dict,
+                "is_start_trigger": False,## 目前宜搭有通知
+            }
+            res = api_instance.entry_data_update(payload)
+            # 兼容两种返回格式：有的返回 {'status':'success',...}，有的直接返回 {'data':{...}}
+            if isinstance(res, dict) and (res.get("status") == "success" or isinstance(res.get("data"), dict)):
+                ok += 1
+            else:
+                fail += 1
+                logger.warning(f"更新失败 data_id:{data_id} 实例:{instance_id} 返回:{res}")
+        except Exception as e:
+            fail += 1
+            error_task_logger.error(f"同步异常 data_id:{data_id} 实例:{instance_id} 错误:{e}", exc_info=True)
+    logger.info(f"完成 同步成功:{ok} 失败:{fail} 跳过:{skip}")

test/续约待办一致性-逐条同步.py

@@ -0,0 +1,58 @@
+import requests
+import pandas as pd
+
+cookies = {
+    'auth_token': 's%3A.9uztgExtmqUJXHCi00hv9SGq6eVYSvH%2BxQSwrox1Yls',
+    'fx-lang': 'zh_cn',
+    'tenantId': 'agndqbuttb7ipfciraxcokgqyu',
+    'AGL_USER_ID': 'a50da526-dd43-4a78-ace1-ba810a6f2168',
+    '_ga': 'GA1.1.626243428.1772260541',
+    '_clck': 'y7ldwu%5E2%5Eg3y%5E0%5E2250',
+    '_ga_JTDW9M3LHZ': 'GS2.1.s1772266909$o3$g0$t1772266909$j60$l0$h0',
+    'sensorsdata2015jssdkcross': '%7B%22distinct_id%22%3A%2257956c24ceedab0c48c17b4e%22%2C%22first_id%22%3A%2219b6dd1a10c148a-063e3a033b10ed-4c657b58-2073600-19b6dd1a10d145b%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22identities%22%3A%22eyIkaWRlbnRpdHlfY29va2llX2lkIjoiMTliNmRkMWExMGMxNDhhLTA2M2UzYTAzM2IxMGVkLTRjNjU3YjU4LTIwNzM2MDAtMTliNmRkMWExMGQxNDViIiwiJGlkZW50aXR5X2xvZ2luX2lkIjoiNTc5NTZjMjRjZWVkYWIwYzQ4YzE3YjRlIn0%3D%22%2C%22history_login_id%22%3A%7B%22name%22%3A%22%24identity_login_id%22%2C%22value%22%3A%2257956c24ceedab0c48c17b4e%22%7D%7D',
+    'sensorsdata2015jssdkcross': '%7B%22distinct_id%22%3A%2257956c24ceedab0c48c17b4e%22%2C%22first_id%22%3A%2219b6dd1a10c148a-063e3a033b10ed-4c657b58-2073600-19b6dd1a10d145b%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22identities%22%3A%22eyIkaWRlbnRpdHlfY29va2llX2lkIjoiMTliNmRkMWExMGMxNDhhLTA2M2UzYTAzM2IxMGVkLTRjNjU3YjU4LTIwNzM2MDAtMTliNmRkMWExMGQxNDViIiwiJGlkZW50aXR5X2xvZ2luX2lkIjoiNTc5NTZjMjRjZWVkYWIwYzQ4YzE3YjRlIn0%3D%22%2C%22history_login_id%22%3A%7B%22name%22%3A%22%24identity_login_id%22%2C%22value%22%3A%2257956c24ceedab0c48c17b4e%22%7D%7D',
+    '_csrf': 's%3AE3ildFF7aOT874ca1lQaPpJh.M4vLvJ9TdzyukOLVlNNYO9ysSRLlssqxQh1%2FwMawRCY',
+    'Hm_lvt_de47dd1629940fe88b02865de93dd9fe': '1774410585,1774417748,1774487358,1774503122',
+    'HMACCOUNT': 'A6A0585E8C70051D',
+    'Hm_lpvt_de47dd1629940fe88b02865de93dd9fe': '1774503273',
+    'GSuvNKHqfvX2r6v7P8HkZv2bow': 's%3ALj5VHNEzUxprmpIJHWpNg7SzUp7GR3H1.yp8HeYMA0ARAeFC0CqxucSfBxvudd4xO76ZwAACWvTc',
+    'JDY_SID': 's%3AZrD40YQAsP9Ui53M1j0lud3AvwbdgzEl.ljjCkitsu2tDtbx0kJTROsKeNh8QfX2mz6SehH%2B%2FD5E',
+}
+
+headers = {
+    'accept': 'application/json, text/plain, */*',
+    'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
+    'content-type': 'application/json',
+    'origin': 'https://www.jiandaoyun.com',
+    'priority': 'u=1, i',
+    'referer': 'https://www.jiandaoyun.com/dashboard/app/675b900991ad2491c69389ca/form/6931063d64187eaf6b927557/edit',
+    'sec-ch-ua': '"Chromium";v="146", "Not-A.Brand";v="24", "Microsoft Edge";v="146"',
+    'sec-ch-ua-mobile': '?0',
+    'sec-ch-ua-platform': '"Windows"',
+    'sec-fetch-dest': 'empty',
+    'sec-fetch-mode': 'cors',
+    'sec-fetch-site': 'same-origin',
+    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/146.0.0.0 Safari/537.36 Edg/146.0.0.0',
+    'x-csrf-token': 'Mape7z8g-8iFP_vxFm8qSzVprTb4XsGfvs1o',
+    'x-jdy-ver': '10.19.6',
+    'x-request-id': '7bb1d48a-0fdb-46b7-bf67-ebd52c1a1f9a',
+    # 'cookie': 'auth_token=s%3A.9uztgExtmqUJXHCi00hv9SGq6eVYSvH%2BxQSwrox1Yls; fx-lang=zh_cn; tenantId=agndqbuttb7ipfciraxcokgqyu; AGL_USER_ID=a50da526-dd43-4a78-ace1-ba810a6f2168; _ga=GA1.1.626243428.1772260541; _clck=y7ldwu%5E2%5Eg3y%5E0%5E2250; _ga_JTDW9M3LHZ=GS2.1.s1772266909$o3$g0$t1772266909$j60$l0$h0; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%2257956c24ceedab0c48c17b4e%22%2C%22first_id%22%3A%2219b6dd1a10c148a-063e3a033b10ed-4c657b58-2073600-19b6dd1a10d145b%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22identities%22%3A%22eyIkaWRlbnRpdHlfY29va2llX2lkIjoiMTliNmRkMWExMGMxNDhhLTA2M2UzYTAzM2IxMGVkLTRjNjU3YjU4LTIwNzM2MDAtMTliNmRkMWExMGQxNDViIiwiJGlkZW50aXR5X2xvZ2luX2lkIjoiNTc5NTZjMjRjZWVkYWIwYzQ4YzE3YjRlIn0%3D%22%2C%22history_login_id%22%3A%7B%22name%22%3A%22%24identity_login_id%22%2C%22value%22%3A%2257956c24ceedab0c48c17b4e%22%7D%7D; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%2257956c24ceedab0c48c17b4e%22%2C%22first_id%22%3A%2219b6dd1a10c148a-063e3a033b10ed-4c657b58-2073600-19b6dd1a10d145b%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22identities%22%3A%22eyIkaWRlbnRpdHlfY29va2llX2lkIjoiMTliNmRkMWExMGMxNDhhLTA2M2UzYTAzM2IxMGVkLTRjNjU3YjU4LTIwNzM2MDAtMTliNmRkMWExMGQxNDViIiwiJGlkZW50aXR5X2xvZ2luX2lkIjoiNTc5NTZjMjRjZWVkYWIwYzQ4YzE3YjRlIn0%3D%22%2C%22history_login_id%22%3A%7B%22name%22%3A%22%24identity_login_id%22%2C%22value%22%3A%2257956c24ceedab0c48c17b4e%22%7D%7D; _csrf=s%3AE3ildFF7aOT874ca1lQaPpJh.M4vLvJ9TdzyukOLVlNNYO9ysSRLlssqxQh1%2FwMawRCY; Hm_lvt_de47dd1629940fe88b02865de93dd9fe=1774410585,1774417748,1774487358,1774503122; HMACCOUNT=A6A0585E8C70051D; Hm_lpvt_de47dd1629940fe88b02865de93dd9fe=1774503273; GSuvNKHqfvX2r6v7P8HkZv2bow=s%3ALj5VHNEzUxprmpIJHWpNg7SzUp7GR3H1.yp8HeYMA0ARAeFC0CqxucSfBxvudd4xO76ZwAACWvTc; JDY_SID=s%3AZrD40YQAsP9Ui53M1j0lud3AvwbdgzEl.ljjCkitsu2tDtbx0kJTROsKeNh8QfX2mz6SehH%2B%2FD5E',
+}
+
+df = pd.read_excel(fr"C:\Users\hp_z66\Downloads\续约服务流程_20260327115935.xlsx",sheet_name="需要查询补充120天节点数据", dtype=str).fillna("")
+
+json_data = {
+    'skip': 0,
+    'limit': 20,
+    'appId': '675b900991ad2491c69389ca',
+    'entryId': '6931063d64187eaf6b927557',
+    'formId': '6931063d64187eaf6b927557',
+    'dataId': '69ab8f89837038e4e81bff21',
+}
+
+response = requests.post(
+    'https://www.jiandaoyun.com/_/admin/data/log/list_changes',
+    cookies=cookies,
+    headers=headers,
+    json=json_data,
+)

test/续约待办宜搭传给简道云-1day.py

@@ -0,0 +1,791 @@
+import os
+from datetime import datetime, timedelta, timezone
+import pandas as pd
+from tqdm import tqdm
+from datetime import datetime, timezone
+import pandas as pd
+import os
+from typing import Dict
+import requests
+import json
+import time
+import numpy as np  # 导入numpy库用于处理numpy数组
+
+output_dir = "output"  # 设置输出目录
+os.makedirs(output_dir, exist_ok=True)
+
+
+class Config:
+    JIANDAOYUN_API_TOKEN = 'Bearer qygHulymo1fekJk4CIZyNKjyQAzG8CFN'  # token
+
+
+class API:
+    def entry_data_list(self, data: dict, replace: bool = False, max_retries: int = 20) -> Dict:  # 获取多条表单数据
+        """
+        获取多条表单数据
+        :param max_retries: 最大重试次数
+        :param replace: 是否替换字段
+        :param data:
+                    api_key: 应用id
+                    entry_id: 表单id
+        :return:
+        """
+
+        url = 'https://api.jiandaoyun.com/api/v5/app/entry/data/list'
+
+        headers = {
+            'Authorization': Config.JIANDAOYUN_API_TOKEN,  # 曹伟应用api测试 app_key
+            'Content-Type': 'application/json'
+        }
+        all_data_batches = []  # 用于存储每次请求返回的数据批次
+        last_data_id = None
+        exit_flag = False
+        while True:
+            payload = json.dumps({
+                "app_id": data['api_key'],  # 应用ID
+                "entry_id": data['entry_id'],  # 表单ID
+                "limit": 90,
+                "data_id": last_data_id,
+                "filter": data.get('filter', None)
+            })
+            retries = 0
+            while retries <= max_retries:
+                try:
+                    res = requests.post(url=url, data=payload, headers=headers, timeout=10)
+                    res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
+                    data_get = res.json()
+                    if data_get["data"]:
+                        all_data_batches.extend(data_get['data'])
+                        last_data_id = data_get['data'][-1].get('_id')
+                        print(f"已获取 {len(all_data_batches)} 条数据")
+                        break  # 成功则跳出循环
+                    else:
+                        if 'data' not in data_get or len(data_get['data']) == 0:
+                            exit_flag = True
+                            break
+                        # logger.warning(f"请求异常, 将重新请求")
+                        retries += 1
+                        time.sleep(0.1)  # 在重试之间稍作停顿
+                except requests.exceptions.RequestException as e:
+                    # logger.warning(f"请求异常: {e}, 将重新请求")
+                    retries += 1
+                    time.sleep(0.1)  # 在重试之间稍作停顿
+            if retries > max_retries:
+                # error_task_logger.error(f"任务 {last_data_id}组 连续{max_retries}次请求失败，放弃此次请求。")
+                all_data_batches.append(None)  # 或者可以选择记录失败的payload以便后续处理
+
+            if exit_flag:
+                break
+
+        # 构建最终返回的字典
+        final_data = {
+            'data': all_data_batches  # 'data' 键对应的值是列表的列表
+        }
+        # logger.info(f"获取了{len(all_data_batches)}条数据")
+        if replace:
+            print("进行了替换")
+            return_data = self.field_replacement(data, final_data)  # 字段替换，由id替换为标签名
+
+            return return_data
+        else:
+            return final_data
+
+    def field_replacement(self, data: dict, data_get: dict) -> dict:
+        """
+        字段替换，将id替换为标签名，即唯一值替换为表单中显示字段的名字
+        :param data: 简道云插件发送过来的data，包含表单id、数据id、应用id
+        :param data_get: 简道云请求的数据，一般是根据数据id获取到表单的数据
+        :return: 将根据数据id获取到的表单数据，进行替换，返回替换后的数据
+        """
+
+        # 获取表单对应字段标签名称
+        widget_list = self.entry_widget_list(data)
+
+        # 检查widget_list是否有效
+        if not widget_list or 'widgets' not in widget_list or not isinstance(widget_list['widgets'], list):
+            raise ValueError("映射表没有接受到数据")
+
+        # 创建一个映射表，将_widget_名称映射到label
+        name_to_label = {widget['name']: widget['label'] for widget in widget_list['widgets']}
+
+        def replace_keys(obj):
+            """递归替换字典中的键名"""
+            if isinstance(obj, dict):
+                new_dict = {}
+                for key, value in obj.items():
+                    new_key = name_to_label.get(key, key)
+                    new_dict[new_key] = replace_keys(value)
+                return new_dict
+            elif isinstance(obj, list):
+                return [replace_keys(item) for item in obj]
+            else:
+                return obj
+
+        # 复制 data_get，避免修改原始数据
+        data_get_copy = json.loads(json.dumps(data_get))  # 深拷贝
+
+        if 'data' in data_get_copy:
+            data_get_copy['data'] = replace_keys(data_get_copy['data'])
+
+        return data_get_copy
+
+    @staticmethod
+    def workflow_instance_get(data: dict, max_retries: int = 20) -> dict:
+        """
+        查询实例流程信息
+        :param max_retries:
+        :param data: 简道云插件发送过来的data，包含应用id
+        :return: 查询简道云流程实例信息返回的结果
+        """
+        url = 'https://api.jiandaoyun.com/api/v6/workflow/instance/get'
+
+        headers = {
+            'Authorization': Config.JIANDAOYUN_API_TOKEN,  # 曹伟应用api测试 appKey
+            'Content-Type': 'application/json'
+        }
+
+        payload = json.dumps({
+            "instance_id": data['data_id'],
+            "tasks_type": 1
+        }
+        )
+        print("payload:", payload)
+        data_get = None
+        retries = 0
+        while retries <= max_retries:
+            try:
+                res = requests.post(url=url, data=payload, headers=headers, timeout=10)
+                # res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
+                data_get = res.json()
+                # print( "返回结果:", data_get)
+                if res.status_code == 200:
+                    break  # 成功则跳出循环
+                else:
+                    # logger.warning(f"请求异常, 将重新请求")
+                    retries += 1
+                    time.sleep(3)  # 在重试之间稍作停顿
+            except requests.exceptions.RequestException as e:
+                # logger.warning(f"请求异常: {e}, 将重新请求")
+                retries += 1
+                time.sleep(0.1)  # 在重试之间稍作停顿
+        if retries > max_retries:
+            # error_task_logger.error(f"任务 {data['data_id']} 连续{max_retries}次请求失败，放弃此次请求。")
+            print("请求失败")
+
+        return data_get
+
+    @staticmethod
+    def entry_data_update(data: dict, max_retries: int = 20) -> dict:  # 修改数据
+        """
+        修改数据
+        :param max_retries: 最大重试次数，此处设置100次
+        :param data: 简道云插件发送过来的data，包含应用id、表单id、数据id等信息
+        :return: 修改数据后简道云返回的结果
+        """
+        url = 'https://api.jiandaoyun.com/api/v5/app/entry/data/update'
+
+        headers = {
+            'Authorization': Config.JIANDAOYUN_API_TOKEN,  # 曹伟应用api测试 appKey
+            'Content-Type': 'application/json'
+        }
+
+        payload = json.dumps({
+            "app_id": data['api_key'],  # 应用ID
+            "entry_id": data['entry_id'],  # 表单ID
+            "data_id": data['data_id'],  # 数据ID
+            "data": data['data'],
+            "is_start_trigger": True
+        }
+        )
+
+        data_get = None
+        retries = 0
+        while retries <= max_retries:
+            try:
+                res: requests.Response = requests.post(url=url, data=payload, headers=headers, timeout=10)
+                res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
+                data_get = res.json()
+                # print(data_get)
+                if res.status_code == 200:
+                    break  # 成功则跳出循环
+                else:
+                    # logger.warning(f"请求异常, 将重新请求")
+                    retries += 1
+                    time.sleep(3)  # 在重试之间稍作停顿
+            except requests.exceptions.RequestException as e:
+                # logger.warning(f"请求异常: {e}, 将重新请求")
+                retries += 1
+                time.sleep(10)  # 在重试之间稍作停顿
+            if retries > max_retries:
+                # error_task_logger.error(f"任务 {data['data_id']} 连续{max_retries}次请求失败，放弃此次请求。")
+                continue
+        return data_get
+
+    @staticmethod
+    def workflow_instance_end(data: dict, max_retries: int = 20) -> dict:
+        """
+        关闭流程
+        :param max_retries:
+        :param data: 简道云插件发送过来的data，包含应用id
+        :return: 查询简道云流程实例信息返回的结果
+        """
+        url = 'https://api.jiandaoyun.com/api/v1/workflow/instance/close'
+
+        headers = {
+            'Authorization': Config.JIANDAOYUN_API_TOKEN,  # 曹伟应用api测试 appKey
+            'Content-Type': 'application/json'
+        }
+
+        payload = json.dumps({
+            "instance_id": data['data_id'],
+        }
+        )
+        print("payload:", payload)
+        data_get = None
+        retries = 0
+        while retries <= max_retries:
+            try:
+                res = requests.post(url=url, data=payload, headers=headers, timeout=10)
+                # res.raise_for_status()  # 检查HTTP响应状态码，如果不等于200会抛出异常
+                data_get = res.json()
+                print("返回结果:", data_get)
+                if res.status_code == 200:
+                    break  # 成功则跳出循环
+                else:
+
+                    retries += 1
+                    time.sleep(3)  # 在重试之间稍作停顿
+            except requests.exceptions.RequestException as e:
+
+                retries += 1
+                time.sleep(0.1)  # 在重试之间稍作停顿
+
+        return data_get
+
+
+class NpEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        else:
+            return super(NpEncoder, self).default(obj)
+
+
+class YDAPI:
+    appKey = "ding5kqocon5s9oph5uq"
+    appSecret = "HL1jgsIIfLAC0eTH0A1m4mwxUDqbgsiPeCCGGE3ocM6qJBTIW7Ivt9drxF_Z4Kb_"
+
+    @staticmethod
+    def get_ids_query(token, formUuid, appType, systemToken, formInstanceIdList=None, max_retries=10, delay=2):
+        """
+        函数功能：读取表单的所有数据，并加入重试机制。
+
+        Args:
+            token (str): 登录验证token，用于API调用的身份验证。
+            formUuid (str): 表单唯一标识符，用于指定需要读取哪个表单的实例数据。
+            page (int): 分页参数，指定请求的数据页码。
+            n (int): 每页显示的数据条数。
+            appType (str): 应用类型标识符，默认为 "APP_UYZ0KG6L0CCNV80GZ66O"
+            systemToken (str): 系统token，默认为固定值
+            instanceStatus (str): 流程实例状态，默认为"RUNNING"
+            max_retries (int): 最大重试次数，默认为10次
+            delay (int): 每次重试之间的延迟秒数，默认为2秒
+
+        Returns:
+            dict: 返回从API获取的流程表单实例数据的JSON解析结果。
+
+        Raises:
+            Exception: 如果达到最大重试次数仍未成功，则抛出异常。
+        """
+
+        attempt = 0
+        api = f'https://api.dingtalk.com/v1.0/yida/forms/instances/ids/query'
+        headers = {
+            "Content-Type": "application/json",
+            "x-acs-dingtalk-access-token": token
+        }
+        formData = {
+            "appType": appType,
+            "systemToken": systemToken,
+            "userId": "yida_pub_account",  # 超级管理员账号
+            "language": "zh_CN",
+            "formUuid": formUuid,
+            "formInstanceIdList": formInstanceIdList,
+        }
+        # print(formData)
+
+        while True:
+            if attempt >= max_retries:
+                break
+
+            try:
+                res = requests.post(api, headers=headers, json=formData)
+                # print(res.json())
+                res.raise_for_status()  # 如果返回状态码不是2xx，抛出异常
+                return res.json()
+
+            except requests.exceptions.RequestException as e:
+
+                time.sleep(delay)
+                attempt += 1
+
+    def generateToken(self) -> str:
+        """
+        函数功能：生成访问令牌（token）
+
+        Returns:
+            str: 返回生成的访问令牌字符串。此token用于后续API调用的身份验证。
+        """
+        token_api = 'https://api.dingtalk.com/v1.0/oauth2/accessToken'
+        data = {
+            "appKey": f"{self.appKey}",
+            "appSecret": f'{self.appSecret}'
+        }
+        res = requests.post(token_api, json=data)
+        token = res.json().get('accessToken')
+        return token
+
+    def read_processes_instances(self, token, formUuid, page, n, appType="APP_UYZ0KG6L0CCNV80GZ66O",
+                                 systemToken="XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2", instanceStatus="RUNNING",
+                                 max_retries=10, delay=2, createFromTimeGMT=None, createToTimeGMT=None,
+                                 modifiedFromTimeGMT=None,
+                                 modifiedToTimeGMT=None, searchFieldJson={}):
+        """
+        函数功能：读取流程表单的所有数据，并加入重试机制。
+
+        Args:
+            token (str): 登录验证token，用于API调用的身份验证。
+            formUuid (str): 表单唯一标识符，用于指定需要读取哪个表单的实例数据。
+            page (int): 分页参数，指定请求的数据页码。
+            n (int): 每页显示的数据条数。
+            appType (str): 应用类型标识符，默认为 "APP_UYZ0KG6L0CCNV80GZ66O"
+            systemToken (str): 系统token，默认为固定值
+            instanceStatus (str): 流程实例状态，默认为"RUNNING"
+            max_retries (int): 最大重试次数，默认为10次
+            delay (int): 每次重试之间的延迟秒数，默认为2秒
+
+        Returns:
+            dict: 返回从API获取的流程表单实例数据的JSON解析结果。
+
+        Raises:
+            Exception: 如果达到最大重试次数仍未成功，则抛出异常。
+        """
+
+        attempt = 0
+        api = f'https://api.dingtalk.com/v1.0/yida/processes/instances?pageNumber={page}&pageSize={n}'
+        headers = {
+            "Content-Type": "application/json",
+            "x-acs-dingtalk-access-token": token
+        }
+        formData = {
+            "appType": appType,
+            "systemToken": systemToken,
+            "userId": "yida_pub_account",  # 超级管理员账号
+            "language": "zh_CN",
+            "formUuid": formUuid,
+            "instanceStatus": instanceStatus,  # 运行中
+            "createFromTimeGMT": createFromTimeGMT,
+            "createToTimeGMT": createToTimeGMT,
+            "modifiedFromTimeGMT": modifiedFromTimeGMT,
+            "modifiedToTimeGMT": modifiedToTimeGMT,
+            "searchFieldJson": json.dumps(
+                searchFieldJson
+            )
+        }
+        # print(formData)
+
+        while True:
+            if attempt >= max_retries:
+                # error_task_logger.error(f"请求失败，已达最大重试次数 {max_retries}，无法获取流程实例数据,跳过本次请求。")
+                break
+
+            try:
+                res = requests.post(api, headers=headers, json=formData)
+                # print(res.json())
+                res.raise_for_status()  # 如果返回状态码不是2xx，抛出异常
+                return res.json()
+
+            except requests.exceptions.RequestException as e:
+                # logger.warning(f"请求异常: {e}，正在尝试第 {attempt + 1} 次重试...")
+                time.sleep(delay)
+                attempt += 1
+
+    def update_from(self, token, formInstanceId, data_new):
+        """
+        函数功能：更新表单内容
+
+        Args:
+            token (str): 登录验证token，用于API调用的身份验证。
+            formInstanceId (str): 表单实例ID，读文件获取。
+            data_new (dict): 新的数据内容，用于替换现有表单实例中的数据。读文件获取。
+
+        Returns:
+            Response: 返回API请求的响应对象。
+        """
+
+        api = f'https://api.dingtalk.com//v1.0/yida/forms/instances'
+
+        headers = {
+            "Content-Type": "application/json",
+            "x-acs-dingtalk-access-token": token
+        }
+
+        payload = {
+            "appType": "APP_UYZ0KG6L0CCNV80GZ66O",
+            "systemToken": "XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2",
+            "userId": "yida_pub_account",  # 曹伟 id
+            "language": "zh_CN",
+            "useLatestVersion": "false",
+            "formInstanceId": formInstanceId,
+            "updateFormDataJson": json.dumps(data_new, cls=NpEncoder),
+
+        }
+
+        res = requests.put(api, headers=headers, json=payload)
+        return res
+
+    def get_approval_records(self, token: str, processInstanceId: str, appType="APP_UYZ0KG6L0CCNV80GZ66O",
+                             systemToken="XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2", max_retries=10, delay=2):
+        """
+        函数功能：获取流程表单的审批记录，适用于"F6客户服务"应用，并且包含重试机制。
+
+        Args:
+            token (str): 登录验证token，用于API调用的身份验证。
+            processInstanceId (str): 流程实例ID，用于标识需要获取审批记录的具体流程实例。
+            appType (str): 应用类型标识符，默认为 "APP_UYZ0KG6L0CCNV80GZ66O"
+            systemToken (str): 系统token，默认为固定值
+            max_retries (int): 最大重试次数，默认为10次
+            delay (int): 每次重试之间的延迟秒数，默认为2秒
+
+        Returns:
+            dict: 返回从API获取的审批记录的JSON解析结果。通常包括审批步骤、审批人、审批时间等信息。
+        """
+        attempt = 0
+        userId = "yida_pub_account"
+        api = f'https://api.dingtalk.com/v1.0/yida/processes/operationRecords?appType={appType}&systemToken={systemToken}&userId={userId}&language=zh_CN&processInstanceId={processInstanceId}'
+        headers = {
+            "Content-Type": "application/json",
+            "x-acs-dingtalk-access-token": token
+        }
+
+        while True:
+            if attempt >= max_retries:
+                # error_task_logger.error(f"请求失败，已达最大重试次数 {max_retries}，无法获取审批数据,跳过本次请求。")
+                break
+
+            try:
+                res = requests.get(api, headers=headers)
+                res.raise_for_status()  # 如果响应状态码不是2xx，则抛出HTTPError
+                return res.json()
+            except (requests.exceptions.RequestException, Exception) as e:
+                # logger.warning(f"请求出现异常: {e}, 正在重试({attempt + 1}/{max_retries})...")
+                time.sleep(delay)  # 等待指定的延迟时间后再次尝试
+                attempt += 1
+
+    def aggree_approval(self, token: str, taskId: str, processInstanceId: str, formData: dict, res_new):
+        """_summary_
+
+        函数功能：同意审批节点   --F6客户服务 应用
+
+        Args:
+            token (str): 登录验证token
+            taskId (str): 获取到的审批节点ID
+            processInstanceId (str): 读取文件获得的实例ID
+            formData (dict): 数据样式
+            res_new (响应值): 从员工ID表里获取到员工名对应的员工ID
+
+        Returns:
+            响应值: 返回请求结果
+        """
+        api = 'https://api.dingtalk.com/v1.0/yida/tasks/execute'
+        headers = {
+            "Content-Type": "application/json",
+            "x-acs-dingtalk-access-token": token
+        }
+        payload = {
+            "outResult": "AGREE",
+            "appType": "APP_UYZ0KG6L0CCNV80GZ66O",
+            "systemToken": "XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2",
+            "remark": "同意(接口自动)",
+            "formDataJson": json.dumps(formData, cls=NpEncoder),
+            "processInstanceId": processInstanceId,
+            "userId": res_new,
+            "language": "zh_CN",
+            "taskId": int(taskId)
+        }
+
+        res = requests.post(api, headers=headers, json=payload)
+        return res
+
+
+api_instance = API()
+yd_api_instance = YDAPI()
+
+
+class YDToJDYRenewalToDo(object):
+    def __init__(self):
+        self.FORMID = "FORM-PE866MD1MJMU0WGLYRFLYEN5YN9L1I55Z7ZUK22"
+        self.appType = "APP_UYZ0KG6L0CCNV80GZ66O"
+        self.systemToken = "XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2"
+        self.token = yd_api_instance.generateToken()
+
+    def load_all_data(self):
+        end_dt = datetime.now() + timedelta(days=1)
+        start_dt = end_dt - timedelta(days=90)
+        start_time = start_dt.strftime("%Y-%m-%d")
+        end_time = end_dt.strftime("%Y-%m-%d")
+
+        yd_data = yd_api_instance.read_processes_instances(
+            token=self.token,
+            formUuid=self.FORMID,
+            page=1,
+            n=100,
+            appType=self.appType,
+            systemToken=self.systemToken,
+            instanceStatus="",
+            modifiedFromTimeGMT=start_time,
+            modifiedToTimeGMT=end_time,
+        )
+
+        all_process_list = []
+
+        PAGES_two = yd_data.get('totalCount') // 100 + 1
+
+        for a in tqdm(range(1, PAGES_two + 1)):
+            try:
+                yd_data = yd_api_instance.read_processes_instances(
+                    token=self.token,
+                    formUuid=self.FORMID,
+                    page=a,
+                    n=100,
+                    appType=self.appType,
+                    systemToken=self.systemToken,
+                    instanceStatus="",
+                    modifiedFromTimeGMT=start_time,
+                    modifiedToTimeGMT=end_time,
+                )
+                all_process_list = all_process_list + yd_data.get("data")
+            except Exception as e:
+                print(f"获取流程实例数据时出错: {e}")
+                continue
+
+        df_current = pd.DataFrame(all_process_list)
+        current_file = f"{output_dir}/{start_time}_{end_time}_all_process_list.csv"
+
+        # === 新增：读取上次文件并计算差值 ===
+        if os.path.exists(current_file):
+            try:
+                df_last = pd.read_csv(current_file)
+            except Exception as e:
+                print(f"读取历史文件失败: {e}")
+                df_last = pd.DataFrame()
+        else:
+            df_last = None  # 明确标记：无历史文件
+
+        id_col = 'processInstanceId'
+
+        if df_last is not None and not df_last.empty and not df_current.empty and id_col in df_current.columns and id_col in df_last.columns:
+            # 有历史文件，计算新增
+            last_ids = set(df_last[id_col].astype(str))
+            current_ids = set(df_current[id_col].astype(str))
+            new_ids = current_ids - last_ids
+            diff_records = df_current[df_current[id_col].astype(str).isin(new_ids)].to_dict('records')
+        else:
+            # 没有历史文件 或 无法比对 → 返回全部当前数据
+            diff_records = df_current.to_dict('records')  # ← 关键修改点
+        # 保存当前全量数据（覆盖）
+        df_current.to_csv(current_file, index=False)
+
+        return diff_records
+
+    def filter_renewal_data(self, all_process_list):
+        update_data_list = []
+        for item in all_process_list:
+            if item.get("data").get("textField_kto3q3ev"):
+                org_id = item.get("data").get("textField_ksydghqw")
+                order_code = item.get("data").get("textField_kto3q3ev")  # 订单编码
+                pay_time = item.get("data").get("dateField_kto3q3ex")  # 订单支付日期
+
+                res = yd_api_instance.get_ids_query(
+                    token=self.token,
+                    formInstanceIdList=[item.get("processInstanceId")],
+                    formUuid="FORM-PE866MD1MJMU0WGLYRFLYEN5YN9L1I55Z7ZUK22",
+                    appType="APP_UYZ0KG6L0CCNV80GZ66O",
+                    systemToken="XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2",
+                )
+
+                payment_amount = None
+                bussiness_type = None
+                # print(res)
+                if res.get("result"):
+                    form_data = res["result"][0]["formData"]
+                    payment_amount = form_data.get("textField_kyjy1kkm")  # "9987"
+                    bussiness_type = form_data.get("textField_kyjy1kkn")  # "续约"
+                # payment_amount = res.get("data").get("textField_kyjy1kkm")  # 支付金额
+                # bussiness_type = res.get("data").get("textField_kyjy1kkn")  # 业务类型
+
+                if pay_time:
+                    # 确保是整数
+                    timestamp_ms = int(pay_time)
+                    # 转为 UTC datetime 对象
+                    pay_datetime_utc = datetime.fromtimestamp(timestamp_ms / 1000, tz=timezone.utc)
+                    print(pay_datetime_utc)  # 例如: 2024-04-05 12:34:38.901000+00:00
+                else:
+                    pay_datetime_utc = None
+
+                update_data_list.append({
+                    "order_code": order_code,
+                    "pay_time": pay_time,
+                    "payment_amount": payment_amount,
+                    "bussiness_type": bussiness_type,
+                    "org_id": org_id
+                })
+        return update_data_list
+
+    def check_jd_ydy_data(self, update_data_list):
+        def extract_widget_value(record, widget_id):
+            value = record.get(widget_id)
+            if isinstance(value, dict) and "value" in value:
+                return value.get("value")
+            return value
+
+        def has_value(value):
+            if value is None:
+                return False
+            if isinstance(value, str):
+                return value.strip() != ""
+            if isinstance(value, (list, dict)):
+                return len(value) > 0
+            return True
+
+        def pick_best_record(records):
+            if not records:
+                return None
+
+            time_fields = [
+                "_updateTime",
+                "_updated_at",
+                "_createTime",
+                "_created_at",
+                "updateTime",
+                "updated_at",
+                "createTime",
+                "created_at",
+            ]
+
+            def extract_sort_value(record):
+                for field in time_fields:
+                    value = record.get(field)
+                    if value is None:
+                        continue
+                    try:
+                        return int(value)
+                    except Exception:
+                        try:
+                            return int(float(value))
+                        except Exception:
+                            continue
+                return 0
+
+            return max(records, key=extract_sort_value)
+
+        def query_jdy_by_org_id(org_id, flow_state=None):
+            cond = [{
+                "field": "_widget_1764820541661",
+                "type": "text",
+                "method": "eq",
+                "value": [org_id]
+            }]
+            if flow_state is not None:
+                cond.append({
+                    "field": "flowState",
+                    "type": "flowstate",
+                    "method": "eq",
+                    "value": [flow_state]
+                })
+
+            payload = {
+                "api_key": "675b900991ad2491c69389ca",
+                "entry_id": "6931063d64187eaf6b927557",
+                "filter": {
+                    "rel": "and",
+                    "cond": cond
+                }
+            }
+            return api_instance.entry_data_list(payload).get("data", [])
+
+        for item in update_data_list:
+            data_list = query_jdy_by_org_id(item.get("org_id"), flow_state=0)
+            if not data_list:
+                data_list = query_jdy_by_org_id(item.get("org_id"), flow_state=None)
+
+            result = pick_best_record(data_list)
+            data_id = result.get("_id") if result else None
+
+            if not data_id:
+                # print(f"未找到订单 {item.get('order_code')} 的简道云数据，跳过。")
+                continue
+
+            # 查询实例状态
+            instance_status = api_instance.workflow_instance_get({"data_id": data_id})
+            task_status = instance_status.get("status", -1)
+            print(f"订单 {item.get('order_code')} 的简道云流程状态为 {task_status}")
+
+            if task_status == 0:
+                print("简道云流程正在进行中，执行流程关闭")
+                api_instance.workflow_instance_end({"data_id": data_id})
+
+            target_values = {
+                "_widget_1764820541674": item.get("order_code"),
+                "_widget_1764820541679": item.get("pay_time"),
+                "_widget_1764820541676": item.get("payment_amount"),
+                "_widget_1764820541680": item.get("bussiness_type"),
+            }
+
+            data_to_update = {}
+            for widget_id, new_value in target_values.items():
+                existing_value = extract_widget_value(result, widget_id)
+                if has_value(existing_value):
+                    continue
+                if not has_value(new_value):
+                    continue
+                data_to_update[widget_id] = {"value": new_value}
+
+            if not data_to_update:
+                print(f"订单 {item.get('order_code')} 简道云字段已有值或无可写入内容，跳过同步")
+                continue
+
+            print("开始同步数据")
+            update_payload = {
+                "api_key": "675b900991ad2491c69389ca",
+                "entry_id": "6931063d64187eaf6b927557",
+                "data_id": data_id,
+                "data": data_to_update
+            }
+            print(update_payload)
+            api_instance.entry_data_update(update_payload)
+            print("数据同步完成")
+
+    def main(self):
+        task_start_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        try:
+            # step1 获取简道云与宜搭数据
+            jd_ydy_data = self.load_all_data()
+            if jd_ydy_data:
+                # step2 过滤已经续约的单子
+                update_data_list = self.filter_renewal_data(jd_ydy_data)
+                # step3 校验简道云是否有进行中的单子并关闭
+                self.check_jd_ydy_data(update_data_list)
+            else:
+                print("本次执行无处理数据")
+        except Exception as e:
+            print(e)
+
+
+if __name__ == '__main__':
+    jd_ydy_renewal_to_do = YDToJDYRenewalToDo()
+    jd_ydy_renewal_to_do.main()

test/续约待办宜搭传给简道云-5min.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 import os
 from datetime import datetime, timedelta, timezone
 import pandas as pd
@@ -193,7 +194,8 @@ class API:
             "app_id": data['api_key'],  # 应用ID
             "entry_id": data['entry_id'],  # 表单ID
             "data_id": data['data_id'],  # 数据ID
-            "data": data['data']
+            "data": data['data'],
+            "is_start_trigger": True
         }
         )
 
@@ -583,20 +585,19 @@ class YDToJDYRenewalToDo(object):
                 print(f"读取历史文件失败: {e}")
                 df_last = pd.DataFrame()
         else:
-            df_last = pd.DataFrame()
+            df_last = None  # 明确标记：无历史文件
 
-        # 假设唯一标识字段为 'instanceId'，请根据实际字段名调整
         id_col = 'processInstanceId'
-        if not df_last.empty and not df_current.empty and id_col in df_current.columns and id_col in df_last.columns:
-            # 转为字符串确保可比
+
+        if df_last is not None and not df_last.empty and not df_current.empty and id_col in df_current.columns and id_col in df_last.columns:
+            # 有历史文件，计算新增
             last_ids = set(df_last[id_col].astype(str))
             current_ids = set(df_current[id_col].astype(str))
             new_ids = current_ids - last_ids
             diff_records = df_current[df_current[id_col].astype(str).isin(new_ids)].to_dict('records')
         else:
-            # 没有历史文件 或 无唯一ID → 返回空 list（按你要求）
-            diff_records = []
-
+            # 没有历史文件 或 无法比对 → 返回全部当前数据
+            diff_records = df_current.to_dict('records')  # ← 关键修改点
         # 保存当前全量数据（覆盖）
         df_current.to_csv(current_file, index=False)
 

test/续约待办宜搭同步简道云.py

@@ -1,33 +0,0 @@
-from datetime import datetime
-import os
-from config import Config
-import pandas as pd
-from back_ground_module import CommonModule
-from api import API
-from log_config import configure_task_logger, configure_error_task_logger
-
-logger = configure_task_logger()
-error_task_logger = configure_error_task_logger()
-output_dir = "output"  # 设置输出目录
-os.makedirs(output_dir, exist_ok=True)
-common_module = CommonModule()
-api_instance = API()
-
-
-class YdToJDYRenewalToDo:
-    def __init__(self):
-        pass
-
-    def load_all_data(self):
-        """
-        从各类来源加载数据上加载数据
-        :return:
-        """
-
-    def main(self):
-        pass
-
-
-if __name__ == '__main__':
-    yd_to_jd_renewal_to_do = YdToJDYRenewalToDo()
-    yd_to_jd_renewal_to_do.main()

test/续约待办派发.py

@@ -122,7 +122,6 @@ class RenewalToDo:
     def load_all_data(self):
         """
         从各类来源加载数据上加载数据
-        :return:
         """
 
         # 数据库获取续约回访数据

test/续约待办派发测试数据.py

@@ -5,7 +5,8 @@ from api import API
 from back_ground_module import CommonModule
 from log_config import configure_task_logger, configure_error_task_logger
 from collections import defaultdict
-
+from datetime import datetime, timezone, timedelta, date, UTC
+from config import Config
 logger = configure_task_logger()
 error_task_logger = configure_error_task_logger()
 api_instance = API()
@@ -14,7 +15,81 @@ output_dir = "output"  # 设置输出目录
 os.makedirs(output_dir, exist_ok=True)
 
 
+import pandas as pd
+import psycopg2
+from datetime import datetime, timedelta, date
+import os
+def get_renewal_details():
+    """
+    从固定的数据库中获取续约待办数据，先拉全量（指定 date_id），再用 CSV 中的门店编码在内存中过滤
+    """
+    try:
+        # 1. 从 CSV 文件中读取门店编码
+        csv_path = r"D:\Idea Project\SaaS_V1.7\test\output\expanded_yd_data.csv"
+        if not os.path.exists(csv_path):
+            error_task_logger.error(f"CSV 文件不存在: {csv_path}")
+            return pd.DataFrame()
+
+        store_df = pd.read_csv(csv_path, dtype=str)
+        if "textField_ksydghqw" not in store_df.columns:
+            error_task_logger.error("CSV 文件中缺少列 'textField_ksydghqw'")
+            return pd.DataFrame()
+
+        store_codes = set(store_df["textField_ksydghqw"].dropna().unique())  # 转为 set 提升查找效率
+        if not store_codes:
+            error_task_logger.warning("CSV 中未找到有效的门店编码")
+            return pd.DataFrame()
+
+        # 2. 连接数据库
+        conn = psycopg2.connect(**Config.CONN_INFO)
+        cursor = conn.cursor()
+
+        # 获取前两天的 date_id（整数格式 YYYYMMDD）
+        now_time = datetime.now()
+        yes_time = now_time + timedelta(days=-2)
+        yes_time_nyr = int(yes_time.strftime('%Y%m%d'))
+
+        # 3. 构造 SQL 查询：不再包含 org_code 过滤
+        sql = """
+            SELECT * 
+            FROM "public"."holo_ads_report_saas_profile_ngv_detail_d"
+            WHERE "date_id" = %s
+        """
+
+        # 执行查询（只传 date_id）
+        cursor.execute(sql, (yes_time_nyr,))
+        rows = cursor.fetchall()
+        all_fields = cursor.description
+
+        # 转换为 DataFrame
+        col = [i[0] for i in all_fields]
+        data_NGV = pd.DataFrame(rows, columns=col) if rows else pd.DataFrame(columns=col)
+
+        # 关闭连接
+        cursor.close()
+        conn.close()
+
+        # 4. 在内存中用 store_codes 过滤 org_code
+        if "org_code" not in data_NGV.columns:
+            error_task_logger.error("数据库结果中缺少 'org_code' 字段")
+            return pd.DataFrame()
+
+        # 确保 org_code 是字符串类型（与 store_codes 一致）
+        data_NGV = data_NGV.copy()  # 避免 SettingWithCopyWarning
+        data_NGV["org_code"] = data_NGV["org_code"].astype(str)
+
+        # 过滤：只保留 org_code 在 store_codes 中的行
+        filtered_data = data_NGV[data_NGV["org_code"].isin(store_codes)].reset_index(drop=True)
+
+        return filtered_data
+
+    except Exception as e:
+        error_task_logger.error(f"获取续约待办数据时出错: {e}", exc_info=True)
+        return pd.DataFrame()
+
 class RenewalToDo:
+    """续约回访待办派发"""
+
     def __init__(self):
         self.renewal_data_list = None
         self.cyclic_increasing = None
@@ -84,6 +159,8 @@ class RenewalToDo:
             "流程状态": "_widget_1765352838610",
             "经营模式": "_widget_1765964381952",
             "公司等级": "_widget_1766130435561",
+            "公司id": "_widget_1766631811839",
+            "订单商品名称": "_widget_1766730385209",
             "提交人": "creator",
             "提交时间": "createTime",
             "更新时间": "updateTime"
@@ -101,6 +178,7 @@ class RenewalToDo:
             "group_grade": "公司等级",
             "technician": "运营专家",
             "manage_model": "经营模式",
+            "id_own_group": "公司id",
         }
         self.subform_field_map = {
             "商品名称": "_widget_1764820541719",
@@ -112,7 +190,7 @@ class RenewalToDo:
             "续约后订单编码": "_widget_1764820541725",
             # 根据实际需要添加更多字段
         }
-        self.renewal_list_map ={
+        self.renewal_list_map = {
 
         }
 
@@ -123,7 +201,9 @@ class RenewalToDo:
         """
 
         # 数据库获取续约回访数据
-        self.data_NGV = pd.read_csv(os.path.join(output_dir, "data_NGV.csv"), encoding="gbk")
+        self.data_NGV = common_module.get_renewal_details()
+        # self.data_NGV = get_renewal_details() # 历史数据
+        self.data_NGV.to_csv("D:\\Idea Project\\SaaS_V1.7\\test\\output\data_NGV1.csv")
 
         # 获取加盟商信息
         self.franchisee = common_module.get_renewal_franchisee_details()
@@ -344,6 +424,10 @@ class RenewalToDo:
         data_NGV['60天自动流转时间'] = data_NGV['过期日'] - pd.Timedelta(days=30)
         data_NGV['30天自动流转时间'] = data_NGV['过期日'] - pd.Timedelta(days=0)
         data_NGV['0天自动流转时间'] = data_NGV['过期日'] + pd.Timedelta(days=90)
+
+        data_NGV['120天是否跟进'] = "主动"
+        data_NGV['60天是否跟进'] = "主动"
+        data_NGV['30天是否跟进'] = "主动"
         # 格式化为字符串（去掉时区）
         for col in ['过期日', '120天自动流转时间', '60天自动流转时间', '30天自动流转时间', '0天自动流转时间']:
             data_NGV[col] = data_NGV[col].dt.strftime('%Y-%m-%d %H:%M:%S')
@@ -356,24 +440,39 @@ class RenewalToDo:
         )
 
         # 新增上次购买价格列
-        # 1. 清洗并拼接类型+价格
-        df_lp = self.last_price[['门店编码', '类型', '价格']].copy()
+        # 1. 清洗数据
+        df_lp = self.last_price[['门店编码', '类型', '订单商品名称', '价格']].copy()
+
+        # 处理“类型”和“订单商品名称”的缺失值
         df_lp['类型'] = df_lp['类型'].fillna('').astype(str)
+        df_lp['订单商品名称'] = df_lp['订单商品名称'].fillna('').astype(str)
+
+        # 处理价格：转数字、四舍五入、填0、转字符串
         df_lp['价格'] = (
             pd.to_numeric(df_lp['价格'], errors='coerce')
             .round().fillna(0).astype(int).astype(str)
         )
-        df_lp['类型_价格'] = df_lp['类型'] + df_lp['价格']
 
-        # 2. 按门店聚合，分号连接
-        agg_df = df_lp.groupby('门店编码', as_index=False)['类型_价格'].apply(';'.join)
+        # 2. 拼接“类型:价格”
+        df_lp['类型_价格'] = df_lp['类型'] + ':' + df_lp['价格']
 
-        # 3. 合并回主表
-        data_NGV = data_NGV.merge(agg_df, on='门店编码', how='left').fillna({'类型_价格': ''})
-        data_NGV.rename(columns={'类型_价格': '上次购买价格'}, inplace=True)
+        # 3. 按门店聚合两列
+        agg_df = df_lp.groupby('门店编码', as_index=False).agg({
+            '类型_价格': lambda x: ';'.join(x),
+            '订单商品名称': lambda x: ';'.join(x)
+        })
 
-        # 4. 处理没有匹配记录的门店（填空或默认值）
-        data_NGV['上次购买价格'] = data_NGV['上次购买价格'].fillna('')
+        # 4. 合并回主表
+        data_NGV = data_NGV.merge(agg_df, on='门店编码', how='left')
+
+        # 5. 填充缺失值为空字符串，并重命名列
+        data_NGV['类型_价格'] = data_NGV['类型_价格'].fillna('')
+        data_NGV['订单商品名称'] = data_NGV['订单商品名称'].fillna('')
+
+        data_NGV.rename(columns={
+            '类型_价格': '上次购买价格',
+            '订单商品名称': '订单商品名称'
+        }, inplace=True)
 
         # 成员字段替换（现在列名是中文）
         staff_name_cols = [
@@ -482,7 +581,7 @@ class RenewalToDo:
 
         payload = {
             "api_key": "675b900991ad2491c69389ca",
-            "entry_id": "6931063d64187eaf6b927557",
+            "entry_id": "6965eec36b73376aa0b5bff8",
             "data_list": records
         }
         print(payload)
@@ -502,11 +601,8 @@ class RenewalToDo:
             data_NGV = self.process_data()
             # step3:数据派发
             self.dispatch_task(data_NGV)
-            # step4:过期日发生变化更新已有表单
 
-            # step5:自动同意原表单
-
-            common_module.send_task_status(task_start_time, "续约回访待办")
+            # common_module.send_task_status(task_start_time, "续约回访待办")
         except Exception as e:
             error_task_logger.error(f"续约回访待办发生错误{e}")
             # common_module.send_task_error(task_start_time, "续约回访待办", str(e))

test/续约待办简道云回传宜搭.py

@@ -233,6 +233,31 @@ class YDAPI:
     appKey = "ding5kqocon5s9oph5uq"
     appSecret = "HL1jgsIIfLAC0eTH0A1m4mwxUDqbgsiPeCCGGE3ocM6qJBTIW7Ivt9drxF_Z4Kb_"
 
+    @staticmethod
+    def _to_int_task_id(task_id):
+        if task_id is None:
+            raise ValueError("taskId is None")
+        if isinstance(task_id, bool):
+            raise ValueError(f"taskId is bool: {task_id}")
+        if isinstance(task_id, int):
+            return task_id
+        if isinstance(task_id, float):
+            if task_id.is_integer():
+                return int(task_id)
+            raise ValueError(f"taskId is non-integer float: {task_id}")
+        if isinstance(task_id, str):
+            s = task_id.strip()
+            if s.isdigit() or (s.startswith("-") and s[1:].isdigit()):
+                return int(s)
+            try:
+                f = float(s)
+            except ValueError as e:
+                raise ValueError(f"taskId is not numeric: {task_id}") from e
+            if f.is_integer():
+                return int(f)
+            raise ValueError(f"taskId is non-integer numeric string: {task_id}")
+        raise ValueError(f"taskId has unsupported type: {type(task_id).__name__}")
+
     def generateToken(self) -> str:
         """
         函数功能：生成访问令牌（token）
@@ -301,10 +326,10 @@ class YDAPI:
         while True:
             if attempt >= max_retries:
                 # error_task_logger.error(f"请求失败，已达最大重试次数 {max_retries}，无法获取流程实例数据,跳过本次请求。")
-                break
+                return {"data": []}
 
             try:
-                res = requests.post(api, headers=headers, json=formData)
+                res = requests.post(api, headers=headers, json=formData, timeout=15)
                 # print(res.json())
                 res.raise_for_status()  # 如果返回状态码不是2xx，抛出异常
                 return res.json()
@@ -313,6 +338,11 @@ class YDAPI:
                 # logger.warning(f"请求异常: {e}，正在尝试第 {attempt + 1} 次重试...")
                 time.sleep(delay)
                 attempt += 1
+            except Exception:
+                time.sleep(delay)
+                attempt += 1
+
+        return {"data": []}
 
     def update_from(self, token, formInstanceId, data_new):
         """
@@ -345,7 +375,7 @@ class YDAPI:
 
         }
 
-        res = requests.put(api, headers=headers, json=payload)
+        res = requests.put(api, headers=headers, json=payload, timeout=15)
         return res
 
     def get_approval_records(self, token: str, processInstanceId: str, appType="APP_UYZ0KG6L0CCNV80GZ66O",
@@ -375,16 +405,17 @@ class YDAPI:
         while True:
             if attempt >= max_retries:
                 # error_task_logger.error(f"请求失败，已达最大重试次数 {max_retries}，无法获取审批数据,跳过本次请求。")
-                break
+                return {"result": []}
 
             try:
-                res = requests.get(api, headers=headers)
+                res = requests.get(api, headers=headers, timeout=15)
                 res.raise_for_status()  # 如果响应状态码不是2xx，则抛出HTTPError
                 return res.json()
             except (requests.exceptions.RequestException, Exception) as e:
                 # logger.warning(f"请求出现异常: {e}, 正在重试({attempt + 1}/{max_retries})...")
                 time.sleep(delay)  # 等待指定的延迟时间后再次尝试
                 attempt += 1
+        return {"result": []}
 
     def aggree_approval(self, token: str, taskId: str, processInstanceId: str, formData: dict, res_new):
         """_summary_
@@ -415,10 +446,10 @@ class YDAPI:
             "processInstanceId": processInstanceId,
             "userId": res_new,
             "language": "zh_CN",
-            "taskId": int(taskId)
+            "taskId": self._to_int_task_id(taskId)
         }
 
-        res = requests.post(api, headers=headers, json=payload)
+        res = requests.post(api, headers=headers, json=payload, timeout=15)
         return res
 
 
@@ -449,9 +480,22 @@ class JDYToYDRenewalToDo(object):
             "30天是否跟进": "_widget_1764820541632",
             "30天处理人": "_widget_1764820541636",
             "30天跟进时间": "_widget_1765352838633",
+            "是否联系上":"_widget_1764820541638",
             "数据ID": "_id"
         }
 
+    @staticmethod
+    def _as_list(val):
+        if val is None:
+            return []
+        if isinstance(val, list):
+            return val
+        if isinstance(val, tuple):
+            return list(val)
+        if isinstance(val, dict):
+            return [val]
+        return []
+
     def load_all_data(self):
         # 获取简道云已派发续约待办，若无数据直接返回
         today_utc = datetime.now(timezone.utc).strftime("%Y-%m-%d")
@@ -463,7 +507,7 @@ class JDYToYDRenewalToDo(object):
                                         "value": ["否"]}]},
                    }
         renewal = api_instance.entry_data_list(payload)
-        self.renewal_data_list = renewal.get("data") or []
+        self.renewal_data_list = self._as_list((renewal or {}).get("data"))
         if not self.renewal_data_list:
             self.renewal_data_df = pd.DataFrame()
             return
@@ -478,7 +522,7 @@ class JDYToYDRenewalToDo(object):
 
         all_data = []
         for _, row in self.renewal_data_df.iterrows():
-            yd_data = yd_api_instance.read_processes_instances(
+            yd_resp = yd_api_instance.read_processes_instances(
                 token=self.token,
                 formUuid=self.FORMID,
                 page=1,
@@ -487,9 +531,12 @@ class JDYToYDRenewalToDo(object):
                 systemToken=self.systemToken,
                 instanceStatus="",
                 searchFieldJson={"textField_ksydghqw": row["_widget_1764820541661"]},
-            ).get("data", [])
+            )
+            yd_data = self._as_list((yd_resp or {}).get("data"))
 
             for record in yd_data:
+                if not isinstance(record, dict):
+                    continue
                 enriched = {**record}
                 enriched.update({k: row.get(v, "") for k, v in self.follow_up_fields.items()})
                 all_data.append(enriched)
@@ -704,20 +751,20 @@ class JDYToYDRenewalToDo(object):
             data = {"data_id": item["数据ID"]}
             jdy_workflow_data = api_instance.workflow_instance_get(data) or {}
             print("简道云流程日志：", jdy_workflow_data)
-            jdy_logs = jdy_workflow_data.get("logs", [])
+            jdy_logs = self._as_list(jdy_workflow_data.get("logs"))
             jdy_logs = sorted(
                 jdy_logs,
                 key=lambda x: parse_dt(x.get("finish_time") or x.get("create_time")),
                 reverse=True,
             )
-            tasks = jdy_workflow_data.get("tasks", []) or []
+            tasks = self._as_list(jdy_workflow_data.get("tasks"))
             pending = [t for t in tasks if t.get("status") == 0]
             task_candidate = (pending[0] if pending else None) or (sorted(
                 tasks,
                 key=lambda x: parse_dt(x.get("create_time")),
                 reverse=True
             )[0] if tasks else {})
-            jdy_result_records = jdy_workflow_data.get("result", []) or []
+            jdy_result_records = self._as_list(jdy_workflow_data.get("result"))
 
             jdy_stage_candidates = [
                 extract_stage_from_text(str(jdy_logs[0].get("flow_name", ""))) if jdy_logs else None,
@@ -740,7 +787,7 @@ class JDYToYDRenewalToDo(object):
                 systemToken=self.systemToken
             ) or {}
             print("宜搭流程日志：", yd_workflow_data)
-            yd_results = yd_workflow_data.get("result", []) or []
+            yd_results = self._as_list(yd_workflow_data.get("result"))
 
             # 展开宜搭 domainList 以获取所有动作
             yd_records = [
@@ -753,7 +800,7 @@ class JDYToYDRenewalToDo(object):
                 key=lambda x: parse_dt(x.get("operateTimeGMT") or x.get("activeTimeGMT")),
                 reverse=True,
             )
-            # 优先使用当前待办（type == TODO），否则用最新一条
+            # 优先使用当前待办（type == "TODO"），否则用最新一条
             yd_todo = next((r for r in yd_records if str(r.get("type")).upper() == "TODO"), None)
             yd_latest = yd_todo or (yd_records[0] if yd_records else {})
             yd_stage = extract_stage_from_text(
@@ -784,6 +831,11 @@ class JDYToYDRenewalToDo(object):
             if not task_id or not operator_user_id:
                 print(f"缺少 taskId 或 operatorUserId，无法自动同意 processInstanceId={item['processInstanceId']}")
                 continue
+            try:
+                task_id = yd_api_instance._to_int_task_id(task_id)
+            except Exception as e:
+                print(f"taskId 非法，跳过自动同意 processInstanceId={item['processInstanceId']} taskId={task_id} err={e}")
+                continue
             try:
                 yd_api_instance.aggree_approval(
                     token=self.token,
@@ -798,10 +850,11 @@ class JDYToYDRenewalToDo(object):
 
     def retrun_jdy(self, yd_update_list):
         for item in yd_update_list:
+            data_id= str(item.get('数据ID'))
             data = {
                 "api_key": "675b900991ad2491c69389ca",
                 "entry_id": "6931063d64187eaf6b927557",
-                "data_id": item.get('数据ID'),
+                "data_id": data_id,
                 "data":
                     {
                         "_widget_1766469131897": {"value": "是"},

test/续约请求接口结果保存.ipynb

@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## 保存boss请求结果",
+   "id": "311a82d4faf8e2d"
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-11-10T06:24:23.858755Z",
+     "start_time": "2025-11-10T06:24:22.994108Z"
+    }
+   },
+   "source": [
+    "# 标准库\n",
+    "import os\n",
+    "import time\n",
+    "import random\n",
+    "import json\n",
+    "import binascii\n",
+    "from datetime import date, timedelta, datetime\n",
+    "from urllib.parse import quote\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# 第三方库\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "from pyDes import des, CBC, PAD_PKCS5\n",
+    "import mysql.connector\n",
+    "from mysql.connector import Error\n",
+    "\n",
+    "# PostgreSQL（如果你用到了）\n",
+    "import psycopg2\n",
+    "\n",
+    "# 自定义模块\n",
+    "from config import Config\n",
+    "from api import API\n",
+    "from back_ground_module import CommonModule\n",
+    "from log_config import configure_task_logger, configure_error_task_logger\n",
+    "\n",
+    "\n",
+    "logger = configure_task_logger()\n",
+    "error_task_logger = configure_error_task_logger()\n",
+    "api_instance = API()\n",
+    "common_module = CommonModule()\n",
+    "output_dir = \"output\"  # 设置输出目录\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "def des_encrypt(s):\n",
+    "    \"\"\"\n",
+    "    DES 加密\n",
+    "    :param s: 原始字符串\n",
+    "    :return: 加密后字符串，16进制\n",
+    "    \"\"\"\n",
+    "    secret_key = 'HwdMBW8o'\n",
+    "    iv = secret_key\n",
+    "    k = des(secret_key, CBC, iv, pad=None, padmode=PAD_PKCS5)\n",
+    "    en = k.encrypt(s, padmode=PAD_PKCS5)\n",
+    "    return binascii.b2a_base64(en, newline=False)\n",
+    "\n",
+    "\n",
+    "def des_descrypt(s):\n",
+    "    \"\"\"\n",
+    "    DES 解密\n",
+    "    :param s: 加密后的字符串，16进制\n",
+    "    :return:  解密后的字符串\n",
+    "    \"\"\"\n",
+    "    secret_key = 'HwdMBW8o'\n",
+    "    iv = secret_key\n",
+    "    k = des(secret_key, CBC, iv, pad=None, padmode=PAD_PKCS5)\n",
+    "    de = k.decrypt(binascii.a2b_base64(s), padmode=PAD_PKCS5)\n",
+    "    return de\n",
+    "\n",
+    "data_NGV = common_module.get_renewal_details()\n",
+    "\n",
+    "\n",
+    "for i in range(0,len(data_NGV[\"date_fmt\"])):\n",
+    "    t = time.time()\n",
+    "    ts = int(round(t * 1000))\n",
+    "    randint = random.randint(100000000, 999999999)\n",
+    "    req = data_NGV['id_own_org'][i] + \"_\" + str(ts) + \"_\" + str(randint)\n",
+    "    str_en = des_encrypt(req)\n",
+    "    req_new = str_en.decode('utf-8')\n",
+    "\n",
+    "    url = f\"http://manage.f6yc.com/hive-admin/py/yida/renewal/orgInfo\"\n",
+    "    data = {\n",
+    "        'req':req_new,\n",
+    "        't':ts,\n",
+    "        'r':randint\n",
+    "    }\n",
+    "    res = requests.post(url,data=data)\n",
+    "    # print(res.json.json())\n",
+    "\n",
+    "    break\n",
+    "\n",
+    "print(len(data_NGV))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "距离今天还有120天的日期是：2026-03-10\n",
+      "29\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/非标

@@ -0,0 +1,71 @@
+,_id,提交人,updater,deleter,提交时间,更新时间,deleteTime,flowState,报备类型,协作内容,情况说明,订单编号,年限,版本,实付金额,商品名称,履约金额,门店编码,门店名称,支付日期,开户/处理日期,业绩归属日期,业绩类型,公司名称,公司ID,报备业绩金额-区域提交,业绩归属小六-区域提交,业绩归属月,是否同步衡石,小六业绩金额,区域业绩金额,报备业绩归属小六,报备业绩归属区域经理,报备业绩归属大区,原业绩归属人,原业绩归属区域经理,原业绩归属大区,小六业绩比例,区域业绩比例,运营专家,业绩动作,提成类型,新签阶段及提成比例,提成金额,SaaS新签提成比例,服务包提成比例,新签提成比例-首年,新签提成比例-非首年,提成动作,业绩类型-聚合,业绩分组,流程是否结束,appId,entryId
+0,68d1039a408016fe13556b06,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:06:50,2025-12-25 15:32:02,,2,多年补差价,,6月订单，9月补差价2年,1757735155184,2,入门版,999,,,CHS202506010300195,上海汨晨汽车服务有限公司,2025-09-13 00:00:00,2025-06-01 00:00:00,2025-09-13 00:00:00,新签,,,,,,是,999.0,999.0,刘鑫烨,张凯,华南沪,刘鑫烨,张凯,华南沪,1.0,1.0,周聪,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+1,68d103cdb8f662bfdf1b7ea2,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:07:41,2025-12-25 15:32:02,,2,新签超3年,,新签5年,1758076816064,2,标准版,3000,,,CHS202302130204882,江阴市华士爱驹汽车养护店,2025-09-17 00:00:00,2025-09-17 00:00:00,2025-09-17 00:00:00,新签,,,,,,是,3000.0,3000.0,赵旭伟,肖军,江苏,赵旭伟,肖军,江苏,1.0,1.0,陈博,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+2,68d103f17f34705c8dbe360a,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:08:17,2025-12-25 15:32:02,,2,新签超3年,,新签5年,1758181826462,2,基础版,1600,,,CHS202504240297202,古城区鸿远轮胎服务中心,2025-09-18 00:00:00,2025-09-18 00:00:00,2025-09-18 00:00:00,新签,,,,,,是,1600.0,1600.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,崔智杰,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+3,68d10415f3728b4cd791630e,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:08:53,2025-12-25 15:32:02,,2,新签超3年,,5年订单,1758259644403,2,基础版,1759,,,CHS202509190309704,金堂县赵镇四达汽修厂,2025-09-19 00:00:00,2025-09-19 00:00:00,2025-09-19 00:00:00,新签,,,,,,是,1759.0,1759.0,陈致欣,肖军,西南,陈致欣,肖军,西南,1.0,1.0,吴间锐,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+4,68d1042ee3ee1af6d0d7f8ee,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:09:18,2025-12-25 15:32:02,,2,新签超3年,,,1756814144233,2,入门版,1000,,,CHS202509020309092,天津市滨海新区安驰汽车修理服务部,2025-09-03 00:00:00,2025-09-02 00:00:00,2025-09-03 00:00:00,新签,,,,,,是,1000.0,1000.0,王鑫,关磊,华北,王鑫,关磊,华北,1.0,1.0,杨挺,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+5,68d104502618b5ea53f4264f,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:09:52,2025-12-25 15:32:02,,2,新签超3年,,,1757245487196,2,基础版,1400,,,CHS202509070309251,车广角盘锦店,2025-09-08 00:00:00,2025-09-07 00:00:00,2025-09-08 00:00:00,新签,,,,,,是,1400.0,1400.0,韩皞,陈庆伟,东北,韩皞,陈庆伟,东北,1.0,1.0,孙旭亮,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+6,68d10471c1d4a4211d2ce420,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:10:25,2025-12-25 15:32:02,,2,新签超3年,,,1757388589517,1,进阶版,1000,,,CHS202509090309331,上海德伽汽车服务中心,2025-09-09 00:00:00,2025-09-09 00:00:00,2025-09-09 00:00:00,新签,,,,,,是,1000.0,1000.0,刘鑫烨,张凯,华南沪,刘鑫烨,张凯,华南沪,1.0,1.0,周聪,新增,,[],,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+7,68d104a081bf67abc88ce650,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:11:12,2025-12-25 15:32:02,,2,新签超3年,,,1757752521945,2,标准版,3000,,,CHS202509130309507,腾冲诚亿汽车修理有限公司,2025-09-14 00:00:00,2025-09-13 00:00:00,2025-09-14 00:00:00,新签,,,,,,是,3000.0,3000.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,崔智杰,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+8,68d104e0304ea14df52c1128,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:12:16,2025-12-25 15:32:02,,2,品牌方协作,电子目录,电子目录,,,,12000,,,,,,,2025-09-22 16:12:16,,,,,,,是,0.0,0.0,杜浩,肖军,江苏,,,,,,,新增,,[],,,,,,无,电子目录,电子目录,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+9,68d104fce01701d04e9ba14d,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:12:44,2025-12-25 15:32:02,,2,品牌方协作,电子目录,,,,,8000,,,,,,,2025-09-22 16:12:44,,,,,,,是,0.0,0.0,韩皞,陈庆伟,东北,,,,,,,新增,,[],,,,,,无,电子目录,电子目录,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+10,68d105177771c4e88d61d725,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-22 16:13:11,2025-12-25 15:32:02,,2,品牌方协作,电子目录,,,,,9000,,,,,,,2025-09-22 16:13:11,,,,,,,是,0.0,0.0,胡楠,景东强,西北,,,,,,,新增,,[],,,,,,无,电子目录,电子目录,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+11,68d2342ba541a358893d61ef,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-23 13:46:19,2025-12-25 15:32:02,,2,多年补差价,,,1758459953833,2,入门版,1000,,,CHS202505070297933,上海义诚汽车服务有限公司,2025-09-22 00:00:00,2025-05-07 00:00:00,2025-09-22 00:00:00,新签,,,,,,是,1000.0,1000.0,刘鑫烨,张凯,华南沪,刘鑫烨,张凯,华南沪,1.0,1.0,周聪,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+12,68d3bc8d99f9d49450ed8b1a,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-24 17:40:29,2025-12-25 15:32:02,,2,品牌方协作,电子目录,电子目录，小六未参与,,,,9000,,,,,,,2025-09-24 17:40:29,,,,,,,是,0.0,0.0,胡楠,景东强,西北,,,,,,,新增,,[],,,,,,无,电子目录,电子目录,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+13,68d494d8c6f070c9f68a4e94,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-25 09:03:20,2025-12-25 15:32:02,,2,新签超3年,,,1758612872486,1,基础版,799,,,CHS202509230309865,回民区青辰宝悦汽车维修中心（个体工商户）,2025-09-23 00:00:00,2025-09-23 00:00:00,2025-09-23 00:00:00,新签,,,,,,是,799.0,799.0,张宏伟,关磊,华北,张宏伟,关磊,华北,1.0,1.0,武宏超,新增,,[],,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+14,68d5f2a0b3bc5add3c3d7a23,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-26 09:55:44,2025-12-25 15:32:02,,2,新签超3年,,,1758789984175,2,入门版,1000,,,CHS202509250310033,济南双江汽车服务有限公司,2025-09-26 00:00:00,2025-09-25 00:00:00,2025-09-26 00:00:00,新签,,,,,,是,1000.0,1000.0,杨旭,关磊,山东,杨旭,关磊,山东,1.0,1.0,王斌,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+15,68d5f2c3f7765d3eddb8506a,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-26 09:56:19,2025-12-25 15:32:02,,2,多年补差价,,,1758787369355,2,旗舰版,4000,,,CHS202505190299143,政德汽修,2025-09-26 00:00:00,2025-05-19 00:00:00,2025-09-26 00:00:00,新签,,,,,,是,4000.0,4000.0,杨旭,关磊,山东,杨旭,关磊,山东,1.0,1.0,王斌,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+16,68d5f2eb8eb300e7d401f7f9,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-26 09:56:59,2025-12-25 15:32:02,,2,新签超3年,,,1758786185157,2,标准版,3120,,,CHS202509250310019,红河州秀林工贸有限公司,2025-09-26 00:00:00,2025-09-25 00:00:00,2025-09-26 00:00:00,新签,,,,,,是,3120.0,3120.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,崔智杰,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+17,68d8882a40f51cce582eddb5,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-28 08:58:18,2025-12-25 15:32:02,,2,多年补差价,,,1758894296058,2,进阶版,2501,,,CHS202107030131629,灵山县车益汽车维修厂,2025-09-27 00:00:00,2025-08-09 00:00:00,2025-09-27 00:00:00,新签,,,,,,是,2501.0,2501.0,黄环宇,张凯,华南沪,黄环宇,张凯,华南沪,1.0,1.0,黄宗祥,新增,,[],125.05,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+18,68d9e2d9710265914f554379,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-29 09:37:29,2025-12-25 15:32:02,,2,跨区新签,,,1759047159482,3,旗舰版,7000,,,CHS202509280310174,荟星行汽车服务有限公司,2025-09-29 00:00:00,2025-09-28 00:00:00,2025-09-29 00:00:00,新签,,,,,,是,3500.0,3500.0,韩皞,陈庆伟,东北,张宏伟,关磊,华北,0.5,0.5,孙旭亮,拆单,,[],315.0,0.135,,,0.135,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+19,68d9f2549d0de29d680f6403,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-29 10:43:32,2025-12-25 15:32:02,,2,新签超3年,,,1759063075745,2,入门版,920,,,CHS202509280310180,宏运汽修,2025-09-29 00:00:00,2025-09-28 00:00:00,2025-09-29 00:00:00,新签,,,,,,是,920.0,920.0,柴铁峰,陈庆伟,东北,柴铁峰,陈庆伟,东北,1.0,1.0,刘立,新增,,[],46.0,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+20,68db3e73890706b7678f34ea,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-30 10:20:35,2025-12-25 15:32:02,,2,新签超3年,,,1757909706794,2,旗舰版,4400,,,CHS202509150309534,监利市壹加汽车服务有限公司,2025-09-15 00:00:00,2025-09-15 00:00:00,2025-09-15 00:00:00,新签,,,,,,是,4400.0,4400.0,陈煜,景东强,华中,陈煜,景东强,华中,1.0,1.0,刘光春,新增,,[],220.0,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+21,68db50ff8e50807e1e84e8f8,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-09-30 11:39:43,2025-12-25 15:32:02,,2,新签超3年,,,1759039606751,1,标准版,1500,,,CHS202509280310149,鄂尔多斯市心成泰汽车维修服务有限公司,2025-09-28 00:00:00,2025-09-28 00:00:00,2025-09-28 00:00:00,新签,,,,,,是,1500.0,1500.0,张宏伟,关磊,华北,张宏伟,关磊,华北,1.0,1.0,武宏超,新增,,[],0.0,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+22,68e71b9216dd2af696d3c489,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-09 10:18:58,2025-12-25 15:32:02,,2,新签超3年,,,1759573927652,1,基础版,800,,,CHS202303010210121,德系专修,2025-10-05 00:00:00,2025-10-04 00:00:00,2025-10-05 00:00:00,新签,,,,,,是,800.0,800.0,刘磊,关磊,华北,刘磊,关磊,华北,1.0,1.0,,新增,,[],0.0,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+23,68e768c27a4eb1ea616434b0,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-09 15:48:18,2025-12-25 15:32:02,,2,新签超3年,,,1759395920361,2,进阶版,2000,,,CHS202510020310341,官渡区众名汽车维修服务经营部,2025-10-03 00:00:00,2025-10-03 00:00:00,2025-10-03 00:00:00,新签,,,,,,是,2000.0,2000.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,,新增,,[],100.0,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+24,68e9ba0cd69c47d29c2e0972,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-11 09:59:40,2025-12-25 15:32:02,,2,新签超3年,,,1760012463416,1,进阶版,1000,,,CHS202510090310521,新乡骏享汽车销售服务有限公司,2025-10-10 00:00:00,2025-10-10 00:00:00,2025-10-10 00:00:00,新签,,,,,,是,1000.0,1000.0,王兵帅,张凯,河南,王兵帅,张凯,河南,1.0,1.0,邢恒岭,新增,,[],0.0,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+25,68eda452872200f9abed2d5d,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-14 09:16:02,2025-12-25 15:32:02,,2,多年补差价,,,1760180223661,2,进阶版,1999,,,CHS202507090303811,上海洗事临门汽车服务有限公司,2025-10-12 00:00:00,2025-07-09 00:00:00,2025-10-12 00:00:00,新签,,,,,,是,1999.0,1999.0,刘鑫烨,张凯,华南沪,刘鑫烨,张凯,华南沪,1.0,1.0,周聪,新增,,[],99.95,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+26,68f830d486f984a8f5d58949,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-22 09:18:12,2025-12-25 15:32:02,,2,电销业绩统计,,,1760341341750,3,基础版,2900,,,CHS202411020284735,深圳康得新KDX大膜王星级甄选店,2025-10-13 00:00:00,2025-10-13 00:00:00,2025-10-13 00:00:00,新签,,,,,,是,0.0,2900.0,严冬延,张凯,华南沪,耿渝淇,张凯,,,1.0,,新增,,[],130.5,0.135,,,0.135,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+27,68f98a2a9cdcf060fcebf670,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-23 09:51:38,2025-12-25 15:32:02,,2,新签超3年,,,1761122509436,2,进阶版,2000,,,CHS202409190281922,海口小拇指汽车服务,2025-10-23 00:00:00,2025-10-22 00:00:00,2025-10-23 00:00:00,新签,,,,,,是,2000.0,2000.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,,新增,,[],100.0,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+28,68fecdec710ccbf6abfdcf9c,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-27 09:42:04,2025-12-25 15:32:02,,2,新签超3年,,,1761365304042,2,标准版,3200,,,CHS202510250311422,博越汽修,2025-10-25 00:00:00,2025-10-25 00:00:00,2025-10-25 00:00:00,新签,,,,,,是,3200.0,3200.0,王有军,陈庆伟,浙皖,王有军,陈庆伟,浙皖,1.0,1.0,,新增,,[],160.0,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+29,6901795327c25049c38f1e2b,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-29 10:17:55,2025-12-25 15:32:02,,1,新签超3年,,,1761644690009,2,基础版,1600,,,CHS202510280311584,宜良捷驰汽车修理厂,2025-10-29 00:00:00,2025-10-29 00:00:00,2025-10-29 00:00:00,新签,,,,,,是,1600.0,1600.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,,新增,,[],80.0,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+30,690179cf34ed36233a8166fd,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-29 10:19:59,2025-12-25 15:32:02,,1,跨区新签,,,1761611436641,1,旗舰版,4499,,,CHS202510280311527,易道大咖乌鲁木齐东坪店,2025-10-28 00:00:00,2025-10-28 00:00:00,2025-10-28 00:00:00,新签,,,,,,是,2249.5,2249.5,孙振华,景东强,西北,潘志强,张凯,华南沪,0.5,0.5,,拆单,,[],0.0,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+31,69041f3e38fb7000e0ffc32e,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-10-31 10:30:22,2025-12-25 15:32:02,,1,新签超3年,,,1761816948126,2,至尊版,8000,,,CHS202510300312383,意嘉易春天里店,2025-10-31 00:00:00,2025-10-31 00:00:00,2025-10-31 00:00:00,新签,,,,,,是,8000.0,8000.0,范启超,肖军,西南,范启超,肖军,西南,1.0,1.0,,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+32,69095a58a9520f1eeaf2afcb,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-04 09:43:52,2025-12-25 15:32:02,,1,新签超3年,,,1762158897286,2,基础版,5000,续约旗舰版-门店管理系统2年,2500.0,CHS202511030312575,宣威市龙泉汽车有限公司,2025-11-04 00:00:00,2025-11-04 00:00:00,2025-11-04 00:00:00,新签,,,,,,是,5000.0,5000.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+33,690aae75afd572c006769a86,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-05 09:55:01,2025-12-25 15:32:02,,1,新签超3年,,,1762245527558,2,进阶版,2240,续约进阶版2年,1120.0,CHS202312020253454,乐山郭建军,2025-11-05 00:00:00,2025-11-05 00:00:00,2025-11-05 00:00:00,新签,,,,,,是,2240.0,2240.0,陈致欣,肖军,西南,陈致欣,肖军,西南,1.0,1.0,,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+34,690aae91ff9575eb8a758dc3,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-05 09:55:29,2025-12-25 15:32:02,,1,多年补差价,,,1761960739508,2,基础版,1101,续约基础版-门店管理系统2年,550.5,CHS202509230309853,都江堰市爱都汽车维修有限责任公司,2025-11-01 00:00:00,2025-09-23 00:00:00,2025-11-01 00:00:00,新签,,,,,,是,1101.0,1101.0,陈致欣,肖军,西南,陈致欣,肖军,西南,1.0,1.0,吴间锐,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+35,690aaec3f23eb35e6e394e54,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-05 09:56:19,2025-12-25 15:32:02,,1,多年补差价,,,1761955976569,2,基础版,1101,续约基础版-门店管理系统2年,550.5,CHS202509150309531,三越汽车音响,2025-11-01 00:00:00,2025-09-12 00:00:00,2025-11-01 00:00:00,新签,,,,,,是,1101.0,1101.0,陈致欣,肖军,西南,陈致欣,肖军,西南,1.0,1.0,吴间锐,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+36,690c00df267ca78f0a86da76,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-06 09:58:55,2025-12-25 15:32:02,,1,新签超3年,,,1762263761814,2,入门版,1600,续约入门版2年,800.0,CHS202105140124767,茂名市电白区华南汽车维修有限公司,2025-11-05 00:00:00,2025-11-04 00:00:00,2025-11-05 00:00:00,新签,,,,,,是,1600.0,1600.0,严冬延,张凯,华南沪,严冬延,张凯,华南沪,1.0,1.0,黄宗祥,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+37,690d4feafeb41d02c491646b,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-07 09:48:26,2025-12-25 15:32:02,,1,多年补差价,,,1762418825828,2,旗舰版,3599,续约旗舰版-门店管理系统2年,1799.5,CHS202508090305351,山海车服,2025-11-07 00:00:00,2025-08-09 00:00:00,2025-11-07 00:00:00,新签,上海山高海深汽车服务有限公司,15975930111903944708,,,,是,3599.0,3599.0,刘鑫烨,张凯,华南沪,刘鑫烨,张凯,华南沪,1.0,1.0,周聪,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+38,690d5114ebf892f67c83c3d9,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-07 09:53:24,2025-12-25 15:32:02,,1,新签超3年,,,1762401310694,2,进阶版,2240,续约进阶版2年,1120.0,CHS202003250058491,博伟汽修(新南路),2025-11-06 00:00:00,2025-11-06 00:00:00,2025-11-06 00:00:00,新签,博伟汽修,10546443563986851726,,,,是,2240.0,2240.0,胡仲远,肖军,西南,胡仲远,肖军,西南,1.0,1.0,吴间锐,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+39,6915529caca516e7c6c28b71,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-13 11:38:04,2025-12-25 15:32:02,,1,多年补差价,,,1762933140216,2,基础版,1998,续约基础版-门店管理系统2年,999.0,CHS202209140188566,德奥养车,2025-11-12 00:00:00,2025-11-08 00:00:00,2025-11-12 00:00:00,新签,德奥养车,11240984669917483539,,,,是,1998.0,1998.0,陈晨,关磊,华北,陈晨,关磊,华北,1.0,1.0,杨挺,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+40,691a76c508e9a99cb7e65565,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-17 09:13:41,2025-12-25 15:32:02,,1,新签超3年,,,1763100107052,2,标准版,3120,续约标准版-门店管理系统2年,1560.0,CHS202511150313111,云南锡业集团汽车技术服务有限公司,2025-11-14 00:00:00,2025-11-14 00:00:00,2025-11-14 00:00:00,新签,云南锡业集团汽车技术服务（文山服务中心）,16011444960305905673,,,,是,3120.0,3120.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,崔智杰,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+41,691c0a70d6f09daa8e0af427,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-18 13:56:00,2025-12-25 15:32:02,,1,多年补差价,,,1763366190883,2,入门版,999,续约入门版2年,499.5,CHS202509020309077,宜嘉养车,2025-11-17 00:00:00,2025-09-02 00:00:00,2025-11-17 00:00:00,新签,宜嘉养车,15984698892155383884,,,,是,999.0,999.0,刘鑫烨,张凯,华南沪,刘鑫烨,张凯,华南沪,1.0,1.0,周聪,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+42,691c0a845f80e95f659f9b26,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-18 13:56:20,2025-12-25 15:32:02,,1,新签超3年,,,1763351228539,1,进阶版,1000,续约进阶版,1000.0,CHS202511170313156,郑州欧汇汽车维修有限公司,2025-11-17 00:00:00,2025-11-17 00:00:00,2025-11-17 00:00:00,新签,郑州欧汇汽车维修有限公司,16012185257256194131,,,,是,1000.0,1000.0,王兵帅,张凯,河南,王兵帅,张凯,河南,1.0,1.0,邢恒岭,新增,,[],,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+43,691c0a9576f2783074f62089,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-18 13:56:37,2025-12-25 15:32:02,,1,新签超3年,,,1763370516248,2,标准版,3120,续约标准版-门店管理系统2年,1560.0,CHS202511170313182,文山万霆新能源科技有限责任公司,2025-11-18 00:00:00,2025-11-18 00:00:00,2025-11-18 00:00:00,新签,文山万霆新能源科技有限责任公司,16012268807246610438,,,,是,3120.0,3120.0,李壮壮,肖军,西南,李壮壮,肖军,西南,1.0,1.0,崔智杰,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+44,691d39d9de406a40ae9c1859,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-19 11:30:33,2025-12-25 15:32:02,,1,多年补差价,,,1763447333680,2,入门版,800,续约入门版2年,400.0,CHS202510070310394,盛发汽车维修保养中心,2025-11-18 00:00:00,2025-10-07 00:00:00,2025-11-18 00:00:00,新签,滨海凯盛汽修养护中心,15997329870740811799,,,,是,800.0,800.0,杜浩,肖军,江苏,杜浩,肖军,江苏,1.0,1.0,霍创业,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+45,691e88e504528289184a3d00,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-20 11:20:05,2025-12-25 15:32:02,,1,多年补差价,,,1763522175031,2,基础版,2300,续约基础版-门店管理系统2年,1150.0,CHS202511190313271,徐宝行汽车服务有限公司,2025-11-19 00:00:00,2025-11-19 00:00:00,2025-11-19 00:00:00,新签,徐州徐宝行汽车服务有限公司,16012883105031421976,,,,是,2300.0,2300.0,赵涛,肖军,江苏,赵涛,肖军,江苏,1.0,1.0,霍创业,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+46,691fdb3878dc061019100add,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-21 11:23:36,2025-12-25 15:32:02,,1,多年补差价,,,1763619899692,2,入门版,700,续约入门版2年,350.0,CHS202510230311351,梵远汽车服务,2025-11-20 00:00:00,2025-10-23 00:00:00,2025-11-20 00:00:00,新签,南京市雨花台区梵远汽车配件销售中心,16003167838416171074,,,,是,700.0,700.0,杜浩,肖军,江苏,杜浩,肖军,江苏,1.0,1.0,霍创业,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+47,6927e49b21b02c48ca7bb1f5,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-27 13:41:47,2025-12-25 15:32:02,,1,多年补差价,,,1764149126832,2,进阶版,1799,续约进阶版2年,899.5,CHS202511080312789,鼎鹏汽车服务中心,2025-11-27 00:00:00,2025-11-08 00:00:00,2025-11-27 00:00:00,新签,贵阳市云岩区鼎鹏汽车养护中心,16008912692744061003,,,,是,1799.0,1799.0,熊斌,肖军,西南,熊斌,肖军,西南,1.0,1.0,吴间锐,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+48,6928ffbe82767255aabb44be,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-28 09:49:50,2025-12-25 15:32:02,,1,品牌方协作,电子目录,,,,,18000,,,,,,,2025-11-27 00:00:00,新签,,,,,,是,9000.0,18000.0,梁柱,张凯,华南沪,,,,0.5,1.0,黄宗祥,新增,,[],,,,,,无,电子目录,电子目录,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+49,6928fff076d8fe5abdd61266,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-28 09:50:40,2025-12-25 15:32:02,,1,新签超3年,,,1762590806733,2,基础版,1600,续约基础版-门店管理系统2年,800.0,CHS202511080312804,华营昌检车线店,2025-11-09 00:00:00,2025-11-08 00:00:00,2025-11-09 00:00:00,新签,阳泉华营昌汽修连锁,10907434497378123878,,,,是,1600.0,1600.0,刘剑桥,关磊,华北,刘剑桥,关磊,华北,1.0,1.0,杨挺,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+50,692900560e8ab9d6604193f2,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-28 09:52:22,2025-12-25 15:32:02,,1,品牌方协作,电子目录,,,,,5000,,,,,,,2025-11-07 00:00:00,新签,,,,,,是,2500.0,5000.0,赵旭伟,肖军,江苏,,,,0.5,1.0,陈博,新增,,[],,,,,,无,电子目录,电子目录,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+51,6929055f9e94d391e9cd53dd,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-11-28 10:13:51,2025-12-25 15:32:02,,1,新签超3年,,,1762739543304,2,入门版,1200,续约入门版2年,600.0,CHS202511090312841,小欢汽车维修,2025-11-10 00:00:00,2025-11-10 00:00:00,2025-11-10 00:00:00,新签,义县稍户营子镇小欢汽车维修处（个体工商户）,16009461720019927075,,,,是,1200.0,1200.0,韩皞,陈庆伟,东北,韩皞,陈庆伟,东北,1.0,1.0,孙旭亮,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+52,692cfbd8da792279e268feef,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-01 10:22:16,2025-12-25 15:32:02,,1,多年补差价,,,1764382941584,2,入门版,800,续约入门版2年,400.0,CHS202510170310927,盐城市大丰区辰煜汽车服务有限公司,2025-11-29 00:00:00,2025-10-18 00:00:00,2025-11-29 00:00:00,新签,盐城市大丰区辰煜汽车服务有限公司,10546443563782178538,,,,是,800.0,800.0,杜浩,肖军,江苏,杜浩,肖军,江苏,1.0,1.0,霍创业,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+53,692cfbe7ff1b578004611ddc,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-01 10:22:31,2025-12-25 15:32:02,,1,新签超3年,,,1764333612264,2,标准版,3600,续约标准版-门店管理系统2年,1800.0,CHS202511280313670,厦门市鑫车宝汽车服务有限公司,2025-11-29 00:00:00,2025-11-28 00:00:00,2025-11-29 00:00:00,新签,厦门市鑫车宝汽车服务有限公司,16016206505594359823,,,,是,3600.0,3600.0,郭锦城,张凯,华南沪,郭锦城,张凯,华南沪,1.0,1.0,周聪,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+54,692cfc00bc219a1741c27ffc,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-01 10:22:56,2025-12-25 15:32:02,,1,多年补差价,,,1764502026835,2,进阶版,2001,续约进阶版2年,1000.5,CHS202511250313539,天门市小拇指汽车维修服务有限公司,2025-12-01 00:00:00,2025-11-26 00:00:00,2025-12-01 00:00:00,新签,天门市小拇指汽车维修服务有限公司,16015175963579027532,,,,是,2001.0,2001.0,陈煜,景东强,华中,陈煜,景东强,华中,1.0,1.0,刘光春,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+55,6938df09cc655df92928b5b3,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-10 10:46:33,2025-12-25 15:32:02,,1,品牌方协作,电子目录,,,,,8000,,,,,,,2025-12-10 00:00:00,新签,,,8000,,2025-12-01 00:00:00,是,4000.0,8000.0,孙旭亮,陈庆伟,东北,,,,0.5,1.0,孙旭亮,新增,,[],,,,,,无,电子目录,电子目录,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+56,69391bbdcdfe09bce4c98cd7,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-10 15:05:33,2025-12-25 15:32:02,,1,新签超3年,,,1764473175419,2,进阶版,2000,续约进阶版2年,1000.0,CHS202511300313735,唐山市路南新腾云汽车维修服务站,2025-11-30 00:00:00,2025-11-30 00:00:00,2025-11-30 00:00:00,新签,唐山市路南新腾云汽车维修服务站,16016880445115371607,,,,是,2000.0,2000.0,王鑫,关磊,华北,王鑫,关磊,华北,1.0,1.0,杨挺,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+57,693b7c9a25d3e9c841729ed3,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-12 10:23:22,2025-12-25 15:32:02,,1,新签超3年,,,1765420934693,2,入门版,1000,续约入门版2年,500.0,CHS202512110314232,西昌安宁美车度汽修,2025-12-11 00:00:00,2025-12-11 00:00:00,2025-12-11 00:00:00,新签,西昌安宁美车度汽车喷漆中心,16020868221515104344,,,,是,1000.0,1000.0,杨君毅,肖军,西南,杨君毅,肖军,西南,1.0,1.0,吴间锐,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+58,693f6a268691ed316215299b,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-15 09:53:42,2025-12-25 15:32:02,,1,跨区新签,,,1765158079178,3,基础版,3000,基础版-门店管理系统3年,1000.0,CHS202512080314089,鹊大师（大同店）,2025-12-08 00:00:00,2025-12-08 00:00:00,2025-12-08 00:00:00,新签,广之源汽车一站式服务中心,16000651004727029792,,,,是,1500.0,1500.0,张宏伟,关磊,华北,韩皞,关磊,东北,0.5,0.5,杨挺,拆单,,[],,0.135,,,0.135,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+59,693f6a41b051e5517a47eed5,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-15 09:54:09,2025-12-25 15:32:02,,1,跨区新签,,,1765358368741,3,至尊版,11997,至尊版-门店管理系统3年,3999.0,CHS202512100314217,鞍山尊荣万顺汽车销售有限公司,2025-12-10 00:00:00,2025-12-10 00:00:00,2025-12-10 00:00:00,新签,鞍山尊荣万顺汽车销售有限公司,16020612536361586766,,,,是,5998.5,5998.5,宋小涛,陈庆伟,东北,刘磊,陈庆伟,华北,0.5,0.5,孙旭亮,拆单,,[],,0.135,,,0.135,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+60,693f6a522b2bc18aef5bec96,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-15 09:54:26,2025-12-25 15:32:02,,1,多年补差价,,,1765259877744,2,进阶版,2500,续约进阶版2年,1250.0,CHS202509170309603,平湖市万里汽车大修厂,2025-12-09 00:00:00,2025-09-17 00:00:00,2025-12-09 00:00:00,新签,平湖市万里汽车大修厂,15990048390801023028,,,,是,2500.0,2500.0,王有军,陈庆伟,浙皖,王有军,陈庆伟,浙皖,1.0,1.0,魏子淇,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+61,6948aa20b9b289e0b6b0a8d1,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-22 10:17:04,2025-12-25 15:32:02,,1,新签超3年,,,1766292766298,2,入门版,800,续约入门版2年,400.0,CHS202512210314689,博晏爱玩车维修中心,2025-12-21 00:00:00,2025-12-21 00:00:00,2025-12-21 00:00:00,新签,义县稍户营子镇博晏爱玩车汽车服务维修中心,16024514498417168447,,,,是,800.0,800.0,韩皞,陈庆伟,东北,韩皞,陈庆伟,东北,1.0,1.0,孙旭亮,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+62,6948abbc111dbba0cb0d4fd9,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-22 10:23:56,2025-12-25 15:32:02,,1,多年补差价,,,1766125609196,2,至尊版,8999,续约至尊版-门店管理系统2年,4499.5,CHS202506260302952,烟台福泰汽车服务有限公司,2025-12-19 00:00:00,2025-06-26 00:00:00,2025-12-19 00:00:00,新签,福泰汽车服务有限公司,11240984669918394572,,,,是,8999.0,8999.0,宗川涵,关磊,山东,宗川涵,关磊,山东,1.0,1.0,王斌,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+63,6949f9571fc4fc20b158497f,"{'name': '陈庆伟', 'username': '025366033037741985', 'status': 1, 'type': 0, 'departments': [122311528, 122229573], 'integrate_id': '025366033037741985'}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-23 10:07:19,2025-12-30 17:35:22,,1,跨区新签,,该单是刘磊跨区浙江的客户，王蔚东培训的，关磊已经让小六把业绩和提成都给王蔚东了，100%算王蔚东的,1765263063272,1,基础版,1199,基础版-门店管理系统,1199.0,CHS202512090314165,舟山市于瑞汽车修理有限公司,2025-12-09 00:00:00,2025-12-09 00:00:00,2025-12-09 00:00:00,新签,舟山市于瑞汽车修理有限公司,16020236055869423684,1199,,2025-12-01 00:00:00,否,599.5,599.5,刘磊,关磊,华北,刘磊,陈庆伟,华北,0.5,0.5,魏子淇,拆单,,[],,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+64,694a09358daf16de8df90b12,"{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-23 11:15:01,2025-12-25 15:32:02,,1,新签超3年,,,1766389436606,1,旗舰版,2125,续约旗舰版-门店管理系统,2125.0,CHS202512220314748,PM汽车服务,2025-12-22 00:00:00,2025-12-22 00:00:00,2025-12-22 00:00:00,新签,PM汽车服务,16024929481844097081,2125,,2025-12-01 00:00:00,是,2125.0,2125.0,杨旭,关磊,山东,杨旭,关磊,山东,1.0,1.0,王斌,新增,,[],,0.0,,,0.0,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+65,694b80373f7c91e02f438916,"{'name': '张凯', 'username': '1525590028775887', 'status': 1, 'type': 0, 'departments': [122283546, 127595486, 122514491], 'integrate_id': '1525590028775887'}","{'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}",,2025-12-24 13:55:03,2025-12-25 15:32:02,,1,多年补差价,,新签一个月补1599两年，共计3198三年基础版,1766474568560,2,基础版,1599,续约基础版-门店管理系统2年,799.5,CHS202512070314056,贺州市德宝汽车养护有限公司,2025-12-23 00:00:00,2025-12-07 00:00:00,2025-12-23 00:00:00,新签,贺州市德宝汽车养护有限公司,16019465729355059235,1599,黄宗祥,2025-12-01 00:00:00,是,1599.0,1599.0,黄宗祥,张凯,华南沪,黄宗祥,张凯,华南沪,1.0,1.0,黄宗祥,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+66,694cfd1d990c750feb7392f8,"{'name': '景东强', 'username': '232229053125844557', 'status': 1, 'type': 0, 'departments': [122472424, 122503482], 'integrate_id': '232229053125844557'}","{'name': '金鹏', 'username': '02545960101197726', 'status': 1, 'type': 0, 'departments': [448339551], 'integrate_id': '02545960101197726'}",,2025-12-25 17:00:13,2025-12-26 09:45:45,,1,多年补差价,,此客户约定进阶版3年3899元；11月8号付款一年进阶版1999元，12月25日补尾款1900元。胡冰区域,1766652296888,2,进阶版,1900,续约进阶版2年,950.0,CHS202511080312811,宜春晴天汽车服务有限公司,2025-12-25 00:00:00,2025-11-08 00:00:00,2025-12-25 00:00:00,新签,宜春晴天汽车服务有限公司,16009000270293930057,1900,胡冰,2025-12-01 00:00:00,是,1900.0,1900.0,胡冰,景东强,华中,胡冰,景东强,华中,1.0,1.0,金华斌,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+67,69536f0a92ee436c9af5e238,"{'name': '肖军', 'username': '311003461041349', 'status': 1, 'type': 0, 'departments': [122314630, 122323520], 'integrate_id': '311003461041349'}","{'name': '金鹏', 'username': '02545960101197726', 'status': 1, 'type': 0, 'departments': [448339551], 'integrate_id': '02545960101197726'}",,2025-12-30 14:19:54,2025-12-31 10:05:48,,1,多年补差价,,,1767075482440,2,基础版,1101,续约基础版-门店管理系统2年,550.5,CHS202511170313151,成都鑫城南汽车,2025-12-30 00:00:00,2025-11-17 00:00:00,2025-12-30 00:00:00,新签,成都鑫城南汽车,16012170912208031813,1101,陈致欣,2025-12-01 00:00:00,是,1101.0,1101.0,陈致欣,肖军,西南,陈致欣,肖军,西南,1.0,1.0,吴间锐,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+68,6953889d92e8e1b524429a46,"{'name': '景东强', 'username': '232229053125844557', 'status': 1, 'type': 0, 'departments': [122472424, 122503482], 'integrate_id': '232229053125844557'}","{'name': '金鹏', 'username': '02545960101197726', 'status': 1, 'type': 0, 'departments': [448339551], 'integrate_id': '02545960101197726'}",,2025-12-30 16:09:01,2025-12-30 16:15:23,,1,跨区新签,,此客户在孙婷婷区域，但由于客户单店体量较大，于是让陈煜介入洽谈，故业绩陈煜一半9000元。孙婷婷一半9000元。,1765503409986,3,至尊版,17997,至尊版-门店管理系统3年,5999.0,CHS202512120314279,武汉酷卡驰汽车科技有限公司,2025-12-12 00:00:00,2025-12-12 00:00:00,2025-12-12 00:00:00,新签,武汉酷卡驰汽车科技有限公司,16021219284734738438,18000,陈煜,2025-12-01 00:00:00,是,8998.5,8998.5,孙婷婷,景东强,华中,陈煜,景东强,华中,0.5,0.5,刘光春,拆单,,[],,0.135,,,0.135,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6
+69,6953bd5a65e053d760df4b80,"{'name': '陈庆伟', 'username': '025366033037741985', 'status': 1, 'type': 0, 'departments': [122311528, 122229573], 'integrate_id': '025366033037741985'}","{'name': '金鹏', 'username': '02545960101197726', 'status': 1, 'type': 0, 'departments': [448339551], 'integrate_id': '02545960101197726'}",,2025-12-30 19:54:02,2025-12-31 09:04:54,,1,多年补差价,,新签3个月内补差价，多年购,1766986879209001,2,进阶版,1600,续约进阶版2年,800.0,CHS202510120310664,鹤岗市南山区鑫宏海中外汽车维修站,2025-12-29 00:00:00,2025-10-12 00:00:00,2025-12-29 00:00:00,新签,鹤岗市南山区鑫宏海中外汽车维修站,15999259585890246734,1600,韩皞,2025-12-01 00:00:00,是,1600.0,1600.0,韩皞,陈庆伟,东北,韩皞,陈庆伟,东北,1.0,1.0,刘立,新增,,[],,0.1,,,0.1,无,新签,当月新签开户,是,66b9678280b37f8a276b1d01,68886b7c0382a7249ae0b5d6

test/非标业绩提报根据拆分做复制.py

@@ -0,0 +1,326 @@
+# -*- coding: utf-8 -*-
+import pandas as pd
+import datetime
+from config import Config
+from api import API
+import pymysql  # 使用 pymysql 替代 mysql.connector
+from back_ground_module import CommonModule
+import os
+import mysql.connector
+import pandas as pd
+import json
+import numpy as np
+import mysql.connector
+from mysql.connector import Error
+from log_config import configure_task_logger, configure_error_task_logger
+
+logger = configure_task_logger()
+error_task_logger = configure_error_task_logger()
+api_instance = API()
+common_module = CommonModule()
+output_dir = "output"  # 设置输出目录
+os.makedirs(output_dir, exist_ok=True)
+
+
+class NonStandardPerformanceToBI:
+    """ 非标业绩提报转BI"""
+    def __init__(self):
+        self.dealer_service_data = None
+        self.field_mapping = {
+            "报备类型": "_widget_1753770875899",
+            "协作内容": "_widget_1753770875915",
+            "情况说明": "_widget_1753770875944",
+            "订单编号": "_widget_1753770875887",
+            "实付金额": "_widget_1753770875889",
+            "门店编码": "_widget_1753770875890",
+            "门店名称": "_widget_1753770875888",
+            "版本": "_widget_1753770875891",
+            "年限": "_widget_1753948745953",
+            "支付日期": "_widget_1753770875893",
+            "开户/处理日期": "_widget_1753770875894",
+            "小六业绩金额": "_widget_1753770875898",
+            "区域业绩金额": "_widget_1753770875937",
+            "报备业绩归属区域经理": "_widget_1753770875903",
+            "报备业绩归属大区": "_widget_1753866196486",
+            "原业绩归属人": "_widget_1753856032683",
+            "原业绩归属区域经理": "_widget_1753866196485",
+            "小六业绩比例": "_widget_1753770875917",
+            "区域业绩比例": "_widget_1753770875921",
+            "运营专家": "_widget_1753770875902",
+            "提成类型": "_widget_1753778922504",
+            "SaaS新签提成比例": "_widget_1753770875949",
+            "服务包提成比例": "_widget_1753778922567",
+            "提成金额": "_widget_1753770875948",
+            "新签提成比例-首年": "_widget_1753778922503",
+            "新签提成比例-非首年": "_widget_1753778922548",
+            "新签阶段及提成比例": "_widget_1753778656359",
+            "业绩动作":"_widget_1756708722933",
+            "提成动作":"_widget_1756708722932",
+            "新签阶段及提成比例.选择提成阶段": "_widget_1753778656359._widget_1753778656361",
+            "新签阶段及提成比例.新签阶段": "_widget_1753778656359._widget_1753948745962",
+            "新签阶段及提成比例.提成比例": "_widget_1753778656359._widget_1753778656362",
+            "业绩类型":"_widget_1753770875966",
+            "报备业绩归属小六":"_widget_1753770875901",
+            "原业绩归属大区":"_widget_1755159216098",
+            "业绩分类":"_widget_1758706882564",
+            "流程是否结束":"_widget_1761633418013",
+            "业绩类型-聚合":"_widget_1758706882564",
+            "业绩分组":"_widget_1762417447169",
+            "商品名称":"_widget_1762219744898",
+            "履约金额":"_widget_1762220516367",
+            "业绩归属日期":"_widget_1762417447127",
+            "公司名称":"_widget_1762420723743",
+            "公司ID":"_widget_1762420723744",
+            "报备业绩金额-区域提交":"_widget_1766375035236",
+            "业绩归属小六-区域提交":"_widget_1766461143813",
+            "业绩归属月":"_widget_1766375035265",
+            "是否同步衡石":"_widget_1766484337844",
+            "提交人": "creator",
+            "提交时间": "createTime",
+            "更新时间": "updateTime"
+        }
+
+        # 定义需要特殊处理的列表字段及其内部字段映射
+        self.list_fields_config = {
+            "新签阶段及提成比例": {
+                "_widget_1753778656361": "选择提成阶段",
+                "_widget_1753948745962": "新签阶段",
+                "_widget_1753778656362": "提成比例"
+            },
+            # 可以在这里添加其他列表字段的配置
+            # "另一个列表字段": {
+            #     "原始字段名1": "映射后字段名1",
+            #     "原始字段名2": "映射后字段名2"
+            # }
+        }
+
+    def load_all_data(self):
+        # 获取非标业绩提报数据
+        payload = {"api_key": "66b9678280b37f8a276b1d01",
+                   "entry_id": "68886b7c0382a7249ae0b5d6",
+                   }
+        dealer_service = api_instance.entry_data_list(payload)
+        self.dealer_service_data = dealer_service.get("data")  # api请求格式，将数据封装在data字典里
+
+    def process_list_field(self, field_value, field_config):
+        """通用方法：处理列表类型的字段"""
+        if not isinstance(field_value, (list, np.ndarray)):
+            return field_value
+
+        processed_list = []
+        for item in field_value:
+            if not isinstance(item, dict):
+                processed_list.append(item)
+                continue
+
+            processed_item = {}
+            for original_key, mapped_key in field_config.items():
+                if original_key in item:
+                    # 处理包含id的字典字段
+                    if isinstance(item[original_key], dict) and "id" in item[original_key]:
+                        processed_item[mapped_key] = item[original_key]["id"]
+                    else:
+                        processed_item[mapped_key] = item[original_key]
+                else:
+                    processed_item[mapped_key] = None
+            processed_list.append(processed_item)
+        return processed_list
+
+    def data_process(self):
+        df = pd.DataFrame(self.dealer_service_data)
+        # 反转映射字典
+        reverse_mapping = {v: k for k, v in self.field_mapping.items()}
+        # 1.列明替换
+        df.columns = [reverse_mapping.get(col, col) for col in df.columns]
+
+        # 只保留流程是否结束为是的内容
+        df = df[df["流程是否结束"] == "是"]
+
+        # 2.成员字段取值
+        user_columns = ["报备业绩归属小六", "报备业绩归属区域经理", "原业绩归属人", "原业绩归属区域经理", "运营专家","业绩归属小六-区域提交"]
+
+        for col in user_columns:
+            df[col] = df[col].map(lambda x: x.get("name", "") if isinstance(x, dict) else "")
+
+        # 3.日期字段转为北京时间
+        time_columns = ["支付日期", "开户/处理日期", "提交时间", "更新时间", "业绩归属月", "业绩归属日期"]
+
+        for col in time_columns:
+            # 1. 解析为 datetime，并明确指定为 UTC（即使原始字符串无时区）
+            dt_utc = pd.to_datetime(df[col], errors='coerce', utc=True)
+
+            # 2. 转换为北京时间
+            dt_beijing = dt_utc.dt.tz_convert('Asia/Shanghai')
+
+            # 3. 去掉时区信息（变成 naive datetime），然后格式化为字符串
+            df[col] = dt_beijing.dt.tz_localize(None).dt.strftime('%Y-%m-%d %H:%M:%S')
+
+        # 4.业绩动作等于拆单做复制
+
+        # 4.1. 定义条件
+        mask = df['业绩动作'] == '拆单'
+
+        # 4.2. 复制满足条件的行
+        new_rows = df[mask].copy()  # ⚠️ 一定要用 .copy() 避免 SettingWithCopyWarning
+
+        # 3. 修改新行中的某些列
+        new_rows['小六业绩金额'] = -new_rows['小六业绩金额']
+        new_rows['区域业绩金额'] = -new_rows['区域业绩金额']
+        new_rows['报备业绩归属小六'] = new_rows['原业绩归属人']
+        new_rows['报备业绩归属区域经理'] = new_rows['原业绩归属区域经理']
+        new_rows['报备业绩归属大区'] = new_rows['原业绩归属大区']
+
+        # 4. 合并回原 DataFrame
+        df = pd.concat([df, new_rows], ignore_index=True)
+
+        # 5.处理所有配置的列表字段
+        if "新签阶段及提成比例" in df.columns:
+            # 先处理订单登记表字段
+            df["新签阶段及提成比例"] = df["新签阶段及提成比例"].apply(
+                lambda x: self.process_list_field(x, self.list_fields_config["新签阶段及提成比例"])
+                if x is not None and (isinstance(x, (list, dict, np.ndarray)) or not pd.isna(x))
+                else None
+            )
+
+            # 拆分行
+            df_exploded = df.explode("新签阶段及提成比例")
+
+            # 将订单登记表中的字段提取到主表中
+            order_fields = self.list_fields_config["新签阶段及提成比例"].values()
+            for field in order_fields:
+                df_exploded[field] = df_exploded["新签阶段及提成比例"].apply(
+                    lambda x: x.get(field) if isinstance(x, dict) else None
+                )
+
+            # 删除原始的订单登记表列
+            df_exploded = df_exploded.drop(columns=["新签阶段及提成比例"])
+
+            # 重置索引
+            df = df_exploded.reset_index(drop=True)
+
+        return df
+
+    def write_to_bi(self, df):
+        # 数据库连接信息
+        HS_DB_Config = Config.HS_DB_Config
+        table_name = "non_standard_performance_to_BI"  # 替换为你的实际表名
+
+        # 建立数据库连接
+        connection = mysql.connector.connect(
+            host=HS_DB_Config["host"],
+            user=HS_DB_Config["user"],
+            password=HS_DB_Config["password"],
+            database=HS_DB_Config["database"]
+        )
+        cursor = connection.cursor()
+
+        try:
+            # 查询表列名
+            cursor.execute(f"SHOW COLUMNS FROM {table_name}")
+            columns_info = cursor.fetchall()
+            db_columns = [col[0] for col in columns_info]  # 提取列名
+            df = df.replace([None, np.nan, pd.NA, 'nan', 'NaN', 'NAN', ''], None)
+            # 保留 DataFrame 中与数据库列名匹配的列
+            filtered_df = df[df.columns.intersection(db_columns)]
+
+            # 如果没有匹配的列，直接返回
+            if filtered_df.empty:
+                print("DataFrame 中没有与数据库表结构匹配的列。")
+                return
+
+            # 筛选列之后，插入前处理 dict 类型
+            filtered_df = filtered_df.copy()
+            for col in filtered_df.columns:
+                if filtered_df[col].apply(lambda x: isinstance(x, (dict, list)) if x is not None else False).any():
+                    filtered_df.loc[:, col] = filtered_df[col].apply(
+                        lambda x: json.dumps(x, ensure_ascii=False) if x is not None else x
+                    )
+
+            # 构建插入语句
+            placeholders = ', '.join(['%s'] * len(filtered_df.columns))
+            # 使用反引号避免特殊列明
+            columns = ', '.join([f"`{col}`" for col in filtered_df.columns])
+            insert_sql = f"INSERT INTO {table_name} ({columns}) VALUES ({placeholders})"
+
+            # 将 DataFrame 写入数据库
+            for _, row in filtered_df.iterrows():
+                cursor.execute(insert_sql, tuple(row))
+
+            connection.commit()
+            logger.info(f"成功写入 {len(filtered_df)} 条记录到 {table_name} 表中。")
+
+        except Exception as e:
+            error_task_logger.error(f"写入数据库时发生错误: {e}")
+            connection.rollback()
+        finally:
+            cursor.close()
+            connection.close()
+
+    def clear_table_data(self):
+        """
+        清空指定 MySQL 表的数据。
+        参数已写死在函数内部，直接调用即可。
+        """
+        # 数据库连接信息
+        HS_DB_Config = {
+            'host': "f6-public.rwlb.rds.aliyuncs.com",
+            'user': "rw_operation_data_relay",
+            'password': "m+q5Z4%IVuF9bf",
+            'database': "f6operation_data_relay"
+        }
+        table_name = "non_standard_performance_to_BI"  # 要清空的表名
+
+        connection = None
+        try:
+            # 建立数据库连接
+            connection = mysql.connector.connect(
+                host=HS_DB_Config["host"],
+                user=HS_DB_Config["user"],
+                password=HS_DB_Config["password"],
+                database=HS_DB_Config["database"]
+            )
+            if connection.is_connected():
+                cursor = connection.cursor()
+
+                # 使用TRUNCATE清空表数据
+                cursor.execute(f"TRUNCATE TABLE {table_name}")
+                connection.commit()
+
+                logger.info(f"成功清空表 {table_name} 中的所有数据")
+
+        except Error as e:
+            error_task_logger.error(f"清空表时发生错误: {e}")
+            if connection and connection.is_connected():
+                connection.rollback()
+        finally:
+            if connection and connection.is_connected():
+                cursor.close()
+                connection.close()
+                logger.info("数据库连接已关闭")
+
+    def main(self):
+        task_start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        try:
+            logger.info("任务开始")
+            # step1: 获取数据
+            self.load_all_data()
+            logger.info("加载数据完成")
+            # step2:数据处理
+            df = self.data_process()
+            # df.to_csv(os.path.join(output_dir, "new_dealer_service_order_to_bi.csv"))
+            logger.info("数据处理完成")
+            # step3:数据库删除
+            self.clear_table_data()
+            logger.info("目标数据库已清空")
+            # step4:数据写入BI
+            self.write_to_bi(df)
+            logger.info("数据已写入数据库中")
+            common_module.send_task_status(task_start_time, "非标业绩提报转BI")
+        except Exception as e:
+            error_task_logger.error(f"非标业绩提报转BI发生错误{e}")
+            common_module.send_task_error(task_start_time,"非标业绩提报转BI", str(e))
+
+
+if __name__ == '__main__':
+    start = NonStandardPerformanceToBI()
+    start.main()

tools/BI.ipynb

@@ -237,8 +237,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-12-25T06:47:43.233398500Z",
-     "start_time": "2025-12-25T06:47:42.837753600Z"
+     "end_time": "2026-01-06T08:14:03.891128500Z",
+     "start_time": "2026-01-06T08:14:03.535415Z"
     }
    },
    "cell_type": "code",
@@ -254,10 +254,10 @@
     "    }  # 衡时数据库链接配置-mysql\n",
     "# table_name = \"new_dealer_service_order_to_bi\"  # 替换为你的实际表名\n",
     "\n",
-    "table_name = \"partner_settlement_to_BI\"\n",
-    "column_name = \"是否同步\"\n",
-    "new_column_type = \"VARCHAR(255)\"  # 目标数据类型\n",
-    "# new_column_type = \"DATETIME\"  # 目标数据类型\n",
+    "table_name = \"f6_denominator_adjustment\"\n",
+    "column_name = \"归属月份\"\n",
+    "# new_column_type = \"VARCHAR(255)\"  # 目标数据类型\n",
+    "new_column_type = \"DATETIME\"  # 目标数据类型\n",
     "\n",
     "try:\n",
     "    # 连接数据库\n",
@@ -316,12 +316,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✅ 成功添加字段: `是否同步`\n",
+      "✅ 成功添加字段: `归属月份`\n",
       "数据库连接已关闭\n"
      ]
     }
    ],
-   "execution_count": 24
+   "execution_count": 1
   },
   {
    "metadata": {},
@@ -421,8 +421,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-11T01:39:55.579785Z",
-     "start_time": "2025-06-11T01:39:55.295469Z"
+     "end_time": "2026-04-03T09:13:22.881255Z",
+     "start_time": "2026-04-03T09:13:20.171270200Z"
     }
    },
    "cell_type": "code",
@@ -432,31 +432,31 @@
     "from mysql.connector import Error\n",
     "\n",
     "# 数据库连接信息\n",
-    "host = \"rm-uf6r230vbtxf5gdz63o.mysql.rds.aliyuncs.com\"\n",
-    "user = \"rw_operation_data_relay\"\n",
-    "password = \"m+q5Z4%IVuF9bf\"\n",
-    "database = \"f6operation_data_relay\"\n",
-    "table_name = \"yida_process_time_statistics\"  # 要操作的表名\n",
+    "BI_CONN_host = \"f6-public.rwlb.rds.aliyuncs.com\"\n",
+    "BI_CONN_INFO_database = \"f6operation_data_relay\"\n",
+    "BI_CONN_INFO_user = \"rw_operation_data_relay\"\n",
+    "BI_CONN_INFO_password = \"m+q5Z4%IVuF9bf\"\n",
+    "table_name = \"gp_monthly_renewal_rate_new\"  # 要操作的表名\n",
     "# table_name = \"thailand_store_data_email\" # 要操作的表名\n",
     "min_id_to_delete = 127821  # 要删除的最小ID值\n",
     "\n",
     "# 连接数据库\n",
     "try:\n",
     "    connection = mysql.connector.connect(\n",
-    "        host=host,\n",
-    "        user=user,\n",
-    "        password=password,\n",
-    "        database=database\n",
+    "        host=BI_CONN_host,\n",
+    "        user=BI_CONN_INFO_user,\n",
+    "        password=BI_CONN_INFO_password,\n",
+    "        database=BI_CONN_INFO_database\n",
     "    )\n",
     "\n",
     "    if connection.is_connected():\n",
     "        cursor = connection.cursor()\n",
     "\n",
     "        # 使用DELETE删除ID大于等于127821的数据\n",
-    "        cursor.execute(f\"DELETE FROM {table_name} WHERE id >= {min_id_to_delete}\")\n",
+    "        # cursor.execute(f\"DELETE FROM {table_name} WHERE id >= {min_id_to_delete}\")\n",
+    "        cursor.execute(f\"DELETE FROM GP_monthly_renewal_rate_new WHERE 月分区（仅用于存储每月最后一天截至数据） = '202603';\")\n",
     "\n",
     "        connection.commit()\n",
-    "        print(f\"成功删除表 {table_name} 中ID大于等于{min_id_to_delete}的所有数据\")\n",
     "\n",
     "except Error as e:\n",
     "    print(f\"删除数据时发生错误: {e}\")\n",
@@ -474,12 +474,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "成功删除表 yida_process_time_statistics 中ID大于等于127821的所有数据\n",
       "数据库连接已关闭\n"
      ]
     }
    ],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "metadata": {},
@@ -552,8 +551,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-21T02:06:22.956060Z",
-     "start_time": "2025-08-21T02:06:22.797879Z"
+     "end_time": "2026-01-13T03:20:16.053485200Z",
+     "start_time": "2026-01-13T03:20:15.845522200Z"
     }
    },
    "cell_type": "code",
@@ -578,7 +577,7 @@
     "}  # 衡时数据库链接配置-mysql\n",
     "\n",
     "# 表名\n",
-    "table_name = \"test\"  # 请替换为实际的表名\n",
+    "table_name = \"jdy_ngv_data_source\"  # 请替换为实际的表名\n",
     "\n",
     "# 连接数据库\n",
     "connection = mysql.connector.connect(\n",
@@ -606,7 +605,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "成功删除表 test\n"
+      "成功删除表 jdy_ngv_data_source\n"
      ]
     }
    ],

tools/保存common_module数据库链接内容.py

@@ -25,6 +25,7 @@ def save_data_yichang_to_csv():
         # data_yichang_S = common_module.get_yichang_details(days_back=1)
         data_yichang_S = common_module.get_ngv_details(days_back=1) # ngv
 
+
         if data_yichang_S is None or data_yichang_S.empty:
             print("未获取到数据或数据为空")
             return
@@ -34,7 +35,7 @@ def save_data_yichang_to_csv():
         
         # 生成文件名（包含日期）
         current_date = datetime.datetime.now().strftime("%Y%m%d")
-        filename = f"data_yichang_{current_date}.csv"
+        filename = f"data_NGV_{current_date}.csv"
         filepath = os.path.join(output_dir, filename)
         
         # 保存到CSV

tools/月初3天删除分子分母表数据.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

tools/重复派发删除.ipynb

@@ -12,8 +12,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-07-17T08:03:51.919981Z",
-     "start_time": "2025-07-17T08:03:39.331174Z"
+     "end_time": "2026-02-25T05:45:52.998049100Z",
+     "start_time": "2026-02-25T05:45:49.628616100Z"
     }
    },
    "source": [
@@ -21,7 +21,7 @@
     "from api import API\n",
     "\n",
     "api_instance = API()\n",
-    "df = pd.read_excel(r\"C:\\Users\\Administrator.DESKTOP-7IC2USJ\\Downloads\\客户资料_20250801111301.xlsx\",sheet_name=\"id为空\")\n",
+    "df = pd.read_excel(r\"C:\\Users\\hp_z66\\Downloads\\客户资料_20260225113103.xlsx\",sheet_name=\"Sheet7\")\n",
     "for index, row in df.iterrows():\n",
     "    data_id = row[\"data_id\"]\n",
     "    payload = {\n",
@@ -33,37 +33,25 @@
    ],
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n"
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 5\u001B[39m\n\u001B[32m      2\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mapi\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m API\n\u001B[32m      4\u001B[39m api_instance = API()\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_excel\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43mr\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mC:\u001B[39;49m\u001B[33;43m\\\u001B[39;49m\u001B[33;43mUsers\u001B[39;49m\u001B[33;43m\\\u001B[39;49m\u001B[33;43mhp_z66\u001B[39;49m\u001B[33;43m\\\u001B[39;49m\u001B[33;43mDownloads\u001B[39;49m\u001B[33;43m\\\u001B[39;49m\u001B[33;43m客户资料_20260225113103.xlsx\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43msheet_name\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mSheet7\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      6\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m index, row \u001B[38;5;129;01min\u001B[39;00m df.iterrows():\n\u001B[32m      7\u001B[39m     data_id = row[\u001B[33m\"\u001B[39m\u001B[33mdata_id\u001B[39m\u001B[33m\"\u001B[39m]\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:495\u001B[39m, in \u001B[36mread_excel\u001B[39m\u001B[34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend, engine_kwargs)\u001B[39m\n\u001B[32m    493\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(io, ExcelFile):\n\u001B[32m    494\u001B[39m     should_close = \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m495\u001B[39m     io = \u001B[43mExcelFile\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    496\u001B[39m \u001B[43m        \u001B[49m\u001B[43mio\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    497\u001B[39m \u001B[43m        \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m=\u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    498\u001B[39m \u001B[43m        \u001B[49m\u001B[43mengine\u001B[49m\u001B[43m=\u001B[49m\u001B[43mengine\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    499\u001B[39m \u001B[43m        \u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    500\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    501\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m engine \u001B[38;5;129;01mand\u001B[39;00m engine != io.engine:\n\u001B[32m    502\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m    503\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mEngine should not be specified when passing \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    504\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33man ExcelFile - ExcelFile already has the engine set\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    505\u001B[39m     )\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1567\u001B[39m, in \u001B[36mExcelFile.__init__\u001B[39m\u001B[34m(self, path_or_buffer, engine, storage_options, engine_kwargs)\u001B[39m\n\u001B[32m   1564\u001B[39m \u001B[38;5;28mself\u001B[39m.engine = engine\n\u001B[32m   1565\u001B[39m \u001B[38;5;28mself\u001B[39m.storage_options = storage_options\n\u001B[32m-> \u001B[39m\u001B[32m1567\u001B[39m \u001B[38;5;28mself\u001B[39m._reader = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_engines\u001B[49m\u001B[43m[\u001B[49m\u001B[43mengine\u001B[49m\u001B[43m]\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1568\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_io\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1569\u001B[39m \u001B[43m    \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m=\u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1570\u001B[39m \u001B[43m    \u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1571\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:553\u001B[39m, in \u001B[36mOpenpyxlReader.__init__\u001B[39m\u001B[34m(self, filepath_or_buffer, storage_options, engine_kwargs)\u001B[39m\n\u001B[32m    541\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    542\u001B[39m \u001B[33;03mReader using openpyxl engine.\u001B[39;00m\n\u001B[32m    543\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    550\u001B[39m \u001B[33;03m    Arbitrary keyword arguments passed to excel engine.\u001B[39;00m\n\u001B[32m    551\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    552\u001B[39m import_optional_dependency(\u001B[33m\"\u001B[39m\u001B[33mopenpyxl\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m553\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[34;43m__init__\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[32m    554\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    555\u001B[39m \u001B[43m    \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m=\u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    556\u001B[39m \u001B[43m    \u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    557\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:573\u001B[39m, in \u001B[36mBaseExcelReader.__init__\u001B[39m\u001B[34m(self, filepath_or_buffer, storage_options, engine_kwargs)\u001B[39m\n\u001B[32m    571\u001B[39m \u001B[38;5;28mself\u001B[39m.handles.handle.seek(\u001B[32m0\u001B[39m)\n\u001B[32m    572\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m573\u001B[39m     \u001B[38;5;28mself\u001B[39m.book = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mload_workbook\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mhandles\u001B[49m\u001B[43m.\u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    574\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m:\n\u001B[32m    575\u001B[39m     \u001B[38;5;28mself\u001B[39m.close()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:572\u001B[39m, in \u001B[36mOpenpyxlReader.load_workbook\u001B[39m\u001B[34m(self, filepath_or_buffer, engine_kwargs)\u001B[39m\n\u001B[32m    568\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mopenpyxl\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m load_workbook\n\u001B[32m    570\u001B[39m default_kwargs = {\u001B[33m\"\u001B[39m\u001B[33mread_only\u001B[39m\u001B[33m\"\u001B[39m: \u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[33m\"\u001B[39m\u001B[33mdata_only\u001B[39m\u001B[33m\"\u001B[39m: \u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[33m\"\u001B[39m\u001B[33mkeep_links\u001B[39m\u001B[33m\"\u001B[39m: \u001B[38;5;28;01mFalse\u001B[39;00m}\n\u001B[32m--> \u001B[39m\u001B[32m572\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mload_workbook\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    573\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    574\u001B[39m \u001B[43m    \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdefault_kwargs\u001B[49m\u001B[43m \u001B[49m\u001B[43m|\u001B[49m\u001B[43m \u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    575\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\openpyxl\\reader\\excel.py:348\u001B[39m, in \u001B[36mload_workbook\u001B[39m\u001B[34m(filename, read_only, keep_vba, data_only, keep_links, rich_text)\u001B[39m\n\u001B[32m    318\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Open the given filename and return the workbook\u001B[39;00m\n\u001B[32m    319\u001B[39m \n\u001B[32m    320\u001B[39m \u001B[33;03m:param filename: the path to open or a file-like object\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    344\u001B[39m \n\u001B[32m    345\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    346\u001B[39m reader = ExcelReader(filename, read_only, keep_vba,\n\u001B[32m    347\u001B[39m                      data_only, keep_links, rich_text)\n\u001B[32m--> \u001B[39m\u001B[32m348\u001B[39m \u001B[43mreader\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    349\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m reader.wb\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\openpyxl\\reader\\excel.py:291\u001B[39m, in \u001B[36mExcelReader.read\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m    289\u001B[39m \u001B[38;5;28mself\u001B[39m.read_manifest()\n\u001B[32m    290\u001B[39m action = \u001B[33m\"\u001B[39m\u001B[33mread strings\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m291\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mread_strings\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    292\u001B[39m action = \u001B[33m\"\u001B[39m\u001B[33mread workbook\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    293\u001B[39m \u001B[38;5;28mself\u001B[39m.read_workbook()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\openpyxl\\reader\\excel.py:147\u001B[39m, in \u001B[36mExcelReader.read_strings\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m    145\u001B[39m strings_path = ct.PartName[\u001B[32m1\u001B[39m:]\n\u001B[32m    146\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m.archive.open(strings_path,) \u001B[38;5;28;01mas\u001B[39;00m src:\n\u001B[32m--> \u001B[39m\u001B[32m147\u001B[39m     \u001B[38;5;28mself\u001B[39m.shared_strings = \u001B[43mreader\u001B[49m\u001B[43m(\u001B[49m\u001B[43msrc\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\openpyxl\\reader\\strings.py:18\u001B[39m, in \u001B[36mread_string_table\u001B[39m\u001B[34m(xml_source)\u001B[39m\n\u001B[32m     16\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m _, node \u001B[38;5;129;01min\u001B[39;00m iterparse(xml_source):\n\u001B[32m     17\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m node.tag == STRING_TAG:\n\u001B[32m---> \u001B[39m\u001B[32m18\u001B[39m         text = \u001B[43mText\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfrom_tree\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnode\u001B[49m\u001B[43m)\u001B[49m.content\n\u001B[32m     19\u001B[39m         text = text.replace(\u001B[33m'\u001B[39m\u001B[33mx005F_\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m     20\u001B[39m         node.clear()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\openpyxl\\descriptors\\serialisable.py:74\u001B[39m, in \u001B[36mSerialisable.from_tree\u001B[39m\u001B[34m(cls, node)\u001B[39m\n\u001B[32m     71\u001B[39m     attrib[\u001B[33m\"\u001B[39m\u001B[33mattr_text\u001B[39m\u001B[33m\"\u001B[39m] = node.text\n\u001B[32m     73\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m el \u001B[38;5;129;01min\u001B[39;00m node:\n\u001B[32m---> \u001B[39m\u001B[32m74\u001B[39m     tag = \u001B[43mlocalname\u001B[49m\u001B[43m(\u001B[49m\u001B[43mel\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     75\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m tag \u001B[38;5;129;01min\u001B[39;00m KEYWORDS:\n\u001B[32m     76\u001B[39m         tag = \u001B[33m\"\u001B[39m\u001B[33m_\u001B[39m\u001B[33m\"\u001B[39m + tag\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mD:\\Program Files\\anaconda3\\envs\\SaaS\\Lib\\site-packages\\openpyxl\\xml\\functions.py:81\u001B[39m, in \u001B[36mlocalname\u001B[39m\u001B[34m(node)\u001B[39m\n\u001B[32m     79\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mcomment\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m     80\u001B[39m m = NS_REGEX.match(node.tag)\n\u001B[32m---> \u001B[39m\u001B[32m81\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mm\u001B[49m\u001B[43m.\u001B[49m\u001B[43mgroup\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mlocalname\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "\u001B[31mKeyboardInterrupt\u001B[39m: "
      ]
     }
    ],
@@ -1528,8 +1516,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-01T08:12:57.996465Z",
-     "start_time": "2025-08-01T08:10:21.860850Z"
+     "end_time": "2026-02-26T02:48:25.972319100Z",
+     "start_time": "2026-02-26T02:34:28.428344700Z"
     }
    },
    "cell_type": "code",
@@ -1538,7 +1526,7 @@
     "from api import API\n",
     "\n",
     "api_instance = API()\n",
-    "df = pd.read_excel(r\"C:\\Users\\Administrator.DESKTOP-7IC2USJ\\Downloads\\客户资料_20250801111301.xlsx\",sheet_name=\"id为空\")\n",
+    "df = pd.read_excel(r\"C:\\Users\\hp_z66\\Downloads\\客户资料_20260226092528.xlsx\",sheet_name=\"Sheet3\")\n",
     "for index, row in df.iterrows():\n",
     "    data_id = row[\"data_id\"]\n",
     "    payload = {\n",
@@ -1549,733 +1537,7 @@
     "    api_instance.entry_data_delete(payload)"
    ],
    "id": "8a06c19850c522b3",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n",
-      "返回结果: {'status': 'success'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "execution_count": 1
   },
   {
@@ -2317,6 +1579,39 @@
     }
    ],
    "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## 商机问题跟进表",
+   "id": "9e39df3c1ef0ccbc"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-31T06:24:52.996465800Z",
+     "start_time": "2026-03-31T06:21:23.287329300Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "from api import API\n",
+    "\n",
+    "api_instance = API()\n",
+    "df = pd.read_excel(r\"C:\\Users\\hp_z66\\Downloads\\商机问题跟进表_20260331114857.xlsx\",sheet_name=\"Sheet8\")\n",
+    "for index, row in df.iterrows():\n",
+    "    data_id = row[\"data_id\"]\n",
+    "    payload = {\n",
+    "        \"api_key\": \"675b900991ad2491c69389ca\",\n",
+    "        \"entry_id\": \"67f8b1d3307bad317abc3a9a\",\n",
+    "        \"data_id\": data_id,\n",
+    "    }\n",
+    "    api_instance.entry_data_delete(payload)"
+   ],
+   "id": "9982ace96792b53c",
+   "outputs": [],
+   "execution_count": 1
   }
  ],
  "metadata": {

yd_api.py

@@ -97,7 +97,6 @@ class YDAPI:
                 break
             try:
                 res = requests.get(api, headers=headers, params=formData)
-
                 return res.json()
             except (requests.exceptions.RequestException, Exception) as e:
                 print(f"请求出现异常: {e}, 正在重试({attempt + 1}/{max_retries})...")
@@ -155,7 +154,7 @@ class YDAPI:
                                  systemToken="XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2", instanceStatus="RUNNING",
                                  max_retries=10, delay=2, createFromTimeGMT=None, createToTimeGMT=None,
                                  modifiedFromTimeGMT=None,
-                                 modifiedToTimeGMT=None, searchFieldJson={}):
+                                 modifiedToTimeGMT=None, searchFieldJson={},useAlias=False, instanceIdList=None):
         """
         函数功能：读取流程表单的所有数据，并加入重试机制。
 
@@ -169,6 +168,7 @@ class YDAPI:
             instanceStatus (str): 流程实例状态，默认为"RUNNING"
             max_retries (int): 最大重试次数，默认为10次
             delay (int): 每次重试之间的延迟秒数，默认为2秒
+            instanceIdList (list): 实例ID列表，用于精确查询
 
         Returns:
             dict: 返回从API获取的流程表单实例数据的JSON解析结果。
@@ -196,8 +196,11 @@ class YDAPI:
             "modifiedToTimeGMT": modifiedToTimeGMT,
             "searchFieldJson": json.dumps(
                 searchFieldJson
-            )
+            ),
+            "useAlias": useAlias,
         }
+        if instanceIdList:
+            formData["instanceIdList"] = instanceIdList
         # print(formData)
 
         while True:
@@ -460,39 +463,40 @@ class YDAPI:
     def get_form_structures(self, token, formUuid, appType="APP_UYZ0KG6L0CCNV80GZ66O",
                             systemToken="XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2", max_retries=20):
         """
-        函数功能：获取表单结构 # 宜搭废弃
-
-        Args:
-            token (str): 登录验证token，用于API调用的身份验证。
-            formUuid (str): 表单的UUID，标识要获取结构的表单。
-            appType
-
-        Returns:
-            响应值: 如果请求成功，则返回服务器的JSON响应；如果请求失败或无响应，则返回一个表示获取成功信息的字符串。
+        获取表单结构（宜搭）
+        注意：该接口必须使用 GET 方法，参数通过 query string 传递
         """
 
-        api = f'https://api.dingtalk.com/v1.0/yida/forms/formFields'
-        headers = {
-            "Content-Type": "application/json",
-            "x-acs-dingtalk-access-token": token
-        }
-        retries = 0
-        data_get = {}
-        payload = {
-            "formUuid": formUuid,
+        api_base = 'https://api.dingtalk.com/v1.0/yida/forms/formFields'
+
+        # 构造查询参数
+        params = {
             "appType": appType,
             "systemToken": systemToken,
+            "formUuid": formUuid,
             "userId": "2268275546837446",
         }
+
+        headers = {
+            "x-acs-dingtalk-access-token": token
+        }
+
+        retries = 0
         while retries < max_retries:
-            res = requests.post(api, headers=headers, json=payload)
-            if res.status_code == 200:
-                data_get = res.json()
-                break
-            else:
-                retries += 1
-                time.sleep(0.1)
-        return data_get
+            try:
+                # 使用 GET，并传入 params（自动编码为 query string）
+                res = requests.get(api_base, headers=headers, params=params, timeout=10)
+                if res.status_code == 200:
+                    return res.json()
+                else:
+                    print(f"请求失败，状态码: {res.status_code}, 响应: {res.text}")
+            except Exception as e:
+                print(f"请求异常: {e}")
+
+            retries += 1
+            time.sleep(0.1)
+
+        return {}  # 或抛出异常
 
 
 class NpEncoder(json.JSONEncoder):