diff --git a/.idea/csv-editor.xml b/.idea/csv-editor.xml
index 632bb9b..dc000ef 100644
--- a/.idea/csv-editor.xml
+++ b/.idea/csv-editor.xml
@@ -24,6 +24,13 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\test\output\last_price.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
       </map>
     </option>
   </component>
diff --git a/back_ground_module/common_module.py b/back_ground_module/common_module.py
index d7828ae..54960c5 100644
--- a/back_ground_module/common_module.py
+++ b/back_ground_module/common_module.py
@@ -262,7 +262,7 @@ class CommonModule:
             )
             cursor = conn.cursor()
 
-            sql = f"""SELECT * FROM saas_renewal_rate_org_code_version_amount_saas;"""
+            sql = f"""SELECT * FROM org_code_total_paid_amount;"""
             # 执行语句并获取结果集
             cursor.execute(sql)
             rows = cursor.fetchall()
@@ -274,7 +274,7 @@ class CommonModule:
             else:  # 如果没有数据，返回空 DataFrame
                 data_NGV = pd.DataFrame()
             headers = [
-                "门店编码", "上次购买价格",
+                "门店编码", "类型", "占位", "价格"
             ]
 
             data_NGV.columns = headers
@@ -319,7 +319,7 @@ class CommonModule:
                 "应续约月份", "商户中心id", "门店id", "门店编码", "门店名称", "是否主店",
                 "商品名称", "应续约日", "公司id", "公司名称", "公司等级", "加盟商名称",
                 "开户时间", "开户渠道来源", "门店状态", "大区", "小区", "省份", "城市",
-                "区域经理", "运营负责人", "技术专家", "商品数量", "分母金额", "是否续约", "elt时间","月分区",
+                "区域经理", "运营负责人", "技术专家", "上次购买数量", "分母金额", "是否续约", "elt时间", "月分区",
             ]
 
             data_NGV.columns = headers
diff --git a/test/result.json b/test/result.json
index 1b1d0c8..420ba75 100644
--- a/test/result.json
+++ b/test/result.json
@@ -44,9 +44,123 @@
       '_widget_1764820541678': {
         'value': '195159084238961158'
       },
-      '_widget_1764820541717': {
-        'value': "[{'_widget_1764820541719': {'value': '员工账号'}, '_widget_1764820541720': {'value': Decimal('0.551200000000000000')}, '_widget_1764820541721': {'value': '2025-02-17 00:00:00'}, '_widget_1764820541722': {'value': None}, '_widget_1764820541723': {'value': None}, '_widget_1764820541724': {'value': None}, '_widget_1764820541725': {'value': None}}, {'_widget_1764820541719': {'value': '商用车车型解析'}, '_widget_1764820541720': {'value': Decimal('0.322800000000000000')}, '_widget_1764820541721': {'value': '2025-02-17 00:00:00'}, '_widget_1764820541722': {'value': None}, '_widget_1764820541723': {'value': None}, '_widget_1764820541724': {'value': None}, '_widget_1764820541725': {'value': None}}, {'_widget_1764820541719': {'value': '企微助手'}, '_widget_1764820541720': {'value': Decimal('589.000000000000000000')}, '_widget_1764820541721': {'value': '2025-08-28 00:00:00'}, '_widget_1764820541722': {'value': None}, '_widget_1764820541723': {'value': None}, '_widget_1764820541724': {'value': None}, '_widget_1764820541725': {'value': None}}, {'_widget_1764820541719': {'value': '保险机器人'}, '_widget_1764820541720': {'value': Decimal('137.802600000000000000')}, '_widget_1764820541721': {'value': '2025-08-28 00:00:00'}, '_widget_1764820541722': {'value': None}, '_widget_1764820541723': {'value': None}, '_widget_1764820541724': {'value': None}, '_widget_1764820541725': {'value': None}}, {'_widget_1764820541719': {'value': '员工账号'}, '_widget_1764820541720': {'value': Decimal('21.623800000000000000')}, '_widget_1764820541721': {'value': '2025-04-06 00:00:00'}, '_widget_1764820541722': {'value': None}, '_widget_1764820541723': {'value': None}, '_widget_1764820541724': {'value': None}, '_widget_1764820541725': {'value': None}}]"
-      },
+      '_widget_1764820541717': [
+        {
+          '_widget_1764820541719': {
+            'value': '员工账号'
+          },
+          '_widget_1764820541720': {
+            'value': 0.5512
+          },
+          '_widget_1764820541721': {
+            'value': '2025-02-17 00:00:00'
+          },
+          '_widget_1764820541722': {
+            'value': None
+          },
+          '_widget_1764820541723': {
+            'value': None
+          },
+          '_widget_1764820541724': {
+            'value': None
+          },
+          '_widget_1764820541725': {
+            'value': None
+          }
+        },
+        {
+          '_widget_1764820541719': {
+            'value': '商用车车型解析'
+          },
+          '_widget_1764820541720': {
+            'value': 0.3228
+          },
+          '_widget_1764820541721': {
+            'value': '2025-02-17 00:00:00'
+          },
+          '_widget_1764820541722': {
+            'value': None
+          },
+          '_widget_1764820541723': {
+            'value': None
+          },
+          '_widget_1764820541724': {
+            'value': None
+          },
+          '_widget_1764820541725': {
+            'value': None
+          }
+        },
+        {
+          '_widget_1764820541719': {
+            'value': '企微助手'
+          },
+          '_widget_1764820541720': {
+            'value': 589.0
+          },
+          '_widget_1764820541721': {
+            'value': '2025-08-28 00:00:00'
+          },
+          '_widget_1764820541722': {
+            'value': None
+          },
+          '_widget_1764820541723': {
+            'value': None
+          },
+          '_widget_1764820541724': {
+            'value': None
+          },
+          '_widget_1764820541725': {
+            'value': None
+          }
+        },
+        {
+          '_widget_1764820541719': {
+            'value': '保险机器人'
+          },
+          '_widget_1764820541720': {
+            'value': 137.8026
+          },
+          '_widget_1764820541721': {
+            'value': '2025-08-28 00:00:00'
+          },
+          '_widget_1764820541722': {
+            'value': None
+          },
+          '_widget_1764820541723': {
+            'value': None
+          },
+          '_widget_1764820541724': {
+            'value': None
+          },
+          '_widget_1764820541725': {
+            'value': None
+          }
+        },
+        {
+          '_widget_1764820541719': {
+            'value': '员工账号'
+          },
+          '_widget_1764820541720': {
+            'value': 21.6238
+          },
+          '_widget_1764820541721': {
+            'value': '2025-04-06 00:00:00'
+          },
+          '_widget_1764820541722': {
+            'value': None
+          },
+          '_widget_1764820541723': {
+            'value': None
+          },
+          '_widget_1764820541724': {
+            'value': None
+          },
+          '_widget_1764820541725': {
+            'value': None
+          }
+        }
+      ],
       '_widget_1764820541865': {
         'value': '2026-07-15 01:59:32'
       }
@@ -93,9 +207,31 @@
       '_widget_1764820541678': {
         'value': '0103433535667605'
       },
-      '_widget_1764820541717': {
-        'value': "[{'_widget_1764820541719': {'value': '员工账号'}, '_widget_1764820541720': {'value': Decimal('300.000000000000000000')}, '_widget_1764820541721': {'value': '2025-10-08 00:00:00'}, '_widget_1764820541722': {'value': None}, '_widget_1764820541723': {'value': None}, '_widget_1764820541724': {'value': None}, '_widget_1764820541725': {'value': None}}]"
-      },
+      '_widget_1764820541717': [
+        {
+          '_widget_1764820541719': {
+            'value': '员工账号'
+          },
+          '_widget_1764820541720': {
+            'value': 300.0
+          },
+          '_widget_1764820541721': {
+            'value': '2025-10-08 00:00:00'
+          },
+          '_widget_1764820541722': {
+            'value': None
+          },
+          '_widget_1764820541723': {
+            'value': None
+          },
+          '_widget_1764820541724': {
+            'value': None
+          },
+          '_widget_1764820541725': {
+            'value': None
+          }
+        }
+      ],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -142,9 +278,7 @@
       '_widget_1764820541678': {
         'value': '054169455736370109'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 03:03:11'
       }
@@ -191,9 +325,7 @@
       '_widget_1764820541678': {
         'value': '180720602439816818'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -240,9 +372,7 @@
       '_widget_1764820541678': {
         'value': '171128505620867604'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 07:42:56'
       }
@@ -289,9 +419,7 @@
       '_widget_1764820541678': {
         'value': '0237196424846034'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 08:16:39'
       }
@@ -338,9 +466,31 @@
       '_widget_1764820541678': {
         'value': '052909055823302010'
       },
-      '_widget_1764820541717': {
-        'value': "[{'_widget_1764820541719': {'value': '轮胎寄存'}, '_widget_1764820541720': {'value': Decimal('64.468500000000000000')}, '_widget_1764820541721': {'value': '2025-04-16 00:00:00'}, '_widget_1764820541722': {'value': None}, '_widget_1764820541723': {'value': None}, '_widget_1764820541724': {'value': None}, '_widget_1764820541725': {'value': None}}]"
-      },
+      '_widget_1764820541717': [
+        {
+          '_widget_1764820541719': {
+            'value': '轮胎寄存'
+          },
+          '_widget_1764820541720': {
+            'value': 64.4685
+          },
+          '_widget_1764820541721': {
+            'value': '2025-04-16 00:00:00'
+          },
+          '_widget_1764820541722': {
+            'value': None
+          },
+          '_widget_1764820541723': {
+            'value': None
+          },
+          '_widget_1764820541724': {
+            'value': None
+          },
+          '_widget_1764820541725': {
+            'value': None
+          }
+        }
+      ],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -387,9 +537,7 @@
       '_widget_1764820541678': {
         'value': '0103433535667605'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 00:37:33'
       }
@@ -436,9 +584,7 @@
       '_widget_1764820541678': {
         'value': '283227523421943504'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 06:39:40'
       }
@@ -485,9 +631,7 @@
       '_widget_1764820541678': {
         'value': '283227523421943504'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 09:13:04'
       }
@@ -532,9 +676,7 @@
       '_widget_1764820541678': {
         'value': '1253235059942945'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -581,9 +723,7 @@
       '_widget_1764820541678': {
         'value': '054169455736370109'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 07:44:19'
       }
@@ -630,9 +770,7 @@
       '_widget_1764820541678': {
         'value': '195159084238961158'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 03:36:05'
       }
@@ -679,9 +817,7 @@
       '_widget_1764820541678': {
         'value': '293429301023738442'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -728,9 +864,7 @@
       '_widget_1764820541678': {
         'value': '3704680936560271'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 00:28:43'
       }
@@ -777,9 +911,7 @@
       '_widget_1764820541678': {
         'value': '0103433535667605'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 02:19:27'
       }
@@ -826,9 +958,7 @@
       '_widget_1764820541678': {
         'value': '054169455736370109'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -875,9 +1005,7 @@
       '_widget_1764820541678': {
         'value': '3704680936560271'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -924,9 +1052,7 @@
       '_widget_1764820541678': {
         'value': '054169455736370109'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -973,9 +1099,7 @@
       '_widget_1764820541678': {
         'value': '293429301023738442'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1022,9 +1146,7 @@
       '_widget_1764820541678': {
         'value': '054169455736370109'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1071,9 +1193,7 @@
       '_widget_1764820541678': {
         'value': '055512041727184572'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1120,9 +1240,7 @@
       '_widget_1764820541678': {
         'value': '0237196424846034'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1169,9 +1287,7 @@
       '_widget_1764820541678': {
         'value': '3048162260682931'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-15 01:32:58'
       }
@@ -1218,9 +1334,31 @@
       '_widget_1764820541678': {
         'value': '180720602439816818'
       },
-      '_widget_1764820541717': {
-        'value': "[{'_widget_1764820541719': {'value': '员工账号'}, '_widget_1764820541720': {'value': Decimal('208.000000000000000000')}, '_widget_1764820541721': {'value': '2025-04-15 00:00:00'}, '_widget_1764820541722': {'value': None}, '_widget_1764820541723': {'value': None}, '_widget_1764820541724': {'value': None}, '_widget_1764820541725': {'value': None}}]"
-      },
+      '_widget_1764820541717': [
+        {
+          '_widget_1764820541719': {
+            'value': '员工账号'
+          },
+          '_widget_1764820541720': {
+            'value': 208.0
+          },
+          '_widget_1764820541721': {
+            'value': '2025-04-15 00:00:00'
+          },
+          '_widget_1764820541722': {
+            'value': None
+          },
+          '_widget_1764820541723': {
+            'value': None
+          },
+          '_widget_1764820541724': {
+            'value': None
+          },
+          '_widget_1764820541725': {
+            'value': None
+          }
+        }
+      ],
       '_widget_1764820541865': {
         'value': '2026-07-15 05:06:51'
       }
@@ -1265,9 +1403,7 @@
       '_widget_1764820541678': {
         'value': '171128505620867604'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1312,9 +1448,7 @@
       '_widget_1764820541678': {
         'value': '171128505620867604'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1359,9 +1493,7 @@
       '_widget_1764820541678': {
         'value': '195159084238961158'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1406,9 +1538,7 @@
       '_widget_1764820541678': {
         'value': '054169455736370109'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1453,9 +1583,7 @@
       '_widget_1764820541678': {
         'value': '171128505620867604'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
@@ -1502,9 +1630,7 @@
       '_widget_1764820541678': {
         'value': '180720602439816818'
       },
-      '_widget_1764820541717': {
-        'value': '[]'
-      },
+      '_widget_1764820541717': [],
       '_widget_1764820541865': {
         'value': '2026-07-14 16:00:00'
       }
diff --git a/test/借车宝异常待办(已改完待上线).py b/test/借车宝异常待办(已改完待上线).py
new file mode 100644
index 0000000..8b3f23b
--- /dev/null
+++ b/test/借车宝异常待办(已改完待上线).py
@@ -0,0 +1,388 @@
+from datetime import timedelta, datetime
+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
+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()
+
+
+class JCBEfficientCarPickup:
+    """接车宝日常回访"""
+
+    def __init__(self):
+        # 使用 pymysql 连接数据库
+        self.daily_revisit_list = None
+        self.field_mapping = {}
+        self.staff_id_list = None
+        self.customer_service_list = None
+
+    def load_cus_data(self):
+        # 获取接车宝客服表单
+        payload = {"api_key": "6717470a0b3975ef583c6df1",
+                   "entry_id": "67b6f2462f9ac03b783d409a",
+                   }
+        customer_service = api_instance.entry_data_list(payload)
+        customer_service_list = customer_service.get("data")  # api请求格式，将数据封装在data字典里
+        return customer_service_list
+
+    def today_customer_service_list(self):
+        # 获取今日接车宝派发客服顺序
+        global is_customer_service_data_id
+        today_customer_service_list = []
+        all_customer_service_list = []
+        today_customer_service_start_list = []
+        for row_items in self.load_cus_data():
+            # print(row_items)
+            customer_service_name_id = row_items.get("_widget_1740042824214", {}).get("username", {})
+            customer_service_name = row_items.get("_widget_1740042824214", {}).get("name", {})
+            customer_service_state = row_items.get("_widget_1740117343937", {})
+            is_last_day_end = row_items.get("_widget_1740042824216", {})
+            customer_service_data_id = row_items.get("_id", {})
+            print(customer_service_name, customer_service_name_id, customer_service_state, is_last_day_end)
+            all_customer_service_list.append(
+                [customer_service_name, customer_service_name_id, customer_service_state, is_last_day_end,
+                 customer_service_data_id])
+            if is_last_day_end == "是":  # 判断是否是下次开始位置
+                last_day_end_customer_service = customer_service_name_id
+                is_customer_service_data_id = row_items.get("_id", {})
+
+        split_index = None
+        for index, row in enumerate(all_customer_service_list):
+            print(row[3])
+            if row[3] == "是":
+                split_index = index
+                print(f"找到索引 {index}")
+                break
+
+        if split_index is not None:
+            # 根据索引切割列表
+            first_part = all_customer_service_list[split_index:]  # 索引位置及之后的行
+            second_part = all_customer_service_list[:split_index]  # 索引位置之前的行
+            # 调换两个子列表的位置并重新组合
+            today_customer_service_start_list = first_part + second_part
+        else:
+            # 如果没有找到"是"，保持原列表不变
+            today_customer_service_start_list = all_customer_service_list
+            pass
+
+        for index, row in enumerate(today_customer_service_start_list):
+            if row[2] == "开":
+                today_customer_service_list.append(row[1])
+
+        return today_customer_service_list, is_customer_service_data_id, all_customer_service_list
+
+    def send_request(self, df):
+        if df is None or df.empty:  # 检查DataFrame是否为None或空
+            logger.info("当前派发数据为空或None，跳过此派发")
+            return
+
+        today_customer_service_list, is_customer_service_data_id, all_customer_service_list = self.today_customer_service_list()
+        # 初始化派发索引
+        next_dispatcher_index = 0
+
+        # 显式循环分配跟进人
+        follow_up_persons = []
+        for _ in range(len(df)):
+            follow_up_person = today_customer_service_list[next_dispatcher_index]
+            follow_up_persons.append(follow_up_person)
+            next_dispatcher_index = (next_dispatcher_index + 1) % len(today_customer_service_list)
+
+        # 添加跟进人到 DataFrame
+        df["跟进人"] = follow_up_persons
+
+        # 获取下一个派发人
+        next_dispatcher = today_customer_service_list[next_dispatcher_index]
+
+        new_sign_abnormal_data = [self.row_to_dict(row, self.field_mapping) for index, row in
+                                  df.iterrows()]
+
+        data = {'api_key': Config.EFFICIENT_CAR_PICKUP_APP_ID, 'entry_id': Config.EFFICIENT_CAR_PICKUP_ENTRY_ID,
+                "data_list": new_sign_abnormal_data}
+
+        result = api_instance.entry_data_batch_create(data)
+        logger.info(f"数据发送成功:{result}")
+
+        data1 = {"api_key": Config.EFFICIENT_CAR_PICKUP_APP_ID,
+                 "entry_id": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_SERVICE_ID,
+                 "data_id": is_customer_service_data_id,
+                 "data":
+                     {"_widget_1740042824216": {"value": ""}, }
+                 }  # 原来的是"_widget_1740042824216": {"value": "是"},修改昨日截至人员
+        next_customer_service_data_id = None
+        for index, row in enumerate(all_customer_service_list):
+            print(row[3])
+            if row[1] == next_dispatcher:
+                next_customer_service_data_id = row[4]
+                break
+
+        data2 = {"api_key": Config.EFFICIENT_CAR_PICKUP_APP_ID,
+                 "entry_id": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_SERVICE_ID,
+                 "data_id": next_customer_service_data_id,
+                 "data":
+                     {"_widget_1740042824216": {"value": "是"}, }}
+
+        api_instance.entry_data_update(data1)
+        result2 = api_instance.entry_data_update(data2)
+        logger.info(f"明日派发人员信息已修改:{result2}")
+
+    def load_all_data(self):
+        # 获取接车宝日常回访单
+        payload = {"api_key": "6717470a0b3975ef583c6df1",
+                   "entry_id": "67174710da507490d8ac12c1",
+                   }
+        daily_revisit = api_instance.entry_data_list(payload)
+        self.daily_revisit_list = daily_revisit.get("data")  # api请求格式，将数据封装在data字典里
+
+    def main(self):
+        task_start_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        try:
+            logger.info(f"接车宝日常回访开始执行")
+            data_JCB = common_module.get_jcb_details()
+            if data_JCB is None:
+                logger.error("获取接车宝数据失败，返回None")
+                raise ValueError("获取接车宝数据失败，返回None")
+            self.load_all_data()
+
+            logger.info(f"数据加载完成")
+
+            # data_JCB.to_csv(os.path.join(output_dir, 'JCB_all_data.csv'), index=False)
+            self.fields()
+
+            # 新签异常待办回访。
+            # 当前日期
+            current_date = datetime.now()
+            current_date = current_date + timedelta(days=0)
+            current_date_str = current_date.strftime("%Y-%m-%d")
+
+            seven_days_ago = current_date - timedelta(days=7)
+            seven_days_ago = seven_days_ago.date()
+            # print(three_days_ago)
+            new_sign_abnormal = []
+
+            for index, row in data_JCB.iterrows():
+                new_row = row.copy()
+                # 先转成字符串，再解析回 date 对象
+                new_row['开户日'] = datetime.strptime(str(row['开户日']), "%Y-%m-%d").date()
+                if new_row['开户日'] == seven_days_ago and row['当月开单天数'] == 0:
+                    # print(row['账号'], row['开户日'], row['当月开单天数'])
+                    row["日期"] = datetime.strptime(str(row['开户日']), "%Y-%m-%d").date()
+                    row['日期'] = row["日期"].strftime("%Y-%m-%d")
+                    new_sign_abnormal.append(row)
+
+            new_sign_abnormal = pd.DataFrame(new_sign_abnormal) if new_sign_abnormal else None
+            if new_sign_abnormal is not None and not new_sign_abnormal.empty:
+                new_sign_abnormal["表单类型"] = "新签异常待办"
+                new_sign_abnormal["派发日期"] = current_date_str
+                # self.send_request(new_sign_abnormal)  # 发送请求
+                logger.info(f"新签异常待办回访完成")
+            else:
+                logger.info(f"新签异常待办回访无数据，跳过")
+
+            # 异常待办
+            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()
+
+            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()
+
+            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 self.daily_revisit_list
+            ])
+
+            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)
+
+            # 发送或跳过
+            if not abnormal_data.empty:
+                abnormal_data = abnormal_data[:40]
+                # self.send_request(abnormal_data)
+                logger.info(f"异常待办完成，共 {len(abnormal_data)} 条")
+            else:
+                logger.info("异常待办无数据，跳过")
+
+            # 优质客户转商机
+            # current_date = datetime.now()
+            thirty_days_ago = current_date - timedelta(days=30)
+            sixty_days_ago = current_date - timedelta(days=60)
+            thirty_days_ago = thirty_days_ago.date()
+            sixty_days_ago = sixty_days_ago.date()
+            customer_to_opportunity = []
+            for index, row in data_JCB.iterrows():
+                new_row = row.copy()
+                # 先转成字符串，再解析回 date 对象
+                new_row['到期日'] = datetime.strptime(str(row['到期日']), "%Y-%m-%d").date()
+                if new_row['到期日'] == thirty_days_ago and row['近一周开单量'] >= 3 and row[
+                    'G状态：近30天开单大于等于10天'] == 1:
+                    print(row['账号'], row['到期日'], row['当月开单天数'], row['当月G天数'])
+                    row["日期"] = datetime.strptime(str(row['开户日']), "%Y-%m-%d").date()
+                    row['日期'] = row["日期"].strftime("%Y-%m-%d")
+                    customer_to_opportunity.append(row)
+                    # 推送给客服
+                    pass
+                if new_row['到期日'] == sixty_days_ago and row['近一周开单量'] >= 3 and row[
+                    'G状态：近30天开单大于等于10天'] == 1:
+                    print(row['账号'], row['到期日'], row['当月开单天数'], row['当月G天数'])
+                    row["日期"] = datetime.strptime(str(row['开户日']), "%Y-%m-%d").date()
+                    row['日期'] = row["日期"].strftime("%Y-%m-%d")
+                    customer_to_opportunity.append(row)
+                    # 推送给客服
+                    pass
+
+            customer_to_opportunity = pd.DataFrame(customer_to_opportunity) if customer_to_opportunity else None
+            if customer_to_opportunity is not None and not customer_to_opportunity.empty:
+                customer_to_opportunity["表单类型"] = "续约优质客户转商机"
+                customer_to_opportunity["派发日期"] = current_date_str
+                # self.send_request(customer_to_opportunity)
+                logger.info(f"优质客户转商机完成")
+            else:
+                logger.info(f"优质客户转商机无数据，跳过")
+
+            # 过期7天客服回访
+            # current_date = datetime.now()
+            seven_days_ago = current_date - timedelta(days=7)
+            seven_days_ago = seven_days_ago.date()
+            outdated_30 = []
+            for index, row in data_JCB.iterrows():
+                new_row = row.copy()
+                new_row['到期日'] = datetime.strptime(str(row['到期日']), "%Y-%m-%d").date()
+                # seven_days_ago = seven_days_ago.date()
+                # print(row['到期日'], seven_days_ago)
+                if new_row['到期日'] == seven_days_ago and row['客户状态'] == "过期":
+                    print(row['账号'], row['到期日'], row['当月开单天数'], row['当月G天数'])
+                    row["日期"] = datetime.strptime(str(row['开户日']), "%Y-%m-%d").date()
+                    row['日期'] = row["日期"].strftime("%Y-%m-%d")
+                    outdated_30.append(row)
+                    # 推送给客服
+                    pass
+
+            outdated_30 = pd.DataFrame(outdated_30) if outdated_30 else None
+            if outdated_30 is not None and not outdated_30.empty:
+                outdated_30["表单类型"] = "过期7天回访"
+                outdated_30["派发日期"] = current_date_str
+                # self.send_request(outdated_30)
+                logger.info(f"过期7天客服回访完成")
+            else:
+                logger.info(f"过期7天客服回访无数据，跳过")
+
+            common_module.send_task_status(task_start_time, "接车宝日常派发")
+            logger.info(f"接车宝日常派发执行完成")
+        except Exception as e:
+            common_module.send_task_error(task_start_time, "接车宝日常派发", str(e))
+            error_task_logger.error(f"接车宝日常派发执行出错：{e}")
+
+    @staticmethod
+    def row_to_dict(row, field_mapping):
+        """将一行数据转换为指定格式的字典"""
+        result = {}
+        # print(field_mapping)
+        for col_name, widget_id in field_mapping.items():
+            # print(col_name, widget_id)
+            if col_name in row:
+                value = row[col_name]
+                clean_value = None if pd.isna(value) else value
+                result[widget_id] = {"value": clean_value}
+        return result
+
+    def fields(self):
+        self.field_mapping = {"日期": "_widget_1739252804406", "产品名称": "_widget_1739252804397",
+                              "账号": "_widget_1739258942667", "联系手机号": "_widget_1739252804407",
+                              "使用时长": "_widget_1739252804409", "开户日": "_widget_1739252804396",
+                              "到期日": "_widget_1739252804408", "续约日": "_widget_1739252804410",
+                              "客户状态": "_widget_1739252804400", "近一周开单量": "_widget_1739252804413",
+                              "近一周是否活跃": "_widget_1739252804414",
+                              "G状态：近30天开单大于等于10天": "_widget_1739252804415",
+                              "当月开单天数": "_widget_1739252804416", "近30天开单天数": "_widget_1739252804417",
+                              "当月G天数": "_widget_1739252804418", "日分区": "_widget_1739252804419",
+                              "表单类型": "_widget_1739951204545", "派发日期": "_widget_1740036367181",
+                              "跟进人": "_widget_1740043340255",
+                              }
+
+
+if __name__ == "__main__":
+    start = JCBEfficientCarPickup()
+    start.main()
diff --git a/test/续约待办派发.py b/test/续约待办派发.py
index cecb317..c42e1e3 100644
--- a/test/续约待办派发.py
+++ b/test/续约待办派发.py
@@ -16,6 +16,7 @@ os.makedirs(output_dir, exist_ok=True)
 
 class RenewalToDo:
     def __init__(self):
+        self.renewal_data_list = None
         self.cyclic_increasing = None
         self.franchisee = None
         self.last_price = None
@@ -75,9 +76,13 @@ class RenewalToDo:
             "业务类型（续约、升级）": "_widget_1764820541680",
             "连锁门店待办同步处理": "_widget_1764820541681",
             "选择需要同步的门店名称": "_widget_1765330820391",
-            "过期日后90天日期": "_widget_1764820541865",
+            "120天自动流转时间": "_widget_1764820541865",
+            "60天自动流转时间": "_widget_1765964381895",
+            "30天自动流转时间": "_widget_1765964381896",
+            "0天自动流转时间": "_widget_1765964381897",
             "当前所处节点": "_widget_1765352838609",
             "流程状态": "_widget_1765352838610",
+            "经营模式": "_widget_1765964381952",
             "提交人": "creator",
             "提交时间": "createTime",
             "更新时间": "updateTime"
@@ -93,7 +98,7 @@ class RenewalToDo:
             "contact_mobile": "联系手机号",
             "service_impl_principal": "专属运营顾问",
             "technician": "运营专家",
-            "expiry_time_plus_90d": "过期日后90天日期",
+            "manage_model": "经营模式",
         }
         self.subform_field_map = {
             "商品名称": "_widget_1764820541719",
@@ -105,6 +110,9 @@ 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 ={
+
+        }
 
     def load_all_data(self):
         """
@@ -144,6 +152,15 @@ class RenewalToDo:
             print("加载省市区人员关系表失败")
             self.province_staff_id_list = []
 
+        # 获取已派发续约待办(进行中)
+        payload = {"api_key": "6694d3c4fcb69ca9a111a6c4",
+                   "entry_id": "6769204a1902c9341340a1bc",
+                   "filter": {"rel": "and",
+                              "cond": [{"field": "flowState", "type": "flowstate", "method": "eq", "value": [0]}]},
+                   }
+        renewal = api_instance.entry_data_list(payload)
+        self.renewal_data_list = renewal.get("data")
+
     @staticmethod
     def replace_names_with_staff_ids(df, name_columns, staff_id_list):
         """
@@ -178,11 +195,8 @@ class RenewalToDo:
         return df
 
     @staticmethod
-    def row_to_dict(row, field_mapping, subform_fields=None):
+    def row_to_dict(row, field_mapping):
         """将一行数据转换为指定格式的字典"""
-        if subform_fields is None:
-            subform_fields = set()
-
         result = {}
         for col_name, widget_id in field_mapping.items():
             if col_name not in row:
@@ -201,11 +215,8 @@ class RenewalToDo:
                 else:
                     clean_value = value
 
-            # 子表单字段直接赋值，其他包 {"value": ...}
-            if widget_id in subform_fields:
-                result[widget_id] = clean_value
-            else:
-                result[widget_id] = {"value": clean_value}
+            # 所有字段统一包 {"value": ...}，包括子表单
+            result[widget_id] = {"value": clean_value}
         return result
 
     @staticmethod
@@ -325,10 +336,13 @@ class RenewalToDo:
             .dt.tz_convert('UTC')
         )
 
-        # 新增一列：过期日后90天
-        data_NGV['过期日后90天日期'] = data_NGV['过期日'] + pd.Timedelta(days=90)
+        # 新增4列：辅助时间字段
+        data_NGV['120天自动流转时间'] = data_NGV['过期日'] - pd.Timedelta(days=60)
+        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)
         # 格式化为字符串（去掉时区）
-        for col in ['过期日', '过期日后90天日期']:
+        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')
 
         # 新增加盟商列
@@ -339,11 +353,24 @@ class RenewalToDo:
         )
 
         # 新增上次购买价格列
-        data_NGV = data_NGV.merge(
-            self.last_price[['门店编码', '上次购买价格']],
-            on='门店编码',
-            how='left'
+        # 1. 清洗并拼接类型+价格
+        df_lp = self.last_price[['门店编码', '类型', '价格']].copy()
+        df_lp['类型'] = df_lp['类型'].fillna('').astype(str)
+        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)
+
+        # 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)
+
+        # 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('')
 
         # 成员字段替换（现在列名是中文）
         staff_name_cols = [
@@ -422,9 +449,6 @@ class RenewalToDo:
                 return None
 
         # === Step 3: 遍历主表每一行，构建最终提交记录 ===
-        # 定义哪些字段是子表单（widget ID 集合）
-        subform_widget_ids = {self.field_map["周期性增购"]}  # 即 {"_widget_1764820541717"}
-
         for _, row in data_NGV.iterrows():
             row_dict = row.to_dict()
 
@@ -441,11 +465,11 @@ class RenewalToDo:
             if not customer_service:
                 no_customer_service_data.append(row_dict)
 
-            # 3.3 注入周期性增购子表单（直接赋 list，不转字符串！）
+            # 3.3 注入周期性增购子表单
             row_dict["周期性增购"] = cyclic_subforms.get(org_code, [])
 
-            # 3.4 转换为 widget 格式（注意传入 subform_widget_ids）
-            widget_record = self.row_to_dict(row_dict, self.field_map, subform_fields=subform_widget_ids)
+            # 3.4 转换为 widget 格式
+            widget_record = self.row_to_dict(row_dict, self.field_map)
             records.append(widget_record)
 
         # === Step 4: 批量提交 ===
@@ -464,6 +488,7 @@ class RenewalToDo:
         logger.info(f"已提交 {len(records)} 条数据进行派发")
 
     def main(self):
+
         task_start_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         try:
             logger.info("任务开始")
@@ -474,6 +499,9 @@ 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")
         except Exception as e:
diff --git a/test/续约待办派发测试数据.py b/test/续约待办派发测试数据.py
new file mode 100644
index 0000000..e69de29