saas1.6日志更新
This commit is contained in:
@@ -1,18 +1,29 @@
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from datetime import date, timedelta, datetime
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import holidays
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from datetime import timedelta, datetime
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from config import Config
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import pandas as pd
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import pymysql # 使用 pymysql 替代 mysql.connector
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from log_config import configure_task_logger, configure_error_task_logger
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from api import API
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from back_ground_module import CommonModule
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from log_config import configure_task_logger, configure_error_task_logger
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# 获取已经配置好的常规日志记录器
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logger = configure_task_logger()
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# 获取已经配置好的错误任务日志记录器
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error_task_logger = configure_error_task_logger()
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common_module = CommonModule()
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api_instance = API()
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# 保存为CSV文件
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output_dir = "output" # 设置输出目录
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# 创建输出目录（如果不存在）
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import os
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os.makedirs(output_dir, exist_ok=True)
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global last_day_end_customer_service, is_customer_service_data_id, customer_service_data_id
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class JCBAbnormalRevisit:
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"""接车宝异常回访"""
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def __init__(self):
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# 使用 pymysql 连接数据库
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self.daily_revisit_list = None
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@@ -47,6 +58,7 @@ class JCBAbnormalRevisit:
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def today_customer_service_list(self):
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# 获取今日接车宝派发客服顺序
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global is_customer_service_data_id
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today_customer_service_list = []
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all_customer_service_list = []
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today_customer_service_start_list = []
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@@ -112,7 +124,7 @@ class JCBAbnormalRevisit:
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df.iterrows()]
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data = {'api_key': Config.EFFICIENT_CAR_PICKUP_APP_ID, 'entry_id': "67174710da507490d8ac12c1",
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"data_list": new_sign_abnormal_data} # 派发数据
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"data_list": new_sign_abnormal_data} # 派发数据
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api_instance.entry_data_batch_create(data)
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@@ -133,197 +145,190 @@ class JCBAbnormalRevisit:
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"entry_id": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_SERVICE_ID,
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"data_id": next_customer_service_data_id,
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"data":
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{"_widget_1740042824216": {"value": "是"}, }}# 明日派发起点人员
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{"_widget_1740042824216": {"value": "是"}, }} # 明日派发起点人员
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api_instance.entry_data_update(data1)
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api_instance.entry_data_update(data2)
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def main(self):
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self.load_all_data()
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task_start_time =datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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task_start_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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try:
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logger.info(f"开始执行")
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self.load_all_data()
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data_JCB = common_module.get_jcb_details()
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logger.info(f"数据加载完成")
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# data_JCB.to_csv(os.path.join(output_dir, 'JCB_all_data.csv'), index=False)
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self.fields()
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data_JCB = common_module.get_jcb_details()
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# 异常待办回访 近1个月开单为0客户
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# 当前日期
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current_date = datetime.now()
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current_date = current_date + timedelta(days=1)
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current_date_str = current_date.strftime("%Y-%m-%d")
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# current_date = datetime.now()
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thirty_days_ago = current_date - timedelta(days=30)
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thirty_days_ago = thirty_days_ago.date()
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abnormal_data = []
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JDY_abnormal_data = []
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JDY_revisit_data = []
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# df = pd.read_csv(os.path.join(output_dir, "JCB_异常待办.csv")) # 读取异常待办表
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# print(df)
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for index, row in data_JCB.iterrows():
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new_row = row.copy()
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new_row['开户日'] = datetime.strptime(new_row['开户日'], "%Y-%m-%d").date()
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if new_row['开户日'] < thirty_days_ago and row['近30天开单天数'] == 0 and row['客户状态'] == "留存":
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# print(row['账号'], row['开户日'], row['近30天开单天数'], row["客户状态"])
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row["日期"] = datetime.strptime(row['开户日'], "%Y-%m-%d").date()
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row['日期'] = row["日期"].strftime("%Y-%m-%d")
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abnormal_data.append(row)
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# 推送给客服
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abnormal_data = pd.DataFrame(abnormal_data)
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abnormal_data["表单类型"] = "异常待办"
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abnormal_data["派发日期"] = current_date_str
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# abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办.xlsx'), index=False) # 派发B(所有异常待办)
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# 保存为CSV文件
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output_dir = "output" # 设置输出目录
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for abnormal_items in self.abnormal_list:
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last_send_date = abnormal_items.get("_widget_1740723898405", {}) # 派发日期
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last_30_days_orders = abnormal_items.get("_widget_1740723898401", {}) # 近30天开单数
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phone = abnormal_items.get("_widget_1740723898391", {}) # 手机号
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account = abnormal_items.get("_widget_1740723898390", {}) # 账号
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data_id = abnormal_items.get("_id", {}) # 数据id
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JDY_abnormal_data.append([data_id, account, phone, last_send_date, last_30_days_orders])
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# 创建输出目录（如果不存在）
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import os
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os.makedirs(output_dir, exist_ok=True)
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JDY_abnormal_data = pd.DataFrame(JDY_abnormal_data,
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columns=["数据id", "账号", "联系手机号", "派发日期",
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"近30天开单天数"]) # 派发A（简道云上异常待办）
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# JDY_abnormal_data.columns = ["数据id", "账号", "联系手机号", "派发日期", "近30天开单天数"]
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# JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_云端异常待办.xlsx'), index=False) # 派发A
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# data_JCB.to_csv(os.path.join(output_dir, 'JCB_all_data.csv'), index=False)
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self.fields()
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# 将 '联系手机号' 列转换为字符串类型
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JDY_abnormal_data['联系手机号'] = JDY_abnormal_data['联系手机号'].astype(str).str.replace('.0', '')
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abnormal_data['联系手机号'] = abnormal_data['联系手机号'].astype(str)
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# JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_云端异常待办.xlsx'), index=False) # 派发A
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# abnormal_data.to_excel(os.path.join(output_dir, 'JCB_今日异常待办.xlsx'), index=False) # 派发B
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# 异常待办回访 近1个月开单为0客户
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# 当前日期
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current_date = datetime.now()
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current_date = current_date + timedelta(days=1)
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current_date_str = current_date.strftime("%Y-%m-%d")
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# current_date = datetime.now()
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thirty_days_ago = current_date - timedelta(days=30)
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thirty_days_ago = thirty_days_ago.date()
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abnormal_data = []
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JDY_abnormal_data = []
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JDY_revisit_data = []
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# df = pd.read_csv(os.path.join(output_dir, "JCB_异常待办.csv")) # 读取异常待办表
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# print(df)
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for index, row in data_JCB.iterrows():
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new_row = row.copy()
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new_row['开户日'] = datetime.strptime(new_row['开户日'], "%Y-%m-%d").date()
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if new_row['开户日'] < thirty_days_ago and row['近30天开单天数'] == 0 and row['客户状态'] == "留存":
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# print(row['账号'], row['开户日'], row['近30天开单天数'], row["客户状态"])
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row["日期"] = datetime.strptime(row['开户日'], "%Y-%m-%d").date()
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row['日期'] = row["日期"].strftime("%Y-%m-%d")
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abnormal_data.append(row)
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# 推送给客服
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abnormal_data = pd.DataFrame(abnormal_data)
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abnormal_data["表单类型"] = "异常待办"
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abnormal_data["派发日期"] = current_date_str
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# abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办.xlsx'), index=False) # 派发B(所有异常待办)
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today = datetime.now().weekday()
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for abnormal_items in self.abnormal_list:
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last_send_date = abnormal_items.get("_widget_1740723898405", {}) # 派发日期
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last_30_days_orders = abnormal_items.get("_widget_1740723898401", {}) # 近30天开单数
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phone = abnormal_items.get("_widget_1740723898391", {}) # 手机号
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account = abnormal_items.get("_widget_1740723898390", {}) # 账号
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data_id = abnormal_items.get("_id", {}) # 数据id
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JDY_abnormal_data.append([data_id, account, phone, last_send_date, last_30_days_orders])
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# 随机抽40条派发
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df_40 = pd.DataFrame()
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if 0 <= today <= 4:
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# if 1>2:
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# 假设 JDY_abnormal_data 和 abnormal_data 都有重复列 '重复列'
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df3 = pd.merge(JDY_abnormal_data, abnormal_data, on=["联系手机号", "账号"], how='inner',
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suffixes=('', '_y'))
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# 删除以 _y 结尾的列（即来自右侧 DataFrame 的重复列）
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df3 = df3.loc[:, ~df3.columns.str.endswith('_y')]
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df3['派发日期'] = pd.to_datetime(df3['派发日期']).dt.strftime("%Y-%m-%d")
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# df3.to_excel(os.path.join(output_dir, 'JCB_异常待办情况1.xlsx'),
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# index=False, ) # B存在，A存在 ，今日派发与历史派发都存在，派发并删历史
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JDY_abnormal_data = pd.DataFrame(JDY_abnormal_data,
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columns=["数据id", "账号", "联系手机号", "派发日期",
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"近30天开单天数"]) # 派发A（简道云上异常待办）
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# JDY_abnormal_data.columns = ["数据id", "账号", "联系手机号", "派发日期", "近30天开单天数"]
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# JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_云端异常待办.xlsx'), index=False) # 派发A
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df_40 = df3[df3.index < 40]
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# df_40.to_excel(os.path.join(output_dir, 'JCB_异常待办情况2.xlsx'), index=False, )
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# 将 '联系手机号' 列转换为字符串类型
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JDY_abnormal_data['联系手机号'] = JDY_abnormal_data['联系手机号'].astype(str).str.replace('.0', '')
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abnormal_data['联系手机号'] = abnormal_data['联系手机号'].astype(str)
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# JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_云端异常待办.xlsx'), index=False) # 派发A
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# abnormal_data.to_excel(os.path.join(output_dir, 'JCB_今日异常待办.xlsx'), index=False) # 派发B
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for index, row in df_40.iterrows(): # 删除已推送的数据
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delete_data = {"api_key": Config.EFFICIENT_CAR_PICKUP_APP_ID,
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"entry_id": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_HISTORY_ID,
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"data_id": row["数据id"]}
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# print(delete_data)
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api_instance.entry_data_delete(delete_data)
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logger.info(f"已删除随机抽取40条数据")
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today = datetime.now().weekday()
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# 随机抽40条派发
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df_40 = pd.DataFrame()
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if 0 <= today <= 4:
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# if 1>2:
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# 假设 JDY_abnormal_data 和 abnormal_data 都有重复列 '重复列'
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df3 = pd.merge(JDY_abnormal_data, abnormal_data, on=["联系手机号", "账号"], how='inner',
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suffixes=('', '_y'))
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# B不存在A存在 今日派发不存在，历史存在，删历史
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# 使用 outer 合并，并添加指示器列 _merge
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df_merged = pd.merge(JDY_abnormal_data, abnormal_data, on=["联系手机号", "账号"], how='outer',
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indicator=True,
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suffixes=('', '_y')) # outer保留所有数据，indicator标注来源
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# 筛选出只存在于 JDY_abnormal_data 中的行
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df_a_not_in_b = df_merged[df_merged['_merge'] == 'left_only']
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# 删除以 _y 结尾的列（即来自右侧 DataFrame 的重复列）
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df3 = df3.loc[:, ~df3.columns.str.endswith('_y')]
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df3['派发日期'] = pd.to_datetime(df3['派发日期']).dt.strftime("%Y-%m-%d")
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# df3.to_excel(os.path.join(output_dir, 'JCB_异常待办情况1.xlsx'),
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# index=False, ) # B存在，A存在 ，今日派发与历史派发都存在，派发并删历史
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df_40 = df3[df3.index < 40]
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# df_40.to_excel(os.path.join(output_dir, 'JCB_异常待办情况2.xlsx'), index=False, )
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for index, row in df_40.iterrows(): # 删除已推送的数据
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df_a_not_in_b = df_a_not_in_b.loc[:, ~df_a_not_in_b.columns.str.endswith('_y')]
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df_a_not_in_b['派发日期'] = pd.to_datetime(df_a_not_in_b['派发日期']).dt.strftime("%Y-%m-%d")
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# 保存到 Excel 文件
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# df_a_not_in_b.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_A存在B不存在.xlsx'), index=False)
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for index, row in df_a_not_in_b.iterrows(): # 删除已推送的数据
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delete_data = {"api_key": Config.EFFICIENT_CAR_PICKUP_APP_ID,
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"entry_id": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_HISTORY_ID,
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"data_id": row["数据id"]}
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# print(delete_data)
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api_instance.entry_data_delete(delete_data)
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logger.info("已删除派发后数据")
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# B不存在A存在 今日派发不存在，历史存在，删历史
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# 使用 outer 合并，并添加指示器列 _merge
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df_merged = pd.merge(JDY_abnormal_data, abnormal_data, on=["联系手机号", "账号"], how='outer', indicator=True,
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suffixes=('', '_y')) # outer保留所有数据，indicator标注来源
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# 筛选出只存在于 JDY_abnormal_data 中的行
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df_a_not_in_b = df_merged[df_merged['_merge'] == 'left_only']
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# 删除以 _y 结尾的列（即来自右侧 DataFrame 的重复列）
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df_a_not_in_b = df_a_not_in_b.loc[:, ~df_a_not_in_b.columns.str.endswith('_y')]
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df_a_not_in_b['派发日期'] = pd.to_datetime(df_a_not_in_b['派发日期']).dt.strftime("%Y-%m-%d")
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# 保存到 Excel 文件
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# df_a_not_in_b.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_A存在B不存在.xlsx'), index=False)
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for index, row in df_a_not_in_b.iterrows(): # 删除已推送的数据
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delete_data = {"api_key": Config.EFFICIENT_CAR_PICKUP_APP_ID,
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"entry_id": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_HISTORY_ID,
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"data_id": row["数据id"]}
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# print(delete_data)
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api_instance.entry_data_delete(delete_data)
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# B存在A不存在 今日派发存在，历史不存在，为新增异常，直接派发
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df_merged = pd.merge(JDY_abnormal_data, abnormal_data, on=["联系手机号", "账号"], how='outer',
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indicator=True,
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suffixes=('_x', '')) # outer保留所有数据，indicator标注来源
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# df_merged.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_134434.xlsx'), index=False)
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# 筛选出只存在于 JDY_abnormal_data 中的行
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df_b_not_in_a = df_merged[df_merged['_merge'] == 'right_only']
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# df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_111.xlsx'), index=False)
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# 删除以 _y 结尾的列（即来自右侧 DataFrame 的重复列）
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df_b_not_in_a = df_b_not_in_a.loc[:, ~df_b_not_in_a.columns.str.endswith('_x')]
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# df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_122.xlsx'), index=False)
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df_b_not_in_a['派发日期'] = pd.to_datetime(df_b_not_in_a['派发日期']).dt.strftime("%Y-%m-%d")
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# 保存到 Excel 文件
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# df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在.xlsx'), index=False)
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# B存在A不存在 今日派发存在，历史不存在，为新增异常，直接派发
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df_merged = pd.merge(JDY_abnormal_data, abnormal_data, on=["联系手机号", "账号"], how='outer', indicator=True,
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suffixes=('_x', '')) # outer保留所有数据，indicator标注来源
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# df_merged.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_134434.xlsx'), index=False)
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# 筛选出只存在于 JDY_abnormal_data 中的行
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df_b_not_in_a = df_merged[df_merged['_merge'] == 'right_only']
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# df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_111.xlsx'), index=False)
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# 删除以 _y 结尾的列（即来自右侧 DataFrame 的重复列）
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df_b_not_in_a = df_b_not_in_a.loc[:, ~df_b_not_in_a.columns.str.endswith('_x')]
|
||||
# df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_122.xlsx'), index=False)
|
||||
df_b_not_in_a['派发日期'] = pd.to_datetime(df_b_not_in_a['派发日期']).dt.strftime("%Y-%m-%d")
|
||||
# 保存到 Excel 文件
|
||||
# df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在.xlsx'), index=False)
|
||||
# 合并两个当日派发的df
|
||||
df_abnormal_data = pd.concat([df_40, df_b_not_in_a], ignore_index=True)
|
||||
# df_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_合并当日派发.xlsx'), index=False)
|
||||
|
||||
# 合并两个当日派发的df
|
||||
df_abnormal_data = pd.concat([df_40, df_b_not_in_a], ignore_index=True)
|
||||
# df_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_合并当日派发.xlsx'), index=False)
|
||||
for abnormal_items in self.daily_revisit_list: # 遍历云端已经派发的数据
|
||||
account = abnormal_items.get("_widget_1739258942667", {}) # 账号
|
||||
sub_date = abnormal_items.get("createTime", {}) # 提交时间
|
||||
update_date = abnormal_items.get("updateTime", {}) # 更新时间
|
||||
entry_style = abnormal_items.get("_widget_1739951204545", {}) # 表单类型
|
||||
entry_type = abnormal_items.get("flowState", {}) # 表单状态 0流转中 1流转完成 2 手动结束
|
||||
|
||||
for abnormal_items in self.daily_revisit_list: # 遍历云端已经派发的数据
|
||||
account = abnormal_items.get("_widget_1739258942667", {}) # 账号
|
||||
sub_date = abnormal_items.get("createTime", {}) # 提交时间
|
||||
update_date = abnormal_items.get("updateTime", {}) # 更新时间
|
||||
entry_style = abnormal_items.get("_widget_1739951204545", {}) # 表单类型
|
||||
entry_type = abnormal_items.get("flowState", {}) # 表单状态 0流转中 1流转完成 2 手动结束
|
||||
data_id = abnormal_items.get("_id", {}) # 数据id
|
||||
JDY_revisit_data.append([data_id, account, sub_date, update_date, entry_style, entry_type])
|
||||
|
||||
data_id = abnormal_items.get("_id", {}) # 数据id
|
||||
JDY_revisit_data.append([data_id, account, sub_date, update_date, entry_style, entry_type])
|
||||
JDY_revisit_data = pd.DataFrame(JDY_revisit_data)
|
||||
JDY_revisit_data.columns = ["数据id", "账号", "提交时间", "更新时间", "表单类型", "表单状态"]
|
||||
# JDY_revisit_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_原始数据.xlsx'), index=False)
|
||||
|
||||
JDY_revisit_data = pd.DataFrame(JDY_revisit_data)
|
||||
JDY_revisit_data.columns = ["数据id", "账号", "提交时间", "更新时间", "表单类型", "表单状态"]
|
||||
# JDY_revisit_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_原始数据.xlsx'), index=False)
|
||||
filtered_data = JDY_revisit_data[JDY_revisit_data['表单类型'] == '异常待办'] # 过滤表单类型
|
||||
# filtered_data = filtered_data[filtered_data['表单状态'] == 1] # 过滤表单状态
|
||||
# filtered_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_过滤数据.xlsx'), index=False)
|
||||
|
||||
filtered_data = JDY_revisit_data[JDY_revisit_data['表单类型'] == '异常待办'] # 过滤表单类型
|
||||
# filtered_data = filtered_data[filtered_data['表单状态'] == 1] # 过滤表单状态
|
||||
# filtered_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_过滤数据.xlsx'), index=False)
|
||||
filtered_data['提交时间'] = pd.to_datetime(filtered_data['提交时间']).dt.strftime("%Y-%m-%d")
|
||||
latest_update_time = filtered_data.groupby('账号')['提交时间'].max().reset_index()
|
||||
latest_update_time.rename(columns={'提交时间': '最新提交时间'}, inplace=True)
|
||||
|
||||
filtered_data['提交时间'] = pd.to_datetime(filtered_data['提交时间']).dt.strftime("%Y-%m-%d")
|
||||
latest_update_time = filtered_data.groupby('账号')['提交时间'].max().reset_index()
|
||||
latest_update_time.rename(columns={'提交时间': '最新提交时间'}, inplace=True)
|
||||
filtered_data_with_latest = pd.merge(
|
||||
filtered_data,
|
||||
latest_update_time,
|
||||
left_on=['账号', '提交时间'],
|
||||
right_on=['账号', '最新提交时间']
|
||||
)
|
||||
|
||||
# 过滤出每个账号中提交时间为最新的记录
|
||||
latest_JDY_abnormal_data = filtered_data_with_latest[
|
||||
filtered_data_with_latest['提交时间'] == filtered_data_with_latest['最新提交时间']
|
||||
]
|
||||
# latest_JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_最新数据_1.xlsx'), index=False)
|
||||
|
||||
filtered_data_with_latest = pd.merge(
|
||||
filtered_data,
|
||||
latest_update_time,
|
||||
left_on=['账号', '提交时间'],
|
||||
right_on=['账号', '最新提交时间']
|
||||
)
|
||||
latest_JDY_abnormal_data['提交时间'] = pd.to_datetime(latest_JDY_abnormal_data['提交时间']).dt.strftime(
|
||||
"%Y-%m-%d")
|
||||
|
||||
# 过滤出每个账号中提交时间为最新的记录
|
||||
latest_JDY_abnormal_data = filtered_data_with_latest[
|
||||
filtered_data_with_latest['提交时间'] == filtered_data_with_latest['最新提交时间']
|
||||
]
|
||||
# latest_JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_最新数据_1.xlsx'), index=False)
|
||||
thirty_days_ago = (current_date - timedelta(days=30)).strftime("%Y-%m-%d")
|
||||
|
||||
final_JDY_abnormal_data = latest_JDY_abnormal_data[
|
||||
latest_JDY_abnormal_data['提交时间'] > thirty_days_ago] # 筛选出提交时间为近30天的数据
|
||||
|
||||
latest_JDY_abnormal_data['提交时间'] = pd.to_datetime(latest_JDY_abnormal_data['提交时间']).dt.strftime("%Y-%m-%d")
|
||||
# final_JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_最新数据.xlsx'), index=False)
|
||||
|
||||
thirty_days_ago = (current_date - timedelta(days=30)).strftime("%Y-%m-%d")
|
||||
df_abnormal_data = df_abnormal_data[~df_abnormal_data['账号'].isin(final_JDY_abnormal_data['账号'])]
|
||||
# empty_num = df_abnormal_data['手机号'].isnull().sum()
|
||||
df_abnormal_data = df_abnormal_data[df_abnormal_data["联系手机号"] != "None"]
|
||||
# df_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_派发数据.xlsx'), index=False)
|
||||
|
||||
final_JDY_abnormal_data = latest_JDY_abnormal_data[latest_JDY_abnormal_data['提交时间'] > thirty_days_ago] # 筛选出提交时间为近30天的数据
|
||||
self.send_request(df_abnormal_data)
|
||||
common_module.send_task_status(task_start_time, "接车宝异常派发")
|
||||
logger.info("接车宝异常派发完成")
|
||||
|
||||
# final_JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_最新数据.xlsx'), index=False)
|
||||
|
||||
df_abnormal_data = df_abnormal_data[~df_abnormal_data['账号'].isin(final_JDY_abnormal_data['账号'])]
|
||||
# empty_num = df_abnormal_data['手机号'].isnull().sum()
|
||||
df_abnormal_data = df_abnormal_data[df_abnormal_data["联系手机号"] != "None"]
|
||||
# df_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_派发数据.xlsx'), index=False)
|
||||
|
||||
self.send_request(df_abnormal_data)
|
||||
common_module.send_task_status(task_start_time, "接车宝异常派发")
|
||||
|
||||
|
||||
# df_abnormal_data = [self.row_to_dict(row, self.field_mapping) for index, row in
|
||||
# df_abnormal_data.iterrows()]
|
||||
#
|
||||
# data = {'api_key': Config.EFFICIENT_CAR_PICKUP_APP_ID, 'entry_id':"67174710da507490d8ac12c1",
|
||||
# "data_list": df_abnormal_data}
|
||||
#
|
||||
#
|
||||
# result = api_instance.entry_data_batch_create(data)
|
||||
except Exception as e:
|
||||
common_module.send_task_error(task_start_time, "接车宝异常派发", str(e))
|
||||
error_task_logger.error(f"接车宝异常派发执行异常: {e}")
|
||||
|
||||
@staticmethod
|
||||
def row_to_dict(row, field_mapping):
|
||||
|
||||
Reference in New Issue
Block a user