389 lines
18 KiB
Python
389 lines
18 KiB
Python
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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from back_ground_module import CommonModule
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from api import API
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from log_config import configure_task_logger, configure_error_task_logger
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from datetime import datetime, timedelta, timezone
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import pandas as pd
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import os
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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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# 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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common_module = CommonModule()
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api_instance = API()
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class JCBEfficientCarPickup:
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"""接车宝日常回访"""
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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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self.field_mapping = {}
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self.staff_id_list = None
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self.customer_service_list = None
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def load_cus_data(self):
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# 获取接车宝客服表单
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payload = {"api_key": "6717470a0b3975ef583c6df1",
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"entry_id": "67b6f2462f9ac03b783d409a",
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}
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customer_service = api_instance.entry_data_list(payload)
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customer_service_list = customer_service.get("data") # api请求格式，将数据封装在data字典里
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return customer_service_list
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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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for row_items in self.load_cus_data():
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# print(row_items)
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customer_service_name_id = row_items.get("_widget_1740042824214", {}).get("username", {})
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customer_service_name = row_items.get("_widget_1740042824214", {}).get("name", {})
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customer_service_state = row_items.get("_widget_1740117343937", {})
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is_last_day_end = row_items.get("_widget_1740042824216", {})
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customer_service_data_id = row_items.get("_id", {})
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print(customer_service_name, customer_service_name_id, customer_service_state, is_last_day_end)
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all_customer_service_list.append(
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[customer_service_name, customer_service_name_id, customer_service_state, is_last_day_end,
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customer_service_data_id])
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if is_last_day_end == "是": # 判断是否是下次开始位置
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last_day_end_customer_service = customer_service_name_id
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is_customer_service_data_id = row_items.get("_id", {})
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split_index = None
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for index, row in enumerate(all_customer_service_list):
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print(row[3])
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if row[3] == "是":
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split_index = index
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print(f"找到索引 {index}")
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break
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if split_index is not None:
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# 根据索引切割列表
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first_part = all_customer_service_list[split_index:] # 索引位置及之后的行
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second_part = all_customer_service_list[:split_index] # 索引位置之前的行
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# 调换两个子列表的位置并重新组合
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today_customer_service_start_list = first_part + second_part
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else:
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# 如果没有找到"是"，保持原列表不变
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today_customer_service_start_list = all_customer_service_list
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pass
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for index, row in enumerate(today_customer_service_start_list):
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if row[2] == "开":
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today_customer_service_list.append(row[1])
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return today_customer_service_list, is_customer_service_data_id, all_customer_service_list
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def send_request(self, df):
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if df is None or df.empty: # 检查DataFrame是否为None或空
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logger.info("当前派发数据为空或None，跳过此派发")
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return
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today_customer_service_list, is_customer_service_data_id, all_customer_service_list = self.today_customer_service_list()
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# 初始化派发索引
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next_dispatcher_index = 0
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# 显式循环分配跟进人
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follow_up_persons = []
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for _ in range(len(df)):
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follow_up_person = today_customer_service_list[next_dispatcher_index]
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follow_up_persons.append(follow_up_person)
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next_dispatcher_index = (next_dispatcher_index + 1) % len(today_customer_service_list)
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# 添加跟进人到 DataFrame
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df["跟进人"] = follow_up_persons
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# 获取下一个派发人
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next_dispatcher = today_customer_service_list[next_dispatcher_index]
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new_sign_abnormal_data = [self.row_to_dict(row, self.field_mapping) for index, row in
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df.iterrows()]
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data = {'api_key': Config.EFFICIENT_CAR_PICKUP_APP_ID, 'entry_id': Config.EFFICIENT_CAR_PICKUP_ENTRY_ID,
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"data_list": new_sign_abnormal_data}
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result = api_instance.entry_data_batch_create(data)
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logger.info(f"数据发送成功:{result}")
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data1 = {"api_key": Config.EFFICIENT_CAR_PICKUP_APP_ID,
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"entry_id": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_SERVICE_ID,
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"data_id": is_customer_service_data_id,
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"data":
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{"_widget_1740042824216": {"value": ""}, }
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} # 原来的是"_widget_1740042824216": {"value": "是"},修改昨日截至人员
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next_customer_service_data_id = None
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for index, row in enumerate(all_customer_service_list):
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print(row[3])
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if row[1] == next_dispatcher:
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next_customer_service_data_id = row[4]
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break
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data2 = {"api_key": Config.EFFICIENT_CAR_PICKUP_APP_ID,
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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"}, }}
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api_instance.entry_data_update(data1)
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result2 = api_instance.entry_data_update(data2)
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logger.info(f"明日派发人员信息已修改:{result2}")
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def load_all_data(self):
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# 获取接车宝日常回访单
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payload = {"api_key": "6717470a0b3975ef583c6df1",
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"entry_id": "67174710da507490d8ac12c1",
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}
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daily_revisit = api_instance.entry_data_list(payload)
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self.daily_revisit_list = daily_revisit.get("data") # api请求格式，将数据封装在data字典里
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def main(self):
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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"接车宝日常回访开始执行")
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data_JCB = common_module.get_jcb_details()
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if data_JCB is None:
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logger.error("获取接车宝数据失败，返回None")
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raise ValueError("获取接车宝数据失败，返回None")
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self.load_all_data()
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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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# 新签异常待办回访。
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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=0)
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current_date_str = current_date.strftime("%Y-%m-%d")
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seven_days_ago = current_date - timedelta(days=7)
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seven_days_ago = seven_days_ago.date()
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# print(three_days_ago)
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new_sign_abnormal = []
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for index, row in data_JCB.iterrows():
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new_row = row.copy()
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# 先转成字符串，再解析回 date 对象
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new_row['开户日'] = datetime.strptime(str(row['开户日']), "%Y-%m-%d").date()
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if new_row['开户日'] == seven_days_ago and row['当月开单天数'] == 0:
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# print(row['账号'], row['开户日'], row['当月开单天数'])
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row["日期"] = datetime.strptime(str(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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new_sign_abnormal.append(row)
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new_sign_abnormal = pd.DataFrame(new_sign_abnormal) if new_sign_abnormal else None
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if new_sign_abnormal is not None and not new_sign_abnormal.empty:
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new_sign_abnormal["表单类型"] = "新签异常待办"
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new_sign_abnormal["派发日期"] = current_date_str
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# self.send_request(new_sign_abnormal) # 发送请求
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logger.info(f"新签异常待办回访完成")
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else:
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logger.info(f"新签异常待办回访无数据，跳过")
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# 异常待办
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current_local = datetime.now() + timedelta(days=-1) # tz-naive，代表本地时间
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current_date_str = current_local.strftime("%Y-%m-%d")
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# 计算30天前的本地日期（用于开户日判断）
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thirty_days_ago_local = (current_local - timedelta(days=30)).date()
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abnormal_data = []
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for index, row in data_JCB.iterrows():
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try:
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# 开户日是本地日期字符串，解析为 date 对象
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open_date = datetime.strptime(str(row['开户日']), "%Y-%m-%d").date()
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except (ValueError, TypeError):
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continue # 跳过无效日期
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if (
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open_date < thirty_days_ago_local
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and row['近30天开单天数'] == 0
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and row['客户状态'] == "留存"
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):
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new_row = row.copy()
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new_row["日期"] = open_date.strftime("%Y-%m-%d")
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abnormal_data.append(new_row)
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abnormal_data = pd.DataFrame(abnormal_data) if abnormal_data else pd.DataFrame()
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if not abnormal_data.empty:
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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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# 清洗手机号（仅去除浮点型 .0）
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def clean_phone(x):
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if pd.isna(x) or x == "" or x == "None":
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return ""
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s = str(x)
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if s.endswith('.0') and s[:-2].isdigit():
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return s[:-2]
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return s
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abnormal_data['联系手机号'] = abnormal_data['联系手机号'].apply(clean_phone)
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# 构建云端已派发记录 DataFrame
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df_cloud = pd.DataFrame([
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{
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"数据id": item.get("_id", ""),
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"账号": item.get("_widget_1739258942667", ""),
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"提交时间": item.get("createTime", ""),
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"表单类型": item.get("_widget_1739951204545", "")
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}
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for item in self.daily_revisit_list
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])
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recent_accounts = set()
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if not df_cloud.empty and not abnormal_data.empty:
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# 将 createTime 转为 UTC 时间（强制统一时区）
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df_cloud["提交时间"] = pd.to_datetime(df_cloud["提交时间"], utc=True, errors="coerce")
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df_cloud = df_cloud.dropna(subset=["提交时间"])
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# 筛选“异常待办”
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df_abnormal_cloud = df_cloud[df_cloud["表单类型"] == "异常待办"]
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if not df_abnormal_cloud.empty:
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# 每个账号保留最新一条
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df_recent = df_abnormal_cloud.sort_values("提交时间").groupby("账号", as_index=False).tail(1)
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current_utc = datetime.now(timezone.utc)
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cutoff_utc = pd.Timestamp(current_utc) - pd.Timedelta(days=30)
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# 安全比较：两边都是 UTC
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recent_accounts = set(df_recent[df_recent["提交时间"] > cutoff_utc]["账号"])
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# 剔除已派发账号 + 过滤有效手机号
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if not abnormal_data.empty:
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abnormal_data = abnormal_data[
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(~abnormal_data["账号"].isin(recent_accounts)) &
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(abnormal_data["联系手机号"].notna()) &
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(abnormal_data["联系手机号"] != "") &
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(abnormal_data["联系手机号"] != "None")
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]
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# # 保存结果
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output_path = os.path.join(output_dir, "异常待办1.csv")
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abnormal_data.to_csv(output_path, index=False)
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# 发送或跳过
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if not abnormal_data.empty:
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abnormal_data = abnormal_data[:40]
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# self.send_request(abnormal_data)
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logger.info(f"异常待办完成，共 {len(abnormal_data)} 条")
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else:
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logger.info("异常待办无数据，跳过")
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# 优质客户转商机
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# current_date = datetime.now()
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thirty_days_ago = current_date - timedelta(days=30)
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sixty_days_ago = current_date - timedelta(days=60)
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thirty_days_ago = thirty_days_ago.date()
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sixty_days_ago = sixty_days_ago.date()
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customer_to_opportunity = []
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for index, row in data_JCB.iterrows():
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new_row = row.copy()
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# 先转成字符串，再解析回 date 对象
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new_row['到期日'] = datetime.strptime(str(row['到期日']), "%Y-%m-%d").date()
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if new_row['到期日'] == thirty_days_ago and row['近一周开单量'] >= 3 and row[
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'G状态：近30天开单大于等于10天'] == 1:
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print(row['账号'], row['到期日'], row['当月开单天数'], row['当月G天数'])
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row["日期"] = datetime.strptime(str(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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customer_to_opportunity.append(row)
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# 推送给客服
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pass
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if new_row['到期日'] == sixty_days_ago and row['近一周开单量'] >= 3 and row[
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'G状态：近30天开单大于等于10天'] == 1:
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print(row['账号'], row['到期日'], row['当月开单天数'], row['当月G天数'])
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row["日期"] = datetime.strptime(str(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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customer_to_opportunity.append(row)
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# 推送给客服
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pass
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customer_to_opportunity = pd.DataFrame(customer_to_opportunity) if customer_to_opportunity else None
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if customer_to_opportunity is not None and not customer_to_opportunity.empty:
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customer_to_opportunity["表单类型"] = "续约优质客户转商机"
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customer_to_opportunity["派发日期"] = current_date_str
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# self.send_request(customer_to_opportunity)
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logger.info(f"优质客户转商机完成")
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else:
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logger.info(f"优质客户转商机无数据，跳过")
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# 过期7天客服回访
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# current_date = datetime.now()
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seven_days_ago = current_date - timedelta(days=7)
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seven_days_ago = seven_days_ago.date()
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outdated_30 = []
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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['到期日'] = datetime.strptime(str(row['到期日']), "%Y-%m-%d").date()
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# seven_days_ago = seven_days_ago.date()
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# print(row['到期日'], seven_days_ago)
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if new_row['到期日'] == seven_days_ago and row['客户状态'] == "过期":
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print(row['账号'], row['到期日'], row['当月开单天数'], row['当月G天数'])
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row["日期"] = datetime.strptime(str(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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outdated_30.append(row)
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# 推送给客服
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pass
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outdated_30 = pd.DataFrame(outdated_30) if outdated_30 else None
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if outdated_30 is not None and not outdated_30.empty:
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outdated_30["表单类型"] = "过期7天回访"
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outdated_30["派发日期"] = current_date_str
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# self.send_request(outdated_30)
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logger.info(f"过期7天客服回访完成")
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else:
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logger.info(f"过期7天客服回访无数据，跳过")
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common_module.send_task_status(task_start_time, "接车宝日常派发")
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logger.info(f"接车宝日常派发执行完成")
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except Exception as e:
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common_module.send_task_error(task_start_time, "接车宝日常派发", str(e))
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error_task_logger.error(f"接车宝日常派发执行出错：{e}")
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@staticmethod
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def row_to_dict(row, field_mapping):
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"""将一行数据转换为指定格式的字典"""
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result = {}
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# print(field_mapping)
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for col_name, widget_id in field_mapping.items():
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# print(col_name, widget_id)
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if col_name in row:
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value = row[col_name]
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clean_value = None if pd.isna(value) else value
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result[widget_id] = {"value": clean_value}
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return result
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def fields(self):
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self.field_mapping = {"日期": "_widget_1739252804406", "产品名称": "_widget_1739252804397",
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"账号": "_widget_1739258942667", "联系手机号": "_widget_1739252804407",
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"使用时长": "_widget_1739252804409", "开户日": "_widget_1739252804396",
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"到期日": "_widget_1739252804408", "续约日": "_widget_1739252804410",
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"客户状态": "_widget_1739252804400", "近一周开单量": "_widget_1739252804413",
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"近一周是否活跃": "_widget_1739252804414",
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"G状态：近30天开单大于等于10天": "_widget_1739252804415",
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"当月开单天数": "_widget_1739252804416", "近30天开单天数": "_widget_1739252804417",
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"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()
|