613 lines
26 KiB
Python
613 lines
26 KiB
Python
import os
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from datetime import datetime
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import pandas as pd
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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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from collections import defaultdict
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from datetime import datetime, timezone, timedelta, date, UTC
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from config import Config
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logger = configure_task_logger()
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error_task_logger = configure_error_task_logger()
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api_instance = API()
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common_module = CommonModule()
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output_dir = "output" # 设置输出目录
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os.makedirs(output_dir, exist_ok=True)
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import pandas as pd
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import psycopg2
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from datetime import datetime, timedelta, date
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import os
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def get_renewal_details():
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"""
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从固定的数据库中获取续约待办数据，先拉全量（指定 date_id），再用 CSV 中的门店编码在内存中过滤
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"""
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try:
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# 1. 从 CSV 文件中读取门店编码
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csv_path = r"D:\Idea Project\SaaS_V1.7\test\output\expanded_yd_data.csv"
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if not os.path.exists(csv_path):
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error_task_logger.error(f"CSV 文件不存在: {csv_path}")
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return pd.DataFrame()
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store_df = pd.read_csv(csv_path, dtype=str)
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if "textField_ksydghqw" not in store_df.columns:
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error_task_logger.error("CSV 文件中缺少列 'textField_ksydghqw'")
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return pd.DataFrame()
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store_codes = set(store_df["textField_ksydghqw"].dropna().unique()) # 转为 set 提升查找效率
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if not store_codes:
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error_task_logger.warning("CSV 中未找到有效的门店编码")
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return pd.DataFrame()
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# 2. 连接数据库
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conn = psycopg2.connect(**Config.CONN_INFO)
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cursor = conn.cursor()
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# 获取前两天的 date_id（整数格式 YYYYMMDD）
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now_time = datetime.now()
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yes_time = now_time + timedelta(days=-2)
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yes_time_nyr = int(yes_time.strftime('%Y%m%d'))
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# 3. 构造 SQL 查询：不再包含 org_code 过滤
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sql = """
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SELECT *
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FROM "public"."holo_ads_report_saas_profile_ngv_detail_d"
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WHERE "date_id" = %s
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"""
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# 执行查询（只传 date_id）
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cursor.execute(sql, (yes_time_nyr,))
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rows = cursor.fetchall()
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all_fields = cursor.description
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# 转换为 DataFrame
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col = [i[0] for i in all_fields]
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data_NGV = pd.DataFrame(rows, columns=col) if rows else pd.DataFrame(columns=col)
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# 关闭连接
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cursor.close()
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conn.close()
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# 4. 在内存中用 store_codes 过滤 org_code
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if "org_code" not in data_NGV.columns:
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error_task_logger.error("数据库结果中缺少 'org_code' 字段")
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return pd.DataFrame()
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# 确保 org_code 是字符串类型（与 store_codes 一致）
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data_NGV = data_NGV.copy() # 避免 SettingWithCopyWarning
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data_NGV["org_code"] = data_NGV["org_code"].astype(str)
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# 过滤：只保留 org_code 在 store_codes 中的行
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filtered_data = data_NGV[data_NGV["org_code"].isin(store_codes)].reset_index(drop=True)
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return filtered_data
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except Exception as e:
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error_task_logger.error(f"获取续约待办数据时出错: {e}", exc_info=True)
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return pd.DataFrame()
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class RenewalToDo:
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"""续约回访待办派发"""
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def __init__(self):
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self.renewal_data_list = None
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self.cyclic_increasing = None
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self.franchisee = None
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self.last_price = None
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self.province_staff_id_list = None
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self.json_list = None
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self.data_NGV = None
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self.staff_id_list = None
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self.NGV_data_list = None
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self.field_map = {
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"关联数据": "_widget_1764820541663",
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"公司名称": "_widget_1764820541616",
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"门店名称": "_widget_1764820541617",
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"门店编码": "_widget_1764820541661",
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"加盟商": "_widget_1764820541618",
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"过期日": "_widget_1764820541672",
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"Saas版本": "_widget_1764820541623",
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"上次购买价格": "_widget_1764820541624",
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"联系人": "_widget_1764820541621",
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"联系手机号": "_widget_1764820541622",
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"专属运营顾问": "_widget_1764820541625",
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"区域客服": "_widget_1764820541715",
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"运营专家": "_widget_1764820541678",
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"120天是否跟进": "_widget_1764820541628",
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"120天处理人": "_widget_1764820541634",
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"120天跟进时间": "_widget_1765352838631",
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"60天是否跟进": "_widget_1764820541630",
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"60天处理人": "_widget_1764820541635",
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"60天跟进时间": "_widget_1765352838632",
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"30天是否跟进": "_widget_1764820541632",
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"30天处理人": "_widget_1764820541636",
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"30天跟进时间": "_widget_1765352838633",
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"是否联系上客户": "_widget_1764820541638",
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"客户现阶段问题分类": "_widget_1764820541641",
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"未联系上原因字段": "_widget_1765330820509",
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"联系情况及问题说明": "_widget_1764820541653",
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"潜在商机": "_widget_1764820541657",
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"商机详情": "_widget_1764820541659",
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"门店续约意愿": "_widget_1764820541654",
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"不续约原因": "_widget_1764820541700",
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"产品原因": "_widget_1764820541707",
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"服务问题": "_widget_1764820541709",
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"门店原因": "_widget_1764820541711",
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"价格原因": "_widget_1764820541713",
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"不续约具体情况说明": "_widget_1764820541702",
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"回访完成方式": "_widget_1764820541697",
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"周期性增购": "_widget_1764820541717",
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"周期性增购.商品名称": "_widget_1764820541717._widget_1764820541719",
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"周期性增购.分母金额": "_widget_1764820541717._widget_1764820541720",
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"周期性增购.应续约日": "_widget_1764820541717._widget_1764820541721",
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"周期性增购.上次购买数量": "_widget_1764820541717._widget_1764820541722",
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"周期性增购.不续约原因": "_widget_1764820541717._widget_1764820541723",
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"周期性增购.是否愿意续约": "_widget_1764820541717._widget_1764820541724",
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"周期性增购.续约后订单编码": "_widget_1764820541717._widget_1764820541725",
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"订单编码": "_widget_1764820541674",
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"订单支付日期": "_widget_1764820541679",
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"本次-实付金额（元）": "_widget_1764820541676",
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"业务类型（续约、升级）": "_widget_1764820541680",
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"连锁门店待办同步处理": "_widget_1764820541681",
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"选择需要同步的门店名称": "_widget_1765330820391",
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"120天自动流转时间": "_widget_1764820541865",
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"60天自动流转时间": "_widget_1765964381895",
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"30天自动流转时间": "_widget_1765964381896",
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"0天自动流转时间": "_widget_1765964381897",
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"当前所处节点": "_widget_1765352838609",
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"流程状态": "_widget_1765352838610",
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"经营模式": "_widget_1765964381952",
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"公司等级": "_widget_1766130435561",
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"公司id": "_widget_1766631811839",
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"订单商品名称": "_widget_1766730385209",
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"提交人": "creator",
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"提交时间": "createTime",
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"更新时间": "updateTime"
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}
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self.cn_field_map = {
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"related_data": "关联数据",
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"group_name": "公司名称",
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"org_name": "门店名称",
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"org_code": "门店编码",
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"expiry_time": "过期日",
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"saas_edition_fmt": "Saas版本",
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"contacts": "联系人",
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"contact_mobile": "联系手机号",
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"service_impl_principal": "专属运营顾问",
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"group_grade": "公司等级",
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"technician": "运营专家",
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"manage_model": "经营模式",
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"id_own_group": "公司id",
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}
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self.subform_field_map = {
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"商品名称": "_widget_1764820541719",
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"分母金额": "_widget_1764820541720",
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"应续约日": "_widget_1764820541721",
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"上次购买数量": "_widget_1764820541722",
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"不续约原因": "_widget_1764820541723",
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"是否愿意续约": "_widget_1764820541724",
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"续约后订单编码": "_widget_1764820541725",
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# 根据实际需要添加更多字段
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}
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self.renewal_list_map = {
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}
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def load_all_data(self):
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"""
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从各类来源加载数据上加载数据
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:return:
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"""
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# 数据库获取续约回访数据
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self.data_NGV = common_module.get_renewal_details()
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# self.data_NGV = get_renewal_details() # 历史数据
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self.data_NGV.to_csv("D:\\Idea Project\\SaaS_V1.7\\test\\output\data_NGV1.csv")
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# 获取加盟商信息
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self.franchisee = common_module.get_renewal_franchisee_details()
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self.franchisee.to_csv(os.path.join(output_dir, "franchisee.csv"))
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# 获取上次购买价格
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self.last_price = common_module.get_renewal_last_price_details()
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self.last_price.to_csv(os.path.join(output_dir, "last_price.csv"))
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# 周期性增购
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self.cyclic_increasing = common_module.get_cyclic_increasing_renewal_details()
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self.cyclic_increasing.to_csv(os.path.join(output_dir, "cyclic_increasing.csv"))
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# 获取NGV数据
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payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "675bb02bd2d53c2034c665e4"}
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self.NGV_data_list = api_instance.entry_data_list(payload).get("data")
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# 获取简道云员工id
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payload = {"api_key": "6694d3c4fcb69ca9a111a6c4",
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"entry_id": "6769204a1902c9341340a1bc",
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}
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staff_id = api_instance.entry_data_list(payload)
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self.staff_id_list = staff_id.get("data") # api请求格式，将数据封装在data字典里
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# 省市区人员关系表
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payload = {"api_key": "675b900991ad2491c69389ca", "entry_id": "676512ac3e54dc3159460c0a"}
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json_dict = api_instance.entry_data_list(payload)
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if json_dict and "data" in json_dict:
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self.province_staff_id_list = json_dict.get("data")
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else:
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print("加载省市区人员关系表失败")
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self.province_staff_id_list = []
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# 获取已派发续约待办(进行中)
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payload = {"api_key": "675b900991ad2491c69389ca",
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"entry_id": "6931063d64187eaf6b927557",
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"filter": {"rel": "and",
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"cond": [{"field": "flowState", "type": "flowstate", "method": "eq", "value": [0]}]},
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}
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renewal = api_instance.entry_data_list(payload)
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self.renewal_data_list = renewal.get("data")
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@staticmethod
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def replace_names_with_staff_ids(df, name_columns, staff_id_list):
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"""
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将 DataFrame 中多个姓名列替换为对应的员工ID。
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:param staff_id_list: 简道云获取到员工id
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:param df: pandas.DataFrame
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:param name_columns: list[str]，需要替换的姓名列名列表，例如 ["col1", "col2"]
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:return: 修改后的 DataFrame（原列被替换）
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"""
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# 1. 构建姓名 -> 员工ID 的映射字典（只做一次）
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name_to_id = {}
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for item in staff_id_list or []:
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name = item.get("_widget_1734942794144")
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staff_id = item.get("_widget_1734942794145")
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if name and staff_id:
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name_to_id[str(name).strip()] = str(staff_id)
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# 2. 对每个指定的列进行替换
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df = df.copy() # 避免修改原始数据
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for col in name_columns:
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if col not in df.columns:
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continue # 跳过不存在的列
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# 替换：姓名 → ID，找不到的保留原值（可改为 fillna(None)）
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df[col] = (
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df[col]
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.astype(str)
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.str.strip()
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.map(name_to_id)
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.fillna(df[col])
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)
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return df
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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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for col_name, widget_id in field_mapping.items():
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if col_name not in row:
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continue
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value = row[col_name]
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# 处理：如果 value 是容器类型（list, dict, tuple, np.ndarray），不进行 pd.isna 判断
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if isinstance(value, (list, dict, tuple)) or (hasattr(value, '__len__') and not isinstance(value, str)):
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clean_value = value
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else:
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# 标量类型：安全使用 pd.isna
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if pd.isna(value):
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clean_value = None
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elif isinstance(value, pd.Timestamp):
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clean_value = value.strftime('%Y-%m-%dT%H:%M:%SZ')
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else:
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clean_value = value
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# 所有字段统一包 {"value": ...}，包括子表单
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result[widget_id] = {"value": clean_value}
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return result
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@staticmethod
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def en_row_to_cn_row(en_row, en_to_cn_map):
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"""
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将英文字段的行数据转换为中文字段的行数据
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:param en_row: dict 或 pandas.Series，key 为英文字段
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:param en_to_cn_map: dict, 英文字段名 -> 中文字段名
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:return: dict，key 为中文字段名
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"""
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cn_row = {}
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for en_key, value in en_row.items():
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if en_key in en_to_cn_map:
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cn_key = en_to_cn_map[en_key]
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cn_row[cn_key] = value
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# 可选：忽略无法映射的字段，或记录警告
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return cn_row
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@staticmethod
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def get_customer_service_by_location(province_name, city_name, area_name, staff_id_list):
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"""
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直接遍历 self.staff_id_list，根据省市区匹配续约回访客服。
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:return: 客服用户名（str），未找到则返回提示信息
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"""
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if not all([province_name, city_name, area_name]):
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return "数据缺失: 省市区不完整"
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for item in staff_id_list or []:
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try:
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prov = item.get('_widget_1734677164861', '').strip()
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city = item.get('_widget_1734677164862', '').strip()
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area = item.get('_widget_1734677164863', '').strip()
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if (prov == province_name.strip() and
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city == city_name.strip() and
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area == area_name.strip()):
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# 提取客服用户名
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staff_info = item.get('_widget_1734677164869', {}) # 续约回访客服
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username = staff_info.get('username')
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return username if username else "数据缺失: 客服用户名为空"
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except Exception:
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continue # 跳过格式异常的记录
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return "数据缺失: 未找到对应的续约回访客服"
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def build_subform_records(
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self,
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df: pd.DataFrame,
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group_by_col: str,
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field_mapping: dict,
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) -> dict:
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"""
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通用子表单预处理函数：将子表单 DataFrame 转换为 {group_key: [subform_record1, subform_record2, ...]} 的字典。
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:param df: 子表单数据 DataFrame，列名为中文（如 "商品名称", "分母金额"）
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:param group_by_col: 用于分组的列名（如 "门店编码"）
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:param field_mapping: 字段映射字典，{中文字段名: widget_id}，例如 {"商品名称": "_widget_xxx"}
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:return: dict，key 为 group_by_col 的值，value 为该组对应的子表单记录列表，
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每条记录是 {widget_id: {"value": clean_value}} 的 dict
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"""
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if df.empty:
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return defaultdict(list)
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result = defaultdict(list)
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target_fields = set(field_mapping.keys())
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for _, row in df.iterrows():
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row_dict = row.to_dict()
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group_key = row_dict.get(group_by_col)
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if not group_key or (isinstance(group_key, str) and group_key.strip() == ""):
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warning_msg = f"子表单行缺少分组字段 '{group_by_col}'，跳过: {row_dict}"
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# 构建单条子表单记录
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sub_record = {}
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for field_cn, widget_id in field_mapping.items():
|
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val = row_dict.get(field_cn)
|
||
|
||
# 清理值
|
||
if pd.isna(val):
|
||
clean_val = None
|
||
elif hasattr(val, 'to_eng_string'): # Decimal
|
||
try:
|
||
clean_val = float(val)
|
||
except (ValueError, TypeError):
|
||
clean_val = str(val)
|
||
elif isinstance(val, pd.Timestamp):
|
||
clean_val = val.strftime('%Y-%m-%d %H:%M:%S')
|
||
else:
|
||
clean_val = val
|
||
|
||
sub_record[widget_id] = {"value": clean_val}
|
||
|
||
result[group_key].append(sub_record)
|
||
|
||
return result
|
||
|
||
def process_data(self):
|
||
"""
|
||
数据处理加工
|
||
:return: 处理后的 DataFrame，列名为中文
|
||
"""
|
||
data_NGV = self.data_NGV.copy() # 避免修改原始数据
|
||
|
||
# === 将英文字段名替换为中文字段名 ===
|
||
# 但只重命名存在的列
|
||
rename_map = {en: cn for en, cn in self.cn_field_map.items() if en in data_NGV.columns}
|
||
data_NGV.rename(columns=rename_map, inplace=True)
|
||
|
||
# 日期字段处理（使用中文列名）
|
||
time_columns = ['过期日']
|
||
data_NGV[time_columns] = data_NGV[time_columns].apply(
|
||
lambda col: pd.to_datetime(col, errors='coerce')
|
||
.dt.tz_localize('Asia/Shanghai')
|
||
.dt.tz_convert('UTC')
|
||
)
|
||
|
||
# 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)
|
||
|
||
data_NGV['120天是否跟进'] = "主动"
|
||
data_NGV['60天是否跟进'] = "主动"
|
||
data_NGV['30天是否跟进'] = "主动"
|
||
# 格式化为字符串（去掉时区）
|
||
for col in ['过期日', '120天自动流转时间', '60天自动流转时间', '30天自动流转时间', '0天自动流转时间']:
|
||
data_NGV[col] = data_NGV[col].dt.strftime('%Y-%m-%d %H:%M:%S')
|
||
|
||
# 新增加盟商列
|
||
data_NGV = data_NGV.merge(
|
||
self.franchisee[['门店编码', '加盟商']],
|
||
on='门店编码',
|
||
how='left'
|
||
)
|
||
|
||
# 新增上次购买价格列
|
||
# 1. 清洗数据
|
||
df_lp = self.last_price[['门店编码', '类型', '订单商品名称', '价格']].copy()
|
||
|
||
# 处理“类型”和“订单商品名称”的缺失值
|
||
df_lp['类型'] = df_lp['类型'].fillna('').astype(str)
|
||
df_lp['订单商品名称'] = df_lp['订单商品名称'].fillna('').astype(str)
|
||
|
||
# 处理价格：转数字、四舍五入、填0、转字符串
|
||
df_lp['价格'] = (
|
||
pd.to_numeric(df_lp['价格'], errors='coerce')
|
||
.round().fillna(0).astype(int).astype(str)
|
||
)
|
||
|
||
# 2. 拼接“类型:价格”
|
||
df_lp['类型_价格'] = df_lp['类型'] + ':' + df_lp['价格']
|
||
|
||
# 3. 按门店聚合两列
|
||
agg_df = df_lp.groupby('门店编码', as_index=False).agg({
|
||
'类型_价格': lambda x: ';'.join(x),
|
||
'订单商品名称': lambda x: ';'.join(x)
|
||
})
|
||
|
||
# 4. 合并回主表
|
||
data_NGV = data_NGV.merge(agg_df, on='门店编码', how='left')
|
||
|
||
# 5. 填充缺失值为空字符串，并重命名列
|
||
data_NGV['类型_价格'] = data_NGV['类型_价格'].fillna('')
|
||
data_NGV['订单商品名称'] = data_NGV['订单商品名称'].fillna('')
|
||
|
||
data_NGV.rename(columns={
|
||
'类型_价格': '上次购买价格',
|
||
'订单商品名称': '订单商品名称'
|
||
}, inplace=True)
|
||
|
||
# 成员字段替换（现在列名是中文）
|
||
staff_name_cols = [
|
||
"专属运营顾问",
|
||
"运营专家",
|
||
]
|
||
data_NGV = self.replace_names_with_staff_ids(data_NGV, staff_name_cols, self.staff_id_list)
|
||
|
||
return data_NGV
|
||
|
||
def dispatch_task(self, data_NGV):
|
||
"""
|
||
拆分为三个独立动作（输入 data_NGV 列名为中文）：
|
||
1. 获取关联数据（NGV_data_id）
|
||
2. 获取区域客服（regional_customer_service）
|
||
3. 字段映射与格式化（中文 → widget），正确处理子表单
|
||
"""
|
||
records = []
|
||
no_customer_service_data = []
|
||
|
||
# === 使用通用函数预处理周期性增购子表单 ===
|
||
cyclic_subforms = self.build_subform_records(
|
||
df=self.cyclic_increasing,
|
||
group_by_col="门店编码",
|
||
field_mapping=self.subform_field_map,
|
||
)
|
||
|
||
# === Step 1: 构建 门店编码 → NGV 数据ID 映射 ===
|
||
org_code_to_ngv_id = {}
|
||
for ngv_item in self.NGV_data_list or []:
|
||
org_code = ngv_item.get("_widget_1734062123071")
|
||
ngv_id = ngv_item.get("_id")
|
||
if org_code and ngv_id:
|
||
org_code_to_ngv_id[org_code] = ngv_id
|
||
|
||
# === Step 2: 定义获取区域客服的函数 ===
|
||
def get_regional_customer_service(row):
|
||
province = row.get("省份") or row.get("province_name")
|
||
city = row.get("城市") or row.get("city_name")
|
||
area = row.get("区县") or row.get("district_name") or row.get("area_name")
|
||
org_code = row.get("门店编码")
|
||
|
||
# 若省市区缺失，尝试从 NGV 补全
|
||
if not all([province, city, area]) or any(
|
||
v in [None, '', 'None', 'NA'] for v in [province, city, area]
|
||
):
|
||
ngv_record = next(
|
||
(item for item in self.NGV_data_list
|
||
if item.get("_widget_1734062123071") == org_code),
|
||
None
|
||
)
|
||
if ngv_record:
|
||
province = ngv_record.get("_widget_1734062123090")
|
||
city = ngv_record.get("_widget_1734062123092")
|
||
area = ngv_record.get("_widget_1734062123094")
|
||
logger.info(f"【从NGV补全省市区】门店 {org_code}: {province}, {city}, {area}")
|
||
|
||
if not all([province, city, area]) or any(
|
||
v in [None, '', 'None', 'NA'] for v in [province, city, area]
|
||
):
|
||
logger.warning(f"【省市区信息缺失】门店 {org_code} 省市区不完整，客服设为空")
|
||
return None
|
||
|
||
customer_service = self.get_customer_service_by_location(
|
||
str(province).strip(),
|
||
str(city).strip(),
|
||
str(area).strip(),
|
||
self.province_staff_id_list
|
||
)
|
||
|
||
if customer_service and "数据缺失" not in str(customer_service):
|
||
logger.info(f"【派发客服】门店 {org_code} 派发给客服: {customer_service}")
|
||
return customer_service
|
||
else:
|
||
logger.warning(f"未找到区域客服，请检查门店编码: {org_code}")
|
||
return None
|
||
|
||
# === Step 3: 遍历主表每一行，构建最终提交记录 ===
|
||
for _, row in data_NGV.iterrows():
|
||
row_dict = row.to_dict()
|
||
|
||
# 3.1 关联数据（NGV ID）
|
||
org_code = row_dict.get("门店编码")
|
||
ngv_id = org_code_to_ngv_id.get(org_code)
|
||
row_dict["关联数据"] = ngv_id if ngv_id else None
|
||
if not ngv_id:
|
||
logger.warning(f"未找到关联数据，请检查门店编码: {org_code}")
|
||
|
||
# 3.2 区域客服
|
||
customer_service = get_regional_customer_service(row_dict)
|
||
row_dict["区域客服"] = customer_service
|
||
if not customer_service:
|
||
no_customer_service_data.append(row_dict)
|
||
|
||
# 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
|
||
widget_record = self.row_to_dict(row_dict, self.field_map)
|
||
records.append(widget_record)
|
||
|
||
# === Step 4: 批量提交 ===
|
||
if not records:
|
||
logger.info("无数据需要派发")
|
||
return
|
||
|
||
payload = {
|
||
"api_key": "675b900991ad2491c69389ca",
|
||
"entry_id": "6965eec36b73376aa0b5bff8",
|
||
"data_list": records
|
||
}
|
||
print(payload)
|
||
|
||
api_instance.entry_data_batch_create(payload)
|
||
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")
|
||
# step1: 获取数据
|
||
self.load_all_data()
|
||
logger.info("加载数据完成")
|
||
# step2:数据处理
|
||
data_NGV = self.process_data()
|
||
# step3:数据派发
|
||
self.dispatch_task(data_NGV)
|
||
|
||
# common_module.send_task_status(task_start_time, "续约回访待办")
|
||
except Exception as e:
|
||
error_task_logger.error(f"续约回访待办发生错误{e}")
|
||
# common_module.send_task_error(task_start_time, "续约回访待办", str(e))
|
||
|
||
|
||
if __name__ == '__main__':
|
||
RenewalToDo().main()
|