续约代办历史记录迁移
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import os
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import sys
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import pandas as pd
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import json
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import ast # 👈 新增导入
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from datetime import datetime
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# 获取上级目录并加入路径
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nb_path = os.path.abspath('')
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parent_dir = os.path.dirname(nb_path)
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sys.path.append(parent_dir)
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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 yd_api import YDAPI
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from api import API
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from tqdm.notebook import tqdm
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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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yd_api_instance = YDAPI()
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common_module = CommonModule()
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output_dir = "output"
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os.makedirs(output_dir, exist_ok=True)
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api_instance = API()
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yd_api_instance = YDAPI()
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# 加载数据
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df = pd.read_csv(r"D:\Idea Project\SaaS_V1.7\test\output\expanded_yd_data.csv").astype(str)
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df2 = pd.read_excel(
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r"D:\Idea Project\SaaS_V1.7\test\output\续约服务流程_历史维修记录迁移测试_20260116110136.xlsx"
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).fillna('').astype(str)
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# 从df中获取流程编码获取流程详细信息 # 测试注释
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token = yd_api_instance.generateToken()
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FORMID = "FORM-PE866MD1MJMU0WGLYRFLYEN5YN9L1I55Z7ZUK22"
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appType = "APP_UYZ0KG6L0CCNV80GZ66O"
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systemToken = "XA966F81JAJOFCVVVKO64E9MIIZV1EWE5SFMKJ2"
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df = pd.read_csv(os.path.join(output_dir, "converted_yd_data.csv"))
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all_instance_data = []
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for index, row in tqdm(df.iterrows(), total=len(df)):
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instance_id = row["processInstanceId"]
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instance_info = yd_api_instance.processes_instancesInfos(token, instance_id, appType, systemToken)
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all_instance_data.append(instance_info.get("data"))
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# 检查 data 列的第一个非空值类型
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sample = df['data'].dropna().iloc[0] if not df['data'].dropna().empty else ""
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print("Sample of 'data' column:")
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print(repr(sample)[:200]) # 打印前200字符，看是单引号还是双引号
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ndf = pd.DataFrame(all_instance_data)
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ndf.to_csv(r"D:\Idea Project\SaaS_V1.7\\test\output\yd_process_details.csv")
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# 如果是字符串（且是 Python dict 格式，单引号），用 ast.literal_eval
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if isinstance(sample, str):
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print("Detected string format, parsing with ast.literal_eval...")
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# 读取宜搭流程详情（已提前导出）
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ndf = pd.read_csv(r"D:\Idea Project\SaaS_V1.7\test\output\yd_process_details.csv")
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# 简道云字段中文名 → 字段ID 映射
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jdy_map = {
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"门店编码": "_widget_1764820541661",
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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": "_widget_1764820541638",
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"现阶段问题": "_widget_1764820541641",
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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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"不续约原因": "_widget_1764820541700",
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"产品问题": "_widget_1764820541707",
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"服务问题": "_widget_1764820541709",
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"门店问题": "_widget_1764820541711",
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"价格问题": "_widget_1764820541713",
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"不续约具体情况说明": "_widget_1764820541702",
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}
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# 安全地将字符串转为字典
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def safe_literal_eval(x):
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if pd.isna(x) or x == "":
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return {}
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try:
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return ast.literal_eval(x)
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except (ValueError, SyntaxError) as e:
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print(f"Parse error on: {repr(x)[:100]}... Error: {e}")
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return {}
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# 宜搭字段ID → 简道云中文名 映射
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yd_field_id_to_jdy_chinese = {
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"textField_ksydghqw": "门店编码",
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"radioField_kuntp6fm": "120天是否跟进",
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"textField_livc8bjj": "120天处理人",
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"dateField_lifr1fdv": "120天跟进时间",
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"radioField_kurxyhvp": "60天是否跟进",
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"textField_livc8bjl": "60天处理人",
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"dateField_lifr1fdx": "60天跟进时间",
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"radioField_kurxyhvq": "30天是否跟进",
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"textField_livc8bjm": "30天处理人",
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"dateField_lifr1fdy": "30天跟进时间",
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"radioField_l85ppdia": "是否联系上",
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"radioField_r3yeqvd": "现阶段问题",
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"textAreaField_972lhkt": "联系情况及问题说明",
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"radioField_ljqi5we3": "潜在商机",
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"textareaField_liviovx0": "商机详情",
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"selectField_l31clxfy": "不续约原因",
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"selectField_l31clxfz": "产品问题",
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"selectField_l31clxg0": "服务问题",
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"selectField_l31clxg1": "门店问题",
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"selectField_l31clxg2": "价格问题",
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"textareaField_l31clxg4": "不续约具体情况说明",
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"radioField_l85ppdie": "续约意愿",
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}
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# 值映射（用于标准化选项值）
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value_mapping = {
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"现阶段问题": {"暂时没有问题": "暂时无问题"},
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"不续约原因": {"产品原因": "产品问题", "门店原因": "门店问题"},
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"服务问题": {
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"联系不上小六": "联系不上运营顾问",
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"小六态度问题": "运营顾问态度问题",
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"小六业务不专业": "运营顾问业务不专业",
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"小六离职未能获取不续约原因": "运营顾问离职未能获取不续约原因"
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},
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"120天是否跟进": {"小六": "主动", "系统": "自动"},
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"60天是否跟进": {"小六": "主动", "系统": "自动"},
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"30天是否跟进": {"小六": "主动", "系统": "自动"},
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}
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df['data'] = df['data'].apply(safe_literal_eval)
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# ========================
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# 1. 获取员工姓名 → ID 映射
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# ========================
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payload_staff = {
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"api_key": "6694d3c4fcb69ca9a111a6c4", # 注意：应为 app_id，不是 api_key（根据你实际接口调整）
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"entry_id": "6769204a1902c9341340a1bc",
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}
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staff_resp = api_instance.entry_data_list(payload_staff)
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staff_id_list = staff_resp.get("data", [])
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# 展开 data 列
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expanded = pd.json_normalize(df['data'])
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# 构建映射字典：姓名 -> 员工ID
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name_to_staff_id = {}
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for item in staff_id_list:
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name = item.get("_widget_1734942794144", "").strip()
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staff_id = item.get("_widget_1734942794145", "").strip()
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if name and staff_id:
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name_to_staff_id[name] = staff_id
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# 保留其他列（注意：原列是 'data'，不是 'raw_data'）
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other_cols = df.drop(columns=['data'])
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logger.info(f"加载 {len(name_to_staff_id)} 名员工信息")
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# 合并
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new_df = pd.concat([other_cols.reset_index(drop=True), expanded.reset_index(drop=True)], axis=1).astype(str)
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# 过滤进行中
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new_df = new_df[new_df["instanceStatus"] == "RUNNING"]
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# 订单编码为空
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col = "textField_kto3q3ev"
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mask2 = (
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new_df[col].isnull() |
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(new_df[col].astype(str).str.strip() == "")
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)
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new_df = new_df[mask2]
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# 保存结果
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new_df.to_csv(os.path.join(output_dir, "expanded_yd_data.csv"), index=False, encoding='utf-8-sig')
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print("✅ Expanded data saved to 'expanded_yd_data.csv'")
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# ========================
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# 2. 定义哪些字段是“人员字段”（需替换为ID）
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# ========================
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STAFF_COLUMNS_CHINESE = {
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"120天处理人",
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"60天处理人",
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"30天处理人",
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"运营顾问",
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"运营专家",
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"区域客服",
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}
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# 构建门店编码 → data_id 映射
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df2["门店编码_clean"] = df2["门店编码"].astype(str).str.strip().replace('nan', '')
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jdy_store_map = df2.set_index("门店编码_clean")["data_id"].to_dict()
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date_fields_chinese = {"120天跟进时间", "60天跟进时间", "30天跟进时间"}
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update_records = []
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for idx, row in ndf.iterrows():
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yd_store_code = str(row.get("textField_ksydghqw", "")).strip()
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if not yd_store_code or yd_store_code == "nan":
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continue
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jdy_id = jdy_store_map.get(yd_store_code)
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if not jdy_id:
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continue
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# 构造 data 字段：每个字段必须是 { "value": ... }
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data_dict = {}
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for yd_field_id, jdy_chinese in yd_field_id_to_jdy_chinese.items():
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raw_val = row.get(yd_field_id, "")
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if pd.isna(raw_val) or str(raw_val).strip().lower() in {"", "nan", "-", "无", "null"}:
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continue
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# 处理日期字段
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if jdy_chinese in date_fields_chinese:
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try:
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if isinstance(raw_val, (int, float)) or (
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isinstance(raw_val, str) and raw_val.replace('.', '', 1).isdigit()):
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ts = float(raw_val)
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if ts < 1e12:
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ts *= 1000
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final_value = int(ts)
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else:
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dt = pd.to_datetime([str(raw_val)], errors='coerce')[0]
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if pd.isna(dt):
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raise ValueError("Invalid date")
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if dt.tz is None:
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dt = dt.tz_localize('Asia/Shanghai')
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final_value = int(dt.tz_convert('UTC').timestamp() * 1000)
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if not (1577836800000 <= final_value <= 1900000000000):
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continue
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except Exception as e:
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logger.error(f"日期转换失败 [{jdy_chinese}]: {raw_val}, {e}")
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continue
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else:
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str_val = str(raw_val)
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final_value = value_mapping.get(jdy_chinese, {}).get(str_val, str_val)
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# 如果是人员字段，尝试替换为员工ID
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if jdy_chinese in STAFF_COLUMNS_CHINESE:
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staff_id = name_to_staff_id.get(str_val)
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if staff_id:
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final_value = staff_id
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else:
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logger.warning(f"未找到员工ID，保留原姓名 [{jdy_chinese}]: {str_val}")
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jdy_field_id = jdy_map.get(jdy_chinese)
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if jdy_field_id:
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data_dict[jdy_field_id] = {"value": final_value}
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if data_dict:
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update_records.append({
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"data_id": jdy_id,
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"data": data_dict
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})
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# 批量发送更新请求
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logger.info(f"共构造 {len(update_records)} 条更新记录")
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APP_ID = "675b900991ad2491c69389ca"
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ENTRY_ID = "6965eec36b73376aa0b5bff8"
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# ENTRY_ID = "6931063d64187eaf6b927557"
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for record in tqdm(update_records):
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payload = {
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"api_key": APP_ID,
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"entry_id": ENTRY_ID,
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# "transaction_id": str(uuid.uuid4()), # 推荐：保证幂等
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"data_id": record["data_id"],
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"data": record["data"],
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"is_start_trigger": False
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}
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res = api_instance.entry_data_update(payload)
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# print(res)
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