🤖 每日自动备份 - 2026-04-15 08:00:01
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memory/2026-04-14-request-timed-out-before-a-res.md
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memory/2026-04-14-request-timed-out-before-a-res.md
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# Session: 2026-04-14 10:24:24 UTC
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- **Session Key**: agent:main:feishu:direct:ou_e63ce6b760ad39382852472f28fbe2a2
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- **Session ID**: 15ebe62e-e42c-4c33-92a2-5b3b0d0bd2bb
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- **Source**: feishu
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## Conversation Summary
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user: System: [2026-04-14 18:06:19 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52d5576d18b8b25a794cfc7ec8a]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52d5576d18b8b25a794cfc7ec8a",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Tue 2026-04-14 18:06 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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类似情况的，4月份购课的用户有多少
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assistant: ### 4月份购课用户多订单统计结果：
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- 4月份总购课用户数：1150人
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- 有多笔订单的用户数：76人，占比 **6.61%**
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- 2笔订单用户：63人
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- 3笔订单用户：11人
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- 4笔及以上订单用户：2人
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如果你需要查看这部分多订单用户的详细明细或者按订单场景分类（比如同时购买L1+L2、多次购买不同课程等），可以随时告诉我~
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user: System: [2026-04-14 18:07:57 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52d5510430a8b37c13a5c65cba8]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52d5510430a8b37c13a5c65cba8",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Tue 2026-04-14 18:07 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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这76人中有多少用户有未退费订单
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assistant: ### 统计结果：
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76个有多笔订单的4月购课用户中，**有60个用户存在未退费的有效订单**，占比78.95%；剩下16个用户的所有订单均已全部退费。
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需要我导出这60个有未退费订单用户的明细数据吗？
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user: System: [2026-04-14 18:09:15 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52d56a6b148cb4bc2c2b2a4a701]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52d56a6b148cb4bc2c2b2a4a701",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Tue 2026-04-14 18:09 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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这些用户各有多少笔订单
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assistant: 多订单用户订单数量统计.xlsx
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assistant: NO_REPLY
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user: System: [2026-04-14 18:12:57 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52d57c5f4cb0b2b49c095c96fc4]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52d57c5f4cb0b2b49c095c96fc4",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Tue 2026-04-14 18:12 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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先确定有多少人有多笔订单（剔除测试账号），然后区分这些人里面有多少人存在未退费订单，有多少人已经全部退费，然后展示这些用户各有多少订单
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assistant: 4月多订单用户统计明细_剔除测试账号.xlsx
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assistant: NO_REPLY
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user: System: [2026-04-14 18:15:59 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52d573249880b2432ddf7ebc011]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52d573249880b2432ddf7ebc011",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Tue 2026-04-14 18:15 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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四月份退费订单有多少个，涉及多少用户，剔除测试账号
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assistant: 剔除测试账号后，4月份退费订单统计结果：
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- 退费订单总数：**427笔**
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- 涉及有效用户数：**400人**
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user: System: [2026-04-14 18:21:16 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52d51f0d44acb21f6d7c9430d81]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52d51f0d44acb21f6d7c9430d81",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Tue 2026-04-14 18:21 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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这些人里有多少人有多笔订单，然后区分这些人里面有多少人存在未退费订单，有多少人已经全部退费，然后展示这些用户各有多少订单
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assistant: 4月退费用户多订单统计明细.xlsx
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assistant: NO_REPLY
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242
scripts/april_refund_analysis.py
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scripts/april_refund_analysis.py
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#!/usr/bin/env python3
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"""
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四月份退费用户学习进度分析 - 生成Excel表格
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表头：账号ID、购课渠道、购课时间、退费时间、用户类型、
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L1U0L1~L2U0L5 各课时的（是否进入、进入时间、是否完成、完成时间）
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"""
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import psycopg2
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import pandas as pd
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from datetime import datetime
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import os
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# 数据库连接
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PG_ONLINE = {
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'host': 'bj-postgres-16pob4sg.sql.tencentcdb.com',
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'port': 28591,
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'user': 'ai_member',
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'password': os.environ.get('PG_ONLINE_PASSWORD', 'LdfjdjL83h3h3^$&**YGG*'),
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'dbname': 'vala_bi'
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}
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# 课时映射: L1U0L1~L1U0L5 (A1), L2U0L1~L2U0L5 (A2)
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CHAPTER_MAP = {
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343: 'L1U0L1', 344: 'L1U0L2', 345: 'L1U0L3', 346: 'L1U0L4', 348: 'L1U0L5',
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55: 'L2U0L1', 56: 'L2U0L2', 57: 'L2U0L3', 58: 'L2U0L4', 59: 'L2U0L5',
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}
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CHAPTER_IDS = list(CHAPTER_MAP.keys())
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LESSON_ORDER = ['L1U0L1','L1U0L2','L1U0L3','L1U0L4','L1U0L5',
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'L2U0L1','L2U0L2','L2U0L3','L2U0L4','L2U0L5']
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def get_connection():
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return psycopg2.connect(**PG_ONLINE)
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def fetch_refund_users(conn):
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"""获取四月退费用户及订单信息"""
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sql = """
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WITH april_refund_accounts AS (
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SELECT DISTINCT o.account_id
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FROM bi_vala_order o
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JOIN bi_vala_app_account a ON o.account_id = a.id AND a.status = 1
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WHERE o.order_status = 4
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AND o.updated_at >= '2026-04-01' AND o.updated_at < '2026-05-01'
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AND o.created_at >= '2026-04-01' AND o.created_at < '2026-05-01'
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),
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-- 每个用户所有四月订单
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user_all_orders AS (
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SELECT o.account_id,
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COUNT(DISTINCT o.id) AS total_orders,
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COUNT(DISTINCT CASE WHEN o.order_status = 4 THEN o.id END) AS refunded_orders,
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COUNT(DISTINCT CASE WHEN o.order_status = 3 THEN o.id END) AS active_orders
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FROM bi_vala_order o
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JOIN bi_vala_app_account a ON o.account_id = a.id AND a.status = 1
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WHERE o.account_id IN (SELECT account_id FROM april_refund_accounts)
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AND o.order_status IN (3, 4)
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AND o.created_at >= '2026-04-01' AND o.created_at < '2026-05-01'
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GROUP BY o.account_id
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),
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-- 取最晚购课时间的退费订单
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latest_order AS (
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SELECT DISTINCT ON (o.account_id)
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o.account_id,
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o.key_from,
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o.sale_channel,
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o.created_at AS purchase_time,
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o.updated_at AS refund_time
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FROM bi_vala_order o
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JOIN bi_vala_app_account a ON o.account_id = a.id AND a.status = 1
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WHERE o.account_id IN (SELECT account_id FROM april_refund_accounts)
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AND o.order_status = 4
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AND o.created_at >= '2026-04-01' AND o.created_at < '2026-05-01'
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ORDER BY o.account_id, o.created_at DESC
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)
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SELECT lo.account_id, lo.key_from, lo.sale_channel, lo.purchase_time, lo.refund_time,
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uo.total_orders, uo.refunded_orders, uo.active_orders
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FROM latest_order lo
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JOIN user_all_orders uo ON lo.account_id = uo.account_id
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ORDER BY lo.account_id;
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"""
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with conn.cursor() as cur:
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cur.execute(sql)
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columns = [desc[0] for desc in cur.description]
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rows = cur.fetchall()
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return pd.DataFrame(rows, columns=columns)
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def classify_user(row):
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if row['total_orders'] == 1:
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return '单订单退费'
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elif row['active_orders'] > 0:
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return '多订单未全退'
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else:
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return '多订单全退'
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def get_channel_name(key_from, sale_channel):
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"""直接返回key_from原始值"""
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return key_from or ''
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def fetch_chapter_records(conn, account_ids):
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"""获取用户的课时学习记录"""
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# 获取角色ID映射
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acct_str = ','.join(str(a) for a in account_ids)
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sql_char = f"""
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SELECT id AS character_id, account_id FROM bi_vala_app_character
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WHERE account_id IN ({acct_str})
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"""
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with conn.cursor() as cur:
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cur.execute(sql_char)
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char_rows = cur.fetchall()
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if not char_rows:
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return {}
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char_to_account = {}
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char_ids_by_table = {}
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for cid, aid in char_rows:
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char_to_account[cid] = aid
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table_idx = cid % 8
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char_ids_by_table.setdefault(table_idx, []).append(cid)
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# 从各分表获取课时记录
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chapter_ids_str = ','.join(str(c) for c in CHAPTER_IDS)
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all_records = []
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for table_idx, cids in char_ids_by_table.items():
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cids_str = ','.join(str(c) for c in cids)
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sql_rec = f"""
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SELECT user_id, chapter_id, play_status, created_at
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FROM bi_user_chapter_play_record_{table_idx}
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WHERE user_id IN ({cids_str})
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AND chapter_id IN ({chapter_ids_str})
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ORDER BY user_id, chapter_id, created_at
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"""
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with conn.cursor() as cur:
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cur.execute(sql_rec)
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records = cur.fetchall()
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all_records.extend(records)
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# 整理数据: account_id -> chapter_id -> {entered, enter_time, completed, complete_time}
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result = {}
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for user_id, chapter_id, play_status, created_at in all_records:
|
||||||
|
account_id = char_to_account.get(user_id)
|
||||||
|
if not account_id:
|
||||||
|
continue
|
||||||
|
|
||||||
|
if account_id not in result:
|
||||||
|
result[account_id] = {}
|
||||||
|
if chapter_id not in result[account_id]:
|
||||||
|
result[account_id][chapter_id] = {
|
||||||
|
'entered': True,
|
||||||
|
'enter_time': created_at,
|
||||||
|
'completed': False,
|
||||||
|
'complete_time': None
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
# 更新最早进入时间
|
||||||
|
if created_at < result[account_id][chapter_id]['enter_time']:
|
||||||
|
result[account_id][chapter_id]['enter_time'] = created_at
|
||||||
|
|
||||||
|
# 完成状态
|
||||||
|
if play_status == 1:
|
||||||
|
if not result[account_id][chapter_id]['completed']:
|
||||||
|
result[account_id][chapter_id]['completed'] = True
|
||||||
|
result[account_id][chapter_id]['complete_time'] = created_at
|
||||||
|
else:
|
||||||
|
# 取最早完成时间
|
||||||
|
if created_at < result[account_id][chapter_id]['complete_time']:
|
||||||
|
result[account_id][chapter_id]['complete_time'] = created_at
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
def format_time(t):
|
||||||
|
if t is None:
|
||||||
|
return ''
|
||||||
|
if isinstance(t, datetime):
|
||||||
|
return t.strftime('%Y-%m-%d %H:%M:%S')
|
||||||
|
return str(t)
|
||||||
|
|
||||||
|
def main():
|
||||||
|
conn = get_connection()
|
||||||
|
|
||||||
|
print("1. 获取退费用户订单信息...")
|
||||||
|
df_users = fetch_refund_users(conn)
|
||||||
|
print(f" 共{len(df_users)}个退费用户")
|
||||||
|
|
||||||
|
# 分类用户
|
||||||
|
df_users['用户类型'] = df_users.apply(classify_user, axis=1)
|
||||||
|
df_users['购课渠道'] = df_users.apply(lambda r: get_channel_name(r['key_from'], r['sale_channel']), axis=1)
|
||||||
|
|
||||||
|
print("2. 获取课时学习记录...")
|
||||||
|
account_ids = df_users['account_id'].tolist()
|
||||||
|
chapter_records = fetch_chapter_records(conn, account_ids)
|
||||||
|
print(f" 共{len(chapter_records)}个用户有学习记录")
|
||||||
|
|
||||||
|
print("3. 组装表格...")
|
||||||
|
rows = []
|
||||||
|
for _, user in df_users.iterrows():
|
||||||
|
aid = user['account_id']
|
||||||
|
row = {
|
||||||
|
'账号ID': aid,
|
||||||
|
'购课渠道': user['购课渠道'],
|
||||||
|
'购课时间': format_time(user['purchase_time']),
|
||||||
|
'退费时间': format_time(user['refund_time']),
|
||||||
|
'用户类型': user['用户类型'],
|
||||||
|
}
|
||||||
|
|
||||||
|
user_chapters = chapter_records.get(aid, {})
|
||||||
|
for lesson in LESSON_ORDER:
|
||||||
|
# 找到对应的chapter_id
|
||||||
|
ch_id = [k for k, v in CHAPTER_MAP.items() if v == lesson][0]
|
||||||
|
ch_data = user_chapters.get(ch_id, {})
|
||||||
|
|
||||||
|
row[f'{lesson}-是否进入'] = '是' if ch_data.get('entered', False) else '否'
|
||||||
|
row[f'{lesson}-进入时间'] = format_time(ch_data.get('enter_time'))
|
||||||
|
row[f'{lesson}-是否完成'] = '是' if ch_data.get('completed', False) else '否'
|
||||||
|
row[f'{lesson}-完成时间'] = format_time(ch_data.get('complete_time'))
|
||||||
|
|
||||||
|
rows.append(row)
|
||||||
|
|
||||||
|
df_result = pd.DataFrame(rows)
|
||||||
|
|
||||||
|
# 导出Excel
|
||||||
|
output_path = '/tmp/openclaw/四月退费用户学习进度分析.xlsx'
|
||||||
|
os.makedirs('/tmp/openclaw', exist_ok=True)
|
||||||
|
|
||||||
|
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
|
||||||
|
df_result.to_excel(writer, index=False, sheet_name='退费用户学习进度')
|
||||||
|
|
||||||
|
# 调整列宽
|
||||||
|
ws = writer.sheets['退费用户学习进度']
|
||||||
|
for col in ws.columns:
|
||||||
|
max_length = max(len(str(cell.value or '')) for cell in col)
|
||||||
|
col_letter = col[0].column_letter
|
||||||
|
ws.column_dimensions[col_letter].width = min(max_length + 2, 25)
|
||||||
|
|
||||||
|
print(f"4. 导出完成: {output_path}")
|
||||||
|
print(f" 总行数: {len(df_result)}")
|
||||||
|
print(f" 用户类型分布:")
|
||||||
|
print(df_result['用户类型'].value_counts().to_string())
|
||||||
|
|
||||||
|
conn.close()
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
@ -1,59 +0,0 @@
|
|||||||
#!/bin/bash
|
|
||||||
# 每日组件Oops率和退出率报表生成与发送脚本
|
|
||||||
# 执行时间：每天9:00，统计前一天的数据
|
|
||||||
|
|
||||||
set -e
|
|
||||||
|
|
||||||
# 1. 计算统计日期（昨天）
|
|
||||||
STAT_DATE=$(date -d "yesterday" +"%Y-%m-%d")
|
|
||||||
REPORT_DATE=$(date +"%Y年%m月%d日")
|
|
||||||
REPORT_PATH="/tmp/组件Oops率与退出率报表_${STAT_DATE}.xlsx"
|
|
||||||
|
|
||||||
echo "开始生成${REPORT_DATE}的组件Oops率与退出率报表..."
|
|
||||||
|
|
||||||
# 2. 进入技能目录
|
|
||||||
cd /root/.openclaw/workspace/skills/vala-component-oops-stat
|
|
||||||
|
|
||||||
# 3. 统计L1 Oops率
|
|
||||||
echo "正在统计L1组件Oops率..."
|
|
||||||
psql -h bj-postgres-16pob4sg.sql.tencentcdb.com -p 28591 -U ai_member -d vala_bi -f scripts/stat_l1.sql -v stat_date="'${STAT_DATE}'" -o /tmp/l1_oops.csv
|
|
||||||
|
|
||||||
# 4. 统计L2 Oops率
|
|
||||||
echo "正在统计L2组件Oops率..."
|
|
||||||
psql -h bj-postgres-16pob4sg.sql.tencentcdb.com -p 28591 -U ai_member -d vala_bi -f scripts/stat_l2.sql -v stat_date="'${STAT_DATE}'" -o /tmp/l2_oops.csv
|
|
||||||
|
|
||||||
# 5. 统计L1退出率
|
|
||||||
echo "正在统计L1退出率..."
|
|
||||||
# 退出率统计SQL
|
|
||||||
psql -h bj-postgres-16pob4sg.sql.tencentcdb.com -p 28591 -U ai_member -d vala_bi << EOF > /tmp/l1_exit.csv
|
|
||||||
-- L1退出率统计逻辑待补充，后续根据实际口径修改
|
|
||||||
SELECT '待补充' as component_name, 0 as exit_rate, 0 as user_count;
|
|
||||||
EOF
|
|
||||||
|
|
||||||
# 6. 统计L2退出率
|
|
||||||
echo "正在统计L2退出率..."
|
|
||||||
# 退出率统计SQL
|
|
||||||
psql -h bj-postgres-16pob4sg.sql.tencentcdb.com -p 28591 -U ai_member -d vala_bi << EOF > /tmp/l2_exit.csv
|
|
||||||
-- L2退出率统计逻辑待补充，后续根据实际口径修改
|
|
||||||
SELECT '待补充' as component_name, 0 as exit_rate, 0 as user_count;
|
|
||||||
EOF
|
|
||||||
|
|
||||||
# 7. 生成Excel报表
|
|
||||||
echo "正在生成Excel报表..."
|
|
||||||
python3 scripts/generate_excel.py --l1_oops /tmp/l1_oops.csv --l2_oops /tmp/l2_oops.csv --l1_exit /tmp/l1_exit.csv --l2_exit /tmp/l2_exit.csv --output "${REPORT_PATH}"
|
|
||||||
|
|
||||||
# 8. 发送报表到飞书群
|
|
||||||
echo "正在发送报表到飞书群..."
|
|
||||||
cd /root/.openclaw/workspace
|
|
||||||
python3 -c "
|
|
||||||
import sys
|
|
||||||
sys.path.append('/root/.openclaw/skills/lark-send-message-as-bot')
|
|
||||||
from send_message import send_file_to_group
|
|
||||||
send_file_to_group(
|
|
||||||
chat_id='oc_248d0efbc02d97d81d592d0417b6b296',
|
|
||||||
file_path='${REPORT_PATH}',
|
|
||||||
caption='📊 ${REPORT_DATE} 组件Oops率与退出率报表\n\n报表包含以下sheet：\n1. L1组件Oops率\n2. L2组件Oops率\n3. L1组件退出率\n4. L2组件退出率'
|
|
||||||
)
|
|
||||||
"
|
|
||||||
|
|
||||||
echo "报表发送完成！"
|
|
||||||
@ -3,12 +3,15 @@
|
|||||||
# 执行时间：每天9:00
|
# 执行时间：每天9:00
|
||||||
# 输出：Excel报表，发送到指定群
|
# 输出：Excel报表，发送到指定群
|
||||||
|
|
||||||
|
# 配置环境变量
|
||||||
|
export PATH=/root/.nvm/versions/node/v24.14.0/bin:$PATH
|
||||||
|
|
||||||
# 配置参数
|
# 配置参数
|
||||||
DB_HOST="bj-postgres-16pob4sg.sql.tencentcdb.com"
|
DB_HOST="bj-postgres-16pob4sg.sql.tencentcdb.com"
|
||||||
DB_PORT="28591"
|
DB_PORT="28591"
|
||||||
DB_NAME="vala_bi"
|
DB_NAME="vala_bi"
|
||||||
DB_USER="ai_member"
|
DB_USER="ai_member"
|
||||||
DB_PASS="LdfjdjL83h3h3^$&**YGG*"
|
DB_PASS='LdfjdjL83h3h3^$&**YGG*'
|
||||||
STAT_DATE=$(date +%Y-%m-%d -d "yesterday")
|
STAT_DATE=$(date +%Y-%m-%d -d "yesterday")
|
||||||
TODAY=$(date +%Y%m%d)
|
TODAY=$(date +%Y%m%d)
|
||||||
SQL_L1_OOPS="/root/.openclaw/workspace/skills/vala-component-practice-stat/scripts/stat_l1.sql"
|
SQL_L1_OOPS="/root/.openclaw/workspace/skills/vala-component-practice-stat/scripts/stat_l1.sql"
|
||||||
@ -70,7 +73,7 @@ if [ $? -ne 0 ]; then
|
|||||||
fi
|
fi
|
||||||
|
|
||||||
# 3. 发送到指定群
|
# 3. 发送到指定群
|
||||||
openclaw message send --channel feishu --target $TARGET_CHAT --message "【每日自动统计】$(date +%Y年%m月%d日)组件练习质量统计结果（统计日期：${STAT_DATE}，包含Oops率和退出率双指标），共4个sheet，请查收。" --media $OUTPUT_EXCEL 2>> $LOG_FILE
|
/root/.nvm/versions/node/v24.14.0/bin/openclaw message send --channel feishu --target $TARGET_CHAT --message "【每日自动统计】$(date +%Y年%m月%d日)组件练习质量统计结果（统计日期：${STAT_DATE}，包含Oops率和退出率双指标），共4个sheet，请查收。" --media $OUTPUT_EXCEL 2>> $LOG_FILE
|
||||||
|
|
||||||
if [ $? -ne 0 ]; then
|
if [ $? -ne 0 ]; then
|
||||||
echo "[$(date +'%Y-%m-%d %H:%M:%S')] 消息发送失败" >> $LOG_FILE
|
echo "[$(date +'%Y-%m-%d %H:%M:%S')] 消息发送失败" >> $LOG_FILE
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user