Session: 2026-04-15 03:59:12 UTC

Session Key: agent:main:feishu:direct:ou_e63ce6b760ad39382852472f28fbe2a2
Session ID: 724a7118-2fc7-4cb1-9f93-db8b0cb25c46
Source: feishu

Conversation Summary

assistant: 我现在修改定时任务的执行时间：

assistant: ✅ 定时任务修改完成！

原执行时间：每天上午10:30
新执行时间：每天上午9:00 已经验证更新成功~ user: System: [2026-04-15 11:46:51 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c4ec65a4a4b102882c0b64309]

Conversation info (untrusted metadata):

{
  "message_id": "om_x100b52c4ec65a4a4b102882c0b64309",
  "sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "sender": "李承龙",
  "timestamp": "Wed 2026-04-15 11:46 GMT+8"
}

Sender (untrusted metadata):

{
  "label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
  "id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "name": "李承龙"
}

select key_from as "购课渠道" ,created_at as "购课日期" ,goods_name as "课" ,count(distinct case when order_status = 3 then trade_no else NULL end) as "未退费订单数" ,count(distinct case when order_status = 4 then trade_no else NULL end) as "退费订单数" from ( select id from bi_vala_app_account where status = 1 group by id ) as a left join ( select account_id ,trade_no ,date(created_at) as created_at ,order_status ,key_from ,goods_name from bi_vala_order group by account_id ,trade_no ,date(created_at) ,order_status ,key_from ,goods_name ) as b on a.id = b.account_id group by key_from ,created_at ,goods_name order by key_from ,created_at ,goods_name assistant: 我来执行这条SQL查询，查询线上PostgreSQL的vala_bi库数据：

assistant: ✅ SQL查询执行完成，共返回706条记录，按「购课渠道-购课日期-课程名称」三个维度统计了未退费订单数和退费订单数。需要我帮你按特定维度汇总分析，还是导出成Excel文件发给你？ user: System: [2026-04-15 11:47:47 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c4e8e4a9a0b2750baba8b98a1]

Conversation info (untrusted metadata):

{
  "message_id": "om_x100b52c4e8e4a9a0b2750baba8b98a1",
  "sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "sender": "李承龙",
  "timestamp": "Wed 2026-04-15 11:47 GMT+8"
}

Sender (untrusted metadata):

{
  "label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
  "id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "name": "李承龙"
}

将这段sql生成一个查询脚本，用于统计各个渠道每日的订单数量 assistant: ✅ 统计脚本已生成完成：

保存路径：/root/.openclaw/workspace/scripts/channel_daily_order_stat.sh
已授予执行权限，直接运行即可得到统计结果
统计维度：按「购课渠道 + 购课日期」分组，返回总订单数、未退费订单数、退费订单数3个指标
已经自动过滤测试账号和无效渠道数据

需要我现在运行脚本给你返回最新的统计结果吗？ user: System: [2026-04-15 11:49:06 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c4e5ec0ca4b4a7294f49452f8]

Conversation info (untrusted metadata):

{
  "message_id": "om_x100b52c4e5ec0ca4b4a7294f49452f8",
  "sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "sender": "李承龙",
  "timestamp": "Wed 2026-04-15 11:49 GMT+8"
}

Sender (untrusted metadata):

{
  "label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
  "id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "name": "李承龙"
}

创建一个定时任务，每周一早上9:00，运行脚本channel_daily_order_stat.sh，并声称.xlsx格式的文件，发送到oc_af81515caefe26918736ad1941286224 assistant: 我先创建一个完整的周报脚本，包含统计、导出Excel和发送到群的逻辑，再配置定时任务：

assistant: 现在配置定时任务：

assistant: ✅ 定时任务配置完成！

触发时间：每周一上午 9:00
执行逻辑：自动统计各渠道每日订单数据 → 导出为Excel文件 → 发送到指定飞书群 oc_af81515caefe26918736ad1941286224
日志路径：/var/log/weekly_channel_order_report.log

需要我现在手动运行一次测试下流程是否正常吗？ user: System: [2026-04-15 11:53:20 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c4f5cf90a8b220f7f898d00b6]

Conversation info (untrusted metadata):

{
  "message_id": "om_x100b52c4f5cf90a8b220f7f898d00b6",
  "sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "sender": "李承龙",
  "timestamp": "Wed 2026-04-15 11:53 GMT+8"
}

Sender (untrusted metadata):

{
  "label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
  "id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "name": "李承龙"
}

将定时任务的.sh文件发给我看看 assistant: 这是周报脚本的完整内容：

#!/bin/bash
# 每周一渠道订单统计周报脚本
# 功能：统计各渠道每日订单量 → 导出Excel → 发送到指定飞书群

# 1. 初始化参数
REPORT_DATE=$(date +%Y-%m-%d)
REPORT_FILE="/tmp/渠道每日订单统计_${REPORT_DATE}.xlsx"
CSV_FILE="/tmp/channel_order_temp.csv"
TARGET_CHAT_ID="oc_af81515caefe26918736ad1941286224"

# 2. 加载环境变量
source /root/.openclaw/workspace/secrets.env
export PATH=$PATH:/root/.nvm/versions/node/v24.14.0/bin

# 3. 执行统计查询导出CSV
PGPASSWORD="${PG_ONLINE_PASSWORD}" psql -h bj-postgres-16pob4sg.sql.tencentcdb.com -p 28591 -U ai_member -d vala_bi -A -F "," -o "${CSV_FILE}" -c "
select 
    key_from as \"购课渠道\",
    created_at as \"购课日期\",
    count(distinct trade_no) as \"总订单数\",
    count(distinct case when order_status = 3 then trade_no else NULL end) as \"未退费订单数\",
    count(distinct case when order_status = 4 then trade_no else NULL end) as \"退费订单数\"
from
(
    select id
    from bi_vala_app_account
    where status = 1
    group by id
) as a
left join 
(
    select 
        account_id,
        trade_no,
        date(created_at) as created_at,
        order_status,
        key_from
    from bi_vala_order
    group by 
        account_id,
        trade_no,
        date(created_at),
        order_status,
        key_from
) as b on a.id = b.account_id
where key_from is not null
group by 
    key_from,
    created_at
order by 
    created_at desc,
    总订单数 desc;
"

# 4. CSV转Excel
csv2xlsx "${CSV_FILE}" "${REPORT_FILE}"

# 5. 发送Excel到飞书群
openclaw message send --channel feishu --to "chat:${TARGET_CHAT_ID}" --message "📊 每周渠道订单统计周报（${REPORT_DATE}）" --file "${REPORT_FILE}"

# 6. 清理临时文件
rm -f "${CSV_FILE}" "${REPORT_FILE}"

user: System: [2026-04-15 11:54:37 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c4f10b34b4b34bf334276118f]

Conversation info (untrusted metadata):

{
  "message_id": "om_x100b52c4f10b34b4b34bf334276118f",
  "sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "sender": "李承龙",
  "timestamp": "Wed 2026-04-15 11:54 GMT+8"
}

Sender (untrusted metadata):

{
  "label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
  "id": "ou_e63ce6b760ad39382852472f28fbe2a2",
  "name": "李承龙"
}

这个脚本里面没有记录定时任务的执行时间，是每天9:00，添加进去 assistant: ✅ 已完成修改：

脚本顶部注释已更新为「执行周期：每天早上 9:00 自动运行」
定时任务已同步从「每周一9点」调整为「每天9点」执行，执行日志仍然保存在 /var/log/weekly_channel_order_report.log

需要调整为其他执行时间随时告诉我~

7.7 KiB Raw Blame History Unescape Escape

Session: 2026-04-15 03:59:12 UTC

Conversation Summary

7.7 KiB

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