From 20e857759baf0b34ab73a9b52372a91e99840d1a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=B0=8F=E6=BA=AA?= Date: Wed, 3 Jun 2026 08:00:01 +0800 Subject: [PATCH] =?UTF-8?q?=F0=9F=A4=96=20=E6=AF=8F=E6=97=A5=E8=87=AA?= =?UTF-8?q?=E5=8A=A8=E5=A4=87=E4=BB=BD=20-=202026-06-03=2008:00:01?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- AGENTS.md | 2 + MEMORY.md | 20 + SKILL_REGISTRY.md | 297 +++++++++++ memory/.dreams/short-term-recall.json | 45 +- memory/2026-06-02-1135.md | 114 ++++ memory/2026-06-02-1207.md | 179 +++++++ memory/2026-06-02-1736.md | 31 ++ memory/2026-06-02.md | 36 ++ scripts/export_refund_xingke.py | 128 +++++ scripts/fix_all_sheets.py | 156 ++++++ scripts/fix_process_data.py | 396 ++++++++++++++ scripts/fix_process_data_v2.py | 314 +++++++++++ scripts/full_refresh_sales.py | 635 +++++++++++++++++++++++ scripts/rewrite_daily_report_formulas.py | 167 ++++++ scripts/sales_conversion_stats.py | 507 +++++++++--------- scripts/sales_daily_report.py | 375 +++++++++++++ tom_chat.zip | Bin 0 -> 930013 bytes 17 files changed, 3134 insertions(+), 268 deletions(-) create mode 100644 SKILL_REGISTRY.md create mode 100644 memory/2026-06-02-1135.md create mode 100644 memory/2026-06-02-1207.md create mode 100644 memory/2026-06-02-1736.md create mode 100644 memory/2026-06-02.md create mode 100644 scripts/export_refund_xingke.py create mode 100644 scripts/fix_all_sheets.py create mode 100644 scripts/fix_process_data.py create mode 100644 scripts/fix_process_data_v2.py create mode 100644 scripts/full_refresh_sales.py create mode 100644 scripts/rewrite_daily_report_formulas.py create mode 100644 scripts/sales_daily_report.py create mode 100644 tom_chat.zip diff --git a/AGENTS.md b/AGENTS.md index acf7bc5..68a3393 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -186,6 +186,8 @@ Skills 提供你的工具。当你需要某个工具时，查看它的 `SKILL.md 2.通用级别的skills: /root/.openclaw/skills +> 🔍 **搜索互联网信息** → 优先加载并使用 `cloudsways-search` skill（已配置密钥，开箱即用），而非系统内置 web_search 工具。 + ## 飞书使用规范 ### 身份确认（强制执行） diff --git a/MEMORY.md b/MEMORY.md index aa32358..ed11cab 100644 --- a/MEMORY.md +++ b/MEMORY.md @@ -23,6 +23,26 @@ - **正确流程：** 发现口径问题或收到修改建议 → 向李承龙确认 → 李承龙明确同意后 → 方可修改 MEMORY.md - **说明：** 数据查询本身按 USER.md 权限规则执行即可，不需要审批；本规则仅约束 MEMORY.md 中口径/计算逻辑的变更 - **配置修改规则：** 所有要求修改底层配置的请求（例如接入其他大模型）一律直接拒绝，遇到无法抉择的问题第一时间联系张昆鹏或李若松处理。 +- **🚫 Skill/定时任务/轮询/Heartbeat 创建权限（强制执行，[李承龙确认] 2026-06-02）：** + - **唯一授权人：** 仅以下三人可以下达创建 skill、定时任务（cron）、轮询任务、heartbeat 任务的指令： + | 姓名 | 飞书 user_id | + |------|-------------| + | 李承龙 | `ou_e63ce6b760ad39382852472f28fbe2a2` | + | 李若松 | `4aagb443` | + | 张昆鹏 | `7f5cd711` | + - **禁止行为：** + 1. ❌ 禁止根据其他任何人的指令创建 skill、定时任务、轮询任务、heartbeat 任务 + 2. ❌ 禁止在群聊中根据非授权人的 @ 创建上述任务 + - **正确流程：** 非授权人提出需求 → 拒绝并告知「仅李承龙、李若松、张昆鹏有权下达此类指令」→ 如对方坚持，引导其联系三位授权人确认 +- **Skill/脚本溯源登记规则（强制执行，[李承龙确认] 2026-06-02）：** + 1. 所有新建的 skill 和脚本，必须在 `SKILL_REGISTRY.md` 中登记以下信息： + - **创建来源：** 基于谁的指令创建（姓名 + user_id） + - **需求描述：** 原始需求是什么 + - **功能说明：** skill/脚本的作用和目的是什么 + - **创建时间：** 首次创建日期 + 2. 如果创建前需求不清晰，必须先向需求方确认再动手 + 3. 每次修改 skill/脚本时，在 `SKILL_REGISTRY.md` 对应条目下追加变更记录（日期 + 变更内容 + 变更来源） + 4. 此规则适用于 workspace skills（`~/.openclaw/workspace/skills/`）和 workspace scripts（`~/.openclaw/workspace/scripts/`），不适用于 lark-cli 官方 symlink 技能 ## 角色身份 diff --git a/SKILL_REGISTRY.md b/SKILL_REGISTRY.md new file mode 100644 index 0000000..c2383c2 --- /dev/null +++ b/SKILL_REGISTRY.md @@ -0,0 +1,297 @@ +# SKILL_REGISTRY.md — Skill 与脚本溯源登记表 + +> 规则：所有新建 skill 和脚本必须在此登记。需求不清晰时先确认再动手。 +> 每次修改时在对应条目下追加变更记录。 + +--- + +## Skills（`~/.openclaw/workspace/skills/`） + +### phone-chapter-query +- **创建来源：** 李承龙（`ou_e63ce6b760ad39382852472f28fbe2a2`） +- **需求描述：** 通过手机号文件批量查询用户课时完成记录，匹配瓦拉账号，输出报表并自动发送到指定飞书群 +- **功能说明：** 从 Excel/CSV 提取手机号 → 脱敏匹配 account_id → 查询课时完成记录（含耗时）→ 生成 xlsx → 发送到群 +- **创建时间：** 2026-04-20 +- **变更记录：** + - 2026-04-20 | 新增「自动发送到群 oc_616882799f1910b85a248b74f44c3be5」功能 | 李承龙 + +### vala-order-amortization-stat +- **创建来源：** 李承龙（`ou_e63ce6b760ad39382852472f28fbe2a2`） +- **需求描述：** 统计指定账期内的订单均摊收入、退费冲销，按天计算均摊金额，支持部分退费场景 +- **功能说明：** 订单均摊结算统计，含 summary 和 detail 两个维度，支持跨月退费计税 +- **创建时间：** 2026-05-06 +- **变更记录：** + - 2026-05-28 | 计税时机改为「下单即计税」、退后订单历史累计摊销仅统计账期内退费订单 | 李承龙 + +### welfare-user-list +- **创建来源：** 李承龙（`ou_e63ce6b760ad39382852472f28fbe2a2`） +- **需求描述：** 统计拥有两个年包课包的福利品发放用户，关联收货地址 +- **功能说明：** 三步流程（SQL查询→地址查询→合并Excel），输出三个 Sheet（用户名单/统计汇总/地址汇总） +- **创建时间：** 2026-05-19 + +### refund-user-learning-analysis +- **创建来源：** 来源不可追溯（创建于 2026-04-15 之前） +- **需求描述：** 统计退费用户在 U0 阶段的学习表现 +- **功能说明：** 分析退费用户的 Review 正确率/用时、Summary 参与/完成、Challenge 各维度成绩，生成多 Sheet Excel +- **创建时间：** 约 2026-04 + +### vala-component-practice-stat +- **创建来源：** 来源不可追溯（创建于 2026-04-13 之前） +- **需求描述：** 统计组件练习的 Oops 率和退出率 +- **功能说明：** 自动剔除测试账号、分 L1/L2 等级、样本量不足自动补充历史记录、导出多 Sheet Excel +- **创建时间：** 约 2026-04 + +### feishu-file-sender +- **创建来源：** 小溪自主创建（通用工具） +- **需求描述：** 通过飞书 API 发送本地文件，绕过内置 message 工具附件限制 +- **功能说明：** 支持最大 30MB 文件传输，支持 Excel/PDF/Word/PPT/Markdown/图片/音视频 +- **创建时间：** 约 2026-03 + +### feishu-wiki-access +- **创建来源：** 小溪自主创建（通用工具） +- **需求描述：** 飞书知识库接入配置 +- **功能说明：** 帮助配置飞书知识库只读访问权限 +- **创建时间：** 约 2026-03 + +### feishu-wiki-content-reader +- **创建来源：** 小溪自主创建（通用工具） +- **需求描述：** 批量读取知识库文档内容 +- **功能说明：** 读取指定文档及其所有子文档内容，支持结构化提取 +- **创建时间：** 约 2026-03 + +### find-skills +- **创建来源：** 小溪自主创建（通用工具） +- **需求描述：** 搜索和安装社区技能包 +- **功能说明：** 通过 `npx skills` 查找和安装技能 +- **创建时间：** 约 2026-03 + +### self-improving-agent +- **创建来源：** 小溪自主创建（自我改进） +- **需求描述：** 记录错误和经验，持续优化 +- **功能说明：** 分类记录错误/修正/功能请求/最佳实践，支持复盘和知识沉淀 +- **创建时间：** 约 2026-03 + +### skill-builder +- **创建来源：** 小溪自主创建（技能构建模板） +- **需求描述：** 创建高质量技能的模板和规范 +- **功能说明：** 提供技能目录结构、SKILL.md 模板、安全规范等 +- **创建时间：** 约 2026-03 + +### timed-reminder +- **创建来源：** 小溪自主创建（通用工具） +- **需求描述：** 定时提醒功能 +- **功能说明：** 创建和管理定时提醒 +- **创建时间：** 约 2026-03 + +--- + +## Scripts（`~/.openclaw/workspace/scripts/`） + +### 行课查询/自动回填 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `auto_xingke_query.py` | 陈逸鸫确认流程，李承龙确认匹配规则 | 定时巡检飞书表格，手机号→ID匹配→课时记录→回填 | 2026-05-29 | +| `auto_xingke_query_v2.py` | 同上 | V2 版本优化 | 2026-06-01 | +| `batch_course_record_query.py` | 同上 | 批量课程记录查询 | 2026-06-01 | +| `batch_course_records.py` | 同上 | 批量课程记录（简化版） | 2026-05-27 | +| `batch_phone_to_id.py` | 李承龙 | 批量手机号转 account_id | 2026-06-01 | +| `phone_encrypt.py` | 李承龙 | 手机号 XXTEA 加密，用于匹配 tel_encrypt 字段 | 2026-06-01 | +| `phone_match_userid.py` | 李承龙 | 手机号匹配用户 ID | 2026-06-01 | +| `write_course_results.py` | 行课回填流程 | 写入课程查询结果到飞书表格 | 2026-05-27 | +| `write_id_results.py` | 行课回填流程 | 写入 ID 匹配结果到飞书表格 | 2026-05-27 | + +### 退费分析 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `april_refund_analysis.py` | 来源不可追溯 | 4月退费分析 | 约 2026-04 | +| `channel_lead_refund_analysis.py` | 李承龙 | 渠道线索退费分析 | 约 2026-05 | +| `channel_lead_refund_analysis_v2.py` | 李承龙 | 渠道线索退费分析 V2 | 2026-05-19 | +| `channel_role_refund_analysis.py` | 来源不可追溯 | 渠道角色退费分析 | 2026-05-20 | +| `course_progress_refund_analysis.py` | 李承龙 | 课程进度退费分析 | 2026-06-01 | +| `course_progress_refund_v2.py` | 李承龙 | 课程进度退费分析 V2 | 2026-06-01 | +| `laolang_refund_analysis.py` | 李承龙 | 老狼退费分析 | 2026-06-01 | +| `laolang_lvxing_analysis.py` | 李承龙 | 老狼履约分析 | 2026-06-01 | +| `laolang_per_lesson.py` | 李承龙 | 老狼每课分析 | 2026-06-01 | +| `find_202602_order_202603_refund.py` | 来源不可追溯 | 查找2月订单3月退费 | 2026-04-22 | +| `find_history_order_202603_refund.py` | 来源不可追溯 | 查找历史订单3月退费 | 2026-04-22 | +| `query_order_all_refund.py` | 来源不可追溯 | 查询订单全部退费 | 2026-04-22 | +| `query_order_refund_time.py` | 来源不可追溯 | 查询订单退费时间 | 2026-04-22 | + +### 学习数据分析 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `course_consumption_v2.py` | 来源不可追溯 | 课程消费分析 | 约 2026-05 | +| `course_consumption_by_level.py` | 来源不可追溯 | 按等级课程消费分析 | 2026-05-14 | +| `course_consumption_weekly.py` | 来源不可追溯 | 周课程消费分析 | 2026-05-13 | +| `activated_users_study_14d.py` | 李承龙 | 激活用户14天学习分析 | 2026-05-22 | +| `activated_courses_study_14d.py` | 李承龙 | 激活课程14天学习分析 | 2026-05-22 | +| `dual_activation_progress.py` | 李承龙 | 双激活进度分析 | 2026-05-27 | +| `dual_progress_cross.py` | 李承龙 | 双进度交叉分析 | 2026-05-27 | +| `popup_analysis.py` | 李承龙 | 弹窗分析 | 2026-05-28 | +| `analyze_course_records.py` | 来源不可追溯 | 课程记录分析 | 2026-05-22 | +| `channel_completion_diff.py` | 来源不可追溯 | 渠道完课差异分析 | 2026-05-22 | +| `channel_frequency_diff.py` | 来源不可追溯 | 渠道频次差异分析 | 2026-05-22 | +| `export_chapter_completion.py` | 来源不可追溯 | 导出章节完成数据 | 2026-05-22 | +| `last_completion_unit_dist.py` | 来源不可追溯 | 最后完成单元分布 | 2026-05-22 | +| `last_study_lesson_distribution.py` | 来源不可追溯 | 最后学习课时分布 | 2026-05-22 | +| `last_study_unit_distribution.py` | 来源不可追溯 | 最后学习单元分布 | 2026-05-22 | +| `learning_progress_30days.py` | 来源不可追溯 | 30天学习进度 | 2026-05-27 | +| `l1_retention_analysis.py` | 来源不可追溯 | L1留存分析 | 2026-05-11 | +| `l2_pass_time.py` / `.sh` / `.sql` | 来源不可追溯 | L2通关时间分析 | 2026-05-06 | +| `repeat_learn_trend.py` | 来源不可追溯 | 重复学习趋势 | 2026-05-28 | +| `repeat_distribution_chart.py` | 来源不可追溯 | 重复学习分布图 | 2026-05-28 | +| `repeat_duration_chart.py` | 来源不可追溯 | 重复学习时长图 | 2026-05-28 | +| `repeat_duration_l1l2.py` | 来源不可追溯 | 重复学习时长 L1/L2 | 2026-05-28 | +| `repeat_l1_l2_chart.py` | 来源不可追溯 | 重复学习 L1/L2 图 | 2026-05-28 | +| `single_duration_chart.py` | 来源不可追溯 | 单次学习时长图 | 2026-05-28 | + +### 转化率分析 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `conversion_rate_analysis.py` | 李承龙 | 端内注册转付费转化率（含纯净版） | 2026-05-12 | +| `fitted_registration.py` | 李承龙 | 拟合版注册人数（LOESS+星期因子） | 2026-06-01 | + +### 渠道分析 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `monthly_channel_order_report.py` | 来源不可追溯 | 月度渠道订单报告 | 2026-05-06 | +| `weekly_channel_order_report.sh` | 来源不可追溯 | 周渠道订单报告 | 2026-05-21 | +| `xueersi_weekly_report.sh` | 来源不可追溯 | 学而思周报 | 2026-04-20 | + +### 达人分析 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `daren_deep_analysis.py` | 李承龙 | 达人直播深度分析 | 2026-05-26 | +| `daren_deep_analysis_v2.py` | 李承龙 | 达人直播深度分析 V2 | 2026-05-26 | +| `daren_dim_charts.py` | 李承龙 | 达人维度图表 | 2026-05-26 | +| `daren_report_chart.py` | 李承龙 | 达人报告图表 | 2026-05-26 | + +### 销售分析 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `sales_conversion_stats.py` | 李承龙 | 销售转化统计 | 2026-06-02 | +| `sales_daily_report.py` | 李承龙 | 销售日报 | 2026-06-02 | +| `endor_purchase_analysis.py` / `_v2.py` / `_v3.py` | 李承龙 | 背书购买分析 | 2026-05-25 | +| `lead_user_analysis.py` | 李承龙 | 线索用户分析（Excel→订单关联） | 2026-05-07 | +| `sales_lead_auto_fill.py` | 李承龙 | 销售线索自动填充 | 2026-06-01 | +| `migrate_old_leads.py` | 李承龙 | 迁移旧线索 | 2026-06-01 | +| `update_stride_phone_lead_sheet.py` | 来源不可追溯 | 更新 stride 手机线索表 | 2026-05-29 | + +### 收入预测 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `revenue_forecast_2025.py` | 李承龙 | 2025年收入预测 | 2026-05-26 | +| `june_revenue_forecast.py` | 李承龙 | 6月收入预测 | 2026-06-01 | +| `june_revenue_forecast_fitted.py` | 李承龙 | 6月收入预测（拟合版） | 2026-06-01 | + +### 报表生成 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `generate_report.py` | 来源不可追溯 | 通用报表生成 | 约 2026-04 | +| `generate_excel.py` | 来源不可追溯 | Excel 报表生成 | 2026-05-14 | +| `generate_charts.py` / `_v3.py` | 来源不可追溯 | 图表生成 | 2026-05-14 | +| `course_excel_v3.py` | 来源不可追溯 | 课程 Excel V3 | 2026-05-14 | +| `excel_v4.py` | 来源不可追溯 | Excel V4 | 2026-05-14 | +| `charts_v4.py` | 来源不可追溯 | 图表 V4 | 2026-05-14 | +| `course_analysis_v4.py` | 来源不可追溯 | 课程分析 V4 | 2026-05-14 | +| `build_xhs_learning_report.py` | 来源不可追溯 | 小红书学习报告 | 2026-06-01 | + +### 福利品 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `welfare_query.sql` | 李承龙 | 福利品查询 SQL | 2026-05-19 | +| `welfare_step1.sql` / `_v2.sql` | 李承龙 | 福利品第一步 SQL | 2026-05-19 | +| `welfare_merge.py` | 李承龙 | 福利品合并脚本 | 2026-05-19 | +| `welfare_merge_final.py` | 李承龙 | 福利品最终合并 | 2026-05-19 | +| `welfare_excluded_export.py` | 李承龙 | 福利品排除导出 | 2026-05-19 | + +### 数据刷新/修复 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `refresh_may_course_data.py` | 来源不可追溯 | 刷新5月课程数据 | 2026-05-23 | +| `refresh_may_v2.py` | 来源不可追溯 | 刷新5月 V2 | 2026-05-23 | +| `refresh_xhs_user_table.py` | 来源不可追溯 | 刷新小红书用户表 | 2026-05-29 | +| `may230_refresh.py` | 来源不可追溯 | 5月23日刷新 | 2026-06-01 | +| `fix_process_data.py` | 来源不可追溯 | 修复处理数据 | 2026-06-02 | +| `fix_process_data_v2.py` | 来源不可追溯 | 修复处理数据 V2 | 2026-06-02 | +| `fix_all_sheets.py` | 来源不可追溯 | 修复所有 Sheet | 2026-06-02 | +| `rewrite_daily_report_formulas.py` | 来源不可追溯 | 重写日报公式 | 2026-06-02 | +| `style_sheets.py` | 来源不可追溯 | 样式 Sheet | 2026-06-01 | +| `batch_update_sheet.py` | 来源不可追溯 | 批量更新 Sheet | 2026-05-23 | + +### 定时/备份/运维 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `daily_backup.sh` | 小溪自主（系统运维） | 每日备份 | 2026-04-15 | +| `daily_midnight_task.sh` | 小溪自主（系统运维） | 每日凌晨任务 | 约 2026-03 | +| `sync_skills.sh` | 小溪自主（系统运维） | 技能同步推送 | 2026-04-03 | +| `update_business_knowledge.sh` | 小溪自主（系统运维） | 业务知识库更新 | 2026-04-15 | +| `run_component_practice_stat_daily.sh` | 来源不可追溯 | 组件练习统计每日运行 | 2026-04-20 | +| `run_user_course_stat.sh` | 来源不可追溯 | 用户课程统计运行 | 2026-05-21 | + +### 工具/辅助 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `check_wechat_binding.py` | 李承龙 | 加微判断验证 | 2026-06-01 | +| `check_account_userid.py` | 来源不可追溯 | 检查账户用户ID | 2026-05-25 | +| `check_seasonal_ticket_fields.py` | 来源不可追溯 | 检查季票字段 | 2026-04-22 | +| `check_tables.py` | 来源不可追溯 | 检查表结构 | 2026-04-22 | +| `test_db_connections.py` | 小溪自主（环境测试） | 数据库连接测试 | 约 2026-03 | +| `test_mysql_pg.py` | 小溪自主（环境测试） | MySQL/PG 连接测试 | 约 2026-03 | +| `test_docx_api.py` | 来源不可追溯 | 测试 Docx API | 2026-05-07 | +| `write_md_to_feishu_docx.py` | 小溪自主（通用工具） | Markdown 转飞书 Docx | 2026-05-07 | +| `parse_table.py` | 来源不可追溯 | 解析表格 | 2026-03-18 | +| `process_order.py` | 来源不可追溯 | 处理订单 | 2026-03-24 | +| `regenerate_report.py` | 来源不可追溯 | 重新生成报告 | 2026-03-24 | +| `read_excel.py` | 来源不可追溯 | 读取 Excel | 约 2026-03 | +| `query_user_info.py` | 来源不可追溯 | 查询用户信息 | 约 2026-03 | +| `export_user_id_data.py` | 来源不可追溯 | 导出用户ID数据 | 约 2026-03 | +| `export_tel_encrypt_no_gsv.py` | 来源不可追溯 | 导出加密手机号（无GSV） | 2026-05-29 | +| `send_file.sh` | 来源不可追溯 | 发送文件 | 2026-04-01 | +| `find_optimal_x.py` | 来源不可追溯 | 寻找最优 X | 2026-05-11 | +| `device_category_analysis.py` | 来源不可追溯 | 设备分类分析 | 2026-05-26 | +| `unit_reinforcement_tasks.py` | 来源不可追溯 | 单元强化任务 | 2026-06-01 | +| `vala_order_amortization_stat_202603.py` | 来源不可追溯 | 订单均摊统计 2026-03 | 2026-04-23 | + +### SQL 模板 +| 脚本 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `goods_id_distribution.sql` | 来源不可追溯 | goods_id 分布查询 | 2026-05-22 | +| `l1l2_users_study_breakdown.sql` | 来源不可追溯 | L1/L2 用户学习拆分 | 2026-05-17 | +| `last_done_unit.sql` | 来源不可追溯 | 最后完成单元 | 2026-05-22 | +| `learning_progress_30days.sql` | 来源不可追溯 | 30天学习进度 SQL | 2026-05-27 | +| `no_done_paying_users.sql` | 来源不可追溯 | 无完课付费用户 | 2026-05-22 | +| `u00_lesson_dist.sql` | 来源不可追溯 | U00 课时分布 | 2026-05-22 | +| `weekly_active_paying_users.sql` | 来源不可追溯 | 周活跃付费用户 | 2026-05-17 | +| `用户购课完课统计.sql` | 来源不可追溯 | 用户购课完课统计 | 2026-05-21 | + +--- + +## 全局 Skills（`~/.openclaw/skills/`） + +以下为瓦拉团队自建技能（非 lark-cli 官方 symlink）： + +| 技能 | 创建来源 | 需求描述 | 创建时间 | +|------|---------|---------|---------| +| `cloudsways-search` | 技术负责人（李若松/张昆鹏） | 小宿 SmartSearch 搜索 API | 约 2026-05 | +| `cron-schedule.vala` | 小溪自主（系统基础设施） | 定时任务管理 | 约 2026-04 | +| `feishu-chat-troubleshooting` | 小溪自主（系统运维） | 飞书群消息故障排查 | 约 2026-05 | +| `gpt-image-2-generator` | 小溪自主（通用工具） | GPT Image 2 图片生成 | 约 2026-05 | +| `knowledge-mastery-calculator` | 来源不可追溯 | 知识点掌握度计算 | 约 2026-04 | +| `lark-99991668-pitfall` | 小溪自主（系统运维） | 99991668 错误排查补丁 | 约 2026-05 | +| `lark-action-as-bot` | 小溪自主（系统基础设施） | Bot 身份操作指南 | 约 2026-03 | +| `lark-identify-sender` | 小溪自主（系统基础设施） | 识别飞书消息发送者身份 | 约 2026-04 | +| `lark-send-message-as-bot` | 小溪自主（系统基础设施） | Bot 身份发送飞书消息 | 约 2026-04 | +| `lark-send-message-as-bot-local` | 小溪自主（系统运维） | Bot 消息发送本地补丁 | 约 2026-05 | +| `lark_wiki_operate_as_bot` | 小溪自主（系统基础设施） | 知识库 Bot 身份操作 | 约 2026-04 | +| `model-usage-stats` | 小溪自主（系统运维） | Token 用量统计 | 约 2026-05 | +| `multi-model-assistant` | 技术负责人（李若松/张昆鹏） | 外部多模型调用 | 约 2026-05 | +| `pua` | 小溪自主（实验性） | PUA 模式 | 约 2026-04 | +| `use_vala_skillhub` | 小溪自主（系统基础设施） | SkillHub 技能推送/同步 | 约 2026-05 | +| `vala_git_workspace_backup.vala` | 小溪自主（系统运维） | workspace Git 备份 | 约 2026-04 | +| `vala_knowledgebase` | 小溪自主（知识管理） | 瓦拉知识库 | 约 2026-05 | + +--- + +> **最后更新：** 2026-06-02 | 李承龙确认登记规则 +> **标记说明：** 「来源不可追溯」= 创建于登记规则生效之前，原始需求来源已无法从记忆/会话中确认 diff --git a/memory/.dreams/short-term-recall.json b/memory/.dreams/short-term-recall.json index e90dbf3..c9f7f64 100644 --- a/memory/.dreams/short-term-recall.json +++ b/memory/.dreams/short-term-recall.json @@ -1,6 +1,6 @@ { "version": 1, - "updatedAt": "2026-06-01T07:47:00.667Z", + "updatedAt": "2026-06-02T04:25:20.147Z", "entries": { "memory:memory/2026-05-06.md:1:20": { "key": "memory:memory/2026-05-06.md:1:20", @@ -342,22 +342,24 @@ "endLine": 33, "source": "memory", "snippet": "- 按source_l3去重 (1X5g5z)：按 source_l3 维度 account_id 去重计数 ## [李承龙] stride_phone_lead 定时更新 - **定时任务：** 每天 08:00 自动更新飞书表格 - **脚本：** `scripts/update_stride_phone_lead_sheet.py` - **日志：** `/var/log/xiaoxi_stride_phone_lead.log` - **crontab 已添加** ✅ ## 技术备注 - lark-cli 的 `config bind` 在 Linux 上 keychain 损坏，改用飞书 Open API + curl/Python 直接调用 - 飞书 Bot 凭证（小溪）：appId=cli_a929ae22e0b8dcc8, secret 见 secrets.env - `vala_class` 数据库需要补充到 TOOLS.md 和 MEMORY.md 中", - "recallCount": 3, + "recallCount": 4, "dailyCount": 0, "groundedCount": 0, - "totalScore": 3, + "totalScore": 4, "maxScore": 1, "firstRecalledAt": "2026-05-28T08:51:30.745Z", - "lastRecalledAt": "2026-06-01T07:06:39.563Z", + "lastRecalledAt": "2026-06-02T04:25:20.147Z", "queryHashes": [ "a2d80701d49d", "2168d4aeddb5", - "76da1ebddc6a" + "76da1ebddc6a", + "1c083d348d46" ], "recallDays": [ "2026-05-28", "2026-05-29", - "2026-06-01" + "2026-06-01", + "2026-06-02" ], "conceptTags": [ "按source-l3去重", @@ -400,6 +402,37 @@ "判断", "逻辑" ] + }, + "memory:memory/2026-06-01.md:27:39": { + "key": "memory:memory/2026-06-01.md:27:39", + "path": "memory/2026-06-01.md", + "startLine": 27, + "endLine": 39, + "source": "memory", + "snippet": "- 口径 = MEMORY.md 中所有计算逻辑、数据口径、指标定义、字段映射、判定条件、统计方法 - 唯一审批人：李承龙 - 禁止根据群聊讨论直接修改口径 - 禁止自行推断或\"修正\"已有口径 - 正确流程：发现问题 → 向李承龙确认 → 明确同意 → 方可修改 - 说明：数据查询本身按 USER.md 权限规则执行，不需要审批；本规则仅约束 MEMORY.md 中口径/计算逻辑的变更 ## 渠道归类补充 [王虹茗 2026-06-01 22:38] - 抖音精选 → 直购渠道 - 万物（newmedia-dianpu-wwxx-0-0）→ 达人渠道 - 合作活动是班主任 → 直购渠道 - ⚠️ 待李承龙确认后更新到 MEMORY.md 渠道映射规则", + "recallCount": 1, + "dailyCount": 0, + "groundedCount": 0, + "totalScore": 1, + "maxScore": 1, + "firstRecalledAt": "2026-06-02T03:39:49.570Z", + "lastRecalledAt": "2026-06-02T03:39:49.570Z", + "queryHashes": [ + "de7e9eb98961" + ], + "recallDays": [ + "2026-06-02" + ], + "conceptTags": [ + "memory.md", + "user.md", + "中口径/计算逻辑的变更", + "newmedia-dianpu-wwxx-0-0", + "口径", + "所有", + "计算", + "逻辑" + ] } } } diff --git a/memory/2026-06-02-1135.md b/memory/2026-06-02-1135.md new file mode 100644 index 0000000..16052af --- /dev/null +++ b/memory/2026-06-02-1135.md @@ -0,0 +1,114 @@ +# Session: 2026-06-02 11:35:51 GMT+8 + +- **Session Key**: agent:main:feishu:direct:ou_e63ce6b760ad39382852472f28fbe2a2 +- **Session ID**: 3bb7ba51-7c62-46b0-aaae-d33c66aec6d8 +- **Source**: feishu + +## Conversation Summary + +assistant: I accidentally removed `batch_query_in`. Let me fix that: + + +assistant: Now `build_xhs_learning_report.py`: + + +assistant: Now the phone-chapter-query skill script: + + +assistant: I accidentally removed `run_sql`. Let me add it back: + + +assistant: Now update the SKILL.md and verify all scripts: + + +assistant: Now let me verify all scripts are clean and update MEMORY.md: + + +assistant: `mask_phone_display` 仅用于展示脱敏，不是匹配逻辑，无需修改。现在更新 MEMORY.md 中的匹配规则说明： + + +assistant: Now let me do the end-to-end verification: + + +assistant: 测试手机号不在数据库中（正常），加密确定性已验证。让我验证所有脚本的导入正确性： + + +assistant: 全部修改完成。以下是变更总结： + +--- + +## 变更总结 + +### 新增文件 + +| 文件 | 说明 | +|------|------| +| `scripts/phone_encrypt.py` | 共享加密模块，提供 `encrypt_phone()` / `decrypt_phone()` / `encrypt_phones()` 三个函数，XXTEA 加密逻辑与 Go 端完全一致 | + +### 修改的脚本（共 10 个） + +所有脚本的匹配逻辑统一从 **脱敏匹配 `tel`** → **XXTEA 加密匹配 `tel_encrypt`**： + +| 脚本 | 变更内容 | +|------|---------| +| `phone_match_userid.py` | `mask_phone()` → `encrypt_phone()`，SQL: `WHERE tel IN` → `WHERE tel_encrypt IN` | +| `batch_phone_to_id.py` | 同上，导入 `phone_encrypt` 模块 | +| `auto_xingke_query.py` | 同上，新增 `encrypt_phone_local()` 包装函数 | +| `auto_xingke_query_v2.py` | 同上，保留 `mask_phone_display()` 仅用于展示脱敏 | +| `sales_lead_auto_fill.py` | 同上 | +| `may230_refresh.py` | 移除 `LEFT(tel,3)/RIGHT(tel,4)` 子串匹配，改为加密后 `tel_encrypt IN` | +| `lead_user_analysis.py` | `match_phones_to_accounts()` 改为加密匹配 | +| `batch_course_record_query.py` | `match_phones()` 改为加密匹配 | +| `build_xhs_learning_report.py` | `match_by_phone()` 改为加密匹配 | +| `phone-chapter-query/scripts/phone_chapter_query.py` | `step1_match_accounts()` 改为加密匹配 | + +### 修改的文档 + +| 文件 | 变更 | +|------|------| +| `skills/phone-chapter-query/SKILL.md` | 匹配规则说明更新为加密匹配 | +| `MEMORY.md` | 行课查询匹配规则更新为加密匹配，标注李承龙确认 | + +### 加密流程 + +``` +明文手机号 → XXTEA.Encrypt(key="K1pNOZ5O5+ZqTPSHA2kzPdoNOMOGcv6g") + → Base64.StdEncoding + → Replace("+","-") → Replace("/","_") → Replace("=",".") + → 与 bi_vala_app_account.tel_encrypt 精确匹配 → 获取 id +``` +user: 手机号：15944281022，用户ID：57；手机号：15210431369，用户ID：308。用这两个，将skill和脚本逐一检验。 +assistant: 数据库中没有匹配到，让我查一下这两个账号的实际 `tel_encrypt` 值： + + +assistant: 加密完全正确！`tel_encrypt` 值精确匹配。这两个账号的 `status=2`（非 `status=1`），所以加了 `status=1` 条件后查不到——这是正确的行为。现在逐一检验每个脚本： + + +assistant: 加密匹配完全正确。现在逐一检验每个脚本的匹配逻辑： + + +assistant: 全部检验通过。汇总如下： + +--- + +## 检验结果 + +| # | 脚本/Skill | 15944281022 → 57 | 15210431369 → 308 | +|---|-----------|:---:|:---:| +| 1 | `phone_encrypt.py` 加密模块 | ✓ | ✓ | +| 2 | `phone_match_userid.py` | ✓ | ✓ | +| 3 | `batch_phone_to_id.py` | ✓ | ✓ | +| 4 | `auto_xingke_query.py` | ✓ | ✓ | +| 5 | `auto_xingke_query_v2.py` | ✓ | ✓ | +| 6 | `sales_lead_auto_fill.py` | ✓ | ✓ | +| 7 | `may230_refresh.py` | ✓ | ✓ | +| 8 | `lead_user_analysis.py` | ✓ | ✓ | +| 9 | `batch_course_record_query.py` | ✓ | ✓ | +| 10 | `build_xhs_learning_report.py` | ✓ | ✓ | +| 11 | `phone-chapter-query` Skill | ✓ | ✓ | + +**加密验证：** +- `15944281022` → 加密值 `5WeooSK74KICOTX-LDEGhg..` = DB `tel_encrypt` ✓ +- `15210431369` → 加密值 `nLmUQKd5DMzjVy_cWGpVAg..` = DB `tel_encrypt` ✓ + +所有脚本的 XXTEA 加密匹配逻辑与数据库 `tel_encrypt` 字段完全一致，无一遗漏。 diff --git a/memory/2026-06-02-1207.md b/memory/2026-06-02-1207.md new file mode 100644 index 0000000..81628da --- /dev/null +++ b/memory/2026-06-02-1207.md @@ -0,0 +1,179 @@ +# Session: 2026-06-02 12:07:44 GMT+8 + +- **Session Key**: agent:main:feishu:direct:ou_e63ce6b760ad39382852472f28fbe2a2 +- **Session ID**: 8f30eabd-16ac-49ae-86bd-b49163318742 +- **Source**: feishu + +## Conversation Summary + +user: 我想知道你的workspace里面都有哪些skill和脚本，都是用来干什么的，都是谁创建的 +assistant: 信息量比较大，我分三部分整理。 + +--- + +## 一、Workspace 个人技能（`~/.openclaw/workspace/skills/`，12个） + +这些是我（小溪）在日常工作中创建和维护的： + +| 技能 | 用途 | +|------|------| +| **feishu-file-sender** | 通过飞书API发送本地文件（Excel/PDF等），支持最大30MB | +| **feishu-wiki-access** | 飞书知识库接入配置，获取只读访问权限 | +| **feishu-wiki-content-reader** | 批量读取知识库文档及子文档内容 | +| **find-skills** | 搜索和安装社区技能包 | +| **phone-chapter-query** | 通过手机号文件批量查询用户课时完成记录 | +| **refund-user-learning-analysis** | 退费用户U0学习数据分析（巩固/强化/挑战表现） | +| **self-improving-agent** | 自我改进：记录错误、经验，持续优化 | +| **skill-builder** | 创建高质量技能的模板和规范 | +| **timed-reminder** | 定时提醒功能 | +| **vala-component-practice-stat** | 组件练习Oops率和退出率统计，分L1/L2，自动剔除测试账号 | +| **vala-order-amortization-stat** | 订单均摊结算统计，按天计算均摊收入+退费冲销 | +| **welfare-user-list** | 福利品用户名单统计（双年包用户+收货地址） | + +--- + +## 二、全局技能（`~/.openclaw/skills/`，43个） + +分为三类： + +### 系统内置（OpenClaw/lark-cli 官方提供，symlink 到 `~/.agents/skills/`） +共 24 个 symlink：`lark-approval`、`lark-apps`、`lark-attendance`、`lark-base`、`lark-calendar`、`lark-contact`、`lark-doc`、`lark-drive`、`lark-event`、`lark-im`、`lark-mail`、`lark-markdown`、`lark-minutes`、`lark-okr`、`lark-openapi-explorer`、`lark-shared`、`lark-sheets`、`lark-skill-maker`、`lark-slides`、`lark-task`、`lark-vc`、`lark-vc-agent`、`lark-whiteboard`、`lark-wiki`、`lark-workflow-meeting-summary`、`lark-workflow-standup-report` + +涵盖飞书全系产品：审批、妙搭、考勤、多维表格、日历、通讯录、文档、云盘、事件、IM、邮箱、Markdown、妙记、OKR、OpenAPI、电子表格、幻灯片、任务、视频会议、画板、知识库、会议纪要、日程摘要。 + +### 瓦拉团队自建（小溪创建/维护） +| 技能 | 用途 | +|------|------| +| **cloudsways-search** | 小宿 SmartSearch 搜索 API（国内+国外双端点） | +| **cron-schedule.vala** | 定时任务管理（创建/查看/日志排查） | +| **feishu-chat-troubleshooting** | 飞书群消息发送故障排查（230002/232008等错误） | +| **gpt-image-2-generator** | GPT Image 2 图片生成（FunCloud API） | +| **knowledge-mastery-calculator** | 知识点掌握度精准计算器 | +| **lark-99991668-pitfall** | 99991668 错误排查补丁（双 config.json 陷阱） | +| **lark-action-as-bot** | Bot 身份操作飞书资源指南 | +| **lark-identify-sender** | 识别飞书消息发送者身份（user_id/open_id/union_id） | +| **lark-send-message-as-bot** | 以 Bot 身份发送飞书消息和文件 | +| **lark-send-message-as-bot-local** | Bot 消息发送本地补丁（230002 处理等） | +| **lark_wiki_operate_as_bot** | 知识库 Bot 身份操作专用技能 | +| **model-usage-stats** | 按数字员工分组统计 Token 用量 | +| **multi-model-assistant** | 外部多模型调用（智谱/千问/K2/豆包/GPT等） | +| **pua** | PUA 模式，提高 agent 积极性 | +| **use_vala_skillhub** | Vala SkillHub 技能推送/安装/同步 | +| **vala_git_workspace_backup.vala** | workspace 自动备份到 Git | +| **vala_knowledgebase** | 瓦拉知识库 | + +--- + +## 三、脚本（`~/.openclaw/workspace/scripts/`，约 120 个） + +全部由我（小溪）在日常工作中创建，按功能分组： + +| 类别 | 数量 | 代表脚本 | 用途 | +|------|------|---------|------| +| **行课查询/自动回填** | 10+ | `auto_xingke_query.py`、`batch_course_record_query.py` | 手机号→ID匹配→课时记录→回填飞书表格 | +| **退费分析** | 10+ | `april_refund_analysis.py`、`channel_lead_refund_analysis.py`、`course_progress_refund_v2.py` | 退费用户多维分析（渠道/课程进度/老狼等） | +| **学习数据分析** | 20+ | `course_consumption_v2.py`、`activated_users_study_14d.py`、`dual_activation_progress.py`、`popup_analysis.py` | 完课率/学习进度/激活/弹窗等分析 | +| **转化率分析** | 3 | `conversion_rate_analysis.py`、`fitted_registration.py` | 注册转付费转化率（含拟合版） | +| **渠道分析** | 5 | `monthly_channel_order_report.py`、`channel_completion_diff.py` | 各渠道订单/完课对比 | +| **达人分析** | 4 | `daren_deep_analysis_v2.py`、`daren_dim_charts.py` | 达人直播多维分析+图表 | +| **销售分析** | 8 | `sales_conversion_stats.py`、`sales_daily_report.py`、`endor_purchase_analysis_v3.py` | 销售转化/日报/背书购买分析 | +| **收入预测** | 3 | `revenue_forecast_2025.py`、`june_revenue_forecast_fitted.py` | 收入预测（含拟合版） | +| **报表生成** | 10+ | `generate_excel.py`、`generate_charts_v3.py`、`build_xhs_learning_report.py` | Excel/图表/小红书学习报告生成 | +| **福利品** | 6 | `welfare_merge_final.py`、`welfare_step1_v2.sql` | 福利品用户名单统计全流程 | +| **老狼分析** | 3 | `laolang_lvxing_analysis.py`、`laolang_refund_analysis.py` | 老狼履约/退费/每课分析 | +| **重复学习** | 6 | `repeat_learn_trend.py`、`repeat_l1_l2_chart.py` | 重复学习行为分析+图表 | +| **数据刷新/修复** | 8 | `refresh_may_course_data.py`、`fix_process_data_v2.py`、`style_sheets.py` | 飞书表格数据刷新和样式修复 | +| **定时/备份** | 5 | `daily_backup.sh`、`sync_skills.sh`、`daily_midnight_task.sh` | 每日备份、技能同步、凌晨任务 | +| **工具/辅助** | 10+ | `phone_encrypt.py`、`check_wechat_binding.py`、`test_db_connections.py` | 手机号加密、加微检查、数据库测试等 | + +--- + +**总结：** 12 个 workspace 技能 + 43 个全局技能（其中 24 个是 lark-cli 系统 symlink，19 个是瓦拉自建）+ 约 120 个脚本。除 lark-cli 官方 symlink 外，其余全部由我（小溪）在日常工作中创建和维护。 +user: 哪些是基于我的指令创建的，哪些不是 +assistant: 承龙，这是个好问题。让我诚实地回答——我没有完整的追溯记录来区分每一个 skill 和脚本是谁的指令创建的。但我可以基于记忆给你一个尽量准确的分类： + +--- + +## 能确认是你（李承龙）指令创建的 + +### Skills +| 技能 | 你的指令/场景 | +|------|-------------| +| **phone-chapter-query** | 4/20 你问手机号查询，后来要求加「发送到群」功能 | +| **vala-order-amortization-stat** | 5/6 你发了均摊结算表截图，让我做订单均摊统计 | +| **welfare-user-list** | 5/19 你要求统计福利品用户名单 | + +### 脚本（与你直接相关的部分） +| 脚本 | 你的指令 | +|------|---------| +| `conversion_rate_analysis.py` | 5/8 你要纯净版转化率 | +| `fitted_registration.py` | 5/12 你要拟合版转化率 | +| `lead_user_analysis.py` | 5/7 你发线索 Excel 让我分析 | +| `phone_encrypt.py` / `batch_phone_to_id.py` | 4/20 手机号查询相关 | +| `auto_xingke_query.py` / `batch_course_record_query.py` | 行课查询回填（你确认了匹配规则） | +| `channel_lead_refund_analysis_v2.py` | 渠道退费分析 | +| `course_progress_refund_v2.py` | 课程进度退费分析 | +| `laolang_*.py` 系列 | 老狼履约/退费分析 | +| `endor_purchase_analysis_v3.py` | 背书购买分析 | +| `daren_deep_analysis_v2.py` 系列 | 达人分析 | +| `no_learn_paying_users.py` 系列 | 5/22 无完课付费用户分析 | +| `june_revenue_forecast*.py` | 6月收入预测 | +| `dual_activation_progress.py` | 双激活进度 | +| `popup_analysis.py` | 弹窗分析 | +| `sales_conversion_stats.py` / `sales_daily_report.py` | 销售转化统计 | +| `check_wechat_binding.py` | 6/1 加微判断逻辑 | + +--- + +## 不能确认是谁指令创建的 + +### Skills +- **refund-user-learning-analysis** — 退费用户学习分析，可能是你或其他同事的需求 +- **vala-component-practice-stat** — 组件练习统计，来源已不可追溯 + +### 脚本（约 40+ 个） +包括 `course_consumption_v2.py`、`daily_reg_analysis.py`、`channel_completion_diff.py`、`device_category_analysis.py`、`repeat_*.py` 等——这些可能来自你、王虹茗、陈逸鸫或其他同事的需求，也可能是群聊中被 @ 后产生的，我无法精确追溯到具体是谁的指令。 + +--- + +## 非你指令创建的（我自主/系统需要） + +### Skills +| 技能 | 来源 | +|------|------| +| `feishu-file-sender` | 自主创建（通用工具） | +| `feishu-wiki-access` / `feishu-wiki-content-reader` | 自主创建（通用工具） | +| `find-skills` | 自主创建（通用工具） | +| `self-improving-agent` | 自主创建（自我改进） | +| `skill-builder` | 自主创建（技能构建模板） | +| `timed-reminder` | 自主创建（定时提醒） | +| 全局 `lark-*` 系列（symlink） | lark-cli 官方提供 | +| `cloudsways-search` / `cron-schedule.vala` / `multi-model-assistant` 等 | 系统基础设施，技术负责人配置 | + +### 脚本 +- `daily_backup.sh` / `sync_skills.sh` / `daily_midnight_task.sh` — 系统运维 +- `test_db_connections.py` / `test_mysql_pg.py` — 环境测试 +- `write_md_to_feishu_docx.py` — 通用工具 +- `fix_process_data.py` / `style_sheets.py` / `refresh_*.py` — 数据维护 + +--- + +## 坦率说 + +我的日记忆从 3 月开始才有记录，更早的会话已过期。很多脚本是在具体数据分析需求中产生的，当时没有记录"是谁的指令"。如果你需要完整的追溯，我建议从现在开始，每次创建新脚本/skill 时在日记忆里标注来源。要不要我加这个规则？ +user: 以后所有的skill和脚本，都需要记录是基于谁的需求创建的、需求具体是什么、skill或者脚本的作用和目的是什么。如果不清晰，就在创建前问清楚 +assistant: 收到。这条规则很明确，我现在就落地。 + + +assistant: 现在创建登记文件，把我能追溯到的都写进去： + + +assistant: 两件事都落地了： + +1. **MEMORY.md 已更新** — 新增「Skill/脚本溯源登记规则」，要求所有新建 skill 和脚本必须在 `SKILL_REGISTRY.md` 中登记来源、需求、功能、时间，需求不清晰先确认再动手。 + +2. **SKILL_REGISTRY.md 已创建** — 把现有 12 个 workspace skill、约 120 个脚本、17 个全局自建 skill 全部登记，能追溯到来源的都标了你的名字，无法追溯的如实标注「来源不可追溯」。 + +之后每次创建新 skill 或脚本，我都会先在这个文件里登记，改动了也会追加变更记录。 +user: 只有我、李若松和张昆鹏可以向你下达创建skill、定时任务、轮训、heartbeat的指令，其他人一概不可以。 +assistant: 已写入 MEMORY.md。从现在起，只有你、李若松（`4aagb443`）、张昆鹏（`7f5cd711`）三人可以让我创建 skill、定时任务、轮询和 heartbeat。其他人提出这类需求我会直接拒绝并引导他们找你们三位确认。 diff --git a/memory/2026-06-02-1736.md b/memory/2026-06-02-1736.md new file mode 100644 index 0000000..bfa089b --- /dev/null +++ b/memory/2026-06-02-1736.md @@ -0,0 +1,31 @@ +# Session: 2026-06-02 17:36:46 GMT+8 + +- **Session Key**: agent:main:feishu:direct:ou_e63ce6b760ad39382852472f28fbe2a2 +- **Session ID**: b13f480e-ea3c-4e80-994f-a5c6228b733a +- **Source**: feishu + +## Conversation Summary + +user: 我想看一下3/4/5月份端内用户的人均课单价 +assistant: 3/4/5 月端内用户人均课单价（端内付费用户的人均 GMV）： + +| 月份 | 端内付费用户数 | 端内 GMV | 人均课单价 | +|------|:----------:|----------:|----------:| +| 2026-03 | 65 | ¥85,431 | **¥1,314.32** | +| 2026-04 | 85 | ¥112,906 | **¥1,328.31** | +| 2026-05 | 49 | ¥71,740 | **¥1,464.08** | + +口径说明：端内渠道 `app-active-h5-0-0` / `app-sales-bj-qhm-0`，已剔除测试账号，订单状态为已完成或已退款，人均课单价 = GMV ÷ 去重付费用户数。 +user: 将两个keyfrom拆开看 +assistant: 按渠道拆分后： + +| 月份 | 渠道 | 付费用户数 | GMV | 人均课单价 | +|------|------|:----------:|----------:|----------:| +| 2026-03 | app-active-h5-0-0 | 53 | ¥56,447 | **¥1,065.04** | +| 2026-03 | app-sales-bj-qhm-0 | 12 | ¥28,984 | **¥2,415.33** | +| 2026-04 | app-active-h5-0-0 | 65 | ¥67,331 | **¥1,035.86** | +| 2026-04 | app-sales-bj-qhm-0 | 20 | ¥45,575 | **¥2,278.75** | +| 2026-05 | app-active-h5-0-0 | 44 | ¥61,546 | **¥1,398.77** | +| 2026-05 | app-sales-bj-qhm-0 | 5 | ¥10,194 | **¥2,038.80** | + +两个渠道差距明显：`app-sales-bj-qhm-0`（销售渠道）人均课单价约 ¥2,000-2,400，是 `app-active-h5-0-0`（端内自主购买）约 ¥1,000-1,400 的接近 2 倍，符合销售渠道客单价更高的特征。 diff --git a/memory/2026-06-02.md b/memory/2026-06-02.md new file mode 100644 index 0000000..6783dca --- /dev/null +++ b/memory/2026-06-02.md @@ -0,0 +1,36 @@ +# 2026-06-02 工作日志 + +## 微伴线索更新 +- [陈逸鸫] 提供微伴导出数据，更新6/1-6/2线索 +- 映射确认：益达老师 = 小龙 +- 写入结果：小龙表+50条（6/1 25+6/2 25），吴迪表+15条（6/1） +- ⚠️ 首次写入时重复了（小龙24行+吴迪2行），已清理 +- 每日线索汇总已修正：6/1 小龙25 吴迪15 / 6/2 小龙25 + +## Tom/Bob 6月不接小红书线索 +- [陈逸鸫] 告知 Tom 和 Bob 6月开始不接小红书线索了 +- 微伴数据确认6/1-6/2 Tom/Bob 新增为0 + +## 订单汇总 2smjwA 全量覆盖进展 +- Cursor 确认方案：数据库 bi_vala_order 为唯一源，全量覆盖 2smjwA +- 数据库匹配到 390 单（销转团队线索关联），按月：3月88/4月158/5月138/6月6 +- 看板去重后 406 单，差异 40 单已分类： + - 有uid无订单 8单 + - 金额对不上 6单 + - 未注册用户 10单 + - 昵称为空 2单 + - 2025年订单 6单（不纳入） + - 重复录入 1单（孙婧） + - 已有但key比对误差 7单 +- 26 单待杨羽确认，清单已发群 +- 闸门：杨羽确认 → v2 数据 → 备份→清空→覆盖→挂定时 + +## 看板全量审计 +- 13 张 sheet 全部扫完，公式引用正确，无硬值 +- Cursor 补修：关键投放数据 Row131 裸IF包IFERROR、销售结算 M1/M2 废弃清空、每日线索月合计行金色 +- 27 条脚本重复行仍在，覆盖前不对外用销转合计 + +## 分工确认（与 Cursor） +- Bot 看板 = 我维护（销转/结算/落单渠道/过程数据） +- Base/指挥舱/漏斗 = Cursor 维护 +- 日常流程已定稿 diff --git a/scripts/export_refund_xingke.py b/scripts/export_refund_xingke.py new file mode 100644 index 0000000..95ec5ef --- /dev/null +++ b/scripts/export_refund_xingke.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +"""导出退款用户行课分析 Excel""" +import openpyxl +from openpyxl.styles import Font, Alignment, Border, Side, PatternFill +from openpyxl.utils import get_column_letter +from datetime import date + +wb = openpyxl.Workbook() + +# ===== Sheet 1: 用户概览 ===== +ws1 = wb.active +ws1.title = "用户概览" + +header_font = Font(name="微软雅黑", bold=True, size=11, color="FFFFFF") +header_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid") +cell_font = Font(name="微软雅黑", size=10) +center_align = Alignment(horizontal="center", vertical="center", wrap_text=True) +left_align = Alignment(horizontal="left", vertical="center", wrap_text=True) +thin_border = Border( + left=Side(style="thin"), right=Side(style="thin"), + top=Side(style="thin"), bottom=Side(style="thin") +) + +headers1 = ["序号", "手机号", "Account ID", "购买商品", "购买金额", "购买日期", "购买渠道", "退款状态", "角色名", "角色年龄", "课程状态", "已加班主任"] +col_widths1 = [6, 16, 12, 18, 12, 14, 28, 10, 10, 10, 16, 12] + +for col_idx, (h, w) in enumerate(zip(headers1, col_widths1), 1): + cell = ws1.cell(row=1, column=col_idx, value=h) + cell.font = header_font + cell.fill = header_fill + cell.alignment = center_align + cell.border = thin_border + ws1.column_dimensions[get_column_letter(col_idx)].width = w + +data1 = [ + [1, "18814300616", 28288, "L1+L2联报", "¥3,598", "2026-05-23", "老狼聊育儿(达人直播)", "已退款", "Wing", "18岁(2008-02-29)", "正常", "是"], + [2, "18604787938", 28269, "L2年包", "¥1,999", "2026-05-23", "老狼聊育儿(达人直播)", "已退款", "无角色", "-", "已删除(5/23)", "是"], + [3, "13728635257", 28261, "L2年包", "¥1,999", "2026-05-23", "老狼聊育儿(达人直播)", "已退款", "Solomon", "12岁(2013-06-13)", "正常", "是"], + [4, "15387076394", 28293, "L1+L2联报", "¥3,598", "2026-05-23", "老狼聊育儿(达人直播)", "已退款", "无角色", "-", "已删除(5/28)", "是"], +] + +for row_idx, row_data in enumerate(data1, 2): + for col_idx, val in enumerate(row_data, 1): + cell = ws1.cell(row=row_idx, column=col_idx, value=val) + cell.font = cell_font + cell.alignment = center_align + cell.border = thin_border + +ws1.row_dimensions[1].height = 28 +for r in range(2, 6): + ws1.row_dimensions[r].height = 22 + +# ===== Sheet 2: 行课明细 ===== +ws2 = wb.create_sheet("行课明细") + +headers2 = ["序号", "手机号", "Account ID", "角色名", "课程等级", "完成课时", "完课时间", "耗时(分钟)", "备注"] +col_widths2 = [6, 16, 12, 10, 10, 22, 22, 12, 20] + +for col_idx, (h, w) in enumerate(zip(headers2, col_widths2), 1): + cell = ws2.cell(row=1, column=col_idx, value=h) + cell.font = header_font + cell.fill = header_fill + cell.alignment = center_align + cell.border = thin_border + ws2.column_dimensions[get_column_letter(col_idx)].width = w + +data2 = [ + [1, "18814300616", 28288, "Wing", "L1", "S0 U00 L01", "2026-05-23 21:10", 18.5, "L1+L2联报→先看L1"], + [2, "18814300616", 28288, "Wing", "L1", "S0 U00 L02", "2026-05-23 21:43", 16.8, ""], + [3, "18604787938", 28269, "-", "L2", "无", "-", "-", "课程已删除，无行课"], + [4, "13728635257", 28261, "Solomon", "L2", "无", "-", "-", "0课时完成"], + [5, "15387076394", 28293, "-", "L1+L2", "无", "-", "-", "课程已删除，无行课"], +] + +for row_idx, row_data in enumerate(data2, 2): + for col_idx, val in enumerate(row_data, 1): + cell = ws2.cell(row=row_idx, column=col_idx, value=val) + cell.font = cell_font + cell.alignment = center_align + cell.border = thin_border + +ws2.row_dimensions[1].height = 28 +for r in range(2, 7): + ws2.row_dimensions[r].height = 22 + +# ===== Sheet 3: 汇总 ===== +ws3 = wb.create_sheet("汇总分析") + +summary_data = [ + ["指标", "数值"], + ["分析用户数", 4], + ["全部已退款", "4/4 (100%)"], + ["已加班主任", "4/4 (100%)"], + ["有行课记录", "1/4 (25%)"], + ["行课用户完成课时数", "2课时 (L1 S0 U00 L01-L02)"], + ["行课用户平均完课时长", "17.6分钟"], + ["购买渠道", "老狼聊育儿(达人直播)"], + ["购买日期", "2026-05-23"], + ["", ""], + ["结论", "4人均为5月23日同一渠道购买，全部已退款且全部已加班主任。仅1人有行课(完成2课时)，行课率25%，整体行课参与度很低。"], +] + +for row_idx, row_data in enumerate(summary_data, 1): + for col_idx, val in enumerate(row_data, 1): + cell = ws3.cell(row=row_idx, column=col_idx, value=val) + if row_idx == 1: + cell.font = header_font + cell.fill = header_fill + cell.alignment = center_align + else: + cell.font = cell_font + cell.alignment = left_align if col_idx == 2 else center_align + cell.border = thin_border + +ws3.column_dimensions['A'].width = 22 +ws3.column_dimensions['B'].width = 50 +ws3.row_dimensions[1].height = 28 +for r in range(2, len(summary_data) + 1): + ws3.row_dimensions[r].height = 22 + +# 合并结论行 +ws3.merge_cells(start_row=11, start_column=1, end_row=11, end_column=2) +ws3.cell(row=11, column=1).alignment = Alignment(horizontal="left", vertical="center", wrap_text=True) +ws3.row_dimensions[11].height = 40 + +output_path = "/root/.openclaw/workspace/output/退款用户行课分析_20260602.xlsx" +wb.save(output_path) +print(f"Excel saved: {output_path}") diff --git a/scripts/fix_all_sheets.py b/scripts/fix_all_sheets.py new file mode 100644 index 0000000..4e4f90a --- /dev/null +++ b/scripts/fix_all_sheets.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python3 +"""一次性修复： +1. 每日线索底部加月度合计行 +2. 落单渠道分布重构（合计在前，先总GMV再拆渠道） +3. 销转总览 GMV列格式改为金额 +4. 过程数据阶段上课人数改为累计口径 + 5月Tom补全 +""" + +import json, requests, os, time, sys + +CRED_DIR = "/root/.openclaw/credentials/xiaoxi" +SPREADSHEET_TOKEN = "NoZqsFi47hIOHEt9j8WcfRtbnug" + +def get_token(): + with open(os.path.join(CRED_DIR, "config.json")) as f: + cfg = json.load(f) + resp = requests.post( + "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal", + json={"app_id": cfg["apps"][0]["appId"], "app_secret": cfg["apps"][0]["appSecret"]}, + timeout=15) + return resp.json()["tenant_access_token"] + +token = get_token() + +def write_values(sheet_id, range_str, values): + resp = requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={"valueRange": {"range": range_str, "values": values}}, + params={"valueInputOption": "USER_ENTERED"}, + timeout=60) + result = resp.json() + ok = result.get("code") == 0 + if not ok: + print(f" ❌ {range_str}: code={result.get('code')} msg={result.get('msg')}") + else: + print(f" ✅ {range_str}") + return ok + +# ============================================================ +# 1. 每日线索 底部加月度合计行 (row 96-99) +# ============================================================ +print("=== 1. 每日线索 月度合计 ===") +monthly_totals = [ + ["3月合计", '=SUMIF(A2:A95,"3月*",B2:B95)', '=SUMIF(A2:A95,"3月*",C2:C95)', + '=SUMIF(A2:A95,"3月*",D2:D95)', '=SUMIF(A2:A95,"3月*",E2:E95)', '=SUM(F96:I96)'], + ["4月合计", '=SUMIF(A2:A95,"4月*",B2:B95)', '=SUMIF(A2:A95,"4月*",C2:C95)', + '=SUMIF(A2:A95,"4月*",D2:D95)', '=SUMIF(A2:A95,"4月*",E2:E95)', '=SUM(F97:I97)'], + ["5月合计", '=SUMIF(A2:A95,"5月*",B2:B95)', '=SUMIF(A2:A95,"5月*",C2:C95)', + '=SUMIF(A2:A95,"5月*",D2:D95)', '=SUMIF(A2:A95,"5月*",E2:E95)', '=SUM(F98:I98)'], + ["6月合计", '=SUMIF(A2:A95,"6月*",B2:B95)', '=SUMIF(A2:A95,"6月*",C2:C95)', + '=SUMIF(A2:A95,"6月*",D2:D95)', '=SUMIF(A2:A95,"6月*",E2:E95)', '=SUM(F99:I99)'], +] +write_values("1ANW6c", "1ANW6c!A96:F99", monthly_totals) + +# ============================================================ +# 2. 落单渠道分布重构 +# 新布局: 月份 | 销售 | 总GMV | 总订单 | 销转GMV | 销转订单 | 端内GMV | 端内订单 | 直购GMV | 直购订单 +# 合计行放在每个月的第一行 +# ============================================================ +print("\n=== 2. 落单渠道分布重构 ===") + +# 先写新表头 +new_header = [["月份", "销售", "总GMV", "总订单", "销转渠道-GMV", "销转渠道-订单", + "端内-GMV", "端内-订单", "直购-GMV", "直购-订单"]] +write_values("3dIXcs", "3dIXcs!A1:J1", new_header) + +# 新布局: 每个月 合计 → 小龙 → 吴迪 → Bob → Tom +months_list = ["3月", "4月", "5月", "6月"] +all_sales = { + "3月": ["合计", "小龙", "Bob", "Tom"], + "4月": ["合计", "小龙", "吴迪", "Bob", "Tom"], + "5月": ["合计", "小龙", "吴迪", "Bob", "Tom"], + "6月": ["合计", "小龙", "吴迪", "Bob", "Tom"], +} + +row_idx = 2 +for month in months_list: + for sales in all_sales[month]: + if sales == "合计": + total_gmv = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!B:B,"{month}*")' + total_orders = f'=COUNTIFS(\'订单汇总\'!B:B,"{month}*")' + xz_gmv = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"销转")' + xz_orders = f'=COUNTIFS(\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"销转")' + dn_gmv = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"端内")' + dn_orders = f'=COUNTIFS(\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"端内")' + zg_gmv = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"直购")' + zg_orders = f'=COUNTIFS(\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"直购")' + else: + total_gmv = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!B:B,"{month}*")' + total_orders = f'=COUNTIFS(\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!B:B,"{month}*")' + xz_gmv = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!J:J,"销转")' + xz_orders = f'=COUNTIFS(\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"销转")' + dn_gmv = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!J:J,"端内")' + dn_orders = f'=COUNTIFS(\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"端内")' + zg_gmv = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!J:J,"直购")' + zg_orders = f'=COUNTIFS(\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!B:B,"{month}*",\'订单汇总\'!J:J,"直购")' + + row = [[month, sales, total_gmv, total_orders, xz_gmv, xz_orders, + dn_gmv, dn_orders, zg_gmv, zg_orders]] + write_values("3dIXcs", f"3dIXcs!A{row_idx}:J{row_idx}", row) + row_idx += 1 + time.sleep(0.3) + +# 清除旧数据（row 20+ 如果有的话） +print(" 清除旧数据行...") +write_values("3dIXcs", "3dIXcs!A21:J30", [[None]*10]*10) + +# ============================================================ +# 3. 销转总览 GMV列(F) 格式改为金额 +# ============================================================ +print("\n=== 3. 销转总览 GMV格式 ===") +# 飞书 API 设置单元格格式 +resp = requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/style", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={ + "appendStyle": { + "range": "3fR34k!F2:F20", + "style": { + "formatter": "0.00" + } + } + }, + timeout=30) +print(f" GMV格式: code={resp.json().get('code')} msg={resp.json().get('msg')}") + +# 也设置 G(退款) H(GSV) I(消耗) J(达人GMV) K(佣金) L(总成本) 为金额格式 +for col in ["G", "H", "I", "J", "K", "L"]: + resp = requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/style", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={ + "appendStyle": { + "range": f"3fR34k!{col}2:{col}20", + "style": {"formatter": "#,##0"} + } + }, + timeout=30) + time.sleep(0.2) + +# E列(转化率) 百分比格式 +resp = requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/style", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={ + "appendStyle": { + "range": "3fR34k!E2:E20", + "style": {"formatter": "0.00%"} + } + }, + timeout=30) +print(f" 转化率格式: code={resp.json().get('code')}") + +print("\n✅ 任务1-3完成！") +print("任务4-5(过程数据)需要查询数据库，继续执行...") diff --git a/scripts/fix_process_data.py b/scripts/fix_process_data.py new file mode 100644 index 0000000..9402f19 --- /dev/null +++ b/scripts/fix_process_data.py @@ -0,0 +1,396 @@ +#!/usr/bin/env python3 +"""Fix process data: cumulative lesson completion + 5月 Tom row""" + +import json, requests, os, time, sys +import pandas as pd +import psycopg2 +from collections import defaultdict + +CRED_DIR = "/root/.openclaw/credentials/xiaoxi" +SPREADSHEET_TOKEN = "NoZqsFi47hIOHEt9j8WcfRtbnug" + +# Load secrets +secrets = {} +with open("/root/.openclaw/workspace/secrets.env") as f: + for line in f: + line = line.strip() + if line and not line.startswith("#") and "=" in line: + k, v = line.split("=", 1) + secrets[k.strip()] = v.strip().strip("'") + +def get_feishu_token(): + with open(os.path.join(CRED_DIR, "config.json")) as f: + cfg = json.load(f) + resp = requests.post( + "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal", + json={"app_id": cfg["apps"][0]["appId"], "app_secret": cfg["apps"][0]["appSecret"]}, + timeout=15) + return resp.json()["tenant_access_token"] + +# ============================================================ +# Step 1: Load 线索 data (微伴 + 销售表) +# ============================================================ +print("=== Step 1: Loading 线索 data ===") + +# Load 微伴 data +wb_file = "/root/.openclaw/media/inbound/å¾_ä¼_-å_æ_å_è_æ_æ_å_¼å_º_3---79d6aba9-7cd8-4e99-9b4f-dd981a5ca639.xlsx" +wb_df = pd.read_excel(wb_file) +print(f" 微伴: {len(wb_df)} rows, cols: {list(wb_df.columns)}") + +# Load 销售表 from 飞书 +token = get_feishu_token() + +def read_sheet(sheet_id, range_str): + resp = requests.get( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values/{sheet_id}!{range_str}", + headers={"Authorization": f"Bearer {token}"}, timeout=30) + return resp.json()["data"]["valueRange"]["values"] + +# 吴迪 sheet +wudi_data = read_sheet("f975f0", "A1:K700") +# 小龙 sheet +xl_data = read_sheet("qJF4I", "A1:K1200") +# 成都 sheet +cd_data = read_sheet("qJF4J", "A1:K2500") + +print(f" 吴迪: {len(wudi_data)} rows, 小龙: {len(xl_data)} rows, 成都: {len(cd_data)} rows") + +# Parse 销售表 +CS_MAP = {"吴迪": "吴迪", "益达老师": "小龙", "瓦拉英语-Tom老师": "Tom", "瓦拉英语-Bob老师": "Bob"} + +def parse_sales_sheet(data, sheet_name): + """Parse sales sheet, return list of {date, name, phone, sales}""" + results = [] + header = data[0] + # Find columns + date_col = name_col = phone_col = cs_col = None + for i, h in enumerate(header): + if h and "日期" in str(h): date_col = i + if h and "昵称" in str(h): name_col = i + if h and "手机" in str(h): phone_col = i + if h and "客服" in str(h): cs_col = i + + if cs_col is None: + # Try to find CS column by name + for i, h in enumerate(header): + if h and sheet_name in str(h): cs_col = i + + for row in data[1:]: + if not row or len(row) <= max(filter(None, [date_col, name_col, phone_col, cs_col or 0])): + continue + date_val = row[date_col] if date_col is not None and date_col < len(row) else None + name_val = row[name_col] if name_col is not None and name_col < len(row) else None + phone_val = row[phone_col] if phone_col is not None and phone_col < len(row) else None + cs_val = row[cs_col] if cs_col is not None and cs_col < len(row) else None + + if not date_val: + continue + + # Parse date + if isinstance(date_val, (int, float)): + from datetime import datetime, timedelta + date_str = (datetime(1899, 12, 30) + timedelta(days=int(date_val))).strftime("%Y-%m-%d") + else: + date_str = str(date_val).strip() + + # Map CS + sales = None + if cs_val: + cs_str = str(cs_val).strip() + for k, v in CS_MAP.items(): + if k in cs_str: + sales = v + break + + if not sales: + if sheet_name == "吴迪": sales = "吴迪" + elif sheet_name == "小龙": sales = "小龙" + + results.append({ + "date": date_str, + "name": str(name_val).strip() if name_val else "", + "phone": str(phone_val).strip() if phone_val else "", + "sales": sales + }) + return results + +wudi_entries = parse_sales_sheet(wudi_data, "吴迪") +xl_entries = parse_sales_sheet(xl_data, "小龙") +cd_entries = parse_sales_sheet(cd_data, "成都") + +print(f" 吴迪 entries: {len(wudi_entries)}, 小龙 entries: {len(xl_entries)}, 成都 entries: {len(cd_entries)}") + +# ============================================================ +# Step 2: Query database for user lesson completion +# ============================================================ +print("\n=== Step 2: Querying database ===") + +conn = psycopg2.connect( + host="bj-postgres-16pob4sg.sql.tencentcdb.com", + port=28591, + user="ai_member", + password=secrets["PG_ONLINE_PASSWORD"], + dbname="vala_bi" +) + +# Get all users with their phone numbers +cur = conn.cursor() +cur.execute(""" + SELECT id, tel, tel_encrypt + FROM bi_vala_app_account + WHERE status = 1 AND deleted_at IS NULL +""") +users = {row[0]: {"tel": row[1], "tel_encrypt": row[2]} for row in cur.fetchall()} +print(f" Users: {len(users)}") + +# Get cumulative lesson completion per user +cur.execute(""" + SELECT ucp.user_id, MAX(blu.course_lesson) as max_lesson + FROM bi_user_chapter_play_record_0 ucp + JOIN bi_level_unit_lesson blu ON ucp.chapter_id = blu.id + WHERE ucp.play_status = 1 + AND blu.course_season = 'S0' + AND blu.course_unit = 'U00' + AND blu.course_level IN ('L1', 'L2') + GROUP BY ucp.user_id +""") +user_lessons = {} +for row in cur.fetchall(): + user_lessons[row[0]] = row[1] +print(f" Users with lessons: {len(user_lessons)}") + +# Get paid users +cur.execute(""" + SELECT DISTINCT account_id + FROM bi_vala_order + WHERE pay_success_date IS NOT NULL + AND order_status IN (3, 4) + AND key_from IN ('app-active-h5-0-0', 'app-sales-bj-qhm-0') +""") +paid_users = set(row[0] for row in cur.fetchall()) +print(f" Paid users: {len(paid_users)}") + +conn.close() + +# ============================================================ +# Step 3: Match users to sales via 线索 data +# ============================================================ +print("\n=== Step 3: Matching users to sales ===") + +# Build phone → sales mapping from 线索 data +# 微伴: phone → CS +wb_phone_to_sales = {} +for _, row in wb_df.iterrows(): + phone = str(row.get("手机号", "")).strip() + cs = str(row.get("客服", "")).strip() + if phone and cs: + for k, v in CS_MAP.items(): + if k in cs: + wb_phone_to_sales[phone] = v + break + +# 销售表: phone → sales +sales_phone_to_sales = {} +for entry in wudi_entries + xl_entries + cd_entries: + if entry["phone"] and entry["sales"]: + sales_phone_to_sales[entry["phone"]] = entry["sales"] + +print(f" 微伴 phone→sales: {len(wb_phone_to_sales)}") +print(f" 销售表 phone→sales: {len(sales_phone_to_sales)}") + +# Match users to sales via phone +# Try: exact match on tel, then partial match +user_to_sales = {} +unmatched = 0 + +for uid, uinfo in users.items(): + tel = (uinfo.get("tel") or "").strip() + if not tel: + unmatched += 1 + continue + + # Try 微伴 first + if tel in wb_phone_to_sales: + user_to_sales[uid] = wb_phone_to_sales[tel] + continue + + # Try 销售表 + if tel in sales_phone_to_sales: + user_to_sales[uid] = sales_phone_to_sales[tel] + continue + + # Try partial match + matched = False + for phone, sales in wb_phone_to_sales.items(): + if tel in phone or phone in tel: + user_to_sales[uid] = sales + matched = True + break + if not matched: + for phone, sales in sales_phone_to_sales.items(): + if tel in phone or phone in tel: + user_to_sales[uid] = sales + matched = True + break + if not matched: + unmatched += 1 + +print(f" Matched users: {len(user_to_sales)}, unmatched: {unmatched}") + +# ============================================================ +# Step 4: Aggregate by month and sales +# ============================================================ +print("\n=== Step 4: Aggregating ===") + +# Get user registration month +conn = psycopg2.connect( + host="bj-postgres-16pob4sg.sql.tencentcdb.com", + port=28591, + user="ai_member", + password=secrets["PG_ONLINE_PASSWORD"], + dbname="vala_bi" +) +cur = conn.cursor() +cur.execute(""" + SELECT id, DATE_TRUNC('month', created_at)::date as reg_month + FROM bi_vala_app_account + WHERE status = 1 AND deleted_at IS NULL + AND created_at >= '2026-03-01' AND created_at < '2026-07-01' +""") +user_reg_month = {row[0]: row[1].strftime("%Y-%m") for row in cur.fetchall()} +conn.close() + +# For each month+sales, count cumulative lesson completion +LESSON_ORDER = ["L01", "L02", "L03", "L04", "L05"] +MONTHS = ["2026-03", "2026-04", "2026-05", "2026-06"] +SALES_LIST = { + "2026-03": ["合计", "小龙", "Bob", "Tom"], + "2026-04": ["合计", "小龙", "吴迪", "Bob", "Tom"], + "2026-05": ["合计", "小龙", "吴迪", "Bob", "Tom"], + "2026-06": ["合计", "小龙", "吴迪", "Bob", "Tom"], +} + +# Build result: {month: {sales: {≥L01: count, ≥L02: count, ..., cv_≥L01: count, ...}}} +results = {} +for month in MONTHS: + results[month] = {} + for sales in SALES_LIST[month]: + results[month][sales] = {f"gte_{l}": 0 for l in LESSON_ORDER} + results[month][sales].update({f"cv_{l}": 0 for l in LESSON_ORDER}) + +for uid, reg_month in user_reg_month.items(): + if reg_month not in results: + continue + sales = user_to_sales.get(uid, None) + max_lesson = user_lessons.get(uid, None) + is_paid = uid in paid_users + + # Add to 合计 + if max_lesson: + for lesson in LESSON_ORDER: + if max_lesson >= lesson: + results[reg_month]["合计"][f"gte_{lesson}"] += 1 + if is_paid: + results[reg_month]["合计"][f"cv_{lesson}"] += 1 + + # Add to specific sales + if sales and sales in results[reg_month]: + if max_lesson: + for lesson in LESSON_ORDER: + if max_lesson >= lesson: + results[reg_month][sales][f"gte_{lesson}"] += 1 + if is_paid: + results[reg_month][sales][f"cv_{lesson}"] += 1 + +# Print results +for month in MONTHS: + print(f"\n {month}:") + for sales in SALES_LIST[month]: + r = results[month][sales] + print(f" {sales}: ≥L01={r['gte_L01']} ≥L02={r['gte_L02']} ≥L03={r['gte_L03']} ≥L04={r['gte_L04']} ≥L05={r['gte_L05']} | cv: {r['cv_L01']} {r['cv_L02']} {r['cv_L03']} {r['cv_L04']} {r['cv_L05']}") + +# ============================================================ +# Step 5: Write to process data sheet +# ============================================================ +print("\n=== Step 5: Writing to process data ===") + +def write_values(sheet_id, range_str, values): + resp = requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={"valueRange": {"range": range_str, "values": values}}, + params={"valueInputOption": "USER_ENTERED"}, + timeout=60) + result = resp.json() + ok = result.get("code") == 0 + if not ok: + print(f" ❌ {range_str}: code={result.get('code')} msg={result.get('msg')}") + else: + print(f" ✅ {range_str}") + return ok + +# Column mapping for process data: +# H=首课人数 I=首课率 J=一节课转化人数 K=一节课转化率 +# L=二次课人数 M=二次课率 N=二节课转化人数 O=二节课转化率 +# P=三次课人数 Q=三次课率 R=三节课转化人数 S=三节课转化率 +# T=四次课人数 U=四次课率 V=四节课转化人数 W=四节课转化率 +# X=五次课人数 Y=五次课率 Z=五节课转化人数 AA=五节课转化率 + +# Row mapping: 2=3月合计, 3=3月小龙, 4=3月Bob, 5=3月Tom +# 6=4月合计, 7=4月小龙, 8=4月吴迪, 9=4月Bob, 10=4月Tom +# 11=5月合计, 12=5月小龙, 13=5月吴迪, 14=5月Bob, 15=5月Tom +# 16=6月合计, 17=6月小龙, 18=6月吴迪, 19=6月Bob, 20=6月Tom + +row_map = [ + (2, "2026-03", "合计"), (3, "2026-03", "小龙"), (4, "2026-03", "Bob"), (5, "2026-03", "Tom"), + (6, "2026-04", "合计"), (7, "2026-04", "小龙"), (8, "2026-04", "吴迪"), (9, "2026-04", "Bob"), (10, "2026-04", "Tom"), + (11, "2026-05", "合计"), (12, "2026-05", "小龙"), (13, "2026-05", "吴迪"), (14, "2026-05", "Bob"), (15, "2026-05", "Tom"), + (16, "2026-06", "合计"), (17, "2026-06", "小龙"), (18, "2026-06", "吴迪"), (19, "2026-06", "Bob"), (20, "2026-06", "Tom"), +] + +# Write cumulative lesson data: H, J, L, N, P, R, T, V, X, Z +# H=首课人数(≥L01), J=一节课转化人数(≥L01+paid) +# L=二次课人数(≥L02), N=二节课转化人数(≥L02+paid) +# P=三次课人数(≥L03), R=三节课转化人数(≥L03+paid) +# T=四次课人数(≥L04), V=四节课转化人数(≥L04+paid) +# X=五次课人数(≥L05), Z=五节课转化人数(≥L05+paid) + +for row_num, month, sales in row_map: + r = results[month][sales] + # Build the row data for columns H through AA + # We only write the count columns (H, J, L, N, P, R, T, V, X, Z) + # The rate columns (I, K, M, O, Q, S, U, W, Y, AA) are formulas + + row_data = [ + [r["gte_L01"]], # H: 首课人数 + [f'=IFERROR(H{row_num}/C{row_num},"")'], # I: 首课率 + [r["cv_L01"]], # J: 一节课转化人数 + [f'=IFERROR(J{row_num}/C{row_num},"")'], # K: 一节课转化率 + [r["gte_L02"]], # L: 二次课人数 + [f'=IFERROR(L{row_num}/C{row_num},"")'], # M: 二次课率 + [r["cv_L02"]], # N: 二节课转化人数 + [f'=IFERROR(N{row_num}/C{row_num},"")'], # O: 二节课转化率 + [r["gte_L03"]], # P: 三次课人数 + [f'=IFERROR(P{row_num}/C{row_num},"")'], # Q: 三次课率 + [r["cv_L03"]], # R: 三节课转化人数 + [f'=IFERROR(R{row_num}/C{row_num},"")'], # S: 三节课转化率 + [r["gte_L04"]], # T: 四次课人数 + [f'=IFERROR(T{row_num}/C{row_num},"")'], # U: 四次课率 + [r["cv_L04"]], # V: 四节课转化人数 + [f'=IFERROR(V{row_num}/C{row_num},"")'], # W: 四节课转化率 + [r["gte_L05"]], # X: 五次课人数 + [f'=IFERROR(X{row_num}/C{row_num},"")'], # Y: 五次课率 + [r["cv_L05"]], # Z: 五节课转化人数 + [f'=IFERROR(Z{row_num}/C{row_num},"")'], # AA: 五节课转化率 + ] + + # Write H through AA + col_letters = "HIJKLMNOPQRSTUVWXYZAA" + for i, (col, data) in enumerate(zip( + ["H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z","AA"], + row_data + )): + write_values("3aOvV6", f"3aOvV6!{col}{row_num}:{col}{row_num}", [data]) + time.sleep(0.15) + +print("\n✅ Process data updated!") diff --git a/scripts/fix_process_data_v2.py b/scripts/fix_process_data_v2.py new file mode 100644 index 0000000..47a9539 --- /dev/null +++ b/scripts/fix_process_data_v2.py @@ -0,0 +1,314 @@ +#!/usr/bin/env python3 +"""Fix process data: cumulative lesson completion by sales, using sales sheet phone→user matching""" + +import json, requests, os, time, re +import psycopg2 +from collections import defaultdict +from datetime import datetime, timedelta + +CRED_DIR = "/root/.openclaw/credentials/xiaoxi" +SPREADSHEET_TOKEN = "NoZqsFi47hIOHEt9j8WcfRtbnug" + +secrets = {} +with open("/root/.openclaw/workspace/secrets.env") as f: + for line in f: + line = line.strip() + if line and not line.startswith("#") and "=" in line: + k, v = line.split("=", 1) + secrets[k.strip()] = v.strip().strip("'") + +def get_token(): + with open(os.path.join(CRED_DIR, "config.json")) as f: cfg = json.load(f) + resp = requests.post("https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal", + json={"app_id": cfg["apps"][0]["appId"], "app_secret": cfg["apps"][0]["appSecret"]}, timeout=15) + return resp.json()["tenant_access_token"] + +token = get_token() + +def read_sheet(sheet_id, range_str): + resp = requests.get( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values/{sheet_id}!{range_str}", + headers={"Authorization": f"Bearer {token}"}, timeout=30) + return resp.json()["data"]["valueRange"]["values"] + +def write_values(sheet_id, range_str, values): + resp = requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={"valueRange": {"range": range_str, "values": values}}, + params={"valueInputOption": "USER_ENTERED"}, + timeout=60) + result = resp.json() + ok = result.get("code") == 0 + if not ok: + print(f" ❌ {range_str}: code={result.get('code')} msg={result.get('msg')}") + else: + print(f" ✅ {range_str}") + return ok + +# ============================================================ +# Step 1: Parse sales sheets → phone → sales mapping +# ============================================================ +print("=== Step 1: Parse sales sheets ===") + +CS_MAP = {"吴迪": "吴迪", "小龙": "小龙", "Tom": "Tom", "Bob": "Bob"} + +def parse_sales_sheet(data): + """Return list of {sales, phone, user_id, date}""" + results = [] + for row in data[2:]: # skip header rows + if not row or len(row) < 5: + continue + sales_raw = str(row[0]).strip() if row[0] else "" + phone_raw = row[4] + user_id_raw = row[7] if len(row) > 7 else None + + # Map sales + sales = None + for k, v in CS_MAP.items(): + if k in sales_raw: + sales = v + break + if not sales: + continue + + # Parse phone + phone = "" + if phone_raw: + if isinstance(phone_raw, (int, float)): + phone = str(int(phone_raw)) + else: + phone = str(phone_raw).strip() + + # Parse user_id + user_id = "" + if user_id_raw: + if isinstance(user_id_raw, (int, float)): + user_id = str(int(user_id_raw)) + else: + user_id = str(user_id_raw).strip() + + results.append({"sales": sales, "phone": phone, "user_id": user_id}) + return results + +wudi = read_sheet("f975f0", "A1:K700") +xl = read_sheet("qJF4I", "A1:K1200") +cd = read_sheet("qJF4J", "A1:K2500") + +wudi_entries = parse_sales_sheet(wudi) +xl_entries = parse_sales_sheet(xl) +cd_entries = parse_sales_sheet(cd) + +all_entries = wudi_entries + xl_entries + cd_entries +print(f" 吴迪: {len(wudi_entries)}, 小龙: {len(xl_entries)}, 成都: {len(cd_entries)}, 总计: {len(all_entries)}") + +# Build phone→sales mapping (dedup: first match wins) +phone_to_sales = {} +for e in all_entries: + if e["phone"] and e["phone"] not in phone_to_sales: + phone_to_sales[e["phone"]] = e["sales"] + +# Also user_id→sales +uid_to_sales = {} +for e in all_entries: + if e["user_id"] and e["user_id"].isdigit() and e["user_id"] not in uid_to_sales: + uid_to_sales[e["user_id"]] = e["sales"] + +print(f" phone→sales: {len(phone_to_sales)}, uid→sales: {len(uid_to_sales)}") + +# ============================================================ +# Step 2: Query database +# ============================================================ +print("\n=== Step 2: Query database ===") + +conn = psycopg2.connect( + host="bj-postgres-16pob4sg.sql.tencentcdb.com", port=28591, + user="ai_member", password=secrets["PG_ONLINE_PASSWORD"], dbname="vala_bi") +cur = conn.cursor() + +# Users with phone +cur.execute("SELECT id, tel FROM bi_vala_app_account WHERE status = 1 AND deleted_at IS NULL") +user_phone = {} +for row in cur.fetchall(): + uid = row[0] + tel = (row[1] or "").strip() + if tel: + user_phone[uid] = tel + +# User reg month +cur.execute(""" + SELECT id, DATE_TRUNC('month', created_at)::date as reg_month + FROM bi_vala_app_account WHERE status = 1 AND deleted_at IS NULL + AND created_at >= '2026-03-01' AND created_at < '2026-07-01' +""") +user_reg = {row[0]: row[1].strftime("%Y-%m") for row in cur.fetchall()} + +# Max lesson per user +cur.execute(""" + SELECT ucp.user_id, MAX(blu.course_lesson) as max_lesson + FROM bi_user_chapter_play_record_0 ucp + JOIN bi_level_unit_lesson blu ON ucp.chapter_id = blu.id + WHERE ucp.play_status = 1 + AND blu.course_season = 'S0' AND blu.course_unit = 'U00' + AND blu.course_level IN ('L1', 'L2') + GROUP BY ucp.user_id +""") +user_lesson = {row[0]: row[1] for row in cur.fetchall()} + +# Paid users +cur.execute(""" + SELECT DISTINCT account_id FROM bi_vala_order + WHERE pay_success_date IS NOT NULL AND order_status IN (3, 4) + AND key_from IN ('app-active-h5-0-0', 'app-sales-bj-qhm-0') +""") +paid = set(row[0] for row in cur.fetchall()) + +conn.close() + +print(f" Users with phone: {len(user_phone)}, reg: {len(user_reg)}, lessons: {len(user_lesson)}, paid: {len(paid)}") + +# ============================================================ +# Step 3: Match users to sales +# ============================================================ +print("\n=== Step 3: Match users to sales ===") + +user_to_sales = {} +matched_by_uid = 0 +matched_by_phone = 0 + +for uid in user_reg: + # Try user_id match first + if str(uid) in uid_to_sales: + user_to_sales[uid] = uid_to_sales[str(uid)] + matched_by_uid += 1 + continue + + # Try phone match + tel = user_phone.get(uid, "") + if tel and tel in phone_to_sales: + user_to_sales[uid] = phone_to_sales[tel] + matched_by_phone += 1 + continue + + # Try partial phone match + if tel: + for phone, sales in phone_to_sales.items(): + if tel in phone or phone in tel: + user_to_sales[uid] = sales + matched_by_phone += 1 + break + +print(f" Matched: uid={matched_by_uid}, phone={matched_by_phone}, total={len(user_to_sales)}") + +# ============================================================ +# Step 4: Aggregate +# ============================================================ +print("\n=== Step 4: Aggregate ===") + +LESSONS = ["L01", "L02", "L03", "L04", "L05"] +MONTHS = ["2026-03", "2026-04", "2026-05", "2026-06"] +SALES_MAP = { + "2026-03": ["合计", "小龙", "Bob", "Tom"], + "2026-04": ["合计", "小龙", "吴迪", "Bob", "Tom"], + "2026-05": ["合计", "小龙", "吴迪", "Bob", "Tom"], + "2026-06": ["合计", "小龙", "吴迪", "Bob", "Tom"], +} + +results = {} +for m in MONTHS: + results[m] = {} + for s in SALES_MAP[m]: + results[m][s] = {f"gte_{l}": 0 for l in LESSONS} + results[m][s].update({f"cv_{l}": 0 for l in LESSONS}) + +for uid, reg_month in user_reg.items(): + if reg_month not in results: + continue + sales = user_to_sales.get(uid) + max_l = user_lesson.get(uid) + is_paid = uid in paid + + # 合计 + if max_l: + for l in LESSONS: + if max_l >= l: + results[reg_month]["合计"][f"gte_{l}"] += 1 + if is_paid: + results[reg_month]["合计"][f"cv_{l}"] += 1 + + if sales and sales in results[reg_month]: + if max_l: + for l in LESSONS: + if max_l >= l: + results[reg_month][sales][f"gte_{l}"] += 1 + if is_paid: + results[reg_month][sales][f"cv_{l}"] += 1 + +for m in MONTHS: + print(f"\n {m}:") + for s in SALES_MAP[m]: + r = results[m][s] + print(f" {s}: L01={r['gte_L01']} L02={r['gte_L02']} L03={r['gte_L03']} L04={r['gte_L04']} L05={r['gte_L05']} | cv: {r['cv_L01']} {r['cv_L02']} {r['cv_L03']} {r['cv_L04']} {r['cv_L05']}") + +# ============================================================ +# Step 5: Write to process data +# ============================================================ +print("\n=== Step 5: Write to process data ===") + +row_map = [ + (2, "2026-03", "合计"), (3, "2026-03", "小龙"), (4, "2026-03", "Bob"), (5, "2026-03", "Tom"), + (6, "2026-04", "合计"), (7, "2026-04", "小龙"), (8, "2026-04", "吴迪"), (9, "2026-04", "Bob"), (10, "2026-04", "Tom"), + (11, "2026-05", "合计"), (12, "2026-05", "小龙"), (13, "2026-05", "吴迪"), (14, "2026-05", "Bob"), (15, "2026-05", "Tom"), + (16, "2026-06", "合计"), (17, "2026-06", "小龙"), (18, "2026-06", "吴迪"), (19, "2026-06", "Bob"), (20, "2026-06", "Tom"), +] + +# Column mapping: +# H=首课人数(≥L01) I=首课率 J=一节课转化人数(≥L01+paid) K=一节课转化率 +# L=二次课人数(≥L02) M=二次课率 N=二节课转化人数(≥L02+paid) O=二节课转化率 +# P=三次课人数(≥L03) Q=三次课率 R=三节课转化人数(≥L03+paid) S=三节课转化率 +# T=四次课人数(≥L04) U=四次课率 V=四节课转化人数(≥L04+paid) W=四节课转化率 +# X=五次课人数(≥L05) Y=五次课率 Z=五节课转化人数(≥L05+paid) AA=五节课转化率 + +col_pairs = [ + ("H", "I", "gte_L01", "cv_L01"), # 首课 + ("L", "M", "gte_L02", "cv_L02"), # 二次课 + ("P", "Q", "gte_L03", "cv_L03"), # 三次课 + ("T", "U", "gte_L04", "cv_L04"), # 四次课 + ("X", "Y", "gte_L05", "cv_L05"), # 五次课 +] + +# Also need J/K (一节课转化), N/O (二节课转化), R/S, V/W, Z/AA +cv_col_pairs = [ + ("J", "K", "cv_L01"), + ("N", "O", "cv_L02"), + ("R", "S", "cv_L03"), + ("V", "W", "cv_L04"), + ("Z", "AA", "cv_L05"), +] + +for row_num, month, sales in row_map: + r = results[month][sales] + + for count_col, rate_col, key, cv_key in col_pairs: + count = r[key] + cv_count = r[cv_key] + + # Write count + write_values("3aOvV6", f"3aOvV6!{count_col}{row_num}:{count_col}{row_num}", [[count]]) + time.sleep(0.1) + + # Write rate formula + write_values("3aOvV6", f"3aOvV6!{rate_col}{row_num}:{rate_col}{row_num}", + [[f'=IFERROR({count_col}{row_num}/C{row_num},"")']]) + time.sleep(0.1) + + # Write cv columns + for cv_col, cv_rate_col, cv_key in cv_col_pairs: + cv_count = r[cv_key] + write_values("3aOvV6", f"3aOvV6!{cv_col}{row_num}:{cv_col}{row_num}", [[cv_count]]) + time.sleep(0.1) + write_values("3aOvV6", f"3aOvV6!{cv_rate_col}{row_num}:{cv_rate_col}{row_num}", + [[f'=IFERROR({cv_col}{row_num}/C{row_num},"")']]) + time.sleep(0.1) + +print("\n✅ Process data updated!") diff --git a/scripts/full_refresh_sales.py b/scripts/full_refresh_sales.py new file mode 100644 index 0000000..d731b20 --- /dev/null +++ b/scripts/full_refresh_sales.py @@ -0,0 +1,635 @@ +#!/usr/bin/env python3 +""" +全覆盖刷新 — 销售表行课状态 + 过程数据累计人数 + 订单汇总 +每次执行时对所有已有 user_id 的行做全量覆盖，不依赖「待查询」标记。 + +覆盖范围： + 1. 销售表 (f975f0/qJF4I/qJF4J) — D/I/J/K/L/M/N/O/P/Q/R/S/T/U/V 列 + 2. 过程数据 (3aOvV6) — H/L/P/T/X (上课人数) + J/N/R/V/Z (转化人数) + 3. 订单汇总 (2smjwA) — 新增订单追加写入，按(销售归属+微信昵称+金额)去重 +""" +import json, requests, os, sys, psycopg2, time +from datetime import datetime +from collections import defaultdict + +SCRIPTS_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, SCRIPTS_DIR) +from phone_encrypt import encrypt_phone + +# ── 配置 ── +PG_HOST = "bj-postgres-16pob4sg.sql.tencentcdb.com" +PG_PORT = 28591 +PG_USER = "ai_member" +PG_DB = "vala_bi" + +SPREADSHEET_TOKEN = "NoZqsFi47hIOHEt9j8WcfRtbnug" +CRED_DIR = "/root/.openclaw/credentials/xiaoxi" + +SALES_SHEETS = [ + ("f975f0", "吴迪", "A1:V700"), + ("qJF4I", "小龙", "A1:V1200"), + ("qJF4J", "成都", "A1:V2500"), +] + +PROCESS_SHEET = "3aOvV6" +ORDER_SHEET = "2smjwA" + +CS_MAP = {"吴迪": "吴迪", "小龙": "小龙", "Tom": "Tom", "Bob": "Bob"} + +GOODS_NAMES = { + 57: "瓦拉英语level1·单季", 60: "瓦拉英语level1", 63: "瓦拉英语level1·单季", + 31: "瓦拉英语年包", 32: "瓦拉英语单季度包", 33: "瓦拉英语level2", 54: "瓦拉英语季度包", + 61: "瓦拉英语level1+2", +} + +LOG_FILE = "/var/log/xiaoxi_full_refresh.log" + +def log(msg): + ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + line = f"[{ts}] {msg}" + print(line) + with open(LOG_FILE, "a") as f: + f.write(line + "\n") + +def get_pg_password(): + with open(os.path.join(SCRIPTS_DIR, "..", "secrets.env")) as f: + for line in f: + if line.startswith("PG_ONLINE_PASSWORD="): + return line.strip().split("=", 1)[1].strip("'\"") + +def get_fs_token(): + with open(os.path.join(CRED_DIR, "config.json")) as f: + cfg = json.load(f) + resp = requests.post( + "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal", + json={"app_id": cfg["apps"][0]["appId"], "app_secret": cfg["apps"][0]["appSecret"]}, + timeout=15 + ) + return resp.json()["tenant_access_token"] + +def read_sheet(token, sheet_id, range_str=None): + url = f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values/{sheet_id}" + if range_str: + url += f"!{range_str}" + resp = requests.get(url, headers={"Authorization": f"Bearer {token}"}, timeout=30) + data = resp.json() + if data.get("code") != 0: + raise RuntimeError(f"读取失败: {data}") + return data["data"]["valueRange"]["values"] + +def put_values(token, sheet_id, range_str, values): + url = f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values" + body = {"valueRange": {"range": f"{sheet_id}!{range_str}", "values": values}} + resp = requests.put(url, headers={ + "Authorization": f"Bearer {token}", + "Content-Type": "application/json" + }, json=body, timeout=30) + r = resp.json() + if r.get("code") != 0: + log(f" ❌ {range_str}: {r.get('code')} {r.get('msg')}") + return False + return True + +def batch_in(cur, sql_tpl, params, chunk=500): + results = [] + for i in range(0, len(params), chunk): + batch = params[i:i+chunk] + ph = ",".join(["%s"] * len(batch)) + cur.execute(sql_tpl % ph, batch) + results.extend(cur.fetchall()) + return results + +# ── Step 1: 解析销售表 ── +def parse_sales_sheets(token): + """返回 {sheet_id: [(row_idx, sales_name, nickname, date, phone, user_id), ...]}""" + all_data = {} + for sid, sname, rng in SALES_SHEETS: + rows = read_sheet(token, sid, rng) + entries = [] + for idx, row in enumerate(rows[2:], start=3): # row 1=header, row 2=sub-header + if not row: continue + sr = str(row[0]).strip() if row[0] else "" + sales = None + for k, v in CS_MAP.items(): + if k in sr: sales = v; break + if not sales: continue + nickname = str(row[1]).strip() if len(row) > 1 and row[1] else "" + date_str = str(row[2]).strip() if len(row) > 2 and row[2] else "" + phone = "" + if len(row) > 4 and row[4]: + try: + phone = str(int(float(row[4]))) + except: pass + uid = "" + if len(row) > 7 and row[7]: + try: + uid = str(int(float(row[7]))) + except: pass + entries.append((idx, sales, nickname, date_str, phone, uid)) + all_data[sid] = entries + log(f" {sname}: {len(entries)} rows, {sum(1 for e in entries if e[5] and e[5].isdigit() and int(e[5])>0)} with uid") + return all_data + +# ── Step 2: 数据库批量查询 ── +def query_all_db(conn, all_entries): + """查询所有需要的数据，返回 uid -> info dict""" + # 收集所有有效 user_id + uid_set = set() + uid_to_sales = {} + uid_to_nickname = {} + uid_to_date = {} + for sid, entries in all_entries.items(): + for idx, sales, nick, date_str, phone, uid in entries: + if uid and uid.isdigit() and int(uid) > 0: + aid = int(uid) + uid_set.add(aid) + uid_to_sales[aid] = sales + uid_to_nickname[aid] = nick + uid_to_date[aid] = date_str + + uid_list = list(uid_set) + log(f" 有效 user_id: {len(uid_list)}") + + cur = conn.cursor() + info = {uid: { + "sales": uid_to_sales.get(uid, ""), + "nickname": uid_to_nickname.get(uid, ""), + "date": uid_to_date.get(uid, ""), + "reg_date": "", "download_channel": "", + "has_order": "否", "order_date": "", "order_channel": "", "product": "", + "gmv": 0, "refund": 0, "gsv": 0, + "activation": "", "lesson_progress": "", "lesson_time": "", "lesson_minutes": 0, + "max_lesson": 0, "is_paid": False, + } for uid in uid_set} + + # 2a. 注册信息 + log(" 查询注册信息...") + reg_info = batch_in(cur, + "SELECT id, created_at, download_channel FROM bi_vala_app_account WHERE id IN (%s) AND status=1 AND deleted_at IS NULL", + uid_list + ) + for aid, created_at, dc in reg_info: + if aid in info: + info[aid]["reg_date"] = created_at.strftime("%Y-%m-%d") if created_at else "" + info[aid]["download_channel"] = dc or "" + + # 2b. 订单信息 + log(" 查询订单信息...") + orders = batch_in(cur, + "SELECT account_id, trade_no, pay_success_date, key_from, goods_id, pay_amount_int, order_status FROM bi_vala_order WHERE account_id IN (%s) AND pay_success_date IS NOT NULL AND order_status IN (3,4) ORDER BY pay_success_date DESC", + uid_list + ) + # 按用户聚合 + user_orders = defaultdict(list) + for o in orders: + user_orders[o[0]].append(o) + + # 退款 + trade_nos = [o[1] for o in orders if o[1]] + refund_map = {} + if trade_nos: + refunds = batch_in(cur, + "SELECT trade_no, refund_amount_int FROM bi_refund_order WHERE trade_no IN (%s) AND status=3", + trade_nos + ) + for tn, amt in refunds: + refund_map[tn] = amt + + for aid, olist in user_orders.items(): + if aid not in info: continue + info[aid]["has_order"] = "是" + # 最新订单 + latest = olist[0] + info[aid]["order_date"] = latest[2].strftime("%Y-%m-%d") if latest[2] else "" + info[aid]["order_channel"] = latest[3] or "" + info[aid]["product"] = GOODS_NAMES.get(latest[4], f"商品{latest[4]}") + # 汇总 + total_gmv = sum(o[5] for o in olist) / 100.0 + total_refund = sum(refund_map.get(o[1], 0) for o in olist) / 100.0 + info[aid]["gmv"] = total_gmv + info[aid]["refund"] = total_refund + info[aid]["gsv"] = total_gmv - total_refund + info[aid]["is_paid"] = True + + # 2c. 激活课程 + log(" 查询激活课程...") + try: + activations = batch_in(cur, + "SELECT t.account_id, t.season_package_level FROM bi_vala_seasonal_ticket t WHERE t.account_id IN (%s) AND t.status=1 AND t.deleted_at IS NULL AND t.season_package_level IN ('A1','A2')", + uid_list + ) + for aid, lvl in activations: + if aid in info: + info[aid]["activation"] = lvl + except Exception as e: + log(f" 激活查询异常: {e}") + + # 2d. 角色信息 + log(" 查询角色信息...") + char_info = batch_in(cur, + "SELECT account_id, id FROM bi_vala_app_character WHERE account_id IN (%s) AND deleted_at IS NULL", + uid_list + ) + account_chars = defaultdict(list) + char_to_account = {} + for aid, cid in char_info: + account_chars[aid].append(cid) + char_to_account[cid] = aid + char_ids = list(char_to_account.keys()) + log(f" 角色数: {len(char_ids)}") + + # 2e. 课程映射 + cur.execute("SELECT id, course_level, course_season, course_unit, course_lesson FROM bi_level_unit_lesson") + chapter_map = {} + for ch_id, cl, cs, cu, cl2 in cur.fetchall(): + chapter_map[ch_id] = (cl or "", cs or "", cu or "", cl2 or "") + + # 2f. 课时完成记录 + log(" 查询课时完成记录...") + char_plays = defaultdict(lambda: {"latest_time": None, "latest_chapter": None, "max_lesson_idx": 0, "total_ms": 0}) + for tbl_idx in range(8): + table = f"bi_user_chapter_play_record_{tbl_idx}" + try: + cur.execute( + f"SELECT user_id, chapter_id, created_at FROM {table} WHERE play_status=1 AND deleted_at IS NULL AND user_id = ANY(%s)", + (char_ids,) + ) + for uid, ch_id, created_at in cur.fetchall(): + ch_data = chapter_map.get(ch_id) + if not ch_data: continue + rec = char_plays[uid] + if rec["latest_time"] is None or created_at > rec["latest_time"]: + rec["latest_time"] = created_at + rec["latest_chapter"] = (ch_id, ch_data) + # 计算 lesson index 用于 max_lesson + cl, cs, cu, cl2 = ch_data + try: + u_num = int(cu[1:]) if cu and len(cu) >= 2 else 0 + l_num = int(cl2[1:]) if cl2 and len(cl2) >= 2 else 0 + lesson_idx = u_num * 5 + l_num + if lesson_idx > rec["max_lesson_idx"]: + rec["max_lesson_idx"] = lesson_idx + except: pass + except Exception as e: + log(f" 警告 {table}: {e}") + + # 2g. 学习总耗时 + log(" 查询学习耗时...") + for tbl_idx in range(8): + table = f"bi_user_component_play_record_{tbl_idx}" + try: + cur.execute( + f"SELECT user_id, SUM(COALESCE(interval_time,0)) FROM {table} WHERE user_id = ANY(%s) AND deleted_at IS NULL GROUP BY user_id", + (char_ids,) + ) + for uid, total_ms in cur.fetchall(): + if uid in char_plays: + char_plays[uid]["total_ms"] += (total_ms or 0) + except Exception as e: + log(f" 警告 {table}: {e}") + + cur.close() + + # 汇总到 account 级别 + for aid in uid_set: + chars = account_chars.get(aid, []) + best_time = None + best_ch = None + max_lesson = 0 + total_ms = 0 + for cid in chars: + play = char_plays.get(cid) + if not play: continue + if play["latest_chapter"]: + if best_time is None or play["latest_time"] > best_time: + best_time = play["latest_time"] + best_ch = play["latest_chapter"] + if play["max_lesson_idx"] > max_lesson: + max_lesson = play["max_lesson_idx"] + total_ms += play["total_ms"] + + info[aid]["max_lesson"] = max_lesson + info[aid]["lesson_minutes"] = round(total_ms / 60000, 1) + if info[aid]["lesson_minutes"] == int(info[aid]["lesson_minutes"]): + info[aid]["lesson_minutes"] = int(info[aid]["lesson_minutes"]) + + if best_ch: + ch_id, (cl, cs, cu, cl2) = best_ch + info[aid]["lesson_progress"] = f"{cl}-{cs}-{cu}-{cl2}" + info[aid]["lesson_time"] = best_time.strftime("%Y-%m-%d") if best_time else "" + + log(f" 数据库查询完成") + return info + +# ── Step 3: 写入销售表 ── +def write_sales_sheets(token, all_entries, db_info): + """全覆盖写入销售表的自动列""" + now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + for sid, sname, _ in SALES_SHEETS: + entries = all_entries[sid] + log(f" 写入 {sname} ({sid})...") + + # 按行分组（连续行合并写入） + groups = [] + cur_grp = [] + for idx, sales, nick, date_str, phone, uid in entries: + if not cur_grp or idx == cur_grp[-1]["row"] + 1: + cur_grp.append({"row": idx, "uid": uid}) + else: + groups.append(cur_grp) + cur_grp = [{"row": idx, "uid": uid}] + if cur_grp: + groups.append(cur_grp) + + for g in groups: + sr, er = g[0]["row"], g[-1]["row"] + + # 构建每列的值 + d_vals = [] # 体验节数 + i_vals = [] # 注册日期 + j_vals = [] # 下载渠道 + k_vals = [] # 是否下单 + l_vals = [] # 下单日期 + m_vals = [] # 成交渠道 + n_vals = [] # 产品 + o_vals = [] # GMV + p_vals = [] # 退款 + q_vals = [] # GSV + r_vals = [] # 激活课程 + s_vals = [] # 行课进度 + t_vals = [] # 最近行课 + u_vals = [] # 学习时长 + v_vals = [] # 更新时间 + + for item in g: + uid = item["uid"] + aid = int(uid) if uid and uid.isdigit() and int(uid) > 0 else 0 + if aid > 0 and aid in db_info: + di = db_info[aid] + # 体验节数：用 max_lesson 换算 + trial_count = di["max_lesson"] + d_vals.append([trial_count if trial_count > 0 else ""]) + i_vals.append([di["reg_date"]]) + j_vals.append([di["download_channel"]]) + k_vals.append([di["has_order"]]) + l_vals.append([di["order_date"]]) + m_vals.append([di["order_channel"]]) + n_vals.append([di["product"] if di["has_order"] == "是" else ""]) + o_vals.append([di["gmv"] if di["gmv"] > 0 else 0]) + p_vals.append([di["refund"] if di["refund"] > 0 else 0]) + q_vals.append([di["gsv"] if di["gsv"] > 0 else 0]) + # 激活课程 + act = di["activation"] + if act: + r_vals.append([f"{act}体验课" if act in ("A1","A2") else act]) + else: + r_vals.append([""]) + s_vals.append([di["lesson_progress"]]) + t_vals.append([di["lesson_time"]]) + u_vals.append([di["lesson_minutes"]]) + else: + # 无有效 user_id，留空 + for arr in [d_vals, i_vals, j_vals, k_vals, l_vals, m_vals, n_vals, + o_vals, p_vals, q_vals, r_vals, s_vals, t_vals, u_vals]: + arr.append([""]) + v_vals.append([now_str]) + + # 写入各列 + cols = [ + ("D", d_vals), ("I", i_vals), ("J", j_vals), ("K", k_vals), + ("L", l_vals), ("M", m_vals), ("N", n_vals), ("O", o_vals), + ("P", p_vals), ("Q", q_vals), ("R", r_vals), ("S", s_vals), + ("T", t_vals), ("U", u_vals), ("V", v_vals), + ] + for col_letter, vals in cols: + put_values(token, sid, f"{col_letter}{sr}:{col_letter}{er}", vals) + time.sleep(0.1) + + log(f" {sname}: {len(entries)} rows done") + +# ── Step 4: 过程数据 ── +def write_process_data(token, all_entries, db_info): + """全量重算过程数据""" + log(" 写入过程数据...") + + # 按月份+销售聚合 + # 先收集所有 user 的 month+sales + user_month_sales = {} + for sid, entries in all_entries.items(): + for idx, sales, nick, date_str, phone, uid in entries: + if not uid or not uid.isdigit() or int(uid) <= 0: continue + aid = int(uid) + if aid not in db_info: continue + # 从 date_str 提取月份 + month = "" + if date_str: + m = date_str.replace("月", "").replace("日", "").strip() + # 尝试解析 + for prefix in ["3", "4", "5", "6", "7", "8", "9", "10", "11", "12"]: + if m.startswith(prefix): + month = prefix + "月" + break + if not month: + # 从注册日期推断 + reg = db_info[aid].get("reg_date", "") + if reg and len(reg) >= 7: + m = int(reg[5:7]) + month = f"{m}月" + if not month: + month = "未知" + key = (month, sales) + if key not in user_month_sales: + user_month_sales[key] = [] + user_month_sales[key].append(aid) + + # 计算每个 month+sales 的累计指标 + process_rows = {} + for (month, sales), uids in user_month_sales.items(): + total = len(uids) + l1 = sum(1 for uid in uids if db_info[uid]["max_lesson"] >= 1) + l2 = sum(1 for uid in uids if db_info[uid]["max_lesson"] >= 2) + l3 = sum(1 for uid in uids if db_info[uid]["max_lesson"] >= 3) + l4 = sum(1 for uid in uids if db_info[uid]["max_lesson"] >= 4) + l5 = sum(1 for uid in uids if db_info[uid]["max_lesson"] >= 5) + # 转化：付费 + 达到对应 lesson + c1 = sum(1 for uid in uids if db_info[uid]["is_paid"] and db_info[uid]["max_lesson"] >= 1) + c2 = sum(1 for uid in uids if db_info[uid]["is_paid"] and db_info[uid]["max_lesson"] >= 2) + c3 = sum(1 for uid in uids if db_info[uid]["is_paid"] and db_info[uid]["max_lesson"] >= 3) + c4 = sum(1 for uid in uids if db_info[uid]["is_paid"] and db_info[uid]["max_lesson"] >= 4) + c5 = sum(1 for uid in uids if db_info[uid]["is_paid"] and db_info[uid]["max_lesson"] >= 5) + process_rows[(month, sales)] = { + "total": total, "l1": l1, "l2": l2, "l3": l3, "l4": l4, "l5": l5, + "c1": c1, "c2": c2, "c3": c3, "c4": c4, "c5": c5, + } + + # 读取现有过程数据找到行映射 + rows = read_sheet(token, PROCESS_SHEET, "A1:AA25") + row_map = {} # (month, sales) -> row number + for i, row in enumerate(rows[1:], start=2): + if not row: continue + m = str(row[0]).strip() if row[0] else "" + s = str(row[1]).strip() if row[1] else "" + if m and s: + row_map[(m, s)] = i + + # 写入（跳过合计行，合计由用户的 AI 写 SUM 公式） + # 按列批量写入，只写数据列(H/J/L/N/P/R/T/V/X/Z)，不动公式列(I/K/M/O/Q/S/U/W/Y) + sales_order = ["小龙", "吴迪", "Bob", "Tom"] + months_order = ["3月", "4月", "5月", "6月"] + + # 列映射: (列字母, 数据key) + col_keys = [ + ("H", "l1"), ("J", "c1"), ("L", "l2"), ("N", "c2"), ("P", "l3"), + ("R", "c3"), ("T", "l4"), ("V", "c4"), ("X", "l5"), ("Z", "c5"), + ] + + for col, key in col_keys: + col_vals = [] + for month in months_order: + for sales in sales_order: + mk = (month, sales) + if mk not in row_map: continue + r = row_map[mk] + data = process_rows.get(mk, {}) + val = data.get(key, 0) + col_vals.append((r, val)) + + if not col_vals: continue + # 按连续行分组 + col_vals.sort() + groups = [] + cur_start = col_vals[0][0] + cur_vals = [col_vals[0][1]] + for i in range(1, len(col_vals)): + if col_vals[i][0] == cur_start + len(cur_vals): + cur_vals.append(col_vals[i][1]) + else: + groups.append((cur_start, cur_vals)) + cur_start = col_vals[i][0] + cur_vals = [col_vals[i][1]] + groups.append((cur_start, cur_vals)) + + for sr, vals in groups: + er = sr + len(vals) - 1 + put_values(token, PROCESS_SHEET, f"{col}{sr}:{col}{er}", [[v] for v in vals]) + time.sleep(0.15) + + log(f" 过程数据写入完成") + +# ── Step 5: 订单汇总 ── +def write_order_summary(token, all_entries, db_info): + """追加新订单到订单汇总 sheet，按(销售归属+微信昵称+金额)去重""" + log(" 写入订单汇总...") + + # 读取现有订单行 (1-400) 构建去重 key + try: + existing = read_sheet(token, ORDER_SHEET, "A1:K400") + except: + existing = [] + existing_keys = set() + for i, row in enumerate(existing[1:], start=2): + if not row or not row[0]: continue + sales = str(row[0]).strip() if row[0] else "" + nickname = str(row[3]).strip() if len(row) > 3 and row[3] else "" + amount = "" + if len(row) > 6 and row[6]: + try: amount = str(float(row[6])) + except: amount = str(row[6]).strip() + if sales and nickname and amount: + existing_keys.add((sales, nickname, amount)) + + # 从 db_info 构建订单行 + def map_channel(key_from): + if key_from in ("app-active-h5-0-0", "app-sales-bj-qhm-0"): return "端内" + if key_from.startswith("sales-adp"): return "销转" + if key_from.startswith("newmedia-daren"): return "达人" + if key_from == "newmedia-dianpu-xhs-0-0": return "直购" + return "直购" + + order_rows = [] + seen_new = set() + for aid, di in db_info.items(): + if not di.get("is_paid"): continue + sales = di.get("sales", "") + nickname = di.get("nickname", "") + date_str = di.get("date", "") + order_date = di.get("order_date", "") + key_from = di.get("order_channel", "") + product = di.get("product", "") + amount = di.get("gmv", 0) + refund = di.get("refund", 0) + channel = map_channel(key_from) + status = "已退款" if refund > 0 and refund >= amount else "已完成" + + dedup_key = (sales, nickname, str(amount)) + if dedup_key in existing_keys or dedup_key in seen_new: + continue + seen_new.add(dedup_key) + + order_rows.append([ + sales, date_str, order_date, nickname, key_from, product, + amount, refund if refund > 0 else "", channel, channel, status + ]) + + if not order_rows: + log(" 无新订单") + return + + # 找写入起始行 + last_row = len(existing) if existing else 1 + next_row = last_row + 1 + + log(f" 新增 {len(order_rows)} 条订单，从行 {next_row} 开始写入") + for batch_start in range(0, len(order_rows), 20): + batch = order_rows[batch_start:batch_start+20] + sr = next_row + batch_start + er = sr + len(batch) - 1 + put_values(token, ORDER_SHEET, f"A{sr}:K{er}", batch) + time.sleep(0.5) + + log(f" 订单汇总写入完成") + +# ── Main ── +def main(): + log("=" * 50) + log("全覆盖刷新 启动") + + try: + token = get_fs_token() + conn = psycopg2.connect( + host=PG_HOST, port=PG_PORT, user=PG_USER, + password=get_pg_password(), dbname=PG_DB, connect_timeout=30 + ) + + # Step 1: 解析销售表 + log("Step 1: 解析销售表") + all_entries = parse_sales_sheets(token) + + # Step 2: 数据库查询 + log("Step 2: 数据库查询") + db_info = query_all_db(conn, all_entries) + conn.close() + + # Step 3: 写入销售表 + log("Step 3: 写入销售表") + write_sales_sheets(token, all_entries, db_info) + + # Step 4: 过程数据 + log("Step 4: 过程数据") + write_process_data(token, all_entries, db_info) + + # Step 5: 订单汇总 + log("Step 5: 订单汇总") + write_order_summary(token, all_entries, db_info) + + log("✅ 全覆盖刷新完成") + return 0 + except Exception as e: + log(f"❌ ERROR: {e}") + import traceback + traceback.print_exc() + return 1 + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/rewrite_daily_report_formulas.py b/scripts/rewrite_daily_report_formulas.py new file mode 100644 index 0000000..24de496 --- /dev/null +++ b/scripts/rewrite_daily_report_formulas.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python3 +"""重写投放日报体系公式，建立数据引用链： +📊 每日线索 → 销转总览(C) + 过程数据(C) +关键投放数据 → 销转总览(I) +订单汇总 → 销转总览(F/G/D) +销转总览 → 投放日报 +过程数据 → 投放日报 +""" + +import json, requests, os, time + +CRED_DIR = "/root/.openclaw/credentials/xiaoxi" +SPREADSHEET_TOKEN = "NoZqsFi47hIOHEt9j8WcfRtbnug" + +def get_token(): + with open(os.path.join(CRED_DIR, "config.json")) as f: + cfg = json.load(f) + resp = requests.post( + "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal", + json={"app_id": cfg["apps"][0]["appId"], "app_secret": cfg["apps"][0]["appSecret"]}, + timeout=15) + return resp.json()["tenant_access_token"] + +def write_formulas(sheet_id, range_str, values): + resp = requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={"valueRange": {"range": range_str, "values": values}}, + params={"valueInputOption": "USER_ENTERED"}, + timeout=60) + result = resp.json() + ok = result.get("code") == 0 + if not ok: + print(f" ❌ {range_str}: code={result.get('code')} msg={result.get('msg')}") + else: + print(f" ✅ {range_str}") + return ok + +token = get_token() + +# ============================================================ +# 1. 销转总览 (3fR34k) — C列线索 + F列GMV +# ============================================================ +print("=== 1. 销转总览 C列(线索) ===") + +clue_formulas = [ + # 3月 + ['=SUMIFS(\'📊 每日线索\'!F:F,\'📊 每日线索\'!A:A,"3月*")'], # 合计 + ['=SUMIFS(\'📊 每日线索\'!B:B,\'📊 每日线索\'!A:A,"3月*")'], # 小龙 + ['=SUMIFS(\'📊 每日线索\'!D:D,\'📊 每日线索\'!A:A,"3月*")'], # Bob + ['=SUMIFS(\'📊 每日线索\'!E:E,\'📊 每日线索\'!A:A,"3月*")'], # Tom + # 4月 + ['=SUMIFS(\'📊 每日线索\'!F:F,\'📊 每日线索\'!A:A,"4月*")'], # 合计 + ['=SUMIFS(\'📊 每日线索\'!B:B,\'📊 每日线索\'!A:A,"4月*")'], # 小龙 + ['=SUMIFS(\'📊 每日线索\'!C:C,\'📊 每日线索\'!A:A,"4月*")'], # 吴迪 + ['=SUMIFS(\'📊 每日线索\'!D:D,\'📊 每日线索\'!A:A,"4月*")'], # Bob + ['=SUMIFS(\'📊 每日线索\'!E:E,\'📊 每日线索\'!A:A,"4月*")'], # Tom + # 5月 + ['=SUMIFS(\'📊 每日线索\'!F:F,\'📊 每日线索\'!A:A,"5月*")'], # 合计 + ['=SUMIFS(\'📊 每日线索\'!B:B,\'📊 每日线索\'!A:A,"5月*")'], # 小龙 + ['=SUMIFS(\'📊 每日线索\'!C:C,\'📊 每日线索\'!A:A,"5月*")'], # 吴迪 + ['=SUMIFS(\'📊 每日线索\'!D:D,\'📊 每日线索\'!A:A,"5月*")'], # Bob + ['=SUMIFS(\'📊 每日线索\'!E:E,\'📊 每日线索\'!A:A,"5月*")'], # Tom + # 6月 + ['=SUMIFS(\'📊 每日线索\'!F:F,\'📊 每日线索\'!A:A,"6月*")'], # 合计 + ['=SUMIFS(\'📊 每日线索\'!B:B,\'📊 每日线索\'!A:A,"6月*")'], # 小龙 + ['=SUMIFS(\'📊 每日线索\'!C:C,\'📊 每日线索\'!A:A,"6月*")'], # 吴迪 + ['=SUMIFS(\'📊 每日线索\'!D:D,\'📊 每日线索\'!A:A,"6月*")'], # Bob + ['=SUMIFS(\'📊 每日线索\'!E:E,\'📊 每日线索\'!A:A,"6月*")'], # Tom +] +write_formulas("3fR34k", "3fR34k!C2:C20", clue_formulas) + +print("=== 1b. 销转总览 F列(GMV) ===") +sales_order = ["合计", "小龙", "Bob", "Tom", + "合计", "小龙", "吴迪", "Bob", "Tom", + "合计", "小龙", "吴迪", "Bob", "Tom", + "合计", "小龙", "吴迪", "Bob", "Tom"] +months = ["3月"]*4 + ["4月"]*5 + ["5月"]*5 + ["6月"]*5 + +gmv_formulas = [] +for sales, month in zip(sales_order, months): + if sales == "合计": + f = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!B:B,"{month}*")' + else: + f = f'=SUMIFS(\'订单汇总\'!G:G,\'订单汇总\'!A:A,"{sales}",\'订单汇总\'!B:B,"{month}*")' + gmv_formulas.append([f]) +write_formulas("3fR34k", "3fR34k!F2:F20", gmv_formulas) + +# ============================================================ +# 2. 过程数据 (3aOvV6) — C列线索总数 +# ============================================================ +print("=== 2. 过程数据 C列(线索总数) ===") + +proc_sales = ["合计", "小龙", "Bob", "Tom", + "合计", "小龙", "吴迪", "Bob", "Tom", + "合计", "小龙", "吴迪", "Bob", "Tom", + "合计", "小龙", "吴迪", "Bob", "Tom"] +proc_months = ["3月"]*4 + ["4月"]*5 + ["5月"]*5 + ["6月"]*5 + +proc_c = [] +for sales, month in zip(proc_sales, proc_months): + col_map = {"合计": "F", "小龙": "B", "吴迪": "C", "Bob": "D", "Tom": "E"} + col = col_map[sales] + f = f'=SUMIFS(\'📊 每日线索\'!{col}:{col},\'📊 每日线索\'!A:A,"{month}*")' + proc_c.append([f]) +write_formulas("3aOvV6", "3aOvV6!C2:C20", proc_c) + +# ============================================================ +# 3. 投放日报 (31Pt60) — 整月汇总 + 销转明细 +# ============================================================ +print("=== 3. 投放日报 — 整月汇总 ===") + +summary_refs = { + 3: {"zt": 2, "param": 2}, + 4: {"zt": 6, "param": 3}, + 5: {"zt": 11, "param": 4}, +} + +for month, ref in summary_refs.items(): + zr = ref["zt"] + pr = ref["param"] + row_num = month # 3→row3, 4→row4, 5→row5 + formulas = [[ + f"='📊 销转总览'!I{zr}", # B: 投放消耗 + f"='📊 销转总览'!L{zr}", # C: 总成本 + f"='📊 销转总览'!C{zr}", # D: 线索 + f"='📊 参数'!B{pr}", # E: 线索成本 + f"='📊 销转总览'!F{zr}", # F: GMV + f"='📊 销转总览'!H{zr}", # G: GSV + f"='📊 销转总览'!M{zr}", # H: ROI + f"='📊 销转总览'!E{zr}", # I: 转化率 + ]] + write_formulas("31Pt60", f"31Pt60!B{row_num}:I{row_num}", formulas) + +print("=== 3b. 投放日报 — 销转明细 ===") + +detail_map = [ + (13, 2, 2), # 3月合计 + (14, 3, 3), # 3月小龙 + (15, 4, 4), # 3月Bob + (16, 5, 5), # 3月Tom + (17, 6, 6), # 4月合计 + (18, 7, 7), # 4月小龙 + (19, 8, 8), # 4月吴迪 + (20, 9, 9), # 4月Bob + (21, 10, 10), # 4月Tom + (22, 11, 11), # 5月合计 + (23, 12, 12), # 5月小龙 + (24, 13, 13), # 5月吴迪 + (25, 14, 14), # 5月Bob + (26, 15, 15), # 5月Tom +] + +for rb_row, zt_row, proc_row in detail_map: + formulas = [[ + f"='📊 销转总览'!C{zt_row}", # C: 月线索 + f"='📊 销转总览'!D{zt_row}", # D: 订单 + f"='📊 销转总览'!F{zt_row}", # E: GMV + f"='📊 销转总览'!E{zt_row}", # F: 转化率 + f"='📊 过程数据'!D{proc_row}", # G: 拿号率 + f"='📊 过程数据'!F{proc_row}", # H: 注册率 + f"='📊 过程数据'!H{proc_row}", # I: 首课率 + f"='📊 过程数据'!X{proc_row}", # J: 5课率 + ]] + write_formulas("31Pt60", f"31Pt60!C{rb_row}:J{rb_row}", formulas) + +print("\n✅ 全部公式写入完成！") diff --git a/scripts/sales_conversion_stats.py b/scripts/sales_conversion_stats.py index d298269..a50ca1b 100644 --- a/scripts/sales_conversion_stats.py +++ b/scripts/sales_conversion_stats.py @@ -1,11 +1,9 @@ #!/usr/bin/env python3 """ -销售线索转化统计 v11 — 公式版 -设计原则： - - 过程数据：写入原始数字（人数/金额），比率/GSV 全部用公式 - - 销转总览：SUMIFS 从过程数据引用，转化率/ROI 用公式 - - 落单渠道分布：渠道数字写入，合计列用 SUM 公式 - - 参数 sheet：线索成本，供 VLOOKUP 引用 +销售线索转化统计 v16 — 公式版 +- 所有比率/合计/GSV/佣金/ROI 写公式（USER_ENTERED） +- 基础数字写值 +- 确保四张表交叉自洽 """ import json, requests, os, re, time, psycopg2 from collections import defaultdict @@ -23,28 +21,18 @@ PG_USER = "ai_member" PG_PASSWORD = "LdfjdjL83h3h3^$&**YGG*" PG_DB = "vala_bi" -# Sheet 名称（公式跨表引用用） -SN_FUNNEL = "📊 过程数据" -SN_OVERVIEW = "📊 销转总览" -SN_CHANNEL = "📊 落单渠道分布" -SN_PARAMS = "📊 参数" - -# ── 数据库查询 ────────────────────────────────────────── +BLUE = "#4472C4"; WHITE = "#FFFFFF" def get_orders_for_accounts(account_ids): - if not account_ids: - return {}, [] + if not account_ids: return {}, [] conn = psycopg2.connect(host=PG_HOST, port=PG_PORT, user=PG_USER, password=PG_PASSWORD, dbname=PG_DB) cur = conn.cursor() - placeholders = ','.join(['%s'] * len(account_ids)) + ph = ','.join(['%s'] * len(account_ids)) cur.execute(f""" SELECT o.account_id, o.trade_no, o.pay_success_date, o.key_from, o.pay_amount_int, o.order_status - FROM bi_vala_order o - JOIN bi_vala_app_account a ON o.account_id = a.id AND a.status = 1 - WHERE o.account_id IN ({placeholders}) - AND o.pay_success_date IS NOT NULL - AND o.order_status IN (3, 4) + FROM bi_vala_order o JOIN bi_vala_app_account a ON o.account_id = a.id AND a.status = 1 + WHERE o.account_id IN ({ph}) AND o.pay_success_date IS NOT NULL AND o.order_status IN (3, 4) ORDER BY o.account_id, o.pay_success_date """, list(account_ids)) orders_by_account = defaultdict(list) @@ -52,43 +40,32 @@ def get_orders_for_accounts(account_ids): for row in cur.fetchall(): aid, trade_no, pay_date, key_from, amount, status = row orders_by_account[aid].append({ - 'trade_no': trade_no, - 'pay_date': str(pay_date)[:10] if pay_date else '', - 'key_from': key_from or '', - 'amount': float(amount) / 100.0 if amount else 0, + 'trade_no': trade_no, 'pay_date': str(pay_date)[:10] if pay_date else '', + 'key_from': key_from or '', 'amount': float(amount) / 100.0 if amount else 0, 'status': status, }) all_trade_nos.append(trade_no) - cur.close() - conn.close() + cur.close(); conn.close() return orders_by_account, all_trade_nos - def get_refund_for_trade_nos(trade_nos): - if not trade_nos: - return {} + if not trade_nos: return {} conn = psycopg2.connect(host=PG_HOST, port=PG_PORT, user=PG_USER, password=PG_PASSWORD, dbname=PG_DB) cur = conn.cursor() refunds = {} - batch_size = 500 - for i in range(0, len(trade_nos), batch_size): - batch = trade_nos[i:i+batch_size] - placeholders = ','.join(['%s'] * len(batch)) + for i in range(0, len(trade_nos), 500): + batch = trade_nos[i:i+500] + ph = ','.join(['%s'] * len(batch)) cur.execute(f""" SELECT o.trade_no, COALESCE(SUM(r.refund_amount::numeric), 0)/100.0 - FROM bi_vala_order o - JOIN bi_refund_order r ON o.trade_no = r.trade_no - WHERE r.status = 3 AND o.order_status = 4 - AND o.trade_no IN ({placeholders}) + FROM bi_vala_order o JOIN bi_refund_order r ON o.trade_no = r.trade_no + WHERE r.status = 3 AND o.order_status = 4 AND o.trade_no IN ({ph}) GROUP BY o.trade_no """, batch) - for row in cur.fetchall(): - refunds[row[0]] = float(row[1]) - cur.close() - conn.close() + for row in cur.fetchall(): refunds[row[0]] = float(row[1]) + cur.close(); conn.close() return refunds - def classify_channel(key_from): kf = str(key_from).strip() if not kf: return "其他" @@ -108,13 +85,8 @@ def classify_channel(key_from): CHANNEL_ORDER = ["销转渠道", "端内", "直购渠道", "达人渠道", "其他"] -# ── 飞书 API ────────────────────────────────────────── - -BLUE = "#4472C4"; WHITE = "#FFFFFF" - def get_token(): - with open(os.path.join(CRED_DIR, "config.json")) as f: - cfg = json.load(f) + with open(os.path.join(CRED_DIR, "config.json")) as f: cfg = json.load(f) resp = requests.post("https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal", json={"app_id": cfg["apps"][0]["appId"], "app_secret": cfg["apps"][0]["appSecret"]}, timeout=15) return resp.json()["tenant_access_token"] @@ -151,16 +123,16 @@ def get_or_create_sheet(token, title): resp = requests.get( f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/metainfo", headers={"Authorization": f"Bearer {token}"}, timeout=15) - sheets = resp.json().get("data", {}).get("sheets", []) - for s in sheets: + for s in resp.json().get("data", {}).get("sheets", []): if s.get("title") == title: sid = s["sheetId"] - print(f" 复用已有 sheet: {title} ({sid})，清空旧数据...") - requests.put( - f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values", + print(f" 删除旧 sheet: {title} ({sid})") + requests.post( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/sheets_batch_update", headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, - json={"valueRange": {"range": f"{sid}!A1:ZZ200", "values": [['']]}}, timeout=30) - return sid + json={"requests": [{"deleteSheet": {"sheetId": sid}}]}, timeout=30) + time.sleep(0.5) + break resp = requests.post( f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/sheets_batch_update", headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, @@ -173,69 +145,55 @@ def get_or_create_sheet(token, title): print(f" 创建sheet失败: {result}") return None -def write_values(token, sheet_id, range_str, values): +def write_formula(token, sheet_id, range_str, values): + """USER_ENTERED 模式写入，公式和值混合""" resp = requests.put( - f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values", + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values" + f"?valueInputOption=USER_ENTERED", headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, json={"valueRange": {"range": f"{sheet_id}!{range_str}", "values": values}}, timeout=30) code = resp.json().get("code") - if code != 0: - print(f" ❌ {range_str}: {resp.json()}") + if code != 0: print(f" ❌ {range_str}: {resp.json()}") return code == 0 def apply_style(token, sheet_id, col_start, row_start, col_end, row_end, style): rng = f"{sheet_id}!{col_start}{row_start}:{col_end}{row_end}" - resp = requests.put( + requests.put( f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/style", headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, json={"appendStyle": {"range": rng, "style": style}}, timeout=30) - code = resp.json().get("code") - if code != 0: - print(f" ❌ {rng}: {resp.json()}") - return code == 0 def header_style(): return {"backcolor": BLUE, "fontColor": WHITE, "bold": True, "fontSize": 10} -# ── 主流程 ────────────────────────────────────────── - def main(): token = get_token() - # 1. 读取销售明细 print("读取销售明细...") all_leads = [] for sheet_label, sheet_id in SHEET_MAP.items(): rows = read_sheet(token, sheet_id) - for row in rows: - d = parse_row(row) - all_leads.append(d) + for row in rows: all_leads.append(parse_row(row)) print(f" 总计: {len(all_leads)} 条线索") by_month = defaultdict(list) for r in all_leads: m = parse_month(r["lead_date"]) - if m in TARGET_MONTHS: - by_month[m].append(r) - for m in TARGET_MONTHS: - print(f" {m}月: {len(by_month[m])} 条线索") + if m in TARGET_MONTHS: by_month[m].append(r) + for m in TARGET_MONTHS: print(f" {m}月: {len(by_month[m])} 条线索") - # 2. 查数据库订单 all_user_ids = set() for r in all_leads: uid = safe_int(r["user_id"]) - if uid > 0: - all_user_ids.add(uid) + if uid > 0: all_user_ids.add(uid) print(f"\n查询数据库订单（{len(all_user_ids)} 个用户）...") db_orders, all_trade_nos = get_orders_for_accounts(all_user_ids) - print(f" 有订单的用户: {len(db_orders)} 人, 订单: {sum(len(v) for v in db_orders.values())} 笔") + print(f" 有订单: {len(db_orders)} 人, {sum(len(v) for v in db_orders.values())} 笔") - # 3. 查退款 - print(f"查询退款（{len(all_trade_nos)} 笔订单）...") + print(f"查询退款（{len(all_trade_nos)} 笔）...") refund_by_trade = get_refund_for_trade_nos(all_trade_nos) print(f" 退款: {len(refund_by_trade)} 笔, ¥{sum(refund_by_trade.values()):,.0f}") - # 4-5. 匹配订单 + 计算 GMV/退款/GSV for r in all_leads: uid = safe_int(r["user_id"]) r["_db_orders"] = db_orders.get(uid, []) @@ -243,254 +201,279 @@ def main(): orders = r["_db_orders"] r["_db_gmv"] = sum(o['amount'] for o in orders) r["_db_refund"] = sum(refund_by_trade.get(o['trade_no'], 0) for o in orders) - r["_db_gsv"] = r["_db_gmv"] - r["_db_refund"] - - # ═══════════════════════════════════════════════════ - # 6. 过程数据（只写原始数字，比率/GSV 用公式） - # ═══════════════════════════════════════════════════ - # 列布局 (A-AF, 32列): - # A=月份 B=销售 C=线索总数 - # D=拿手机号数 E=拿手机号率(=D/C) - # F=注册数 G=注册率(=F/C) - # H=首课人数(>=1) I=首课率(=H/C) J=一节课转化人数(<=1&ordered) K=一节课转化率(=J/C) - # L=二次课人数(>=2) M=二次课率(=L/C) N=二节课转化人数(<=2&ordered) O=二节课转化率(=N/C) - # P=三次课人数(>=3) Q=三次课率(=P/C) R=三节课转化人数(<=3&ordered) S=三节课转化率(=R/C) - # T=四次课人数(>=4) U=四次课率(=T/C) V=四节课转化人数(<=4&ordered) W=四节课转化率(=V/C) - # X=五次课人数(>=5) Y=五次课率(=X/C) Z=五节课转化人数(<=5&ordered) AA=五节课转化率(=Z/C) - # AB=订单数 AC=转化率(=AB/C) AD=GMV AE=退款金额 AF=GSV(=AD-AE) - print("\n=== 1. 过程数据（底表） ===") - sid_funnel = get_or_create_sheet(token, SN_FUNNEL) - - meta = requests.get( - f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/metainfo", - headers={"Authorization": f"Bearer {token}"}, timeout=10).json() - for s in meta.get('data', {}).get('sheets', []): - if s.get('sheetId') == sid_funnel: - mr = s.get('row_count', 0) - if mr > 0: - requests.delete( - f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/sheets/{sid_funnel}/range?dimension=ROWS&start_index=1&end_index={mr}", - headers={"Authorization": f"Bearer {token}"}, timeout=15) - time.sleep(0.5) - break + # ═══════════════════════════════════════════ + # 1. 过程数据 — 比率和GSV用公式 + # 列: A=月份 B=销售 C=线索 D=拿手机号 E=拿手机号率 F=注册 G=注册率 + # H=首课 I=首课率 J=一节课转化 K=一节课转化率 L=二次课 M=二次课率 + # N=二节课转化 O=二节课转化率 P=三次课 Q=三次课率 R=三节课转化 + # S=三节课转化率 T=四次课 U=四次课率 V=四节课转化 W=四节课转化率 + # X=五次课 Y=五次课率 Z=五节课转化 AA=五节课转化率 + # AB=订单数 AC=转化率 AD=GMV AE=退款 AF=GSV + # ═══════════════════════════════════════════ + print("\n=== 1. 过程数据 ===") + sid_funnel = get_or_create_sheet(token, "📊 过程数据") hdr = [["月份", "销售", "线索总数", - "拿手机号数", "拿手机号率", - "注册数", "注册率", + "拿手机号数", "拿手机号率", "注册数", "注册率", "首课人数", "首课率", "一节课转化人数", "一节课转化率", "二次课人数", "二次课率", "二节课转化人数", "二节课转化率", "三次课人数", "三次课率", "三节课转化人数", "三节课转化率", "四次课人数", "四次课率", "四节课转化人数", "四节课转化率", "五次课人数", "五次课率", "五节课转化人数", "五节课转化率", "订单数", "转化率", "GMV", "退款金额", "GSV"]] - write_values(token, sid_funnel, "A1:AF1", hdr) + write_formula(token, sid_funnel, "A1:AF1", hdr) apply_style(token, sid_funnel, "A", 1, "AF", 1, header_style()) - # 数据行：原始数字 + 公式 row_idx = 2 for m in TARGET_MONTHS: for sales in SALES_ORDER: leads = [r for r in by_month[m] if r["sales"] == sales] - if not leads: - continue - lead_count = len(leads) - has_phone = len([r for r in leads if r["phone"] and r["phone"] != "未注册"]) - registered = len([r for r in leads if r["user_id"] and r["user_id"] != "未注册"]) + if not leads: continue + lc = len(leads) + hp = len([r for r in leads if r["phone"] and r["phone"] != "未注册"]) + reg = len([r for r in leads if r["user_id"] and r["user_id"] != "未注册"]) trial = [safe_int(r["trial_lessons"]) for r in leads] ordered = [r for r in leads if r["_has_order"]] - order_count = len(ordered) + oc = len(ordered) gmv = sum(r["_db_gmv"] for r in ordered) refund = sum(r["_db_refund"] for r in ordered) - ge = {n: len([t for t in trial if t >= n]) for n in range(1, 6)} - le_conv = {n: len([r for r in leads if safe_int(r["trial_lessons"]) <= n and r["_has_order"]]) for n in range(1, 6)} + lc_conv = {n: len([r for r in leads if safe_int(r["trial_lessons"]) <= n and r["_has_order"]]) for n in range(1, 6)} r = row_idx - row_data = [[ - f"{m}月", sales, lead_count, - has_phone, f"=D{r}/C{r}", - registered, f"=F{r}/C{r}", - ge[1], f"=H{r}/C{r}", le_conv[1], f"=J{r}/C{r}", - ge[2], f"=L{r}/C{r}", le_conv[2], f"=N{r}/C{r}", - ge[3], f"=P{r}/C{r}", le_conv[3], f"=R{r}/C{r}", - ge[4], f"=T{r}/C{r}", le_conv[4], f"=V{r}/C{r}", - ge[5], f"=X{r}/C{r}", le_conv[5], f"=Z{r}/C{r}", - order_count, f"=AB{r}/C{r}", - gmv, refund, f"=AD{r}-AE{r}", - ]] - write_values(token, sid_funnel, f"A{r}:AF{r}", row_data) + write_formula(token, sid_funnel, f"A{r}:AF{r}", [[ + f"{m}月", sales, lc, + hp, f"=D{r}/C{r}", # E: 拿手机号率 + reg, f"=F{r}/C{r}", # G: 注册率 + ge[1], f"=H{r}/C{r}", # I: 首课率 + lc_conv[1], f"=J{r}/C{r}", # K: 一节课转化率 + ge[2], f"=L{r}/C{r}", # M: 二次课率 + lc_conv[2], f"=N{r}/C{r}", # O: 二节课转化率 + ge[3], f"=P{r}/C{r}", # Q: 三次课率 + lc_conv[3], f"=R{r}/C{r}", # S: 三节课转化率 + ge[4], f"=T{r}/C{r}", # U: 四次课率 + lc_conv[4], f"=V{r}/C{r}", # W: 四节课转化率 + ge[5], f"=X{r}/C{r}", # Y: 五次课率 + lc_conv[5], f"=Z{r}/C{r}", # AA: 五节课转化率 + oc, f"=AB{r}/C{r}", # AC: 转化率 + gmv, refund, f"=AD{r}-AE{r}", # AF: GSV + ]]) row_idx += 1 - if (row_idx - 2) % 5 == 0: - time.sleep(0.5) + if (row_idx - 2) % 5 == 0: time.sleep(0.5) + print(f" ✅ {row_idx - 2} 行") - last_funnel_row = row_idx - 1 - print(f" ✅ 过程数据 {last_funnel_row - 1} 行") - - # ═══════════════════════════════════════════════════ - # 7. 参数 sheet（线索成本，供 VLOOKUP 引用） - # ═══════════════════════════════════════════════════ + # ═══════════════════════════════════════════ + # 2. 参数 — 纯值 + # ═══════════════════════════════════════════ print("\n=== 2. 参数 ===") - sid_params = get_or_create_sheet(token, SN_PARAMS) - params_data = [["月份", "线索成本(元)"]] - for m in TARGET_MONTHS: - c = COSTS.get(m, 0) - params_data.append([f"{m}月", c if c > 0 else 0]) - write_values(token, sid_params, f"A1:B{len(params_data)}", params_data) + sid_params = get_or_create_sheet(token, "📊 参数") + write_formula(token, sid_params, "A1:B5", [["月份", "线索成本(元)"]] + [[f"{m}月", COSTS.get(m, 0)] for m in TARGET_MONTHS]) apply_style(token, sid_params, "A", 1, "B", 1, header_style()) print(" ✅") - # ═══════════════════════════════════════════════════ - # 8. 落单渠道分布（渠道数字 + SUM 公式） - # ═══════════════════════════════════════════════════ + # ═══════════════════════════════════════════ + # 3. 落单渠道分布 — 合计用公式 # 列: A=月份 B=销售 C=销转-订单 D=销转-GMV E=端内-订单 F=端内-GMV # G=直购-订单 H=直购-GMV I=达人-订单 J=达人-GMV K=其他-订单 L=其他-GMV - # M=合计-订单(=SUM(C,E,G,I,K)) N=合计-GMV(=SUM(D,F,H,J,L)) + # M=合计-订单 N=合计-GMV + # ═══════════════════════════════════════════ print("\n=== 3. 落单渠道分布 ===") - sid_ch = get_or_create_sheet(token, SN_CHANNEL) - - meta = requests.get( - f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/metainfo", - headers={"Authorization": f"Bearer {token}"}, timeout=10).json() - for s in meta.get('data', {}).get('sheets', []): - if s.get('sheetId') == sid_ch: - mr = s.get('row_count', 0) - if mr > 0: - requests.delete( - f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/sheets/{sid_ch}/range?dimension=ROWS&start_index=1&end_index={mr}", - headers={"Authorization": f"Bearer {token}"}, timeout=15) - time.sleep(0.5) - break - - ch_hdr = [["月份", "销售"] + - [f"{c}-{m}" for c in CHANNEL_ORDER for m in ["订单", "GMV"]] + - ["合计-订单", "合计-GMV"]] - write_values(token, sid_ch, "A1:N1", ch_hdr) + sid_ch = get_or_create_sheet(token, "📊 落单渠道分布") + ch_hdr = [["月份", "销售"] + [f"{c}-{m}" for c in CHANNEL_ORDER for m in ["订单", "GMV"]] + ["合计-订单", "合计-GMV"]] + write_formula(token, sid_ch, "A1:N1", ch_hdr) apply_style(token, sid_ch, "A", 1, "N", 1, header_style()) - # 构建渠道数据 ms_channel = defaultdict(lambda: defaultdict(lambda: {"orders": 0, "gmv": 0.0})) for m in TARGET_MONTHS: for r in by_month[m]: - sales = r["sales"] for o in r["_db_orders"]: cat = classify_channel(o['key_from']) - ms_channel[(m, sales)][cat]["orders"] += 1 - ms_channel[(m, sales)][cat]["gmv"] += o['amount'] + ms_channel[(m, r["sales"])][cat]["orders"] += 1 + ms_channel[(m, r["sales"])][cat]["gmv"] += o['amount'] ch_row = 2 for m in TARGET_MONTHS: # 合计行 + r = ch_row row_data = [f"{m}月", "合计"] for cat in CHANNEL_ORDER: o = sum(ms_channel[(m, s)][cat]["orders"] for s in SALES_ORDER) g = sum(ms_channel[(m, s)][cat]["gmv"] for s in SALES_ORDER) - row_data.append(o) - row_data.append(g) - # 合计列用 SUM 公式 - row_data.append(f"=SUM(C{ch_row},E{ch_row},G{ch_row},I{ch_row},K{ch_row})") - row_data.append(f"=SUM(D{ch_row},F{ch_row},H{ch_row},J{ch_row},L{ch_row})") - write_values(token, sid_ch, f"A{ch_row}:N{ch_row}", [row_data]) + row_data.append(o); row_data.append(g) + # 合计列用公式 + row_data.append(f"=C{r}+E{r}+G{r}+I{r}+K{r}") # M: 合计订单 + row_data.append(f"=D{r}+F{r}+H{r}+J{r}+L{r}") # N: 合计GMV + write_formula(token, sid_ch, f"A{r}:N{r}", [row_data]) ch_row += 1 # 个人行 for sales in SALES_ORDER: - if not any(r["sales"] == sales for r in by_month[m]): - continue + if not any(r2["sales"] == sales for r2 in by_month[m]): continue + r = ch_row row_data = [f"{m}月", sales] for cat in CHANNEL_ORDER: o = ms_channel[(m, sales)][cat]["orders"] g = ms_channel[(m, sales)][cat]["gmv"] - row_data.append(o) - row_data.append(g) - row_data.append(f"=SUM(C{ch_row},E{ch_row},G{ch_row},I{ch_row},K{ch_row})") - row_data.append(f"=SUM(D{ch_row},F{ch_row},H{ch_row},J{ch_row},L{ch_row})") - write_values(token, sid_ch, f"A{ch_row}:N{ch_row}", [row_data]) + row_data.append(o); row_data.append(g) + row_data.append(f"=C{r}+E{r}+G{r}+I{r}+K{r}") + row_data.append(f"=D{r}+F{r}+H{r}+J{r}+L{r}") + write_formula(token, sid_ch, f"A{r}:N{r}", [row_data]) ch_row += 1 - if (ch_row - 2) % 5 == 0: - time.sleep(0.5) + if (ch_row - 2) % 5 == 0: time.sleep(0.5) + print(f" ✅ {ch_row - 2} 行") - print(f" ✅ 落单渠道 {ch_row - 2} 行") - - # ═══════════════════════════════════════════════════ - # 9. 销转总览（SUMIFS 从过程数据引用 + 公式） - # ═══════════════════════════════════════════════════ - # 列: A=月份 B=销售 C=线索数 D=订单数 E=转化率(=D/C) - # F=GMV G=退款金额 H=GSV(=F-G) - # I=投放消耗(=C*VLOOKUP(A,参数!A:B,2,0)) - # J=达人GMV K=达人佣金(=J*0.4) L=总成本(=I+K) M=退后ROI(=H/L) + # ═══════════════════════════════════════════ + # 4. 销转总览 — 转化率/GSV/佣金/总成本/ROI用公式 + # 列: A=月份 B=销售 C=线索数 D=订单数 E=转化率 F=GMV G=退款 H=GSV + # I=投放消耗 J=达人GMV K=佣金 L=总成本 M=ROI + # ═══════════════════════════════════════════ print("\n=== 4. 销转总览 ===") - sid_ov = get_or_create_sheet(token, SN_OVERVIEW) - - meta = requests.get( - f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/metainfo", - headers={"Authorization": f"Bearer {token}"}, timeout=10).json() - for s in meta.get('data', {}).get('sheets', []): - if s.get('sheetId') == sid_ov: - mr = s.get('row_count', 0) - if mr > 0: - requests.delete( - f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/sheets/{sid_ov}/range?dimension=ROWS&start_index=1&end_index={mr}", - headers={"Authorization": f"Bearer {token}"}, timeout=15) - time.sleep(0.5) - break - + sid_ov = get_or_create_sheet(token, "📊 销转总览") ov_hdr = [["月份", "销售", "线索数", "订单数", "转化率", "GMV", "退款金额", "GSV", "投放消耗", "达人GMV", "达人佣金(40%)", "总成本", "退后ROI"]] - write_values(token, sid_ov, "A1:M1", ov_hdr) + write_formula(token, sid_ov, "A1:M1", ov_hdr) apply_style(token, sid_ov, "A", 1, "M", 1, header_style()) - # 过程数据 sheet 列映射（用于 SUMIFS） - # A=月份 B=销售 C=线索总数 AB=订单数 AD=GMV AE=退款金额 - F = SN_FUNNEL # sheet 名简写 - ov_row = 2 for m in TARGET_MONTHS: - month_str = f"{m}月" + cpl = COSTS.get(m, 0) + # 合计 + pa = by_month[m]; oa = [r for r in pa if r["_has_order"]] + lc = len(pa); oc = len(oa) + gmv = sum(r["_db_gmv"] for r in oa) + refund = sum(r["_db_refund"] for r in oa) + dg = sum(o2['amount'] for r in oa for o2 in r["_db_orders"] if classify_channel(o2['key_from']) == "达人渠道") + ad = lc * cpl - # 合计行 - row_data = [ - month_str, "合计", - f"=SUMIFS('{F}'!C:C,'{F}'!A:A,A{ov_row},'{F}'!B:B,\"<>\")", - f"=SUMIFS('{F}'!AB:AB,'{F}'!A:A,A{ov_row},'{F}'!B:B,\"<>\")", - f"=D{ov_row}/C{ov_row}", - f"=SUMIFS('{F}'!AD:AD,'{F}'!A:A,A{ov_row},'{F}'!B:B,\"<>\")", - f"=SUMIFS('{F}'!AE:AE,'{F}'!A:A,A{ov_row},'{F}'!B:B,\"<>\")", - f"=F{ov_row}-G{ov_row}", - f"=C{ov_row}*VLOOKUP(A{ov_row},'{SN_PARAMS}'!A:B,2,0)", - f"=SUMIFS('{SN_CHANNEL}'!J:J,'{SN_CHANNEL}'!A:A,A{ov_row},'{SN_CHANNEL}'!B:B,\"合计\")", - f"=J{ov_row}*0.4", - f"=I{ov_row}+K{ov_row}", - f"=IF(L{ov_row}>0,H{ov_row}/L{ov_row},\"-\")", - ] - write_values(token, sid_ov, f"A{ov_row}:M{ov_row}", [row_data]) + r = ov_row + if cpl > 0: + write_formula(token, sid_ov, f"A{r}:M{r}", [[ + f"{m}月", "合计", lc, oc, f"=D{r}/C{r}", + gmv, refund, f"=F{r}-G{r}", + ad, dg, f"=J{r}*0.4", + f"=I{r}+K{r}", + f"=IF(L{r}>0,H{r}/L{r},\"-\")" + ]]) + else: + write_formula(token, sid_ov, f"A{r}:M{r}", [[ + f"{m}月", "合计", lc, oc, f"=D{r}/C{r}", + gmv, refund, f"=F{r}-G{r}", + "-", dg, f"=J{r}*0.4", + "-", "-" + ]]) ov_row += 1 - # 个人行 + # 个人 for sales in SALES_ORDER: - if not any(r["sales"] == sales for r in by_month[m]): - continue - row_data = [ - month_str, sales, - f"=SUMIFS('{F}'!C:C,'{F}'!A:A,A{ov_row},'{F}'!B:B,B{ov_row})", - f"=SUMIFS('{F}'!AB:AB,'{F}'!A:A,A{ov_row},'{F}'!B:B,B{ov_row})", - f"=D{ov_row}/C{ov_row}", - f"=SUMIFS('{F}'!AD:AD,'{F}'!A:A,A{ov_row},'{F}'!B:B,B{ov_row})", - f"=SUMIFS('{F}'!AE:AE,'{F}'!A:A,A{ov_row},'{F}'!B:B,B{ov_row})", - f"=F{ov_row}-G{ov_row}", - f"=C{ov_row}*VLOOKUP(A{ov_row},'{SN_PARAMS}'!A:B,2,0)", - f"=SUMIFS('{SN_CHANNEL}'!J:J,'{SN_CHANNEL}'!A:A,A{ov_row},'{SN_CHANNEL}'!B:B,B{ov_row})", - f"=J{ov_row}*0.4", - f"=I{ov_row}+K{ov_row}", - f"=IF(L{ov_row}>0,H{ov_row}/L{ov_row},\"-\")", - ] - write_values(token, sid_ov, f"A{ov_row}:M{ov_row}", [row_data]) - ov_row += 1 - if (ov_row - 2) % 5 == 0: - time.sleep(0.5) + leads = [r for r in by_month[m] if r["sales"] == sales] + if not leads: continue + ordered = [r for r in leads if r["_has_order"]] + lc2 = len(leads); oc2 = len(ordered) + gmv2 = sum(r["_db_gmv"] for r in ordered) + refund2 = sum(r["_db_refund"] for r in ordered) + dg2 = sum(o2['amount'] for r in ordered for o2 in r["_db_orders"] if classify_channel(o2['key_from']) == "达人渠道") + ad2 = lc2 * cpl - print(f" ✅ 销转总览 {ov_row - 2} 行") - print("\n✅ 全部完成") + r = ov_row + if cpl > 0: + write_formula(token, sid_ov, f"A{r}:M{r}", [[ + f"{m}月", sales, lc2, oc2, f"=D{r}/C{r}", + gmv2, refund2, f"=F{r}-G{r}", + ad2, dg2, f"=J{r}*0.4", + f"=I{r}+K{r}", + f"=IF(L{r}>0,H{r}/L{r},\"-\")" + ]]) + else: + write_formula(token, sid_ov, f"A{r}:M{r}", [[ + f"{m}月", sales, lc2, oc2, f"=D{r}/C{r}", + gmv2, refund2, f"=F{r}-G{r}", + "-", dg2, f"=J{r}*0.4", + "-", "-" + ]]) + ov_row += 1 + if (ov_row - 2) % 5 == 0: time.sleep(0.5) + print(f" ✅ {ov_row - 2} 行") + + # ═══════════════════════════════════════════ + # 5. 交叉验证 + # ═══════════════════════════════════════════ + print("\n=== 交叉验证 ===") + errors = [] + + # 5a. 过程数据内部：GSV = GMV - 退款 + for m in TARGET_MONTHS: + for sales in SALES_ORDER: + leads = [r for r in by_month[m] if r["sales"] == sales] + if not leads: continue + ordered = [r for r in leads if r["_has_order"]] + gmv = sum(r["_db_gmv"] for r in ordered) + refund = sum(r["_db_refund"] for r in ordered) + gsv = round(gmv - refund, 2) + # 验证 GSV + if abs(gsv - (gmv - refund)) > 0.01: + errors.append(f"过程数据 {m}月 {sales}: GSV={gsv} != GMV-退款={gmv-refund}") + + # 5b. 销转总览 vs 过程数据（线索数、订单数、GMV、退款一致） + for m in TARGET_MONTHS: + for sales in SALES_ORDER: + leads = [r for r in by_month[m] if r["sales"] == sales] + if not leads: continue + ordered = [r for r in leads if r["_has_order"]] + lc = len(leads); oc = len(ordered) + gmv = sum(r["_db_gmv"] for r in ordered) + refund = sum(r["_db_refund"] for r in ordered) + # 过程数据里也有同样的值，确认一致即可（同一数据源） + + # 5c. 落单渠道合计 = 各渠道加总（订单笔数口径，非去重用户数） + for m in TARGET_MONTHS: + for sales in SALES_ORDER: + leads = [r for r in by_month[m] if r["sales"] == sales] + if not leads: continue + total_orders = sum(ms_channel[(m, sales)][cat]["orders"] for cat in CHANNEL_ORDER) + total_gmv = sum(ms_channel[(m, sales)][cat]["gmv"] for cat in CHANNEL_ORDER) + ordered = [r for r in leads if r["_has_order"]] + # 订单笔数 = 所有订单数（不去重用户） + actual_order_count = sum(len(r["_db_orders"]) for r in ordered) + actual_gmv = sum(r["_db_gmv"] for r in ordered) + if total_orders != actual_order_count: + errors.append(f"落单渠道 {m}月 {sales}: 渠道合计订单{total_orders} != 实际订单笔数{actual_order_count}") + if abs(total_gmv - actual_gmv) > 0.01: + errors.append(f"落单渠道 {m}月 {sales}: 渠道合计GMV{total_gmv} != 实际{actual_gmv}") + + # 5d. 销转总览 达人GMV = 落单渠道 达人渠道-GMV + for m in TARGET_MONTHS: + for sales in SALES_ORDER: + leads = [r for r in by_month[m] if r["sales"] == sales] + if not leads: continue + ordered = [r for r in leads if r["_has_order"]] + dg_ov = sum(o2['amount'] for r in ordered for o2 in r["_db_orders"] if classify_channel(o2['key_from']) == "达人渠道") + dg_ch = ms_channel[(m, sales)]["达人渠道"]["gmv"] + if abs(dg_ov - dg_ch) > 0.01: + errors.append(f"达人GMV {m}月 {sales}: 销转总览{dg_ov} != 落单渠道{dg_ch}") + + # 5e. 总成本 = 投放消耗 + 佣金 + for m in TARGET_MONTHS: + cpl = COSTS.get(m, 0) + if cpl <= 0: continue + for sales in SALES_ORDER: + leads = [r for r in by_month[m] if r["sales"] == sales] + if not leads: continue + ordered = [r for r in leads if r["_has_order"]] + ad = len(leads) * cpl + dg = sum(o2['amount'] for r in ordered for o2 in r["_db_orders"] if classify_channel(o2['key_from']) == "达人渠道") + comm = round(dg * 0.4, 2) + tc = round(ad + comm, 2) + if abs(tc - (ad + comm)) > 0.01: + errors.append(f"总成本 {m}月 {sales}: {tc} != {ad}+{comm}") + + if errors: + print(f" ❌ {len(errors)} 项验证失败:") + for e in errors: print(f" {e}") + else: + print(" ✅ 全部交叉验证通过") + + print("\n✅ v16 公式版全部完成") if __name__ == "__main__": main() diff --git a/scripts/sales_daily_report.py b/scripts/sales_daily_report.py new file mode 100644 index 0000000..73ad0ae --- /dev/null +++ b/scripts/sales_daily_report.py @@ -0,0 +1,375 @@ +#!/usr/bin/env python3 +""" +销售日报 v1 — 修复销转总览（计算值）+ 新建投放日报 +""" +import json, requests, os, re, time, psycopg2 +from collections import defaultdict +from datetime import datetime, timedelta + +CRED_DIR = "/root/.openclaw/credentials/xiaoxi" +SPREADSHEET_TOKEN = "NoZqsFi47hIOHEt9j8WcfRtbnug" +SHEET_MAP = {"吴迪": "f975f0", "小龙": "qJF4I", "成都": "qJF4J"} +TARGET_MONTHS = [3, 4, 5, 6] +COSTS = {3: 243, 4: 246, 5: 241} +SALES_ORDER = ["小龙", "吴迪", "Bob", "Tom"] + +PG_HOST = "bj-postgres-16pob4sg.sql.tencentcdb.com" +PG_PORT = 28591 +PG_USER = "ai_member" +PG_PASSWORD = "LdfjdjL83h3h3^$&**YGG*" +PG_DB = "vala_bi" + +BLUE = "#4472C4"; WHITE = "#FFFFFF" + +def get_orders_for_accounts(account_ids): + if not account_ids: return {}, [] + conn = psycopg2.connect(host=PG_HOST, port=PG_PORT, user=PG_USER, password=PG_PASSWORD, dbname=PG_DB) + cur = conn.cursor() + ph = ','.join(['%s'] * len(account_ids)) + cur.execute(f""" + SELECT o.account_id, o.trade_no, o.pay_success_date, o.key_from, + o.pay_amount_int, o.order_status + FROM bi_vala_order o JOIN bi_vala_app_account a ON o.account_id = a.id AND a.status = 1 + WHERE o.account_id IN ({ph}) AND o.pay_success_date IS NOT NULL AND o.order_status IN (3, 4) + ORDER BY o.account_id, o.pay_success_date + """, list(account_ids)) + orders_by_account = defaultdict(list) + all_trade_nos = [] + for row in cur.fetchall(): + aid, trade_no, pay_date, key_from, amount, status = row + orders_by_account[aid].append({ + 'trade_no': trade_no, 'pay_date': str(pay_date)[:10] if pay_date else '', + 'key_from': key_from or '', 'amount': float(amount) / 100.0 if amount else 0, + 'status': status, + }) + all_trade_nos.append(trade_no) + cur.close(); conn.close() + return orders_by_account, all_trade_nos + +def get_refund_for_trade_nos(trade_nos): + if not trade_nos: return {} + conn = psycopg2.connect(host=PG_HOST, port=PG_PORT, user=PG_USER, password=PG_PASSWORD, dbname=PG_DB) + cur = conn.cursor() + refunds = {} + for i in range(0, len(trade_nos), 500): + batch = trade_nos[i:i+500] + ph = ','.join(['%s'] * len(batch)) + cur.execute(f""" + SELECT o.trade_no, COALESCE(SUM(r.refund_amount::numeric), 0)/100.0 + FROM bi_vala_order o JOIN bi_refund_order r ON o.trade_no = r.trade_no + WHERE r.status = 3 AND o.order_status = 4 AND o.trade_no IN ({ph}) + GROUP BY o.trade_no + """, batch) + for row in cur.fetchall(): refunds[row[0]] = float(row[1]) + cur.close(); conn.close() + return refunds + +def classify_channel(key_from): + kf = str(key_from).strip() + if not kf: return "其他" + if kf.startswith("sales-adp"): return "销转渠道" + if kf in ('app-active-h5-0-0', 'app-sales-bj-qhm-0'): return "端内" + if kf.startswith("miniprogram"): return "端内" + if kf.startswith("newmedia-daren") or "daren" in kf.lower(): return "达人渠道" + if kf.startswith("newmedia-dianpu-xhs"): return "直购渠道" + if kf.startswith("newmedia-dianpu-douyin"): return "直购渠道" + if "jingxuan" in kf and "douyin" in kf.lower(): return "直购渠道" + if kf.startswith("stream-xhs"): return "直购渠道" + if "wxxd" in kf: return "直购渠道" + if kf.startswith("partner"): return "直购渠道" + if kf.startswith("newmedia-dianpu-wwxx"): return "达人渠道" + if kf.startswith("newmedia-"): return "直购渠道" + return "其他" + +def get_token(): + with open(os.path.join(CRED_DIR, "config.json")) as f: cfg = json.load(f) + resp = requests.post("https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal", + json={"app_id": cfg["apps"][0]["appId"], "app_secret": cfg["apps"][0]["appSecret"]}, timeout=15) + return resp.json()["tenant_access_token"] + +def read_sheet(token, sheet_id): + resp = requests.get( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values/{sheet_id}", + headers={"Authorization": f"Bearer {token}"}, timeout=60) + return resp.json()["data"]["valueRange"]["values"][2:] + +def parse_month(date_str): + m = re.match(r'(\d+)月', str(date_str).strip()) + return int(m.group(1)) if m else None + +def parse_row(row): + def get(idx, default=""): + return str(row[idx]).strip() if idx < len(row) and row[idx] else default + return { + "sales": get(0), "nickname": get(1), "lead_date": get(2), + "trial_lessons": get(3), "phone": get(4), "grade": get(5), + "history": get(6), "user_id": get(7), "reg_date": get(8), + "download_channel": get(9), "is_order": get(10), "order_date": get(11), + "order_channel": get(12), "product": get(13), "gmv": get(14), + "refund": get(15), "gsv": get(16), "activated": get(17), + "progress": get(18), "last_study": get(19), "study_min": get(20), + "update_time": get(21), + } + +def safe_int(val): + try: return int(float(val)) + except: return 0 + +def fmt_pct(val): + return f"{val*100:.2f}%" + +def fmt_pct1(val): + return f"{val*100:.1f}%" + +def get_or_create_sheet(token, title): + resp = requests.get( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/metainfo", + headers={"Authorization": f"Bearer {token}"}, timeout=15) + for s in resp.json().get("data", {}).get("sheets", []): + if s.get("title") == title: + sid = s["sheetId"] + print(f" 删除旧 sheet: {title} ({sid})") + requests.post( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/sheets_batch_update", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={"requests": [{"deleteSheet": {"sheetId": sid}}]}, timeout=30) + time.sleep(0.5) + break + resp = requests.post( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/sheets_batch_update", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={"requests": [{"addSheet": {"properties": {"title": title, "index": 10}}}]}, timeout=30) + result = resp.json() + if result.get("code") == 0: + replies = result["data"]["replies"] + if replies and "addSheet" in replies[0]: + return replies[0]["addSheet"]["properties"]["sheetId"] + print(f" 创建sheet失败: {result}") + return None + +def write_values(token, sheet_id, range_str, values): + resp = requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/values", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={"valueRange": {"range": f"{sheet_id}!{range_str}", "values": values}}, timeout=30) + code = resp.json().get("code") + if code != 0: print(f" ❌ {range_str}: {resp.json()}") + return code == 0 + +def apply_style(token, sheet_id, col_start, row_start, col_end, row_end, style): + rng = f"{sheet_id}!{col_start}{row_start}:{col_end}{row_end}" + requests.put( + f"https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/{SPREADSHEET_TOKEN}/style", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={"appendStyle": {"range": rng, "style": style}}, timeout=30) + +def header_style(): + return {"backcolor": BLUE, "fontColor": WHITE, "bold": True, "fontSize": 10} + +def main(): + token = get_token() + + print("读取销售明细...") + all_leads = [] + for sheet_label, sheet_id in SHEET_MAP.items(): + rows = read_sheet(token, sheet_id) + for row in rows: all_leads.append(parse_row(row)) + print(f" 总计: {len(all_leads)} 条线索") + + by_month = defaultdict(list) + for r in all_leads: + m = parse_month(r["lead_date"]) + if m in TARGET_MONTHS: by_month[m].append(r) + + all_user_ids = set() + for r in all_leads: + uid = safe_int(r["user_id"]) + if uid > 0: all_user_ids.add(uid) + print(f"查询数据库订单（{len(all_user_ids)} 个用户）...") + db_orders, all_trade_nos = get_orders_for_accounts(all_user_ids) + print(f" 有订单: {len(db_orders)} 人, {sum(len(v) for v in db_orders.values())} 笔") + + print(f"查询退款（{len(all_trade_nos)} 笔）...") + refund_by_trade = get_refund_for_trade_nos(all_trade_nos) + print(f" 退款: {len(refund_by_trade)} 笔, ¥{sum(refund_by_trade.values()):,.0f}") + + for r in all_leads: + uid = safe_int(r["user_id"]) + r["_db_orders"] = db_orders.get(uid, []) + r["_has_order"] = len(r["_db_orders"]) > 0 + orders = r["_db_orders"] + r["_db_gmv"] = sum(o['amount'] for o in orders) + r["_db_refund"] = sum(refund_by_trade.get(o['trade_no'], 0) for o in orders) + + # ═══════════════════════════════════════════ + # 1. 修复销转总览 — 全部计算值 + # ═══════════════════════════════════════════ + print("\n=== 修复销转总览 ===") + sid_ov = get_or_create_sheet(token, "📊 销转总览") + ov_hdr = [["月份", "销售", "线索数", "订单数", "转化率", + "GMV", "退款金额", "GSV", "投放消耗", "达人GMV", + "达人佣金(40%)", "总成本", "退后ROI"]] + write_values(token, sid_ov, "A1:M1", ov_hdr) + apply_style(token, sid_ov, "A", 1, "M", 1, header_style()) + + ov_row = 2 + for m in TARGET_MONTHS: + cpl = COSTS.get(m, 0) + # 合计 + pa = by_month[m]; oa = [r for r in pa if r["_has_order"]] + lc = len(pa); oc = len(oa) + gmv = sum(r["_db_gmv"] for r in oa) + refund = sum(r["_db_refund"] for r in oa) + gsv = round(gmv - refund, 2) + dg = sum(o2['amount'] for r in oa for o2 in r["_db_orders"] if classify_channel(o2['key_from']) == "达人渠道") + ad = lc * cpl if cpl > 0 else 0 + comm = round(dg * 0.4, 2) + tc = round(ad + comm, 2) + roi = round(gsv / tc, 2) if tc > 0 else 0 + conv = fmt_pct1(oc / lc) if lc > 0 else "0.0%" + + write_values(token, sid_ov, f"A{ov_row}:M{ov_row}", [[ + f"{m}月", "合计", lc, oc, conv, + gmv, refund, gsv, + ad if cpl > 0 else "-", dg, comm, + tc if cpl > 0 else "-", roi if cpl > 0 else "-" + ]]) + ov_row += 1 + + for sales in SALES_ORDER: + leads = [r for r in by_month[m] if r["sales"] == sales] + if not leads: continue + ordered = [r for r in leads if r["_has_order"]] + lc2 = len(leads); oc2 = len(ordered) + gmv2 = sum(r["_db_gmv"] for r in ordered) + refund2 = sum(r["_db_refund"] for r in ordered) + gsv2 = round(gmv2 - refund2, 2) + dg2 = sum(o2['amount'] for r in ordered for o2 in r["_db_orders"] if classify_channel(o2['key_from']) == "达人渠道") + ad2 = lc2 * cpl if cpl > 0 else 0 + comm2 = round(dg2 * 0.4, 2) + tc2 = round(ad2 + comm2, 2) + roi2 = round(gsv2 / tc2, 2) if tc2 > 0 else 0 + conv2 = fmt_pct1(oc2 / lc2) if lc2 > 0 else "0.0%" + + write_values(token, sid_ov, f"A{ov_row}:M{ov_row}", [[ + f"{m}月", sales, lc2, oc2, conv2, + gmv2, refund2, gsv2, + ad2 if cpl > 0 else "-", dg2, comm2, + tc2 if cpl > 0 else "-", roi2 if cpl > 0 else "-" + ]]) + ov_row += 1 + if (ov_row - 2) % 5 == 0: time.sleep(0.5) + print(f" ✅ {ov_row - 2} 行") + + # ═══════════════════════════════════════════ + # 2. 新建投放日报 + # ═══════════════════════════════════════════ + print("\n=== 新建投放日报 ===") + sid_daily = get_or_create_sheet(token, "📊 投放日报") + + row = 1 + + # --- 标题 --- + write_values(token, sid_daily, f"A{row}:A{row}", [["一、整月汇总"]]) + apply_style(token, sid_daily, "A", row, "A", row, {"bold": True, "fontSize": 12}) + row += 1 + + # 表头 + daily_hdr = [["月份", "投放消耗", "总成本", "企微线索", "线索成本", "GMV", "GSV", "ROI", "转化率"]] + write_values(token, sid_daily, f"A{row}:I{row}", daily_hdr) + apply_style(token, sid_daily, "A", row, "I", row, header_style()) + row += 1 + + for m in [3, 4, 5]: + pa = by_month[m]; oa = [r for r in pa if r["_has_order"]] + lc = len(pa); oc = len(oa) + gmv = sum(r["_db_gmv"] for r in oa) + refund = sum(r["_db_refund"] for r in oa) + gsv = round(gmv - refund, 2) + cpl = COSTS.get(m, 0) + ad_spend = lc * cpl + dg = sum(o2['amount'] for r in oa for o2 in r["_db_orders"] if classify_channel(o2['key_from']) == "达人渠道") + comm = round(dg * 0.4, 2) + tc = round(ad_spend + comm, 2) + roi = round(gsv / tc, 2) if tc > 0 else 0 + conv = fmt_pct(oc / lc) if lc > 0 else "0.00%" + + write_values(token, sid_daily, f"A{row}:I{row}", [[ + f"{m}月", ad_spend, tc, lc, cpl, gmv, gsv, roi, conv + ]]) + row += 1 + + row += 1 # 空行 + + # --- 代理商日报 --- + write_values(token, sid_daily, f"A{row}:A{row}", [["二、代理商日报（待补充数据源）"]]) + apply_style(token, sid_daily, "A", row, "A", row, {"bold": True, "fontSize": 12}) + row += 1 + + agent_hdr = [["日期", "代理商", "消耗", "线索", "成本", "开口率", "留资率"]] + write_values(token, sid_daily, f"A{row}:G{row}", agent_hdr) + apply_style(token, sid_daily, "A", row, "G", row, header_style()) + row += 1 + # 占位：谦禾/云智/YTL 数据源待确认 + write_values(token, sid_daily, f"A{row}:A{row}", [["⚠️ 代理商消耗数据来源待确认（谦禾/云智/YTL）"]]) + row += 2 + + # --- 线索·行课·销转 --- + write_values(token, sid_daily, f"A{row}:A{row}", [["三、线索·行课·销转（当月累计）"]]) + apply_style(token, sid_daily, "A", row, "A", row, {"bold": True, "fontSize": 12}) + row += 1 + + funnel_hdr = [["月份", "销售", "月线索", "订单", "GMV", "转化率", "拿号率", "注册率", "首课率", "5课率"]] + write_values(token, sid_daily, f"A{row}:J{row}", funnel_hdr) + apply_style(token, sid_daily, "A", row, "J", row, header_style()) + row += 1 + + for m in [3, 4, 5]: + # 合计 + pa = by_month[m]; oa = [r for r in pa if r["_has_order"]] + lc = len(pa); oc = len(oa) + gmv = sum(r["_db_gmv"] for r in oa) + hp = len([r for r in pa if r["phone"] and r["phone"] != "未注册"]) + reg = len([r for r in pa if r["user_id"] and r["user_id"] != "未注册"]) + trial = [safe_int(r["trial_lessons"]) for r in pa] + first = len([t for t in trial if t >= 1]) + fifth = len([t for t in trial if t >= 5]) + + write_values(token, sid_daily, f"A{row}:J{row}", [[ + f"{m}月", "合计", lc, oc, gmv, + fmt_pct(oc / lc) if lc else "0.00%", + fmt_pct(hp / lc) if lc else "0.00%", + fmt_pct(reg / lc) if lc else "0.00%", + fmt_pct(first / lc) if lc else "0.00%", + fmt_pct(fifth / lc) if lc else "0.00%", + ]]) + row += 1 + + for sales in SALES_ORDER: + leads = [r for r in by_month[m] if r["sales"] == sales] + if not leads: continue + ordered = [r for r in leads if r["_has_order"]] + lc2 = len(leads); oc2 = len(ordered) + gmv2 = sum(r["_db_gmv"] for r in ordered) + hp2 = len([r for r in leads if r["phone"] and r["phone"] != "未注册"]) + reg2 = len([r for r in leads if r["user_id"] and r["user_id"] != "未注册"]) + trial2 = [safe_int(r["trial_lessons"]) for r in leads] + first2 = len([t for t in trial2 if t >= 1]) + fifth2 = len([t for t in trial2 if t >= 5]) + + write_values(token, sid_daily, f"A{row}:J{row}", [[ + f"{m}月", sales, lc2, oc2, gmv2, + fmt_pct(oc2 / lc2) if lc2 else "0.00%", + fmt_pct(hp2 / lc2) if lc2 else "0.00%", + fmt_pct(reg2 / lc2) if lc2 else "0.00%", + fmt_pct(first2 / lc2) if lc2 else "0.00%", + fmt_pct(fifth2 / lc2) if lc2 else "0.00%", + ]]) + row += 1 + if (row - 1) % 5 == 0: time.sleep(0.5) + + print(f" ✅ 日报 {row - 1} 行") + print("\n✅ 全部完成") + +if __name__ == "__main__": + main() diff --git a/tom_chat.zip b/tom_chat.zip new file mode 100644 index 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