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logs/backup.log
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9d6999a..5bd5873 master -> master
[2026-06-19 08:10:02] 工作区备份成功：自动备份 2026-06-19 08:10:01
[2026-06-20 08:10:01] 开始备份工作区...
[master 68241dc] 自动备份 2026-06-20 08:10:01
2 files changed, 1 insertion(+), 3 deletions(-)
delete mode 100644 tmp_daily_summary.md
remote: . Processing 1 references
remote: Processed 1 references in total
To https://git.valavala.com/ai_member_only/ai_member_xiaoban
5bd5873..68241dc master -> master
[2026-06-20 08:10:01] 工作区备份成功：自动备份 2026-06-20 08:10:01
[2026-06-21 08:10:01] 开始备份工作区...

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"snippet": "# 2026-05-24 工作日志 ## 新建技能: studytime-analysis [刘庆逊提出] 创建学习时间分析技能，分析角色完课记录的规律。 ### 技能结构 - `skills/studytime-analysis/SKILL.md` — 技能定义 - `skills/studytime-analysis/scripts/studytime_analysis.py` — Python 分析脚本 ### 分析维度 1. **一周时间分布**（排除寒暑假1-2月、7-8月）：周一~周日各天完课数、时段分布（上午/中午/下午/晚上）、周末是否上课 2. **跨周学习趋势**（包含寒暑假全部数据）：总周数、周均完课数、连续性、中断周检测、前后半段趋势对比、突增/骤降检测 3. **完课记录明细表**（全部数据）：日期/时间/星期/时段/级别/课程ID ### 数据源 - PostgreSQL Online（vala 库） - 核心表：`user_chapter_play_record_0~7`（8张分表，无 `bi_` 前缀） - 筛选：`play_status = 1` - 注意：表在 PostgreSQL 而非 MySQL，表名无 `bi_` 前缀 ### 寒暑假规则 - 一周分布分析时排除 1-2 月（寒假）和 7-8 月（暑假）—— 因为寒暑假作息与平时差异大，混在一起会干扰时段分析 - 跨周趋势和明细表包含全部数据（含寒暑假） - 报告中区分标注数据范围 ### 触发方式 用户说「学习时间分析 [角色ID]」即可触发 ### 已测试角色",
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"snippet": "- 一周分布分析时排除 1-2 月（寒假）和 7-8 月（暑假）—— 因为寒暑假作息与平时差异大，混在一起会干扰时段分析 - 跨周趋势和明细表包含全部数据（含寒暑假） - 报告中区分标注数据范围 ### 触发方式 用户说「学习时间分析 [角色ID]」即可触发 ### 已测试角色 - 2343、2344：无完课记录（play_status=2，未完成） - 2840：276条记录，秋季集中型用户 - 25976：265条，246条在W16周一天完成（A2批量），疑似系统批量标记 - 2895：188条，长期稳定学习型用户，36周几乎不间断，非寒暑假晚上为主，寒暑假上午为主 ### 技术要点 - psycopg2 的 `%(param_name)s` 命名参数必须正确匹配，UNION ALL 多个子查询需要不同参数名 - PostgreSQL 返回的 `updated_at` 是 tz-aware datetime - `datetime.fromisocalendar(year, week, 1)` 获取某周周一的日期 ### 同步 - 已推送到 SkillHub（`studytime-analysis.xiaoban`） - 已 commit 到 Git 远程仓库 - 已通知 Cris（李若松） ### 增强: 报告开头加入角色基本信息 (2026-05-24) [刘庆逊提出] 在 studytime-analysis 输出中加入角色基本信息，包括： - 角色ID、账号ID、角色名字、性别、年龄、账号手机号后4位 **数据源（新增）**： - MySQL Onli",
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"snippet": "### 增强: 报告开头加入角色基本信息 (2026-05-24) [刘庆逊提出] 在 studytime-analysis 输出中加入角色基本信息，包括： - 角色ID、账号ID、角色名字、性别、年龄、账号手机号后4位 **数据源（新增）**： - MySQL Online `vala_user` 库 - `vala_app_character` 表：id, account_id, nickname, gender(0=女/1=男), birthday(varchar \"YYYY-MM-DD\") - `vala_app_account` 表：id, tel(已脱敏如 186****1625) - 手机号已脱敏，直接取后4位；年龄从 birthday 计算 **修改文件**： - `skills/studytime-analysis/scripts/studytime_analysis.py`：新增 MySQL 连接函数 `get_mysql_connection()` 和 `fetch_role_info(role_id)`，更新 `format_report()` 输出基本角色信息 - 已验证 2895 正常运行输出 - 已同步 SkillHub + Git ### Unit 显示修复: 季度名称 → 全局单元编号 (2026-05-24) [刘庆逊提出] HTML 报告中 Unit 列显示错误——显示的是季度名称（如\"小镇时光\"\"钢铁之心\"）而非单元数字（0-48）。 **根因分析**： - `vala_game_chapter`（MySQL）无 `unit_",
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"snippet": "- `skills/studytime-analysis/scripts/studytime_analysis.py` — 重写 `fetch_chapter_info_map()`，新增全局 unit_index 计算；HTML 模板更新为 Level/Unit/Lesson 三列 - 已为角色 32009（zyl）重新生成 HTML 并发送 - 已同步 Git + SkillHub ## 新建技能: studycourse-analysis (2026-05-24) [刘庆逊提出] 创建角色上课情况分析技能，从四维度分析角色学习数据。 ### 技能结构 - `skills/studycourse-analysis/SKILL.md` — 技能定义 - `skills/studycourse-analysis/scripts/studycourse_analysis.py` — Python 分析脚本 ### 四步分析 1. **基础信息**：角色姓名/年龄/账号ID/手机号后4位/注册时间/购买渠道/设备/首末次完课 2. **完课耗时**：平均值/中位数、异常检测(<10min / >20min)、前后半段趋势 3. **中互动正确率**：Perfect/Good/Oops/Pass/Failed 占比和趋势 4. **知识巩固**：完成率、正确率得分分布 ### 数据源 | 类型 | 库 | 表 | 用途 | |------|-----|-----|------| | MySQL vala_user | vala_app_character | 角色信息、pu",
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"snippet": "# 2026-05-13 工作日志 ## 文档权限规则修正 [Cris 确认] **问题：** AGENTS.md 中「权限告知规则」未区分用户身份，对所有用户（包括业务负责人李应瑛）一视同仁地回复「请添加Bot为知识空间成员」。这导致业务负责人被不必要的技术细节阻塞。 **修正：** 更新 AGENTS.md 第3条权限告知规则，按用户身份分级处理： - 业务负责人（刘庆逊、李应瑛）→ 不告知权限问题，直接联系 Cris 处理 - 其他用户 → 保持原有提示 **验证：** 对 `小斑` 文档（Tn23wQkUQilduAkvgwscTGhgnUd）执行了完整的读/写（追加）/删除测试，Bot身份权限全部正常。 ## lark-cli 绑定 完成了 lark-cli 与 OpenClaw 的绑定（bot-only 模式），后续飞书 API 调用无需额外配置。",
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"snippet": "**陈逸鸫提到 Cursor 可以写公式** — 待确认他用的是什么工具/API。可能路径：Cursor 用了不同端点（Sheets v3？）、不同 auth 方式、或者直接用 Feishu MCP。 ### ~22:05 日报「公式映射」现状 **事实：** C1HVN2 Section 三当前数字是 `compute_lesson_activation.py` / `pipeline.py step 3` 写入的静态值，不是真正的 Feishu 公式。API 层无法写入公式。 **正确的「公式映射」方案：** 不强求单元格公式，而是 pipeline 脚本作为\"公式\"—从源表取数→计算→写入。Section 一/二的现有自动化就是这样运行的。需验证 pipeline step 3 是否已覆盖新增的当日行课/7天首课行，如果没覆盖就加进去。 ### 当前 C1HVN2 待修复项 1. Row 18（企微新增）TEXT 格式 → 需陈逸鸫手动「清除格式」 2. Section 四（销转情况）Row 26-29 数字也是 text 格式 → 同样需手动清除 3. 当日行课/7天首课数据 → pipeline 脚本需覆盖 Section 三写入",
"recallCount": 4,
"recallCount": 1,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 4,
"totalScore": 1,
"maxScore": 1,
"firstRecalledAt": "2026-05-31T23:25:36.480Z",
"lastRecalledAt": "2026-06-20T12:07:03.489Z",
"lastRecalledAt": "2026-05-31T23:25:36.480Z",
"queryHashes": [
"679cdd7bd3a8",
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],
"recallDays": [
"2026-06-01",
"2026-06-20"
"2026-06-01"
],
"conceptTags": [
"待确认他用的是什么工具/api",
@ -1672,25 +1626,22 @@
"endLine": 624,
"source": "memory",
"snippet": "6. **build_pipeline 脚本** — 新增 `scripts/compute_lesson_activation.py`（PG→2aNzzy V/W） **关键数据源映射：** 进线=2aNzzy C列日期 → 用户ID=2aNzzy → PG user_course_detail 首课日期 → 比对同天 → 写回 2aNzzy V/W ### 待办汇总 1. 📋 陈逸鸫确认行课转化改动方案 → 一口气改 6 处 2. 📋 王虹茗 user_id 获取（需她发消息或陈逸鸫截图） 3. 📋 数据转发王虹茗 + 写入大麦查询输出表 fd42b8 4. 📋 销售看板 build 挂住问题排查 5. 📋 Image2 生图任务执行 6. ⏳ 全量 pipeline 聚光验证（子进程） ### 20:52 行课转化全量改动完成 [陈逸鸫确认] 行课记录新增指标：当日进线→当天行课 + 7天首课，6处改动已完成4处： | # | 位置 | 改动 | 状态 | |---|------|------|------| | 1 | 2aNzzy | V/W/X 三列（首课日期/当日行课/7日内行课） | ✅ | | 2 | 3PRySY | AE-AH 四列（当日行课/当日行课率/7日内首课/7日内首课率）| ✅ | | 3 | C1HVN2 | 合并 三+五 →「线索→行课转化」| ✅ | | 4 | Base | 行课销售月(4)/5月漏斗(3)/销转销售月(2) 加字段 | ⚠️ bot权限不足，需手动 | | 5 | funnel-daily 看板 |",
"recallCount": 5,
"recallCount": 3,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 5,
"totalScore": 3,
"maxScore": 1,
"firstRecalledAt": "2026-05-31T23:25:36.480Z",
"lastRecalledAt": "2026-06-20T12:04:19.862Z",
"lastRecalledAt": "2026-06-18T03:21:04.151Z",
"queryHashes": [
"679cdd7bd3a8",
"6d1afbed352e",
"872ac2ac6438",
"72255b156849",
"38aaeeb23a92"
"872ac2ac6438"
],
"recallDays": [
"2026-06-01",
"2026-06-12",
"2026-06-18",
"2026-06-20"
"2026-06-18"
],
"conceptTags": [
"build-pipeline",
@ -1807,21 +1758,18 @@
"endLine": 649,
"source": "memory",
"snippet": "| 4 | Base | 行课销售月(4)/5月漏斗(3)/销转销售月(2) 加字段 | ⚠️ bot权限不足，需手动 | | 5 | funnel-daily 看板 | 加行课转化指标 | 📋 待 build 脚本改 | | 6 | build 脚本 | compute_lesson_activation.py | ✅ | **合并后的 C1HVN2 Section 三：** ``` 三、线索→行课转化（5月27日） 指标 小龙 吴迪 Bob Tom 日汇总 企微新增 13 6 14 15 48 当日行课 0 0 0 0 0 当日行课率 0% 0% 0% 0% 0% 7天线索→首课 1 2 2 2 10 7天首课率 7.7% 33.3% 14.3% 13.3% 20.8% ``` **5月全月数据（3PRySY 公式自动计算）：** Bob 当日行课率 7.4% > Tom 3.7% > 其余 0%；7天首课率 吴迪 28.6% > 小龙 14.3% **关键实现细节：** - 2aNzzy V: PG chapter_settlement_data 最早日期 - 2aNzzy W: 进线当天有 chapter 活动=1 - 2aNzzy X: 进线 7 天内有 chapter 活动=1 - 3PRySY",
"recallCount": 3,
"recallCount": 1,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 3,
"totalScore": 1,
"maxScore": 1,
"firstRecalledAt": "2026-05-31T23:27:40.582Z",
"lastRecalledAt": "2026-06-20T12:04:19.862Z",
"lastRecalledAt": "2026-05-31T23:27:40.582Z",
"queryHashes": [
"0be022b45645",
"72255b156849",
"38aaeeb23a92"
"0be022b45645"
],
"recallDays": [
"2026-06-01",
"2026-06-20"
"2026-06-01"
],
"conceptTags": [
"funnel-daily",
@ -1872,23 +1820,20 @@
"endLine": 18,
"source": "memory",
"snippet": "# 2026-05-27 工作日志 ## 小红书渠道财务预测模型（陈逸鸫） - [陈逸鸫] 需要搭建小红书渠道 2026年6-12月的财务预测模型 - 关键参数：客单价 ¥2,500、退费率 3%、销售佣金 10%、月进量基准 1750 条、转化率基准 10%、CPL 275 元、无固定成本 - K12 季节性系数自行设定（7月/8月/12月为大月） - 用 sonnet 模型搭框架，后续用 opus 或 deepseek 做深度分析 - **结论**：当前参数下单元经济亏损（CAC ¥2,750 > 单客净收入 ¥2,182.5），盈亏平衡需 CVR≥12.6% 或 ASP≥¥3,150 或 CPL≤¥218 - **待确认**：用户需提供可调整的参数方向 ## 瓦拉星球漫画化方案（陈逸鸫） - [陈逸鸫] 希望将瓦拉星球游戏剧情做成日漫风连载漫画，发布到小红书，用于拓展品牌人群资产，引导英语课程试听 - 已探索飞书 VALA Academy wiki space（space_id: `7487521512283799556`），获取了完整游戏世界观： - 四大势力：地球地下城、艾斯环空间站、万得港海盗巢、Vala 新大陆 - 核心角色：Fio（15 岁女海盗王）、Nook 鳄鱼（思维芯片联合发明人，被背叛）、Lauren（主角母亲，失踪/牺牲）、Pemberton（反派/校长）、主角（开拓者后代） - 经过多次迭代优化开场方案：",
"recallCount": 4,
"recallCount": 2,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 4,
"totalScore": 2,
"maxScore": 1,
"firstRecalledAt": "2026-06-02T12:23:22.229Z",
"lastRecalledAt": "2026-06-20T12:07:31.032Z",
"lastRecalledAt": "2026-06-04T03:24:12.941Z",
"queryHashes": [
"2d6e2b982050",
"c4fd4a35d234",
"12f794f89047",
"88f6bf45fe89"
"c4fd4a35d234"
],
"recallDays": [
"2026-06-02",
"2026-06-04",
"2026-06-20"
"2026-06-04"
],
"conceptTags": [
"2026年6-12月的财务预测模型",
@ -2430,23 +2375,21 @@
"endLine": 30,
"source": "memory",
"snippet": "- 发现核心 bug：`pick_valid_order()` 只取最新一笔有效订单，未做 GSV 聚合 - **根因：** 18621578529（account_id=13147）有 2 笔有效订单（P785... GSV=1,999 + 3734... GSV=1,599），汇总只收了最新那笔 1,599，丢了 1,999 - **影响范围：** 13 个账户各 2 笔有效订单，漏 GSV ¥23,185 - **修复（v2.1）：** - 新增 `aggregate_valid_orders()` 函数：同一账户多笔有效订单 GSV/GMV/退款累加 - 订单号取未退款那笔（多笔未退款取最新），产品列多单用 `+` 拼接 - Step 4 线索绑单仍用 `pick_valid_order()` 不变 - 已更新 `sales_leads_full_refresh.py` + `skills/full-data-refresh/SKILL.md` - **数据源确认：** 大麦和小溪查的是同一个 PG `vala_bi.bi_vala_order`，数据一致 - **MySQL Makee 交易系统：** 大麦有 read_only 权限但 vala/vala_user/vala_order 库中无订单表，无法直接对比交易系统后台",
"recallCount": 4,
"recallCount": 3,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 4,
"totalScore": 3,
"maxScore": 1,
"firstRecalledAt": "2026-06-18T00:09:26.923Z",
"lastRecalledAt": "2026-06-20T03:20:58.925Z",
"lastRecalledAt": "2026-06-19T12:45:15.278Z",
"queryHashes": [
"ee468f64688e",
"d83437ba9cad",
"290e7f4aaa74",
"4cbb3d2aa18d"
"290e7f4aaa74"
],
"recallDays": [
"2026-06-18",
"2026-06-19",
"2026-06-20"
"2026-06-19"
],
"conceptTags": [
"pick-valid-order",
@ -2497,20 +2440,18 @@
"endLine": 12,
"source": "memory",
"snippet": "# 2026-06-19 ## v2_fill 首次跑（陈逸鸫 18:06） - 表: CP7BsOjYdhtcmft5iz2csIaHnKe（细水新架构版） - 脚本: `scripts/damai_v2_fill.py` - ① 线索明细 (7fdb4b): F列95个手机号 → XXTEA加密 → PG匹配 → K列UID 63个（新增12个） - ② 订单明细 (vrYbiX): DB查询63个UID → 17行订单 → 写入A-P（Q/R未填） - db_info: 2026-06-19 18:21:44 - 使用实习虾 app (cli_aa898f32d4799bea) 的 tenant token 绕过 xiaoban bot 的 sheets:spreadsheet:read 权限缺失 - 注意: clear_range 用空字符串覆盖而非空数组（飞书API拒绝空数组）",
"recallCount": 2,
"recallCount": 1,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 2,
"totalScore": 1,
"maxScore": 1,
"firstRecalledAt": "2026-06-19T12:45:15.278Z",
"lastRecalledAt": "2026-06-20T03:20:58.925Z",
"lastRecalledAt": "2026-06-19T12:45:15.278Z",
"queryHashes": [
"290e7f4aaa74",
"4cbb3d2aa18d"
"290e7f4aaa74"
],
"recallDays": [
"2026-06-19",
"2026-06-20"
"2026-06-19"
],
"conceptTags": [
"v2-fill",
@ -2530,20 +2471,18 @@
"endLine": 49,
"source": "memory",
"snippet": "- **大麦**: full_refresh · 手机/UID/行课回填 · 订单汇总 merge · 完成后群回「full_refresh 完成」 - **小溪**: 不再参与 Bot 刷新 ### 5. 验收标准 - gate X = 汇总 W（当前 406=406） - 绑单审计 E1–E9 全部 0 - 孤儿 X = 0 ### 6. 脚本修改清单 - `bot_sales_step2_refresh.py`: DB 层改为逐单存储 + `pick_valid_order()` + Y≠1 不写 X - `sales_leads_full_refresh.py`: 同上 + 汇总改为 gate 全量重建 - `refresh_order_summary.py`: A-W(23列) + 渠道分类改用 L 列 - 新增 `audit_lead_primary_order_bind.py`: 线索绑单审计脚本 ### 7. 环境修复 - `secrets.env` 需要软链接: `ln -sf /root/.openclaw/workspace/secrets.env /root/.openclaw/workspace-xiaoban/secrets.env` ### 8. Skill 文档已更新 - `skills/full-data-refresh/SKILL.md` → v2 定稿，含 6 条核心架构规则 - 协作契约: `xhs-ark-dashboard/docs/bot-full-refresh-v2.md` - 大麦侧主文档: `xhs-ark-dash",
"recallCount": 2,
"recallCount": 1,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 2,
"totalScore": 1,
"maxScore": 1,
"firstRecalledAt": "2026-06-19T12:45:15.278Z",
"lastRecalledAt": "2026-06-20T03:20:58.925Z",
"lastRecalledAt": "2026-06-19T12:45:15.278Z",
"queryHashes": [
"290e7f4aaa74",
"4cbb3d2aa18d"
"290e7f4aaa74"
],
"recallDays": [
"2026-06-19",
"2026-06-20"
"2026-06-19"
],
"conceptTags": [
"full-refresh",
@ -2563,21 +2502,18 @@
"endLine": 709,
"source": "memory",
"snippet": "### ~21:55 lark-cli `+write` bug 修复 **根因：** `bin/lark-cli-impl` 的 `+write` action 从未读取 `--value-input-option` 参数，PUT URL 里也没加 `?valueInputOption=USER_ENTERED`。参数被完全忽略。 **修复：** ```python vio = pargs.get(\"value_input_option\", \"RAW\") url_params = f\"?valueInputOption={vio}\" result = api(\"PUT\", f\"{SHEETS_V2}/spreadsheets/{token}/values{url_params}\", ...) ``` 同理 `+append` 也加了参数支持。 **验证：** 修复后写 int 到全新单元格 → 飞书正确显示为数字 ✅。但 Row 18 旧单元格 TEXT 格式仍覆盖显示。 ### ~22:00 飞书 Sheets API 公式求值测试 **结论：公式无法通过 API 写入并求值。** 即使 `valueInputOption=USER_ENTERED` 修复后，在全新单元格写 `=1+2` → FormattedValue 显示文本 \"=1+2\"，不计算为 3。 **飞书 Sheets API v2 的 USER_ENTERED 的作用：** 数字/日期解析正常 → 公式不支持求值。 **陈逸鸫提到 Cursor 可以写公式** — 待确认他用的是什么工具/AP",
"recallCount": 3,
"recallCount": 1,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 3,
"totalScore": 1,
"maxScore": 1,
"firstRecalledAt": "2026-06-19T12:45:15.278Z",
"lastRecalledAt": "2026-06-20T12:07:31.033Z",
"lastRecalledAt": "2026-06-19T12:45:15.278Z",
"queryHashes": [
"290e7f4aaa74",
"4cbb3d2aa18d",
"ed77f7d79595"
"290e7f4aaa74"
],
"recallDays": [
"2026-06-19",
"2026-06-20"
"2026-06-19"
],
"conceptTags": [
"lark-cli",
@ -2589,38 +2525,6 @@
"sheets-v2",
"数字/日期解析正常"
]
},
"memory:memory/2026-05-28.md:661:691": {
"key": "memory:memory/2026-05-28.md:661:691",
"path": "memory/2026-05-28.md",
"startLine": 661,
"endLine": 691,
"source": "memory",
"snippet": "### 待执行 (下次回话) ```bash # 1. 修复 snapshot_funnel_daily.py 两处 bug # 2. 重建 snapshot + 看板 cd /root/.openclaw/workspace-xiaoban/xhs-ark-dashboard source scripts/xiaoban_env.sh python3 scripts/snapshot_funnel_daily.py --date 2026-05-28 python3 scripts/build_funnel_dashboard.py --month 5 ``` ### ~21:50 C1HVN2 日报格式修复 [陈逸鸫] 报 C1HVN2 Section 三「线索→行课转化」格式乱七八糟，数字是 text 而非数字格式。 **清理完成：** - G23:G25/G30 stray 值清除 ✅ - Row 19（当日行课）/ Row 21（7天首课）→ int 型正常 ✅ - Row 20/22（百分比行）→ 用 USER_ENTERED 写入 \"0.0%\"/\"14.3%\" 字符串 ✅ - H 列测试残渣清除 ✅ **⚠️ Row 18（企微新增）仍是 text 格式** — 那些单元格有历史残留 TEXT 格式，底层值已是整数但飞书 UI 强制显示为文本。API 无法修改 cell numberFormat（`+batch-set-style` 是 stub，`repeatCell` 调飞书 API 返回 90204）。**需陈逸鸫在飞书里选中 B18:F18 →",
"recallCount": 2,
"dailyCount": 0,
"groundedCount": 0,
"totalScore": 2,
"maxScore": 1,
"firstRecalledAt": "2026-06-20T12:04:14.084Z",
"lastRecalledAt": "2026-06-20T12:04:19.862Z",
"queryHashes": [
"72255b156849",
"38aaeeb23a92"
],
"recallDays": [
"2026-06-20"
],
"conceptTags": [
"snapshot-funnel-daily.py",
"scripts/xiaoban-env.sh",
"scripts/snapshot-funnel-daily.py",
"g25/g30",
"20/22",
"user-entered",
"0.0",
"14.3"
]
}
}
}

16
scripts/damai_v2_fill.py
View File

@ -163,14 +163,6 @@ def parse_date(date_str):
return f"2026-{month:02d}-{day:02d}"
def check_timing(entry_time_str, order_date_str):
"""时序判断: 下单日期 ≥ 进线日期 → '是', 否则 '' """
entry_date = parse_date(entry_time_str)
if not entry_date or not order_date_str:
return ""
return "是" if order_date_str >= entry_date else ""
# ═══════════════════════════════════════════════════════════════
# ① 线索明细: F→K UID
# ═══════════════════════════════════════════════════════════════
@ -396,8 +388,8 @@ def fill_order_details(token, leads):
# N=渠道归属
channel = classify_channel(key_from)
# O=时序有效 (下单≥进线→是)
timing_valid = check_timing(entry_time, order_time)
# O=时序有效 (先留空，由自动规则填充)
timing_valid = ""
# P=备注
remark = ""
@ -410,10 +402,10 @@ def fill_order_details(token, leads):
log(f" 生成 {len(order_rows)} 行订单数据")
# Clear 订单明细 (A3:R5000)
# Clear 订单明细 (A3:P5000)
log(" 清空订单明细...")
# 先清空大范围
clear_range(token, ORDERS_SHEET_ID, "A3:R5000")
clear_range(token, ORDERS_SHEET_ID, "A3:P5000")
time.sleep(0.5)
# 写入订单数据 (分批, 每批最多 400 行 × 16 列 = 6400 格, 留余量用 250 行/批)