每日总结更新 20260625
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xiaoban
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- `oc_db7359c4d5d0bc61403c54bf3c46812a`（成员：陈逸鸫、王虹茗、吴迪、姜小龙）
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- `oc_55cb96a8f6c7fa4020253fb2854371c5`「麦动乾坤💰」（陈逸鸫 2026-06-17 确认加入）
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- `oc_52a41ba082e3a5ca8d8e1ed43e00984b`「王璐辰, 陈逸鸫」（陈逸鸫 2026-06-17 确认加入）
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- `oc_90b444c8b12a86361bc6625f6013a8cb`「上海教研Group叫什么群名好」（李应瑛 2026-06-24 确认加入）
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## 数据服务边界（陈逸鸫 2026-05-28）
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@ -684,3 +684,11 @@ To https://git.valavala.com/ai_member_only/ai_member_xiaoban
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2b75352..3fa2aa8 master -> master
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[2026-06-23 08:10:01] 工作区备份成功：自动备份 2026-06-23 08:10:01
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[2026-06-24 08:10:01] 开始备份工作区...
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[master b770715] 自动备份 2026-06-24 08:10:01
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2 files changed, 1 insertion(+), 3 deletions(-)
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delete mode 100644 tmp_daily_summary.md
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remote: . Processing 1 references
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remote: Processed 1 references in total
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To https://git.valavala.com/ai_member_only/ai_member_xiaoban
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3fa2aa8..b770715 master -> master
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[2026-06-24 08:10:01] 工作区备份成功：自动备份 2026-06-24 08:10:01
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@ -123,3 +123,10 @@
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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": "- `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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|
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"snippet": "# 2026-05-25 工作日志 ## user-info 技能重写 [刘庆逊提出] 修复 `user-info` 技能，使其匹配线上实际数据库结构。 ### 问题 旧脚本引用的表（`bi_vala_app_account`、`account_login`、`account_detail_info`、`bi_vala_order`、`bi_level_unit_lesson`）在线上数据库均不存在。 ### 修复内容 - **scripts/query_user_info.py** 完整重写： - 表名改为实际线上表：`vala_user.vala_app_account`、`vala_user.vala_app_character`、`vala_order.vala_seasonal_ticket`、PG `user_chapter_play_record_0~7` - 手机号查询通过 `tel LIKE '前缀%后缀'` 脱敏匹配 - Chapter → Level/Unit/Lesson 映射复用 studytime-analysis 的 `fetch_chapter_info_map()` 逻辑 - 订单数据改用 `vala_seasonal_ticket`（赛季通票），因线上无标准订单表 - 设备/地域信息标注为暂不可用（线上无对应表） - PG 时区处理：`created_at` 为 tz-aware，统一转 naive 比较 - **SKILL.md** 更新至 v2.0.0，补充数据覆盖说明 - **references/dat",
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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": "**根因分析**： - `vala_game_chapter`（MySQL）无 `unit_index` 字段 - `big_map_chapter`（PostgreSQL）有 `unit_index` 字段，但仅包含 A1 数据，且与 `vala_game_chapter` 无直接关联键 - 两者 ID 空间不重叠（big_map: ~1720-2070，game_chapter: ~55-399），UUID 也不匹配 **映射方案**： - 每个 season_package 内，`lesson_type=1` 的章节按 `id` 排序，每 5 个连续章节组成一个单元 - Season 0（序章/L1-U0）：所有章节属于 Unit 0 - Season 1-4：每个 season 有 12 个单元（60 个 lesson 章节） - 全局 unit_index = base_offset(season_of_quarter) + unit_within_season - base_offset: 0→0, 1→1, 2→13, 3→25, 4→37 **关键 Bug**：初版按 `season_of_quarter` 分组时 A1 和 A2 混在一起，因为相同季度值合并了。修复：改为按 `(level, season_of_quarter)` 分组。 **验证结果**： - A1: Unit 0-48（49 个单元），与 big_map_chapter 的 unit_index 范围一致 - A2: Unit 0-49（50 个单元，比 A1 多 1 个） *",
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"snippet": "- 448 人有新进展（相比小溪历史快照） - pull → 3PRySY 口径对齐 12/12 ✅ - lesson_cache → C1HVN2 16 格 ✅ - 变化有限（PG=正式课，小溪=体验课，口径不同） - sync_base (4b) → 多维表格 8 张 ✅ 21.2s ### 18:54 漏斗看板发布待解决 - funnel HTML 已构建（scripts/build_funnel_dashboard.py ✅） - 妙搭发布 `apps +html-publish` 需要 `--as user`，bot 模式不支持 - 系统 lark-cli `/usr/local/lib/node_modules/@anthropic/lark-cli` 可能支持 - 待确认：服务器是否已 `lark-cli auth login --as user` ### 19:00 陈逸鸫派 Image2 生图任务 **任务：** L1-S1-U1《秘密基地》5 课投放用小地图底图 - 模型：gpt-image-2 · 3:4 · 2K · 不要文字 - 风格：太阳朋克 + L1 场景 - 5 张 PNG：U1-L1～U1-L5 - FUNCLOUD_API_KEY 在小研 workspace `.env` 中可用（`fc_eea138933b02b4797ce0779ffb637d8b8a6368db7b435dfdab7b4be1cd254d98`） - Brief 文档 `/docx/KsVadUTmooO7yYxHaGmc1R0Bn5b` + 投放手册 `/do",
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"snippet": "- 18 单 | GMV ¥44,375 | 退款 ¥0 - 渠道：销转 11 / 达人 4 / 端内 3 - 客单价：¥599×2 / ¥1,999×9 / ¥3,598×7 - 订单日期分布在 11 天（04/23–05/15） **看板发布全流程梳理：** - 服务器 build HTML + Base sync → DM 陈逸鸫 → Mac 妙搭 `html-publish` + `access-scope-set` - 三个看板 App ID：漏斗 `app_4k886pmc9x6yt` · 指挥舱 `app_4k79smc6fa1kf` · 销售 `app_4k7qkz9wrga74` - 当前 cron 不自动发布看板，仅 build HTML **王虹茗身份确认：** wanghongming@makee.com，user_id 未获取 - 尝试 `lark-cli contact +search-user wanghongming@makee.com` → 失败：bot API 缺少 `search:user` scope - 替代方案：让她发消息给大麦（系统自动获取 user_id），或陈逸鸫截图资料页 **同事查询三级场景定稿：** 1. 常规只读 → 直接查，append 输出表 2. 权限外用户 → 先通知业务负责人，再决定是否返回 3. 写操作 → 回复「这会影响生产数据，已转 @陈逸鸫 确认」 ### 20:20 行课转化分析 [陈逸鸫需求] **需求：行课记录新增当日进线→当天行课 + 7天首课率 + 销售排名 + 日报展示** **",
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"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 |",
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"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",
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"snippet": "# 2026-06-03 工作日志 ## 招聘：商务经理候选人筛选 ### 李泓晔 一面评价 [王虹茗反馈] - **结论：不通过** - 太油了，管理成本高 - 塑品能力不行 - 如果做得不好会出去说瓦拉产品不行（甩锅风险） - 商务能力和资源应该还好，主要是风格不匹配 ### 大麦自省：简历分析的盲区 [2026-06-04] - **数字会骗人：** 看到GMV 1200w+、6倍增长就默认硬实力强，但面试中\"太油了\"恰恰说明此人擅长包装。简历数据要打折扣看。 - **\"能挑毛病\"≠\"懂产品\"：** 面试纪要里批评公司产品，我当时解读为深度理解，但面试官看到的是甩锅前置——\"做不好会说产品不行\"。 - **风格匹配和硬能力同等重要：** 只评了硬能力，完全忽略了管理成本、团队融入、价值观匹配。 - **改进：** 后续分析增加\"风格/文化匹配\"维度，对简历数据标注置信度（高/中/低），面试策略中增加\"风格验证\"类问题。 ### 宋宗佩 一面评价 [王虹茗反馈] - **结论：不通过（本岗位）** - 欠缺达播资源，不具备本岗位相关经验 - 工作内容主要为销售leads课包，对渠道情况、合作模式较为了解",
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@ -2291,20 +2327,22 @@
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"snippet": "- **改进：** 后续分析增加\"风格/文化匹配\"维度，对简历数据标注置信度（高/中/低），面试策略中增加\"风格验证\"类问题。 ### 宋宗佩 一面评价 [王虹茗反馈] - **结论：不通过（本岗位）** - 欠缺达播资源，不具备本岗位相关经验 - 工作内容主要为销售leads课包，对渠道情况、合作模式较为了解 - 能关注到leads后端的承接、转化并进行优化动作 - **后续方向：** 后面做leads课投放的商务渠道时可以再考虑 - **教训：** 简历中\"达人直售商务经理\"\"在线增长负责人\"等title容易让人高估其达播能力，实际核心能力在CPA/leads渠道运营而非达人直播资源 ### 简历评估优化点 [王虹茗指示] - 需区分\"达播商务\"和\"leads渠道商务\"两类能力，不能仅凭title判断 - 达播商务核心验证：是否有自有达人资源、是否有直播操盘经验、是否有达人分级运营体系 - Leads渠道商务核心验证：CPA/CPS渠道拓展、TMK/短信/进校/社群分销、leads转化链路优化 - 宋宗佩案例说明：title含\"达人直售\"\"增长负责人\"，但实际经验偏leads课包销售，非达人直播资源型 ### 候选人状态追踪 | 候选人 | 状态 | 备注 |",
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@ -2517,20 +2555,22 @@
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"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）",
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"v2-fill",
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@ -2550,20 +2590,22 @@
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"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",
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"conceptTags": [
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"full-refresh",
|
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@ -2641,6 +2683,37 @@
|
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"0.0",
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|
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"snippet": "# 2026-06-16 工作日志 ## 陈逸鸫 - 细水入海 full_refresh v2 定稿 **来源：** 陈逸鸫（`ou_f981d4811369c954b3597908ca93a01c`） **核心变更（6/16 定稿）：** ### 1. 订单汇总列结构变更 - 从 A-X(24列) → A-W(23列)：去掉原 W「有效成单」列，订单号从 X 左移到 W - V=渠道归属，W=订单号 ### 2. 线索只绑有效单 - `pick_valid_order()`: GSV>0 · 非全额退 · K≥C，取最新一笔 - Y=1 时 K/L/X/N/O/P/Z 全写有效主单真实值 - 已退单不写旧 X/L - 无有效单 → Y=0，K/L/X 留空 ### 3. 汇总 gate 全量覆盖（非 DB 扩行） - 唯一真源 = 三表 Y=1 gate 的 unique X - Step 4 + Step 5 同一 run，共用 `pick_valid_order()` + `db_info` - 汇总 W = 三表 X（gate 同源，不是 merge 再查 DB） - clear → gate 全量覆盖，不保留旧 W - 同 X 多进线 → 只保留行号最小的 1 行 ### 4. 分工定稿 - **Cursor**: 微伴/旧表同步 · 三键去重 · V/W 公式 · 验单 · 撞库消解 - **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**:",
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"aa2a119a58a5"
|
||||
],
|
||||
"recallDays": [
|
||||
"2026-06-24"
|
||||
],
|
||||
"conceptTags": [
|
||||
"full-refresh",
|
||||
"6/16",
|
||||
"a-x",
|
||||
"a-w",
|
||||
"pick-valid-order",
|
||||
"k/l/x/n/o/p/z",
|
||||
"x/l",
|
||||
"k/l/x"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
BIN
output/L1_U0_Oops率Top3组件_完整汇总.xlsx
Normal file
BIN
output/L1_U0_Oops率Top3组件_完整汇总.xlsx
Normal file
Binary file not shown.
BIN
output/U0-2_自我介绍类组件_最近50条记录.xlsx
Normal file
BIN
output/U0-2_自我介绍类组件_最近50条记录.xlsx
Normal file
Binary file not shown.
BIN
output/U0-4_U0-5_Oops率Top3组件_最近20条记录.xlsx
Normal file
BIN
output/U0-4_U0-5_Oops率Top3组件_最近20条记录.xlsx
Normal file
Binary file not shown.
BIN
output/中互动组件聚合统计_20260624.xlsx
Normal file
BIN
output/中互动组件聚合统计_20260624.xlsx
Normal file
Binary file not shown.
159
output/手机号查询角色ID方法.md
Normal file
159
output/手机号查询角色ID方法.md
Normal file
@ -0,0 +1,159 @@
|
||||
# 手机号 → 账号ID → 角色ID 检索方法
|
||||
|
||||
## 数据关系
|
||||
|
||||
```
|
||||
手机号 (明文)
|
||||
│ XXTEA 加密
|
||||
▼
|
||||
tel_encrypt (密文) account_id
|
||||
│ │
|
||||
▼ ▼
|
||||
vala_app_account ──────────► vala_app_character
|
||||
(账号表) 1:N 关联 (角色表)
|
||||
```
|
||||
|
||||
- **一个账号** (`vala_app_account`) 可以有 **多个角色** (`vala_app_character`)
|
||||
- 关联字段：`vala_app_character.account_id = vala_app_account.id`
|
||||
|
||||
## 数据库
|
||||
|
||||
| 项目 | 值 |
|
||||
|------|-----|
|
||||
| 数据库 | MySQL 线上环境 |
|
||||
| Host | `bj-cdb-dh2fkqa0.sql.tencentcdb.com` |
|
||||
| Port | `27751` |
|
||||
| 库名 | `vala_user` |
|
||||
| 用户 | `read_only` |
|
||||
|
||||
## 表结构
|
||||
|
||||
### vala_app_account（账号表）
|
||||
|
||||
| 字段 | 类型 | 说明 |
|
||||
|------|------|------|
|
||||
| `id` | bigint | 账号ID（主键） |
|
||||
| `tel` | varchar(20) | 手机号（脱敏显示，如 `158****7007`） |
|
||||
| `tel_encrypt` | varchar(100) | 手机号密文（用于精确匹配） |
|
||||
|
||||
### vala_app_character（角色表）
|
||||
|
||||
| 字段 | 类型 | 说明 |
|
||||
|------|------|------|
|
||||
| `id` | bigint | 角色ID（主键） |
|
||||
| `account_id` | bigint | 所属账号ID |
|
||||
| `nickname` | varchar(20) | 角色昵称 |
|
||||
| `gender` | tinyint(1) | 性别 |
|
||||
| `birthday` | varchar(50) | 生日 |
|
||||
| `purchase_season_package` | text | 已购赛季包 |
|
||||
|
||||
## 手机号加密方式
|
||||
|
||||
手机号在数据库中以密文存储，加密算法为 **XXTEA + Base64 URL-safe**。
|
||||
|
||||
### 加密参数
|
||||
|
||||
| 参数 | 值 |
|
||||
|------|-----|
|
||||
| 算法 | XXTEA |
|
||||
| 密钥 | `K1pNOZ5O5+ZqTPSHA2kzPdoNOMOGcv6g` |
|
||||
| 编码 | Base64（标准 → URL-safe：`+`→`-`, `/`→`_`, `=`→`.`） |
|
||||
|
||||
### Python 加密代码
|
||||
|
||||
```python
|
||||
import xxtea
|
||||
import base64
|
||||
|
||||
KEY = "K1pNOZ5O5+ZqTPSHA2kzPdoNOMOGcv6g"
|
||||
|
||||
def encrypt_phone(phone: str) -> str:
|
||||
"""加密明文手机号，返回与数据库 tel_encrypt 字段一致的密文"""
|
||||
encrypted = xxtea.encrypt(phone.encode(), KEY.encode())
|
||||
result = base64.b64encode(encrypted).decode()
|
||||
result = result.replace("+", "-").replace("/", "_").replace("=", ".")
|
||||
return result
|
||||
|
||||
def decrypt_phone(encrypted: str) -> str:
|
||||
"""解密 tel_encrypt 还原明文手机号"""
|
||||
restored = encrypted.replace("-", "+").replace("_", "/").replace(".", "=")
|
||||
decrypted = xxtea.decrypt(base64.b64decode(restored), KEY.encode())
|
||||
return decrypted.decode()
|
||||
```
|
||||
|
||||
### 加密示例
|
||||
|
||||
| 明文手机号 | 密文 (tel_encrypt) |
|
||||
|-----------|-------------------|
|
||||
| `15849377007` | `CxMOc6z56aYjE73r8OSAog..` |
|
||||
|
||||
## 查询步骤
|
||||
|
||||
### 步骤 1：加密手机号
|
||||
|
||||
```bash
|
||||
python3 -c "
|
||||
import xxtea, base64
|
||||
KEY = 'K1pNOZ5O5+ZqTPSHA2kzPdoNOMOGcv6g'
|
||||
phone = '15849377007'
|
||||
encrypted = xxtea.encrypt(phone.encode(), KEY.encode())
|
||||
result = base64.b64encode(encrypted).decode()
|
||||
print(result.replace('+', '-').replace('/', '_').replace('=', '.'))
|
||||
"
|
||||
# 输出: CxMOc6z56aYjE73r8OSAog..
|
||||
```
|
||||
|
||||
### 步骤 2：用密文查询账号和角色
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
a.id AS account_id,
|
||||
a.tel,
|
||||
c.id AS character_id,
|
||||
c.nickname,
|
||||
c.gender,
|
||||
c.birthday,
|
||||
c.purchase_season_package,
|
||||
c.created_at
|
||||
FROM vala_app_account a
|
||||
LEFT JOIN vala_app_character c ON c.account_id = a.id
|
||||
WHERE a.tel_encrypt = 'CxMOc6z56aYjE73r8OSAog..';
|
||||
```
|
||||
|
||||
### 步骤 3：解读结果
|
||||
|
||||
```
|
||||
account_id tel character_id nickname gender birthday purchase_season_package
|
||||
18279 158****7007 23600 Morris 1 2021-09-09 [16,17,18,19,20]
|
||||
18279 158****7007 23686 Nathan 1 2018-03-13 [16]
|
||||
```
|
||||
|
||||
- **账号ID**: 18279
|
||||
- **角色**: 23600 (Morris)、23686 (Nathan)
|
||||
- 一个账号下可能有多个角色（一个孩子一个角色）
|
||||
|
||||
## 完整查询脚本
|
||||
|
||||
`scripts/phone_encrypt.py` 已封装加密/解密/MD5 功能，可直接使用：
|
||||
|
||||
```python
|
||||
from phone_encrypt import encrypt_phone, decrypt_phone, phone_md5
|
||||
|
||||
# 加密
|
||||
cipher = encrypt_phone("15849377007")
|
||||
# → "CxMOc6z56aYjE73r8OSAog.."
|
||||
|
||||
# 解密
|
||||
phone = decrypt_phone(cipher)
|
||||
# → "15849377007"
|
||||
|
||||
# MD5（用于跨系统关联）
|
||||
md5 = phone_md5("15849377007")
|
||||
```
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. **tel 字段是脱敏的**（如 `158****7007`），不能直接用于精确匹配
|
||||
2. **必须用 tel_encrypt 密文匹配**，密文由 XXTEA 加密生成
|
||||
3. **一个账号可以有多个角色**，查询结果可能返回多行
|
||||
4. 测试环境和线上环境的 `tel_encrypt` 值相同（加密算法一致）
|
||||
BIN
output/核心听力组件_聚合统计.xlsx
Normal file
BIN
output/核心听力组件_聚合统计.xlsx
Normal file
Binary file not shown.
BIN
output/离开森林_1000517_全部Oops记录.xlsx
Normal file
BIN
output/离开森林_1000517_全部Oops记录.xlsx
Normal file
Binary file not shown.
376
scripts/mid_component_aggregation.py
Normal file
376
scripts/mid_component_aggregation.py
Normal file
@ -0,0 +1,376 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
中互动组件按类型聚合统计 + 元数据导出（逐表聚合版）
|
||||
|
||||
策略：逐表查询+聚合，Python 端合并，避免大 UNION ALL 超时。
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
from datetime import datetime
|
||||
from typing import Any, Dict, List, Optional, Set, Tuple
|
||||
|
||||
import psycopg2
|
||||
import pymysql
|
||||
import pandas as pd
|
||||
|
||||
C_TYPE_NAME_MAPPING = {
|
||||
"mid_dialog_choose": "对话选择",
|
||||
"mid_dialog_express": "对话表达",
|
||||
"mid_dialog_fillin": "对话填空",
|
||||
"mid_dialog_repeat": "对话跟读",
|
||||
"mid_dialog_select": "对话选择",
|
||||
"mid_dialog_sentence": "对话组句",
|
||||
"mid_image_choose": "图片选择",
|
||||
"mid_image_drag": "图片拖拽",
|
||||
"mid_image_multiple": "图片多选",
|
||||
"mid_image_sequence": "图片排序",
|
||||
"mid_message_combine": "消息组合",
|
||||
"mid_message_fillin": "消息填空",
|
||||
"mid_message_sentence": "消息组句",
|
||||
"mid_message_spell": "消息拼写",
|
||||
"mid_message_trace": "消息描红",
|
||||
"mid_message_word": "消息选词",
|
||||
"mid_grammar_cloze": "语法挖空",
|
||||
"mid_grammar_sentence": "语法组句",
|
||||
"mid_pron_pron": "发音互动",
|
||||
"mid_sentence_dialogue": "句子对话",
|
||||
"mid_sentence_makeSentence": "句子造句",
|
||||
"mid_sentence_material": "句子材料",
|
||||
"mid_sentence_voice": "句子语音",
|
||||
"mid_vocab_fillBlank": "词汇填空",
|
||||
"mid_vocab_image": "词汇图片",
|
||||
"mid_vocab_instruction": "词汇指令",
|
||||
"mid_vocab_item": "词汇物品",
|
||||
}
|
||||
|
||||
|
||||
def get_pg_conn():
|
||||
return psycopg2.connect(
|
||||
host=os.getenv("PG_DB_HOST"),
|
||||
port=os.getenv("PG_DB_PORT"),
|
||||
user=os.getenv("PG_DB_USER"),
|
||||
password=os.getenv("PG_DB_PASSWORD"),
|
||||
database=os.getenv("PG_DB_DATABASE"),
|
||||
)
|
||||
|
||||
|
||||
def get_mysql_conn():
|
||||
return pymysql.connect(
|
||||
host=os.getenv("MYSQL_HOST"),
|
||||
user=os.getenv("MYSQL_USERNAME"),
|
||||
password=os.getenv("MYSQL_PASSWORD"),
|
||||
database="vala_test",
|
||||
port=int(os.getenv("MYSQL_PORT", 3306)),
|
||||
charset="utf8mb4",
|
||||
)
|
||||
|
||||
|
||||
def count_help_actions(user_behavior_info: Any) -> int:
|
||||
if not user_behavior_info:
|
||||
return 0
|
||||
try:
|
||||
if isinstance(user_behavior_info, str):
|
||||
data = json.loads(user_behavior_info)
|
||||
elif isinstance(user_behavior_info, list):
|
||||
data = user_behavior_info
|
||||
else:
|
||||
return 0
|
||||
if not isinstance(data, list):
|
||||
return 0
|
||||
count = 0
|
||||
for item in data:
|
||||
if isinstance(item, dict):
|
||||
opt = item.get("submitOpt", "")
|
||||
if opt and "help" in str(opt).lower():
|
||||
count += 1
|
||||
return count
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
return 0
|
||||
|
||||
|
||||
def query_table_aggregation(cursor, table: str) -> Dict[str, Dict]:
|
||||
"""查询单表的聚合数据"""
|
||||
query = f"""
|
||||
SELECT
|
||||
c_type,
|
||||
COUNT(*) AS total_records,
|
||||
COUNT(DISTINCT user_id) AS user_count,
|
||||
|
||||
SUM(CASE WHEN play_result = 'Perfect' THEN 1 ELSE 0 END) AS perfect_count,
|
||||
SUM(CASE WHEN play_result = 'Pass' THEN 1 ELSE 0 END) AS pass_count,
|
||||
SUM(CASE WHEN play_result IN ('Oops', 'Opps') THEN 1 ELSE 0 END) AS oops_count,
|
||||
SUM(CASE WHEN play_result = 'None' THEN 1 ELSE 0 END) AS none_count,
|
||||
AVG(interval_time) AS avg_interval_time,
|
||||
SUM(interval_time) AS sum_interval_time
|
||||
FROM {table}
|
||||
WHERE c_type LIKE 'mid_%%' AND c_type != ''
|
||||
AND play_status = 1
|
||||
GROUP BY c_type
|
||||
"""
|
||||
cursor.execute(query)
|
||||
result = {}
|
||||
for row in cursor.fetchall():
|
||||
ct = row[0]
|
||||
result[ct] = {
|
||||
"total_records": int(row[1]),
|
||||
"user_count": int(row[2]),
|
||||
|
||||
"perfect_count": int(row[3]),
|
||||
"pass_count": int(row[4]),
|
||||
"oops_count": int(row[5]),
|
||||
"none_count": int(row[6]),
|
||||
"avg_interval_time": float(row[7]) if row[7] else 0,
|
||||
"sum_interval_time": float(row[8]) if row[8] else 0,
|
||||
}
|
||||
return result
|
||||
|
||||
|
||||
def query_table_exit(cursor, table: str) -> Dict[str, int]:
|
||||
"""查询单表的退出数"""
|
||||
query = f"""
|
||||
SELECT c_type, COUNT(*) as cnt
|
||||
FROM {table}
|
||||
WHERE c_type LIKE 'mid_%%' AND c_type != ''
|
||||
AND play_status = 2
|
||||
GROUP BY c_type
|
||||
"""
|
||||
cursor.execute(query)
|
||||
return {row[0]: int(row[1]) for row in cursor.fetchall()}
|
||||
|
||||
|
||||
def query_table_help_samples(cursor, table: str, limit: int = 5000) -> Dict[str, List[int]]:
|
||||
"""从单表抽样查询帮助次数"""
|
||||
query = f"""
|
||||
SELECT c_type, user_behavior_info
|
||||
FROM {table}
|
||||
WHERE c_type LIKE 'mid_%%' AND c_type != ''
|
||||
AND play_status = 1
|
||||
AND user_behavior_info IS NOT NULL
|
||||
AND user_behavior_info != '[]'
|
||||
LIMIT {limit}
|
||||
"""
|
||||
cursor.execute(query)
|
||||
result: Dict[str, List[int]] = defaultdict(list)
|
||||
for row in cursor.fetchall():
|
||||
ct, ubi = row[0], row[1]
|
||||
result[ct].append(count_help_actions(ubi))
|
||||
return dict(result)
|
||||
|
||||
|
||||
def query_table_used_ids(cursor, table: str) -> Set[Tuple[str, str]]:
|
||||
"""查询单表已使用的组件ID"""
|
||||
query = f"""
|
||||
SELECT DISTINCT c_type, c_id
|
||||
FROM {table}
|
||||
WHERE c_type LIKE 'mid_%%' AND c_type != ''
|
||||
AND play_status = 1
|
||||
"""
|
||||
cursor.execute(query)
|
||||
return {(row[0], str(row[1])) for row in cursor.fetchall()}
|
||||
|
||||
|
||||
def merge_aggregations(per_table: List[Dict[str, Dict]]) -> Dict[str, Dict]:
|
||||
"""合并多表聚合结果"""
|
||||
merged: Dict[str, Dict] = {}
|
||||
for table_data in per_table:
|
||||
for ct, d in table_data.items():
|
||||
if ct not in merged:
|
||||
merged[ct] = {
|
||||
"total_records": 0,
|
||||
"user_count": 0,
|
||||
|
||||
"perfect_count": 0,
|
||||
"pass_count": 0,
|
||||
"oops_count": 0,
|
||||
"none_count": 0,
|
||||
"sum_interval_time": 0.0,
|
||||
"interval_count": 0,
|
||||
}
|
||||
m = merged[ct]
|
||||
m["total_records"] += d["total_records"]
|
||||
m["perfect_count"] += d["perfect_count"]
|
||||
m["pass_count"] += d["pass_count"]
|
||||
m["oops_count"] += d["oops_count"]
|
||||
m["none_count"] += d["none_count"]
|
||||
m["sum_interval_time"] += d["sum_interval_time"]
|
||||
m["interval_count"] += d["total_records"]
|
||||
|
||||
return merged
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="中互动组件按类型聚合统计")
|
||||
parser.add_argument("--output-dir", default="output", help="输出目录")
|
||||
args = parser.parse_args()
|
||||
|
||||
print("=" * 60)
|
||||
print("中互动组件按类型聚合统计（逐表聚合版）")
|
||||
print("=" * 60)
|
||||
|
||||
pg_conn = get_pg_conn()
|
||||
cursor = pg_conn.cursor()
|
||||
|
||||
# ===== 1. 逐表查询聚合 + 退出 + 帮助 + 已用ID =====
|
||||
print("\n[1/4] 逐表查询...")
|
||||
all_aggs = []
|
||||
all_exits: Dict[str, int] = {}
|
||||
all_helps: Dict[str, List[int]] = defaultdict(list)
|
||||
all_used_ids: Set[Tuple[str, str]] = set()
|
||||
all_used_ids_by_type: Dict[str, Set[str]] = defaultdict(set)
|
||||
|
||||
for i in range(8):
|
||||
table = f"user_component_play_record_{i}"
|
||||
print(f" {table}...", end=" ", flush=True)
|
||||
|
||||
agg = query_table_aggregation(cursor, table)
|
||||
exits = query_table_exit(cursor, table)
|
||||
helps = query_table_help_samples(cursor, table)
|
||||
ids = query_table_used_ids(cursor, table)
|
||||
|
||||
all_aggs.append(agg)
|
||||
for ct, cnt in exits.items():
|
||||
all_exits[ct] = all_exits.get(ct, 0) + cnt
|
||||
for ct, vals in helps.items():
|
||||
all_helps[ct].extend(vals)
|
||||
all_used_ids.update(ids)
|
||||
for ct, cid in ids:
|
||||
all_used_ids_by_type[ct].add(cid)
|
||||
|
||||
total = sum(d["total_records"] for d in agg.values())
|
||||
print(f"完成({total}条, {len(agg)}种类型)")
|
||||
|
||||
cursor.close()
|
||||
pg_conn.close()
|
||||
|
||||
# ===== 2. 合并聚合结果 =====
|
||||
print("\n[2/4] 合并聚合结果...")
|
||||
merged = merge_aggregations(all_aggs)
|
||||
|
||||
# 计算 user_count（需要去重，这里用近似：取最大值）
|
||||
# 实际 user_count 跨表可能重复，这里用 SQL 再查一次精确值
|
||||
print(" 计算精确用户数...")
|
||||
pg_conn2 = get_pg_conn()
|
||||
cursor2 = pg_conn2.cursor()
|
||||
for ct in merged:
|
||||
# 精确查询每种类型的去重用户数
|
||||
parts = []
|
||||
for i in range(8):
|
||||
parts.append(f"""
|
||||
SELECT DISTINCT user_id FROM user_component_play_record_{i}
|
||||
WHERE c_type = %s AND play_status = 1
|
||||
""")
|
||||
union = " UNION ".join(parts)
|
||||
query = f"SELECT COUNT(*) FROM ({union}) AS u"
|
||||
cursor2.execute(query, tuple([ct] * 8))
|
||||
merged[ct]["user_count"] = cursor2.fetchone()[0]
|
||||
cursor2.close()
|
||||
pg_conn2.close()
|
||||
|
||||
# ===== 3. 构建输出 DataFrame =====
|
||||
print("\n[3/4] 构建输出...")
|
||||
rows = []
|
||||
for ct, m in sorted(merged.items(), key=lambda x: x[1]["total_records"], reverse=True):
|
||||
total = m["total_records"]
|
||||
perfect = m["perfect_count"]
|
||||
passed = m["pass_count"]
|
||||
oops = m["oops_count"]
|
||||
exit_cnt = all_exits.get(ct, 0)
|
||||
|
||||
perfect_pct = perfect / total * 100 if total > 0 else 0
|
||||
pass_pct = passed / total * 100 if total > 0 else 0
|
||||
oops_pct = oops / total * 100 if total > 0 else 0
|
||||
exit_pct = exit_cnt / (total + exit_cnt) * 100 if (total + exit_cnt) > 0 else 0
|
||||
|
||||
helps = all_helps.get(ct, [])
|
||||
avg_help = sum(helps) / len(helps) if helps else 0
|
||||
|
||||
avg_time = m["sum_interval_time"] / m["interval_count"] if m["interval_count"] > 0 else 0
|
||||
avg_time_sec = avg_time / 1000
|
||||
|
||||
display_name = C_TYPE_NAME_MAPPING.get(ct, ct)
|
||||
|
||||
rows.append({
|
||||
"组件类型(c_type)": ct,
|
||||
"组件名称": display_name,
|
||||
"组件个数": len(all_used_ids_by_type.get(ct, set())),
|
||||
"总完成人数": m["user_count"],
|
||||
"总记录数": total,
|
||||
"Perfect数": perfect,
|
||||
"Perfect率": f"{perfect_pct:.1f}%",
|
||||
"Pass数": passed,
|
||||
"Pass率": f"{pass_pct:.1f}%",
|
||||
"Oops数": oops,
|
||||
"Oops率": f"{oops_pct:.1f}%",
|
||||
"退出数": exit_cnt,
|
||||
"退出率": f"{exit_pct:.1f}%",
|
||||
"平均帮助次数": round(avg_help, 2),
|
||||
"平均耗时(秒)": round(avg_time_sec, 1),
|
||||
})
|
||||
|
||||
result_df = pd.DataFrame(rows)
|
||||
|
||||
# ===== 4. 获取 MySQL 元数据 =====
|
||||
print(" 获取 MySQL 组件元数据...")
|
||||
mysql_conn = get_mysql_conn()
|
||||
try:
|
||||
query = """
|
||||
SELECT c_type, c_id, title, component_config,
|
||||
audio_list, text_analysis, related_path,
|
||||
created_at, updated_at
|
||||
FROM middle_interaction_component
|
||||
WHERE c_type IS NOT NULL AND c_type != ''
|
||||
ORDER BY c_type, c_id
|
||||
"""
|
||||
meta_df = pd.read_sql_query(query, mysql_conn)
|
||||
finally:
|
||||
mysql_conn.close()
|
||||
|
||||
meta_df["_key"] = meta_df.apply(lambda r: (r["c_type"], str(r["c_id"])), axis=1)
|
||||
meta_df = meta_df[meta_df["_key"].isin(all_used_ids)].drop(columns=["_key"])
|
||||
meta_df["组件名称"] = meta_df["c_type"].map(C_TYPE_NAME_MAPPING).fillna(meta_df["c_type"])
|
||||
meta_cols = ["c_type", "组件名称", "c_id", "title", "component_config",
|
||||
"audio_list", "text_analysis", "related_path", "created_at", "updated_at"]
|
||||
meta_df = meta_df[meta_cols]
|
||||
|
||||
# ===== 5. 输出 Excel =====
|
||||
print("\n[4/4] 输出 Excel...")
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
date_str = datetime.now().strftime("%Y%m%d")
|
||||
filename = f"中互动组件聚合统计_{date_str}.xlsx"
|
||||
output_path = os.path.join(args.output_dir, filename)
|
||||
|
||||
with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
|
||||
result_df.to_excel(writer, sheet_name="按类型聚合统计", index=False)
|
||||
meta_df.to_excel(writer, sheet_name="组件元数据明细", index=False)
|
||||
|
||||
from openpyxl.utils import get_column_letter
|
||||
for sheet_name in ["按类型聚合统计", "组件元数据明细"]:
|
||||
ws = writer.sheets[sheet_name]
|
||||
for col_idx, col_cells in enumerate(ws.columns, 1):
|
||||
max_len = 0
|
||||
for cell in col_cells:
|
||||
if cell.value:
|
||||
val = str(cell.value)
|
||||
char_len = sum(2 if ord(c) > 127 else 1 for c in val)
|
||||
max_len = max(max_len, char_len)
|
||||
ws.column_dimensions[get_column_letter(col_idx)].width = min(max_len + 4, 60)
|
||||
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f"导出完成！")
|
||||
print(f"文件: {output_path}")
|
||||
print(f"Sheet1「按类型聚合统计」: {len(result_df)} 行")
|
||||
print(f"Sheet2「组件元数据明细」: {len(meta_df)} 行")
|
||||
print(f"{'=' * 60}")
|
||||
|
||||
print("\n📊 聚合统计预览:")
|
||||
print(result_df[["组件名称", "组件个数", "总完成人数", "总记录数",
|
||||
"Perfect率", "Pass率", "Oops率", "退出率",
|
||||
"平均帮助次数", "平均耗时(秒)"]].to_string(index=False))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
2
tmp_daily_summary.md
Normal file
2
tmp_daily_summary.md
Normal file
@ -0,0 +1,2 @@
|
||||
=== 每日总结 20260625 ===
|
||||
## 昨日关键进展
|
||||
Loading…
Reference in New Issue
Block a user