From 9df48681919da6cb0ec2ab879467c7da59220f36 Mon Sep 17 00:00:00 2001
From: simonkoson <28867558@qq.com>
Date: Fri, 3 Jul 2026 21:25:38 +0800
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- 修正 DeepSeek 模型名 deepseek-chat → deepseek-v4-pro
- 摘要块：修复 **粗体** markdown 渲染、左右块可滚动、左块固定粉红色系
- 新增 prompt4（内容摘要卡）+ prompt5（诊断报告）+ 三处 prompt5 优化
- 新增 004 迁移（episodes +content_digest JSONB）、导入脚本、摘要卡生成脚本
- 更新 CLAUDE.md 状态栏/进度/交接备注/关键决策

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 CLAUDE.md                                     |   30 +-
 ai-labeling/prompts/prompt4_content_digest.md |  108 ++
 .../prompts/prompt5_diagnosis_report.md       |  153 ++
 ai-labeling/scripts/gen_content_digest.py     |  260 ++++
 backend/app/api/analytics.py                  |    4 +-
 backend/app/models/episode.py                 |    3 +
 backend/sql/004_add_content_digest.sql        |    9 +
 doco/CLAUDE.md                                |    7 +-
 docs/api_credentials_inventory.md             |    8 +-
 .../components/Analytics/DiagnosisSummary.jsx |   49 +-
 frontend/src/pages/Analytics/Analytics.css    |   24 +-
 note/寄存条Doco文稿整理子项目已外迁.md        |    4 +-
 package-lock.json                             | 1291 ++++++++++++++++-
 package.json                                  |    7 +
 scripts/import_content_digests.py             |   78 +
 15 files changed, 1993 insertions(+), 42 deletions(-)
 create mode 100644 ai-labeling/prompts/prompt4_content_digest.md
 create mode 100644 ai-labeling/prompts/prompt5_diagnosis_report.md
 create mode 100644 ai-labeling/scripts/gen_content_digest.py
 create mode 100644 backend/sql/004_add_content_digest.sql
 create mode 100644 package.json
 create mode 100644 scripts/import_content_digests.py

diff --git a/CLAUDE.md b/CLAUDE.md
index 9029e77..721427c 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -14,7 +14,7 @@
   ## 🔖 状态栏（每次结束 session 前必须更新这三行）
 
   - **最后更新**：Claude（顾问+动手）｜ 2026-07-03
-  - **当前状态一句话**：收视分析看板 React 前端 L1–L4 已实现并验收（指标卡+走势图+季度/编导/题材对比+双引擎象限图）。25 期真实收视+AI 标签已导入。下一步：L4 AI 诊断报告（新 session 讨论）。
+  - **当前状态一句话**：L4 AI 诊断报告已实现（DeepSeek V4 Pro 生成、摘要块+详情页、三档自适应、22期内容摘要卡已入库）。收视分析看板 L1-L4 全部完成。
   - **下一个动手的人从这里开始**：见下方「⏩ 交接备注」
 
   ---
@@ -74,16 +74,17 @@
 
   ## 3. 当前进度（动态，核心交接区 — 以最新快照为准）
 
-  - **已完成至**：收视分析看板 React 前端 L1–L4（指标卡、走势图、季度/编导/题材对比、双引擎象限图），25 期真实数据已导入。
-  - **正在做**：无。下一步为 L4 AI 诊断报告，制片人将开新 session 讨论。
-  - **卡点/待解**：AI 诊断报告方案待细化（双步骤生成+制片人人工验证，方案草稿见 `memory/project_ai_diagnosis_plan.md`）。
-  - **Schema 状态**：知识库两表 schema 已就绪，**Phase 3 不再改表**。episodes 表已通过 003 迁移加 7 列 AI 标签字段（看板升级子项目 2026-06-26 执行），25 期数据含 AI 标签已就位。
+  - **已完成至**：收视分析看板 L1-L4 全部完成，含 L4 AI 诊断报告（DeepSeek V4 Pro 生成、摘要块三档自适应+详情页 Markdown 渲染、22期内容摘要卡入库）。
+  - **正在做**：无。
+  - **卡点/待解**：无硬卡点。下一刀候选见待办。
+  - **Schema 状态**：episodes 表已通过 003（+7 AI 标签列）和 004（+content_digest JSONB）迁移。知识库两表不再改。
 
   ---
 
   ## 4. 已完成（只追加，最新在上）
 
-  - [2026-07-03] **收视分析看板 React 前端实现（L1–L4）**：① 指标卡 5 列（均值/基础达标率/摸高完成率含四档动画/期次数/年度目标）；② 走势折线图（dataZoom 滑块+确认按钮范围过滤，下游模块联动）；③ 双列对比区（左列季度+编导、右列题材饼图+条形图）；④ 双引擎象限图（题材热度×叙事结构散点，气泡编码份额/三色/钩子强度）。附带：25 期真实收视+AI 标签数据导入、侧边栏 fixed 定位、滑块滚轮冲突修复。后端 analytics API 增返 4 个 AI 标签字段。
+  - [2026-07-03] **L4 AI 诊断报告**：① Prompt 4（内容摘要卡，MiMo 生成）+ Prompt 5（诊断报告，DeepSeek V4 Pro）撰写完成；② 22 期文稿摘要卡批量生成并导入 episodes.content_digest（004 迁移）；③ 后端 POST /api/analytics/diagnosis-report 端点（组装数据+调 DeepSeek+内存缓存）；④ 前端摘要块 DiagnosisSummary（左右两块、三档自适应色调、可滚动、粗体渲染）+ 详情页 DiagnosisReport（react-markdown 渲染+重新生成+免责声明）；⑤ 话题性（社交货币）三维框架设计（大众认知度/降维切口/惊奇密度）。模型选型：DeepSeek V4 Pro 胜出（vs Qwen-Max 对比测试）。
+  - [2026-07-03] **收视分析看板 React 前端实现（L1–L3）**：① 指标卡 5 列（均值/基础达标率/摸高完成率含四档动画/期次数/年度目标）；② 走势折线图（dataZoom 滑块+确认按钮范围过滤，下游模块联动）；③ 双列对比区（左列季度+编导、右列题材饼图+条形图）；④ 双引擎象限图（题材热度×叙事结构散点，气泡编码份额/三色/钩子强度）。附带：25 期真实收视+AI 标签数据导入、侧边栏 fixed 定位、滑块滚轮冲突修复。后端 analytics API 增返 4 个 AI 标签字段。
   - [2026-07-03] 看板升级子项目原型阶段收工：L2 打标流水线就绪（GT v0.6.0 / 25 期 / 三个 Prompt 可用）+ L3 HTML 原型验证通过（双引擎象限图+5 模块+视觉定稿）+ 003 迁移已执行（episodes +7 AI 标签列）。交付清单见寄存条。
   - [2026-05-27] Phase 3 Task 3/3.1/3.2：知识库树形视图（按来源大类→二级维度分组、左右布局、节点联动、展开收起；修出处筛选/宽度跳动/排版）。commit `3409d48`。
   - [2026-05-27] Phase 3 Task 2：知识库管理后台最小版（上传/列表/删除）。
@@ -96,7 +97,7 @@
 
   ## 5. 待办（下一刀候选，开局前定先做哪件）
 
-  - [ ] **L4 AI 诊断报告**：收视分析看板最后一个模块，双步骤生成（先 AI 出草稿→制片人审核发布），方案草稿见 `memory/project_ai_diagnosis_plan.md`，制片人将开新 session 讨论。
+  - [x] ~~L4 AI 诊断报告~~ → 已完成（2026-07-03）
   - [ ] **下一刀三选一**：① 语义搜索界面（不依赖任何材料，随时能开，是 Phase 4a 硬门槛）；② PDF 原文关联 + 大文件存储架构（需 Opus 审方案，优先级较高）；③ 界面像素级打磨+视觉规范统一。
   - [ ] PDF 大文件存储：**大文件不入库，单独文件仓库，DB 只存地址指针**（md 正文+向量留库参与检索；pdf 仅按需调阅）。地基一次定对，避免上云返工。
   - [ ] 200+ Obsidian md 批量录入（**建议在 PDF 存储方案定后做**；先试 10 篇验证解析/落位再全量，每篇都真调 MiniMax 耗额度）。
@@ -124,14 +125,21 @@
   - embedding 用 **MiniMax embo-01**：请求用 `texts` 数组 + `type`（db/query），返回在 `vectors` 字段，需 API Key + GroupId；花 MiniMax 账号额度（与 Claude token 两笔账，一篇几厘钱）。
   - **PDF 等大文件不入数据库**，单独文件仓库 + DB 存指针（详见待办，待立项）。
   - **收视分析看板 dataZoom 模式**：只用 slider（禁 `type:'inside'` 避免滚轮冲突），拖滑块更新 `zoomRange` 但不立即过滤，用户点「应用此范围」按钮才写入 `appliedRange` → `filteredEpisodes`；走势图始终用全量 `episodes`，其余模块（指标卡/对比/象限图）用 `filteredEpisodes`。
-  - **收视分析看板页面顺序**：指标卡 → 走势图 → AI 诊断报告（占位） → 双引擎象限图 → 双列对比（左列季度+编导堆叠、右列题材跨两行）。
+  - **收视分析看板页面顺序**：指标卡 → 走势图 → AI 诊断报告（摘要块） → 双引擎象限图 → 双列对比（左列季度+编导堆叠、右列题材跨两行）。
+  - **L4 AI 诊断报告模型选型**：DeepSeek V4 Pro（胜出，分析深度+论证结构优于 Qwen-Max）。不搞多模型共识——诊断报告是完整分析文章，逻辑连贯性比多家一致更重要。
+  - **L4 摘要块视觉**：左块固定粉红色系（`#c0584f`），右块跟 tier 三档变色（绿/蓝/红）。两块均可滚动查看完整内容。
+  - **L4 话题性框架**：社交货币理论三维（大众认知度/降维切口/惊奇密度），写入摘要卡不作为独立 AI 标签字段。
+  - **L4 prompt5 约束**：① 提及期次必须用「第X期《节目名》」格式；② 话题性必须独立分析段；③ 军事装备分类须准确（枪械≠冷兵器）。
 
   ---
 
   ## 7. ⏩ 交接备注（换人/换账号 0 摩擦续上）
 
-  - **收视分析看板前端 L1–L4 已验收**，下一步是 L4 AI 诊断报告（新 session）。页面布局顺序已定稿：指标卡 → 走势图 → AI 诊断报告（占位） → 双引擎象限图 → 双列对比（左:季度+编导, 右:题材）。
-  - **看板前端新增文件**：`frontend/src/components/Analytics/`（QuarterCompare / EditorCompare / TopicCompare / QuadrantChart）；`scripts/`（import_real_episodes.py / fix_label_mapping.py，一次性导入脚本，已用完）。
+  - **收视分析看板 L1-L4 全部完成**。页面布局：指标卡 → 走势图 → AI 诊断报告（摘要块） → 双引擎象限图 → 双列对比（左:季度+编导, 右:题材）。
+  - **L4 AI 诊断报告新增文件**：后端 `backend/app/api/analytics.py`（POST 端点）；前端 `DiagnosisSummary.jsx`（摘要块）+ `DiagnosisReport.jsx/.css`（详情页）；Prompt 文件 `ai-labeling/prompts/prompt4_content_digest.md` + `prompt5_diagnosis_report.md`；批量脚本 `ai-labeling/scripts/gen_content_digest.py` + `scripts/import_content_digests.py`。
+  - **L4 依赖**：`backend/.env` 需配 `DEEPSEEK_API_KEY`；前端需 `react-markdown` 包（已装）；后端需 `openai` 包（已装）。
+  - **004 迁移已执行**：episodes 表 +content_digest JSONB 列，22 期摘要卡已导入。
+  - **看板前端其他文件**：`frontend/src/components/Analytics/`（QuarterCompare / EditorCompare / TopicCompare / QuadrantChart）；`scripts/`（import_real_episodes.py / fix_label_mapping.py / import_content_digests.py，一次性导入脚本）。
   - **schema 红线**：Phase 3 不改知识库两表；`topics` / `topic_embeddings` 属 Phase 4a，**勿碰**；五类 `source_type` 枚举固定不增减，细分杂志/军报=改表=须 Opus 审。
   - **`logs/` 是受保护资产**：Act 模式严禁 Cline 改；phase 日志由顾问协助生成。Task 3 三轮内容待并入 `phase3_log`（Task 3 段）。
   - **避雷（Phase 3 实战教训）**：
@@ -151,6 +159,6 @@
 
   ## 9. 待确认 / 开放问题（需制片人拍板，AI 别自行假设）
 
-  - [ ] AI 诊断报告（L4）方案细化（新 session 讨论）。
+  - [x] ~~AI 诊断报告（L4）方案细化~~ → 已完成（2026-07-03）
   - [ ] PDF 文件存哪、笔记如何关联中台内文件位置（Backlog #2 的前提）。
   - [ ] 视觉风格参考图、6 名编导初始画像（各 200 字）、甘特图样本 —— 后续 Phase 需要。
diff --git a/ai-labeling/prompts/prompt4_content_digest.md b/ai-labeling/prompts/prompt4_content_digest.md
new file mode 100644
index 0000000..5bf6741
--- /dev/null
+++ b/ai-labeling/prompts/prompt4_content_digest.md
@@ -0,0 +1,108 @@
+# Prompt 4：节目内容摘要卡生成
+
+## 角色
+
+你是一位资深电视节目分析师，擅长从军事科技类节目文稿中提炼核心信息。你的任务是将一篇 5000-7000 字的节目文稿压缩为一份 150-200 字的结构化摘要卡，供后续的收视诊断分析系统使用。
+
+## 背景知识
+
+你分析的是央视《军事科技》栏目的节目文稿。该栏目每周一期，每期约 27 分钟，面向对军事装备和国防科技感兴趣的大众观众（非专业军事人员）。
+
+文稿由以下段落类型构成：
+- 【导视】：片头预告，通常包含本期核心悬念或看点
+- 【主持人N】：演播室主持人串场，负责起承转合
+- 【解说N】：画外音解说，承载主要信息量
+- 【三维动画解说N】：配合三维动画的技术原理讲解
+- 【专家N】：专家访谈，提供专业解读
+- 【街采】：街头采访普通观众（部分期次有）
+
+## 输出格式
+
+请严格按以下 JSON 格式输出，不要输出任何其他内容：
+
+```json
+{
+  "核心切口": "一句话（20-40字），概括本期节目用什么角度切入军事科技话题",
+  "叙事亮点": [
+    "第一个亮点（30-50字）：本期最出彩的段落、转折、对比或论证设计",
+    "第二个亮点（30-50字，可选）：如有第二个突出的叙事设计"
+  ],
+  "观众门槛": "低/中/高 — 一句话说明（20-30字）",
+  "话题性": {
+    "总评": "强/中/弱",
+    "大众认知度": "高/中/低 — 一句话理由（15-25字）",
+    "降维切口": "强/中/弱 — 一句话理由（15-25字）",
+    "惊奇密度": "高/中/低 — 一句话理由（15-25字）"
+  },
+  "潜在弱点": [
+    "第一个弱点（30-50字）：节奏、深度、趣味性、信息密度等方面的不足"
+  ],
+  "时效关联": "有/无 — 如有则说明借势了什么热点/节日/新装备亮相（20-40字）"
+}
+```
+
+## 各字段评估标准
+
+### 核心切口
+提炼本期节目的"一句话卖点"——制片人用什么角度把军事科技话题包装成观众愿意点开的内容。重点是"角度"而非"题材"。
+
+### 叙事亮点
+关注文稿中最能抓住观众的叙事设计，例如：
+- 出人意料的对比（如丑装备 vs 美装备、竞标失败者 vs 成功者）
+- 引人入胜的历史故事或真实事件
+- 巧妙的类比或降维解释
+- 层层递进的悬念设置
+- 街头采访带来的反差或趣味
+
+### 观众门槛
+评估一个对军事完全不了解的普通观众，能否顺畅理解本期内容：
+- **低**：日常概念切入（如颜值、仿生、玩具），无需军事知识储备
+- **中**：需要一定常识（如知道航母是什么、战斗机分代），但节目有充分铺垫
+- **高**：涉及专业概念（如气动布局、相控阵雷达原理），且节目未充分降维
+
+### 话题性（社交货币评估）
+话题性的本质是：观众看完这期节目后，在多大程度上愿意主动跟别人聊起它——在微信群、饭桌上、茶歇时说"你看了昨天那期军事科技吗"。
+
+三个判据：
+
+**大众认知度**：节目涉及的核心装备或概念，普通人听没听说过？
+- 高：航母、坦克、AK47、隐身战机、核潜艇——街上随便拉个人都知道
+- 中：预警机、驱逐舰、无人机——看过新闻的人大概知道
+- 低：鸭翼气动布局、舰载相控阵雷达、弹道导弹防御层级——需要军事爱好者才懂
+
+**降维切口**：节目的切入角度，不懂军事的人能不能秒懂、想聊？
+- 强：用日常概念（颜值、仿生、玩具、进化论）包装军事话题，任何人都能参与讨论
+- 中：切口有一定趣味（竞标失败者的命运、谁更强），但仍需基本军事兴趣
+- 弱：直接讲技术原理或装备参数，非爱好者难以产生讨论欲
+
+**惊奇密度**：全片中有几个让观众"哇！"的瞬间？
+- 高：3个以上反直觉事实、震撼对比、意外反转（如"丑的反而打赢了""这个国家竟然拦截过黑鸟"）
+- 中：1-2个有趣的知识点或对比，但整体平稳
+- 低：全片信息性为主，缺少让人惊讶或想转述的瞬间
+
+三项中占 2 项以上为"强"，占 1 项为"中"，0 项为"弱"。
+
+### 潜在弱点
+从"观众会不会中途换台"的角度审视，常见弱点包括：
+- 信息密度过高，观众消化不了
+- 中段节奏拖沓，缺乏新的刺激点
+- 技术讲解过于抽象，缺少具象化呈现
+- 案例之间缺乏递进关系，像在念清单
+- 结论过于笼统，缺少有记忆点的金句
+- 同类题材短期内重复出现（如连续多期讲枪械）
+
+### 时效关联
+判断本期播出时是否借势了外部事件：
+- 新装备亮相/首飞/下水/交付
+- 军事冲突或国际安全事件
+- 国庆/建军节等军事相关节日
+- 航展/军事展会
+- 如文稿中未提及任何时事背景，标"无"即可
+
+## 注意事项
+
+1. 摘要卡的读者不是观众，而是后续的 AI 诊断系统——用分析性语言，不要用宣传性语言。
+2. 每个字段的评估必须基于文稿内容本身，不要凭题目猜测。
+3. 话题性评估关注的是"传播潜力"而非"内容质量"——一期制作精良但话题封闭的节目可能话题性弱，一期制作普通但切口有趣的节目可能话题性强。
+4. 潜在弱点要具体到本期内容，不要给泛泛的评价。
+5. 总字数控制在 150-200 字（JSON 内纯文本，不含 key 和格式符号）。
diff --git a/ai-labeling/prompts/prompt5_diagnosis_report.md b/ai-labeling/prompts/prompt5_diagnosis_report.md
new file mode 100644
index 0000000..5bc8289
--- /dev/null
+++ b/ai-labeling/prompts/prompt5_diagnosis_report.md
@@ -0,0 +1,153 @@
+# Prompt 5：收视诊断报告生成
+
+## 角色
+
+你是央视《军事科技》栏目的资深收视分析顾问。你的任务是基于一组节目的收视数据、AI 标签和内容摘要，撰写一份有深度、有细节的收视诊断分析报告。
+
+你的分析对象是"节目"，不是"人"。报告中不对任何编导进行评价、排名或点名。
+
+## 背景知识
+
+### 栏目基本情况
+《军事科技》是央视国防军事频道的周播节目，每期约 27 分钟，面向对军事装备和国防科技感兴趣的大众观众。栏目 8 人团队（制片人、责编、6 名编导）。
+
+### 收视颜色判定（与通常直觉相反）
+- 🔴 红色 = 优秀：收视份额 **高于** 摸高目标
+- 🔵 蓝色 = 达标：收视份额介于基础目标与摸高目标之间
+- 🟢 绿色 = 待提升：收视份额 **低于** 基础目标
+
+### 核心分析框架：双引擎模型
+收视表现由两个"引擎"驱动，加上一个独立维度：
+
+**引擎 1：题材热度（地基）**
+热门装备（航母、隐身战机）、热点事件自带观众基础。即使叙事一般，热门题材也能撑住收视。
+
+**引擎 2：叙事结构（放大器）**
+有贯穿全片的主线悬念（主线演进）能放大收视，板块式并列结构则依赖每个板块自身吸引力。
+
+**独立维度：开篇钩子**
+前 1-2 分钟决定观众是否换台。强钩子能挽留犹豫的观众，弱钩子让潜在观众流失。
+
+**核心规律：两条腿至少占一条。** 冷门题材 + 并列结构 = 高风险组合。
+
+### 话题性（社交货币）
+话题性 = 观众看完后愿不愿意主动跟别人聊这期节目。由三个因素决定：
+- 大众认知度：核心装备/概念普通人知不知道
+- 降维切口：节目角度非军迷能不能秒懂、想聊
+- 惊奇密度：全片有多少个让人"哇！"的瞬间
+
+## 输入数据格式
+
+你会收到以下结构化数据：
+
+```
+分析范围：第X期 至 第Y期（共N期）
+年度目标：基础目标 0.XXXX，摸高目标 0.XXXX
+整体统计：平均份额、达标率、摸高完成率等
+
+逐期数据：
+| 期号 | 节目名 | 份额 | 判定 | 题材类型 | 叙事结构 | 钩子强度 | 话题性 |
+| ... |
+
+各期内容摘要卡：
+第X期《节目名》：
+  核心切口：...
+  叙事亮点：...
+  观众门槛：...
+  话题性：总评/大众认知度/降维切口/惊奇密度
+  潜在弱点：...
+  时效关联：...
+```
+
+## 输出要求
+
+### 报告色调（三档自适应）
+
+根据所选范围的平均收视份额，自动切换分析语气：
+
+**当平均份额 < 基础目标时（危险区）：**
+- 语气严厉、直接，不回避问题
+- 用词示例："必须正视""亮红灯""持续恶化""如不及时调整"
+- 重点放在问题诊断和止损建议
+
+**当平均份额介于基础目标与摸高目标之间时（达标区）：**
+- 语气客观、偏鼓励，帮助团队找准发力方向
+- 用词示例："整体达标但仍有提升空间""值得关注的是""建议进一步"
+- 重点放在原因分析和提振方向
+
+**当平均份额 > 摸高目标时（优秀区）：**
+- 语气肯定、激励，提炼可复用的成功经验
+- 用词示例："表现亮眼""成功经验值得推广""再接再厉"
+- 重点放在经验总结和保持势头
+
+### 报告结构
+
+请按以下结构输出（用 Markdown 格式）：
+
+---
+
+#### 一、总体判断（2-3句话）
+
+用一句话定调整体表现，再用 1-2 句话点出最突出的特征或趋势。要求：精准、有冲击力、能让读者立刻把握全局。
+
+#### 二、核心发现（3-5条）
+
+每条发现必须：
+- 以加粗的结论性判断开头
+- 紧跟具体数据佐证（期号、份额、百分比）
+- 点到具体节目名称（**必须用"第X期《节目名》"格式，禁止只写期号不带节目名**——读者记不住期号对应哪期节目）
+- 50-80 字/条
+
+核心发现应覆盖以下维度（不必全覆盖，选最有价值的）：
+- 收视走势的关键转折点或趋势
+- 题材类型与收视的关联规律
+- 叙事结构（主线演进 vs 并列结构）的效果差异
+- 钩子强度与观众留存的关系
+- 话题性与收视的对应关系
+- 异常值（特别高或特别低）的原因
+
+#### 三、深度分析（3-4段）
+
+这是报告的核心价值区。每段 80-120 字，要求：
+- 不是简单地重复数据，而是**解释为什么**
+- 结合内容摘要卡中的具体信息（节目的切口、叙事亮点、观众门槛、潜在弱点）
+- 运用双引擎模型解释收视高低的因果关系
+- 指出**可迁移的规律**，而不是就事论事
+- **必须进行跨期归纳**：这份报告分析的是一段时间内的多期节目，你要找出这些节目的共性规律。如果整体表现好，要总结"这批节目做对了什么、共同特征是什么"；如果整体表现差，要总结"这批节目共同踩了什么坑"；如果好坏参半，要对比分析"好的那几期和差的那几期，差异到底在哪"。逐期点评是数据罗列，跨期归纳才是分析价值。
+
+分析角度示例：
+- "第X期《节目名》虽然是冷门题材（XX），但凭借'XX'的降维切口和强钩子逆袭到XX份额，证明切口设计能弥补题材劣势"
+- "第X期《节目名》至第Y期《节目名》连续N期低于基础线，共同特征是XX类题材+并列结构+XX门槛，说明……"
+- "对比同为XX题材的第X期《节目名》（份额XX）和第Y期《节目名》（份额XX），差异主要在于……"
+
+**话题性/社交货币维度必须独立分析：** 深度分析中必须有一段专门分析话题性（社交货币）维度。不要笼统地说"话题性偏弱"，而要拆到大众认知度、降维切口、惊奇密度三个子维度，指出这批节目在哪个子维度上集体失分、哪个子维度是个别节目的亮点。话题性分析的价值在于：告诉制片人"观众为什么看完不想跟人聊"，而非重复收视数字。
+
+#### 四、行动建议（2-3条）
+
+每条建议必须：
+- 具体可执行，不说正确的废话
+- 基于前面的分析逻辑推导出来
+- 面向选题策划和节目制作环节
+- 40-60 字/条
+
+好的建议示例：
+- "控制XX类题材的连续出现频次，建议间隔至少N期，中间插入XX类题材缓冲"
+- "XX类节目建议优先采用主线演进结构，第X期已验证该组合的收视拉动效果"
+
+差的建议示例（避免）：
+- "提高节目质量"（太空）
+- "加强选题策划"（无具体方向）
+- "某编导应该……"（不评价人）
+
+---
+
+## 注意事项
+
+1. **只分析节目，不评价编导。** 报告中不出现任何编导姓名，不对编导进行排名、评价或建议。
+1.5. **全文使用"第X期《节目名》"格式。** 每次提及具体期次都必须同时带期号和节目名，不允许只写"第X期"而不带节目名。读者不会记住期号与节目的对应关系。
+1.6. **军事装备分类须准确。** 枪械（机枪、步枪、冲锋枪等）属于轻武器/热兵器，不是冷兵器；舰炮属于海军武器系统，不是"冷兵器"。请使用准确的军事分类术语。
+2. **数据驱动，不凭感觉。** 每个判断都要有数据或具体案例支撑。
+3. **写给制片人看。** 语言简洁有力，不堆术语，不写学术论文。制片人熟悉每一期节目，不需要你复述剧情，只需要你指出规律和原因。
+4. **实事求是。** 数据好就说好，数据差就说差，不要为了平衡而硬找优点或硬找缺点。
+5. **报告总字数 600-900 字。** 这是一份内部工作文档，不是公开发表的研究报告。
+6. **外部因素诚实标注。** 如果某期收视异常可能受外部因素影响（如同时段重大事件、特殊节假日），但你无法确认，可以标注"可能受外部因素影响，建议结合当期播出环境分析"，不要编造外部因素。
diff --git a/ai-labeling/scripts/gen_content_digest.py b/ai-labeling/scripts/gen_content_digest.py
new file mode 100644
index 0000000..d2f6b1c
--- /dev/null
+++ b/ai-labeling/scripts/gen_content_digest.py
@@ -0,0 +1,260 @@
+"""
+gen_content_digest.py - 批量生成 22 期节目内容摘要卡
+用法:
+  cd E:\tps-dashboard\ai-labeling
+  python scripts/gen_content_digest.py
+
+功能:
+  - 读取 prompt4_content_digest.md 作为 system prompt
+  - 遍历 doco/deliverables/ 下所有融合A稿 .docx 文件
+  - 用 python-docx 提取文稿文本，调用 MiMo API 生成结构化摘要卡
+  - 支持断点续跑（已存在的 digest 文件自动跳过）
+"""
+
+import sys
+sys.stdout.reconfigure(encoding='utf-8')
+sys.stderr.reconfigure(encoding='utf-8')
+
+import os
+import re
+import json
+import time
+from pathlib import Path
+from datetime import datetime
+from openai import OpenAI
+from dotenv import load_dotenv
+from docx import Document
+
+# 加载 .env（优先加载 ai-labeling 目录下的 .env）
+SCRIPT_DIR = Path(__file__).parent
+BASE_DIR = SCRIPT_DIR.parent  # ai-labeling/
+load_dotenv(BASE_DIR / ".env")
+load_dotenv()  # 也尝试加载项目根目录的 .env
+
+# 目录配置
+DELIVERABLES_DIR = BASE_DIR.parent / "doco" / "deliverables"
+PROMPTS_DIR = BASE_DIR / "prompts"
+EXPERIMENTS_DIR = BASE_DIR / "experiments" / "content_digests"
+
+# MiMo API 配置（与 run_labeling.py 一致）
+MIMO_CONFIG = {
+    "base_url": "https://api.xiaomimimo.com/v1",
+    "model_name": "mimo-v2.5-pro",
+    "api_key_env": "MIMO_API_KEY",
+}
+
+
+def load_system_prompt() -> str:
+    """加载 prompt4_content_digest.md 作为 system prompt。"""
+    prompt_file = PROMPTS_DIR / "prompt4_content_digest.md"
+    if not prompt_file.exists():
+        raise FileNotFoundError(f"找不到 prompt 文件: {prompt_file}")
+    return prompt_file.read_text(encoding="utf-8")
+
+
+def parse_filename(filename: str) -> dict:
+    """
+    从文件名解析元信息。
+    文件名格式: 第02期_20260113_武器进化论：海战颠覆者_付天雨_融合A稿.docx
+    返回: {"ep": 2, "date": "2026-01-13", "title": "...", "editor": "..."}
+    """
+    name = filename.replace(".docx", "")
+    parts = name.split("_")
+    if len(parts) < 4:
+        return None
+    # 解析期号
+    ep_match = re.search(r'第(\d+)期', parts[0])
+    if not ep_match:
+        return None
+    ep = int(ep_match.group(1))
+    # 解析日期（YYYYMMDD -> YYYY-MM-DD）
+    raw_date = parts[1]
+    if len(raw_date) == 8 and raw_date.isdigit():
+        date = f"{raw_date[:4]}-{raw_date[4:6]}-{raw_date[6:8]}"
+    else:
+        date = raw_date
+    title = parts[2]
+    editor = parts[3]
+    return {"ep": ep, "date": date, "title": title, "editor": editor}
+
+
+def extract_docx_text(filepath: Path) -> str:
+    """用 python-docx 提取 .docx 文件的全部文本，逐段拼接。"""
+    doc = Document(str(filepath))
+    paragraphs = []
+    for para in doc.paragraphs:
+        text = para.text.strip()
+        if text:
+            paragraphs.append(text)
+    return "\n".join(paragraphs)
+
+
+def build_user_message(meta: dict, body: str) -> str:
+    """构造 user message：元信息 + 文稿全文。"""
+    return (
+        f"期号：第{meta['ep']:02d}期\n"
+        f"播出日期：{meta['date']}\n"
+        f"节目名：{meta['title']}\n"
+        f"编导：{meta['editor']}\n"
+        f"\n以下是节目文稿全文：\n\n{body}"
+    )
+
+
+def extract_json_from_response(raw: str) -> dict:
+    """从模型响应中提取 JSON，兼容推理模型的<think>...输出。"""
+    # 先去掉<think>...标签及其内容
+    text = re.sub(r'<think>.*?</think>', '', raw, flags=re.DOTALL)
+    text = text.strip()
+    # 去掉 markdown 代码块
+    text = re.sub(r'^```(?:json)?\s*', '', text)
+    text = re.sub(r'\s*```$', '', text)
+    text = text.strip()
+    # 从第一个 { 开始，到最后一个 } 结束
+    first_brace = text.find('{')
+    last_brace = text.rfind('}')
+    if first_brace != -1 and last_brace != -1 and last_brace >= first_brace:
+        json_str = text[first_brace:last_brace + 1]
+        return json.loads(json_str)
+    # 兜底：直接尝试解析
+    return json.loads(text)
+
+
+def call_mimo(system_prompt: str, user_prompt: str) -> dict:
+    """调用 MiMo API 生成摘要卡，返回解析后的 JSON dict。"""
+    api_key = os.environ.get(MIMO_CONFIG["api_key_env"])
+    if not api_key:
+        raise EnvironmentError(
+            f"环境变量 {MIMO_CONFIG['api_key_env']} 未设置，请检查 ai-labeling/.env 或根目录 .env 文件"
+        )
+    client = OpenAI(
+        api_key=api_key,
+        base_url=MIMO_CONFIG["base_url"],
+    )
+    response = client.chat.completions.create(
+        model=MIMO_CONFIG["model_name"],
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        temperature=0.0,
+        # 关闭 thinking（与 run_labeling.py 一致）
+        extra_body={"thinking": {"type": "disabled"}},
+    )
+    raw = response.choices[0].message.content
+    return extract_json_from_response(raw)
+
+
+def collect_docx_files() -> list[dict]:
+    """收集所有 .docx 文件并按期号排序。"""
+    files = []
+    for f in sorted(DELIVERABLES_DIR.iterdir()):
+        if not f.name.endswith(".docx"):
+            continue
+        meta = parse_filename(f.name)
+        if meta is None:
+            print(f"⚠️  跳过无法解析的文件: {f.name}")
+            continue
+        meta["filepath"] = f
+        files.append(meta)
+    # 按期号排序
+    files.sort(key=lambda x: x["ep"])
+    return files
+
+
+def main():
+    # 确保输出目录存在
+    EXPERIMENTS_DIR.mkdir(parents=True, exist_ok=True)
+
+    # 加载 system prompt
+    system_prompt = load_system_prompt()
+    print(f"✅ 已加载 system prompt: prompt4_content_digest.md")
+
+    # 收集 docx 文件
+    docx_files = collect_docx_files()
+    print(f"✅ 找到 {len(docx_files)} 个融合A稿 docx 文件\n")
+
+    all_digests = []
+    skipped = 0
+    success = 0
+    failed = 0
+
+    for i, meta in enumerate(docx_files):
+        ep = meta["ep"]
+        out_file = EXPERIMENTS_DIR / f"ep{ep:02d}_digest.json"
+
+        # 断点续跳：已存在则跳过
+        if out_file.exists():
+            print(f"[{i+1}/{len(docx_files)}] ep{ep:02d} 已存在，跳过")
+            try:
+                existing = json.loads(out_file.read_text(encoding="utf-8"))
+                all_digests.append(existing)
+            except Exception:
+                pass
+            skipped += 1
+            continue
+
+        print(f"[{i+1}/{len(docx_files)}] 正在处理 ep{ep:02d} - {meta['title']} ...")
+
+        try:
+            # 提取 docx 文本
+            body = extract_docx_text(meta["filepath"])
+            if not body.strip():
+                print(f"  ⚠️  ep{ep:02d} 文稿内容为空，跳过")
+                failed += 1
+                continue
+
+            # 构造 user message
+            user_msg = build_user_message(meta, body)
+
+            # 调用 MiMo
+            result = call_mimo(system_prompt, user_msg)
+
+            # 构造输出
+            output = {
+                "ep": ep,
+                "date": meta["date"],
+                "title": meta["title"],
+                "editor": meta["editor"],
+                "filename": meta["filepath"].name,
+                "digest": result,
+                "generated_at": datetime.now().isoformat(),
+            }
+
+            # 写入单期文件
+            out_file.write_text(
+                json.dumps(output, ensure_ascii=False, indent=2),
+                encoding="utf-8",
+            )
+            all_digests.append(output)
+            print(f"  ✅ ep{ep:02d} 摘要卡已生成 -> {out_file.name}")
+            success += 1
+
+        except json.JSONDecodeError as e:
+            print(f"  ⚠️  ep{ep:02d} LLM 返回的不是合法 JSON: {e}")
+            failed += 1
+        except Exception as e:
+            print(f"  ❌ ep{ep:02d} 处理失败: {e}")
+            failed += 1
+
+        # 限流保护：每期之间 sleep 1 秒
+        if i < len(docx_files) - 1:
+            time.sleep(1)
+
+    # 写入汇总文件
+    summary_file = EXPERIMENTS_DIR / "_all_digests.json"
+    summary_file.write_text(
+        json.dumps(all_digests, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+
+    print("\n" + "=" * 60)
+    print(f"📊 处理完成:")
+    print(f"   成功: {success}")
+    print(f"   跳过(已存在): {skipped}")
+    print(f"   失败: {failed}")
+    print(f"   汇总文件: {summary_file}")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/backend/app/api/analytics.py b/backend/app/api/analytics.py
index 3a91a20..386df51 100644
--- a/backend/app/api/analytics.py
+++ b/backend/app/api/analytics.py
@@ -295,7 +295,7 @@ def generate_diagnosis_report(
         base_url="https://api.deepseek.com",
     )
     response = client.chat.completions.create(
-        model="deepseek-chat",
+        model="deepseek-v4-pro",
         messages=[
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": user_message},
@@ -322,7 +322,7 @@ def generate_diagnosis_report(
         },
         "report_markdown": report_markdown,
         "generated_at": datetime.now().isoformat(),
-        "model": "deepseek-chat",
+        "model": "deepseek-v4-pro",
         "disclaimer": "本报告基于已入库的收视数据、节目标签及内容摘要生成，未纳入同时段竞品、社会热点等外部因素。分析结论难免挂一漏万，仅供栏目内部讨论参考，不构成节目决策依据。",
     }
 
diff --git a/backend/app/models/episode.py b/backend/app/models/episode.py
index f4196c0..4369811 100644
--- a/backend/app/models/episode.py
+++ b/backend/app/models/episode.py
@@ -69,6 +69,9 @@ class Episode(SQLModel, table=True):
     opening_hook: str | None = Field(default=None, max_length=10)           # 开篇钩子(单选)
     ai_label_confidence: str | None = Field(default=None, max_length=10)    # draft/reviewed/inferred
 
+    # ── 内容摘要卡（AI 生成，供诊断报告输入）──
+    content_digest: Any | None = Field(default=None, sa_column=Column(JSONB))
+
     created_at: datetime | None = Field(
         default=None,
         sa_column=Column(SADateTime(timezone=True), nullable=False, server_default=sa_func.now()),
diff --git a/backend/sql/004_add_content_digest.sql b/backend/sql/004_add_content_digest.sql
new file mode 100644
index 0000000..e539665
--- /dev/null
+++ b/backend/sql/004_add_content_digest.sql
@@ -0,0 +1,9 @@
+-- 004_add_content_digest.sql
+-- 给 episodes 表添加内容摘要卡字段（AI 生成的节目内容结构化摘要，供诊断报告使用）
+-- 前置：执行前先 pg_dump 备份
+
+ALTER TABLE episodes
+  ADD COLUMN IF NOT EXISTS content_digest JSONB;
+
+COMMENT ON COLUMN episodes.content_digest
+  IS '节目内容摘要卡（AI生成JSONB：核心切口/叙事亮点/观众门槛/话题性/潜在弱点/时效关联）';
\ No newline at end of file
diff --git a/doco/CLAUDE.md b/doco/CLAUDE.md
index d02c88a..97551d5 100644
--- a/doco/CLAUDE.md
+++ b/doco/CLAUDE.md
@@ -16,9 +16,9 @@
 
 ## 🔖 状态栏 (STATUS — 每次结束 session 前必须更新这三行)
 
-- **最后更新**：Claude Code ｜ 2026-06-26
-- **当前状态一句话**：**doco 子项目开发收尾。** 22 期融合A稿全部产出（含说话人区分重跑），已归拢至 `doco/deliverables/`。第01期（武器装备里的形状规律）缺A稿暂跳过。下一步：带 22 期成品回 TPS 主项目知识库批量导入。
-- **下一个动手的人从这里开始**：见下方「⏩ 交接备注」
+- **最后更新**：Claude Code ｜ 2026-07-03
+- **当前状态一句话**：**doco 子项目阶段性收工。** 22 期融合A稿全部产出，成品在 `doco/deliverables/`。主项目寄存条交接核查已完成（2026-07-03）：寄存条状态表已更新、api_credentials_inventory 已更新（MiMo/讯飞）。下一步由主项目侧执行批量导入知识库。
+- **下一个动手的人从这里开始**：doco 开发完成，无待办。主项目侧批量导入走 Phase 3 上传/embedding 链路。
 
 ---
 
@@ -89,6 +89,7 @@
 
 ## 4. 已完成（只追加，最新在最上）
 
+- [2026-07-03 ｜Claude Code] **主项目寄存条交接核查完成。** 更新内容：① 主项目 `note/寄存条Doco文稿整理子项目已外迁.md` 底部状态表从"等子项目出 PRD v3 启动 P1"更新为"开发收尾（2026-07-03）"；② `docs/api_credentials_inventory.md` doco 节更新——讯飞改已激活、DeepSeek Vision 改为 Ollama 本地 DeepSeek-OCR、新增小米 MiMo 2.5 Pro（替代原 Anthropic Claude API 条目）；③ doco CLAUDE.md 状态栏更新。交付物核查：22 份 docx + 20 份 md 在 `deliverables/`、术语词典 `term_dict.json` 10600 行、测试 `test_video_split.py` 存在。
 - [2026-06-26 ｜Claude Code] **doco 子项目收尾：ep021/022 补稿 + 说话人重跑 + 成品归拢。** 本 session 完成：① **ep021（海战颠覆者）、ep022（硬核脑洞）两期新补 A 稿全流程 P1→C4 跑通**——ep021 骨架 LLM 自动生成失败（`extract_a_paragraphs()` 过滤空段后重编号，LLM 用了 raw docx 下标），手写骨架 JSON（32 段=26 normal+6 ignore 粗体章节标题）通过校验；ep022 骨架 LLM 自动通过（38 段全 normal，inline header）；ep021 C3 review 13 条（均 OCR 修正，专名零替换）、C4 punct_ok 25/26；ep022 C3 review 5 条、C4 punct_ok 全过；② **cli.py 路径 bug 修复**：`episode_dir = Path("programs")/episode_id` 相对路径在子进程中二次解析导致路径翻倍（`programs/ep021/programs/ep021/...`），加 `.resolve()` 修复——此 bug 从未触发因 ep004 用 `--skip-p1` 且 `_batch_run.py` 用绝对路径；③ **ep007、ep010 C4 说话人区分重跑**（06-24 批量重跑时这两期被遗漏，融合A稿时间戳仍为 06-23），重跑完成、产物已更新；④ **排播对照表** `doco/data/2026episode_list.xlsx`（25 期播出日期/期次/节目名/编导/收视）导入，建立 ep→播出顺序完整映射；⑤ **成品归拢** `doco/deliverables/` 文件夹：22 份融合A稿按播出顺序统一命名（`第XX期_YYYYMMDD_节目名_编导_融合A稿.docx`），缺第01期（武器装备里的形状规律，无A稿）；⑥ 通哥人工核验 ep021/022 质量高，仅两处小毛刺（ep021 一段话归错段落、ep022 一个标签应为三维动画），确认技术路径有效。
 - [2026-06-23 ｜Claude Code] **批量化基础设施完工 + ep004 全流程通过 + 16 期批量跑启动**。本 session 完成：① ep004（枪王对决，最难·小剧场密·58段）全流程 P1→C4 一次通过，C4 punct_ok 58/58 全过、confidence 全 ≥0.80（batch_size=25 效果显著，无批次回退）；② 新增 `doco run` 一键全流程命令（Cline 实现，Opus 审核），参数 `--episode-id / --a-script / --input-video / --batch-size / --skip-p1`，ep004 `--skip-p1` 验证通过；③ C3 SYSTEM_PROMPT 收紧专名铁律（防 ASR 同音字替换权威 B稿专名）；④ Ollama 并发提至 16 路（`OLLAMA_NUM_PARALLEL=16`，8 路实测 GPU 96%、显存充裕）；⑤ 16 期新目录建立 + 文件拷贝 + 骨架批量生成并集中核验（真名零泄露、隔断/ignore 正确）；⑥ `_batch_run.py` 批量脚本启动跑 ep005-ep020。**doco PRD 完成标准已与通哥确定**：20 期全出稿 → 带成品回 TPS 主项目知识库批量导入。
 - [2026-06-22 晚｜Claude Code] **ep002 C4 审核完毕，全流程收工**。Cline 跑完 C4 compose 出稿 `20260127潜艇的仿生之路_穆佩弦_融合A稿.docx`。Opus 审核核验：硬校验（汉字零改）全过、733 行全覆盖、标点回退 0 段。**但发现两个问题**：① Cline 自报空段名称错了两个（报"解说3/解说6"，实际是"三维动画解说3/解说8"）、隔断数也报错（报 3 个，实际 4 个）——再次印证不信 Cline 自检；② **9/19 批次（47%）LLM 对齐失败走回退**（全 confidence=0.30），本次因失败批次恰在大段中间未翻车，但属黄色预警。**分段偏差根因**：A 稿是拍摄前剧本，专家采访段与实际播出内容差异巨大（A 稿一句话提纲 vs 专家自由发挥两分钟），LLM 无法正确匹配——这是信息不对应，非算法问题。通哥手动批改分段（~10 处高亮标签），内容本身正确。**结论（通哥拍板）**：治本靠编导给贴近播出版的稿子，现阶段接受"程序保证文字零改 + 编导手调分段"的模式。
diff --git a/docs/api_credentials_inventory.md b/docs/api_credentials_inventory.md
index b9e6f0f..509c0f0 100644
--- a/docs/api_credentials_inventory.md
+++ b/docs/api_credentials_inventory.md
@@ -25,9 +25,9 @@
 
 | 子模块 | API 服务 | Key 类型 | 开通日 | 激活状态 | 到期日 | 责任人 | 备注 |
 |---|---|---|---|---|---|---|---|
-| doco | 讯飞开放平台 - 录音文件转写(标准版) | APP_ID + SECRET_KEY | 2026-06-12 | 待激活(需走 0 元购买) | 2027-06-12 | 制片人 | demo 凭证已过期,需新申请 |
-| doco | DeepSeek Vision | API_KEY | 2026-06-12 | 已激活 | — | 制片人 | doco OCR 用 |
-| doco | Anthropic Claude API | API_KEY | 2026-06-12 | 已激活 | — | 制片人 | AI 融合层(P3) |
+| doco | 讯飞开放平台 - 录音文件转写(标准版) | APP_ID + SECRET_KEY | 2026-06-12 | 已激活 | 2027-06-12 | 制片人 | C2 ASR 转写，22 期已跑完 |
+| doco | Ollama 本地 DeepSeek-OCR | 本地部署 | 2026-06-12 | 已激活 | — | 制片人 | P2 本地 OCR，16 路并发，4090D 24GB |
+| doco | 小米 MiMo 2.5 Pro | API_KEY | 2026-06-22 | 已激活 | — | 制片人 | AI 融合层(C1/C3/C4)，OpenAI 兼容端，替代原 DeepSeek v4-pro |
 
 ---
 
@@ -41,4 +41,4 @@
 
 ---
 
-*最后更新: 2026-06-12*
+*最后更新: 2026-07-03*
diff --git a/frontend/src/components/Analytics/DiagnosisSummary.jsx b/frontend/src/components/Analytics/DiagnosisSummary.jsx
index ed341ab..268b0f7 100644
--- a/frontend/src/components/Analytics/DiagnosisSummary.jsx
+++ b/frontend/src/components/Analytics/DiagnosisSummary.jsx
@@ -27,32 +27,49 @@ function extractSection(markdown, startMarker, endMarkers) {
   return markdown.substring(afterStart, endIdx).trim()
 }
 
-/** 清理 markdown 为纯文本（去掉标题标记、多余符号等） */
-function cleanMarkdown(text) {
+/** 将 markdown 文本按行拆分，保留 **bold** 为 <strong> */
+function parseMarkdownLines(text) {
   if (!text) return []
   return text
     .split('\n')
-    .map((line) => line.replace(/^#+\s*/, '').replace(/^\*\*(.+?)\*\*/, '$1').trim())
+    .map((line) => line.replace(/^#+\s*/, '').trim())
     .filter((line) => line.length > 0)
 }
 
+/** 将行内 **text** 转为 React 元素 */
+function renderInlineMarkdown(text) {
+  const parts = text.split(/(\*\*[^*]+\*\*)/)
+  return parts.map((part, i) => {
+    const boldMatch = part.match(/^\*\*(.+)\*\*$/)
+    if (boldMatch) {
+      return <strong key={i}>{boldMatch[1]}</strong>
+    }
+    return part
+  })
+}
+
+const LEFT_STYLE = {
+  color: '#c0584f',
+  bg: 'rgba(192,88,79,0.06)',
+}
+
 const TIER_CONFIG = {
   danger: {
     leftTitle: '问题聚焦',
     rightTitle: '病因与预警',
-    color: '#7aa874',
+    rightColor: '#7aa874',
     rightBg: 'rgba(122,168,116,0.06)',
   },
   on_target: {
     leftTitle: '核心发现',
     rightTitle: '病因与提振建议',
-    color: '#5b8db8',
+    rightColor: '#5b8db8',
     rightBg: 'rgba(91,141,184,0.06)',
   },
   excellent: {
     leftTitle: '高光复盘',
     rightTitle: '经验总结',
-    color: '#c0584f',
+    rightColor: '#c0584f',
     rightBg: 'rgba(192,88,79,0.06)',
   },
 }
@@ -144,8 +161,8 @@ function DiagnosisSummary({ episodes, yearlyTarget, selectedYear }) {
   // 行动建议：从 "#### 四、行动建议" 到末尾
   let actions = extractSection(markdown, '#### 四、行动建议', [])
 
-  const coreLines = cleanMarkdown(coreFindings)
-  const actionLines = cleanMarkdown(actions)
+  const coreLines = parseMarkdownLines(coreFindings)
+  const actionLines = parseMarkdownLines(actions)
 
   return (
     <div className="analytics-chart-card">
@@ -176,30 +193,30 @@ function DiagnosisSummary({ episodes, yearlyTarget, selectedYear }) {
         </div>
       ) : report ? (
         <div className="diagnosis-summary-content">
-          {/* 左块 */}
+          {/* 左块（固定粉红色系） */}
           <div
             className="diagnosis-summary-left"
-            style={{ borderColor: config.color }}
+            style={{ borderColor: LEFT_STYLE.color, background: LEFT_STYLE.bg }}
           >
-            <div className="diagnosis-block-title">{config.leftTitle}</div>
+            <div className="diagnosis-block-title" style={{ color: LEFT_STYLE.color }}>{config.leftTitle}</div>
             {coreLines.length > 0 ? (
               coreLines.map((line, i) => (
-                <p key={i} style={{ margin: '0 0 8px 0' }}>{line}</p>
+                <p key={i} style={{ margin: '0 0 8px 0' }}>{renderInlineMarkdown(line)}</p>
               ))
             ) : (
               <p>报告已生成，请查看完整报告</p>
             )}
           </div>
 
-          {/* 右块 */}
+          {/* 右块（跟随 tier 变色） */}
           <div
             className="diagnosis-summary-right"
-            style={{ background: config.rightBg }}
+            style={{ borderColor: config.rightColor, background: config.rightBg }}
           >
-            <div className="diagnosis-block-title">{config.rightTitle}</div>
+            <div className="diagnosis-block-title" style={{ color: config.rightColor }}>{config.rightTitle}</div>
             {actionLines.length > 0 ? (
               actionLines.map((line, i) => (
-                <p key={i} style={{ margin: '0 0 8px 0' }}>{line}</p>
+                <p key={i} style={{ margin: '0 0 8px 0' }}>{renderInlineMarkdown(line)}</p>
               ))
             ) : (
               <p>报告已生成，请查看完整报告</p>
diff --git a/frontend/src/pages/Analytics/Analytics.css b/frontend/src/pages/Analytics/Analytics.css
index 871631e..abd8b01 100644
--- a/frontend/src/pages/Analytics/Analytics.css
+++ b/frontend/src/pages/Analytics/Analytics.css
@@ -276,15 +276,33 @@
   font-size: 13px;
   line-height: 1.8;
   color: #444;
+  max-height: 220px;
+  overflow-y: auto;
+  position: relative;
+}
+
+.diagnosis-summary-left::-webkit-scrollbar,
+.diagnosis-summary-right::-webkit-scrollbar {
+  width: 4px;
+}
+
+.diagnosis-summary-left::-webkit-scrollbar-thumb,
+.diagnosis-summary-right::-webkit-scrollbar-thumb {
+  background: rgba(0,0,0,0.12);
+  border-radius: 2px;
+}
+
+.diagnosis-summary-left::-webkit-scrollbar-track,
+.diagnosis-summary-right::-webkit-scrollbar-track {
+  background: transparent;
 }
 
 .diagnosis-summary-left {
-  border-left: 4px solid currentColor;
-  background: rgba(255, 255, 255, 0.3);
+  border-left: 3px solid currentColor;
 }
 
 .diagnosis-summary-right {
-  background: rgba(255, 255, 255, 0.3);
+  border-left: 3px solid currentColor;
 }
 
 .diagnosis-block-title {
diff --git a/note/寄存条Doco文稿整理子项目已外迁.md b/note/寄存条Doco文稿整理子项目已外迁.md
index c73a8eb..bcd2660 100644
--- a/note/寄存条Doco文稿整理子项目已外迁.md
+++ b/note/寄存条Doco文稿整理子项目已外迁.md
@@ -102,5 +102,5 @@ Doco 子项目交付物的接口约定,见**子项目主 Brief §六**(交付什
 
 | 子项目           | 外迁日期   | 寄存条文件                 | 状态                      |
 | ---------------- | ---------- | -------------------------- | ------------------------- |
-| TPS 看板分析升级 | 2026-06-12 | `寄存条_看板升级已外迁.md` | 设计阶段                  |
-| Doco 文稿整理    | 2026-06-12 | 本文件                     | 等子项目出 PRD v3 启动 P1 |
\ No newline at end of file
+| TPS 看板分析升级 | 2026-06-12 | `寄存条_看板升级已外迁.md` | 原型阶段收工（2026-07-03） |
+| Doco 文稿整理    | 2026-06-12 | 本文件                     | **开发收尾（2026-07-03）**：22 期融合A稿已产出，成品在 `doco/deliverables/`，待批量导入主项目知识库 |
\ No newline at end of file
diff --git a/package-lock.json b/package-lock.json
index 4e7e27c..36e720c 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -2,5 +2,1294 @@
   "name": "tps-dashboard",
   "lockfileVersion": 3,
   "requires": true,
-  "packages": {}
+  "packages": {
+    "": {
+      "dependencies": {
+        "echarts": "^6.1.0",
+        "echarts-for-react": "^3.0.6",
+        "react-markdown": "^10.1.0"
+      }
+    },
+    "node_modules/@types/debug": {
+      "version": "4.1.13",
+      "resolved": "https://registry.npmjs.org/@types/debug/-/debug-4.1.13.tgz",
+      "integrity": "sha512-KSVgmQmzMwPlmtljOomayoR89W4FynCAi3E8PPs7vmDVPe84hT+vGPKkJfThkmXs0x0jAaa9U8uW8bbfyS2fWw==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/ms": "*"
+      }
+    },
+    "node_modules/@types/estree": {
+      "version": "1.0.9",
+      "resolved": "https://registry.npmjs.org/@types/estree/-/estree-1.0.9.tgz",
+      "integrity": "sha512-GhdPgy1el4/ImP05X05Uw4cw2/M93BCUmnEvWZNStlCzEKME4Fkk+YpoA5OiHNQmoS7Cafb8Xa3Pya8m1Qrzeg==",
+      "license": "MIT"
+    },
+    "node_modules/@types/estree-jsx": {
+      "version": "1.0.5",
+      "resolved": "https://registry.npmjs.org/@types/estree-jsx/-/estree-jsx-1.0.5.tgz",
+      "integrity": "sha512-52CcUVNFyfb1A2ALocQw/Dd1BQFNmSdkuC3BkZ6iqhdMfQz7JWOFRuJFloOzjk+6WijU56m9oKXFAXc7o3Towg==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/estree": "*"
+      }
+    },
+    "node_modules/@types/hast": {
+      "version": "3.0.4",
+      "resolved": "https://registry.npmjs.org/@types/hast/-/hast-3.0.4.tgz",
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 }
diff --git a/package.json b/package.json
new file mode 100644
index 0000000..80db86b
--- /dev/null
+++ b/package.json
@@ -0,0 +1,7 @@
+{
+  "dependencies": {
+    "echarts": "^6.1.0",
+    "echarts-for-react": "^3.0.6",
+    "react-markdown": "^10.1.0"
+  }
+}
diff --git a/scripts/import_content_digests.py b/scripts/import_content_digests.py
new file mode 100644
index 0000000..7f679c4
--- /dev/null
+++ b/scripts/import_content_digests.py
@@ -0,0 +1,78 @@
+"""
+一次性脚本：将 AI 生成的内容摘要卡写入 episodes 表的 content_digest 字段
+数据来源：ai-labeling/experiments/content_digests/_all_digests.json
+前置：先手动执行 backend/sql/004_add_content_digest.sql 添加字段
+"""
+
+import sys
+import os
+import json
+from datetime import date
+
+# Windows 终端中文输出
+sys.stdout.reconfigure(encoding='utf-8')
+sys.stderr.reconfigure(encoding='utf-8')
+
+# 加 backend 到 path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'backend'))
+
+from app.db.session import get_session
+from app.models.episode import Episode
+from app.models.user import User  # FK 解析需要
+from sqlmodel import select
+
+# ── 1. 读 digest 数据 ──
+project_root = os.path.join(os.path.dirname(__file__), '..')
+digest_file = os.path.join(project_root, 'ai-labeling', 'experiments', 'content_digests', '_all_digests.json')
+
+with open(digest_file, 'r', encoding='utf-8') as f:
+    all_digests = json.load(f)
+
+print(f"读入 {len(all_digests)} 条摘要记录")
+
+# ── 2. 逐条匹配并写入 ──
+session = next(get_session())
+success = 0
+skipped = 0
+mismatch = 0
+
+for d in all_digests:
+    ep_num = d['ep']
+    digest_date = d['date']  # "2026-04-21" 格式
+
+    # 查 episode
+    ep = session.exec(select(Episode).where(Episode.episode_number == ep_num)).first()
+    if not ep:
+        print(f"⚠️  ep{ep_num:02d} 在数据库中不存在，跳过")
+        skipped += 1
+        continue
+
+    # 校验日期
+    if str(ep.air_date) != digest_date:
+        print(f"⚠️  ep{ep_num:02d} 日期不匹配: DB={ep.air_date}, digest={digest_date}，跳过")
+        mismatch += 1
+        continue
+
+    # 写入（只存 digest 字段，不存外壳的 ep/date/title/editor/filename/generated_at）
+    ep.content_digest = d['digest']
+    session.add(ep)
+    success += 1
+    print(f"✅ ep{ep_num:02d} {ep.program_name} <- digest 已写入")
+
+session.commit()
+
+# ── 3. 打印统计 ──
+print()
+print(f"=== 统计 ===")
+print(f"成功写入: {success}")
+print(f"跳过(不存在): {skipped}")
+print(f"跳过(日期不匹配): {mismatch}")
+print(f"总记录: {len(all_digests)}")
+
+# ── 4. 验证：所有 episodes 的 content_digest 状态 ──
+print()
+print(f"=== 验证: episodes content_digest 状态 ===")
+all_eps = session.exec(select(Episode).order_by(Episode.episode_number)).all()
+for e in all_eps:
+    has_digest = "✅" if e.content_digest is not None else "❌"
+    print(f"ep{e.episode_number:02d} | {e.program_name} | {e.air_date} | digest: {has_digest}")
\ No newline at end of file