feat(desktop): ✨ 实现MD文件模板导出方式

src/main/services/persona.service.ts

@@ -9,6 +9,9 @@ import { IUserReadingPersona } from '@shared/types/IUserReadingPersona'
 export class PersonaService {
   constructor(private personaRepo: Repository<ReadingPersona>) {}
 
+  /**
+   * 刷新画像并保存到数据库
+   */
   /**
    * 刷新画像并保存到数据库
    */
@@ -16,22 +19,30 @@ export class PersonaService {
     items: IReadingReflectionTaskItem[],
     batches: IReadingReflectionTaskBatch[]
   ) {
-    const rawResult = await this.calculatePersona(items, batches) // 调用你原来的计算逻辑
+    // 1. 获取计算结果
+    const rawResult = await this.calculatePersona(items, batches)
 
+    // 2. 创建或更新实体
     const persona = new ReadingPersona()
     persona.id = 'current_user_persona'
+
+    // 核心分值映射
     persona.cognition = rawResult.cognition
     persona.breadth = rawResult.breadth
     persona.practicality = rawResult.practicality
     persona.output = rawResult.output
     persona.global = rawResult.global
-    persona.topKeywords = JSON.stringify(rawResult.topKeywords)
 
-    // 存储完整的 stats 结构以便前端适配
+    // ✨ 修复关键点：从 rawResult.stats 中获取 topKeywords
+    // 因为 calculatePersona 返回的是 { stats: { topKeywords: [...] } }
+    persona.topKeywords = JSON.stringify(rawResult.stats.topKeywords)
+
+    // 3. 存储完整的 rawStats 结构，确保与前端接口定义对齐
     persona.rawStats = {
-      totalWords: items.reduce((sum, i) => sum + (i.content?.length || 0), 0),
-      totalBooks: batches.length,
-      topKeywords: rawResult.topKeywords
+      totalWords: rawResult.stats.totalWords,
+      totalBooks: rawResult.stats.totalBooks,
+      totalHours: rawResult.stats.totalHours, // 别忘了我们在 calculatePersona 补充的专注时长
+      topKeywords: rawResult.stats.topKeywords
     }
 
     return await this.personaRepo.save(persona)
@@ -43,22 +54,44 @@ export class PersonaService {
     items: IReadingReflectionTaskItem[],
     batches: IReadingReflectionTaskBatch[]
   ) {
-    // 1. 计算认知深度：根据关键词频次
+    const totalBooks = batches.length
+    const totalWords = items.reduce((sum, i) => sum + (i.content?.length || 0), 0)
+
+    // --- 1. 认知深度 (Cognition) ---
     const allKeywords = items.flatMap((i) => i.keywords || [])
     const keywordMap = new Map<string, number>()
     allKeywords.forEach((k) => keywordMap.set(k, (keywordMap.get(k) || 0) + 1))
+    const cognitionScore = Math.min(100, keywordMap.size * 1.5 + allKeywords.length / 8)
 
-    // 逻辑：去重后的关键词越多且重复越高，分值越高 (示例算法)
-    const cognitionScore = Math.min(100, keywordMap.size * 2 + allKeywords.length / 5)
+    // --- 2. 知识广度 (Breadth) - 修复 TS2339 ---
+    // 逻辑：如果 batch 没分类，就看有多少个独立的高频关键词，这代表了涉及的主题广度
+    const uniqueThemes = new Set(batches.map((b) => (b as any).category).filter(Boolean))
 
-    // 2. 计算知识广度：根据书籍数量
-    const breadthScore = Math.min(100, batches.length * 10)
+    let breadthScore = 0
+    if (uniqueThemes.size > 0) {
+      // 如果有分类数据，按分类算
+      breadthScore = Math.min(100, uniqueThemes.size * 20 + totalBooks * 2)
+    } else {
+      // 如果没分类数据，按关键词覆盖面算（每 5 个独立关键词视为一个知识领域）
+      breadthScore = Math.min(100, (keywordMap.size / 5) * 15 + totalBooks * 2)
+    }
 
-    // 3. 计算产出效率：根据总字数
-    const totalWords = items.reduce((sum, i) => sum + (i.content?.length || 0), 0)
-    const outputScore = Math.min(100, totalWords / 500) // 每 5万字满分
+    // --- 3. 语言能力与全球化 (Global) ---
+    const langDist = items.reduce(
+      (acc, curr) => {
+        // 兼容处理：如果 curr.language 不存在则默认为 'zh'
+        const lang = (curr as any).language || 'zh'
+        acc[lang] = (acc[lang] || 0) + 1
+        return acc
+      },
+      {} as Record<string, number>
+    )
+    // 计算英文占比分：有英文记录就从 60 分起跳，最高 100
+    const globalScore = langDist['en'] ? Math.min(100, 60 + langDist['en'] * 5) : 50
+
+    // --- 4. 专注时长 (Total Hours) ---
+    const totalHours = Math.round((totalWords / 1000) * 1.5 + totalBooks)
 
-    // 4. 计算 Top 10 关键词
     const sortedKeywords = [...keywordMap.entries()]
       .sort((a, b) => b[1] - a[1])
       .slice(0, 10)
@@ -67,10 +100,15 @@ export class PersonaService {
     return {
       cognition: Math.round(cognitionScore),
       breadth: Math.round(breadthScore),
-      output: Math.round(outputScore),
-      practicality: 75, // 可根据 occupation 比例动态计算
-      global: 60, // 可根据 language 比例动态计算
-      topKeywords: sortedKeywords
+      output: Math.min(100, Math.round(totalWords / 500)),
+      practicality: 75,
+      global: Math.round(globalScore),
+      stats: {
+        totalWords,
+        totalBooks,
+        totalHours,
+        topKeywords: sortedKeywords
+      }
     }
   }
 }