feat(desktop): ✨ 实现一些功能

1. 实现了用户阅读画像

2. 实现了全局检索功能

src/main/services/persona.service.ts

@@ -0,0 +1,100 @@
+import { Repository } from 'typeorm'
+import { ReadingPersona } from '@main/db/entities/ReadingPersona'
+import {
+  IReadingReflectionTaskBatch,
+  IReadingReflectionTaskItem
+} from '@shared/types/IReadingReflectionTask'
+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
+   */
+  async refreshPersona(
+    items: IReadingReflectionTaskItem[],
+    batches: IReadingReflectionTaskBatch[]
+  ) {
+    const rawResult = await this.calculatePersona(items, batches) // 调用你原来的计算逻辑
+
+    const persona = new ReadingPersona()
+    persona.id = 'current_user_persona'
+    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
+    persona.rawStats = {
+      totalWords: items.reduce((sum, i) => sum + (i.content?.length || 0), 0),
+      totalBooks: batches.length,
+      topKeywords: rawResult.topKeywords
+    }
+
+    return await this.personaRepo.save(persona)
+  }
+  /**
+   * 从数据库聚合数据并计算画像分值
+   */
+  async calculatePersona(
+    items: IReadingReflectionTaskItem[],
+    batches: IReadingReflectionTaskBatch[]
+  ) {
+    // 1. 计算认知深度：根据关键词频次
+    const allKeywords = items.flatMap((i) => i.keywords || [])
+    const keywordMap = new Map<string, number>()
+    allKeywords.forEach((k) => keywordMap.set(k, (keywordMap.get(k) || 0) + 1))
+
+    // 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. 计算知识广度：根据书籍数量
+    const breadthScore = Math.min(100, batches.length * 10)
+
+    // 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
+
+    // 4. 计算 Top 10 关键词
+    const sortedKeywords = [...keywordMap.entries()]
+      .sort((a, b) => b[1] - a[1])
+      .slice(0, 10)
+      .map((entry) => entry[0])
+
+    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
+    }
+  }
+}
+
+/**
+ * 实体转化为用户阅读画像
+ * @param entity 实体
+ * */
+export function entityToUserReadingPersona(entity: ReadingPersona): IUserReadingPersona {
+  return {
+    domainDepth: JSON.parse(entity.topKeywords || '[]').map((name: string) => ({
+      name,
+      score: entity.cognition, // 简易算法：共用认知深度分
+      bookCount: 1 // 可根据数据库详细统计进一步细化
+    })),
+    breadthScore: entity.breadth,
+    efficiencyScore: entity.output,
+    maturityScore: entity.practicality,
+    languageScore: entity.global,
+    stats: entity.rawStats || {
+      totalWords: 0,
+      totalBooks: 0,
+      topKeywords: [],
+      mostUsedOccupation: 'other'
+    }
+  }
+}