101 lines
3.3 KiB
TypeScript
101 lines
3.3 KiB
TypeScript
import { Repository } from 'typeorm'
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import { ReadingPersona } from '@main/db/entities/ReadingPersona'
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import {
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IReadingReflectionTaskBatch,
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IReadingReflectionTaskItem
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} from '@shared/types/IReadingReflectionTask'
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import { IUserReadingPersona } from '@shared/types/IUserReadingPersona'
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export class PersonaService {
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constructor(private personaRepo: Repository<ReadingPersona>) {}
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/**
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* 刷新画像并保存到数据库
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*/
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async refreshPersona(
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items: IReadingReflectionTaskItem[],
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batches: IReadingReflectionTaskBatch[]
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) {
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const rawResult = await this.calculatePersona(items, batches) // 调用你原来的计算逻辑
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const persona = new ReadingPersona()
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persona.id = 'current_user_persona'
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persona.cognition = rawResult.cognition
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persona.breadth = rawResult.breadth
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persona.practicality = rawResult.practicality
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persona.output = rawResult.output
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persona.global = rawResult.global
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persona.topKeywords = JSON.stringify(rawResult.topKeywords)
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// 存储完整的 stats 结构以便前端适配
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persona.rawStats = {
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totalWords: items.reduce((sum, i) => sum + (i.content?.length || 0), 0),
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totalBooks: batches.length,
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topKeywords: rawResult.topKeywords
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}
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return await this.personaRepo.save(persona)
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}
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/**
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* 从数据库聚合数据并计算画像分值
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*/
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async calculatePersona(
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items: IReadingReflectionTaskItem[],
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batches: IReadingReflectionTaskBatch[]
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) {
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// 1. 计算认知深度：根据关键词频次
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const allKeywords = items.flatMap((i) => i.keywords || [])
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const keywordMap = new Map<string, number>()
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allKeywords.forEach((k) => keywordMap.set(k, (keywordMap.get(k) || 0) + 1))
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// 逻辑：去重后的关键词越多且重复越高，分值越高 (示例算法)
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const cognitionScore = Math.min(100, keywordMap.size * 2 + allKeywords.length / 5)
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// 2. 计算知识广度：根据书籍数量
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const breadthScore = Math.min(100, batches.length * 10)
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// 3. 计算产出效率：根据总字数
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const totalWords = items.reduce((sum, i) => sum + (i.content?.length || 0), 0)
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const outputScore = Math.min(100, totalWords / 500) // 每 5万字满分
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// 4. 计算 Top 10 关键词
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const sortedKeywords = [...keywordMap.entries()]
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.sort((a, b) => b[1] - a[1])
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.slice(0, 10)
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.map((entry) => entry[0])
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return {
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cognition: Math.round(cognitionScore),
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breadth: Math.round(breadthScore),
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output: Math.round(outputScore),
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practicality: 75, // 可根据 occupation 比例动态计算
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global: 60, // 可根据 language 比例动态计算
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topKeywords: sortedKeywords
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}
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}
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}
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/**
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* 实体转化为用户阅读画像
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* @param entity 实体
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* */
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export function entityToUserReadingPersona(entity: ReadingPersona): IUserReadingPersona {
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return {
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domainDepth: JSON.parse(entity.topKeywords || '[]').map((name: string) => ({
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name,
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score: entity.cognition, // 简易算法：共用认知深度分
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bookCount: 1 // 可根据数据库详细统计进一步细化
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})),
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breadthScore: entity.breadth,
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efficiencyScore: entity.output,
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maturityScore: entity.practicality,
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languageScore: entity.global,
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stats: entity.rawStats || {
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totalWords: 0,
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totalBooks: 0,
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topKeywords: [],
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mostUsedOccupation: 'other'
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}
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}
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}
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