420 lines
12 KiB
TypeScript
420 lines
12 KiB
TypeScript
import { query } from './database';
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// AI 評分結果的 TypeScript 介面
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export interface AIEvaluationResult {
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projectTitle: string;
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overallScore: number;
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totalPossible: number;
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grade: string;
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performanceStatus: string;
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recommendedStars: number;
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analysisDate: string;
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criteria: {
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name: string;
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score: number;
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maxScore: number;
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weight: number;
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weightedScore: number;
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feedback: string;
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strengths: string[];
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improvements: string[];
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}[];
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overview: {
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excellentItems: number;
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improvementItems: number;
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overallPerformance: number;
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};
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detailedAnalysis: {
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summary: string;
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keyFindings: string[];
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};
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improvementSuggestions: {
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overallSuggestions: string;
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maintainStrengths: {
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title: string;
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description: string;
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}[];
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keyImprovements: {
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title: string;
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description: string;
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suggestions: string[];
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}[];
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actionPlan: {
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phase: string;
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description: string;
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}[];
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};
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}
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// 評分項目名稱對應到 criteria_items 表的 ID
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const criteriaNameToId: Record<string, number> = {
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"應用實務性": 52,
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"創新性": 53,
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"成效與效益": 54,
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"擴散與可複用性": 55,
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"簡報與表達": 56
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};
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export class EvaluationUploadService {
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/**
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* 上傳 AI 評分結果到資料庫
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* @param projectId 專案 ID
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* @param aiResult AI 評分結果
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* @param analysisDuration 分析耗時（秒）
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* @param aiModelVersion AI 模型版本
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* @returns 創建的 evaluation ID
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*/
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static async uploadEvaluationResult(
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projectId: number,
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aiResult: AIEvaluationResult,
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analysisDuration?: number,
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aiModelVersion: string = 'gemini-1.5-pro'
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): Promise<number> {
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try {
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// 1. 創建 evaluations 記錄
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const evaluationId = await this.createEvaluationRecord(
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projectId,
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aiResult,
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analysisDuration,
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aiModelVersion
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);
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// 2. 創建 evaluation_scores 記錄
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await this.createEvaluationScores(evaluationId, aiResult);
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// 3. 創建 evaluation_feedback 記錄
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await this.createEvaluationFeedback(evaluationId, aiResult);
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return evaluationId;
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} catch (error) {
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console.error('上傳評分結果時發生錯誤:', error);
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throw error;
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}
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}
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/**
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* 創建 evaluations 記錄
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*/
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private static async createEvaluationRecord(
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projectId: number,
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aiResult: AIEvaluationResult,
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analysisDuration?: number,
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aiModelVersion: string = 'gemini-1.5-pro'
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): Promise<number> {
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const sql = `
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INSERT INTO evaluations (project_id, overall_score, max_possible_score, grade, analysis_duration, ai_model_version, status, error_message)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?)
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`;
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const result = await query(sql, [
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projectId,
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aiResult.overallScore,
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aiResult.totalPossible,
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aiResult.grade,
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analysisDuration,
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aiModelVersion,
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'completed',
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null
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]);
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return result.insertId;
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}
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/**
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* 創建 evaluation_scores 記錄
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*/
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private static async createEvaluationScores(
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evaluationId: number,
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aiResult: AIEvaluationResult
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): Promise<void> {
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const sql = `
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INSERT INTO evaluation_scores (evaluation_id, criteria_item_id, score, max_score, weight, weighted_score, percentage)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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`;
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for (const criteria of aiResult.criteria) {
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const criteriaItemId = criteriaNameToId[criteria.name];
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if (!criteriaItemId) {
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console.warn(`找不到評分項目 "${criteria.name}" 的 ID`);
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continue;
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}
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const percentage = (criteria.score / criteria.maxScore) * 100;
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await query(sql, [
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evaluationId,
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criteriaItemId,
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criteria.score,
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criteria.maxScore,
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criteria.weight,
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criteria.weightedScore,
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percentage
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]);
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}
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}
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/**
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* 創建 evaluation_feedback 記錄
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*/
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private static async createEvaluationFeedback(
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evaluationId: number,
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aiResult: AIEvaluationResult
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): Promise<void> {
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const sql = `
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INSERT INTO evaluation_feedback (evaluation_id, criteria_item_id, feedback_type, content, sort_order)
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VALUES (?, ?, ?, ?, ?)
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`;
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let sortOrder = 1;
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// 1. 整體反饋
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await query(sql, [
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evaluationId,
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null,
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'overall',
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aiResult.detailedAnalysis.summary,
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sortOrder++
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]);
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// 2. 各項標準的反饋
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for (const criteria of aiResult.criteria) {
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const criteriaItemId = criteriaNameToId[criteria.name];
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if (!criteriaItemId) continue;
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// 標準反饋
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await query(sql, [
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evaluationId,
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criteriaItemId,
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'criteria',
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criteria.feedback,
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sortOrder++
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]);
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// 優點反饋
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for (const strength of criteria.strengths) {
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await query(sql, [
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evaluationId,
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criteriaItemId,
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'strength',
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strength,
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sortOrder++
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]);
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}
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// 改進建議反饋
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for (const improvement of criteria.improvements) {
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await query(sql, [
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evaluationId,
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criteriaItemId,
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'improvement',
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improvement,
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sortOrder++
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]);
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}
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}
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// 3. 改進建議的整體建議
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await query(sql, [
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evaluationId,
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null,
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'improvement',
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aiResult.improvementSuggestions.overallSuggestions,
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sortOrder++
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]);
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// 4. 保持優勢
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for (const strength of aiResult.improvementSuggestions.maintainStrengths) {
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await query(sql, [
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evaluationId,
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null,
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'strength',
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`${strength.title}: ${strength.description}`,
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sortOrder++
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]);
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}
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// 5. 關鍵改進建議
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for (const improvement of aiResult.improvementSuggestions.keyImprovements) {
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await query(sql, [
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evaluationId,
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null,
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'improvement',
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`${improvement.title}: ${improvement.description}`,
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sortOrder++
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]);
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}
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// 6. 行動計劃
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for (const plan of aiResult.improvementSuggestions.actionPlan) {
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await query(sql, [
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evaluationId,
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null,
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'improvement',
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`${plan.phase}: ${plan.description}`,
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sortOrder++
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]);
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}
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}
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/**
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* 更新 criteria_item_id 對應關係
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* @param newMapping 新的對應關係
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*/
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static updateCriteriaMapping(newMapping: Record<string, number>): void {
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Object.assign(criteriaNameToId, newMapping);
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}
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/**
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* 獲取當前的 criteria_item_id 對應關係
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*/
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static getCriteriaMapping(): Record<string, number> {
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return { ...criteriaNameToId };
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}
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}
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// 預設的 AI 評分結果（用於測試）
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export const defaultAIEvaluationResult: AIEvaluationResult = {
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"projectTitle": "ITBU_人咧 PeoplePing 智能出勤系統",
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"overallScore": 78.5,
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"totalPossible": 100,
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"grade": "A-",
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"performanceStatus": "表現良好",
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"recommendedStars": 4,
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"analysisDate": "2024-10-27",
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"criteria": [
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{
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"name": "應用實務性",
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"score": 8,
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"maxScore": 10,
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"weight": 30,
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"weightedScore": 24,
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"feedback": "系統設計貼近實際需求，解決了主管和員工的痛點，具備可行性。",
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"strengths": [
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"明確指出解決了主管無法即時掌握團隊出勤狀況和員工查詢流程繁瑣的問題",
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"提供了 Dify Chat Flow 和 Web Dashboard 兩個使用者介面",
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"簡潔明瞭地說明了系統架構和流程"
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],
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"improvements": [
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"可以加入更多實際案例或數據，例如導入前的數據和導入後的對比數據",
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"更詳細地說明系統的安全性及數據隱私保護措施"
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]
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},
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{
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"name": "創新性",
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"score": 6,
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"maxScore": 10,
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"weight": 15,
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"weightedScore": 9,
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"feedback": "利用 Dify Chat Flow 整合自然語言處理，提升使用者體驗，但整體創新性不算高。",
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"strengths": [
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"結合 Dify Chat Flow 和 Web Dashboard，提供了多樣化的使用方式",
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"利用自然語言處理，讓使用者能以更自然的方式進行出勤登記"
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],
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"improvements": [
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"可以探索更多創新應用，例如結合生物識別技術、位置服務等",
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"進一步提升自然語言理解能力，處理更多複雜的出勤場景"
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]
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},
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{
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"name": "成效與效益",
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"score": 9,
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"maxScore": 10,
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"weight": 25,
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"weightedScore": 22.5,
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"feedback": "量化了系統帶來的效率提升，數據明確，具有說服力。",
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"strengths": [
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"使用數據明確地呈現了系統帶來的效率提升，例如出勤登錄時間縮短 83%，月報彙整時間縮減 99%",
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"系統錯誤率控制在 1% 以下，展現了系統的穩定性"
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],
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"improvements": [
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"可以更詳細地說明數據的計算方法和資料來源"
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]
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},
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{
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"name": "擴散與可複用性",
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"score": 7,
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"maxScore": 10,
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"weight": 20,
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"weightedScore": 14,
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"feedback": "系統架構清晰，模組化設計良好，具備一定程度的可複用性。",
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"strengths": [
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"系統架構清晰，各模組功能明確",
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"後續優化與應用擴展規劃合理，展現了系統的擴展性"
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],
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"improvements": [
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"可以更詳細地說明系統的模組化設計，以及如何方便地複製到其他部門"
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]
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},
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{
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"name": "簡報與表達",
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"score": 8,
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"maxScore": 10,
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"weight": 10,
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"weightedScore": 8,
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"feedback": "簡報內容結構清晰，表達流暢，但部分幻燈片內容略顯簡潔。",
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"strengths": [
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"簡報目錄清晰，結構合理",
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"圖表使用恰當，數據呈現清晰"
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],
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"improvements": [
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"某些幻燈片可以添加更多圖表或說明，使內容更豐富完整",
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"幻燈片 5 的「人咧~聊天流程展示」需要更具體的內容"
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]
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}
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],
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"overview": {
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"excellentItems": 1,
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"improvementItems": 0,
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"overallPerformance": 78.5
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},
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"detailedAnalysis": {
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"summary": "整體而言，此專案具備良好的應用實務性、成效與效益，以及擴散與可複用性。創新性方面尚有提升空間，簡報表達也需進一步完善。",
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"keyFindings": [
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"系統有效解決了實際問題，並量化了效益提升",
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"系統架構清晰，具備一定程度的可複用性",
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"創新性方面仍有提升空間，可以探索更多創新應用"
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]
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},
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"improvementSuggestions": {
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"overallSuggestions": "整體而言，專案表現良好，建議著重提升創新性，並補充更多數據和案例，以增强說服力。",
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"maintainStrengths": [
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{
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"title": "數據驅動的效益呈現",
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"description": "有效地利用數據量化了系統帶來的效率提升，這部分是專案的優勢，應該持續保持。"
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}
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],
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"keyImprovements": [
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{
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"title": "提升創新性",
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"description": "探索更多創新應用，例如結合生物識別技術、位置服務等，提升系統的競爭力。",
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"suggestions": [
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"研究市場上最新的技術，尋求更多創新點",
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"考慮與其他系統整合，擴展系統功能"
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]
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},
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{
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"title": "完善數據說明和案例",
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"description": "提供更詳細的數據計算方法和資料來源，並添加更多實際案例，以增强簡報的說服力。",
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"suggestions": [
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"提供導入前後的數據對比",
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"加入更多使用者反饋和成功案例"
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]
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}
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],
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"actionPlan": [
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{
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"phase": "短期目標（1-2週）",
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"description": "完善數據說明和案例，補充更多細節。"
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},
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{
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"phase": "中期目標（1個月）",
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"description": "研究新的技術和應用場景，探討系統的創新升級。"
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},
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{
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"phase": "長期目標（3個月）",
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"description": "完成系統的創新升級，並將其應用到更多部門。"
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}
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]
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}
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};
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