376 lines
13 KiB
JavaScript
376 lines
13 KiB
JavaScript
const mysql = require('mysql2/promise');
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// 資料庫配置
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const dbConfig = {
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host: process.env.DB_HOST || 'localhost',
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user: process.env.DB_USER || 'root',
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password: process.env.DB_PASSWORD || '',
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database: process.env.DB_NAME || 'ai_scoring_app',
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timezone: '+08:00',
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};
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// AI JSON 結果 (從終端輸出中提取)
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const 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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// 評分項目名稱對應到 criteria_items 表的 ID
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// 這些 ID 需要根據實際資料庫中的 criteria_items 來調整
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const criteriaNameToId = {
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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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async function parseAndUploadEvaluation() {
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let connection;
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try {
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console.log('🔗 連接到資料庫...');
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connection = await mysql.createConnection(dbConfig);
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console.log('✅ 資料庫連接成功');
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// 假設專案 ID 為 1，實際使用時需要根據實際情況調整
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const projectId = 1;
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// 1. 創建 evaluations 記錄
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console.log('📝 創建 evaluations 記錄...');
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const evaluationData = {
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project_id: projectId,
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overall_score: aiEvaluationResult.overallScore,
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max_possible_score: aiEvaluationResult.totalPossible,
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grade: aiEvaluationResult.grade,
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analysis_duration: null, // 可以從實際分析時間計算
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ai_model_version: 'gemini-1.5-pro',
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status: 'completed',
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error_message: null
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};
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const evaluationSql = `
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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 [evaluationResult] = await connection.execute(evaluationSql, [
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evaluationData.project_id,
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evaluationData.overall_score,
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evaluationData.max_possible_score,
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evaluationData.grade,
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evaluationData.analysis_duration,
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evaluationData.ai_model_version,
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evaluationData.status,
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evaluationData.error_message
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]);
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const evaluationId = evaluationResult.insertId;
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console.log(`✅ 創建 evaluations 記錄成功，ID: ${evaluationId}`);
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// 2. 創建 evaluation_scores 記錄
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console.log('📊 創建 evaluation_scores 記錄...');
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const evaluationScoresData = aiEvaluationResult.criteria.map(criteria => ({
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evaluation_id: evaluationId,
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criteria_item_id: criteriaNameToId[criteria.name],
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score: criteria.score,
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max_score: criteria.maxScore,
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weight: criteria.weight,
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weighted_score: criteria.weightedScore,
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percentage: (criteria.score / criteria.maxScore) * 100
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}));
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for (const scoreData of evaluationScoresData) {
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const scoreSql = `
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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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await connection.execute(scoreSql, [
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scoreData.evaluation_id,
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scoreData.criteria_item_id,
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scoreData.score,
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scoreData.max_score,
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scoreData.weight,
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scoreData.weighted_score,
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scoreData.percentage
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]);
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}
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console.log(`✅ 創建 ${evaluationScoresData.length} 筆 evaluation_scores 記錄成功`);
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// 3. 創建 evaluation_feedback 記錄
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console.log('💬 創建 evaluation_feedback 記錄...');
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const feedbackData = [];
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// 3.1 整體反饋
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feedbackData.push({
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evaluation_id: evaluationId,
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criteria_item_id: null,
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feedback_type: 'overall',
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content: aiEvaluationResult.detailedAnalysis.summary,
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sort_order: 1
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});
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// 3.2 各項標準的反饋
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aiEvaluationResult.criteria.forEach((criteria, index) => {
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// 標準反饋
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feedbackData.push({
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evaluation_id: evaluationId,
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criteria_item_id: criteriaNameToId[criteria.name],
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feedback_type: 'criteria',
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content: criteria.feedback,
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sort_order: index + 2
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});
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// 優點反饋
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criteria.strengths.forEach((strength, strengthIndex) => {
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feedbackData.push({
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evaluation_id: evaluationId,
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criteria_item_id: criteriaNameToId[criteria.name],
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feedback_type: 'strength',
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content: strength,
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sort_order: (index + 2) * 100 + strengthIndex + 1
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});
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});
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// 改進建議反饋
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criteria.improvements.forEach((improvement, improvementIndex) => {
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feedbackData.push({
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evaluation_id: evaluationId,
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criteria_item_id: criteriaNameToId[criteria.name],
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feedback_type: 'improvement',
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content: improvement,
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sort_order: (index + 2) * 100 + improvementIndex + 50
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});
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});
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});
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// 3.3 改進建議的整體建議
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feedbackData.push({
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evaluation_id: evaluationId,
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criteria_item_id: null,
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feedback_type: 'improvement',
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content: aiEvaluationResult.improvementSuggestions.overallSuggestions,
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sort_order: 1000
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});
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// 3.4 保持優勢
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aiEvaluationResult.improvementSuggestions.maintainStrengths.forEach((strength, index) => {
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feedbackData.push({
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evaluation_id: evaluationId,
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criteria_item_id: null,
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feedback_type: 'strength',
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content: `${strength.title}: ${strength.description}`,
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sort_order: 1001 + index
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});
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});
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// 3.5 關鍵改進建議
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aiEvaluationResult.improvementSuggestions.keyImprovements.forEach((improvement, index) => {
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feedbackData.push({
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evaluation_id: evaluationId,
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criteria_item_id: null,
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feedback_type: 'improvement',
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content: `${improvement.title}: ${improvement.description}`,
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sort_order: 2000 + index
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});
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});
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// 3.6 行動計劃
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aiEvaluationResult.improvementSuggestions.actionPlan.forEach((plan, index) => {
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feedbackData.push({
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evaluation_id: evaluationId,
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criteria_item_id: null,
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feedback_type: 'improvement',
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content: `${plan.phase}: ${plan.description}`,
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sort_order: 3000 + index
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});
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});
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// 插入所有反饋資料
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for (const feedback of feedbackData) {
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const feedbackSql = `
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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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await connection.execute(feedbackSql, [
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feedback.evaluation_id,
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feedback.criteria_item_id,
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feedback.feedback_type,
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feedback.content,
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feedback.sort_order
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]);
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}
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console.log(`✅ 創建 ${feedbackData.length} 筆 evaluation_feedback 記錄成功`);
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console.log('🎉 所有資料上傳完成！');
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console.log(`📊 評分結果: ${aiEvaluationResult.overallScore}/${aiEvaluationResult.totalPossible} (${aiEvaluationResult.grade})`);
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console.log(`📝 專案: ${aiEvaluationResult.projectTitle}`);
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} catch (error) {
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console.error('❌ 處理過程中發生錯誤:', error);
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throw error;
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} finally {
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if (connection) {
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await connection.end();
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console.log('🔌 資料庫連接已關閉');
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}
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}
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}
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// 執行解析和上傳
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if (require.main === module) {
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parseAndUploadEvaluation()
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.then(() => {
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console.log('✅ 腳本執行完成');
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process.exit(0);
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})
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.catch((error) => {
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console.error('❌ 腳本執行失敗:', error);
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process.exit(1);
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});
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}
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module.exports = { parseAndUploadEvaluation, aiEvaluationResult, criteriaNameToId };
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