feat: 新增多模块功能与服务实现
新增广告计划、用户资产、B2B交易、合规规则等核心模型 实现爬虫工作器、贸易服务、现金流预测等业务服务 添加RBAC权限测试、压力测试等测试用例 完善扩展程序的消息处理与内容脚本功能 重构应用入口与文档生成器 更新项目规则与业务闭环分析文档
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338
extension/src/background/ABTestOptimizationService.ts
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338
extension/src/background/ABTestOptimizationService.ts
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import { Logger } from '../utils/Logger';
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export interface TestResult {
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testId: string;
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variations: VariationResult[];
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metrics: {
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[metric: string]: number;
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};
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startDate: string;
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endDate: string;
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sampleSize: number;
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statisticalSignificance: number;
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}
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export interface VariationResult {
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id: string;
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name: string;
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metrics: {
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[metric: string]: number;
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};
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sampleSize: number;
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conversionRate?: number;
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engagementRate?: number;
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revenuePerUser?: number;
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retentionRate?: number;
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}
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export interface OptimizationGoal {
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type: 'maximize' | 'minimize';
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metric: string;
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targetValue?: number;
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weight?: number;
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}
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export interface OptimizationRecommendation {
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recommendedVariation: string;
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confidence: number;
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expectedImprovement: number;
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optimizationActions: OptimizationAction[];
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riskAssessment: 'low' | 'medium' | 'high';
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implementationSteps: string[];
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followUpTests: FollowUpTest[];
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}
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export interface OptimizationAction {
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id: string;
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description: string;
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priority: 'high' | 'medium' | 'low';
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expectedImpact: number;
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implementationEffort: 'low' | 'medium' | 'high';
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}
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export interface FollowUpTest {
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testName: string;
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description: string;
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recommendedTiming: string;
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estimatedDuration: number;
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}
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export class ABTestOptimizationService {
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private logger = new Logger('ABTestOptimizationService');
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async optimizeTest(
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testResult: TestResult,
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optimizationGoals: OptimizationGoal[],
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traceId: string
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): Promise<OptimizationRecommendation> {
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try {
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this.logger.info('开始优化A/B测试', {
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testId: testResult.testId,
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variationCount: testResult.variations.length,
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traceId
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});
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const bestVariation = this.identifyBestVariation(testResult, optimizationGoals);
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const confidence = this.calculateConfidence(testResult, bestVariation.id);
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const expectedImprovement = this.calculateExpectedImprovement(testResult, bestVariation.id);
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const optimizationActions = this.generateOptimizationActions(testResult, bestVariation, optimizationGoals);
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const risk = this.assessRisk(testResult, bestVariation, optimizationGoals);
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const implementationSteps = this.generateImplementationSteps(optimizationActions);
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const followUpTests = this.recommendFollowUpTests(testResult, bestVariation, optimizationGoals);
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const recommendation: OptimizationRecommendation = {
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recommendedVariation: bestVariation.id,
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confidence,
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expectedImprovement,
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optimizationActions,
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riskAssessment: risk,
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implementationSteps,
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followUpTests
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};
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this.logger.info('A/B测试优化完成', {
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recommendedVariation: bestVariation.id,
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confidence,
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expectedImprovement,
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traceId
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});
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return recommendation;
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} catch (error) {
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this.logger.error('优化A/B测试失败', {
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error: error instanceof Error ? error.message : '未知错误',
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traceId
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});
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throw error;
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}
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}
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private identifyBestVariation(testResult: TestResult, optimizationGoals: OptimizationGoal[]): VariationResult {
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let bestVariation: VariationResult = testResult.variations[0];
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let bestScore = -Infinity;
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testResult.variations.forEach(variation => {
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let score = 0;
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optimizationGoals.forEach(goal => {
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const value = variation.metrics[goal.metric] || 0;
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const weight = goal.weight || 1;
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if (goal.type === 'maximize') {
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score += value * weight;
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} else {
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score += (1 / (value + 0.001)) * weight;
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}
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});
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if (score > bestScore) {
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bestScore = score;
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bestVariation = variation;
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}
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});
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return bestVariation;
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}
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private calculateConfidence(testResult: TestResult, bestVariationId: string): number {
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const bestVariation = testResult.variations.find(v => v.id === bestVariationId);
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if (!bestVariation) return 0;
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const otherVariations = testResult.variations.filter(v => v.id !== bestVariationId);
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const significantWins = otherVariations.filter(other => {
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return this.isStatisticallySignificant(bestVariation!, other);
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});
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return Math.min(1, significantWins.length / otherVariations.length * 0.8 + 0.2);
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}
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private calculateExpectedImprovement(testResult: TestResult, bestVariationId: string): number {
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const bestVariation = testResult.variations.find(v => v.id === bestVariationId);
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if (!bestVariation) return 0;
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const otherVariations = testResult.variations.filter(v => v.id !== bestVariationId);
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if (otherVariations.length === 0) return 0;
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const averagePerformance = otherVariations.reduce((sum, v) => {
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const metricValues = Object.values(v.metrics);
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return sum + metricValues.reduce((sum, val) => sum + val, 0) / metricValues.length;
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}, 0) / otherVariations.length;
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const bestPerformance = Object.values(bestVariation.metrics).reduce((sum, val) => sum + val, 0) / Object.values(bestVariation.metrics).length;
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return (bestPerformance - averagePerformance) / averagePerformance;
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}
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private generateOptimizationActions(
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testResult: TestResult,
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bestVariation: VariationResult,
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optimizationGoals: OptimizationGoal[]
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): OptimizationAction[] {
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const actions: OptimizationAction[] = [];
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optimizationGoals.forEach(goal => {
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const bestValue = bestVariation.metrics[goal.metric];
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if (bestValue === undefined) return;
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const otherVariations = testResult.variations.filter(v => v.id !== bestVariation.id);
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const averageValue = otherVariations.reduce((sum, v) => sum + (v.metrics[goal.metric] || 0), 0) / otherVariations.length;
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const improvement = (bestValue - averageValue) / averageValue;
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if (Math.abs(improvement) > 0.05) {
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actions.push({
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id: `action_${goal.metric}`,
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description: `优化 ${goal.metric} 指标，采用 ${bestVariation.name} 的策略`,
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priority: Math.abs(improvement) > 0.15 ? 'high' : Math.abs(improvement) > 0.08 ? 'medium' : 'low',
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expectedImpact: Math.abs(improvement),
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implementationEffort: Math.abs(improvement) > 0.15 ? 'medium' : 'low'
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});
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}
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});
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if (actions.length === 0) {
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actions.push({
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id: 'action_no_change',
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description: '保持当前策略，继续监控性能',
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priority: 'low',
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expectedImpact: 0,
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implementationEffort: 'low'
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});
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}
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return actions.sort((a, b) => {
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const priorityOrder = { high: 3, medium: 2, low: 1 };
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return priorityOrder[b.priority] - priorityOrder[a.priority];
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});
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}
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private assessRisk(
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testResult: TestResult,
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bestVariation: VariationResult,
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optimizationGoals: OptimizationGoal[]
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): 'low' | 'medium' | 'high' {
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let riskScore = 0;
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if (testResult.sampleSize < 500) riskScore += 2;
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else if (testResult.sampleSize < 1000) riskScore += 1;
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if (testResult.statisticalSignificance > 0.1) riskScore += 2;
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else if (testResult.statisticalSignificance > 0.05) riskScore += 1;
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const otherVariations = testResult.variations.filter(v => v.id !== bestVariation.id);
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const closeCompetitors = otherVariations.filter(other => {
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const bestScore = Object.values(bestVariation.metrics).reduce((sum, val) => sum + val, 0);
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const otherScore = Object.values(other.metrics).reduce((sum, val) => sum + val, 0);
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return (otherScore / bestScore) > 0.95;
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});
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if (closeCompetitors.length > 0) riskScore += 1;
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const hasRevenueGoal = optimizationGoals.some(goal => goal.metric.includes('revenue'));
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if (hasRevenueGoal) riskScore += 1;
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if (riskScore >= 5) return 'high';
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if (riskScore >= 3) return 'medium';
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return 'low';
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}
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private generateImplementationSteps(actions: OptimizationAction[]): string[] {
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const steps: string[] = [];
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steps.push('1. 审核优化建议，确认推荐的变体');
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steps.push('2. 制定实施计划，包括时间线和负责人');
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actions.forEach((action, index) => {
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steps.push(`${index + 3}. 实施 ${action.description}（优先级：${action.priority}）`);
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});
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steps.push(`${actions.length + 3}. 监控实施后的性能指标`);
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steps.push(`${actions.length + 4}. 记录实施结果，用于后续分析`);
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return steps;
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}
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private recommendFollowUpTests(
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testResult: TestResult,
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bestVariation: VariationResult,
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optimizationGoals: OptimizationGoal[]
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): FollowUpTest[] {
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const tests: FollowUpTest[] = [];
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if (optimizationGoals.some(goal => goal.type === 'maximize' && goal.metric.includes('conversion'))) {
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tests.push({
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testName: '转化率优化深度测试',
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description: '进一步优化已识别的高转化率策略',
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recommendedTiming: '2周后',
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estimatedDuration: 7
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});
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}
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if (optimizationGoals.some(goal => goal.type === 'maximize' && goal.metric.includes('revenue'))) {
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tests.push({
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testName: '收入优化扩展测试',
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description: '测试不同定价策略和促销组合',
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recommendedTiming: '3周后',
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estimatedDuration: 14
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});
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}
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if (tests.length === 0) {
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tests.push({
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testName: '性能监控测试',
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description: '持续监控实施后的性能变化',
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recommendedTiming: '1周后',
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estimatedDuration: 7
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});
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}
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return tests;
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}
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private isStatisticallySignificant(variationA: VariationResult, variationB: VariationResult): boolean {
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const metricsA = Object.values(variationA.metrics);
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const metricsB = Object.values(variationB.metrics);
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if (metricsA.length === 0 || metricsB.length === 0) return false;
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const meanA = metricsA.reduce((sum, val) => sum + val, 0) / metricsA.length;
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const meanB = metricsB.reduce((sum, val) => sum + val, 0) / metricsB.length;
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const varianceA = metricsA.reduce((sum, val) => sum + Math.pow(val - meanA, 2), 0) / metricsA.length;
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const varianceB = metricsB.reduce((sum, val) => sum + Math.pow(val - meanB, 2), 0) / metricsB.length;
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const standardError = Math.sqrt(varianceA / metricsA.length + varianceB / metricsB.length);
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const zScore = Math.abs(meanA - meanB) / standardError;
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return zScore > 1.96; // 95% confidence
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}
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async validateOptimization(recommendation: OptimizationRecommendation, traceId: string): Promise<{ valid: boolean; warnings: string[] }> {
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const warnings: string[] = [];
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if (recommendation.confidence < 0.7) {
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warnings.push('置信度较低，建议增加样本量或延长测试时间');
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}
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if (recommendation.expectedImprovement < 0.05) {
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warnings.push('预期改进较小，可能不值得实施');
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}
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if (recommendation.riskAssessment === 'high') {
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warnings.push('风险评估为高，建议谨慎实施');
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}
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if (recommendation.optimizationActions.length === 0) {
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warnings.push('未生成优化行动，建议重新评估测试结果');
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}
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this.logger.info('优化建议验证完成', {
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warningCount: warnings.length,
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traceId
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});
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return {
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valid: warnings.length < 3,
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warnings
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};
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}
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}
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