3-4. LLM优化结构
概述
LLM优化结构是Mei智能决策的核心大脑，负责将用户的自然语言转化为精确的区块链操作。这个系统就像一个经验丰富的投资顾问，不仅理解您说的话，更能理解您没说出口的需求。通过多层次的智能分析和优化算法，确保每个用户请求都能以最高效、最安全的方式执行。
核心架构设计
多阶段处理流水线
自然语言输入 (Natural Language Input)
↓
意图识别与分析 (Intent Recognition & Analysis)
↓
上下文理解与记忆 (Context Understanding & Memory)
↓
任务分解与规划 (Task Decomposition & Planning)
↓
路径优化与选择 (Path Optimization & Selection)
↓
执行监控与调整 (Execution Monitoring & Adjustment)
↓
结果反馈与学习 (Result Feedback & Learning)智能决策引擎
采用分层决策架构，从简单的规则匹配到复杂的机器学习推理。
意图识别与理解
多维度意图分析
不仅仅分析用户说了什么，更要理解用户想要什么。
意图分类系统
interface IntentAnalysis {
// 主要意图类别
primaryIntent: {
category: 'query' | 'transaction' | 'analysis' | 'management' | 'learning';
confidence: number;
subcategory: string;
};
// 隐含意图
implicitIntents: {
riskTolerance: 'conservative' | 'moderate' | 'aggressive';
timeHorizon: 'immediate' | 'short_term' | 'long_term';
investmentGoal: 'profit' | 'learning' | 'diversification';
};
// 情感分析
sentiment: {
overall: 'positive' | 'neutral' | 'negative';
urgency: number; // 0-1
confidence_level: number; // 0-1
};
}上下文感知
理解对话的前后文，提供连贯的服务体验：
class ContextManager {
private conversationHistory: ConversationTurn[] = [];
private userProfile: UserProfile;
private sessionContext: SessionContext;
analyzeContext(userInput: string): ContextAnalysis {
return {
referencedEntities: this.extractEntityReferences(userInput),
temporalContext: this.analyzeTimeReferences(userInput),
conversationFlow: this.analyzeConversationFlow(),
userState: this.inferUserState()
};
}
private inferUserState(): UserState {
// 基于历史对话推断用户当前状态
const recentInteractions = this.getRecentInteractions(5);
return {
knowledgeLevel: this.assessKnowledgeLevel(recentInteractions),
currentFocus: this.identifyCurrentFocus(recentInteractions),
emotionalState: this.analyzeEmotionalProgression(recentInteractions)
};
}
}任务分解与规划
智能任务分解
将复杂的用户请求分解为可执行的原子操作。
分解策略
interface TaskDecomposition {
async decomposeComplexTask(userIntent: UserIntent): Promise<TaskPlan> {
// 1. 识别核心任务
const coreTask = this.identifyCoreTask(userIntent);
// 2. 分析依赖关系
const dependencies = this.analyzeDependencies(coreTask);
// 3. 生成执行计划
const executionPlan = this.generateExecutionPlan(coreTask, dependencies);
// 4. 优化执行顺序
const optimizedPlan = this.optimizeExecutionOrder(executionPlan);
return optimizedPlan;
}
}并行化分析
识别可以并行执行的任务，提升整体效率：
class ParallelizationOptimizer {
identifyParallelTasks(tasks: Task[]): ParallelExecutionPlan {
const dependencyGraph = this.buildDependencyGraph(tasks);
const parallelGroups = this.groupIndependentTasks(dependencyGraph);
return {
sequentialStages: parallelGroups.map(group => ({
parallelTasks: group,
estimatedTime: Math.max(...group.map(task => task.estimatedDuration))
})),
totalEstimatedTime: this.calculateTotalTime(parallelGroups)
};
}
}路径优化算法
多目标优化
在执行效率、成本控制、风险管理之间找到最佳平衡。
优化目标权重
interface OptimizationObjectives {
speed: number; // 执行速度权重
cost: number; // 成本控制权重
reliability: number; // 可靠性权重
privacy: number; // 隐私保护权重
}
class MultiObjectiveOptimizer {
optimizeExecutionPath(
availablePaths: ExecutionPath[],
objectives: OptimizationObjectives,
userConstraints: UserConstraints
): OptimizedPath {
// 计算每条路径的综合评分
const scoredPaths = availablePaths.map(path => ({
path,
score: this.calculateCompositeScore(path, objectives)
}));
// 应用用户约束
const feasiblePaths = scoredPaths.filter(
scored => this.satisfiesConstraints(scored.path, userConstraints)
);
// 选择最优路径
return this.selectOptimalPath(feasiblePaths);
}
}动态路径调整
执行过程中根据实时情况调整路径：
class DynamicPathAdjuster {
async monitorAndAdjust(executionContext: ExecutionContext): Promise<void> {
while (!executionContext.isComplete()) {
const currentMetrics = await this.collectRealTimeMetrics();
if (this.shouldAdjustPath(currentMetrics)) {
const newPath = await this.recalculateOptimalPath(
executionContext.remainingTasks,
currentMetrics
);
await this.applyPathAdjustment(executionContext, newPath);
}
await this.wait(1000); // 每秒检查一次
}
}
}智能学习与适应
用户行为学习
通过观察用户的行为模式，持续优化服务质量。
学习模型
interface UserLearningModel {
// 偏好学习
preferences: {
tradingStyle: 'conservative' | 'balanced' | 'aggressive';
preferredTokens: string[];
riskTolerance: number;
timePreferences: TimePattern[];
};
// 行为模式
behaviorPatterns: {
decisionSpeed: number; // 决策速度
informationDepth: number; // 信息深度偏好
followUpFrequency: number; // 后续关注频率
};
// 反馈学习
feedbackHistory: {
satisfactionScores: number[];
commonComplaints: string[];
successfulInteractions: InteractionPattern[];
};
}个性化优化
基于学习到的用户特征调整服务策略：
class PersonalizationEngine {
async personalizeResponse(
userModel: UserLearningModel,
standardResponse: Response
): Promise<PersonalizedResponse> {
// 调整信息详细程度
const informationLevel = this.determineInformationLevel(userModel);
// 调整风险提示强度
const riskWarningLevel = this.calculateRiskWarningLevel(userModel);
// 个性化推荐
const recommendations = await this.generatePersonalizedRecommendations(userModel);
return {
...standardResponse,
informationLevel,
riskWarningLevel,
personalizedRecommendations: recommendations
};
}
}实时优化机制
性能监控与调优
持续监控系统性能，自动调整优化策略。
性能指标追踪
interface PerformanceMetrics {
responseTime: {
intentRecognition: number;
taskPlanning: number;
pathOptimization: number;
execution: number;
};
accuracy: {
intentAccuracy: number;
executionSuccess: number;
userSatisfaction: number;
};
efficiency: {
resourceUtilization: number;
parallelizationRatio: number;
cacheHitRate: number;
};
}自适应调优
class AdaptiveTuner {
async optimizeSystemParameters(metrics: PerformanceMetrics): Promise<void> {
// 根据响应时间调整并发度
if (metrics.responseTime.execution > this.responseTimeThreshold) {
await this.increaseConcurrency();
}
// 根据准确率调整模型参数
if (metrics.accuracy.intentAccuracy < this.accuracyThreshold) {
await this.refineIntentModel();
}
// 根据资源利用率调整缓存策略
if (metrics.efficiency.cacheHitRate < this.cacheThreshold) {
await this.optimizeCacheStrategy();
}
}
}错误处理与恢复
智能错误诊断
不仅识别错误，更要理解错误的根本原因。
interface ErrorDiagnosis {
errorType: 'user_input' | 'system_internal' | 'blockchain_network' | 'external_service';
severity: 'low' | 'medium' | 'high' | 'critical';
rootCause: string;
suggestedRecovery: RecoveryStrategy[];
userFriendlyExplanation: string;
}
class IntelligentErrorHandler {
async diagnoseAndRecover(error: Error, context: ExecutionContext): Promise<RecoveryResult> {
// 诊断错误
const diagnosis = await this.diagnoseError(error, context);
// 选择恢复策略
const recoveryStrategy = this.selectRecoveryStrategy(diagnosis);
// 执行恢复
const recoveryResult = await this.executeRecovery(recoveryStrategy, context);
// 学习和改进
await this.learnFromError(diagnosis, recoveryResult);
return recoveryResult;
}
}渐进式降级
当系统出现问题时，优雅地降级服务而不是完全失败。
class GracefulDegradation {
async handleServiceDegradation(serviceLevel: ServiceLevel): Promise<DegradedResponse> {
switch (serviceLevel) {
case 'full_service':
return await this.provideFull Service();
case 'limited_service':
return await this.provideLimitedService();
case 'basic_service':
return await this.provideBasicService();
case 'emergency_mode':
return await this.provideEmergencyResponse();
}
}
}安全与隐私保护
敏感信息处理
在LLM处理过程中保护用户隐私和敏感信息。
class PrivacyProtection {
async sanitizeInput(userInput: string): Promise<SanitizedInput> {
// 识别敏感信息
const sensitiveEntities = await this.identifySensitiveEntities(userInput);
// 替换或遮蔽敏感信息
const sanitizedText = this.maskSensitiveData(userInput, sensitiveEntities);
// 保留处理所需的上下文
const preservedContext = this.preserveNecessaryContext(sensitiveEntities);
return {
sanitizedText,
preservedContext,
sensitiveEntities
};
}
}LLM优化结构的设计理念是"智能而不失控制"。通过多层次的智能分析和优化，Mei能够提供类人的服务体验，同时确保每个决策都是可解释、可控制的。这个系统不断学习和进化，但始终以用户的最佳利益为核心目标。
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