性能监控

// 要点先行：用户满意度监控实现

class UserSatisfactionMonitor:
    def __init__(self, analytics_client):
        self.analytics = analytics_client
        self.sentiment_analyzer = SentimentAnalyzer()
    
    def track_user_interaction(self, query, response, user_feedback):
        """跟踪用户交互质量"""
        satisfaction_score = self.calculate_satisfaction(
            query, response, user_feedback
        )
        
        # 记录满意度数据
        self.analytics.track_event('geo_satisfaction', {
            'query': query,
            'response_quality': self.assess_response_quality(response),
            'user_satisfaction': satisfaction_score,
            'follow_up_queries': self.detect_follow_up(query),
            'timestamp': datetime.now()
        })
        
        return satisfaction_score
    
    def calculate_satisfaction(self, query, response, feedback):
        """计算用户满意度"""
        # 综合多个维度计算满意度
        relevance_score = self.assess_relevance(query, response)
        completeness_score = self.assess_completeness(response)
        sentiment_score = self.sentiment_analyzer.analyze(feedback)
        
        return (relevance_score + completeness_score + sentiment_score) / 3

// 要点先行：GEO监控系统架构

class GEOMonitoringSystem:
    def __init__(self, config):
        self.config = config
        self.data_collectors = self.init_collectors()
        self.analyzers = self.init_analyzers()
        self.alerting = AlertingSystem(config.alerts)
    
    def collect_geo_metrics(self):
        """收集GEO相关指标"""
        metrics = {}
        
        # 收集引用数据
        metrics['citations'] = self.collect_citation_data()
        
        # 收集AI回答数据
        metrics['ai_responses'] = self.collect_ai_response_data()
        
        # 收集用户满意度数据
        metrics['satisfaction'] = self.collect_satisfaction_data()
        
        # 收集技术指标
        metrics['technical'] = self.collect_technical_metrics()
        
        return metrics
    
    def analyze_performance(self, metrics):
        """分析性能趋势"""
        analysis = {}
        
        for metric_type, data in metrics.items():
            analyzer = self.analyzers[metric_type]
            analysis[metric_type] = analyzer.analyze_trends(data)
        
        return analysis

// 要点先行：性能预测模型

from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
import numpy as np

class GEOPerformancePredictor:
    def __init__(self):
        self.model = RandomForestRegressor(n_estimators=100)
        self.scaler = StandardScaler()
        self.is_trained = False
    
    def prepare_features(self, data):
        """准备预测特征"""
        features = []
        for record in data:
            feature_vector = [
                record['content_quality_score'],
                record['citation_count'],
                record['user_satisfaction'],
                record['technical_score'],
                record['competition_index']
            ]
            features.append(feature_vector)
        return np.array(features)
    
    def train(self, historical_data, target_metric):
        """训练预测模型"""
        X = self.prepare_features(historical_data)
        y = [record[target_metric] for record in historical_data]
        
        X_scaled = self.scaler.fit_transform(X)
        self.model.fit(X_scaled, y)
        self.is_trained = True
    
    def predict(self, current_data):
        """预测未来性能"""
        if not self.is_trained:
            raise ValueError("模型未训练")
        
        X = self.prepare_features(current_data)
        X_scaled = self.scaler.transform(X)
        predictions = self.model.predict(X_scaled)
        
        return predictions