amberjs 1.17.0

// 智能故障预测引擎
//
// 该模块基于历史数据和实时指标，利用机器学习算法预测潜在的系统故障。

use crate::aiops::anomaly_detection::{Anomaly, AnomalyDetector, BaselineCalculator};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, SystemTime};

/// 系统指标类型
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum MetricType {
    /// CPU 使用率
    CpuUsage,
    /// 内存使用率
    MemoryUsage,
    /// 磁盘使用率
    DiskUsage,
    /// 网络延迟
    NetworkLatency,
    /// 错误率
    ErrorRate,
    /// 吞吐量
    Throughput,
}
/// 系统指标数据点
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemMetric {
    pub metric_type: MetricType,
    pub value: f64,
    pub timestamp: SystemTime,
    pub labels: HashMap<String, String>,
}
/// 时间序列数据点
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeSeriesData {
    pub timestamp: SystemTime,
    pub value: f64,
}
/// 趋势方向
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
    Volatile,
}
/// 趋势分析结果
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendReport {
    pub direction: TrendDirection,
    pub slope: f64,
    pub confidence: f64,
    pub predicted_values: Vec<f64>,
}
/// 故障预测结果
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Prediction {
    pub metric_type: MetricType,
    pub probability: f64,
    pub predicted_time: SystemTime,
    pub severity: PredictionSeverity,
    pub affected_services: Vec<String>,
    pub confidence_score: f64,
    pub recommendations: Vec<String>,
}
/// 预测严重程度
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum PredictionSeverity {
    Low,
    Medium,
    High,
    Critical,
}
/// 趋势分析器
#[derive(Debug)]
pub struct TrendAnalyzer {
    baseline_calculator: Arc<BaselineCalculator>,
}
impl TrendAnalyzer {
    pub fn new() -> Self {
        Self {
            baseline_calculator: Arc::new(Mutex::new(BaselineCalculator::new())),
        }
    }
    /// 分析时间序列趋势
    pub async fn analyze_trends(
        &self,
        time_series: &[TimeSeriesData],
    ) -> Result<TrendReport, Box<dyn std::error::Error>> {
        if time_series.len() < 2 {
            return Err("时间序列数据点不足".into());
        }
        // 计算线性回归斜率
        let (slope, intercept) = self.calculate_linear_regression(time_series)?;
        // 计算置信度
        let confidence: _ = self.calculate_confidence(time_series, slope)?;
        // 确定趋势方向
        let direction: _ = self.determine_direction(slope, confidence)?;
        // 预测未来值
        let predicted_values: _ = self.predict_future_values(time_series, slope, intercept)?;
        Ok(TrendReport {
            direction,
            slope,
            confidence,
            predicted_values,
        })
    }
    fn calculate_linear_regression(
        &self,
        time_series: &[TimeSeriesData],
    ) -> Result<(f64, f64), Box<dyn std::error::Error>> {
        let n: _ = time_series.len() as f64;
        let mut sum_x = 0.0;
        let mut sum_y = 0.0;
        let mut sum_xy = 0.0;
        let mut sum_x2 = 0.0;
        for (i, data) in time_series.iter().enumerate() {
            let x: _ = i as f64;
            let y: _ = data.value;
            sum_x += x;
            sum_y += y;
            sum_xy += x * y;
            sum_x2 += x * x;
        }
        let slope: _ = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x);
        let intercept: _ = (sum_y - slope * sum_x) / n;
        Ok((slope, intercept))
    }
    fn calculate_confidence(
        &self,
        time_series: &[TimeSeriesData],
        slope: f64,
    ) -> Result<f64, Box<dyn std::error::Error>> {
        let mean: f64 = time_series.iter().map(|d| d.value).sum::<f64>() / time_series.len() as f64;
        let variance: f64 = time_series
            .iter()
            .map(|d| (d.value - mean).powi(2))
            .sum::<f64>()
            / time_series.len() as f64;
        // 基于方差计算置信度（方差越小，置信度越高）
        let confidence: _ = 1.0 / (1.0 + variance);
        Ok(confidence.min(1.0).max(0.0))
    }
    fn determine_direction(
        &self,
        slope: f64,
        confidence: f64,
    ) -> Result<TrendDirection, Box<dyn std::error::Error>> {
        let threshold: _ = 0.1;
        if confidence < 0.5 {
            return Ok(TrendDirection::Volatile);
        }
        if slope.abs() < threshold {
            Ok(TrendDirection::Stable)
        } else if slope > 0.0 {
            Ok(TrendDirection::Increasing)
        } else {
            Ok(TrendDirection::Decreasing)
        }
    }
    fn predict_future_values(
        &self,
        time_series: &[TimeSeriesData],
        slope: f64,
        intercept: f64,
    ) -> Result<Vec<f64>, Box<dyn std::error::Error>> {
        let n: _ = time_series.len();
        let mut predictions = Vec::new();
        // 预测未来 5 个时间点的值
        for i in n..n + 5 {
            let predicted_value: _ = slope * i as f64 + intercept;
            predictions.push(predicted_value);
        }
        Ok(predictions)
    }
}
impl Default for TrendAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}
/// 模型训练器
#[derive(Debug)]
pub struct ModelTrainer {
    historical_data: Arc<RwLock<Vec<SystemMetric>>>,
}
impl ModelTrainer {
    pub fn new() -> Self {
        Self {
            historical_data: Arc::new(Mutex::new(Vec::new())),
        }
    }
    /// 训练预测模型
    pub async fn train_model(
        &self,
        metrics: &[SystemMetric],
    ) -> Result<(), Box<dyn std::error::Error>> {
        let mut data = self.historical_data.write().await;
        data.extend_from_slice(metrics);
        // 这里应该实现实际的机器学习模型训练
        // 目前使用简单的统计方法
        Ok(())
    }
    /// 计算故障概率
    pub async fn calculate_failure_probability(
        &self,
        metrics: &[SystemMetric],
    ) -> Result<HashMap<MetricType, f64>, Box<dyn std::error::Error>> {
        let mut probabilities = HashMap::new();
        for metric in metrics {
            let probability: _ = match metric.metric_type {
                MetricType::CpuUsage => self.calculate_cpu_failure_probability(metric.value),
                MetricType::MemoryUsage => self.calculate_memory_failure_probability(metric.value),
                MetricType::DiskUsage => self.calculate_disk_failure_probability(metric.value),
                MetricType::NetworkLatency => {
                    self.calculate_network_failure_probability(metric.value)
                }
                MetricType::ErrorRate => {
                    self.calculate_error_rate_failure_probability(metric.value)
                }
                MetricType::Throughput => {
                    self.calculate_throughput_failure_probability(metric.value)
                }
            };
            probabilities.insert(metric.metric_type, probability);
        }
        Ok(probabilities)
    }
    fn calculate_cpu_failure_probability(&self, cpu_usage: f64) -> f64 {
        if cpu_usage < 50.0 {
            0.1
        } else if cpu_usage < 70.0 {
            0.3
        } else if cpu_usage < 85.0 {
            0.6
        } else if cpu_usage < 95.0 {
            0.85
        } else {
            0.95
        }
    }
    fn calculate_memory_failure_probability(&self, memory_usage: f64) -> f64 {
        if memory_usage < 60.0 {
            0.1
        } else if memory_usage < 75.0 {
            0.4
        } else if memory_usage < 88.0 {
            0.7
        } else if memory_usage < 96.0 {
            0.9
        } else {
            0.98
        }
    }
    fn calculate_disk_failure_probability(&self, disk_usage: f64) -> f64 {
        if disk_usage < 70.0 {
            0.05
        } else if disk_usage < 85.0 {
            0.3
        } else if disk_usage < 95.0 {
            0.8
        } else {
            0.95
        }
    }
    fn calculate_network_failure_probability(&self, latency: f64) -> f64 {
        if latency < 50.0 {
            0.1
        } else if latency < 100.0 {
            0.3
        } else if latency < 200.0 {
            0.6
        } else if latency < 500.0 {
            0.85
        } else {
            0.95
        }
    }
    fn calculate_error_rate_failure_probability(&self, error_rate: f64) -> f64 {
        if error_rate < 0.01 {
            0.05
        } else if error_rate < 0.05 {
            0.4
        } else if error_rate < 0.1 {
            0.7
        } else if error_rate < 0.2 {
            0.9
        } else {
            0.99
        }
    }
    fn calculate_throughput_failure_probability(&self, throughput: f64) -> f64 {
        // 假设正常吞吐量为 1000，低于此值表示故障
        if throughput > 800.0 {
            0.1
        } else if throughput > 500.0 {
            0.4
        } else if throughput > 200.0 {
            0.7
        } else if throughput > 100.0 {
            0.9
        } else {
            0.98
        }
    }
}
impl Default for ModelTrainer {
    fn default() -> Self {
        Self::new()
    }
}
/// 预测引擎
#[derive(Debug)]
pub struct PredictionEngine {
    anomaly_detector: Arc<AnomalyDetector>,
    trend_analyzer: Arc<TrendAnalyzer>,
    model_trainer: Arc<ModelTrainer>,
}
impl PredictionEngine {
    pub fn new() -> Self {
        Self {
            anomaly_detector: Arc::new(AnomalyDetector::new()),
            trend_analyzer: Arc::new(TrendAnalyzer::new()),
            model_trainer: Arc::new(ModelTrainer::new()),
        }
    }
    /// 预测系统故障
    pub async fn predict_failures(
        &self,
        metrics: &[SystemMetric],
    ) -> Result<Vec<Prediction>, Box<dyn std::error::Error>> {
        if metrics.is_empty() {
            return Err("指标数据为空".into());
        }
        let mut predictions = Vec::new();
        // 按指标类型分组
        let metrics_by_type: _ = self.group_metrics_by_type(metrics);
        // 对每种指标类型进行预测
        for (metric_type, type_metrics) in metrics_by_type {
            // 提取数值数据
            let values: Vec<f64> = type_metrics.iter().map(|m| m.value).collect();
            // 检测异常
            let anomalies: _ = self.anomaly_detector.detect_anomalies(&values).await?;
            // 分析趋势
            let time_series: _ = self.convert_to_time_series(&type_metrics);
            let trend_report: _ = self.trend_analyzer.analyze_trends(&time_series).await?;
            // 计算故障概率
            let probabilities: _ = self
                .model_trainer
                .calculate_failure_probability(&type_metrics)
                .await?;
            let probability: _ = probabilities.get(&metric_type).unwrap_or(&0.0);
            // 生成预测（如果概率超过阈值）
            if *probability > 0.5 {
                let prediction: _ =
                    self.create_prediction(metric_type, *probability, &anomalies, &trend_report)?;
                predictions.push(prediction);
            }
        }
        Ok(predictions)
    }
    /// 分析时间序列趋势
    pub async fn analyze_trends(
        &self,
        historical_data: &[TimeSeriesData],
    ) -> Result<TrendReport, Box<dyn std::error::Error>> {
        self.trend_analyzer.analyze_trends(historical_data).await
    }
    /// 计算故障概率
    pub async fn calculate_failure_probability(
        &self,
        system_metrics: &[SystemMetric],
    ) -> Result<f64, Box<dyn std::error::Error>> {
        let probabilities: _ = self
            .model_trainer
            .calculate_failure_probability(system_metrics)
            .await?;
        // 计算综合概率（取最大值）
        let max_probability: _ =
            probabilities
                .values()
                .fold(0.0, |max, &prob| if prob > max { prob } else { max });
        Ok(max_probability)
    }
    fn group_metrics_by_type(
        &self,
        metrics: &[SystemMetric],
    ) -> HashMap<MetricType, Vec<SystemMetric>> {
        let mut grouped = HashMap::new();
        for metric in metrics {
            grouped
                .entry(metric.metric_type)
                .or_insert_with(Vec::new)
                .push(metric.clone());
        }
        grouped
    }
    fn convert_to_time_series(&self, metrics: &[SystemMetric]) -> Vec<TimeSeriesData> {
        metrics
            .iter()
            .map(|m| TimeSeriesData {
                timestamp: m.timestamp,
                value: m.value,
            })
            .collect()
    }
    fn create_prediction(
        &self,
        metric_type: MetricType,
        probability: f64,
        anomalies: &[Anomaly],
        trend_report: &TrendReport,
    ) -> Result<Prediction, Box<dyn std::error::Error>> {
        let severity: _ = if probability > 0.9 {
            PredictionSeverity::Critical
        } else if probability > 0.7 {
            PredictionSeverity::High
        } else if probability > 0.5 {
            PredictionSeverity::Medium
        } else {
            PredictionSeverity::Low
        };
        let affected_services: _ = self.identify_affected_services(metric_type);
        let recommendations: _ =
            self.generate_recommendations(metric_type, probability, anomalies, trend_report)?;
        Ok(Prediction {
            metric_type,
            probability,
            predicted_time: SystemTime::now() + Duration::from_secs(3600), // 1小时后
            severity,
            affected_services,
            confidence_score: trend_report.confidence,
            recommendations,
        })
    }
    fn identify_affected_services(&self, metric_type: MetricType) -> Vec<String> {
        match metric_type {
            MetricType::CpuUsage | MetricType::MemoryUsage => {
                vec!["api-service".to_string(), "worker-service".to_string()]
            }
            MetricType::DiskUsage => {
                vec!["storage-service".to_string()]
            }
            MetricType::NetworkLatency => {
                vec!["gateway".to_string(), "load-balancer".to_string()]
            }
            MetricType::ErrorRate | MetricType::Throughput => {
                vec!["api-service".to_string()]
            }
        }
    }
    fn generate_recommendations(
        &self,
        metric_type: MetricType,
        probability: f64,
        anomalies: &[Anomaly],
        trend_report: &TrendReport,
    ) -> Result<Vec<String>, Box<dyn std::error::Error>> {
        let mut recommendations = Vec::new();
        match metric_type {
            MetricType::CpuUsage => {
                if probability > 0.8 {
                    recommendations.push("立即扩容 CPU 资源".to_string());
                    recommendations.push("检查 CPU 密集型进程".to_string());
                }
                recommendations.push("优化算法和代码".to_string());
            }
            MetricType::MemoryUsage => {
                if probability > 0.8 {
                    recommendations.push("增加内存配额".to_string());
                    recommendations.push("重启内存泄漏服务".to_string());
                }
                recommendations.push("优化内存使用".to_string());
            }
            MetricType::DiskUsage => {
                if probability > 0.8 {
                    recommendations.push("清理临时文件".to_string());
                    recommendations.push("增加磁盘空间".to_string());
                }
                recommendations.push("压缩日志文件".to_string());
            }
            MetricType::NetworkLatency => {
                if probability > 0.8 {
                    recommendations.push("检查网络连接".to_string());
                    recommendations.push("优化网络配置".to_string());
                }
                recommendations.push("启用网络缓存".to_string());
            }
            MetricType::ErrorRate => {
                if probability > 0.8 {
                    recommendations.push("立即检查错误日志".to_string());
                    recommendations.push("回滚最近的变更".to_string());
                }
                recommendations.push("加强错误处理".to_string());
            }
            MetricType::Throughput => {
                if probability > 0.8 {
                    recommendations.push("扩容实例数量".to_string());
                    recommendations.push("优化数据库查询".to_string());
                }
                recommendations.push("启用负载均衡".to_string());
            }
        }
        // 基于趋势添加建议
        match trend_report.direction {
            TrendDirection::Increasing => {
                recommendations.push("趋势显示问题在恶化，建议立即行动".to_string());
            }
            TrendDirection::Volatile => {
                recommendations.push("指标波动较大，建议稳定性分析".to_string());
            }
            _ => {}
        }
        Ok(recommendations)
    }
}
impl Default for PredictionEngine {
    fn default() -> Self {
        Self::new()
    }
}