graph TD
A[数据采集层] --> B[流式处理层]
B --> C[AI诊断引擎]
C --> D[根因分析层]
D --> E[修复执行层]subgraph 数据采集层
A1[Prometheus] --> A2[OpenTelemetry]
A3[日志Agent] --> A4[Kafka]
endsubgraph 流式处理层
B1[窗口聚合] --> B2[异常检测]
B3[指标关联] --> B4[特征提取]
endsubgraph AI诊断引擎
C1[时序预测] --> C2[拓扑分析]
C3[因果推断] --> C4[故障模式匹配]
end

import numpy as np
from river import anomalyclass AdaptiveThresholdDetector:def __init__(self, window_size=300):self.half_amp = anomaly.HalfSpaceTrees(n_trees=5,height=15,window_size=window_size)self.threshold = 0.95def update(self, value, timestamp):score = self.half_amp.score_one({'value': value})self.half_amp = self.half_amp.learn_one({'value': value})# 动态调整阈值if score > self.threshold:self.threshold = min(0.99, self.threshold * 1.01)else:self.threshold = max(0.8, self.threshold * 0.99)return score > self.threshold

import networkx as nx
from causalnex.structure import Dynotearsdef build_causal_graph(metrics_df):# 动态因果发现model = Dynotears(lambda1=0.1,lambda2=0.001,device='cuda')model.fit(metrics_df)# 构建服务拓扑图graph = nx.DiGraph()for i, col in enumerate(metrics_df.columns):for j, effect in enumerate(model.adjacency[i]):if abs(effect) > 0.2:  # 显著因果阈值graph.add_edge(metrics_df.columns[j],col,weight=effect)return graph

def find_root_cause(graph, symptom_node):# 反向传播分析candidates = {}for node in nx.ancestors(graph, symptom_node):# 计算影响因子paths = list(nx.all_simple_paths(graph, node, symptom_node))influence = sum(np.prod([graph[u][v]['weight'] for u, v in zip(p[:-1], p[1:]))for p in paths)candidates[node] = influence# 返回Top3候选根因return sorted(candidates.items(), key=lambda x: x[1], reverse=True)[:3]

from transformers import pipelinelog_analyzer = pipeline("text-classification", model="microsoft/logbert-base"
)def multimodal_diagnosis(metrics, logs, traces):# 指标分析metric_causes = metric_analysis(metrics)# 日志分析log_results = log_analyzer(logs)[0]if log_results['label'] == 'ERROR':log_causes = extract_error_pattern(logs)# 调用链分析trace_root = find_slowest_span(traces)# 证据融合return {'metric_evidence': metric_causes,'log_evidence': log_causes,'trace_evidence': trace_root}

REPAIR_ACTIONS = {"memory_leak": [{"action": "restart_service", "params": {"service": "payment"}},{"action": "scale_out", "params": {"replicas": 2}}],"db_connection_pool_exhausted": [{"action": "execute_sql", "sql": "RELEASE CONNECTIONS"},{"action": "update_config", "file": "db.conf", "key": "max_connections", "value": 200}]
}def execute_repair(root_cause):for action in REPAIR_ACTIONS.get(root_cause, []):if action["action"] == "restart_service":k8s_api.restart_deployment(action["params"]["service"])elif action["action"] == "execute_sql":db_conn.execute(action["sql"])# 执行后验证if validate_fix():return Truereturn False

class RepairExecutor:def __init__(self):self.snapshot_mgr = SnapshotManager()def safe_execute(self, action):# 创建系统快照snap_id = self.snapshot_mgr.create_snapshot()try:execute_action(action)if not health_check():raise RuntimeError("Health check failed after repair")except Exception as e:self.snapshot_mgr.restore_snapshot(snap_id)raise e