models:
– name: anomaly_detection
type: unsupervised
data_sources:
– metrics
– logs
– events
thresholds:
confidence: 0.8
alert_severity: medium

– name: failure_prediction
type: supervised
training_data: ./training_data
prediction_window: 24h

# 启动AI监控服务
# systemctl start ai-monitor

# 检查监控状态
# curl http://ai-monitor:8080/api/monitoring/status
{
“services”: [
{
“name”: “database”,
“status”: “healthy”,
“anomaly_score”: 0.1,
“predicted_failure_probability”: 0.05
},
{
“name”: “storage”,
“status”: “warning”,
“anomaly_score”: 0.75,
“predicted_failure_probability”: 0.45,
“recommendations”: [
“Check disk health”,
“Increase replication factor”
]
},
{
“name”: “network”,
“status”: “healthy”,
“anomaly_score”: 0.2,
“predicted_failure_probability”: 0.1
}
]
}

# 测试异常检测模型
# python test_anomaly_model.py –model ./models/anomaly_model.pkl –test_data ./test_data

Testing anomaly detection model…
Accuracy: 95.2%
Precision: 92.8%
Recall: 94.5%
F1 Score: 93.6%

# 部署异常检测模型
# cp ./models/anomaly_model.pkl /opt/ai-monitor/models/
# systemctl restart ai-monitor

# 查看异常检测结果
# curl http://ai-monitor:8080/api/anomalies
{
“timestamp”: “2026-03-30T10:00:00Z”,
“anomalies”: [
{
“service”: “storage”,
“metric”: “disk_io_latency”,
“value”: 120.5,
“threshold”: 80.0,
“anomaly_score”: 0.85,
“predicted_impact”: “High”,
“recommendation”: “Investigate disk performance issues”
}
]
}

recovery_strategies:
– name: database_recovery
priority: high
triggers:
– database_down
– replication_lag
actions:
– switch_to_standby
– restore_from_backup
– verify_data_integrity

– name: storage_recovery
priority: medium
triggers:
– disk_failure
– storage_full
actions:
– failover_to_replica
– expand_storage
– optimize_storage

# 启动AI恢复服务
# systemctl start ai-recovery

# 模拟故障并测试恢复
# curl -X POST http://ai-recovery:8080/api/simulate_failure -d ‘{“service”: “database”, “failure_type”: “database_down”}’

{
“status”: “success”,
“recovery_plan”: {
“steps”: [
{
“action”: “detect_failure”,
“status”: “completed”,
“duration”: “2s”
},
{
“action”: “switch_to_standby”,
“status”: “completed”,
“duration”: “15s”
},
{
“action”: “verify_data_integrity”,
“status”: “completed”,
“duration”: “30s”
}
],
“total_recovery_time”: “47s”,
“rto_met”: true,
“rpo_met”: true
}
}

rto_goals:
database: 5m
storage: 10m
network: 1m

rpo_goals:
database: 15m
storage: 30m
network: 5m

optimization_strategies:
– name: dynamic_replication
enabled: true
parameters:
min_replication_interval: 5m
max_replication_interval: 60m

# 启动RTO/RPO优化服务
# systemctl start ai-rto-rpo

# 查看RTO/RPO优化结果
# curl http://ai-rto-rpo:8080/api/optimization/status
{
“services”: [
{
“name”: “database”,
“current_rto”: “3m 45s”,
“target_rto”: “5m”,
“rto_status”: “within_target”,
“current_rpo”: “10m 20s”,
“target_rpo”: “15m”,
“rpo_status”: “within_target”,
“optimization_actions”: [
“Adjusted replication frequency to 10m”
]
},
{
“name”: “storage”,
“current_rto”: “8m 15s”,
“target_rto”: “10m”,
“rto_status”: “within_target”,
“current_rpo”: “25m 40s”,
“target_rpo”: “30m”,
“rpo_status”: “within_target”,
“optimization_actions”: [
“Implemented incremental backup strategy”
]
}
]
}

Analyzing infrastructure…
Generating disaster recovery scenarios…
Evaluating recovery strategies…
Optimizing plan…

# 查看生成的容灾计划
# cat ./dr_plan.json
{
“plan_id”: “dr-plan-2026-03-30”,
“generated_at”: “2026-03-30T10:00:00Z”,
“scenarios”: [
{
“name”: “datacenter_outage”,
“probability”: 0.05,
“impact”: “high”,
“recovery_strategy”: “switch_to_secondary_datacenter”,
“estimated_rto”: “15m”,
“estimated_rpo”: “5m”
},
{
“name”: “database_failure”,
“probability”: 0.15,
“impact”: “high”,
“recovery_strategy”: “switch_to_standby_database”,
“estimated_rto”: “3m”,
“estimated_rpo”: “1m”
}
],
“recommendations”: [
“Increase network bandwidth between datacenters”,
“Implement automated failover for critical services”,
“Test recovery procedures quarterly”
]
}

test_strategies:
– name: automated_dr_testing
frequency: weekly
scenarios:
– datacenter_outage
– network_failure
– database_corruption
– storage_failure

# 启动AI测试服务
# systemctl start ai-testing

# 运行容灾测试
# curl -X POST http://ai-testing:8080/api/run_test -d ‘{“scenario”: “datacenter_outage”}’

{
“test_id”: “test-2026-03-30-001”,
“scenario”: “datacenter_outage”,
“status”: “running”,
“start_time”: “2026-03-30T10:00:00Z”
}

# 查看测试结果
# curl http://ai-testing:8080/api/test_results/test-2026-03-30-001
{
“test_id”: “test-2026-03-30-001”,
“scenario”: “datacenter_outage”,
“status”: “completed”,
“start_time”: “2026-03-30T10:00:00Z”,
“end_time”: “2026-03-30T10:15:30Z”,
“duration”: “15m 30s”,
“results”: {
“rto_achieved”: “12m 45s”,
“rpo_achieved”: “3m 20s”,
“objectives_met”: true,
“issues_found”: [
“Network latency during failover”
],
“recommendations”: [
“Optimize network routing”,
“Increase bandwidth”
]
}
}

security_modules:
– name: threat_detection
type: anomaly_detection
data_sources:
– network_traffic
– access_logs
– system_events

– name: intrusion_prevention
type: predictive_analytics
rules: ./security_rules.yaml

# 启动AI安全服务
# systemctl start ai-security

# 查看安全状态
# curl http://ai-security:8080/api/security/status
{
“status”: “secure”,
“threats”: [
{
“type”: “suspicious_access”,
“severity”: “medium”,
“timestamp”: “2026-03-30T09:45:00Z”,
“source”: “192.168.1.100”,
“action”: “blocked”
}
],
“recommendations”: [
“Update firewall rules”,
“Review access control policies”
]
}

## 1. 数据质量与模型训练
– 确保数据质量：收集高质量的监控数据和故障数据
– 持续模型训练：定期更新AI模型，适应系统变化
– 多源数据整合：整合来自不同系统的数据，提高模型准确性

## 2. 系统集成与部署
– 模块化设计：将AI功能模块化，便于集成和维护
– 渐进式部署：从小规模试点开始，逐步扩展
– 混合智能：结合规则引擎和AI模型，提高可靠性

## 3. 监控与优化
– 模型性能监控：监控AI模型的准确性和性能
– 系统集成监控：监控AI系统与容灾系统的集成状态
– 持续优化：根据实际运行数据持续优化AI模型

## 4. 安全与合规
– 数据安全：确保训练数据和模型的安全
– 隐私保护：遵守数据隐私法规
– 合规审计：确保AI系统符合行业合规要求

## 5. 人员培训与管理
– 技能培训：培训运维人员使用AI辅助工具
– 知识转移：建立AI系统的知识文档
– 变更管理：规范AI系统的变更流程