89 lines
4.3 KiB
Python
89 lines
4.3 KiB
Python
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"""AI运维智能体数据模型 - 设备健康评分、异常检测、诊断报告、预测性维护、运营洞察"""
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from sqlalchemy import Column, Integer, String, Float, Boolean, DateTime, ForeignKey, Text, JSON
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from sqlalchemy.sql import func
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from app.core.database import Base
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class DeviceHealthScore(Base):
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"""设备健康评分"""
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__tablename__ = "device_health_scores"
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id = Column(Integer, primary_key=True, autoincrement=True)
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device_id = Column(Integer, ForeignKey("devices.id"), nullable=False, index=True)
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timestamp = Column(DateTime(timezone=True), server_default=func.now(), index=True)
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health_score = Column(Float, nullable=False) # 0-100
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status = Column(String(20), default="healthy") # healthy, warning, critical
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factors = Column(JSON) # {power_stability, efficiency, alarm_frequency, uptime, temperature}
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trend = Column(String(20), default="stable") # improving, stable, degrading
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created_at = Column(DateTime(timezone=True), server_default=func.now())
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class AnomalyDetection(Base):
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"""异常检测记录"""
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__tablename__ = "anomaly_detections"
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id = Column(Integer, primary_key=True, autoincrement=True)
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device_id = Column(Integer, ForeignKey("devices.id"), nullable=False, index=True)
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detected_at = Column(DateTime(timezone=True), server_default=func.now(), index=True)
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anomaly_type = Column(String(50), nullable=False) # power_drop, efficiency_loss, abnormal_temperature, communication_loss, pattern_deviation
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severity = Column(String(20), default="warning") # info, warning, critical
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description = Column(Text)
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metric_name = Column(String(50))
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expected_value = Column(Float)
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actual_value = Column(Float)
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deviation_percent = Column(Float)
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status = Column(String(20), default="detected") # detected, investigating, resolved, false_positive
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resolution_notes = Column(Text)
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created_at = Column(DateTime(timezone=True), server_default=func.now())
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class DiagnosticReport(Base):
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"""AI诊断报告"""
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__tablename__ = "diagnostic_reports"
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id = Column(Integer, primary_key=True, autoincrement=True)
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device_id = Column(Integer, ForeignKey("devices.id"), nullable=False, index=True)
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generated_at = Column(DateTime(timezone=True), server_default=func.now(), index=True)
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report_type = Column(String(20), default="routine") # routine, triggered, comprehensive
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findings = Column(JSON) # [{finding, severity, detail}]
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recommendations = Column(JSON) # [{action, priority, detail}]
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estimated_impact = Column(JSON) # {energy_loss_kwh, cost_impact_yuan}
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status = Column(String(20), default="generated") # generated, reviewed, action_taken
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created_at = Column(DateTime(timezone=True), server_default=func.now())
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class MaintenancePrediction(Base):
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"""预测性维护"""
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__tablename__ = "maintenance_predictions"
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id = Column(Integer, primary_key=True, autoincrement=True)
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device_id = Column(Integer, ForeignKey("devices.id"), nullable=False, index=True)
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predicted_at = Column(DateTime(timezone=True), server_default=func.now())
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component = Column(String(100))
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failure_mode = Column(String(200))
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probability = Column(Float) # 0-1
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predicted_failure_date = Column(DateTime(timezone=True))
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recommended_action = Column(Text)
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urgency = Column(String(20), default="medium") # low, medium, high, critical
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estimated_downtime_hours = Column(Float)
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estimated_repair_cost = Column(Float)
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status = Column(String(20), default="predicted") # predicted, scheduled, completed, false_alarm
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created_at = Column(DateTime(timezone=True), server_default=func.now())
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class OpsInsight(Base):
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"""运营洞察"""
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__tablename__ = "ops_insights"
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id = Column(Integer, primary_key=True, autoincrement=True)
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insight_type = Column(String(50), nullable=False) # efficiency_trend, cost_anomaly, performance_comparison, seasonal_pattern
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title = Column(String(200), nullable=False)
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description = Column(Text)
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data = Column(JSON)
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impact_level = Column(String(20), default="medium") # low, medium, high
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actionable = Column(Boolean, default=False)
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recommended_action = Column(Text)
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generated_at = Column(DateTime(timezone=True), server_default=func.now())
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valid_until = Column(DateTime(timezone=True))
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created_at = Column(DateTime(timezone=True), server_default=func.now())
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