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| class ManufacturingFaultDetection:
"""制造业故障检测系统"""
def __init__(self):
# 多模态数据处理
self.vision_model = YOLOv8("fault_detection.pt")
self.sensor_analyzer = SensorDataAnalyzer()
self.audio_classifier = AudioFaultClassifier()
# 故障知识库
self.fault_kb = FaultKnowledgeBase()
# 决策 Agent
self.diagnostic_agent = self._create_diagnostic_agent()
# 告警系统
self.alert_system = AlertSystem()
def _create_diagnostic_agent(self):
"""创建诊断 Agent"""
tools = [
HistoricalDataTool(),
MaintenanceRecordTool(),
PartSpecificationTool(),
WorkOrderTool()
]
prompt = ChatPromptTemplate.from_messages([
("system", """你是设备故障诊断专家。基于以下信息进行故障分析：
检测数据：
- 视觉检测：{visual_detection}
- 传感器数据：{sensor_data}
- 音频分析：{audio_analysis}
- 历史记录：{historical_data}
诊断要求：
1. 分析故障类型和严重程度
2. 提供可能的原因分析
3. 给出维修建议和优先级
4. 评估停机风险
5. 推荐预防措施
请提供结构化的诊断报告。"""),
("human", "设备ID：{equipment_id}\n异常描述：{anomaly_description}")
])
return create_openai_functions_agent(
llm=ChatOpenAI(model="gpt-4", temperature=0.1),
tools=tools,
prompt=prompt
)
async def detect_and_diagnose(self, equipment_id: str, image_data: bytes, sensor_data: Dict, audio_data: bytes) -> Dict[str, Any]:
"""检测和诊断故障"""
# 1. 多模态检测
visual_result = await self._analyze_visual_data(image_data)
sensor_result = await self._analyze_sensor_data(sensor_data)
audio_result = await self._analyze_audio_data(audio_data)
# 2. 异常判断
anomalies = self._detect_anomalies(visual_result, sensor_result, audio_result)
if not anomalies:
return {"status": "normal", "confidence": 0.95}
# 3. 故障诊断
diagnosis = await self._diagnose_fault(
equipment_id=equipment_id,
visual_detection=visual_result,
sensor_data=sensor_result,
audio_analysis=audio_result,
anomalies=anomalies
)
# 4. 风险评估
risk_level = self._assess_risk(diagnosis, equipment_id)
# 5. 生成告警
if risk_level >= 3: # 高风险
await self._trigger_alert(equipment_id, diagnosis, risk_level)
return {
"status": "fault_detected",
"diagnosis": diagnosis,
"risk_level": risk_level,
"recommended_actions": diagnosis.get("recommendations", []),
"estimated_downtime": diagnosis.get("estimated_downtime", "未知")
}
async def _analyze_visual_data(self, image_data: bytes) -> Dict[str, Any]:
"""视觉数据分析"""
# YOLO 检测
results = self.vision_model(image_data)
detected_faults = []
for result in results:
if result.confidence > 0.7:
detected_faults.append({
"type": result.class_name,
"confidence": result.confidence,
"location": result.bbox,
"severity": self._assess_visual_severity(result)
})
return {
"detected_faults": detected_faults,
"image_quality": self._assess_image_quality(image_data),
"timestamp": datetime.now().isoformat()
}
async def _diagnose_fault(self, **kwargs) -> Dict[str, Any]:
"""故障诊断"""
# 构建诊断上下文
context = {
"equipment_id": kwargs["equipment_id"],
"visual_detection": json.dumps(kwargs["visual_detection"], ensure_ascii=False),
"sensor_data": json.dumps(kwargs["sensor_data"], ensure_ascii=False),
"audio_analysis": json.dumps(kwargs["audio_analysis"], ensure_ascii=False),
"anomaly_description": self._format_anomalies(kwargs["anomalies"])
}
# Agent 诊断
result = await self.diagnostic_agent.ainvoke(context)
# 解析结构化结果
diagnosis = self._parse_diagnosis_result(result["output"])
return diagnosis
|
