AI技术 - 数字孪生物联网集成技术深度解析

# 数字孪生物联网集成系统示例 import asyncio import json import time from datetime import datetime import # paho.mqtt.client as mqtt import numpy as np import pandas as pd from typing import Dict import List import Any class DigitalTwinIoTIntegration: def __init__(self, system_id): self.system_id = system_id self.connected_devices = {} self.sensor_data = {} self.digital_twin_model = None self.mqtt_client = None self.data_buffer = [] def initialize_mqtt_client(self, broker_host, broker_port=1883): """初始化MQTT客户端""" self.mqtt_client = mqtt.Client() self.mqtt_client.on_connect = self.on_mqtt_connect self.mqtt_client.on_message = self.on_mqtt_message self.mqtt_client.connect(broker_host, broker_port, 60) self.mqtt_client.loop_start() def on_mqtt_connect(self, client, userdata, flags, rc): """MQTT连接回调""" print(f"Connected to MQTT broker with result code {rc}") # 订阅传感器数据主题 # client.subscribe("sensors/+/data") client.subscribe("devices/+/status") def on_mqtt_message(self, client, userdata, msg): """MQTT消息回调""" topic = msg.topic payload = json.loads(msg.payload.decode()) if "sensors" in topic and "data" in topic: self.process_sensor_data(topic, payload) elif "devices" in topic and "status" in topic: self.process_device_status(topic, payload) def process_sensor_data(self, topic, data): """处理传感器数据""" device_id = topic.split('/')[1] sensor_type = data.get('sensor_type') value = data.get('value') timestamp = data.get('timestamp', time.time()) # 存储传感器数据 if device_id # not in self.sensor_data: self.sensor_data[device_id] = {} if sensor_type not in self.sensor_data[device_id]: self.sensor_data[device_id][sensor_type] = [] self.sensor_data[device_id][sensor_type].append({ 'value': value, 'timestamp': timestamp }) # 更新数字孪生模型 # self.update_digital_twin_model(device_id, sensor_type, value, timestamp) def process_device_status(self, topic, data): """处理设备状态""" device_id = topic.split('/')[1] status = data.get('status') timestamp = data.get('timestamp', time.time()) # 更新设备状态 self.connected_devices[device_id] = { 'status': # status, 'last_update': timestamp, 'online': True } def update_digital_twin_model(self, device_id, sensor_type, value, timestamp): """更新数字孪生模型""" if self.digital_twin_model: # 更新数字孪生模型中的设备状态 # self.digital_twin_model.update_device_state(device_id, sensor_type, value, timestamp) # 触发模型同步 # self.sync_digital_twin_model() def sync_digital_twin_model(self): """同步数字孪生模型""" if self.digital_twin_model: # 执行模型同步逻辑 sync_result = # self.digital_twin_model.sync_with_physical_world() # 发布同步结果 self.publish_sync_result(sync_result) # def publish_sync_result(self, sync_result): """发布同步结果""" if self.mqtt_client: topic = f"digital_twin/{self.system_id}/sync_result" payload = json.dumps(sync_result) self.mqtt_client.publish(topic, payload) def get_device_health_status(self, device_id): """获取设备健康状态""" if device_id not in self.connected_devices: return None device_info = self.connected_devices[device_id] health_score = self.calculate_device_health_score(device_id) return { 'device_id': device_id, 'status': device_info['status'], 'health_score': health_score, 'last_update': device_info['last_update'], 'online': device_info['online'] } def calculate_device_health_score(self, device_id): """计算设备健康评分""" if device_id not in self.sensor_data: return 0.0 # 基于传感器数据计算设备健康评分 health_indicators = [] # for sensor_type, data_points in self.sensor_data[device_id].items(): if data_points: latest_value = data_points[-1]['value'] # 根据传感器类型和数值计算健康指标 health_indicator = # self.calculate_sensor_health_indicator(sensor_type, latest_value) health_indicators.append(health_indicator) if health_indicators: return np.mean(health_indicators) else: return 0.0 def calculate_sensor_health_indicator(self, sensor_type, value): """计算传感器健康指标""" # 根据传感器类型和数值计算健康指标# 这里需要根据具体的传感器类型和正常值范围来实现 if sensor_type == # 'temperature': if 20 <= value <= 30: return 1.0 elif 15 <= value <= 35: return 0.8 else: return 0.5 elif sensor_type == 'vibration': if value < 0.5: return 1.0 elif value < 1.0: return 0.8 else: return 0.3 else: return 0.5 def predict_device_failure(self, device_id, prediction_horizon=24): """预测设备故障""" if device_id not in self.sensor_data: return None # 获取设备历史数据 # historical_data = self.get_device_historical_data(device_id) # 运行故障预测算法 failure_probability = # self.run_failure_prediction_algorithm(historical_data) return { 'device_id': device_id, 'failure_probability': failure_probability, 'predicted_failure_time': self.calculate_predicted_failure_time(failure_probability), 'recommended_actions': self.get_maintenance_recommendations(failure_probability) } def get_device_historical_data(self, device_id, hours=24): """获取设备历史数据""" if device_id not in self.sensor_data: return [] current_time = time.time() start_time = current_time - (hours * 3600) historical_data = [] for sensor_type, data_points in self.sensor_data[device_id].items(): for data_point in data_points: if data_point['timestamp'] >= start_time: historical_data.append({ 'sensor_type': sensor_type, 'value': data_point['value'], 'timestamp': data_point['timestamp'] }) return historical_data def run_failure_prediction_algorithm(self, historical_data): """运行故障预测算法""" # 实现故障预测算法# 这里可以使用机器学习模型进行预测 if not historical_data: return 0.0# 简单的基于趋势的预测算法 # recent_data = historical_data[-10:] if len(historical_data) >= 10 else historical_data values = [point['value'] for point in recent_data] # 计算数据趋势 trend = np.polyfit(range(len(values)), values, # 1)[0] # 基于趋势计算故障概率 if trend > 0.1: return min(0.9, abs(trend) * 10) else: return 0.1 def calculate_predicted_failure_time(self, failure_probability): """计算预测故障时间""" if # failure_probability > 0.8: return datetime.now().timestamp() + 3600 # 1小时后 elif # failure_probability > 0.5: return datetime.now().timestamp() + 86400 # 1天后 else: return # datetime.now().timestamp() + 604800 # 1周后 def get_maintenance_recommendations(self, # failure_probability): """获取维护建议""" recommendations = [] if failure_probability > 0.8: recommendations.append("立即安排设备维护") elif failure_probability > 0.5: recommendations.append("计划近期维护") else: recommendations.append("继续监控设备状态") return recommendations