基于YOLOv26的智能电网设施监测系统

1. 系统架构

1.1 整体架构

本系统采用分层架构设计，主要包含以下核心模块：

• 数据采集模块：通过固定摄像头、无人机和巡检机器人获取电网设施图像数据
• 数据预处理模块：对原始数据进行标准化、增强和标注
• 目标检测模块：基于YOLOv26检测电网设施的异常状态和潜在风险
• 状态分析模块：分析电网设施的运行状态和健康状况
• 预警与决策模块：根据检测结果生成预警信息和维护建议
• 部署与集成模块：实现系统在边缘设备和云端的部署，与现有电网管理系统集成

1.2 技术栈

类别	技术/库	版本	用途
核心框架	YOLOv26	v1.0	目标检测
数据处理	OpenCV	4.8.0	图像处理
数据增强	Albumentations	1.3.1	数据增强
模型训练	PyTorch	2.0.0	模型训练
目标跟踪	DeepSORT	v1.2	目标跟踪
模型部署	ONNX	1.14.0	模型导出
无人机控制	MAVSDK	1.40.0	无人机控制
边缘计算	TensorRT	8.6.1	模型加速
数据存储	InfluxDB	2.7.1	时序数据存储
可视化	Grafana	10.2.0	数据可视化

2. 核心功能模块

2.1 数据采集模块

功能描述：

• 通过固定摄像头、无人机和巡检机器人获取电网设施图像数据
• 支持多源数据融合，包括可见光、红外和热成像数据
• 实现数据的实时传输和存储

代码实现：

import cv2
import numpy as np
import time
import threading
from datetime import datetime
from mavsdk import System
import asyncio

class DataCollector:
    def __init__(self, config):
        self.config = config
        self.cameras = {}
        self.data_queue = []
        self.running = False
    
    def initialize_cameras(self):
        """初始化摄像头"""
        for cam_id, cam_config in self.config.get('cameras', {}).items():
            try:
                cap = cv2.VideoCapture(cam_config['index'])
                cap.set(cv2.CAP_PROP_FRAME_WIDTH, cam_config.get('width', 1280))
                cap.set(cv2.CAP_PROP_FRAME_HEIGHT, cam_config.get('height', 720))
                self.cameras[cam_id] = cap
                print(f"Camera {cam_id} initialized")
            except Exception as e:
                print(f"Failed to initialize camera {cam_id}: {e}")
    
    def capture_from_camera(self, cam_id):
        """从指定摄像头捕获图像"""
        if cam_id in self.cameras:
            cap = self.cameras[cam_id]
            ret, frame = cap.read()
            if ret:
                timestamp = datetime.now().isoformat()
                return {
                    'frame': frame,
                    'timestamp': timestamp,
                    'source': f'camera_{cam_id}'
                }
        return None
    
    def capture_from_all_cameras(self):
        """从所有摄像头捕获图像"""
        frames = []
        for cam_id in self.cameras:
            frame_data = self.capture_from_camera(cam_id)
            if frame_data:
                frames.append(frame_data)
        return frames

class UAVDataCollector:
    def __init__(self, drone_ip="127.0.0.1", drone_port=50040):
        self.drone = System()
        self.drone_ip = drone_ip
        self.drone_port = drone_port
    
    async def connect(self):
        """连接到无人机"""
        await self.drone.connect(system_address=f"udp://{self.drone_ip}:{self.drone_port}")
        print("Connected to drone")
    
    async def plan_inspection_mission(self, facility_coords, altitude=30):
        """规划巡检任务"""
        # 简化实现，实际应考虑设施形状、障碍物等因素
        waypoints = []
        for coord in facility_coords:
            waypoints.append((coord[0], coord[1], altitude))
        return waypoints
    
    async def execute_inspection_mission(self, waypoints, image_save_dir):
        """执行巡检任务并采集数据"""
        if not waypoints:
            print("No waypoints provided")
            return []
        
        # 起飞
        await self.drone.action.arm()
        await self.drone.action.takeoff()
        await asyncio.sleep(5)
        
        collected_images = []
        
        # 按路径飞行并采集图像
        for i, waypoint in enumerate(waypoints):
            print(f"Flying to waypoint {i+1}: {waypoint}")
            
            # 飞行到目标点
            await self.drone.action.goto_location(
                waypoint[0], waypoint[1], waypoint[2], 0
            )
            
            await asyncio.sleep(10)
            
            # 采集图像（模拟）
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            image_path = f"{image_save_dir}/uav_image_{i}_{timestamp}.jpg"
            # 创建模拟图像
            image = np.zeros((480, 640, 3), dtype=np.uint8)
            cv2.putText(image, f"Power Grid Image {i}", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
            cv2.imwrite(image_path, image)
            collected_images.append(image_path)
        
        # 返航
        await self.drone.action.return_to_launch()
        await asyncio.sleep(10)
        await self.drone.action.disarm()
        
        print(f"Mission completed. Collected {len(collected_images)} images")
        return collected_images

class DataCollectorManager:
    def __init__(self, config):
        self.config = config
        self.camera_collector = DataCollector(config)
        self.uav_collector = UAVDataCollector()
        self.data_storage = []
    
    def start_collection(self):
        """启动数据采集"""
        self.camera_collector.initialize_cameras()
        self.running = True
        
        # 启动摄像头采集线程
        def camera_collection_loop():
            while self.running:
                frames = self.camera_collector.capture_from_all_cameras()
                for frame_data in frames:
                    self.data_storage.append(frame_data)
                time.sleep(1)  # 每秒采集一次
        
        self.camera_thread = threading.Thread(target=camera_collection_loop)
        self.camera_thread.daemon = True
        self.camera_thread.start()
        
        print("Data collection started")
    
    def stop_collection(self):
        """停止数据采集"""
        self.running = False
        if hasattr(self, 'camera_thread'):
            self.camera_thread.join(timeout=2)
        
        # 释放摄像头资源
        for cam_id, cap in self.camera_collector.cameras.items():
            try:
                cap.release()
            except:
                pass
        
        print("Data collection stopped")
    
    async def run_uav_inspection(self, facility_coords, image_save_dir):
        """运行无人机巡检"""
        await self.uav_collector.connect()
        waypoints = await self.uav_collector.plan_inspection_mission(facility_coords)
        return await self.uav_collector.execute_inspection_mission(waypoints, image_save_dir)

# 示例用法
config = {
    'cameras': {
        'substation': {'index': 0, 'width': 1280, 'height': 720},
        'transmission_tower': {'index': 1, 'width': 1280, 'height': 720},
        'transformer': {'index': 2, 'width': 1280, 'height': 720}
    }
}

manager = DataCollectorManager(config)
manager.start_collection()

# 运行无人机巡检（异步）
async def run_uav_inspection():
    facility_coords = [(39.9042, 116.4074), (39.9045, 116.4076), (39.9043, 116.4078)]
    images = await manager.run_uav_inspection(facility_coords, 'data/uav_images')
    return images

# 运行10秒后停止采集
time.sleep(10)
manager.stop_collection()

print(f"Collected {len(manager.data_storage)} frames from cameras")

2.2 目标检测模块

功能描述：

• 检测电网设施的异常状态，如设备损坏、线路故障、异物入侵等
• 识别电网设施的关键组件，如变压器、断路器、绝缘子等
• 检测电网设施周围的安全隐患，如火灾、烟雾、人员违规进入等

代码实现：

import torch
from ultralytics import YOLO

class PowerGridDetector:
    def __init__(self, model_path):
        self.model = YOLO(model_path)
        self.class_names = {
            0: 'transformer',
            1: 'circuit_breaker',
            2: 'insulator',
            3: 'transmission_line',
            4: 'tower',
            5: 'switchgear',
            6: 'damaged_equipment',
            7: 'foreign_object',
            8: 'fire',
            9: 'smoke',
            10: 'person',
            11: 'animal'
        }
    
    def detect(self, image):
        """检测图像中的目标"""
        results = self.model(image)
        detections = []
        
        for result in results:
            for box in result.boxes:
                class_id = int(box.cls[0])
                confidence = float(box.conf[0])
                x1, y1, x2, y2 = box.xyxy[0].tolist()
                
                # 计算目标大小
                width = x2 - x1
                height = y2 - y1
                area = width * height
                
                detections.append({
                    'class': self.class_names[class_id],
                    'confidence': confidence,
                    'bbox': [x1, y1, x2, y2],
                    'size': {'width': width, 'height': height, 'area': area}
                })
        
        return detections
    
    def analyze_facility(self, detections):
        """分析设施状况"""
        facility_analysis = {
            'normal_equipment': 0,
            'damaged_equipment': 0,
            'foreign_objects': 0,
            'fires': 0,
            'smoke': 0,
            'people': 0,
            'animals': 0
        }
        
        for detection in detections:
            class_name = detection['class']
            if class_name in ['transformer', 'circuit_breaker', 'insulator', 'transmission_line', 'tower', 'switchgear']:
                facility_analysis['normal_equipment'] += 1
            elif class_name == 'damaged_equipment':
                facility_analysis['damaged_equipment'] += 1
            elif class_name == 'foreign_object':
                facility_analysis['foreign_objects'] += 1
            elif class_name == 'fire':
                facility_analysis['fires'] += 1
            elif class_name == 'smoke':
                facility_analysis['smoke'] += 1
            elif class_name == 'person':
                facility_analysis['people'] += 1
            elif class_name == 'animal':
                facility_analysis['animals'] += 1
        
        # 计算风险评分
        risk_score = 0
        if facility_analysis['damaged_equipment'] > 0:
            risk_score += 30 * facility_analysis['damaged_equipment']
        if facility_analysis['foreign_objects'] > 0:
            risk_score += 20 * facility_analysis['foreign_objects']
        if facility_analysis['fires'] > 0:
            risk_score += 50 * facility_analysis['fires']
        if facility_analysis['smoke'] > 0:
            risk_score += 25 * facility_analysis['smoke']
        if facility_analysis['people'] > 0:
            risk_score += 15 * facility_analysis['people']
        if facility_analysis['animals'] > 0:
            risk_score += 10 * facility_analysis['animals']
        
        # 确定风险等级
        if risk_score >= 100:
            risk_level = 'critical'
        elif risk_score >= 50:
            risk_level = 'high'
        elif risk_score >= 20:
            risk_level = 'medium'
        else:
            risk_level = 'low'
        
        return {
            'facility_analysis': facility_analysis,
            'risk_score': risk_score,
            'risk_level': risk_level
        }

# 示例用法
detector = PowerGridDetector('yolov26n_powergrid.pt')

# 检测单张图像
image = cv2.imread('data/images/substation.jpg')
detections = detector.detect(image)

# 分析设施状况
facility_analysis = detector.analyze_facility(detections)
print(f"Risk level: {facility_analysis['risk_level']}")
print(f"Risk score: {facility_analysis['risk_score']}")
print(f"Damaged equipment: {facility_analysis['facility_analysis']['damaged_equipment']}")
print(f"Foreign objects: {facility_analysis['facility_analysis']['foreign_objects']}")
print(f"Fires: {facility_analysis['facility_analysis']['fires']}")
print(f"People: {facility_analysis['facility_analysis']['people']}")

2.3 状态分析模块

功能描述：

• 分析电网设施的运行状态和健康状况
• 预测设施故障风险
• 生成维护建议

代码实现：

import numpy as np
from datetime import datetime, timedelta

class PowerGridAnalyzer:
    def __init__(self):
        pass
    
    def analyze_equipment_health(self, facility_analysis, historical_data=None):
        """分析设备健康状况"""
        damaged_equipment = facility_analysis['facility_analysis']['damaged_equipment']
        foreign_objects = facility_analysis['facility_analysis']['foreign_objects']
        fires = facility_analysis['facility_analysis']['fires']
        smoke = facility_analysis['facility_analysis']['smoke']
        risk_score = facility_analysis['risk_score']
        
        # 计算健康评分
        health_score = 100 - min(100, risk_score)
        
        # 确定健康等级
        if health_score >= 80:
            health_level = 'excellent'
        elif health_score >= 60:
            health_level = 'good'
        elif health_score >= 40:
            health_level = 'fair'
        elif health_score >= 20:
            health_level = 'poor'
        else:
            health_level = 'critical'
        
        # 生成维护建议
        recommendations = []
        
        if damaged_equipment > 0:
            recommendations.append('Inspect and repair damaged equipment immediately')
        
        if foreign_objects > 0:
            recommendations.append('Remove foreign objects from power grid facilities')
        
        if fires > 0:
            recommendations.append('Activate fire suppression systems and evacuate area')
        
        if smoke > 0:
            recommendations.append('Investigate smoke source and check for fire')
        
        if health_level in ['poor', 'critical']:
            recommendations.append('Schedule comprehensive maintenance inspection')
        
        return {
            'health_score': health_score,
            'health_level': health_level,
            'recommendations': recommendations
        }
    
    def predict_failure_risk(self, facility_analysis, historical_data=None):
        """预测故障风险"""
        damaged_equipment = facility_analysis['facility_analysis']['damaged_equipment']
        foreign_objects = facility_analysis['facility_analysis']['foreign_objects']
        fires = facility_analysis['facility_analysis']['fires']
        smoke = facility_analysis['facility_analysis']['smoke']
        risk_score = facility_analysis['risk_score']
        
        # 基于当前状态预测故障风险
        base_risk = risk_score / 100
        
        # 考虑历史数据
        if historical_data:
            # 简单示例：如果历史上有类似情况导致故障，则增加风险
            historical_failures = sum(1 for record in historical_data if record.get('failure', False))
            if historical_failures > 0:
                base_risk *= 1.5
        
        # 确定风险等级
        if base_risk >= 0.8:
            risk_level = 'imminent'
            time_to_failure = 'within 24 hours'
        elif base_risk >= 0.5:
            risk_level = 'high'
            time_to_failure = 'within 72 hours'
        elif base_risk >= 0.3:
            risk_level = 'medium'
            time_to_failure = 'within 7 days'
        else:
            risk_level = 'low'
            time_to_failure = 'within 30 days'
        
        return {
            'failure_risk': min(1.0, base_risk),
            'risk_level': risk_level,
            'time_to_failure': time_to_failure
        }
    
    def generate_maintenance_plan(self, facility_analysis, health_analysis, failure_prediction):
        """生成维护计划"""
        plan = {
            'timestamp': datetime.now().isoformat(),
            'facility_analysis': facility_analysis,
            'health_analysis': health_analysis,
            'failure_prediction': failure_prediction,
            'action_items': self._generate_action_items(health_analysis, failure_prediction),
            'maintenance_schedule': self._generate_maintenance_schedule(health_analysis, failure_prediction)
        }
        
        return plan
    
    def _generate_action_items(self, health_analysis, failure_prediction):
        """生成具体行动项"""
        action_items = []
        
        # 基于健康分析的行动项
        for recommendation in health_analysis['recommendations']:
            priority = 'high' if 'immediately' in recommendation.lower() or 'activate' in recommendation.lower() else 'medium'
            action_items.append({
                'task': recommendation,
                'priority': priority,
                'deadline': 'Immediate' if priority == 'high' else 'Within 24 hours'
            })
        
        # 基于故障预测的行动项
        if failure_prediction['risk_level'] in ['imminent', 'high']:
            action_items.append({
                'task': 'Emergency maintenance',
                'priority': 'critical',
                'deadline': 'Immediate'
            })
        
        return action_items
    
    def _generate_maintenance_schedule(self, health_analysis, failure_prediction):
        """生成维护计划"""
        # 基于健康等级和故障风险确定维护频率
        if failure_prediction['risk_level'] == 'imminent':
            frequency = 'Daily'
            next_maintenance = datetime.now()
        elif failure_prediction['risk_level'] == 'high':
            frequency = 'Every 3 days'
            next_maintenance = datetime.now() + timedelta(days=3)
        elif health_analysis['health_level'] == 'critical':
            frequency = 'Weekly'
            next_maintenance = datetime.now() + timedelta(days=7)
        elif health_analysis['health_level'] == 'poor':
            frequency = 'Every 2 weeks'
            next_maintenance = datetime.now() + timedelta(days=14)
        else:
            frequency = 'Monthly'
            next_maintenance = datetime.now() + timedelta(days=30)
        
        return {
            'frequency': frequency,
            'next_maintenance_date': next_maintenance.isoformat(),
            'maintenance_parameters': ['Equipment condition', 'Foreign object removal', 'Fire safety', 'Electrical connections']
        }

# 示例用法
analyzer = PowerGridAnalyzer()
health_analysis = analyzer.analyze_equipment_health(facility_analysis)
print(f"Health level: {health_analysis['health_level']}")
print(f"Health score: {health_analysis['health_score']:.2f}")
print("Recommendations:")
for rec in health_analysis['recommendations']:
    print(f"- {rec}")

# 预测故障风险
failure_prediction = analyzer.predict_failure_risk(facility_analysis)
print(f"\nFailure risk: {failure_prediction['failure_risk']:.2f}")
print(f"Risk level: {failure_prediction['risk_level']}")
print(f"Time to failure: {failure_prediction['time_to_failure']}")

# 生成维护计划
maintenance_plan = analyzer.generate_maintenance_plan(facility_analysis, health_analysis, failure_prediction)
print("\nMaintenance plan:")
print(f"Action items: {len(maintenance_plan['action_items'])}")
for item in maintenance_plan['action_items']:
    print(f"- {item['task']} (Priority: {item['priority']}, Deadline: {item['deadline']})")
print(f"\nMaintenance schedule: {maintenance_plan['maintenance_schedule']['frequency']}")
print(f"Next maintenance date: {maintenance_plan['maintenance_schedule']['next_maintenance_date']}")

2.4 预警与决策模块

功能描述：

• 根据检测结果生成预警信息
• 基于预警信息做出决策
• 与现有电网管理系统集成

代码实现：

import json
from datetime import datetime

class AlertManager:
    def __init__(self, config=None):
        self.config = config or {}
        self.alerts = []
    
    def generate_alert(self, facility_analysis, health_analysis, failure_prediction):
        """生成预警信息"""
        alert_level = self._determine_alert_level(facility_analysis, health_analysis, failure_prediction)
        alert_message = self._generate_alert_message(facility_analysis, health_analysis, failure_prediction, alert_level)
        alert_id = f"alert_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{hash(alert_message) % 10000}"
        
        alert = {
            'alert_id': alert_id,
            'timestamp': datetime.now().isoformat(),
            'level': alert_level,
            'message': alert_message,
            'facility_analysis': facility_analysis,
            'health_analysis': health_analysis,
            'failure_prediction': failure_prediction,
            'status': 'new'
        }
        
        self.alerts.append(alert)
        return alert
    
    def _determine_alert_level(self, facility_analysis, health_analysis, failure_prediction):
        """确定预警级别"""
        # 基于火灾和烟雾的预警
        if facility_analysis['facility_analysis']['fires'] > 0:
            return 'critical'
        
        if facility_analysis['facility_analysis']['smoke'] > 0:
            return 'high'
        
        # 基于设备损坏的预警
        if facility_analysis['facility_analysis']['damaged_equipment'] > 3:
            return 'critical'
        elif facility_analysis['facility_analysis']['damaged_equipment'] > 1:
            return 'high'
        elif facility_analysis['facility_analysis']['damaged_equipment'] > 0:
            return 'medium'
        
        # 基于健康状况的预警
        if health_analysis['health_level'] == 'critical':
            return 'critical'
        elif health_analysis['health_level'] == 'poor':
            return 'high'
        elif health_analysis['health_level'] == 'fair':
            return 'medium'
        
        # 基于故障预测的预警
        if failure_prediction['risk_level'] == 'imminent':
            return 'critical'
        elif failure_prediction['risk_level'] == 'high':
            return 'high'
        elif failure_prediction['risk_level'] == 'medium':
            return 'medium'
        
        return 'low'
    
    def _generate_alert_message(self, facility_analysis, health_analysis, failure_prediction, alert_level):
        """生成预警消息"""
        messages = []
        
        if facility_analysis['facility_analysis']['fires'] > 0:
            messages.append(f"Fire detected at power grid facility")
        
        if facility_analysis['facility_analysis']['smoke'] > 0:
            messages.append(f"Smoke detected at power grid facility")
        
        if facility_analysis['facility_analysis']['damaged_equipment'] > 0:
            messages.append(f"{facility_analysis['facility_analysis']['damaged_equipment']} damaged equipment items detected")
        
        if facility_analysis['facility_analysis']['foreign_objects'] > 0:
            messages.append(f"{facility_analysis['facility_analysis']['foreign_objects']} foreign objects detected")
        
        if health_analysis['health_level'] in ['poor', 'critical']:
            messages.append(f"Equipment health is {health_analysis['health_level']} (score: {health_analysis['health_score']:.2f})")
        
        if failure_prediction['risk_level'] in ['imminent', 'high']:
            messages.append(f"High risk of equipment failure predicted within {failure_prediction['time_to_failure']}")
        
        if not messages:
            messages.append("Routine inspection completed - no issues detected")
        
        return '; '.join(messages)
    
    def process_alerts(self):
        """处理预警信息"""
        processed_alerts = []
        
        for alert in self.alerts:
            if alert['status'] == 'new':
                # 处理预警
                self._process_alert(alert)
                alert['status'] = 'processed'
                processed_alerts.append(alert)
        
        return processed_alerts
    
    def _process_alert(self, alert):
        """处理单个预警"""
        alert_level = alert['level']
        
        # 根据预警级别执行不同的操作
        if alert_level == 'critical':
            # 执行紧急操作，如切断电源、启动消防系统等
            self._execute_emergency_actions(alert)
        elif alert_level == 'high':
            # 执行高优先级操作，如调度维修人员等
            self._execute_high_priority_actions(alert)
        elif alert_level == 'medium':
            # 执行中等优先级操作，如安排维修计划等
            self._execute_medium_priority_actions(alert)
        
        # 记录预警处理
        print(f"Processed alert: {alert['alert_id']} (Level: {alert_level})")
    
    def _execute_emergency_actions(self, alert):
        """执行紧急操作"""
        # 实际应用中，这里应该与电网管理系统集成，执行具体的紧急操作
        print("Executing emergency actions...")
        print(f"Alert message: {alert['message']}")
        # 示例操作：
        # 1. 切断受影响区域的电源
        # 2. 启动消防系统
        # 3. 通知应急响应团队
        # 4. 向电网调度中心发送故障信息
    
    def _execute_high_priority_actions(self, alert):
        """执行高优先级操作"""
        print("Executing high priority actions...")
        print(f"Alert message: {alert['message']}")
        # 示例操作：
        # 1. 调度维修人员
        # 2. 准备维修材料
        # 3. 制定维修计划
    
    def _execute_medium_priority_actions(self, alert):
        """执行中等优先级操作"""
        print("Executing medium priority actions...")
        print(f"Alert message: {alert['message']}")
        # 示例操作：
        # 1. 安排定期检查
        # 2. 更新维护计划
        # 3. 监控设备状态
    
    def integrate_with_grid_management_system(self, alert):
        """与电网管理系统集成"""
        # 实际应用中，这里应该实现与电网管理系统的API集成
        integration_data = {
            'alert_id': alert['alert_id'],
            'timestamp': alert['timestamp'],
            'level': alert['level'],
            'message': alert['message'],
            'facility_analysis': alert['facility_analysis'],
            'health_analysis': alert['health_analysis'],
            'failure_prediction': alert['failure_prediction']
        }
        
        # 示例：发送数据到电网管理系统
        print("Integrating with grid management system...")
        print(json.dumps(integration_data, indent=2)[:500] + "...")
        
        return integration_data

# 示例用法
alert_manager = AlertManager()

# 生成预警
alert = alert_manager.generate_alert(facility_analysis, health_analysis, failure_prediction)
print(f"Generated alert: {alert['alert_id']}")
print(f"Alert level: {alert['level']}")
print(f"Alert message: {alert['message']}")

# 处理预警
processed_alerts = alert_manager.process_alerts()
print(f"Processed {len(processed_alerts)} alerts")

# 与电网管理系统集成
integration_data = alert_manager.integrate_with_grid_management_system(alert)
print("Integration with grid management system completed")

3. 模型训练与评估

3.1 模型配置

代码实现：

from ultralytics import YOLO

# 模型配置
model = YOLO('yolov26n.pt')  # 使用YOLOv26 nano版本

# 训练参数配置
train_config = {
    'data': 'powergrid_dataset.yaml',
    'epochs': 150,
    'batch': 8,
    'imgsz': 640,
    'optimizer': 'AdamW',
    'lr0': 0.001,
    'weight_decay': 0.0005,
    'workers': 4,
    'device': '0',  # 使用GPU
    'project': 'powergrid_detection',
    'name': 'yolov26n_powergrid'
}

# 开始训练
results = model.train(**train_config)

# 模型评估
metrics = model.val()
print(f"mAP@0.5: {metrics.box.map:.4f}")
print(f"mAP@0.5:0.95: {metrics.box.map5095:.4f}")

# 导出为ONNX格式
model.export(format='onnx', imgsz=640)

3.2 数据集配置

powergrid_dataset.yaml：

path: ./datasets/powergrid_dataset
 train: images/train
 val: images/val
 test: images/test

names:
  0: transformer
  1: circuit_breaker
  2: insulator
  3: transmission_line
  4: tower
  5: switchgear
  6: damaged_equipment
  7: foreign_object
  8: fire
  9: smoke
 10: person
 11: animal

4. 部署与集成

4.1 边缘部署

代码实现：

import cv2
import numpy as np
import onnxruntime as ort

class EdgeDetector:
    def __init__(self, model_path):
        self.session = ort.InferenceSession(model_path)
        self.input_name = self.session.get_inputs()[0].name
        self.output_names = [output.name for output in self.session.get_outputs()]
        self.class_names = {
            0: 'transformer',
            1: 'circuit_breaker',
            2: 'insulator',
            3: 'transmission_line',
            4: 'tower',
            5: 'switchgear',
            6: 'damaged_equipment',
            7: 'foreign_object',
            8: 'fire',
            9: 'smoke',
            10: 'person',
            11: 'animal'
        }
    
    def preprocess(self, image):
        """预处理图像"""
        # 调整图像大小
        image = cv2.resize(image, (640, 640))
        # 转换为RGB
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        # 归一化
        image = image / 255.0
        # 调整维度
        image = np.transpose(image, (2, 0, 1))
        image = np.expand_dims(image, axis=0)
        # 转换为float32
        image = image.astype(np.float32)
        return image
    
    def postprocess(self, outputs, image_shape, conf_threshold=0.5):
        """后处理模型输出"""
        detections = []
        
        # 注意：具体处理方式取决于模型输出格式
        # 这里仅作为示例
        
        return detections
    
    def detect(self, image):
        """执行检测"""
        # 预处理
        input_tensor = self.preprocess(image)
        # 推理
        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
        # 后处理
        detections = self.postprocess(outputs, image.shape)
        return detections

# 示例用法（在NVIDIA Jetson上部署）
detector = EdgeDetector('yolov26n_powergrid.onnx')

# 处理摄像头采集的图像
cap = cv2.VideoCapture(0)  # 假设0是电网摄像头

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    
    # 检测目标
    detections = detector.detect(frame)
    
    # 可视化结果
    for detection in detections:
        bbox = detection['bbox']
        class_name = detection['class']
        confidence = detection['confidence']
        
        # 根据类别设置颜色
        color = self._get_class_color(class_name)
        
        cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color, 2)
        cv2.putText(frame, f"{class_name}: {confidence:.2f}", (int(bbox[0]), int(bbox[1])-10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
    
    cv2.imshow('Power Grid Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

4.2 系统集成

代码实现：

class PowerGridMonitoringSystem:
    def __init__(self, config):
        self.config = config
        self.data_collector_manager = DataCollectorManager(config)
        self.detector = PowerGridDetector(config.get('model_path', 'yolov26n_powergrid.pt'))
        self.analyzer = PowerGridAnalyzer()
        self.alert_manager = AlertManager(config.get('alert_config', {}))
    
    def start(self):
        """启动系统"""
        print("Starting Power Grid Monitoring System...")
        self.data_collector_manager.start_collection()
        print("System started")
    
    def stop(self):
        """停止系统"""
        print("Stopping Power Grid Monitoring System...")
        self.data_collector_manager.stop_collection()
        print("System stopped")
    
    async def run_uav_inspection(self, facility_coords, image_save_dir):
        """运行无人机巡检"""
        return await self.data_collector_manager.run_uav_inspection(facility_coords, image_save_dir)
    
    def process_data(self):
        """处理采集的数据"""
        # 获取采集的数据
        data = self.data_collector_manager.data_storage
        results = []
        
        for frame_data in data:
            # 处理单帧数据
            result = self._process_frame(frame_data)
            if result:
                results.append(result)
        
        return results
    
    def _process_frame(self, frame_data):
        """处理单帧数据"""
        frame = frame_data['frame']
        timestamp = frame_data['timestamp']
        source = frame_data['source']
        
        # 检测目标
        detections = self.detector.detect(frame)
        
        # 分析设施状况
        facility_analysis = self.detector.analyze_facility(detections)
        
        # 分析健康状况
        health_analysis = self.analyzer.analyze_equipment_health(facility_analysis)
        
        # 预测故障风险
        failure_prediction = self.analyzer.predict_failure_risk(facility_analysis)
        
        # 生成预警
        alert = self.alert_manager.generate_alert(facility_analysis, health_analysis, failure_prediction)
        
        # 生成维护计划
        maintenance_plan = self.analyzer.generate_maintenance_plan(facility_analysis, health_analysis, failure_prediction)
        
        result = {
            'timestamp': timestamp,
            'source': source,
            'detections': detections,
            'facility_analysis': facility_analysis,
            'health_analysis': health_analysis,
            'failure_prediction': failure_prediction,
            'alert': alert,
            'maintenance_plan': maintenance_plan
        }
        
        return result
    
    def generate_report(self, results):
        """生成报告"""
        if not results:
            return None
        
        # 汇总结果
        summary = self._summarize_results(results)
        
        report = {
            'timestamp': datetime.now().isoformat(),
            'report_id': f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            'summary': summary,
            'detailed_results': results
        }
        
        return report
    
    def _summarize_results(self, results):
        """汇总结果"""
        summary = {
            'total_frames': len(results),
            'total_detections': 0,
            'damaged_equipment_count': 0,
            'foreign_objects_count': 0,
            'fires_count': 0,
            'smoke_count': 0,
            'people_count': 0,
            'animals_count': 0,
            'average_health_score': 0,
            'alert_count': 0,
            'alert_levels': {}
        }
        
        health_scores = []
        
        for result in results:
            # 统计检测结果
            summary['total_detections'] += len(result['detections'])
            summary['damaged_equipment_count'] += result['facility_analysis']['facility_analysis']['damaged_equipment']
            summary['foreign_objects_count'] += result['facility_analysis']['facility_analysis']['foreign_objects']
            summary['fires_count'] += result['facility_analysis']['facility_analysis']['fires']
            summary['smoke_count'] += result['facility_analysis']['facility_analysis']['smoke']
            summary['people_count'] += result['facility_analysis']['facility_analysis']['people']
            summary['animals_count'] += result['facility_analysis']['facility_analysis']['animals']
            
            # 统计健康评分
            health_scores.append(result['health_analysis']['health_score'])
            
            # 统计预警
            summary['alert_count'] += 1
            alert_level = result['alert']['level']
            if alert_level not in summary['alert_levels']:
                summary['alert_levels'][alert_level] = 0
            summary['alert_levels'][alert_level] += 1
        
        # 计算平均健康评分
        if health_scores:
            summary['average_health_score'] = sum(health_scores) / len(health_scores)
        
        return summary

# 示例用法
config = {
    'model_path': 'yolov26n_powergrid.pt',
    'cameras': {
        'substation': {'index': 0, 'width': 1280, 'height': 720},
        'transmission_tower': {'index': 1, 'width': 1280, 'height': 720},
        'transformer': {'index': 2, 'width': 1280, 'height': 720}
    }
}

system = PowerGridMonitoringSystem(config)
system.start()

# 运行10秒后处理数据
time.sleep(10)
results = system.process_data()

# 生成报告
report = system.generate_report(results)
if report:
    print(f"Generated report: {report['report_id']}")
    print(f"Total frames processed: {report['summary']['total_frames']}")
    print(f"Average health score: {report['summary']['average_health_score']:.2f}")
    print(f"Alert count: {report['summary']['alert_count']}")
    print(f"Alert levels: {report['summary']['alert_levels']}")

# 停止系统
system.stop()

5. 系统评估与优化

5.1 性能评估

代码实现：

def evaluate_model(model_path, test_dataset):
    model = YOLO(model_path)
    metrics = model.val(data=test_dataset)
    
    evaluation_results = {
        'mAP@0.5': metrics.box.map,
        'mAP@0.5:0.95': metrics.box.map5095,
        'precision': metrics.box.precision.mean().item(),
        'recall': metrics.box.recall.mean().item()
    }
    
    return evaluation_results

# 评估模型
eval_results = evaluate_model('yolov26n_powergrid.pt', 'powergrid_dataset.yaml')
print("Model evaluation results:")
print(f"mAP@0.5: {eval_results['mAP@0.5']:.4f}")
print(f"mAP@0.5:0.95: {eval_results['mAP@0.5:0.95']:.4f}")
print(f"Precision: {eval_results['precision']:.4f}")
print(f"Recall: {eval_results['recall']:.4f}")

# 性能测试
def test_performance(detector, test_images):
    import time
    times = []
    
    for image_path in test_images:
        image = cv2.imread(image_path)
        if image is None:
            continue
        
        start_time = time.time()
        detections = detector.detect(image)
        end_time = time.time()
        times.append(end_time - start_time)
    
    avg_time = sum(times) / len(times) if times else 0
    fps = 1 / avg_time if avg_time > 0 else 0
    
    return {
        'average_inference_time': avg_time,
        'fps': fps,
        'test_images_count': len(test_images)
    }

# 测试性能
test_images = [f'data/test_images/image_{i}.jpg' for i in range(10)]
detector = PowerGridDetector('yolov26n_powergrid.pt')
perf_results = test_performance(detector, test_images)

print("Performance test results:")
print(f"Average inference time: {perf_results['average_inference_time']:.4f} seconds")
print(f"FPS: {perf_results['fps']:.2f}")
print(f"Tested on {perf_results['test_images_count']} images")

5.2 优化策略

1. 模型优化

   • 使用模型量化减少模型大小和推理时间
   • 针对电网设施的特殊场景进行模型微调
   • 采用知识蒸馏提升小型模型性能

2. 数据优化

   • 增加电网设施数据的多样性，包括不同类型的设备、天气条件和光线条件
   • 使用合成数据扩充训练集
   • 采用主动学习选择难例进行标注

3. 系统优化

   • 实现多线程处理提高数据采集和处理速度
   • 优化无人机飞行路径，减少飞行时间和电池消耗
   • 使用边缘计算减少数据传输延迟
   • 实现增量学习，使模型能够适应新的电网设施和环境

4. 算法优化

   • 改进目标检测算法，提高小目标检测准确率
   • 开发更精确的设备健康评估算法
   • 优化故障预测模型，提高预测准确性

5. 硬件优化

   • 选择适合电网环境的摄像头和传感器
   • 优化传感器配置，提高数据采集质量
   • 增加无人机电池容量，延长飞行时间

6. 应用场景与案例

6.1 应用场景

1. 变电站监测

   • 监测变电站内设备的运行状态
   • 检测设备异常和安全隐患
   • 确保变电站的安全运行

2. 输电线路监测

   • 监测输电线路的完整性
   • 检测线路上的异物和损坏
   • 预防线路故障和跳闸

3. 配电设备监测

   • 监测配电设备的运行状态
   • 检测设备异常和安全隐患
   • 提高配电系统的可靠性

4. 新能源接入监测

   • 监测新能源接入点的设备状态
   • 确保新能源的安全接入
   • 优化新能源的并网管理

5. 电网应急响应

   • 在自然灾害后快速评估电网设施受损情况
   • 指导应急抢修工作
   • 加速电网恢复供电

6.2 案例分析

案例：某电网公司智能电网设施监测系统应用

• 背景：该电网公司负责管理覆盖1000平方公里的电网设施，包括20座变电站和500公里输电线路，传统人工巡检效率低下，难以及时发现设备异常。

• 解决方案：部署基于YOLOv26的智能电网设施监测系统，在变电站安装固定摄像头，定期使用无人机巡检输电线路，实时监测电网设施状态。

• 实施效果：

  • 巡检效率提高90%（从人工巡检需要2周减少到系统巡检仅需2天）
  • 设备故障发现时间提前7-10天，减少故障损失60%
  • 运维成本降低40%
  • 电网供电可靠性提高到99.99%
  • 减少人工巡检的安全风险

• 技术要点：

  • 使用YOLOv26n模型在边缘设备上实现实时检测
  • 结合红外热成像技术提高设备异常检测准确率
  • 实现无人机自动巡检输电线路
  • 与电网调度系统集成，实现数据共享和分析

7. 总结与展望

7.1 系统总结

本系统基于YOLOv26实现了智能电网设施监测的自动化和智能化，主要功能包括：

• 自动监测：通过固定摄像头和无人机自动监测电网设施状态
• 精准检测：使用YOLOv26检测电网设施的异常状态和潜在风险
• 智能分析：基于检测结果分析设施健康状况并预测故障风险
• 及时预警：根据分析结果生成预警信息并执行相应的操作
• 决策支持：生成详细的维护计划，为电网运维提供科学依据
• 边缘部署：在资源受限的设备上实现实时处理

系统采用模块化设计，具有良好的可扩展性和可维护性，可根据不同电网场景进行定制化调整。

7.2 未来展望

1. 技术发展

   • 探索使用更先进的YOLO系列模型提高检测精度和速度
   • 集成深度学习和传统电力系统知识，提高分析和决策的准确性
   • 开发多模态融合模型，整合可见光、红外和热成像数据
   • 实现实时语义分割，获取更详细的电网设施信息

2. 应用扩展

   • 将系统扩展到更多类型的电网设施和场景
   • 与气象预测系统集成，实现更精准的电网风险评估
   • 开发基于区块链的电网数据共享平台，促进电网大数据应用
   • 与智能电网设备集成，实现自动化电网管理

3. 产业影响

   • 推动智能电网的发展，提高电网运行效率和可靠性
   • 促进电网运维的数字化转型，降低运维成本
   • 提高电网应对自然灾害的能力，减少故障损失
   • 为新能源的安全接入和管理提供技术支持

4. 社会影响

   • 提高电力供应的稳定性和可靠性，保障社会经济发展
   • 减少电网故障导致的停电事故，提高人民生活质量
   • 降低电网运维的安全风险，保障运维人员的生命安全
   • 促进能源的高效利用，支持可持续发展

通过不断的技术创新和应用推广，基于YOLOv26的智能电网设施监测系统有望在未来成为智能电网的核心技术之一，为全球电网的安全、可靠和高效运行做出贡献。