变电站表盘破损模糊识别检测基于YOLOv12+Transformer的技术研究

import torch
import torch.nn as nn
import torch.nn.functional as F

class HybridAttentionBlock(nn.Module):
    """
    用于YOLOv12骨干网络的混合注意力模块
    结合CNN的局部特征与Transformer的全局建模能力
    """
    def __init__(self, in_channels, num_heads=8, reduction_ratio=4):
        super(HybridAttentionBlock, self).__init__()
        self.num_heads = num_heads
        head_dim = in_channels // num_heads
        self.scale = head_dim ** -0.5
        
        # 线性映射层
        self.qkv = nn.Conv2d(in_channels, in_channels * 3, kernel_size=1)
        self.proj = nn.Conv2d(in_channels, in_channels, kernel_size=1)
        
        # 局部卷积增强
        self.local_conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1, groups=in_channels)
        
        self.reduction_ratio = reduction_ratio

    def forward(self, x):
        B, C, H, W = x.shape
        
        # 1. 局部特征提取
        local_feat = self.local_conv(x)
        
        # 2. 全局注意力计算
        # Reshape for attention: B, C, H, W -> B, N, C
        qkv = self.qkv(x).reshape(B, 3, self.num_heads, C // self.num_heads, H * W).permute(1, 0, 2, 4, 3)
        q, k, v = qkv[0], qkv[1], qkv[2]
        
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        
        global_feat = (attn @ v).permute(0, 3, 1, 2).reshape(B, C, H, W)
        
        # 3. 特征融合与投影
        out = self.proj(global_feat + local_feat)
        return x + out  # Residual connection

# 模拟输入：Batch=1, Channels=512, H=20, W=20
# 这种结构能有效捕捉表盘破损的长距离纹理依赖

def anomaly_decision_pipeline(detection_result, confidence_threshold=0.85):
    """
    异常决策流水线：结合大模型二次研判逻辑
    """
    alerts = []
    
    for obj in detection_result:
        # 1. 初步筛选
        if obj['confidence'] < confidence_threshold:
            continue
            
        # 2. 规则引擎与大模型研判
        # 例如：如果是"表盘破损"且位于"主变压器"区域，提升告警等级
        if obj['class'] == 'dial_damage' and obj['zone'] == 'main_transformer':
            severity = 'CRITICAL'
            # 调用大模型进行多模态验证（伪代码）
            # llm_verification = call_multimodal_llm(obj['crop_image'], "Confirm damage severity")
        else:
            severity = 'WARNING'
            
        alerts.append({
            "device_id": obj['id'],
            "type": obj['class'],
            "severity": severity,
            "timestamp": obj['time']
        })
        
    return alerts