import torch
from torchmeta.modules import MetaModule, MetaLinear

class MetaDiagnosisNet(MetaModule):
    def __init__(self):
        super(MetaDiagnosisNet, self).__init__()
        self.fc1 = MetaLinear(128, 64)
        self.fc2 = MetaLinear(64, 32)
        self.fc3 = MetaLinear(32, 5)  # 5类典型故障

    def forward(self, x, params=None):
        x = torch.relu(self.fc1(x, params=self.get_subdict(params, 'fc1')))
        x = torch.relu(self.fc2(x, params=self.get_subdict(params, 'fc2')))
        return self.fc3(x, params=self.get_subdict(params, 'fc3'))

# 元训练过程示例
def meta_train():
    model = MetaDiagnosisNet()
    meta_opt = torch.optim.Adam(model.parameters(), lr=0.001)

    for task_batch in task_loader:
        # 计算任务损失
        loss = model(task_batch.support_set)
        # 元梯度更新
        grads = torch.autograd.grad(loss, model.parameters())
        meta_opt.step(grads)

// 边缘设备推理代码片段
void diagnose(float* features, int len) {
    // 特征预处理
    for(int i=0; i<len; i++) {
        features[i] = (features[i] - mean[i])/std[i];
    }

    // 加载预训练模型
    tflite::MicroInterpreter interpreter(model, resolver, tensor_arena, 
                                         arena_size, &micro_alloc);

    // 执行推理
    TfLiteStatus invoke_status = interpreter.Invoke();
    if (invoke_status != kTfLiteOk) {
        handle_error();
    }

    // 获取结果
    float* output = interpreter.output(0);
}