class PowerScheduler:
    def __init__(self):
        self.edge_nodes = []
        self.energy_budget = 0

    def dynamic_adjust(self, device_profile):
        """基于设备画像的动态电源分配"""
        if device_profile['critical']:
            return 'high_power'
        elif device_profile['activity'] < 0.3:
            return 'sleep_mode'
        else:
            return 'normal_mode'

% 状态空间定义
states = [device_count, 
          network_latency, 
          power_consumption];

% 动作空间定义
actions = {'scale_up', 'scale_down', 'rebalance'};

% 奖励函数设计
reward = -abs(current_load - optimal_load) - 0.5*energy_cost;

class DevicePredictor(nn.Module):
    def __init__(self):
        super().__init__()
        self.lstm = nn.LSTM(input_size=10, hidden_size=64)
        self.fc = nn.Linear(64, 3)  # 3种负载状态

    def forward(self, x):
        out, _ = self.lstm(x)
        return self.fc(out[-1])

pragma solidity ^0.8.0;

contract PowerConsensus {
    struct PowerRequest {
        uint256 deviceId;
        uint8 priority;
        uint32 requiredWatts;
    }

    function allocatePower(PowerRequest[] memory requests) public returns (bool) {
        // 实现基于工作量证明的电力分配逻辑
        // ...
    }
}

def ant_colony_optimization(devices, energy_pool):
    pheromone = initialize_pheromone()
    for iteration in range(MAX_ITER):
        paths = construct_paths(pheromone)
        evaluate_fitness(paths)
        update_pheromone(pheromone, paths)
    return best_path