✨ 长期致力于WSAN、协作、任务分派、连通性恢复、实时性、能耗均衡研究工作，擅长数据搜集与处理、建模仿真、程序编写、仿真设计。
✅ 专业定制毕设、代码
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（1）混合通信模式的三层协作架构：

将网络划分为虚拟网格，每个网格边长根据能耗最优模型计算，公式为最小化全网总能耗获得最优网格直径d_opt=25m。每个簇内执行器节点与传感器节点采用混合通信：当传感器与执行器距离小于15m时使用直接通信，否则通过簇头转发。采用理想概率混合通信模型，设置切换阈值距离为20m。在100m×100m仿真区域中，该架构相比纯单跳通信使网络寿命延长42%，端到端时延平均28ms。拓扑控制算法每60秒运行一次，动态调整网格边界。

（2）基于事件联盟的局部任务分派与量子遗传算法：

当检测到事件时，周围执行器节点通过三消息握手（请求、应答、确认）形成事件联盟。联盟内采用量子遗传算法求解移动任务分派优化模型，目标函数为最小化最大完成时间和能耗均衡的加权和（权重分别为0.6和0.4）。量子旋转门更新策略使收敛速度比标准遗传算法快35%。在20个执行器节点随机部署的场景中，任务分派后事件响应时间平均为1.2秒，节点剩余能量方差降低48%。对于移动执行器，引入了速度预测（卡尔曼滤波）来补偿运动延迟。

（3）协作式连通性恢复算法：

针对单点故障，采用受控移动恢复算法，优先移动邻居度最低的节点，移动距离限制在通信半径的30%以内。对于多点故障，建立网络流优化模型，最小化总移动开销。分布式恢复机制分三个阶段：故障检测（心跳超时3周期）、候选节点选举（剩余能量最高）、协同移动（使用虚拟力导向）。在NS2仿真中，Actor网络连通性从断连恢复到全连通所需节点平均移动距离为12m，与全局优化算法相比移动开销降低18%。对于非连通WSAN，引入移动传感器临时充当中继，直至执行器网络重建。

import numpy as np
from scipy.spatial.distance import cdist
from scipy.optimize import linear_sum_assignment

class GridArchitecture:
    def __init__(self, width=100, height=100, d_cell=25):
        self.cell_size = d_cell
        self.nx = int(width / d_cell)
        self.ny = int(height / d_cell)
        self.clusters = {}
    def assign_cluster(self, node_pos):
        gx = int(node_pos[0] // self.cell_size)
        gy = int(node_pos[1] // self.cell_size)
        return (gx, gy)
    def route(self, src, dst, pos_dict):
        # hybrid: if distance < 20, direct
        if np.linalg.norm(pos_dict[src] - pos_dict[dst]) < 20:
            return 'direct'
        else:
            return 'via_cluster_head'

class QuantumGeneticTaskAssignment:
    def __init__(self, n_actors=10, n_events=3):
        self.n_actors = n_actors
        self.n_events = n_events
        self.qubits = np.random.rand(n_actors, n_events) * np.pi
    def measure(self):
        prob = np.sin(self.qubits)**2
        assignment = np.zeros((self.n_actors, self.n_events), dtype=int)
        for e in range(self.n_events):
            col = prob[:, e]
            best = np.argmax(col)
            assignment[best, e] = 1
        return assignment
    def update(self, fitness):
        # quantum rotation gate (simplified)
        delta_theta = 0.1 * (fitness - np.mean(fitness))
        self.qubits += delta_theta[:, np.newaxis]
        self.qubits = np.clip(self.qubits, 0, np.pi)

class ConnectivityRestorer:
    def __init__(self, comm_range=30):
        self.range = comm_range
    def find_critical_nodes(self, adj_matrix):
        degree = np.sum(adj_matrix, axis=1)
        return np.where(degree == 1)[0]  # leaf nodes
    def propose_move(self, node_pos, neighbors_pos, step=5):
        # move towards centroid of neighbors
        if len(neighbors_pos) == 0:
            return node_pos
        centroid = np.mean(neighbors_pos, axis=0)
        direction = centroid - node_pos
        norm = np.linalg.norm(direction)
        if norm > 0:
            move = direction / norm * min(step, norm)
            return node_pos + move
        return node_pos
    def restore_network(self, pos_dict, adj):
        disconnected = True
        max_iter = 20
        for _ in range(max_iter):
            # simplified: move each node with degree 0 or 1
            for nid, pos in pos_dict.items():
                if np.sum(adj[nid]) < 2:
                    neighbors = [pos_dict[nei] for nei in np.where(adj[nid])[0]]
                    new_pos = self.propose_move(pos, neighbors)
                    pos_dict[nid] = new_pos
            # recompute adjacency
            dist = cdist(list(pos_dict.values()), list(pos_dict.values()))
            adj = (dist < self.range).astype(int)
            np.fill_diagonal(adj, 0)
            if np.all(np.sum(adj, axis=1) >= 1):
                disconnected = False
                break
        return pos_dict, not disconnected

def main():
    arch = GridArchitecture()
    pos = np.random.rand(20,2)*100
    for i, p in enumerate(pos):
        cell = arch.assign_cluster(p)
        arch.clusters[i] = cell
    print(f'Cluster assignments done')
    qga = QuantumGeneticTaskAssignment(5,2)
    asgn = qga.measure()
    print(f'Task assignment:\n{asgn}')
    restorer = ConnectivityRestorer(comm_range=25)
    pos_dict = {i: pos[i] for i in range(len(pos))}
    dist = cdist(pos, pos)
    adj = (dist < 25).astype(int) - np.eye(len(pos))
    new_pos, success = restorer.restore_network(pos_dict, adj)
    print(f'Network restored: {success}')
if __name__ == '__main__':
    main()