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(1) 联合区域划分与层间分簇路由的能耗优化算法 (JRDHCR)
为了从源头上降低网络能耗并均衡负载，本文首先对WRSN的拓扑结构进行了重构。传统的静态分簇往往导致靠近基站（Sink）的节点因承担过重的中继任务而过早死亡（热区效应）。JRDHCR算法采用动态簇半径的区域划分方法，根据节点距离Sink的远近自适应调整簇的大小，距离越近簇越小，从而减少簇内通信能耗，为长距离中继预留能量。在分簇阶段，提出基于极大簇的聚类算法，优化了簇首的分布。在最为关键的数据传输阶段，设计了基于中继路由区间划分的层间路由机制。该机制将网络划分为若干层级，规定数据包仅能在相邻层级或特定跨层区间内传输，避免了长距离回传和路由环路。实验表明，JRDHCR显著降低了网络整体能耗，有效缓解了热区效应，延长了网络在无充电介入情况下的基础寿命，为后续的充电规划奠定了良好的网络状态基础。
(2) 考虑抢占更新与双层层次分析法的充电规划算法 (CPUSDAHP)
在能耗优化的基础上，针对移动充电节点（MC）的调度问题，本文提出了CPUSDAHP算法。该算法摒弃了单一的“先到先服务”或“最近距离优先”策略，而是引入了层次分析法（AHP）构建双层决策模型。第一层针对“簇间”规划，综合考虑簇的剩余能量、地理距离、节点密度等宏观指标，计算各簇的充电紧迫度；第二层针对“簇内”规划，细化到具体节点的充电优先级。为了解决低优先级的“饥饿”问题，本文创新性地提出了融合历史信息的抢占更新策略。当MC在前往目标簇的途中，若经过处于“顺路”且能量即将耗尽的次级紧急簇，算法允许打断当前任务进行快速补给（抢占），随后再恢复原计划。这种策略极大地提升了MC的移动路径收益比，减少了无效移动距离，实现了MC能量利用率与网络节点存活率的双重提升。
(3) WRSN能耗优化与充电规划仿真系统设计
为了验证上述算法的有效性并提供可视化的研究平台，本文基于PyQt5和Python开发了一套WRSN仿真系统。该系统集成了网络拓扑生成、能耗模型计算、路由协议模拟以及充电路径动态演示等功能模块。用户可以通过Qt Designer设计的图形界面灵活设置网络参数（如节点数量、初始能量、MC移动速度等），系统后端自动调用JRDHCR和CPUSDAHP算法引擎进行模拟运算。
import heapq import numpy as np class SensorNode: def __init__(self, id, pos, energy): self.id = id self.pos = np.array(pos) self.energy = energy self.cluster_id = -1 class MobileCharger: def __init__(self, start_pos, velocity, max_energy): self.pos = np.array(start_pos) self.velocity = velocity self.energy = max_energy class WRSN_Simulation: def __init__(self, num_nodes, area_size): self.nodes = [SensorNode(i, np.random.rand(2)*area_size, 100) for i in range(num_nodes)] self.mc = MobileCharger([0,0], 5.0, 5000) self.clusters = {} def dynamic_region_division(self, sink_pos): # Mock logic for JRDHCR region division for node in self.nodes: dist = np.linalg.norm(node.pos - sink_pos) # Closer nodes get smaller cluster radius logically if dist < 20: node.cluster_id = 0 elif dist < 50: node.cluster_id = 1 else: node.cluster_id = 2 if node.cluster_id not in self.clusters: self.clusters[node.cluster_id] = [] self.clusters[node.cluster_id].append(node) def calculate_ahp_weights(self): # Simplified AHP weights for charging priority # Criteria: Residual Energy (0.6), Distance (0.3), Node Density (0.1) return np.array([0.6, 0.3, 0.1]) def charging_planning_cpusdahp(self): priority_queue = [] weights = self.calculate_ahp_weights() for cid, nodes in self.clusters.items(): avg_energy = np.mean([n.energy for n in nodes]) cluster_center = np.mean([n.pos for n in nodes], axis=0) dist_to_mc = np.linalg.norm(cluster_center - self.mc.pos) density = len(nodes) # Inverse energy and distance for higher priority score = weights[0]*(100/avg_energy) + weights[1]*(100/dist_to_mc) + weights[2]*density heapq.heappush(priority_queue, (-score, cid, cluster_center)) path = [] while priority_queue: score, cid, target_pos = heapq.heappop(priority_queue) # Preemptive check logic (Simplified) # If passing by a critical cluster, insert it # This is a mock representation of the preemptive strategy current_path_vec = target_pos - self.mc.pos for other_score, other_cid, other_pos in priority_queue: vec_to_other = other_pos - self.mc.pos # Check if 'other' is on the way and critical projection = np.dot(vec_to_other, current_path_vec) / np.linalg.norm(current_path_vec) dist_perp = np.linalg.norm(vec_to_other - projection * (current_path_vec/np.linalg.norm(current_path_vec))) if projection > 0 and projection < np.linalg.norm(current_path_vec) and dist_perp < 5.0: path.append(f"Preemptive Charge Cluster {other_cid}") # Remove from queue logic omitted for brevity path.append(f"Charge Cluster {cid}") self.mc.pos = target_pos self.mc.energy -= 10 # Cost to move return path if __name__ == "__main__": sim = WRSN_Simulation(50, 100) sim.dynamic_region_division(np.array([50,50])) plan = sim.charging_planning_cpusdahp() print(plan)成品代码50-200，定制代码300起，可以直接沟通
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