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在网站中动态效果怎么做,销售网站平台搭建,快速网站开发框架,网页设计代码是怎么加链接✨ 本团队擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导#xff0c;毕业论文、期刊论文经验交流。✅ 专业定制毕设、代码✅ 成品或定制#xff0c;查看文章底部微信二维码(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…✨ 本团队擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导毕业论文、期刊论文经验交流。✅ 专业定制毕设、代码✅ 成品或定制查看文章底部微信二维码(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], axis0) 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(fPreemptive Charge Cluster {other_cid}) # Remove from queue logic omitted for brevity path.append(fCharge 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