针对传统无人机辅助的无线传感器网络数据收集方案仅优化无人机能耗而忽略无线传感器能耗的问题，提出了一种综合考虑无人机和无线传感器能耗的联合优化方案。首先，利用K-means算法和无人机与无线传感器之间的通信阈值进行聚类分析，实现无线传感器的有效分簇。其次，构建了一个多目标优化模型，旨在协同优化传感器能耗和无人机悬停能耗，并利用多目标粒子群算法求解最优的无人机悬停位置和无线传感器发射功率。最后，基于各簇中无人机的最优悬停位置，利用蚁群算法计算无人机的最优飞行路径，以最小化无人机的飞行能耗，从而最小化整个数据收集系统的总能耗。通过仿真实验的结果表明，相较于传统方法，本文所提出的方案在系统能耗上取得了显著效果。特别地，当分簇半径为120 m时，传感器能耗降低了16.2%，无人机能耗降低了24.9%。

A comprehensive joint optimization solution was proposed to address the issue of traditional UAV(unmanned aerial vehicle)-assisted wireless sensor network data collection schemes, where only UAV energy consumption was optimized, while wireless sensor energy consumption is neglected. Firstly, clustering analysis was performed using the K-means algorithm and communication threshold between UAVs and wireless sensors to achieve effective clustering of wireless sensors. Secondly, a multi-objective optimization model was constructed to collaboratively optimize sensor energy consumption and UAV hovering energy consumption. The optimal UAV hovering position and wireless sensor transmission power were determined using a multi-objective particle swarm optimization algorithm. Finally, based on the optimal hovering positions of UAVs in each cluster, an ant colony algorithm was applied to compute the optimal flight path of UAVs, minimizing UAV's flight energy consumption and thus minimizing the overall energy consumption of the entire data collection system. Simulation results indicate that the proposed solution achieves significant improvements in system energy consumption compared to traditional methods. Specifically, when the clustering radius is 120 meters, sensor energy consumption is reduced by 16.2%, and UAV energy consumption is reduced by 24.9%.