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%.