Research on Spatio-Temporal Collaborative Optimization for UAV-based Dynamic Shading Aerosol Deployment Strategies

Traditional environmental shading methods suffer from limited flexibility and restricted coverage. Unmanned aerial vehicles (UAVs), with their high maneuverability, offer an ideal platform for precise execution of dynamic shading tasks. This paper focuses on the spatio-temporal coordinated optimization problem for the multi-deployment of aerosol-based shading units by UAVs. By establishing a unified spatio-temporal coordinate system, precise kinematic models are developed for the UAV, shading capsules, aerosol clouds, and moving interference sources. In a single-deployment scenario, an optimization model incorporating UAV flight parameters and unit activation timing parameters is formulated to maximize the effective shading duration. The particle swarm optimization (PSO) algorithm is employed to derive the optimal deployment strategy through spatio-temporal coordination. To address multi-directional, multi-batch dynamic interferences in complex experimental scenarios, a multi-objective optimization model is extended. This model balances objectives including maximizing total effective shading time, minimizing initial response time, and minimizing resource consumption. The NSGA-II algorithm is employed to obtain a set of Pareto optimal solutions. This research establishes a comprehensive technical approach from theoretical modeling to algorithmic solution. Simulation validation confirms the model's validity and the algorithm's effectiveness, providing theoretical foundations and algorithmic support for the practical application of UAV-mounted aerosol shading systems.