Unmanned aerial vehicle （UAV） communication faces challenges such as path loss and intergroup interference. To meet the discrete users’ communication needs， achieve static deployment of UAV networks， and maximize energy efficiency， this paper studies a multi-ratio concave-convex fractional programming problem. A convex optimization cooperative swarm intelligence strategy is proposed， which decouples the original problem into separate power control and height optimization problems， solving them iteratively. Firstly， a line-of-sight （LoS） probability average path loss model is introduced to study the relationship between deployment height and horizontal distance， as well as the three-dimensional deployment problem through pitch angles. Secondly， a quadratic transformation is utilized to decouple the original problem， aiming to enhance system energy efficiency under the LoS probability link. Finally， a fast feedback particle swarm algorithm is proposed for accurate deployment of heights， addressing the complex multi-objective cooperative optimization problem. Simulation results demonstrate that， under the proposed model， the strategy achieves the balance between algorithm complexity and accuracy， enabling efficient and accurate deployment of UAV base stations.