In emergency communication scenarios, unmanned aerial vehicles (UAVs) serve as aerial data collection platforms that can be rapidly deployed to disaster-stricken areas such as those affected by earthquakes, floods, wildfires, mining accidents and battlefield environments. UAVs are capable of collecting data from wireless IoT devices and transmitting it to the command center, thereby improving the efficiency of rescue decision-making. In post-disaster scenarios, data transmission tasks impose higher requirements on both communication efficiency and data completeness. Meanwhile, the limited energy supply of UAVs makes it challenging to collect large volumes of data efficiently while ensuring the complete transmission of vital information. To address these issues, this paper investigates a wireless communication system assisted by a single UAV, which adopts a multi-user uplink communication mode and a fly-hover-communicate data collection pattern. A joint optimization problem is formulated for device association, UAV hovering location and bandwidth allocation, aiming to maximize the UAV′s coverage utility while minimizing its total energy consumption. First, to enhance the UAV′s coverage utility, a particle swarm optimization (PSO) algorithm initialized with K-means clustering is employed. Then, to minimize energy consumption, we propose a PSO-based two-stage optimization framework that alternately optimizes hovering positions and bandwidth allocation. In particular, a PSO variant incorporating Gaussian perturbation and differential mechanisms is designed for hovering position refinement. Simulation results demonstrate that the proposed method effectively improves both coverage utility and energy efficiency. The coverage utility increased by 13.15% compared to the K-means algorithm, while energy consumption was reduced by 18.24% compared to the approach that only optimizes hovering locations and lower than the scheme where hovering energy and flight energy are optimized separately.