## 2 Related Works
[20] considered delay-sensitive tasks and proposed a solution scheme based on the block coordinate descent method to jointly optimize the UAV trajectory, resource allocation, and offloading decisions, which minimized the total energy consumption of the equipment. In [21], UAVs were used as air data relays, and a heuristic algorithm based on particle swarm optimization was proposed to minimize the execution time of user offloading tasks by jointly optimizing the UAV's task scheduling and flight trajectory. [PERSON] et al. [22] proposed a UAV-assisted edge computing framework based on artificial intelligence and non-orthogonal multiple access, which minimized transmission delay and power consumption by jointly optimizing UAV deployment and resource allocation. [PERSON] et al. [23] proposed a joint dynamic programming and bidding algorithm with the goal of maximizing the minimum secure computing capability of dynamic end users, through joint optimization of UAV offloading decisions, resource allocation, and trajectory planning. [PERSON] et al. [24] proposed a two-stage dynamic optimization method based on Lyapunov optimization theory, which minimized the comprehensive energy consumption of the system by jointly optimizing task offloading, communication resource allocation, and the UAV 3D trajectory. [PERSON] et al. [25] propose a solution based on deep reinforcement learning for the optimization of UAV trajectories and resource allocation problems, which minimizes the energy consumption of mobile users under the constraints of system performance and UAV energy. [PERSON] et al. [26] proposed an alternating iterative method based on block descent in a multi-UAV-assisted network, which minimized the cost function based on energy and delay by jointly optimizing task offloading and MEC server selection decisions, transmission power, flight trajectory, and CPU frequency allocation. Based on the analysis of the existing literature, the following problems still exist in the research on UAV-assisted MEC:  
1. Most of the above studies are based on the premise that user devices are in the same plane, UAVs are flying at a fixed altitude, and the scene environment is naturally open. However, in the practical application of UAV-assisted edge computing, especially in complex scenes such as emergency rescue, the environmental conditions are complex and changeable, and key factors such as obstacles and user positions in 3D space need to be further considered. 2. The current research on performance indicators mainly focuses on latency, throughput, power, flight time, and energy consumption, and most of them are single or simply weighted. To improve system performance, the intrinsic correlation between multiple performance indicators in the MEC model can be comprehensively considered and jointly optimized. 3. Although traditional convex optimization methods are effective in simplifying models, the number of system variables increases and the complexity of the problem significantly increases in large-scale application scenarios. At this point, traditional convex optimization methods will no longer be applicable, and more advantageous metaheuristic algorithms can be considered for the solution.