随着物联网的蓬勃发展，无人机移动边缘计算作为一种新兴的计算范式，将密集型任务卸载到网络边缘服务器上，从而提高用户数据处理能力。该文针对用户任务类型多样化和不同优先级的需求，设计一种量子遗传和凸优化相结合的服务缓存和资源分配算法。在考虑到存储、计算和能耗约束下，通过联合优化服务缓存、用户卸载策略、时隙划分、计算资源分配和飞行轨迹实现用户满意度最大和服务缓存最小化。具体而言，将原问题分解为3个子问题。首先，基于量子遗传算法求解服务缓存和用户卸载子问题；其次，基于拉格朗日对偶函数得到计算资源分配闭式解；而后，利用逐次凸逼近技术求解时隙划分和无人机轨迹优化子问题；最后，将3个子问题多次迭代获得最优解。仿真结果表明该算法能很好的满足用户多样化需求还兼顾较低的服务缓存。

With the booming development of the Internet of Things， mobile edge computing of UAVs， as an emerging computing paradigm， offloads intensive tasks to network edge servers， thereby improving user data processing capacity. This paper designs a service caching and resource allocation algorithm that combines the quantum genetic algorithm and the traditional algorithm to address the needs of diversified and different prioritized user application services. Taking into account storage， computation， and energy constraints， the maximum user satisfaction and minimum service placement cost are achieved by jointly optimizing service caching， user offloading policy， time slot allocation， computational resource allocation， and flight trajectory. Specifically， the original problem is decomposed into three subproblems. First， the subproblem of service caching and user offloading is solved based on the quantum genetic algorithm. Second， the closed-form optimal solution for computational resource allocation is obtained based on the Lagrangian duality function. Then， the subproblem of time slot allocation and UAV trajectory optimization is solved using the successive convex approximation technique. Finally， the three subproblems are iterated several times to obtain their optimal solutions. The simulation results show that the algorithm can satisfy the diversified needs of users well， and can also have low services caching.