Abstract: With the evolution of 5G and 6G communication technologies, various Internet of Things (IoT) devices and artificial intel-ligence applications are proliferating, putting enormous pressure on existing computing power networks. Unmanned Aerial Vehicle (UAV)-enabled Mobile Edge Computing (U-MEC) shows potential to alleviate this pressure and has been recognized as a new paradigm for responding to the data explosion. Nevertheless, the conflict between computing demands and re-source-constrained UAVs poses a great challenge. Recently, researchers have proposed resource management solutions in U-MEC for computing tasks with dependency. However, the repeatability among the tasks was ignored. In this paper, con-sidering repeatability and dependency, we propose a U-MEC paradigm based on a computing power pool for processing computationally intensive tasks, in which UAVs can share information and computing resources. To ensure the effectiveness of computing power pool construction, the problem of balancing the energy consumption of UAVs is formulated through joint optimization of an offloading strategy, task scheduling, and resource allocation. To address this NP-hard problem, we adopt a two-stage alternate optimization algorithm based on a Successive Convex Approximation (SCA) and an improved Genetic Algorithm (GA). The simulation results show that the proposed scheme reduces time consumption by 18.41% and energy consumption by 21.68% on average, which can improve the working efficiency of UAVs.