To reduce the delay of processing intensive computing tasks by terminal equipment in pumped storage power stations , this paper proposes a task offloading algorithm based on edge computing for the Internet of Things (IoT) system of pumped storage power stations. Firstly, the computing tasks are prioritized based on the analytic hierarchy process, and an offloading model is built with the terminal energy consumption as the constraint and the processing delay of terminal computing tasks as the optimization objective. Secondly, the Q-Learning (QL) algorithm is adopted to collect the state transition information, in the hope to obtain the best offloading strategy between the terminal device and the edge node. Finally, Deep Learning (DL) is used to map the relationship between states and actions to avoid a dimensional explosion in the iterative solution of the algorithm. The simulation results show that the proposed method greatly reduces the average delay of computing tasks and can greatly improve the execution efficiency of production operations and safety monitoring associated with pumped storage power stations.