To solve the problem of limited resources of edge servers in grid nodes, an edge computing framework based on a software-defined network is proposed. The Depth Deterministic Strategy Gradient (DDPG) reinforcement learning algorithm is used to allocate the computing and storage resources reasonably in edge servers. First, an edge computing model applied into the software-defined grid network is established. This is to obtain the edge node computing and cache resources and task delay constraints, in order to analyze the MINIP which needs to be solved. TensorFlow is used to build a simulation environment and execute the reinforcement learning algorithm to realize the optimal utilization of edge server storage computing resources by grid edge nodes. It shows that the rewards of reinforcement learning increase, and the total delay of the system using DDPG allocation is lower. This work is supported by the Science and Technology Project of State Grid Corporation of China (No. (SGHEXTOOGCJS2000167) and the National Natural Science Foundation of China (No. 61971190).