Abstract: With the growth of global energy demand and the widespread application of renewable energy sources, distributed energy systems (DES) have become a crucial component of modern energy infrastructures. This paper proposes a distributed energy digital management platform and a resource scheduling optimization method. A distributed energy digital management platform is designed by incorporating edge nodes. This platform adopts an edge-cloud data center architecture and integrates seven functional modules: energy monitoring, operation and maintenance management, energy management, energy analysis, investment infrastructure construction, safety management, and scheduling optimization algorithms. Furthermore, by combining deep reinforcement learning algorithms with pointer networks, an efficient hybrid scheduling optimization algorithm named DND is proposed to optimize the latency of energy requests. The results from platform application and simulation experiments demonstrate that this platform can effectively reduce platform performance bottlenecks.