Computational resource scheduling, through the rational allocation of resources, can significantly enhance the quality of service and user experience, prevent resource overload and wastage, and is crucial for improving the efficiency of edge computing resources management. This paper proposes an edge computing power scheduling algorithm based on reinforcement learning, which, combined with business demand forecasting, allows the scheduling strategy to anticipate and adapt to the dynamic changes in business demands in advance, achieving more precise computing power scheduling. An innovative reward function is designed, taking into account four key factors: latency, edge hit rate, resource utilization balance, and business priority. Simulation experiments have demonstrated that, the proposed algorithm exhibits superior performance in reducing latency, improving edge hit rate, and enhancing the balance of resource utilization on edge servers.