Power Business Routing Optimization Algorithm Considering Overall Balance of Network Performance

Real-time business is crucial to the security and stability of the power system. The routing planning of power business should give priority to the performance requirements of real-time services and achieve the overall balance of network performance. This paper presents a routing optimization algorithm based on fuzzy Q-Learning. The algorithm fuzzifies the link saturation and link importance, and evaluates the link status by establishing a fuzzy logic system. Taking the power business as the agent, the return value is calculated according to the link state. At the same time, considering the service bandwidth and delay requirements, it guides the agent to continuously interact with the environment and find the best route to meet the service requirements. The simulation test shows that the algorithm is an effective route optimization method for power business, which can conduct route planning from a global perspective, and effectively meet the delay demand of real-time business. It achieves the balanced distribution of link importance and link saturation in the whole network.