Intelligent tuning method for service scheduling in electric power communication networks based on operational risk and QoS guarantee - Yang Yu; YueLin Jiang; Zeng Dou; Li Cong; Wei Huang; Qiang Zhang; Yang Hu; YanJun Bi (PLoS ONE, 2025)
In the operational planning of electric power communication networks, a well-structured service scheduling scheme based on the established network topology can significantly enhance the risk prevention capabilities of these networks. Since routing policies directly influence data transmission paths, routing optimization serves as an effective strategy for improving network performance by mitigating transmission risks and threats. This paper introduces an Intelligent Tuning Method for Service Scheduling in Electric Power Communication Networks Based on Operational Risk and Quality of Service (QoS) Guarantee. Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. The improvements to the ant colony algorithm are made in four key areas: the definition of heuristic information, the weighting of parameters, the state selection strategy, and the pheromone update strategy. These enhancements aim to achieve optimal routing scheduling based on risk information. At the same time, a reconfiguration algorithm for power optical communication networks, based on service priority, is proposed for specific service requests. This algorithm provides both a primary routing path and an alternate routing path for service transmission, ensuring the delivery of high-priority services even when both the primary and standby paths are unavailable. Simulation results from an actual power business communication network demonstrate that the algorithm outputs the main and alternate paths with the lowest risk costs. Additionally, the path satisfaction of the proposed algorithm is improved by 7.4% compared to the traditional ant colony algorithm. This improvement validates the accuracy and superiority of the proposed algorithm and offers a valuable reference for ensuring the reliable operation of power optical fiber communication network systems.