Knowledge Graph-Driven Organizational Planning for Shipboard Communications

This paper proposes a ship communication parameter optimization method that integrates knowledge graphs and genetic algorithms to address the complexity and dynamic changes of the maritime communication environment. By constructing a knowledge graph based on RDF and OWL technologies, historical communication data is systematically organized to enhance decision support and real-time update capabilities in communication planning. Utilizing a subgraph isomorphism algorithm, historical plans are selected from the knowledge graph to form the initial population for the genetic algorithm, thereby improving the efficiency and adaptability of the planning process. The genetic algorithm further optimizes communication parameters through crossover and mutation operations, ultimately achieving an efficient and intelligent communication configuration.