An Adaptive Optimization Method for Moored Buoy Site Selection Integrating Ontology Reasoning and Numerical Computation

With the growing diversity and complexity of marine monitoring requirements, the scientific deployment of moored buoys has attracted increasing attention. To address the limitations of traditional methods—such as inconsistent knowledge representation, insufficient logical reasoning capacity, and poor adaptability to dynamic marine environments—this study proposes an adaptive optimization method for moored buoy site selection integrating ontology reasoning and numerical computation. The proposed approach constructs an ontology model covering key concepts such as buoy specifications, monitoring objectives, and deployment requirements, and further defines formalized reasoning rules to enable automated judgment of deployment feasibility, sensor configuration, and spatial conflict resolution for moored buoy siting. Based on this semantic framework, a spatio-temporal comprehensive variation index (STCVI) is established by integrating temperature, salinity, and current velocity to characterize dynamic oceanographic conditions. Furthermore, a coverage-first greedy algorithm is designed to determine buoy deployment locations, enabling dynamic optimization and environmental adaptability of the buoy station layout. To verify the feasibility and adaptability of the proposed method, simulation experiments are conducted in the Beibu Gulf. Two layout scenarios—an appending layout with existing buoys and an independent layout without existing buoys—are designed to test the method’s adaptability under different deployment conditions. By combining Voronoi spatial partitioning and nearest-neighbor distance analysis, the optimized results are quantitatively evaluated in terms of spatial uniformity and observational effectiveness. The results indicate that the proposed method effectively enhances the spatial rationality and monitoring efficiency of buoy deployment, demonstrating strong generality and scalability.