Evaluation of Ship Importance in Offshore Wind Farm Area Based on Fusion Gravity Model in Complex Network

With the rapid expansion of offshore wind farms (OWFs), ensuring maritime safety in adjacent waters has become an increasingly critical challenge. This study proposes an innovative dynamic risk assessment method that integrates a fusion gravity model into a complex network framework to comprehensively evaluate ship importance in OWF areas. By treating ships and wind farms as network nodes and modeling their interactions using AIS data, the method effectively captures spatiotemporal traffic dynamics and precisely quantifies ship importance. Multiple network indicators, including centrality, clustering coefficient, and vertex strength, are fused to comprehensively assess node criticality. A case study in the Yangtze River Estuary empirically demonstrates that ship importance is not static but dynamically and significantly changes with trajectories, interactions with other vessels, and proximity to OWFs, successfully identifying high-risk ships and sensitive OWF areas. The contribution of this research lies in providing a data-driven, quantifiable, novel framework capable of real-time identification of potential threats in maritime traffic. This approach offers direct and practical insights for traffic control, early warning system development, and optimizing maritime traffic management policies, facilitating a shift from reactive response to proactive prevention. Ultimately, it enhances safety supervision efficiency and decision-making support in complex maritime environments, safeguarding the sustainable development of the offshore wind industry.