Intelligent ship traffic supervision system based on distributed blockchain and federated reinforcement learning for collaborative decision optimization

This paper presents an innovative intelligent decision optimization model that integrates distributed blockchain technology with federated reinforcement learning to address critical challenges in ship traffic collaborative supervision. Traditional maritime traffic monitoring systems suffer from data silos, privacy concerns, and centralized decision-making bottlenecks that impede effective multi-jurisdictional coordination. The proposed framework employs a multi-layered architecture consisting of data layer, blockchain layer, federated learning layer, and decision layer to enable secure data sharing while preserving operational autonomy among maritime authorities. The distributed blockchain mechanism ensures data integrity and immutability through cryptographic protocols and smart contracts, while the federated reinforcement learning algorithm enables privacy-preserving collaborative model training without exposing sensitive commercial information. Experimental validation demonstrates superior performance with 93.6% decision accuracy, 520ms average response time, and 285 transactions per second throughput. Case studies involving emergency collision avoidance, abnormal behavior identification, and search-and-rescue coordination confirm the system's practical effectiveness, achieving 40% reduction in incident response times and 60% enhancement in cross-agency collaboration efficiency. The research provides a robust foundation for next-generation maritime traffic management systems that require secure multi-party collaboration and intelligent decision optimization.