Intelligent ship traffic supervision system based on distributed blockchain and federated reinforcement learning for...

Area of Science:

• Maritime Technology
• Artificial Intelligence
• Distributed Systems

Background:

• Traditional maritime traffic monitoring faces challenges like data silos and privacy concerns.
• Centralized decision-making hinders effective multi-jurisdictional coordination in maritime surveillance.
• Lack of secure data sharing impedes operational autonomy among maritime authorities.

Purpose of the Study:

• To develop an intelligent decision optimization model for ship traffic collaborative supervision.
• To integrate distributed blockchain technology with federated reinforcement learning for enhanced maritime traffic management.
• To address data silos, privacy concerns, and decision-making bottlenecks in traditional systems.

Main Methods:

• A multi-layered architecture comprising data, blockchain, federated learning, and decision layers.
• Utilizing distributed blockchain for data integrity, immutability, and secure sharing via smart contracts.
• Employing federated reinforcement learning for privacy-preserving collaborative model training.

Main Results:

• Achieved 93.6% decision accuracy, 520ms average response time, and 285 transactions per second throughput.
• Demonstrated practical effectiveness in emergency collision avoidance, abnormal behavior identification, and search-and-rescue.
• Reported a 40% reduction in incident response times and a 60% enhancement in cross-agency collaboration efficiency.

Conclusions:

• The proposed framework provides a robust foundation for next-generation maritime traffic management systems.
• The integration of blockchain and federated reinforcement learning enables secure multi-party collaboration.
• The model offers intelligent decision optimization while preserving operational autonomy and data privacy.