Data Mining and Optimization of a Port Vessel Behavior Behavioral Model Under the Internet of Things

Abstract

Port traffic is affected by many factors, which may lead to certain problems in terms of maritime traffic safety and efficiency. To further study the maritime traffic flow of ships under the influence of multiple factors, our work combines artificial intelligence and an agent-based architecture to carry out port traffic flow simulation research. Moreover, considering an actual situation, a theoretical research method is used to analyze the feasibility of cellular automata combined with an agent architecture, and the basic framework is constructed. Simultaneously, in order to improve the computational efficiency, a system framework based on the Internet of Things structure is constructed, and the authenticity of the simulation process is improved. In addition, through a field investigation, the corresponding cellular automata model is established, and the cellular automata model and agent architecture are combined to establish the port traffic flow simulation model. Finally, the actual port operational data are collected to simulate various conditions, and the simulation results are obtained. The research shows that the model constructed in this paper has certain effects on the study of ship behavior in ports and can provide a theoretical reference for subsequent related research.

Keywords

• Agent-based architectures
• port vessel behavior
• artificial intelligence
• model simulation
• fog computing