Research on Algorithm Design and Implementation of Intelligent Cargo Management System for Intelligent Ships

This paper proposes an algorithm design and implementation method for intelligent ship cargo management system based on graph neural network. First, in view of the information island and inefficient scheduling problems existing in traditional cargo management systems, graph neural network is introduced. By constructing a dynamic model of ship logistics network nodes and edges, intelligent detision-making of cargo status and logistics paths between different nodes is realized. Secondly, this paper adopts a multi-objective optimization algorithm to globally optimize cargo transportation paths, loading strategies and scheduling plans. Finally, the performance of the system is verified. The simulation results show that the designed algorithm has significant advantages in improving the decision-making efficiency and accuracy of the cargo management system, especially in the multi-node logistics network environment, the cargo scheduling time is shortened by 15.6% and the energy consumption is reduced by 8.2%. Data analysis shows that the system has strong robustness in complex environments, can effectively cope with the interference of various uncertain factors, and ensure the safety and efficiency of cargo management.