Research on Ship Operational Process Monitoring Based on Digital Twin and Large Language Model
ZHANG Zihan
YIN Caiyu
Abstract：With the rapid advancement of intelligent technologies and artificial intelligence,the maritime industry is progress-ing toward a fully intelligent future.However,data heterogeneity arising from differences among equipment manufacturers creates in-formation silos,severely hindering comprehensive monitoring of operational processes.Traditional methods,such as data standard-ization,the Internet of Things(IoT),manual monitoring,and small-scale machine learning,have alleviated this issue to some ex-tent.Nevertheless,they are constrained by compatibility,real-time performance,and model capabilities,making them insufficient to meet the demands of fully intelligent ships.To address these challenges,this study proposes an innovative solution that combines digital twins and large models.Digital twins construct virtual replicas of maritime equipment,integrating multi-source heteroge-neous data to break down information silos.Leveraging this platform,large models enable continuous monitoring of equipment oper-ating states,anomaly detection,and provision of solutions.Research findings demonstrate that this approach possesses real-time monitoring and precise anomaly identification capabilities,offering support for optimizing ship operations and contributing to the ad-vancement of intelligent shipping.
Keywords：ship monitoringdigital twinlarge language modelintelligent ship
Publication Date：2025-10-20
Online Publishing Date：2026-01-16(First online date of this platform, not the publication date of the document)
Pages：5( 1-5 )