Large language model-empowered technology evolution analysis of remote operation centres of maritime autonomous surface ships

Remote Operation Centres (ROCs) are safety-critical shore infrastructures enabling Maritime Autonomous Surface Ships (MASS) through remote supervision, decision-making, and human-machine cooperation. However, fragmented ROC technologies hinder system-level understanding of their interactions, constraining reliability and safety assurance for fully autonomous maritime operations. This study develops a Large Language Model (LLM)-based patent analytics framework that can systematically enable the identification and mapping of ROC-related technologies from patent data. By integrating LLM-assisted topic modelling, complex network analysis, and temporal trend evaluation, the findings provide a data-driven perspective that captures both the evolutionary dynamics and structural interrelations among ROC technologies. An analysis of 973 relevant patents reveals 22 technology domains, with maritime data communication, remote control and teleoperation, remote condition monitoring, human-machine interface, and maritime positioning and navigation emerging as central and highly interconnected clusters. The results indicate that communication and teleoperation form the structural backbone of remote operation development. Temporal analysis shows technology development increased quickly after 2018, reflecting growing integration across sensing, communication, and control systems. The findings provide an evidence base for prioritising safety- and reliability-critical ROC technology development and for informing harmonised standards, operator competency requirements, and targeted investment to support safe, scalable MASS operations.