Empowering digital twins with large language models for global temporal feature learning

Digital Twin (DT), as an efficient technology for virtual-physical interaction, has demonstrated significant application potential in various industries. Intelligent agent-driven digital twin systems excel in analysis, decision-making, and control, making them highly suitable for manufacturing resource scheduling, diagnostic decision-making, and other requirements. However, current intelligent agents have notable deficiencies in adaptability, data utilization, and interpretability. This limitation undermines decision security and acceptability, creating barriers for user intervention. Therefore, this paper introduces a DT multi agent architecture driven by Large Language Models (LLMs). Agents perceive the characteristics of physical systems, particularly their temporal characteristics, by integrating data from various modalities. Multiple agents achieve insights through specific interaction mechanisms, while maintaining traceability. To showcase the advantages and characteristics of this architecture, we developed a typical application scenario for equipment maintenance. The effectiveness of each framework component was validated through ablation experiments. The experimental results suggest that the proposed framework holds promising and extensive application prospects.