A Dynamic Digital Twin System with Robotic Vision for Emergency Management

Ensuring production safety and enabling rapid emergency response in complex industrial environments remains a critical challenge. Traditional inspection robots are often limited by perception delays when confronted with sudden dynamic threats. This paper presents a vision-driven dynamic digital twin system designed to enhance real-time monitoring and emergency management capabilities. The framework constructs high-fidelity 3D models using SolidWorks 2024, Scaniverse 5.0.0, and 3ds Max 2024, and integrates them into a unified digital twin environment via the Unity 3D engine. Its core contribution is a vision-driven dynamic mapping mechanism: robots operating on the Robot Operating System (ROS) and equipped with ZED stereo cameras and embedded YOLOv5m models perform real-time detection, such as personnel and fire sources. Recognized targets trigger the dynamic instantiation of corresponding virtual models from a pre-built library, enabling automated, real-time reconstruction within the digital twin. An integrated service platform further supports early warning, status monitoring, and maintenance functions. Experimental validation confirms that the system satisfies key performance metrics, including data collection completeness exceeding 99.99%, incident detection accuracy of 80%, and state synchronization latency below 90 milliseconds. The system improves the dynamic updating efficiency of digital twins and demonstrates strong potential for proactive safety assurance and efficient emergency response in dynamic industrial settings.