Research on Disaster Early Warning and Emergency Response System for Underground Cables Based on Digital Twin

With the increasing frequency of underground cable failures, traditional early warning methods face numerous challenges, particularly in handling large-scale data and adapting to changes in cable operating conditions, leading to response delays and frequent false alarms. To enhance the efficiency of disaster early warning and emergency response for underground cables, this paper proposes an intelligent approach based on digital twin technology. By integrating cable monitoring data, geographic information, and historical maintenance records, a digital twin model of underground cables is constructed. An improved time-series prediction model is employed, leveraging the Transformer architecture to enhance long-term dependency modeling, and reinforcement learning is incorporated to optimize early warning strategies. Experimental results demonstrate that the proposed system significantly improves fault prediction accuracy, reduces false and missed alarms, and enhances emergency response efficiency.