Resource Optimization Management and Ultra-Early Risk Prevention System for Warehouse Parks Based on Multimodal and Digital Twin Technologies

With the expansion of the scale of the warehouse park, the problem of false alarm or missing alarm caused by the traditional fire detection method is becoming more and more prominent. To this end, a system combining multimodal sensing and digital twin technology is proposed to optimize resource management and achieve ultra-early fire risk prevention. The system collects data by deploying heterogeneous devices such as infrared cameras and temperature and humidity sensors, and transmits it to a digital twin layer based on BIM and 3D modeling for processing using 5G and IIoT technology. The improved YOLOv5 algorithm and CFD simulation are used for dynamic fire risk prediction. The results show that compared with traditional methods, the new system achieves more than 90% accuracy in fire detection, reduces false alarms by 30%, and optimizes emergency response. It is concluded that the system has significantly improved the efficiency of fire warning and safety management, and future plans are to integrate blockchain technology and optimize edge computing models to further improve performance.