Cooperative regulation of hazardous chemicals in electric power sector based on CA-ResNet model - Weiqing Chen; Lei Yu; Yang Zhang; Bin Han; Xiaolong Hao; Yiming Xue (Systems and Soft Computing)
With the advancement of industrialization, the amount of hazardous chemicals in the power industry is constantly increasing, resulting in frequent safety hazards such as leaks, explosions, and other accidents. The main objective of this research is to construct a hazardous chemical identification system based on the Coordinate Attention mechanism and Residual Network (CA-ResNet) model and achieve collaborative supervision to improve the safety management and monitoring efficiency of hazardous chemicals in the electric power industry. The results showed that after 305 iterations, the model reached a stable state with a training loss of 0.17. The recognition accuracy of the model was as high as 0.98. In addition, the collaborative regulatory platform implemented using the model achieved expert satisfaction and user satisfaction rates of 96.3 % and 98.2 %, respectively. The hazardous materials identification model and collaborative monitoring platform developed in this study could help power industry personnel accurately identify, transport, store, and use electricity commodities. By introducing advanced deep learning technology, the platform will significantly improve the response speed and decision-making efficiency of the power industry in the management of dangerous goods, thereby promoting the safe and sustainable development of the industry.