Artificial intelligence-empowered industrial framework for extreme vulnerability analysis

Abstract

Modern warehouse systems for storing fresh and temperature-sensitive goods require stringent operational management, precise temperature control, and coordinated labor efforts. However, these demands often result in challenging and hazardous working conditions, with recent fatal incidents in warehouses worldwide underscoring the urgent need for improved safety management in highly industrialized settings. To address this, a novel framework integrating Internet of Things (IoT) and Digital Twin technologies has been developed to enable efficient real-time tracking and monitoring. The framework incorporates an industrial vulnerability tracking system capable of detecting abnormal conditions and analyzing inactivity sequences to capture precise, location-specific data. Experimental simulations demonstrate that the proposed model significantly outperforms existing methods in extreme industrial environments, achieving a Temporal Efficacy of 40.1 s, Data Acquisition Accuracy of 71.2 %, Classification Efficiency with an Accuracy of 94.23 %, Specificity of 94.36 %, Sensitivity of 93.94 %, and F-Measure of 93.36 %, as well as strong Prediction Performance with a Correlation Coefficient of 0.86 and Error Rate of 0.28, and Stability of 76 %. By enhancing real-time situational awareness and improving vulnerability detection, this framework provides a robust solution to increase safety, reduce accidents, and strengthen operational resilience in hazardous warehouse environments.