A Dynamic Security Assessment Method for Ironmaking Plants Based on Cloud-Edge Collaboration in Reconstructed Networks

Abstract

Security assessment of ironmaking plants is one of the crucial means to promote their sustainable development. However, the disparate nature of subsystems within these plants, along with network inconsistencies and isolated data, obstruct a thorough and timely security assessment. At the same time, it is impossible to achieve the sustainable development goals of reducing the adverse impact of safety on the environment, ensuring economic benefits and the health of employees. This study addresses the complexities of heterogeneous networks, disparate systems, and segregated data that are prevalent in traditional ironmaking plants; and a method to reconstruct the plant’s network and execute security assessments is proposed. This method involves coupling existing systems with new ones to create comprehensive data and resource pools by aggregating information from diverse sources. Subsequently, employing multiple regression models and optimized neural network models at the edge and central cloud facilitates dynamic assessment of security concerns. This method enables concurrent consideration of both regional and overall security analysis and decision-making within the plant. Through simulation testing of 27 functionalized module indicator datasets over the preceding 12 months at a specific ironmaking plant, the efficacy of the proposed theoretical methods and technological approaches in constructing security systems for ironmaking plants is substantiated.