An operational risk assessment method for petrochemical plants based on deep learning

Abstract

Petrochemical plants are an important guarantee for the development of people's lives, and the most important thing in petrochemical plants is the risk assessment method in operations. Based on the research of deep learning algorithms, this paper innovatively proposes a risk assessment method for petrochemical plants based on the combination of human motion simulation based on the micro-Doppler effect and fuzzy hierarchical analysis. The original monitoring image of the petrochemical plant is invoked to identify the target of human movement in the job site and generate the spectral diagram of human movement, and the operation safety risk of the petrochemical plant is assessed through the combination of fuzzy function and hierarchical analysis, which can effectively prevent the illegal actions of the on-site operators and provide protection for the personal safety of the on-site operators. This method plays an important role in improving the safety of petrochemical plants.