Research on Data-Driven Safety Level Assessment System of Refining and Chemical Enterprises

Abstract

The rapid development of refining and chemical enterprises has brought great support to the national economy, but the outstanding security problems of them have also attracted great attention. How to scientifically and accurately evaluate the safety level of refining and chemical enterprises has become a hot topic. The advent of the era of big data has also brought about changes in the study of Safety Level Assessment (SLA) technique. The growth of information volume, the development of big data analysis and computing technology will help to overcome the shortcomings of traditional SLA technologies from a statistical point of view, thus forming a more accurate one. In the above context, this paper focus on the impact of the data on the development of SLA technology and propose the connotation and construction ideas of the data-driven SLA system. The network crawler technology, entropy weight method, Page-Rank algorithm are used to build a scientific and perfect refining and chemical enterprise SLA system, in terms of data collection, index setting, index weight calculation. The limitations of this research and the future direction are also analyzed in this paper. The research results are applied in four refining and chemical enterprises. The application results show that the research results can effectively complete the horizontal comparison between enterprises, reveal the problems and loopholes in management, and it is worth further development and promotion.