Abstract
In order to contain the safety accidents caused by the leakage of dangerous chemicals, chemical enterprises need to use big data to reasonably control the risk of dangerous chemicals. Based on the analysis of related concepts of big data technology and risk management and control, this paper uses AHP and k-means clustering method to construct a risk management and control model of dangerous chemicals in chemical enterprises in the context of big data. Through the consistency check of the judgment matrix A and CR and the weight of all levels of variables, it is found that there are four levels of risk management and control of dangerous chemicals in chemical enterprises, namely targeted safety management, daily safety management, emergency safety management and safety planning management. Each of the four levels contains multiple risk mitigation measures and preventive measures, which can provide reference for the management and control of dangerous chemicals in chemical enterprises.
