Research on Occupational Hazard Supervision System of Petroleum Enterprises Based on Cloud Computing

Abstract

Aiming at the problem that the petroleum industry is a high-occurrence industry with many occupational hazards, in order to comprehensively evaluate the hazards in coal mines, the number of supervisors for petroleum occupational hazards is small, the number of objects to be supervised, the frequency of supervision, and the scope of supervision are large, combined with cloud computing Based on the characteristics of cloud computing, an occupational hazard supervision system for petroleum companies based on cloud computing is proposed. Based on relevant theories, 15 occupational hazard identification indicators such as lifestyle, average working age, cultural level, professional skills, and health level were selected from massive data to establish an occupational hazard evaluation index system. In this paper, the analytic hierarchy process is used to determine the weight of the occupational hazard identification index, and then a BPNN neural network prediction model is established based on the determined weight index system. The qualitative prediction of the input and output of the index is achieved through model solving and model training. The experimental results show that the training error accuracy of BPNN neural network is less than 5%, and the model accuracy meets the requirements. Compared with the error of AHP, the average evaluation error of BPNN is 0.637% lower than that of AHP. The comprehensive evaluation system based on BPNN accords with the actual situation of the enterprise is greater than 97%, so it has certain guiding significance in the evaluation of petroleum occupational diseases.