Application of machine learning to laboratory safety management assessment

Abstract

This paper proposes a new method to evaluate laboratory safety management based on machine learning that aims to address the shortcomings of traditional laboratory safety evaluation methods, including poor accuracy, large influence of human factors, and lack of a unified evaluation system. In this paper, the safety data of laboratories in Southwest University were collected using a safety checklist, and the weight of each factor affecting laboratory safety was analyzed using a Fuzzy Analytic Hierarchy Process (FAHP). The simulation results showed that the model gave a more accurate and reasonable safety risk level of the laboratory, verified the rationality and feasibility of the established method, and realized potential loopholes in the process of laboratory management and college students' operations. According to the evaluation results, the machine learning principle and the existing maintenance knowledge of the evaluation knowledge base are applied to provide effective measures and suggestions for users to complete the process of risk assessment. Through risk assessment, the professional skill and the incident control could be improved. The model was easy to operate and has a good application value for ensuring the personal and property safety of laboratory users.