Research on Diagnosis and Prevention Strategies of Risk-Inducing Factors in University Laboratories Based on Polymorphic Fuzzy Bayesian Networks
The diagnosis of risk factors is an important gateway for preventing and controlling hazardous sources in university laboratories.In response to the shortcomings of existing studies regarding the completeness,diagnostic accuracy,and efficiency of risk factor identification,grounded theory coding techniques are used to deconstruct the causes of accidents in 33 university laboratory cases,extracting 23 major risk factors.Considering the dynamic,polymorphic,and fuzzy characteristics of university laboratory risks,a polymorphic fuzzy Bayesian network model is constructed,utilizing its bidirectional reasoning and importance analysis techniques for risk factor diagnosis.33 laboratory safety accidents in a certain university are taken to validate the effectiveness of the model.The results indicate that the weak safety awareness of students and management personnel,along with their lack of emergency response skills,will increase the likelihood of severe failures in safety culture.Key risk factors leading to university laboratory safety accidents include weak safety awareness,unclear responsibilities,inadequate safety inspections,and insufficient rectification of hidden dangers.These factors should be prioritized for targeted control and management.The polymorphic fuzzy Bayesian network can achieve real-time dynamic risk diagnosis throughout the entire system,comprehensively addressing the complex system risk analysis problems characterized by fuzziness and polymorphism.Based on the model's findings,four corresponding preventive strategies are proposed:energizing laboratory safety management across the entire system,reinforcing safety management of experimental projects throughout the whole process,ensuring comprehensive control of hazardous chemicals,and advancing the overall construction of safety culture in universities.
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