Research on the prevention of chemical leakage accidents driven by semantic recognition
Chemical leakage incident reports contain a wealth of information;however,their utilization remains low.Solely relying on traditional accident analysis theories and methods for post-incident consequence analysis and statistics is insufficient for achieving proactive prevention and minimizing losses.This study establishes a semantic recognition-driven research framework for the proactive prevention of chemical leakage incidents.The Latent Dirichlet Allocation(LDA)topic model is employed to extract causative themes and keywords.The results demonstrate that LDA modeling effectively identifies the causative themes associated with chemical leakage incidents from the reports.Subsequently,keyword co-occurrence network analysis is used to examine the centrality and associative degree of the causative factors.Factor analysis is performed to calculate the impact of these elements,enabling the extraction and effective analysis of potential information from chemical leakage incident reports.This analysis categorizes the causative factors into five clusters:lack of safety awareness,material escape,equipment failure,and others.Using the improved Pointwise Mutual Information(PMI)-based keyword co-occurrence network,critical causative factors,stages,locations,and types of accidents are identified.The analysis reveals that the most significant and strongly associated causative agents are improper personnel operations and inadequate on-site management.Factor analysis further reveals that the most significant contributory cause is hazardous working environments,followed by violations and improper operations.The proposed research framework substantially enhances the extraction and utilization of extensive causative information from accident reports,thereby reducing the subjectivity inherent in evaluations of accident causation.By offering new methods for extracting information from complex,non-standardized accident report texts,this study expands the application of semantic recognition in the field of chemical leakage accident prevention.This advancement is essential for enhancing the identification,prediction,and management of risks associated with chemical leakage incidents.In conclusion,the integration of LDA,keyword co-occurrence network analysis,and factor analysis within this framework provides a systematic,data-driven approach to the proactive prevention and comprehensive analysis of chemical leakage incidents.This approach demonstrates significant practical applicability and holds great potential for enhancing safety management practices.
safety social engineeringchemical accidenttext miningsemantic recognitionterm frequency-inverse document frequency algorithmlatent dirichlet allocation topic model
万方数据
维普
