Construction Method Of Petrochemical Plants Accident Warning And Guidance System Based On Machine Learning

In recent years,the petrochemical production situation in our country has been relatively severe because of frequenlty leakage,fire,explosion and other accidents.The main reason is that there are many potential risks and hazards hidden in the production process of petrochemical plants.Moreover,due to the complex and changeable petrochemical production situation,the operators are easy to misoperate,which causes serious losses to the enterprise.Using the risk analysis method to identify the possible risks in the production process in advance is the premise to ensure the safety of petrochemical production.If the accident warning can be made according to the results of risk analysis during the operation of petrochemical plant,and the operators can take effective measures against the identified risks in time,the occurrence of petrochemical production accidents can be avoided to a large extent.Therefore,it is particularly important to study the risk analysis and accident warning of petrochemical plants.The completed risk analysis reports accumulated by petrochemical enterprises over the years can be regarded as the materials for the centralized presentation of accident risks,which are valuable in extracting the risk factors of petrochemical accidents and mining the potential accident occurrence rules.However,a large number of risk analysis reports that have been completed are not fully utilized.Analysts still rely on the existing experience and knowledge when analyzing the risks of the new production process,and seldom refer to the completed analysis report or accident case.Moreover,the potential accident rules in the risk analysis report have not been fully explored,it is difficult to apply the experience knowledge in the risk analysis report to the petrochemical plant accident early warning.Therefore,it is of great significance to use machine learning technology to mine accident early warning knowledge based on risk analysis data,which can improve the level of safety risk analysis,promote the intelligent prevention and control of petrochemical safety production risk,curb the occurrence of major accidents and ensure the production safety requirements.To prevent petrochemical plant accidents,this paper studies the construction method of the petrochemical plant accident warning and guidance system based on machine learning.The main contents are as follows:(1)Construction of petrochemical plant accident information database:Through analyzing various risk analysis methods of petrochemical enterprises and petrochemical plant accident records,the construction method of the petrochemical plant accident information base is put forward.This paper constructs the petrochemical plant accident information database based on the completed hazard and operability analysis report and the evaluation results of the risk matrix.Considering the results of the analysis report and risk matrix evaluation,the accident information can be fully understood,which can provide a favorable data basis for the subsequent construction of the petrochemical plant accident warning and guidance system based on machine learning.(2)Accident warning knowledge mining based on machine learning:Based on the established petrochemical plant accident information database,the LDA(Latent Dirichlet allocation)clustering model was used to construct the accident causal clustering model,and the hidden causes and consequences in the risk analysis can be excavated.A topic information extraction model was constructed based on part of a speech tagging algorithm to clarify the main content of each cause topic and consequence topic.The Apriori algorithm was used to mine the association rules between the cause topic and the consequence topic.At the same time,the results of the historical data query are combined to construct an accident causal association model and establish accident causal bidirectional inference relationship,which provides an effective basis for building a causal bidirectional inference module based on text data.Combined with the results of the historical data query,the accident causal association model can be constructed,and the accident causal bidirectional reasoning relationship was established,which provides an effective basis for the construction of the causal bidirectional inference module based on text data.By analyzing the data in the petrochemical plant accident information base,a summary table of typical misoperations is established.Combined with natural language processing technology,a method of misoperation analysis is proposed to provide an effective basis for the construction of typical misoperation analysis module.According to the classified equipment data in the petrochemical plant accident information database,the causal clustering model and the correlation model of analysis contents of various types of equipment can be established to clarify the correlation relationship among various analysis contents.The risk prediction model was constructed based on the naive Bayes model to provide technical support for the intelligent risk analysis module of petrochemical equipment.(3)Construction of the petrochemical plant accident warning and guidance system based on machine learning: According to the results of accident warning knowledge mining based on machine learning,Foxtable software was used to construct the causal bidirectional reasoning module based on text data,the typical misoperation analysis module and the petrochemical equipment intelligent risk analysis module,which can be used to construct the petrochemical plant accident warning and guidance system based on machine learning.The system can realize accident risk identification and early warning based on the production description of phenomena in the petrochemical production process,assist the intelligent risk analysis of petrochemical enterprises,and provide reference for the risk analysis,accident risk early warning and emergency rescue guidance in the petrochemical production process.In this paper,risk analysis and machine learning algorithms were combined to mine accident warning knowledge,the petrochemical plant accident warning and guidance system was constructed based on the mining results.This method can realize the risk identification and accident early warning in the petrochemical production process,avoid the dependence of risk analysis on the experience of analysts to the greatest extent,contribute to the realization of informatization and intellectualization of safety risk analysis in petrochemical enterprises,and provide theoretical basis and technical support for reducing and avoiding petrochemical plant accidents.