| The mobile pressure vessel industry is an important part of the healthy development of China’s national economy,and it is also a traditional industry with high risk.Mobile pressure vessels are widely used in many fields,such as industry,energy,people’s life and public transportation.They are usually used to load flammable,explosive and volatile high-risk chemicals.The consequences of accidents will pose a great threat to people’s lives and material safety.Reasonable and scientific risk supervision mode can effectively prevent accidents and reduce accident losses.At present,there are some problems in the mobile pressure vessel industry,such as weak supervision,low informatization level,backward supervision means,no basis for supervision,and low data utilization rate.Therefore,how to build a new regulatory model that meets the safety risk needs of the mobile pressure vessel industry and effectively guide the regulatory work and safe operation of the mobile pressure vessel industry through data science has urgent practical significance.This study explores the application of Internet of things technology and big data technology to the industry,defines the regulatory body,regulatory content and regulatory mode under this mode based on relevant concepts and theoretical research,constructs a mobile pressure vessel safety risk intelligent regulatory mode,identifies the safety risk elements under this mode,and designs the risk early warning method and early warning process under this mode,Explore the use of appropriate theoretical methods to build a scientific mobile pressure vessel safety risk intelligent supervision system structure,in order to improve the supervision efficiency of the mobile pressure vessel industry by building a safety risk intelligent supervision mode.The specific research contents are as follows:(1)It defines the concepts of smart supervision and smart supervision of mobile pressure vessel safety risks,combs and analyzes the relevant theories of smart supervision of mobile pressure vessel safety risks,such as responsive supervision theory,stakeholder theory,risk management theory,system engineering theory,knowledge mapping theory and data mining theory,and designs the theoretical framework of smart supervision of mobile pressure vessel safety risks,That is,supported by the system engineering theory,based on the responsive supervision theory,combined with the stakeholder theory for analysis,and through risk management theory,knowledge mapping theory and data mining theory to achieve intelligent supervision,which is an important theoretical basis for subsequent research.(2)Based on the connotation and responsive supervision theory of mobile pressure vessel safety risk intelligent supervision,this paper defines the supervision subject,supervision object,supervision method and supervision system under the safety risk intelligent supervision mode,and constructs a mobile pressure vessel safety risk intelligent supervision mode;Based on the analysis method of grounded theory,with expert interview materials,mobile pressure vessel safety accident cases and mobile pressure vessel safety laws and regulations as the analysis materials,four main categories of basic management elements,real-time monitoring elements,statistical analysis elements and public service elements as well as 54 related concepts were extracted,and the data acquisition method of safety risk elements was clarified from the perspective of the main categories,Based on this,the safety risk element model of mobile pressure vessel under the safety risk intelligence mode is constructed;At the same time,based on the concept of data sharing,after defining the role orientation and sharing content of data sharing under the mobile pressure vessel safety risk intelligent supervision mode,a data sharing mechanism of safety risk elements under this mode is constructed to mobilize the enthusiasm of all regulatory bodies,promote the cooperation between the bodies,and achieve joint supervision.(3)According to the risk management theory,the text data of accident causes in 30 accident report cases are applied,and combined with the construction process of knowledge map,Bert CRF model is used to extract the named entities of accident causes;The natural processing technology and support vector machine model are used to extract the entity relationship of accident causes;An entity alignment method of accident causes based on vocabulary similarity calculation is proposed.The Neo4 j database is used to identify and store the risk information of mobile pressure vessels based on accident causes,and a risk knowledge map is drawn;By using data mining technologies such as equivalent class transformation algorithm and sliding window model,research and analyze the occurrence probability of risk,transformation trend in adjacent periods,risk assessment and early warning,design risk visualization methods,and push risk early warning information to relevant subjects in a more intuitive form;Based on the characteristics of the safety risk intelligent supervision mode,the main body and functions of risk early warning and the form of risk early warning under this mode are defined,and the implementation process of risk early warning is designed.(4)Analyze the objectives and basic principles of the construction of the mobile pressure vessel safety risk intelligent supervision system,and summarize the system functions of the mobile pressure vessel safety risk intelligent supervision system through questionnaires and sorting out the functions of the relevant industry supervision system according to the needs of different regulators;The interpretive structure model in system engineering is used to divide the system functions and build the structure of mobile pressure vessel safety risk intelligent supervision system;Finally,combined with the framework of the Internet of Things,the overall framework of the mobile pressure vessel safety risk intelligent supervision system is built.This thesis mainly has the following innovative achievements:(1)The concept of intelligent supervision on safety risks of mobile pressure vessels is proposed and defined,and an intelligent supervision mode for safety risks of mobile pressure vessels is constructed,which includes supervision subject,supervision object,supervision method and supervision system;Based on the grounded theory,a safety risk element model based on the intelligent supervision perspective of mobile pressure vessel safety risk is constructed,with basic management elements,real-time monitoring elements,statistical analysis elements and public service elements as the core;Based on the concept of data sharing,a data sharing mechanism under the security risk intelligent supervision mode is designed.(2)According to the knowledge map process,a risk identification method based on Bert CRF model,natural processing technology,support vector machine model and intelligent supervision of security risk based on lexical similarity is designed.The automatic identification of unstructured risk information of accident cause text is completed,and Neo4 j database is used to store and draw the mobile pressure vessel risk knowledge map based on accident cause.(3)A risk evaluation and early warning method for intelligent supervision of security risk based on data mining technology is designed.The algorithm combining equivalence class transformation and association rules is used to analyze the single risk and compound risk level of equipment and enterprises;The sliding window model algorithm is used to analyze the changes in the risk cycle,and the relevant early warning information is displayed in a visual way;The main body and form of early warning are defined,and the risk early warning process of mobile pressure vessel safety risk intelligent supervision is designed.(4)The questionnaire was used to obtain 16 system functions of the mobile pressure vessel safety risk intelligent supervision system,and the 16 system functions were divided into six subsystems through the interpretive structural model of system engineering.Based on the Io T framework,the mobile pressure vessel safety risk intelligent supervision system framework was scientifically and reasonably constructed. |
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