Design And Implementation Of Gas Pipeline Corrosion Assessment System

Urban gas system is one of the important infrastructures of the city.While the construction of urban gas system is developing rapidly,the safety of buried gas pipelines should be attached great importance.For buried gas pipelines,pipeline damage caused by corrosion is the most serious potential hazard.In response to the risk analysis of pipeline corrosion,the gas industry has achieved certain results after years of research,and risk analysis has been gradually standardized.Fuzzy comprehensive evaluation based on analytic hierarchy process and fuzzy comprehensive evaluation based on expert scoring method are widely used pipeline corrosion assessment methods.In these methods,the weights of the factors causing the pipeline corrosion are determined by the analytic hierarchy process and the expert scoring method,and on this basis,the pipeline corrosion risk is quantified by the fuzzy comprehensive evaluation method.However,such methods are computationally intensive and the determination of weights is ambiguous.In recent years,artificial intelligence has developed rapidly,and machine learning has been widely used in many fields.Machine learning technology acquires the regularities from the data and uses the regularities to predict the unknown data,providing a new technical route for the corrosion assessment of buried gas pipelines.Based on the research on traditional gas pipeline corrosion assessment method and in-depth study of machine learning algorithms,this thesis designs a gas pipeline corrosion assessment model based on machine learning method,and optimizes the model by adding environmental factors.Based on the optimized model,the gas pipeline corrosion assessment system is designed and implemented.The system consists of a resource server,an application server,and a client.The resource server is responsible for the storage and data cleaning of gas pipeline related data and environmental data;the application server is responsible for providing various application interfaces,including security factor analysis services and pipeline corrosion assessment services;the client is responsible for the visualization of safety factor analysis results and pipeline corrosion assessment results.The thesis firstly introduces the background and significance of the project,summarizes the current research status of pipeline corrosion assessment technology,and puts forward detailed research objectives and research contents.Then introduces the relevant technologies applied in the research and development process,including database technology,data visualization technology,JavaEE development framework and machine learning algorithms.Then the thesis introduces the traditional pipeline corrosion assessment method of the gas industry,designs a corrosion assessment model based on machine learning method,and optimizes the corrosion assessment model by adding environmental factors.In the design and implementation of the gas pipeline corrosion assessment system,the system requirements analysis and overall framework design are introduced,and the workflow and core class diagram of each functional module are described in detail.Finally,the thesis introduces the system test,including functional test and performance test,demonstrates the use effect of the complete system,and verifies the functional integrity and stability of the system.