Design And Implementation Of PE Pipeline Construction Quality Monitoring Platform

At present,plastic pipes are commonly used in municipal gas projects.Among them,PE pipes are widely used because of their advantages such as corrosion resistance,tightness,flexibility and impact resistance.In the actual use process,natural gas leaked due to the unqualified construction quality of PE pipelines,leading to major safety accidents and causing loss of people's lives and property.Therefore,it is imperative to design and develop a PE pipeline construction quality monitoring platform that can meet the requirements of construction units,construction units and supervision and inspection units.The PE pipeline construction quality monitoring system developed in this paper is implemented using the B / S design pattern combined with the SSH framework,and the database uses My SQL.The system has been tested for good performance and practicality.The content of the paper is mainly divided into the design and implementation of the system and the intelligent supervision of the construction quality..(1)The thesis elaborates the requirements of the PE pipeline construction quality monitoring system,analyzes the factors that need to be controlled when managing the quality of the gas pipeline construction,mainly including the control of personnel,equipment and data.According to the actual project management,the functional requirements required by the system include: Welder management requirements,welding data management requirements,project management requirements,construction quality supervision functions and user authority management requirements.Based on the requirements analysis,the paper expounds the system design,explains the system architecture,and divides the functional modules of the system.Using modeling methods such as flowcharts,class diagrams,and timing diagrams,the functional modules are designed in detail After that,the design of the database structure is given.According to the design results,the main function modules of the system are realized.Finally,the content and results of the system test are given.(2)The thesis introduces the process of image recognition,compares the key steps of the algorithm,and obtains the advantages of deep learning in dealing with image classification problems.In this paper,the relevant theories of convolutional neural networks are studied in detail,and the classical neural network model is analyzed and elaborated.When processing the welder's face image,this paper first analyzes and studies the VGG network model,builds a VGG model suitable for the research object of this article,and improves it by combining it with the Siamese network to construct the Sia VGG network model.Finally,the Sia VGG network model is trained using the LFW face database,and the experimental results show that the improved Sia VGG network model has higher accuracy in welder identity verification.In viewof the different data sets and classification difficulties of welder identity verification and backfill quality inspection,the classic Le Net-5 model is used as the basis for building backfill image classification models during backfill quality inspection,and the backfill images are trained through training on self-collected data sets Classification detection.By using this system,users of relevant units can supervise the entire process of the project,thereby improving the quality of gas pipeline construction management,reducing the occurrence of project risks,and having practical application value..