Research On The Management Of Joint Rectification Of High-speed Railway Engineering Mortar Based On Big Data Technology

As a huge system,railway has a wide coverage,many types of services,complicated work types,and reserves a large amount of data such as planning,operation,and maintenance.The industry characteristics make the integration of railway and big data technology will promote the transformation of railway and can The railway industry creates greater value.Special rectification is an important task for the maintenance of high-speed railway facilities and equipment.The special line rectification fund is an important investment during the operation period of high-speed railways,but there is still no unified management in the management field.The information of special rectification projects is mostly stored in unstructured forms such as texts and reports.It is difficult to analyze the data.Most of the special rectification decisions rely on human experience.Therefore,the use of existing data to assist in the decision of special rectification projects is important for improving the management level of special rectification projects Theoretical and practical significance.The thesis conducts an in-depth study on the rectification and management of mortar seam defects in the CRTS II track slab seam problems of high-speed railways.The big data technology is used to process the existing complex and redundant mortar seam disease data,and the influencing factors and manifestations of the mortar seam disease are discussed,and the separated effective data are summarized to obtain the data set by means of data fusion.The disease influencing factors and manifestations are merged and summarized into disease static feature data and disease dynamic feature data.A new overall feature framework for mortar off-seam disease is proposed,which lays a data foundation for multi-dimensional statistical analysis of mortar off-seam disease information.Based on the overall framework of the mortar off-seam disease,the classification of the damage degree of the disease was studied,and a new disease classification model-BDAJ(Big Data And Judgment)classification model was proposed based on big data technology and algorithm.The analytic hierarchy process and the entropy weight method are used to calculate the weight of the static data and dynamic data of the mortar off-seam disease,and then use machine learning unsupervised learning to perform cluster analysis.Finally,the linear discriminant analysis is used to train the clustering results to obtain the disease classification model.Relying on the classification model,the research proposed a neural network-based mortar joint repair special prediction cost prediction model,through the existing repair joint repair disease cost file and repair cost analysis to get the neural network training set,develop a reasonable neural network structure training The cost prediction model for the special rectification of offseam simplifies the existing cost prediction steps through the data-based cost prediction model.In order to more systematically manage and study the seam disease,this paper constructs the knowledge map of seam disease.Protégé is used to model the on-seam disease data,and the generated RDF data model is stored in the Neo4 j graph database by calling py2 neo to visualize the knowledge map of the seam disease,through which the conflict detection of disease maintenance can be achieved.Finally,the thesis combined with the actual work situation of the mortar seam disease treatment on the railway highspeed railway,combined with the research results,responsibility division,etc.to sort out the process of seam disease management,and got the disease treatment management flow chart.Based on the idea of big data,the thesis studies the management process of seam disease management of high-speed railway,applies big data technology to the project of seam disease repair of high-speed railway,and provides reference for the application of big data in railway.