Design And Realization Of Information Management System For Urban Rail Vehicles

Since the twenty-first century, with the city expanding, the operating pressure of the city’s existing transport system is constantly increasing. Base on this situation, in most cities, the construction and development of public transport system is accelerated. Metro transportation is an important role of the urban rail transit, and the vehicle of metro is the core part of metro transportation. So the maintenance of metro vehicles is the most important segment. In this paper, we propose to establish an information management system, in order to solve the various problems in the maintenance. By this way, we hope to improve the quality of the maintenance, ensure safety of the metro operation and enhance the quality of that.Firstly, we introduced the work of metro vehicle maintenance, and then describe the management mode of the maintenance and the use in detail. At last, we analyzed the current mode of the maintenance and its rules.Secondly we analyzed the drafted the comprehensive needs analysis about the metro maintenance management system. We carried out the analysis about the relevant business about metro maintenance, and the detailed analysis about each specific business module. Based on the specificity of each module, we sorted out their different needs.Then, based on a comprehensive needs analysis as described above, for the system, it described the main principles and requirements for system design, and ideas and solutions. And then we do the analysis, combining the characteristics of the business functions of the system. We constructed of suitable relational database, and created the data tables according to the entity of the system model. On this basis, we implemented database functionalities, and selected the system platform combined with data and information requirements. It’s the foundation for the development of the system;Finally, with a large number of inventory historical data, we can do the analysis and process of the data. Forecast the spare parts demand by the nonlinear neural network models based on the two factors:neural networks model and SVM model. Then on the model AHP and SVM, we classified the spare parts into three kinds:ABC. Combined the forecasting model and classification model, a spare parts inventory control strategy about class A is given...