The Design And Implementation Of Overhaul Service Management System For High-Voltage Client Device

Electric power is closely related to all dimensions of our life.The stable and normal operation of the power system is of great importance to our production and life.A problem that can not be ignored in the operation of power system is the occurrence of power failure.Power cut caused by power failure will cause unexpected losses to social life,and the problem of client device itself is a common cause of power failure.For a more stable run of the system,to reduce the frequency of power failure,it is necessary to regularly do a thorough inspection to the devices,and the condition of each device is not the same,the time and energy needed to invest is also not the same,in order to be able to carry out the work more efficiently,it is necessary to quantify the condition of the device.Based on the above background,this paper sets a power device service system as target.With the help of intelligent method like big data analysis to establish an evaluation model to give device a health score,so we can analysis every device more accurately,and provide advice on work priorities and rationality for staff,and it can improve work efficiency,ultimately lead to improve the health status of the client device and improve the standard of safe operation of power grid,ensure the stable and reliable run of power system,and guarantee the stability of the social economy and people’s life.The main work of this paper is as follows:1)The design and implementation of the web system.The web system has implemented multiple function modules such as data collection,fault analysis,health statistics,device tracking,system management,etc.,which covers the necessary services needed by equipment maintenance and facilitates the use of relevant personnel.The design pattern of front and end separation is taken by web system.The back end takes the MVC architecture and is built on SpringBoot framework,while the front end is built with tools such as Vue,Element and Axios.2)The design and implementation of the model for evaluating device state.The evaluation of device state mainly consists of two parts:the failure rate of the device itself and the condition of the line where the device is.The former uses the long short-memory neural network(LSTM)to predict,while the latter uses the chi-square test and mutual information to select the features,and then evaluates the line through the entropy weight method.