Research And Design Of Remote Monitoring System For Elevator Maintenance

With China’s economy soaring and the acceleration of the aging population,elevators have gradually become a necessity in people’s daily life.The rapid increase in the number of elevators has also increased the pressure on maintenance work.In order to improve the quality of maintenance,standardize the maintenance process,and make the entire maintenance process documented and responsible,this paper has developed a set of elevator maintenance systems.Develop a maintenance management platform based on JAVA,and cooperate with We Chat technician maintenance Mini Program to effectively ensure the quality of elevator maintenance;then build a C4.5 decision tree-BP neural network combination model to predict and analyze elevator failures,and promote the transformation of elevators from on-time maintenance to on-demand maintenance.(1)Firstly,the paper analyzes the functional and non-functional requirements of the system function,divide the maintenance system into maintenance management platform,maintenance mobile terminal(maintenance We Chat Mini Program)and back-end,then implement them according to the requirements.The overall system is built with Spring + Spring MVC + My Batis.The back end is divided into the Controller layer,Service layer and DAO layer,which provides the interface for the maintenance management platform and maintenance applet.The maintenance management platform is the control center of maintenance,and the roles and permissions are assigned through Shiro for the three roles of government,maintenance company and property.The maintenance mobile terminal is provided for maintenance technicians to use,which can complete maintenance related tasks such as applying for technician certification and maintaining punch-in.(2)The real-time running data of elevator is collected through the elevator Io T black box device installed in the corresponding part of the elevator,and then send it to the server through the GPRS network,which can monitor the elevator running status in real time on the maintenance management platform.The BP neural network fault prediction model and the C4.5 decision tree-BP neural network combined fault prediction model were constructed respectively.By comparing and analyzing the model prediction results,It shows that the combined fault prediction model improves the prediction accuracy,and the prediction accuracy rate can reach 99.22%.