Realization And Research On Key Technologies Of Data Terminal On Storage Battery Tele-monitoring System

In electric power, communication, high-speed rail and aviation system batteries as backuppower supply, its application is more and more widely, also more and more important role. In orderto keep the back-up power to effectively work, need to real-time monitoring of the health of thebattery. Therefore, carrying out a research into the battery remote monitoring system, no matter intheory or in engineering applications, has a very important research value. In this paper, we studythe battery remote monitoring data terminals as part of the whole remote monitoring system, torealize the remote real-time data management and analysis, has very important significance.The system uses C/S system architecture, the background using Microsoft SQL Server2005database, the front desk to use Visual Studio2010as a development tool, C#language to completethe battery remote monitoring system for the preparation of the code. Maintenance personnel afterlogin system, may at any time to track of battery health information, and draw out the monitoringdata of graph, convenient maintenance personnel to the effective management of the battery.In this paper, the huge amounts of data query optimization carried on the thorough research,system uses a table partitioning technology to optimize the query efficiency of huge amounts ofdata. The data table partitioning, uses the divide-and-conquer strategy to solve the problem of theperformance of the large data tables. The partition table is not only having a lot of flexibility in datamanagement, but also promoting the efficiency of data query.In order to provide users a kind of personalized, rapid information retrieval service,in this paperwe study the personalized recommendation algorithm.First,from the perspective of user interest toanalysis and establish the user interest model, and the introduction of forgetting factor to update theuser interest model; Finally the personalized recommendation algorithm application in the systemand its performance analysis, the results show that personalized recommendation retrievaleffectively improve the performance of the system.