Design and Implementation of Expert Decision Information System for Intelligent Operation and Maintenance of Traction Power Supply Based on GIS Technology
Xiaojing Zhou, Wu Tan, Shudan Yu, Fenshen Kong, Qingtang Li
{"title":"Design and Implementation of Expert Decision Information System for Intelligent Operation and Maintenance of Traction Power Supply Based on GIS Technology","authors":"Xiaojing Zhou, Wu Tan, Shudan Yu, Fenshen Kong, Qingtang Li","doi":"10.1109/ATEEE54283.2021.00014","DOIUrl":null,"url":null,"abstract":"Electricity is a “necessity” for national economic production and social development and is one of indispensable basic energy sources in today’s society. With the continuous development of computer, remote sensing technology, geographic science, and information science, GIS-based traction power supply intelligent operation and maintenance system has become increasingly mature. Expert decision information systems can make reasonable inferences and judgments based on the knowledge and experience of many experts and use human natural language to explain the results of inferences and judgments. This study investigates the design and implementation of an expert decision information system for intelligent operation and maintenance of traction power supply based on GIS technology. This study mainly introduces the main functions of the intelligent operation and maintenance expert decision-making information system, including equipment online monitoring, abnormal intelligent analysis, and intelligent auxiliary decision making, and expounds on the detailed workflow of each functional module. In addition, this study introduces the key technology of the system and conducts equipment failure early warning function test and performance test. Test results showed that the number of concurrent users increased to 1000, the maximum response time of the system was 2.67s, the maximum CPU occupancy rate was 15.8%, and the maximum occupancy rate of physical memory was 39.6%. These results meet the system performance target, and the system performance test passes.","PeriodicalId":62545,"journal":{"name":"电工电能新技术","volume":"56 1","pages":"26-30"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"电工电能新技术","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.1109/ATEEE54283.2021.00014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Electricity is a “necessity” for national economic production and social development and is one of indispensable basic energy sources in today’s society. With the continuous development of computer, remote sensing technology, geographic science, and information science, GIS-based traction power supply intelligent operation and maintenance system has become increasingly mature. Expert decision information systems can make reasonable inferences and judgments based on the knowledge and experience of many experts and use human natural language to explain the results of inferences and judgments. This study investigates the design and implementation of an expert decision information system for intelligent operation and maintenance of traction power supply based on GIS technology. This study mainly introduces the main functions of the intelligent operation and maintenance expert decision-making information system, including equipment online monitoring, abnormal intelligent analysis, and intelligent auxiliary decision making, and expounds on the detailed workflow of each functional module. In addition, this study introduces the key technology of the system and conducts equipment failure early warning function test and performance test. Test results showed that the number of concurrent users increased to 1000, the maximum response time of the system was 2.67s, the maximum CPU occupancy rate was 15.8%, and the maximum occupancy rate of physical memory was 39.6%. These results meet the system performance target, and the system performance test passes.