基于GIS技术的牵引电源智能运维专家决策信息系统的设计与实现

Xiaojing Zhou, Wu Tan, Shudan Yu, Fenshen Kong, Qingtang Li
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引用次数: 0

摘要

电力是国民经济生产和社会发展的“必需品”,是当今社会不可缺少的基础能源之一。随着计算机、遥感技术、地理科学、信息科学的不断发展,基于gis的牵引供电智能运维系统日趋成熟。专家决策信息系统可以根据众多专家的知识和经验做出合理的推理和判断,并使用人类的自然语言来解释推理和判断的结果。本文研究了基于GIS技术的牵引供电智能运维专家决策信息系统的设计与实现。本研究主要介绍了智能运维专家决策信息系统的主要功能,包括设备在线监测、异常智能分析、智能辅助决策,并阐述了各功能模块的详细工作流程。此外,本研究还介绍了系统的关键技术,并进行了设备故障预警功能试验和性能试验。测试结果表明,并发用户数增加到1000,系统最大响应时间为2.67s, CPU最大占用率为15.8%,物理内存最大占用率为39.6%。这些结果满足系统性能指标,系统性能测试通过。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Implementation of Expert Decision Information System for Intelligent Operation and Maintenance of Traction Power Supply Based on GIS Technology
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.
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