摘要
本文目的是提出基于人工智能技术的机房电气系统工作状态预报方法,以增强机房电气系统稳定性和故障预警能力。利用长短期记忆网络(LSTM)深度学习模型,实现了电压、电流、负载、温湿度关键指标的实时监测及分析。仿真结果显示,随着负载的增加,电压从220 V增加到230 V,电流从18.2 A增加到22.0 A,负载从12.5 kW增加至17.5 kW,温湿度从55%升高至60%。通过执行电压调节、负载均衡以及温湿度控制,当负载发生变化时系统维持电压与电流平稳,避免电压波动给装置带来的冲击。研究证明该控制措施能有效地提高系统运行效率,降低故障发生率,为机房电气系统智能运维与故障预测提供切实可行的解决方案。
This study aims to enhance the stability and fault capability of electrical systems in machine rooms by proposing an AI-based operational state prediction method.Utilizing a Long Short-Term Memory(LSTM)deep learning model,the system enables real-time monitoring and analysis of key parameters,including voltage,current,load,temperature,and humidity.Simulation results indicate parameter variations corresponding to load increases:voltage rose from 220V to 230V,current from 18.2A to 22.0A,load from 12.5kW to 17.5kW,and temperature/humidity from 55%to 60%.Through the implementation of voltage regulation,load balancing,and environmental control strategies,the system maintained stable voltage and current levels during load transitions,mitigating the impact of voltage fluctuations on equipment.The research validates that these control measures effectively improve operational efficiency,reduce fault incidence,and offer a practical solution for intelligent operation,maintenance,and fault prediction of electrical systems in machine rooms.
作者
李战军
Li Zhanjun(Shaanxi Beidou Star Technology Development Co.,Ltd.,Xi'an,Shaanxi,710000,China)
出处
《北斗与空间信息应用技术》
2025年第6期33-36,共4页
Beidou and Spatial Information Application Technology
关键词
机房电气系统
人工智能
LSTM模型
智能运维
Electrical system of machine room
Artificial intelligence
LSTM model
Intelligent operation and maintenance
