基于大数据技术的图书馆自助服务设备故障诊断方法  

Fault diagnosis method of library self-service equipment based on big data technology

作　　者：李铨民 刘勇[1] LI Quanmin;LIU Yong(ShangLuo University,Shangluo Shanxi 726000,China)

机构地区：[1]商洛学院,陕西商洛726000

出　　处：《自动化与仪器仪表》2021年第1期70-72,76,共4页Automation & Instrumentation

基　　金：商洛学院校级科研项目:数字化时代高校智慧图书馆构建与服务研究(No.18SKY019)。

摘　　要：为了提高图书馆自助服务设备的故障检测和智能修复能力,提出基于大数据技术的图书馆自助服务设备故障诊断方法,建立图书馆自助服务设备故障检测的大数据分析模型,结合大数据信息融合方法进行图书馆自助服务设备故障特征信息采样,通过模糊信息聚类分析的方法进行图书馆自助服务设备故障的类别划分,采用大数据技术进行图书馆自助服务设备故障特征的大数据采样和优化融合处理,建立图书馆自助服务设备故障类别特征监测模型,在优化的大数据分类算法下实现对图书馆自助服务设备故障的类别优化判断和自适应寻优。仿真结果表明,采用该方法进行图书馆自助服务设备故障诊断的准确性较高,对故障特征样本信息采样精度较好,提高了故障检测和识别能力。In order to improve the fault detection and intelligent repair capability of library self-service equipment,a fault diagnosis method of library self-service equipment based on big data technology is proposed,a big data analysis model for fault detection of library self-service equipment is established,fault characteristic information of library self-service equipment is sampled by combining a big data information fusion method,classification of library self-service equipment faults is carried out by a fuzzy information clustering analysis method,The big data technology is adopted to sample and optimize the fusion processing of library self-service equipment failure characteristics,and a monitoring model of library self-service equipment failure category characteristics is established.Under the optimized big data classification algorithm,the category optimization judgment and adaptive optimization of library self-service equipment failure are realized.The simulation results show that the fault diagnosis accuracy of library self-service equipment using this method is higher,the sampling accuracy of fault feature sample information is better,and the fault detection and identification capabilities are improved.

关 键 词：大数据技术 图书馆 自助服务设备 故障 诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]