基于改进k-means算法的科研仪器机时智能计算系统  

An Intelligent Calculation System of Machine Time of Research Instruments Based on Improved k-means Algorithm

作　　者：李姜超 谢一航 李辰[1] 苏爽 LI Jiangchao;XIE Yihang;LI Chen;SU Shuang(National Institute of Mertology,Beijing 100029,China)

机构地区：[1]中国计量科学研究院,北京100029

出　　处：《微型电脑应用》2024年第10期156-160,共5页Microcomputer Applications

摘　　要：传统的机时统计常使用人工,不仅效率低,而且成本相对较大,因此在传统的机时计算的基础上提出一种基于改进k均值聚类算法的科研仪器机时智能计算系统。通过对仪器机时电流数据的聚类分析,完成对仪器机时的计算和统计,同时将传统k均值聚类算法进行改进,提升其系统机时计算的准确性。结果表明,使用改进k均值聚类算法后的机时系统在仪器的机时计算中表现更为优异,计算的时间与正常运行时间相同,能够在一定程度上达到0误差标准。由此可见,使用改进聚类算法进行仪器的机时统计能够提升机时计算结果的准确性。Traditional machine time statistics often use manual work,which is not only inefficient but also relatively costly.Therefore,this paper proposes an intelligent calculation system of machine time of research instruments based on an improved k-means algorithm,building upon traditional machine time calculation methods.The system completes the calculation and statis-tics of the machine time of the instruments through cluster analysis of the instrument machine time current data,and simultane-ously improves the traditional k-means clustering algorithm to enhance the accuracy of the machine time calculation.The results show that after using the improved k-means clustering algorithm,the system performs better in the calculation of the machine time of the instruments,the calculated time is the same as uptime,and is able to achieve an error standard of o to a certain ex-tent,This indicates that using the improved clustering algorithm for the machine time statistics of the instruments can enhance the accuracy of machine time calculation results.

关 键 词：仪器机时 K均值聚类算法 智能 电流数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]