基于大数据与关联规则的考评进度动态跟踪系统设计  

Design of Evaluation Progress Dynamic Tracking System Based on Big Data and Association Rules

作　　者：张瑞 张维建 张新峰 刘颖 ZHANG Rui;ZHANG Weijian;ZHANG Xinfeng;LIU Ying(Northwest Branch of State Grid Corporation of China,Xi’an 710048,China;Information Communication Company of State Grid Shaanxi Electric Power Co.,Ltd.,Xi’an 710000,China)

机构地区：[1]国家电网有限公司西北分部,陕西西安710048 [2]国网陕西省电力有限公司信息通信公司,陕西西安710000

出　　处：《微型电脑应用》2024年第4期153-156,共4页Microcomputer Applications

摘　　要：为了提高考评进度动态跟踪效果,设计一个基于大数据与关联规则的考评进度动态跟踪系统。在系统硬件部分设计了微控制器、通信模块、存储器模块与信号采集模块;在系统软件部分,采用大数据挖掘技术挖掘员工相关数据,采用关联规则挖掘数据的频繁项集,构建FP树,计算数据的最小支持度和最小置信度,对数据分类,获得员工绩效的关联信息,并整合信息,完成考评进度动态跟踪系统的设计。实验结果表明,该方法能够准确地对员工绩效打分,并在多并发用户访问与多条数量处理上,有效提高了考评进度动态跟踪效果。In order to improve the dynamic tracking effect of evaluation progress,a dynamic evaluation progress tracking system based on big data and association rules is designed.In the hardware part of the system,the microcontroller,communication module,memory module and signal acquisition module are designed.In the software part of the system,big data mining technology is used to mine employee-related data,association rules are used to mine frequent item sets of data,FP tree is constructed,and data are calculated.The minimum support and confidence of the data are classified,the related information of employee performance is obtained,and the information is integrated to complete the design of the evaluation progress dynamic tracking system.The experimental results show that the method can accurately score employee performance,and effectively improve the dynamic tracking effect of evaluation progress in terms of multiple concurrent user access and multiple number processing.

关 键 词：大数据 关联规则 考评进度 动态跟踪 频繁项集 最小支持度 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]