[1]何 林,刘宇红,张荣芬*.基于关联规则对工业铀测量数据挖掘分析研究[J].计算机技术与发展,2022,32(05):147-152.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 025]
　HE Lin,LIU Yu-hong,ZHANG Rong-fen*.Research on Data Mining and Analysis of Industrial Uranium Measurement Based on Association Rules[J].,2022,32(05):147-152.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 025]
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基于关联规则对工业铀测量数据挖掘分析研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年05期
页码:
147-152
栏目:
应用前沿与综合
出版日期:
2022-05-10

文章信息/Info

Title:
Research on Data Mining and Analysis of Industrial Uranium Measurement Based on Association Rules
文章编号:
1673-629X(2022)05-0147-06
作者:
何 林; 刘宇红; 张荣芬*
贵州大学 大数据与信息工程学院,贵州 贵阳 550025
Author(s):
HE Lin; LIU Yu-hong; ZHANG Rong-fen*
School of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China
关键词:
工业大数据; 智能化生产; 数据挖掘分析; 聚类; 关联规则
Keywords:
industrial big data; intelligent production; data mining and analysis; clustering; association rules
分类号:
TP391. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 05. 025
摘要:
为了推进“ 智能制造” 的进步,大数据技术处理工业制造的大量数据成为了一种高效的方式。 通过采用大数据方法针对工厂工业产品的测量数据进行挖掘分析,挖掘有意义的知识,制定智能化生产方案以提高生产效率,可以避免资源的浪费。 该文主要借助某企业工业铀产品的测量数据开展工业大数据分析研究。 首先进行数据清洗,去除部分冗余和不相干或不可靠的数据,然后根据数据本身的结构特点利用 K - Means 算法对数据进行聚类分析,再结合改进的关联规则算法 Apriori 对测量参数进行挖掘分析,成功得到测量参数与产品质量之间的有价值的关联规则。 最后,根据关联规则结果进行有效性分析。 实验结果表明,该方法减少了大量冗余规则,能够准确挖掘工业大数据有意义的信息,并且准确性得到了明显的提升,有助于提高生产质量,为企业生产调整智能化提供有益的技术支持。
Abstract:
In order to promote the progress of " intelligent manufacturing" ,big data technology has become an efficient way to proces slarge amounts? ? ? ? ?of? data from industrial manufacturing. By using big data methods to mine and analyze the measurement data of factory industrial products, mining meaningful knowledge and formulate intelligent production plans so as to improve the production efficiency can avoid the waste of resources.? ?We mainly use the measurement data of industrial uranium products of a certain enterprise to carry out the industrial big data analysis and research. Firstly,the data is cleaned and then clustered by the K-Means algorithm,and further combined with the improved association rule algorithm Apriori to mine and analyze the measurement parameters,which gives valuable association rules between measurement parameters and product quality successfully. Finally,the validity analysis is carried out according to the results of association rules. Experimental results show that it reduces a large number of redundant rules and can accurately mine meaningful information from industrial big data,and also has a significant improvement in accuracy,which is helpful to improve the production quality and provide useful technology support for intelligent production adjustment of the enterprises.
更新日期/Last Update: 2022-05-10