基于集成模型的BOM近似度量方法

doi:10.3969/j.issn.0372-2112.2019.05.007

电子学报 ›› 2019, Vol. 47 ›› Issue (5): 1023-1028.DOI: 10.3969/j.issn.0372-2112.2019.05.007

基于集成模型的BOM近似度量方法

吴文李¹, 范小朋², 周庚申¹, 黄羿¹, 曹旸¹, 林桂婵¹

1. 中国长城科技集团股份有限公司, 深圳广东 518000;
2. 中国科学院深圳先进技术研究院, 深圳广东 518000

收稿日期:2018-08-22 修回日期:2019-01-02 出版日期:2019-05-25
- 通讯作者:
- 范小朋
- 作者简介:
- 吴文李 女,1986年生.中国科学院深圳先进技术研究所和中国长城科技集团股份有限公司博士后实践基地联合招收博士后.主要研究方向为机器学习、数据挖掘.E-mail:huihuigou@whu.edu.cn

A Novel BOM Similarity Metric Method Based on Ensemble Model

WU Wen-li¹, FAN Xiao-peng², ZHOU Geng-shen¹, HUANG Yi¹, CAO Yang¹, LIN Gui-chan¹

1. China Greatwall Technology Group Co., Ltd, Shenzhen, Guangdong 518000, China;
2. Shenzhen Institutes of Advanced Technology, China Academy of Sciences, Shenzhen, Guangdong 518000, China

Received:2018-08-22 Revised:2019-01-02 Online:2019-05-25 Published:2019-05-25

摘要/Abstract

摘要： 为满足多品种小批次、大规模定制模式下有效划分产品族的需求，全面分析BOM（Bill of Materials，物料清单）所包含的特征，概括已有结构近似方法并提出内容近似度量模型，在此基础上提出组合两者的集成模型.结构近似模型方面，以包含BOM层次结构和物料数量的相邻矩阵表示BOM，利用正交普氏分析法计算BOM与BOM之间的近似程度.内容近似模型方面，从BOM文本中提取有效特征，引入逆向词频法将文本特征转换成机器可识别向量形式，采用余弦近似公式完成向量近似的计算.集成模型提出基于基尼系数的权重分配方法集成结构和内容两种模型.最后，提供测试框架并通过实验评价集成模型较已有方法在模型性能及训练耗时上的优劣.

关键词: 相似性度量, 物料清单, 产品族, 集成模型

Abstract: In order to meet the requirements of grouping product families for advanced manufacturing modes such as mass customization,the features in BOM (Bill of Materials) are comprehensively analyzed,and a concept of BOM structure-based similarity metric model,a content-based similarity metric model,and an ensemble model combined with both are proposed.In the structure-based model,BOMs are represented by adjacent matrixes,including the relationships between materials and the quantity of materials,and the Orthogonal Procrustes Analysis is implemented to measure the similarity among BOMs.While in content-based model,effective text features are extracted from BOMs,being transformed to vectors by TFIDF(Term Frequency-Inverse Document Frequency),and finally being inputted into cosine approximation formula for similarity value.To obtain more accuracy and performance,a weight distribution method based on the Gini coefficient is proposed for the ensemble model.Finally,a test framework is provided and all models are in evaluated experimentally in accuracy and performance.

中图分类号:

TP391.7

吴文李, 范小朋, 周庚申, 等. 基于集成模型的BOM近似度量方法[J]. 电子学报, 2019, 47(5): 1023-1028.

WU Wen-li, FAN Xiao-peng, ZHOU Geng-shen, et al. A Novel BOM Similarity Metric Method Based on Ensemble Model[J]. Acta Electronica Sinica, 2019, 47(5): 1023-1028.

参考文献

[1] Hu X,Peng W,Jin L,Dou J,Zhong Y,Jiang R.A new product family mining method based on PLM database[J].Journal of Central South University,2017,24(11):2513-2523.
[2] Romanowski C J,Nagi R.On comparing bills of materials:a similarity/distance measure for unordered trees[J].IEEE Transactions on Systems,Man and Cybernetics,Part A:Systems and Humans,2015,35(2):249-260.
[3] ZHU H,WANG H,ZHANG G.General bill of material reconfiguration method based on data mining[J].Computer Integreted Manufacturing Systems.2008,14(2):315-321.
[4] Geng J,Zhen M,Tian X,Zhang D.Product lifecycle-oriented BOM similarity metric method[J].China Mechanical Engineering,2008,19(20):2441-2445.
[5] Shih H M.Product structure (BOM)-based product similarity measures using orthogonal procrustes approach[J].Computers & Industrial Engineering,2013,61(3):608-628.
[6] Israt J C,Richi N.Identifying product families using data mining techniques in manufacturing[A].Paradigm.Proceedings of the Twelfth Australasian Data Mining Conference[C].Berlin:Springer's Communication in Computer and Information Science,2014.113-120.
[7] Chowdhury I J,Naya R.A novel method for finding similarities between unordered trees using matrix data model[A].Web Information Systems Engineering-WISE[C].Berlin:Springer's Communication in Computer and Information Science,2013.421-430.
[8] Chen C.Improved TFIDF in big news retrieval:An empirical study[J].Pattern Recognition Letters.2017,93(1):113-122.
[9] Shuo C,Rongxing L,Jie Z.A flexible privacy-preserving framework for singular value decomposition under internet of things environment[A].IFIP International Conference on Trust Management[C].Berlin:Springer,2017.21-37.
[10] Geng Z,Li Y,Han Y,Zhu Q.A novel self-organizing cosine similarity learning network[J].Energy,2018:142(1):400-410.
[11] Caitlin M A,Elizabeth A W,Brian G.Prediction of plant lncRNA by ensemble machine learning classifiers[J].BMC Genomics,2018,19(1):1-11.

基于集成模型的BOM近似度量方法

A Novel BOM Similarity Metric Method Based on Ensemble Model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

[1]	石剑, 刘忠乐, 周敏佳, 文无敌. 一种基于离散Fréchet距离的磁场相似性度量方法[J]. 电子学报, 2018, 46(4): 856-861.
[2]	张桂杰, 张健沛, 杨静, 辛宇. 基于链接相似性聚类的重叠社区识别[J]. 电子学报, 2015, 43(7): 1329-1335.
[3]	许海洋, 庄毅, 顾晶晶. 一种面向嵌入式软件体系结构的形式化建模方法[J]. 电子学报, 2014, 42(8): 1515-1521.
[4]	文成林;周哲;徐晓滨. 一种新的广义梯形模糊数相似性度量方法及在故障诊断中的应用[J]. 电子学报, 2011, 39(3A): 1-6.
[5]	李海林;郭崇慧;邱望仁. 正态云模型相似度计算方法[J]. 电子学报, 2011, 39(11): 2561-2567.
[6]	王玲, 薄列峰, 焦李成. 密度敏感的谱聚类[J]. 电子学报, 2007, 35(8): 1577-1581.