[数据驱动的产品设计]面向智能制造服务的产品设计需求信息可视化方法

摘要/Abstract

摘要： 为实现智能制造服务视角下产品设计需求信息全面、准确、高效和清晰的表达，构建了一种设计需求信息可视化方法。利用网络爬虫工具抓取相关平台上的竞品现状信息、用户反馈信息等产品调研对象的数据文本；利用自然语言处理技术，对采集到的设计需求数据进行预处理和特征提取，从而获得可编码文本数据；采用共现矩阵、聚类图、战略坐标图等方法和工具，对文本数据进行编码与分析，以实现产品设计需求信息的可视化表达，并结合定性调整函数计算需求权重。以某型号谷物联合收割机的竞品现状信息分析为例，验证了所提方法的可行性和有效性。

关键词: 智能制造, 产品设计, 信息可视化, 共现矩阵, 战略坐标图

Abstract: From the perspective of intelligent manufacturing services, a design requirement visualization method was constructed to achieve a comprehensive, accurate, efficient and clear expression of design requirement informations. Firstly, the relevant platforms of the status of the competition informations, user feedback informations and other research object data text were captured by the web crawler tools. Secondly, the design requirement data were reprocessed and features were extracted by natural language processing technology to obtain the encoded text data. Thirdly, the text data were coded and analyzed by means of co-occurrence matrix, cluster graph and strategic coordinate graph to realize the visual expression of product design requirement informations, and to calculate the requirement weights with qualitative adjustment function. In order to verify the feasibility and validity of the method, the analysis of competitive status informations, a type of grain combine harvester was taken as an example.

Key words: intelligent manufacturing, product design, information visualization, co-occurrence matrix, strategic diagram

中图分类号:

TB472

李晓英, 周大涛. [数据驱动的产品设计]面向智能制造服务的产品设计需求信息可视化方法[J]. 中国机械工程.

LI Xiaoying, ZHOU Datao. Product Design Requirement Information Visualization Approach for Intelligent Manufacturing Services[J]. China Mechanical Engineering.

参考文献

[1]张卫, 丁金福, 纪杨建, 等. 工业大数据环境下的智能服务模块化设计[J].中国机械工程, 2019,30(2):167-173.

ZHANG Wei, DING Jingfu, JI Yangjian, et al. Modular Design of Intelligent Service Based on Industrial Big Data[J]. China Mechanical Engineering,2019, 30(2):167-173.

[2]李浩, 顾新建,祁国宁,等. 现代制造服务业的发展模式及中国的发展策略[J].中国机械工程,2012,23(7):798-809.

LI Hao, GU Xinjian, QI Guoning, et al. Research on Development Mode of Modern Manufacturing Services and Its Development Strategy in China[J]. China Mechanical Engineering, 2012, 23(7): 798-809.

[3]DAVIS J, EDGAR T, PORTER J, et al. Smart Manufacturing, Manufacturing Intelligence and Demand-dynamic Performance[J]. Computers & Chemical Engineering, 2012, 47(12):145-156.

[4]张映锋,张党,任杉.智能制造及其关键技术研究现状与趋势综述[J].机械科学与技术,2019,38(3):329-338.

ZHANG Yingfeng, ZHANG Dang, REN Shan. Survey on Current Research and Future Trends of Smart Manufacturing and Its Key Technologies[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(3): 329-338.

[5]周佳军, 姚锡凡, 刘敏, 等. 几种新兴智能制造模式研究评述[J]. 计算机集成制造系统, 2017,23(3):624-639.

ZHOU Jiajun, YAO Xifan, LIU Min, et al. State-of-Art Review on New Emerging Intelligent Manufacturing Paradigms[J]. Computer Integrated Manufacturing Systems, 2017, 23(3): 624-639.

[6]KANG H S, LEE J Y, CHOI S, et al. Smart Manufacturing: Past Research, Present Findings, and Future Directions[J]. International Journal of Precision Engineering and Manufacturing-Green Technology,2016,3(1):111-128.

[7]ROA M A , BERENSON D , HUANG W. Mobile Manipulation: toward Smart Manufacturing[J]. IEEE Robotics & Automation Magazine, 2015, 22(4):14-15.

[8]姚锡凡, 周佳军, 张存吉, 等. 主动制造:大数据驱动的新兴制造范式[J]. 计算机集成制造系统, 2017,23(1):172-185.

YAO Xifan, ZHOU Jiajun, ZHANG Cunji, et al.Proactive Manufacturing—a Big Data Driven Emerging Manufacturing Paradigm[J]. Computer Integrated Manufacturing Systems, 2017, 23(1): 172-185.

[9]张洁, 汪俊亮, 吕佑龙, 等. 大数据驱动的智能制造[J]. 中国机械工程, 2019,30(2):127-133.

ZHANG Jie, WANG Junliang, LYU Youlong, et al. Big Data Driven Intelligent Manufacturing[J]. China Mechanical Engineering,2019,30(2):127-133.

[10]KUSIAK A. Smart Manufacturing Must Embrace Big Data[J]. Nature, 2017, 544(7648):23-25.

[11]SIM H S. Big Data Analysis Methodology for Smart Manufacturing Systems[J]. International Journal of Precision Engineering and Manufacturing, 2019, 20(6):973-982.

[12]ZHONG R , XU C , CHEN C , et al. Big Data Analytics for Physical Internet-based Intelligent Manufacturing Shop Floors[J]. International Journal of Production Research, 2017, 55(9):2610-2621.

[13]LAI C F ,CHIEN W C , YANG L T, et al. LSTM and Edge Computing for Big Data Feature Recognition of Industrial Electrical Equipment[J]. IEEE Transactions on Industrial Informatics, 2019,15(4):2469-2477.

[14]HAN X , WANG Z , HE Y, et al. A Mission Reliability-driven Manufacturing System Health State Evaluation Method Based on Fusion of Operational Data[J]. Sensors, 2019, 19(3):442.

[15]张晨, 李嘉, 王海宁, 等. 大数据在设备健康预测和备件补货中的应用[J]. 中国机械工程, 2019, 30(2): 183-187.

ZHANG Chen, LI Jia, WANG Haining, et al. Applications of Big Data in Equipment Health Status Prediction and Spare Parts Replenishment[J]. China Mechanical Engineering,2019, 30(2): 183-187.

[16]张洁, 高亮, 秦威, 等. 大数据驱动的智能车间运行分析与决策方法体系[J]. 计算机集成制造系统, 2016, 22(5):1120-1128.

ZHANG Jie, GAO Liang, Qing Wei, et al. Big Data-driven Operational Analysis and Decision-making Methodology in Intelligent Workshop[J].Computer Integrated Manufacturing Systems, 2016,22(5):1120-1128.

[17]刘伟杰, 吉卫喜, 张朝阳. 面向智能生产维护的大数据建模分析方法[J]. 中国机械工程, 2019,30(2):159-166.

LIU Weijie, JI Weixi, ZHANG Chaoyang. Big Data Modeling Analysis Method for Intelligent Production Maintenance[J]. China Mechanical Engineering, 2019,30(2):159-166.

[18]REN S , ZHANG Y , LIU Y , et al. A Comprehensive Review of Big Data Analytics Throughout Product Lifecycle to Support Sustainable Smart Manufacturing: a Framework, Challenges and Future Research Directions[J]. Journal of Cleaner Production, 2018，210：1343-1365.

[19]姚锡凡, 雷毅, 葛动元, 等. 驱动制造业从“互联网+”走向“人工智能+”的大数据之道[J]. 中国机械工程, 2019,30(2):134-142.

YAO Xifan, LEI Yi, GE Dongyuan, et al. On Big Data Driving Manufacturing from “Internet Plus” to “AI Plus”[J]. China Mechanical Engineering, 2019, 30(2): 134-142.

[20]张雷,钟言久,袁远,等. 基于数据挖掘的绿色设计中客户需求向工程特性权重转化方法[J].中国机械工程,2019,30(2):174-182.

ZHANG Lei, ZHONG Yanjiu, YUAN Yuan, et al. Method of Transform Customer Demands to Engineering Characteristic Weights in Green Design Based on Data Mining[J].China Mechanical Engineering, 2019, 30(2): 174-182.

[21]司光耀, 王凯, 李文强, 等. 基于大数据和粗糙集的产品需求分析方法研究[J]. 工程设计学报, 2016,23(6):521-529.

SI Guangyao, WANG Kai, LI Wenqiang, et al. Research on Product Demand Analysis Method Based on Big Data and Rough Set[J]. Chinese Journal of Engineering Design, 2016, 23(6): 521-529.

[22]QI J, ZHANG Z, JEON S, et al. Mining Customer Requirements from Online Reviews: a Product Improvement Perspective[J]. Information & Management, 2016, 53(8):951-963.

[23]CHEN M, MAO S, LIU Y. Big Data:a Survey[J]. Mobile Networks and Applications,2014,19(3):171-209.

[24]李晓英, 周大涛. 企业产品开发全过程设计评价流程与方法研究[J]. 科技进步与对策, 2018,35(24):144-149.

LI Xiaoying, ZHOU Datao. Research on the Evaluation Process and Method of the Whole Process Design of Enterprise Product Development[J]. Science and Technology Progress and Policy, 2018, 35(24): 144-149.

[25]曹梅, 朱学芳. 用户信息行为的研究方法体系初探[J]. 情报理论与实践, 2010,33(1):37-40.

CAO Mei, ZHU Xuefang. A Preliminary Study on The Research Method System of User Information Behavior[J]. Information Studies:Theory and Application, 2010, 33(1): 37-40.

[26]刘智慧, 张泉灵. 大数据技术研究综述[J]. 浙江大学学报(工学版), 2014,48(6):957-972.

LIU Zhihui, ZHANG Quanling. Research Overview of Big Data Technology[J]. Journal of Zhejiang University(Engineering Science), 2014, 48(6): 957-972.

[27]ZULKARNAINN, ANSHARI M . Big Data: Concept, Applications & Challenges[C]∥2016 International Conference on Information Management and Technology. Bandung,Indonesia, 2017:307-310.

[28]张华平. NLPIR大数据语义智能分析平台[EB/OL].(2018-12-14). http:∥www.nlpir.org/wordpress.

ZHANG Huaping. NLPIR Big Data Semantic Intelligence Analysis Platform[EB/OL].(2018-12-14).http:∥www.nlpir.org/wordpress.

[29]LAW J, BAUIN S, COURTIAL J P, et al. Policy and the Mapping of Scientific Changer: a Co-word Analysis of Research into Environmental Acidification[J]. Scientometrics, 1988, 14(3/4): 251-264.

[30]刘敏娟, 张学福, 颜蕴. 基于词频、词量、累积词频占比的共词分析词集范围选取方法研究[J]. 图书情报工作, 2016, 60(23): 135-142.

LIU Minjuan, ZHANG Xuefu, YAN Yun. Research on Method of Determining Scope of Word Set in Co-word Analysis Based on Word Frequency, Number of Words,Cumulative Word Frequency in Proportion[J]. Library and Information Service, 2016, 60(23): 135-142.

[31]储节旺,郭春侠.共词分析法的基本原理及EXCEL实现[J].情报科学,2011,29(6):931-934.

CHU Jiewang, GUO Chunxia. General Principles of Co-words Analysis Method and Achieved by EXCEL[J].Information Science, 2011, 29(6):931-934.

[32]李永忠, 陈静, 谢隆腾. 共词分析法中战略坐标图的改进研究[J]. 情报理论与实践, 2019, 42(1): 65-69.

LI Yongzhong, CHENG Jing, XIE Longteng. Improvement of Strategic Diagram in the Co-word Analysis[J]. Information Studies: Theory and Application, 2019, 42(1): 65-69.