[Purpose/Significance] Industrial big data is the catalyst and source power to realize intelligent manufacturing, and it is an important link to promote the digital transformation of traditional manufacturing industry. Further exploring the potential value of industrial big data is of great significance to realize the digital transformation and upgrading of industry.[Method/Process] Combining with the unique attribute characteristics of industrial big data, this paper constructed a three-stage model of industrial big data life cycle based on the perspective of data life cycle and constructed the industrial big data value discovery mode under the perspective of life cycle.[Result/Conclusion] This paper clarifies the data value presented existing in each major stage in the industrial big data life cycle, i.e., the data static value in the data integration stage, the data dynamic value in the data analysis stage, and the data decision value in the data application stage, and explains the power mode of the data value discovery process at different stages, i.e., the "scene+specification" data acquisition mode and orderly data organization mode in the data integration stage, the intelligent data processing mode and adaptive algorithm matching mode in the data analysis stage, and the data collaboration usage mode and forward feedback mode of data value in the data application stage.
[1] 武超.中国科协张彦敏:庞大的传统制造业升级是智能制造今后的主攻方向[EB/OL].[2023-04-07].https://baijiahao.baidu.com/s?id=1647713119513308196&wfr=spider&for=pc.
[2] 中国电子技术标准化研究院.工业大数据白皮书(2019版)[EB/OL].[2023-04-07].http://www.cesi.cn/201904/4955.html.
[3] 苗效东, 吕明元, 张旭东.工业大数据对我国制造业绿色发展的影响-基于省级面板数据的空间效应检验[J].软科学, 2023, 37(3):1-10.
[4] 罗立辉.工业大数据的应用和实践研究[J].信息记录材料, 2022, 23(2):167-169.
[5] 刘阳, 郜志英, 周晓敏, 等.工业数据驱动下薄板冷轧颤振的LSTM智能预报[J].机械工程学报, 2020, 56(11):121-131.
[6] 李敏波, 丁铎, 易泳.基于FP-Growth改进算法的轮胎质量数据分析[J].中国机械工程, 2019, 30(2):244-251.
[7] 彭放, 彭高群, 祁亚茹, 等.基于电力大数据的中国工业增加值现时预测[J].电信科学, 2021, 37(7):115-125.
[8] KUSIAK A.Smart manufacturing must embrace big data[J].Nature, 2017, 544(7648):23-25.
[9] REHMAN M H U, YAQOOB I, SALAH K, et al.The role of big data analytics in industrial Internet of things[J].Future generation computer systems, 2019, 99:247-259.
[10] 孙立, 焦微玲.工业大数据驱动下知识发现与知识服务构建研究[J].情报理论与实践, 2017, 40(11):86-89, 104.
[11] LYTRAS M, VISVIZI A, ZHANG X, et al.Cognitive computing, big data analytics and data driven industrial marketing[J].Industrial marketing management, 2020, 90(3):663-666.
[12] 刘祎, 王玮, 苏芳.工业大数据背景下企业实现数字化转型的案例研究[J].管理学刊, 2020, 33(1):60-69.
[13] HIGGINS S.The DCC curation lifecycle model[J].International journal of digital curation, 2008, 3(1):134-140.
[14] BALL A.Review of data management life cycle Models[DB/OL].[2023-04-07].https://purehost.bath.ac.uk/ws/portalfiles/portal/206543/redm1rep120110ab10.pdf.
[15] DataONE.Data life cycle[EB/OL].[2023-04-07].https://www.dataone.org/data-life-cycle.
[16] 柴天佑, 刘强, 丁进良, 等.工业互联网驱动的流程工业智能优化制造新模式研究展望[J].中国科学:技术科学, 2022, 52(1):14-25.
[17] 郭建红.电解铝工业设备运行因素综合生命周期评估[J].世界有色金属, 2019(4):49, 51.
[18] 王华斌, 蔡桦榕.基于客户体验的工业产品全生命周期数字化研究[J].设计艺术研究, 2022, 12(1):4-7, 26.
[19] 王伟玲.基于价值链的工业数据治理:模型构建与实践指向[J].科技管理研究, 2020, 40(21):233-239.
[20] 汪洋, 王柯, 张桃宁, 等.工业数字化转型中的数据治理研究[J].信息技术与网络安全, 2022, 41(4):25-31.
[21] 吴彤.基于工业大数据陶瓷窑炉的节能降耗优化设计[J].陶瓷科学与艺术, 2022, 56(4):64-65.
[22] 罗立辉.工业大数据的应用和实践研究[J].信息记录材料, 2022, 23(2):167-169.
[23] 李敏波, 王海鹏, 陈松奎, 等.工业大数据分析技术与轮胎销售数据预测[J].计算机工程与应用, 2017, 53(11):100-109.
[24] 陈玲, 王晓飞, 付宇航.制造业企业自生能力与数字化转型路径[J/OL].科学学研究:1-16[2023-04-20].https://doi.org/10.16192/j.cnki.1003-2053.20230413.002.
[25] 孟钰潇, 周西平.基于数据生命周期的公安情报数据治理方法[J].情报探索, 2021(10):33-40.
[26] SINACEPOURFARD A, GARCIA J, MASIP-BRUIN X, et al.A comprehensive scenario agnostic Data LifeCycle model for an efficient data complexity management[C]//2016 IEEE 12th international conference on e-Science(e-Science).Piscataway:IEEE computer society, 2016:276-281.
[27] 陆莉, 沙勇忠, 徐雪峰.基于生命周期的公共安全数据管理模型研究[J].图书与情报, 2019(4):13-21.
[28] 王晨, 贡晓丽.业务驱动, "造一把可靠的锤子"[N].中国科学报, 2018-10-11(05).
[29] 任磊, 贾子翟, 赖李媛君, 等.数据驱动的工业智能:现状与展望[J].计算机集成制造系统, 2022, 28(7):1913-1939.
[30] 慧都大数据.工业互联网到智能制造, 其实工业大数据才是核心关键点[EB/OL].[2023-04-27].https://m.sohu.com/a/298204176_120089034?_trans_=010004_pcwzy.
[31] 朱国晖, 李庆, 梁申麟.基于深度强化学习的服务功能链跨域映射算法[J].计算机应用研究, 2021, 38(6):1834-1837, 1842.
[32] 黄少安, 张华庆, 刘阳荷.数据要素的价值实现与市场化配置[J].东岳论丛, 2022, 43(2):115-121.
[33] 刘业政, 孙见山, 姜元春, 等.大数据的价值发现:4C模型[J].管理世界, 2020, 36(2):129-138, 223.
[34] REN L, MENG Z H, WANG X K, et al.A wide-deep-sequence model-based quality prediction method in industrial process analysis[J].IEEE transactions on neural networks and learning systems, 2020, 31(9):3721-3731.
[35] GUO L, YU Y X, GAO H L, et al.Online remaining useful life prediction of milling cutters based on multi-source data and feature learning[J].IEEE transactions on industrial informatics, 2022, 18(8):5199-5208.
[36] MA M, SUN C, CHEN X F.Deep coupling autoencoder for fault diagnosis with multimodal sensory data[J].IEEE transactions on industrial informatics, 2018, 14(3):1137-1145.
[37] YANG Z, BARALDI P, ZIO E.A multi-branch deep neural network model for failure prognostics based on multimodal data[J].Journal of manufacturing systems, 2021, 59:42-50.
[38] 朱琳, 沈杨, 周川, 等.基于粒子群的工业大数据雾计算多目标优化任务调度算法[J].南京理工大学学报, 2023, 47(1):48-55.
[39] STERKENBURG T F, GRUNWALD P D.The no-free-lunch theorems of supervised learning[J].Synthese, 2021, 199(3/4):9979-10015.
[40] 刘春辉.微增长时期中国汽车产业发展特征及对策研究[J].汽车与配件, 2012(34):18-23.
[41] 莫军.某涂装车间空调节能优化研究[D].长春:吉林大学, 2011.
[42] 韩俊杰, 张川, 孔飞.我国西北地区涂装车间空调系统设计方案探讨[J].建筑热能通风空调, 2020, 39(2):66-69, 99.
[43] 李会哲, 宫殿玺.风机在汽车厂涂装车间的应用[J].汽车工艺与材料, 2011(7):78-82.
