算法溯源简述

王亭亭; 崔伟群

doi:10.12338/j.issn.2096-9015.2022.0270

算法溯源简述

doi: 10.12338/j.issn.2096-9015.2022.0270

王亭亭,
崔伟群^,

中国计量科学研究院，北京 100029

基金项目: 中国计量科学研究院基本科研业务费所自主项目（AKYZZ2238）。

详细信息

作者简介:
王亭亭（1991-），中国计量科学研究院助理研究员，研究方向：数字计量等，邮箱：wangtingting@nim.ac.cn

通讯作者:
崔伟群（1973-），中国计量科学研究院副研究员，研究方向：软件测评、数字计量等，邮箱：cuiwq@nim.ac.cn

中图分类号: TB91-64
计量
- 文章访问数: 347
- HTML全文浏览量: 115
- PDF下载量: 63
- 被引次数: 0
出版历程
- 收稿日期: 2022-11-15
- 录用日期: 2023-01-11
- 修回日期: 2023-06-16
- 网络出版日期: 2023-06-29
- 刊出日期: 2023-05-31

An Overview of Algorithm Traceability

WANG Tingting,
CUI Weiqun^,

National Institute of Metrology, Beijing 100029, China

摘要

摘要: 数字时代，融入人工智能等新一代信息技术的软件算法，是加快推进制造业进行数字化转型的关键要素之一。基于不同工业革命时期测量仪器和标准在量值传递和溯源中表现的特征，以及测量仪器中软件算法对量值准确可靠的影响，提出算法溯源理念，目的是从计量溯源的角度实现对软件算法在程序实现过程中输出量值的定量评价，从而保证软件算法输出量值的准确和可靠。同时给出了算法溯源的定义、参考标准、方法和溯源结果等的综合性描述，并就算法溯源的研究现状进行了讨论，为进一步开展算法溯源的计量技术应用奠定理论基础。
- 计量学 /
- 软件算法 /
- 算法溯源 /
- 标准参考数据 /
- 算法标准
Abstract: In the digital age, software algorithms imbued with artificial intelligence and other next-generation information technologies serve as crucial factors accelerating the digital transformation of the manufacturing sector. The concept of algorithm traceability has been proposed based on the distinct characteristics manifested by measuring instruments and standards during metrological value transfer and traceability in various stages of the industrial revolution, along with the impact of software algorithms within measuring instruments on the accuracy and reliability of metrological values. The objective of this concept is to quantitatively assess the output values of software algorithms during their execution from a metrological traceability perspective, thereby ensuring their precision and dependability. This paper offers a comprehensive exposition of algorithm traceability, including its definition, reference standards, methodologies, and traceability outcomes. Current research trends in algorithm traceability are discussed, laying a theoretical groundwork for further exploration into metrological applications of algorithm traceability.
- metrology /
- software algorithm /
- algorithm traceability /
- standard reference data /
- algorithm standard

HTML全文

图 1 第一阶段校准示意图

Figure 1. Schematic diagram of first stage calibration

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图 2 第二阶段校准示意图

Figure 2. Schematic diagram of second stage calibration

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图 3 软件控制的测量标准的输出过程

Figure 3. Output process of software-controlled measurement standards

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图 4 软件控制的测量仪器的示值过程

Figure 4. Indication process of software-controlled measurement instruments

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图 5 第三阶段校准示意图

Figure 5. Schematic diagram of third stage calibration

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图 6 以算法为主的测量仪器的示值过程

Figure 6. Indication process of algorithm-focused measurement instruments

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图 7 基于检测数据的标准参考数据建立流程

Figure 7. Process of establishing standard reference data based on detection data

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图 8 溯源至标准参考数据的算法溯源过程

Figure 8. Algorithm traceability process tracing back to standard reference data

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图 9 溯源至算法标准的算法溯源过程

Figure 9. Algorithm traceability process tracing back to algorithm standards

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参考文献(31)

[1]	Rojko A. Industry 4.0 concept: Background and overview[J]. International Journal of Interactive Mobile Technologies, 2017, 11(5): 77-90. doi: 10.3991/ijim.v11i5.7072
[2]	Dalenogare L S, Benitez G B, Ayala N F, et al. The expected contribution of Industry 4.0 technologies for industrial performance[J]. International Journal of Production Economics, 2018, 204: 383-394. doi: 10.1016/j.ijpe.2018.08.019
[3]	Ghobakhloo M. Industry 4.0, digitization, and opportunities for sustainability[J]. Journal of Cleaner Production, 2020, 252: 119869. doi: 10.1016/j.jclepro.2019.119869
[4]	Wang J, Wu H, Chen Y. Made in China 2025 and manufacturing strategy decisions with reverse QFD[J]. International Journal of Production Economics, 2020, 224: 107539. doi: 10.1016/j.ijpe.2019.107539
[5]	Zhong R Y, Xu X, Klotz E, et al. Intelligent manufacturing in the context of industry 4.0: a review[J]. Engineering, 2017, 3(5): 616-630. doi: 10.1016/J.ENG.2017.05.015
[6]	Chen Y. Integrated and intelligent manufacturing: perspectives and enablers[J]. Engineering, 2017, 3(5): 588-595. doi: 10.1016/J.ENG.2017.04.009
[7]	Durakbasa N M, Bas G, Bauer J M, et al. Development of an advanced metrology and intelligent quality management as a strategic approach in manufacturing industry[C]. LMPMI, 2014.
[8]	Yin P, Wang J, Lu C. Measuring software test verification for complex workpieces based on virtual gear measuring instrument[J]. Measurement Science Review, 2017, 17(4): 197-207. doi: 10.1515/msr-2017-0023
[9]	汤洁, Härtig F, 石照耀. 计量软件溯源中的TraCIM软件认证系统[J]. 工具技术, 2017, 51(12): 4. doi: 10.3969/j.issn.1000-7008.2017.12.031
[10]	Hopp T H, Levenson M S. Performance Measures for Geometric Fitting in the NIST Algorithm Testing and Evaluation Program for Coordinate Measurement Systems[J]. Journal of Research of the National Institute of Standards and Technology, 1995, 100(5): 563-574. doi: 10.6028/jres.100.042
[11]	Velychko O M, Gaman V V, Gordiyenko T B, et al. Testing of measurement instrument software with the purpose of conformity assessment[J]. Eastern-European Journal of Enterprise Technologies, 2019, 97: 19-26.
[12]	Boccardo D R, Machado R C S, Camara S M, et al. Software validation of medical instruments[C]. IEEE, 2014.
[13]	崔伟群. 计量器具软件测评体系研究[J]. 中国计量, 2010, 174(5): 68-69. doi: 10.3969/j.issn.1006-9364.2010.05.032
[14]	李东升, 郭天太. 量值传递与溯源[M]. 杭州: 浙江大学出版社, 2009.
[15]	Matsumoto E. The History of Electric Measuring Instruments and Active Components[C]. IEEE, 2004.
[16]	Yang F Q. Thinking on the development of software engineering technology[J]. Journal of software, 2005, 16(1): 1-7. doi: 10.1360/jos160001e
[17]	崔伟群. 欧盟关于计量器具软件测评的现状与发展[J]. 中国计量, 2012(8): 19-22. doi: 10.3969/j.issn.1006-9364.2012.08.010
[18]	Roy B K, Santhosh K V. An intelligent instrument for measuring liquid level[C]. IEEE, 2011.
[19]	Topolsky D V, Topolskaya I G, Topolsky N D. Intelligent instrument transformer for control systems of digital substations[C]. IEEE, 2018.
[20]	Khanafer M, Shirmohammadi S. Applied AI in instrumentation and measurement: The deep learning revolution[J]. IEEE Instrumentation & Measurement Magazine, 2020, 23(6): 10-17.
[21]	Cai L, Gao J, Zhao D. A review of the application of deep learning in medical image classification and segmentation[J]. Annals of Translational Medicine, 2020, 8(11): 713. doi: 10.21037/atm.2020.02.44
[22]	Shu X, Wang X, Zang X, et al. Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmark[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(7): 4390-4403.
[23]	Wu D, Zheng S J, Zhang X P, et al. Deep learning-based methods for person re-identification: A comprehensive review[J]. Neurocomputing, 2019, 337: 354-371. doi: 10.1016/j.neucom.2019.01.079
[24]	崔伟群, 田锋, 李颖. 数字计量学导论[M]. 北京: 中国质检出版社, 2014.
[25]	靳浩元, 刘军. 测量不确定度的评定方法及应用研究[J]. 计量科学与技术, 2021, 65(5): 124-131. doi: 10.12338/j.issn.2096-9015.2020.9002
[26]	汪斌, 卢晓华. 一元线性校准曲线不确定度评定与适用条件的讨论[J]. 计量科学与技术, 2022, 66(7): 45-49, 44.
[27]	Thiel F. Digital transformation of legal metrology-the European metrology cloud[J]. OIML Bulletin, 2018, 59(1): 10-21.
[28]	Chalk S J, Coppa D N, Flamenco F, et al. International development of the SI in FAIR digital data[J]. Measurement: Sensors, 2021, 18: 100293. doi: 10.1016/j.measen.2021.100293
[29]	Neyezhmakov P, Zub S, Pivnenko S. Preliminary E-infrastructure for Digital Calibration Certificate[J]. Measurement: Sensors, 2021, 18: 100332. doi: 10.1016/j.measen.2021.100332
[30]	窦晨, 周自力. 数字世界计量的重要性及计量的发展方向探析[J]. 计量科学与技术, 2022, 66(9): 70-73. doi: 10.12338/j.issn.2096-9015.2022.0168
[31]	Varshney A, Garg N, Nagla K S, et al. Challenges in sensors technology for industry 4.0 for futuristic metrological applications[J]. MAPAN, 2021, 36(2): 215-226. doi: 10.1007/s12647-021-00453-1