动态演化过程视角下技术融合生长点识别研究

doi:10.13266/j.issn.0252-3116.2022.07.010

图书情报工作 ›› 2022, Vol. 66 ›› Issue (7): 99-109.DOI: 10.13266/j.issn.0252-3116.2022.07.010

动态演化过程视角下技术融合生长点识别研究

李昌¹, 周锦锦², 杨中楷¹

1. 大连理工大学人文与社会科学学部大连 116024;
2. 东北农业大学经济管理学院哈尔滨 150006

收稿日期:2021-11-02 修回日期:2022-01-15 出版日期:2022-04-05 发布日期:2022-04-15
通讯作者: 杨中楷,教授,博士,博士生导师,通信作者,E-mail:yandayang@163.com。
作者简介:李昌,博士研究生;周锦锦,博士研究生。
基金资助:
本文系国家社会科学基金重大项目"新形势下我国科技创新治理体系现代化研究"（项目编号：20&ZD074）研究成果之一。

Research on the Identification of Technology Fusion Growth Points from the Perspective of Dynamic Evolution Process

Li Chang¹, Zhou Jinjin², Yang Zhongkai¹

1. Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian 116024;
2. College of Economics and Management, Northeast Agricultural University, Harbin 150006

Received:2021-11-02 Revised:2022-01-15 Online:2022-04-05 Published:2022-04-15

摘要/Abstract

摘要： [目的/意义] 提出一种从技术动态演化过程视角识别技术融合生长点的新方法，旨在识别具有生长潜力的技术融合领域。[方法/过程] 首先提出演化路径的两种阶段，归纳技术融合生长点的演化路径；然后针对技术融合生长点的特点，总结显著属性特征，构建识别的指标体系；最后计算属性在演化路径上的变化，实现技术融合生长点的识别。[结果/结论] 通过实验并与已有研究进行对比，认为该方法能有效识别技术融合生长点，并能追溯技术的起源与过程。

关键词: 技术融合生长点, 动态演化过程, 属性特征, 纳米医学

Abstract: [Purpose/Significance] This paper proposes a new method to identify the growth points of technology fusion from the perspective of the dynamic evolution process of technology, aiming to identify the technology fusion fields with growth potential.[Method/Process] First, the study proposed two stages in the evolution path, summarized the evolution path of the technological fusion growth points, and summarized significant attribute characteristics according to the characteristics of the technological fusion growth points, and constructed an index system for recognition. Finally, calculating the changes of attributes on the evolution path realizes the identification of the technological fusion growth points.[Result/Conclusion] Through experiments and comparisons with existing studies, this method can effectively identify the growth points of technology fusion, and trace the origin and process of technology.

Key words: technology fusion growth point, dynamic evolution process, attribute characteristics, nanomedicine

中图分类号:

G306

李昌, 周锦锦, 杨中楷. 动态演化过程视角下技术融合生长点识别研究[J]. 图书情报工作, 2022, 66(7): 99-109.

Li Chang, Zhou Jinjin, Yang Zhongkai. Research on the Identification of Technology Fusion Growth Points from the Perspective of Dynamic Evolution Process[J]. Library and Information Service, 2022, 66(7): 99-109.

参考文献

[1] HACKLIN F, BATTISTINI B, KROGH G. Strategic choices in converging industries[J]. MIT sloan management review, 2013, 55(1):65-73.
[2] JEONG S, LEE S. What drives technology convergence? exploring the influence of technological and resource allocation contexts[J]. Journal of engineering and technology management, 2015, 36(2):78-96.
[3] CURRAN C. The anticipation of converging industries[M]. London:Springer, 2013.
[4] ROCO M C, BAINBRIDGE W S. The new world of discovery, invention, and innovation:convergence of knowledge, technology, and society[J]. Journal of nanoparticle research, 2013, 15(9):1946-1963.
[5] CURRAN C, LEKER J. Patent indicators for monitoring convergence-examples from NFF and ICT[J]. Technological forecasting and social change, 2011, 78(2):256-273.
[6] KARVONEN M, KÄSSI T. Patent citations as a tool for analyzing the early stages of convergence[J]. Technological forecasting and social change, 2013, 80(6):1094-1107.
[7] GATES A J, KE Q, VAROL O, et al. Nature's reach:narrow work has broad impact[J]. Nature,2019, 575(7781):32-34.
[8] MOON S, KIM. On a patent analysis method for technological convergence[J]. Procedia social & behavioral sciences, 2012,40(40):657-663.
[9] SONG C H, ELVERS D, LEKER J. Anticipation of converging technology areas-a refined approach for the identification of attractive fields of innovation[J]. Technological forecasting & social change, 2017, 116(3):98-115.
[10] EILERS K, FRISCHKOM J, EPPINGER E,et al. Patent-based semantic measurement of one-way and two-way technology convergence:the case of ultraviolet light emitting diodes (UV-LEDs)-ScienceDirect[J]. Technological forecasting and social change, 2019, 140(3):341-353.
[11] KIM T S, SOHN S Y. Machine-learning-based deep semantic analysis approach for forecasting new technology convergence[J]. Technological forecasting and social change, 2020, 157:120095.
[12] PARK H, YOON J. Assessing coreness and intermediarity of technology sectors using patent co-classification analysis:the case of korean national R&D[J]. Scientometrics,2014, 98(2):853-890.
[13] 栾春娟,覃雪,黄福. 技术大类之间会聚指数测度的理论与方法[J]. 科技管理研究, 2016, 36(8):188-193.
[14] 李姝影,方曙. 测度技术融合与趋势的数据分析方法研究进展[J]. 数据分析与知识发现, 2017, 1(7):2-12.
[15] YONGRAE C, MINSUNG K, Wolfgang G. Entropy and gravity concepts as new methodological indexes to investigate technological convergence:patent network-based approach[J]. Plos one, 2014, 9(6):e98009.
[16] 冯科,曾德明,周昕.技术融合的动态演化路径[J].科学学研究,2019,37(6):986-995.
[17] LEE C, HONG S, KIM J. Anticipating multi-technology convergence:a machine learning approach using patent information[J]. Scientometrics, 2021, 126(3):1867-1896.
[18] XU H, WINNINK J, YUE Z, et al. Topic-linked innovation paths in science and technology[J]. Journal of informetrics, 2020, 14(2):101014.
[19] DAHLIN K B, BEHRENS D M. When is an invention really radical?[J]. Research policy,2005, 34(5):717-737.
[20] 阿瑟.技术的本质:技术是什么,它是如何进化的[M].曹东溟,王健,译.杭州:浙江人民出版社,2014:329-344.
[21] CAVIGGIOLI F. Technology fusion:Identification and analysis of the drivers of technology convergence using patent data[J]. Technovation, 2016, 55/56:22-32.
[22] ROCO M C. Coherence and divergence of megatrends in science and engineering[J]. Journal of nanoparticle research,2002, 4(1):9-19.
[23] BAINBRIDGE W S, ROCO M C. Science and technology convergence:with emphasis for nanotechnology-inspired convergence[J]. Journal of nanoparticle research,2016, 18(7):211-230.
[24] HACKLIN F. Management of convergence in innovation[J]. Contributions to management science, 2007, 2010(42):1014-1021.
[25] 赵红洲,蒋国华. 知识单元与指数规律[J]. 科学学与科学技术管理, 1984(9):39-41.
[26] 刘则渊. 知识图谱的若干问题思考[R]. 大连:大连理工大学WISE实验室,2010.
[27] HACKLIN F, MARXT C, FAHRNI F. Coevolutionary cycles of convergence:an extrapolation from the ICT industry[J]. Technological forecasting & social change, 2009, 76(6):723-736.
[28] PORTER M E, STERN S. Measuring the "Ideas" production function:evidence from international patent output[J]. Nber working papers series, 2000,9(3):47-57.
[29] ZHOU Y, DONG F, KONG D, et al. Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies[J]. Technological forecasting and social change,2019, 144(7):205-220.
[30] 李长玲,高峰,牌艳欣.试论跨学科潜在知识生长点及其识别方法[J].科学学研究,2021,39(6):1007-1014.
[31] 吴红,伊惠芳,马永新,等. 面向专利技术主题分析的WI-LDA模型研究[J]. 图书情报工作,2018, 62(17):68-74.
[32] RODRIGUEZ A, KIM B, TURKOZ M, et al. New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network[J]. Scientometrics,2015, 103(2):565-581.
[33] MOEHRLE M G, PASSING F. Applying an anchor based patent mapping approach:basic conception and the case of carbon fiber reinforcements[J]. World patent information,2016, 45(1):1-9.
[34] PASSING F, MOEHRLE M G. Measuring technological convergence in the field of smart grids:a semantic patent analysis approach using textual corpora of technologies[C]//2015 Portland international conference on management of engineering and technology. Portland:IEEE, 2015.
[35] RODRIGUEZ A, KIM B, TURKOZ M, et al. New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network[J]. Scientometrics,2015, 103(2):565-581.
[36] MILANEZ D H, FARIA L I L D, AMARAL R M D, et al. Claim-based patent indicators:a novel approach to analyze patent content and monitor technological advances[J]. World patent information,2017, 50(9):64-72.
[37] KIM J, LEE S. Forecasting and identifying multi-technology convergence based on patent data:the case of IT and BT industries in 2020[J]. Scientometrics,2017, 111(1):47-65.
[38] AN J, KIM K, Mortara L, et al. Deriving technology intelligence from patents:preposition-based semantic analysis[J]. Journal of informetrics,2018, 12(1):217-236.
[39] AN X, LI J, XU S, et al. An improved patent similarity measurement based on entities and semantic relations[J]. Journal of informetrics,2021, 15(2):1-16.
[40] SMALL H, BOYACK K W, KLAVANS R. Identifying emerging topics in science and technology[J]. Research policy,2014, 43(8):1450-1467.
[41] BREITZMAN A, THOMAS P. The emerging clusters model:a tool for identifying emerging technologies across multiple patent systems[J]. Research policy, 2015, 44(1):195-205.
[42] PELAZ B, ALEXIOU C, ALVAREZ-PUEBLA R A, et al. Diverse applications of nanomedicine[J]. ACS nano, 2017, 11(3):2313-2381.

动态演化过程视角下技术融合生长点识别研究

Research on the Identification of Technology Fusion Growth Points from the Perspective of Dynamic Evolution Process

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	吕璐成, 赵亚娟, 王学昭, 韩涛, 赵萍, 张迪. 基于表示学习的技术融合差异度测度方法及其效果研究[J]. 图书情报工作, 2022, 66(4): 118-128.
[2]	王康, 高继平, 潘云涛, 陈悦. 多位态研究主题识别及其演化路径方法研究[J]. 图书情报工作, 2021, 65(11): 113-122.
[3]	冯仁涛, 余翔. 生物技术领域发明专利授权影响因素研究[J]. 图书情报工作, 2021, 65(6): 102-109.
[4]	刘小玲, 谭宗颖. 新兴技术主题识别方法研究进展[J]. 图书情报工作, 2020, 64(11): 145-152.
[5]	郑思远, 王学昭. 专利转让视角下技术转移特征指标体系研究[J]. 图书情报工作, 2020, 64(7): 94-102.
[6]	杨冠灿, 刘占麟, 李纲. 基于指数随机图模型的专利引用关系形成机制研究——以奈拉滨药物为例[J]. 图书情报工作, 2019, 63(10): 75-86.
[7]	杨冠灿, 陈亮, 张静, 李纲. 专利引用关系形成的解释框架:一个指数随机图模型视角[J]. 图书情报工作, 2019, 63(5): 100-109.
[8]	张欣, 马瑞敏. 基于改进PageRank算法的核心专利发现研究[J]. 图书情报工作, 2018, 62(10): 106-115.
[9]	储节旺, 曹振祥. 综合性国家科学中心情报保障体系和运行模式构建——以合肥为例[J]. 图书情报工作, 2018, 62(8): 5-13.
[10]	翟东升, 蔡文浩, 张杰, 郭程. 基于图相似度的专利侵权检测方法研究[J]. 图书情报工作, 2018, 62(5): 97-105.
[11]	郑美玉. 基于Innography的农林类高校专利竞争力研究[J]. 图书情报工作, 2018, 62(1): 117-124.
[12]	吴红, 冀方燕. 基于专利申请及审查制度的专利引文评价效能实证研究[J]. 图书情报工作, 2017, 61(19): 89-95.
[13]	周姗姗, 徐坤, 毕强. 科研用户场景下的高校科研数据组织管理架构构建[J]. 图书情报工作, 2017, 61(18): 29-34.
[14]	田丽娜, 杨祖国. 基于“反向引用”视角的中国专利技术与世界科学关联分析[J]. 图书情报工作, 2017, 61(2): 107-113.
[15]	马雨菲, 刘敏榕, 陈振标. 专利视角下企业技术竞争力评价研究——以LTE-Advanced技术为例[J]. 图书情报工作, 2016, 60(21): 96-102.