综述述评

技术演化路径识别:内涵释义与研究进展

  • 黄颖 ,
  • 叶冬梅 ,
  • 丁凤 ,
  • 徐畅 ,
  • 张琳
展开
  • 1 武汉大学信息管理学院 武汉 430072;
    2 武汉大学科教管理与评价中心 武汉 430072;
    3 比利时鲁汶大学ECOOM研究中心 比利时鲁汶 B-3000
黄颖,副教授,博士,博士生导师,E-mail:ying.huang@whu.edu.cn;叶冬梅,博士研究生;丁凤,硕士研究生;徐畅,硕士研究生;张琳,教授,博士,博士生导师。

收稿日期: 2021-11-24

  修回日期: 2022-08-21

  网络出版日期: 2022-12-02

基金资助

本文系国家自然科学基金青年项目"基于多源异构数据的新兴技术演化路径识别与预测研究"(项目编号:72004169)研究成果之一。

Identification of Technology Evolution Pathway: Connotation Interpretation and Research Progress

  • Huang Ying ,
  • Ye Dongmei ,
  • Ding Feng ,
  • Xu Chang ,
  • Zhang Lin
Expand
  • 1 School of Information Management, Wuhan University, Wuhan 430072;
    2 Cener for Science, Technology & Education Assessment (CSTEA), Wuhan University, Wuhan 430072;
    3 Centre for R&D Monitoring (ECOOM) and Department of MSI, KU Leuven, Leuven B-3000

Received date: 2021-11-24

  Revised date: 2022-08-21

  Online published: 2022-12-02

摘要

[目的/意义] 开展面向特定技术领域的技术演化路径识别,有助于梳理技术发展脉络进而对未来的技术发展方向做出合理预测,对识别科技优先领域、合理配置科技资源具有重要意义。[方法/过程] 在梳理技术演化与技术演化路径的相关内涵的基础上,简要辨析了技术演化路径相关概念;进而从专利文献中的不同信息出发,从专利分类、专利引文、专利文本以及融合多种字段信息来总结技术演化路径识别研究的主要进展,并进一步归纳出该主题的整体发展趋势。[结果/结论] 技术演化路径识别研究主要趋势包括:数据来源从单一数据转向多源异构数据融合,研究方法从注重定量转向定性与定量相结合,关注视角从历史演化路径识别转向未来演化路径预测,应用场景由一般性技术到颠覆性技术转变。

本文引用格式

黄颖 , 叶冬梅 , 丁凤 , 徐畅 , 张琳 . 技术演化路径识别:内涵释义与研究进展[J]. 图书情报工作, 2022 , 66(22) : 142 -154 . DOI: 10.13266/j.issn.0252-3116.2022.22.013

Abstract

[Purpose/Significance] Conducting the identification of technological evolution paths for specific technological fields helps sort out the context of historical technology development and make reasonable predictions about future technological development directions. It is of great significance to identify the priority areas of science and technology and rationally allocate scientific and technological resources. [Method/Process] Based on sorting out the connotation of technology evolution and technology evolution pathway, this paper briefly identified the concepts related to technology evolution pathways; then, starting from different information in patent documents, the paper summarized the main progress of technology evolution path identification research from patent classification, patent citation, patent text and fusion of multiple field information, and further summarized the overall development trend of this topic. [Result/Conclusion] The main trends in technology evolution path identification research include: data sources will shift from single data to multi-source heterogeneous data fusion, research methods will shift from focusing on quantitative to combining qualitative and quantitative, focused perspective will shift from historical evolution path identification to future evolution path prediction, and application scenarios will shift from the general technology to disruptive technology.

参考文献

[1] DOSI G. Technological paradigms and technological trajectories:a suggested interpretation of the determinants and directions of technical change[J]. Research policy, 1982, 11(3):147-162.
[2] HUANG Y, ZHU F, PORTER A, et al. Exploring technology evolution pathways to facilitate technology management:from a technology life cycle perspective[J]. IEEE transactions on engineering management, 2021, 68(5):1347-1359.
[3] HASHIMOTO T. Evolutionary linguistics and evolutionary economics[J]. Evolutionary and institutional economics review, 2006, 3(1):27-46.
[4] 张立超, 刘怡君. 技术轨道的跃迁与技术创新的演化发展[J]. 科学学研究, 2015, 33(1):137-145.
[5] 王敏, 银路. 技术演化的集成研究及新兴技术演化[J]. 科学学研究, 2008, 26(3):22-27.
[6] 祖坤琳. 基于专利文献的技术演化分析[D]. 大连:大连理工大学, 2015.
[7] LEE K, LEE S. Patterns of technological innovation and evolution in the energy sector:a patent-based approach[J]. Energy policy, 2013, 59:415-432.
[8] FOSTER R N. The S-curve:a new forecasting tool[M]. Innovation:the attacker's Advantage. New York:Macmillan, 1986.
[9] HENDERSON R M, CLARK K B. Architectural innovation-the reconfiguration of existing product technologies and the failure of established firms[J]. Administrative science quarterly, 1990, 35(1):9-30.
[10] 熊鸿儒, 王毅, 林敏, 等. 技术轨道研究:述评与展望[J]. 科学学与科学技术管理, 2012, 33(7):21-28.
[11] 刘小玲, 谭宗颖. 基于专利网络的技术演进研究方法探索[J]. 科学学研究, 2013, 31(05):651-656,731.
[12] 陈亮, 张志强. 技术演化研究方法进展分析[J]. 图书情报工作, 2012, 56(17):59-66.
[13] 周源, 杜俊飞, 刘宇飞, 等. 基于引用网络和文本挖掘的技术演化路径识别[J]. 情报杂志, 2018, 37(10):76-81.
[14] 黄颖. 基于专利文献的技术演化路径识别方法研究[D]. 北京:北京理工大学, 2018.
[15] LITTLE A D. The strategic management of technology. Cambridge:Arthur D. Little, 1981.
[16] ANDERSON P, TUSHMAN M L. Technological discontinuities and dominant designs:a cyclical model of technological change[J]. Administrative science quarterly, 1990, 35(4):604-633.
[17] 赵莉晓. 基于专利分析的RFID技术预测和专利战略研究——从技术生命周期角度[J]. 科学学与科学技术管理, 2012, 33(11):24-30.
[18] WILLYARD C H, MCCLEES C W. Motorola's technology roadmap process[J]. Research management, 1987, 30(5):13-19.
[19] GALVIN R. Science roadmaps[J]. Science, 1998, 280(5365):803-803.
[20] KOSTOFF R N, SCHALLER R R. Science and technology roadmaps[J]. Engineering management IEEE transactions on, 2001, 48(2):132-143.
[21] PHAAL R, FARRUKH C J P, PROBERT D R. Technology roadmapping-a planning framework for evolution and revolution[J]. Technological forecasting & social change, 2004, 71(1):5-26.
[22] RINNE M. Technology roadmaps:infrastructure for innovation[J]. Technological forecasting & social change, 2004, 71(1):67-80.
[23] 沈君, 高继平, 滕立. 德温特手工代码共现法:一种实用的专利地图法[J]. 科学学与科学技术管理, 2012, 33(1):12-16.
[24] 肖沪卫, 顾震宇. 专利地图方法与应用[M]. 上海:上海交通大学出版社, 2011.
[25] ZHOU X, ZHANG Y, PORTER A L, et al. A patent analysis method to trace technology evolutionary pathways[J]. Scientometrics, 2014, 100(3):705-721.
[26] SUZUKI J, KODAMA F. Technological diversity of persistent innovators in Japan-two case studies of large Japanese firms[J]. Research Policy, 2004, 33(3):531-549.
[27] 栾春娟, 覃雪. 技术部类之间会聚指数测度的方法与指标[J]. 研究与发展管理, 2016, 28(3):67-78.
[28] KRAFFT J, QUATRARO F, SAVIOTTI P P. The knowledge-base evolution in biotechnology:a social network analysis[J]. Economics of innovation and new technology, 2011, 20(5):445-475.
[29] 刘凤朝, 马荣康, 孙玉涛. 基于专利技术共现网络的纳米技术演化路径研究[J]. 科学学研究, 2012, 30(10):1500-1508.
[30] 郑荣, 魏明珠, 高志豪, 等. 基于SCAN-CPM的产业新兴技术识别与演化路径分析:以新能源汽车产业为例[J]. 图书情报工作, 2022,66(11):100-109.
[31] 吴晓燕, 胡雅敏, 陈方. 基于专利共类的技术融合分析框架研究——以合成生物学领域为例[J]. 情报理论与实践, 2021, 44(10):179-184.
[32] 陈悦, 王康, 宋超, 等. 一种用于技术融合与演化路径探测的新方法:技术群相似度时序分析法[J]. 情报学报, 2021, 40(6):565-574.
[33] 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(06):986-995.
[34] 方曙, 胡正银, 庞弘燊, 等. 基于专利文献的技术演化分析方法研究[J]. 图书情报工作, 2011, 55(22):42-46.
[35] LEYDESDORFF L, ALKEMADE F, HEIMERIKS G, et al. Patents as instruments for exploring innovation dynamics:geographic and technological perspectives on "photovoltaic cells"[J]. Scientometrics, 2015, 102(1):629-651.
[36] LEYDESDORFF L. Can technology life-cycles be indicated by diversity in patent classifications? the crucial role of variety[J]. Scientometrics, 2015, 105(3):1441-1451.
[37] 许景龙, 赵亚娟. IPC分类修订中的技术演化研究[J]. 图书情报工作, 2021, 65(15):140-152.
[38] XU Q, GU X, FENG Y. Knowledge adaptability evaluation in view of patent citation in technological evolutionary process:a case study of fuel cell[J]. International journal of software engineering and knowledge engineering, 2015, 25(8):1335-1364.
[39] HUMMON N P, DEREIAN P. Connectivity in a citation network:the development of DNA theory[J]. Social networks, 1989, 11(1):39-63.
[40] 王燕玲. 技术轨道识别研究-以专利引文网络主路径分析为方法[D]. 武汉:武汉大学, 2013.
[41] BATAGELJ V. Efficient algorithms for citation network analysis[A]//BATAGELJ V, DOREIAN P, FERLIGOJ A K, et al. Understanding large temporal networks and spatial networks:exploration, pattern searching, visualization and network evolution. Chichester:John Wiley & Sons Ltd, 2014.
[42] LIU J S, LU L Y Y, HO M H-C. A few notes on main path analysis[J]. Scientometrics, 2019, 119(1):379-391.
[43] VERSPAGEN B. Mapping technological trajectories as patent citation networks:a study on the history of fuel cell research[J]. Advances in complex systems, 2007, 10(1):93-115.
[44] CHOI C, PARK Y. Monitoring the organic structure of technology based on the patent development paths[J]. Technological forecasting and social change, 2009, 76(6):754-768.
[45] FILIPPIN F. Do main paths reflect technological trajectories? applying main path analysis to the semiconductor manufacturing industry[J]. Scientometrics, 2021, 126(8):6443-6477.
[46] LIU J S, LU L Y Y, LU W-M, et al. Data envelopment analysis 1978-2010:a citation-based literature survey[J]. Omega, 2013, 41(1):3-15.
[47] LIU J S, LU L Y Y. An integrated approach for main path analysis:development of the hirsch index as an example[J]. Journal of the American Society for Information Science and Technology, 2012, 63(3):528-542.
[48] HUNG S-C, LIU J S, LU L Y Y, et al. Technological change in lithium iron phosphate battery:the key-route main path analysis[J]. Scientometrics, 2014, 100(1):97-120.
[49] FONTANA R, NUVOLARI A, VERSPAGEN B. Mapping technological trajectories as patent citation networks. an application to data communication standards[J]. Economics of innovation and new technology, 2009, 18(4):311-336.
[50] 杨中楷, 刘佳. 基于专利引文网络的技术轨道识别研究——以太阳能光伏电池板领域为例[J]. 科学学研究, 2011, 29(9):1311-1317.
[51] 彭爱东, 黎欢, 王洋. 基于专利引文网络的技术演进路径研究——以激光显示技术领域为例[J]. 情报理论与实践, 2013, 36(8):57-61.
[52] 樊志伟, 韩芳芳, 刘佳. 引文网络的主路径特征研究——以富勒烯领域为例[J]. 图书情报工作, 2013, 57(3):17-21, 60.
[53] 苗红, 张伟, 黄鲁成. 基于专利引用的碳捕获与封存技术发展研究[J]. 情报杂志, 2013, 32(1):27-32.
[54] MINA A, RAMLOGAN R, TAMPUBOLON G, et al. Mapping evolutionary trajectories:applications to the growth and transformation of medical knowledge[J]. Research policy, 2007, 36(5):789-806.
[55] MOGEE M E, KOLAR R G. Patent co-citation analysis of Eli Lilly & Co. patents[J]. Expert opinion on therapeutic patents, 1999, 9(3):291-305.
[56] HUANG M H, CHIANG LY, CHEN D Z. Constructing a patent citation map using bibliographic coupling:a study of Taiwan's high-tech companies[J]. Scientometrics, 2003, 58(3):489-506.
[57] HSUEH C-C, WANG C-C. The use of social network analysis in knowledge diffusion research from patent data[C]//2009 international conference on advances in social networks analysis and mining. IEEE Computer Society, 2009:393-398.
[58] 颜端武, 白敬毅, 李晨晨, 等. 科技领域技术演进的多路径识别及创新特性分析[J]. 情报理论与实践, 2021, 44(11):99-107.
[59] YOON S, MUN C, RAGHAVAN N, et al. Hierarchical main path analysis to identify decompositional multi-knowledge trajectories[J]. Journal of knowledge management, 2020, 25(2):454-476.
[60] 马俊红, 张文凤, 冯鑫, 等. 克服引文滞后的科技演化主路径测绘[J]. 情报杂志, 2021, 40(5):186-192.
[61] LAI K-K, BHATT P, KUMAR V, et al. Identifying the impact of patent family on the patent trajectory:a case of thin film solar cells technological trajectories[J]. Journal of informetrics, 2021, 15(2):101143.
[62] 万小萍, 刘向, 闫肖婷, 等. 基于关联分析的技术演进路径发现[J]. 情报学报, 2018, 37(11):1087-1094.
[63] 翟东升, 蔡力伟, 张杰, 等. 基于专利的技术融合创新轨道识别模型研究——以云计算技术为例[J]. 情报学报, 2015, 34(4):352-360.
[64] 马瑞敏, 杨雨华. 基于节点重要性的领域主路径发现新探索[J]. 情报杂志, 2018, 37(3):71-78,93.
[65] 程洁琼, 万小萍, 刘向. 技术主路径分析:基于边链接影响力流的路径搜索[J]. 现代情报, 2019, 39(5):24-29,37.
[66] MALHOTRA A, ZHANG H, BEUSE M, et al. How do new use environments influence a technology's knowledge trajectory? a patent citation network analysis of lithium-ion battery technology[J]. Research policy, 2021, 50(9):104318.
[67] 伊惠芳, 刘细文, 龙艺璇. 技术创新全视角下技术机会发现研究进展[J]. 图书情报工作, 2021, 65(7):132-142.
[68] 郭婕婷, 肖国华. 专利分析方法研究[J]. 情报杂志, 2008, 27(1):12-14.
[69] CHEN Y-H, CHEN C-Y, LEE S-C. Technology forecasting of new clean energy:the example of hydrogen energy and fuel cell[J]. African journal of business management, 2010, 4(7):1372-1380.
[70] 吴菲菲, 张亚茹, 黄鲁成, 等. 基于AToT模型的技术主题多维动态演化分析——以石墨烯技术为例[J]. 图书情报工作, 2017, 61(5):95-102.
[71] YOON B, PARK Y. A text-mining-based patent network:analytical tool for high-technology trend[J]. The journal of high technology management research, 2004, 15(1):37-50.
[72] KIM Y G, SUH J H, PARK S C. Visualization of patent analysis for emerging technology[J]. Expert systems with applications, 2008, 34(3):1804-1812.
[73] 刘玉林, 菅利荣. 基于动态专利有向网络的核心技术集群演化分析[J]. 情报杂志, 2021, 40(4):101-108.
[74] 王康, 高继平, 潘云涛, 等. 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.
[75] DEERWESTER S, DUMAIS S T, FURNAS G W, et al. Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 1990, 41(6):391-407.
[76] HOFMANN T. Probabilistic latent semantic analysis[J]. Uncertainty in artificial intelligence, proceedings, 1999, 41(6):289-296.
[77] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3(4/5):993-1022.
[78] DU L, BUNTINE W, JIN H, et al. Sequential latent Dirichlet allocation[J]. Knowledge and information systems, 2012, 31(3):475-503.
[79] 胡吉明, 陈果. 基于动态LDA主题模型的内容主题挖掘与演化[J]. 图书情报工作, 2014, 58(2):138-142.
[80] 陈亮, 张静, 张海超, 等. 层次主题模型在技术演化分析上的应用研究[J]. 图书情报工作, 2017, 61(5):103-108.
[81] 杨超, 朱东华, 汪雪锋, 等. 专利技术主题分析:基于SAO结构的LDA主题模型方法[J]. 图书情报工作, 2017(3):86-96.
[82] 李乾瑞, 郭俊芳, 朱东华. 新兴技术创新机会识别方法研究[J]. 中国软科学, 2018(11):138-147.
[83] YOON J, PARK H, KIM K. Identifying technological competition trends for R&D planning using dynamic patent maps:SAO-based content analysis[J]. Scientometrics, 2013, 94(1):313-331.
[84] 黄鲁成, 张璐, 吴菲菲, 等. 基于突现文献和SAO相似度的新兴主题识别研究[J]. 科学学研究, 2016, 34(6):814-821.
[85] ZHANG Y, ZHOU X, PORTER A L, et al. How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence:"problem & solution" pattern based semantic TRIZ tool and case study[J]. Scientometrics, 2014, 101(2):1375-1389.
[86] CHOI S, KIM H, YOON J, et al. An SAO-based text-mining approach for technology roadmapping using patent information[J]. R&D management, 2013, 43(1):52-74.
[87] WANG X, QIU P, ZHU D, et al. Identification of technology development trends based on subject-action-object analysis:the case of dye-sensitized solar cells[J]. Technological forecasting and social change, 2015, 98:24-46.
[88] WANG X, MA P, HUANG Y, et al. Combining SAO semantic analysis and morphology analysis to identify technology opportunities[J]. Scientometrics, 2017, 111(1):3-24.
[89] 李欣, 谢前前, 黄鲁成, 等. 基于SAO结构语义挖掘的新兴技术演化轨迹研究[J]. 科学学与科学技术管理, 2018, 39(1):17-31.
[90] 马铭, 王超, 许海云, 等. 面向语义信息分析的多层次技术演化轨迹识别方法研究[J]. 图书情报工作, 2022, 66(4):103-117.
[91] 李晓曼, 张学福, 宋红燕, 等. 专利文献技术要素识别方法研究——以纳米肥料领域为例[J]. 图书情报工作, 2020, 64(6):59-68.
[92] 李晓曼. 基于专利要素特征的技术演化分析[D]. 北京:中国农业科学院, 2020.
[93] 刘婷, 张娴, 许海云, 等. 面向技术路径识别的文本挖掘方法应用研究述评[J]. 情报理论与实践, 2020, 43(7):179-185.
[94] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:13013781. 2013.
[95] LE Q, MIKOLOV T. Distributed representations of sentences and documents[C]//International conference on machine learning. PMLR, 2014:1188-1196.
[96] 贾怡炜, 戚湧, 武兰芬. 专利视角下人工智能与车联网技术融合演化研究[J]. 科技进步与对策, 2022:1-10.
[97] 徐红姣, 曾文, 张运良. 基于Word2vec的论文和专利主题关联演化分析方法研究[J]. 情报杂志, 2018, 37(12):36-42.
[98] 杨恒, 王曰芬, 张露. 基于核心专利技术主题识别与演化分析的技术预测[J]. 情报杂志, 2022, 41(7):49-56.
[99] DEVLIN J, CHANG M-W, LEE K, et al. Bert:pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:181004805. 2018.
[100] 翟羽佳, 田静文, 赵玥. 基于BERT-BiLSTM-CRF模型的算法术语抽取与创新演化路径构建研究[J]. 情报科学, 2022, 40(4):71-78.
[101] 胡阿沛, 张静, 张晓宇. 基于专利文献的技术演化分析方法评述[J]. 现代情报, 2013, 33(10):172-176.
[102] 侯剑华, 范二宝. 基于专利家族的核心技术演进分析:以太阳能光伏电池技术为例[J]. 情报杂志, 2014, 33(12):30-35.
[103] 廖列法, 勒孚刚. 基于LDA模型和分类号的专利技术演化研究[J]. 现代情报, 2017, 37(5):13-18.
[104] 侯筱蓉, 司有和, 吴海燕. 基于引文路径分析的专利技术演进图制作的实证研究——以医学内窥镜专利分析为例[J]. 情报学报, 2008, 27(5):788-792.
[105] 吴菲菲, 陈肖微, 黄鲁成, 等. 基于语义相似度的技术多主题演化路径识别方法研究[J]. 情报杂志, 2018, 37(5):91-96.
[106] 陈亮, 杨冠灿, 张静, 等. 面向技术演化分析的多主路径方法研究[J]. 图书情报工作, 2015, 59(10):124-130+115.
[107] LI M. A novel three-dimension perspective to explore technology evolution[J]. Scientometrics, 2015, 105(3):1679-1697.
[108] HUANG Y, ZHU D, QIAN Y, et al. A hybrid method to trace technology evolution pathways:a case study of 3D printing[J]. Scientometrics, 2017, 111(1):185-204.
[109] 陈瑞真, 丁文晴, 金金, 等. 基于科学-技术映射路线图的前沿科技互动模式识别与预测——以中药领域为例[J]. 情报杂志, 2019, 38(6):38-44,79.
[110] SPREAFICO C, RUSSO D, SPREAFICO M. Investigating the evolution of pyrolysis technologies through bibliometric analysis of patents and papers[J]. Journal of analytical and applied pyrolysis, 2021, 159:105021.
[111] 陈稳, 陈伟. 科学与技术对比视角下的前沿主题识别与演化分析[J]. 情报杂志, 2022, 41(1):67-73,163.
[112] 马铭, 王超, 周勇, 等. 基于语义信息的核心技术主题识别与演化趋势分析方法研究[J]. 情报理论与实践, 2021, 44(9):106-113.
[113] 周潇, 黄璐, 马婷婷. 大数据视角下的技术创新路径识别研究[J]. 科研管理, 2017, 38(10):1-9.
[114] 刘建华, 孟战, 姜照华. 基于"要素-结构-功能-成本"视角的丰田混合动力汽车技术演化阶段研究[J]. 科学学与科学技术管理, 2017, 38(12):26-36.
[115] 张维冲, 王芳, 赵洪. 多源信息融合用于新兴技术发展趋势识别——以区块链为例[J]. 情报学报, 2019, 38(11):1166-1176.
[116] 唐恒, 邱悦文. 多源信息视角下的多指标新兴技术主题识别研究——以智能网联汽车领域为例[J]. 情报杂志, 2021, 40(3):81-88.
[117] 胡正银, 方曙. 专利文本技术挖掘研究进展综述[J]. 现代图书情报技术, 2014, 247(6):62-70.
[118] 王园园, 赵亚娟. 基于非负矩阵分解的技术主题演化分析[J]. 图书情报工作, 2018, 62(10):94-105.
[119] 吴蕾, 梁晓贺, 宋红燕. 基于技术关键词的学科领域协同演化分析实证研究[J]. 现代情报, 2019, 39(8):137-142.
[120] ERDI P, MAKOVI K, SOMOGYVARI Z, et al. Prediction of emerging technologies based on analysis of the US patent citation network[J]. Scientometrics, 2013, 95(1):225-242.
[121] 冯立杰, 尤鸿宇, 王金凤. 专利技术创新路径识别及其新颖性评价研究[J]. 情报学报, 2021, 40(5):513-522.
[122] 杨武, 陈培, David G. 专利引证视角下技术轨道演化与技术锁定识别——以光刻技术为例[J]. 科学学研究, 2022, 40(2):209-219.
[123] SUN B, KOLESNIKOV S, GOLDSTEIN A, et al. A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics[J]. Technological forecasting and social change, 2021, 165:120534.
[124] ÉRDI P, MAKOVI K, SOMOGYVÁRI Z, et al. Prediction of emerging technologies based on analysis of the US patent citation network[J]. Scientometrics, 2013, 95(1):225-242.
[125] JANG H J, WOO H-G, LEE C. Hawkes process-based technology impact analysis[J]. Journal of informetrics, 2017, 11(2):511-529.
[126] 张鑫, 文奕, 许海云, 等. Prophet预测-修正的主题强度演化模型——以干细胞领域为实证[J]. 图书情报工作, 2020, 64(8):78-92.
[127] 罗恺, 袁晓东. 基于LDA主题模型与社会网络的专利技术融合趋势研究——以关节机器人为例[J]. 情报杂志, 2021, 40(3):89-97.
[128] ZHANG G, ALLAIRE D, MCADAMS D A, et al. Generating technology evolution prediction intervals using a bootstrap method[J]. Journal of mechanical design, 2019, 141(6):061401.
[129] 张娴, 方曙, 王春华. 专利引证视角下的技术演化研究综述[J]. 科学学与科学技术管理, 2016, 37(3):58-67.
[130] 吴可凡, 王伟, 张世玉, 等. 技术不连续性视角下颠覆性技术识别方法研究[J]. 情报理论与实践, 2022:1-11.
[131] 王超, 马铭, 张伟然, 等. 颠覆性技术关注方向演化研究[J]. 科技进步与对策, 2022, 39(8):19-29.
[132] 李乾瑞, 郭俊芳, 黄颖, 等. 基于突变-融合视角的颠覆性技术主题演化研究[J]. 科学学研究, 2021, 39(12):2129-2139.
文章导航

/