[目的/意义] 立足情报研究视角,提出一套科学有效且可复用推广的关键技术识别方法,以期为国家、地区、企业和创新机构发现、部署、推动关键技术研发前瞻性布局提供情报支撑。[方法/过程] 在关键技术类型及概念界定的基础上,利用文献知识聚类识别热点技术,以各项热点技术为节点构建复杂网络,通过节点二次聚类和可视化方法展现技术结构网络,采用结构洞理论分析网络和节点特性,以此遴选共性技术;利用链路预测方法,预测技术结构网络中的缺失边产生连接的可能性,分析热点技术交叉融合促进创新技术形成的现象,以此识别潜在新兴技术。[结果/结论] 以智能制造领域为例开展关键技术识别的实证研究,通过国家权威规划文件对比和文献资料调研,初步验证方法的可操作性和有效性。
[Purpose/significance] This paper try to propose a scientific, effective and reusable method to identify key technologies based on the perspective of intelligence research. It aims to provide information support for nation, regions, enterprises and innovative institutions to discover, deploy and promote the prospective R&D of key technologies.[Method/process] Based on the definition of key technology and its types, this paper used K-means++ algorithm to cluster scientific papers to identify hotspot technologies. Then it used the hotspot technologies as nodes to construct and visualize complex network through secondary clustering and Gephi. Structural holes theory was adopted to analysis the network and attributes of nodes, and thereby selected generic technologies. Link prediction algorithm was used to predict the missing edges in the network according to the structure, and we can identify the potential emerging technologies based on the phenomenon of cross-fusion of hot technologies to promote the formation of innovative technologies.[Result/conclusion] Taking the Intelligent Manufacturing as an example to carry out empirical research on the method, and validated the operability and effectiveness of the method through national authoritative documents and literature research.
[1] 汪雪锋,赖院根,朱东华.技术威胁理论研究[J].科学学研究,2009,27(2):166-169.
[2] 七丈直弘, 小笠原敦. 第10回科学技術予測調査(ビジョン):国際的視点からのシナリオプランニング[J].年次学術大会講演要旨集, 2015, 30(1):882-885.
[3] 中国科学院技术预见研究组. 中国未来20年技术预见[M]. 北京:科学出版社, 2006.
[4] 李红, 孙绍荣, 刘继云. 上海市科技发展重点领域技术预见的实证研究[J]. 科学学研究, 2005, 23(s1):101-105.
[5] 穆荣平. 北京技术预见:实践与思考[J]. 世界科学, 2003, 15(4):41-43.
[6] UCHIHIRA N. Future direction and roadmap of concurrent system technology[J]. Ieice transactions on fundamentals, 2007, 90(11):2443-2448.
[7] LEE C, KWON O, KIM M, et al. Early identification of emerging technologies:a machine learning approach using multiple patent indicators[J]. Technological forecasting and social change, 2018, 50(127):291-303.
[8] NAGY D, SCHUESSLER J, DUBINSKY A. Defining and identifying disruptive innovations[J]. Industrial marketing management, 2016, 46(57):119-126.
[9] 王昆声, 周晓纪, 龚旭,等. 中国工程科技2035技术预见研究[J]. 中国工程科学, 2017, 19(1):34-42.
[10] SUN J, GAO J, YANG B, et al. Achieving disruptive innovation-forecasting potential technologies based upon technical system evolution by TRIZ[C]//IEEE international conference on management of innovation and technology. Bangkok:IEEE, 2008:18-22.
[11] 李政, 罗晖, 李正风,等. 基于突变理论的科技评价方法初探[J]. 科研管理, 2017,38(s1):193-200.
[12] DOTSIKA F, WATKINS A. Identifying potentially disruptive trends by means of keyword network analysis[J]. Technological forecasting & social change,2017,49(119):114-127.
[13] 白光祖,郑玉荣,吴新年,等.基于文献知识关联的颠覆性技术预见方法研究与实证[J].情报杂志,2017,36(9):38-44.
[14] JIN T, MIYAZAKI K, KAJIKAWA Y. Identification of evolutionary characteristics of emerging technologies:the case of smart grid in Japan[C]//Portland international conference on management of engineering and technology. Honolulu, HI:IEEE, 2017:649-656.
[15] 董放, 董放, 刘宇飞, 等. 基于LDA-SVM论文摘要多分类新兴技术预测[J]. 情报杂志, 2017, 36(7):40-45.
[16] 刘军. 整体网分析讲义:UCINET软件实用指南[M]. 上海:格致出版社/上海人民出版社, 2009.
[17] RAIDER H J. Market structure and innovation[J]. Social science research, 1998, 27(1):1-21.
[18] BLONDEL V D, GUILLAUME J L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of statistical mechanics theory & experiment, 2008:P10008.
[19] 刘书田, 李取浩, 陈文炯, 等. 拓扑优化与增材制造结合:一种设计与制造一体化方法[J]. 航空制造技术, 2017, 60(10):26-31.