[Purpose/Significance]Accurately identifying the innovation fronts is conducive to the forward-looking deployment of innovation strategies by the state,the government and enterprises,and is of positive significance for seizing technological opportunities and winning competitive advantages.[Method/Process]Firstly,build a machine learning model to predict the probability that recently published papers are highly cited and identify potential highly cited papers.At the same time,build a set of index system to evaluate the level of technology innovation and screen high value patents based on the three dimensions of technological novelty,technological uniqueness and technological importance.Then,LDA theme model was used to cluster the potentially highly cited papers and high-value patents respectively,so as to identify scientific innovation fronts,technological innovation fronts and scientific-technological innovation fronts.Finally,according to the created scientific value and technical value index,combined with the theme intensity,build a map of the innovation fronts,and quantitatively interpret the development level and value differences between the innovation fronts.[Result/Conclusion]The empirical research based on intelligent driving vehicle data shows that this method can effectively identify the innovation fronts,show the scientific value,technical value and theme intensity differences between the innovation fronts,and provide references for the technical layout and strategy formulation of countries and enterprises.
[1] JIBU M.Mapping of scientific patenting:toward the development of'J-GLOBAL foresight'[J].Technology analysis&strategic management,2014,26(4):485-498.
[2] 熊彼特.经济发展理论[M].邹建平,译.北京:中国画报出版社,2012.
[3] 吴金希."创新"概念内涵的再思考及其启示[J].学习与探索,2015,37(4):123-127.
[4] PRICE D.Networks of scientific papers[J].Science,1965,149(3683):510-515.
[5] CHEN C. CiteSpace II:detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006, 57(3):359-377.
[6] 杜建,孙轶楠,李永洁,等.从科学-技术交叉处识别创新前沿:方法与实证[J].情报理论与实践,2019,42(1):94-99.
[7] 张雪,张志强,曹玲静,等.学科领域研究前沿识别方法研究进展[J].图书情报工作,2022, 66(12):139-151.
[8] 罗瑞,许海云,董坤.领域前沿识别方法综述[J].图书情报工作,2018,62(23):119-131.
[9] 赖茂生,王琳,李宇宁.情报学前沿领域的调查与分析[J].图书情报工作,2008,52(3):6-10.
[10] GARFIELD E. Citation indexes in sociological and historical research[J]. American documentation, 1963, 14(4):289-291.
[11] SHIBATA N, KAJIKAWA Y, TAKEDA Y, et al. Detecting emerging research fronts based on topological measures in citation networks of scientific publications[J]. Technovation, 2008, 28(11):758-775.
[12] SMALL H, GRIFFITH B C. The structure of scientific literatures I:identifying and graphing specialties[J]. Science studies, 1974, 4(1):17-40.
[13] GLÄNZEL W, CZERWON H. A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level[J]. Scientometrics, 1996, 37(2):195-221.
[14] CALLON M, COURTIAL J P, TURNER W A, et al. From translations to problematic networks:an introduction to co-word analysis[J]. Social science information, 1983, 22(2):191-235.
[15] 黄晓斌,吴高.学科领域研究前沿探测方法研究述评[J].情报学报,2019,38(8):872-880.
[16] 白如江,刘博文,冷伏海.基于多维指标的未来新兴科学研究前沿识别研究[J].情报学报,2020,39(7):747-760.
[17] 王菲菲,刘明.Altmetrics视角下的交叉学科研究前沿探测——以医学信息学领域为例[J].情报学报,2020,39(10):1011-1020.
[18] SMALL H, BOYACK K W, KLAVANS R. Identifying emerging topics in science and technology[J]. Research policy, 2014, 43(8):1450-1467.
[19] 纳尔逊.牛津创新手册[M].北京:知识产权出版社, 2009.
[20] SCHNEIDER J W, COSTAS R. Identifying potential "breakthrough" publications using refined citation analyses:three related explorative approaches[J]. Journal of the Association for Information Science and Technology, 2017, 68(3):709-723.
[21] SCHERER F M,HARHOFF D.Technology policy for a world of skew-distributed outcomes[J].Research policy,2000,29(4/5):559-566.
[22] 莫富传,娄策群.高被引论文应用于研究热点识别的理论依据与路径探索[J].情报理论与实践,2019,42(4):59-63,35.
[23] 孔玲,王效岳,于纯良,等.学术论文离被引有多远——基于影响因素与预测方法的文献述评[J].情报资料工作,2019,40(6):63-72.
[24] DAHLIN K B,BEHRENS D M.When is an invention really radical?Defining and measuring technological radicalness[J].Research policy,2005,34(5):717-737.
[25] 罗家豪,孙巍.基于专利价值的技术成熟度测度与分析方法研究[J].数字图书馆论坛,2022,18(1):17-25.
[26] 马荣康,王艺棠.基于专利相似度的突破性技术发明识别研究——以纳米技术为例[J].科研管理,2021,42(5):153-160.
[27] BREITZMAN A, THOMAS P. Inventor team size as a predictor of the future citation impact of patents[J]. Scientometrics, 2015, 103(2):631-647.
[28] 张彪,吴红,高道斌,等.基于特征融合的高校可转移专利识别研究[J/OL].情报杂志[2022-09-07].http://kns.cnki.net/kcms/detail/61.1167.G3.20220726.1042.002.html.
[29] MARCO A C,SARNOFF J D,CHARLES A W.Patent claims and patentscope[J].Research policy,2019,48(9):103790.
[30] 邱洪华,陆潘冰.基于专利价值影响因素评价的企业专利技术管理策略研究[J].图书情报工作,2016,60(6):77-83.
[31] MIMNO D,WALLACH H,TALLEY E,et al.Optimizing semantic coherence in topic models[C]//Proceedings of the 2011 conference on empirical methods in natural language processing. Edinburgh:Association for Computational Linguistics, 2011:262-272.
[32] JI Y,ZHU X,ZHAO T,et al.Revealing technology innovation, competition and cooperation of self-driving vehicles from patent perspective[J].IEEE Access,2020,8(99):221191-221202.
[33] LI Y,ZHOU Y,MA X,et al.Forecasting the development of self-driving technology in China by multidimensional information[J].Journal of advanced transportation,2021,2021:1693459-1693471.
[34] 吴菲菲,童奕铭,黄鲁成.嵌入社会感知的技术热点主题识别与发展态势分析——基于微信公众平台视域[J].现代情报,2020,40(3):47-57.
[35] 头豹研究院.2021年中国线控系统系列报告:探讨转向控制系统在自动驾驶中的重要性[EB/OL].[2021-05-01].https://pdf.dfcfw.com/pdf/H3_AP202105271494264468_1.pdf?1622129819000.pdf.
[36] 来飞,黄超群,胡博.智能汽车自动驾驶技术的发展与挑战[J].西南大学学报(自然科学版),2019,41(8):124-133.
[37] 中国智能网联汽车产业创新联盟.智能汽车人机交互现状及发展趋势研究报告[EB/OL].[2022-09-07].http://img.sae-china.org/web/2019/03/[003研报]201803智能汽车人机交互现状及发展趋势研究探析.pdf.
[38] 崔明阳,黄荷叶,许庆,等.智能网联汽车架构、功能与应用关键技术[J].清华大学学报(自然科学版),2022,62(3):493-508.
[39] 周云泽,闵超.基于LDA模型与共享语义空间的新兴技术识别——以自动驾驶汽车为例[J].数据分析与知识发现,2022,6(Z1):55-66.
[40] 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.
