[Purpose/Significance] Emerging industrial technologies have a major role in creating or changing the economic structure and development direction of traditional industries. It has important strategic and practical guiding significance for industrial development and reform to identify emerging industrial technologies in a timely and accurate manner and clarify the appropriate development direction. [Method/Process] According to the patent IPC classification information, this paper built an IPC node network, used the structural clustering algorithm for networks (SCAN) to identify industrial clusters and emerging technologies, and analyzed them in detail; then based on the time-series data of the five identified emerging technology patents, it constructed a patent node network and performed CPM to realize critical path analysis under the field of different emerging technologies, making the recognition results more microscopic and refined. Finally, taking the new energy automobile industry as an example, it obtained patent information from INCOPAT patent database for empirical analysis. [Result/Conclusion] Analysis based on SCAN-CPM is a useful supplement to the traditional emerging technology identification method. It displays the technology evolution path from large to small, from coarse to detailed, and provides a new perspective and technical means for comprehensively identifying emerging technologies and detecting technology evolution paths. The empirical analysis finds that: the internal design and external layout of charging piles for new energy vehicles are still the top priority for the development of the industry. Thermal management technology has increasingly become a relatively independent technical field. It is an important technical support for the new energy automobile industry to achieve overtaking on a curve. The evolution and diffusion path is clear. The technical issues such as battery pack environment, motor heat dissipation, battery liquid cooling technology, and heat exchanger materials are important breakthroughs and innovation points in the future. The motor drive control system currently forms a relatively high patent barrier.
Zheng Rong
,
Wei Mingzhu
,
Gao Zhihao
,
Wang Xiaoyu
. SCAN-CPM-Based Industry Emerging Technology Identification and Evolution Path Analysis：Taking the New Energy Automobile Industry as an Example[J]. Library and Information Service, 2022
, 66(11)
: 100
-109
.
DOI: 10.13266/j.issn.0252-3116.2022.11.011
[1] 戴,休梅克.沃顿论新兴技术管理[M].石莹,译.北京:华夏出版社,2002.
[2] ROTOLO D, HICKS D, MARTIN B R. What is an emerging technology?[J].Research policy,2015,44(10):1827-1843.
[3] 周潇.新兴技术热点领域识别及技术路线图研究[D].北京:北京理工大学,2015.
[4] 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.
[5] DOSI G.Technological paradigms and technological trajectories[J].Research ploicy,1982,11(2):147-162.
[6] 吕璐成,赵亚娟.基于专利数据的技术融合研究综述[J].图书情报工作,2021,65(6):138-148.
[7] 卢小宾,杨冠灿,徐硕,等.计量与演化视角下的新兴技术识别研究进展评述[J].情报学报,2020,39(6):651-661.
[8] FUJITA K,KAJIKAWA Y, MORI J, et al. Detecting research fronts using different types of weighted citation networks[J].Journal of engineering and technology management,2014,32(SI):129-146.
[9] 李瑞茜,陈向东.基于专利共类的关键技术识别及技术发展模式研究[J].情报学报,2018,37(5):49-56.
[10] CHOI S,YOON J,KIM K, et al. SAO network analysis of patents for technology trends identification:a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells[J].Scientometrics,2011,88(3):863-883.
[11] 罗建,蔡丽君,史敏.基于专利的两阶段新兴技术识别研究——以图像识别技术为例[J].情报科学,2019,37(12):57-62.
[12] 黄璐,朱一鹤,张嶷.基于加权网络链路预测的新兴技术主题识别研究[J].情报学报,2019,38(4):335-341.
[13] 丁云龙,谭超.作为技术预见工具的技术路线图及其应用前景[J].公共管理学报,2006(4):40-45,108-109.
[14] EPICOCO M. Knowledge patterns and sources of leadership:mapping the semiconductor miniaturization trajectory[J].Research policy,2013,42(1):185-190.
[15] 王玏,吴新年.新兴技术识别方法研究综述[J].图书情报工作,2020,64(4):125-135.
[16] XU X W,YURUK N,FENG Z D, et al. SCAN:a structural clustering algorithm for networks[C]//13th international conference on knowledge discovery and data mining. New York:ACM.2007:824-833.
[17] 孔德婧,董放,陈子婧,等.离群专利视角下的新兴技术预测——基于BERT模型和深度神经网络[J].图书情报工作,2021,65(17):131-141.
[18] NEWMAN M E J, GIRVAN M. Finding and evaluating community structure in networks[J].Physical review E statistical nonlinear & soft matter physics,2004,69(2):26-34.
[19] MARTIN R,BERGSTROM C T. Maps of random walks on complex networks reveal community structure[J].Proceedings of the National Academy of Sciences of the United States of America,2007,105(4):18-23.
[20] BATAGELJ V. Efficient algorithms for citation network analysis[J].Computer science,2003,41(897):1-29.
[21] 宋欣娜,郭颖,席笑文.基于专利文献的多指标新兴技术识别研究[J].情报杂志,2020,39(6):76-81,88.
[22] 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.
[23] 马永红,孔令凯,林超然,等.基于专利挖掘的关键共性技术识别研究[J].情报学报,2020,39(10):1093-1103.作者贡献说明:郑荣:提出研究命题、研究思路及论文修订;魏明珠:负责论文撰写、修改及数据分析与处理;高志豪:负责文献收集及算法校验;王晓宇:负责数据采集及英文翻译。
