摘要
针对战略性新兴产业开展技术融合过程分析,有助于深入理解产业技术的产生过程和发展规律,从而捕捉领域发展动向、推动产业健康发展。本文针对战略性新兴产业中呈现融合发展趋势且备受社会关注的高端装备制造、新一代信息技术、新医药、新能源4个技术领域进行多案例研究,以期识别出技术融合发展的路径和程度。采用基于引用网络和文本信息的知识融合路径分析方法,使用图神经网络同时将论文的引用网络、标题和摘要信息编码为向量;分析4个技术领域的论文数据,识别出了5条技术融合路径。研究结果表明,信息技术与数控设备技术、生物医药与太阳能光伏技术均呈现深度融合的趋势,且前者的融合程度更为深入;数控设备与太阳能光伏技术、信息技术与太阳能光伏技术也呈现融合趋势,但限于发展时间较短而显融合程度较浅;数控设备与生物医药技术领域尚未呈现融合发展的趋势。
The analysis of technology convergence process for strategic emerging industries is helpful to deeply understand the generation process and development law of industrial technology, thereby helping master the development trend of the field and promoting the healthy development of the industry. To identify the trajectory and degree of technology convergence of the strategic emerging industries, this study conducts a multi-case study on four fields which present a trend of convergence and attract social attention, namely, high-end equipment manufacturing, new-generation information technology, new medicine, and new energy. This study adopts a knowledge convergence trajectory analysis method based on citation network and text information. It utilizes a graph neural network model and encodes the citation network, title, and abstract of the publications as vectors. Five knowledge convergence trajectories are identified, after analyzing the data of the selected four technical fields. The research results show that information technology and numerical control equipment, biomedicine and solar photovoltaic technology have shown a trend of deep convergence, respectively;and the convergence of the information technology and numerical control equipment is deeper. Numerical control equipment and solar photovoltaic technology, information technology and solar photovoltaic technology have shown a converging trend, respectively;however, the current degree of convergence is still insufficient, due to the late start of convergence. Numerical control equipment and biomedicine have not shown any trend of convergence.
作者
刘宇飞
苗仲桢
黎凌峰
孔德婧
Liu Yufei;Miao Zhongzhen;Li Lingfeng;Kong Dejing(Center for Strategic Studies,Chinese Academy of Engineering,Beijing 100088,China;School of Public Policy&Management,Tsinghua University,Beijing 100084,China;School of Mechanical Science&Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;School of Modern Post,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《中国工程科学》
CSCD
北大核心
2020年第2期120-129,共10页
Strategic Study of CAE
基金
中国工程院咨询项目“新兴产业发展战略研究(2035)”(2018-ZD-12)
国家自然科学基金项目(91646102,71974107,L1824039)。
关键词
新兴产业
知识融合
图神经网络
引用网络
主题模型
emerging industries
knowledge convergence
graph neural networks
citation network
topic model