摘要
以德温特(Derwent)专利数据库为数据源,综合采用专利情报挖掘、大数据分析、可视化分析等方法对总体态势、研发主体、研发内容进行分析.首先,从时间、技术方向、地理分布角度展示类脑智能技术专利情况,分析类脑智能技术研发总体态势;然后,从专利布局、技术活动、研发质量角度,分析了类脑智能技术研发主要国家和主要机构,识别类脑智能技术优势力量;接着,从主题聚类、关键词突现、技术发展趋势角度,分析类脑智能技术研发热点,构建了颠覆性技术识别指标体系,预测了类脑智能技术领域的颠覆性技术;最后,对结果进行总结分析,为相关研究尤其是构建我国类脑智能技术发展体系和布局提供思路.
The patent data collected in Derwent database were used,and the methods of patent intelligence mining,big data analysis and visual analysis were comprehensively used to insight the overall situation,the core countries and the key organizations,and the technical hotspots and the research frontiers in brain-inspired intelligence field.First of all,aiming at grasping overall situation in the research and development(R&D)of brain-inspired intelligence,the patent situation was displayed from the perspective of time,technological direction,and geographical distribution.Secondly,the main countries and the major institutions was analyzed from the perspective of patent layout,technical activities,and the R&D quality.On this basis,the superior power in the R&D of brain-inspired intelligence field was identified.Third,the R&D hotspots were analyzed from the perspectives of topic clustering,burst keyword,and technology development trends.Then an index system was constructed for disruptive technology identification,and it was employed to predict the disruptive technologies in the field of brain-inspired intelligence field.Finally,the results were summarized and analyzed,which provides ideas and reference for related research,especially for constructing the development system and layout of brain-inspired intelligence technology in China.
作者
梁江海
吴集
刘书雷
LIANG Jianghai;WU Ji;LIU Shulei(Academy of Advanced Studies in Interdisciplinary Research,National University of Defense Technology,Changsha 410073,China)
出处
《华中科技大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2022年第2期96-104,共9页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(61906208).
关键词
类脑智能
专利计量分析
主题聚类
脉冲神经网络
颠覆性技术识别
brain-inspired intelligence
patent analysis
topic clustering
spiking neuron networks
disruptive technology identification