摘要
[研究目的]通过多维技术关联趋势演化模型,可更综合全面地认识与理解行业内部技术之间的影响及发展关系,有助把握行业技术关联发展的具体模式,在提升预测效果、完善预测研究体系方面具有一定意义。[研究方法]基于专利分类代码共现,建立技术之间的关联关系,在技术关联系统性的基础上,运用社会网络分析理论及交叉影响分析理论,结合时间序列分析法,以医学医药行业为研究对象,对该行业的技术关联趋势进行了细粒度剖析,从宏观、中观和微观三个层面分别揭示行业整体技术关联模式、热点技术社群结构演变情况、热点技术路径演化过程,实现对行业视角下的技术关联趋势探索。[研究结论]根据研究结果预测,医学医药行业将向强调技术关联对象的选择及持续加强关联紧密度的模式发展;基于数据算法下的智能化辅助医疗是医学医药行业的重点发展方向。
[Research purpose]The multi-dimensional technological relatedness trend evolution model provides a more comprehensive understanding of the impact and development relationship between technologies within the industry,it helps to grasp the specific pattern of technological relatedness development in the industry,which has certain significance in improving the forecasting effect and perfecting the forecasting research system.[Research method]Based on the co-occurrence of patent classification codes,the relatedness relationship between technologies is established,and on the basis of the systematic nature of technological relatedness,social network analysis theory and cross-impact analysis theory are applied,combined with time series analysis,select the medical and pharmaceutical industry as the example,providing a fine-grained analysis of the technology-related trends in the industry,to reveal the overall technological relatedness pattern of the industry,the evolution of the structure of hot technology communities and the evolution process of hot technology paths from three levels:macro,meso and micro,respectively,so as to explore the technological relatedness trend from the perspective of the industry.[Research conclusion]Based on the results,it is predicted that the industry will develop towards a model that emphasizes the selectivity of technical relevance objects and continues to strengthen the closeness of the relationship.And the intelligent auxiliary medical care based on data algorithms is the key development direction of the medical and pharmaceutical industry.
作者
王莎莎
严素梅
陈荣
李建霞
Wang Shasha;Yan Sumei;Chen Rong;Li Jianxia(Institute of Science and Technology Information,East China University of Science and Technology,Shanghai 200237)
出处
《情报杂志》
CSSCI
北大核心
2021年第11期53-61,共9页
Journal of Intelligence
基金
上海市软科学研究计划项目“基于量化趋势演化模型的技术发展预见与实证研究”(编号:18692109200)研究成果之一。
关键词
技术关联
代码共现
热点技术
专利分析
技术预测
technological relatedness
code co-occurrence
hot technology
patent analysis
technology forecasting