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基于引力模型的机器人标准研制趋势发现研究

Research on the Development Trend of Robot Standards Based on the Gravity Model
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摘要 标准路线图在标准制修订规划和发展过程中具有重要的指导作用。目前,国内外标准路线图的构建主要是在行业调研的基础上,结合专家的知识和经验确定。但不同主体研制的标准路线图差异十分巨大,对于实际的指导作用效果不一,以定量为主的标准趋势发现研究成为重要方向。本文以机器人领域标准为研究对象,基于引力模型,在对标准集群中各个标准的重要性进行度量和排序的基础上,结合文本挖掘和时间序列分析方法,通过编制python程序并应用citespace工具,研究引力值前100名的标准在“主题”和“时间”维度上的特性,发现了我国机器人领域标准存在术语、性能检验、安全等基础性标准重要性高;对国外依赖仍然比较强;机器人标准发展总体可分为4个阶段,特种用途、服务等细分领域标准制修订机会较多等3点重要趋势,并据此对机器人行业标准化发展提出相关建议。 The standard roadmap plays an important guiding role in the process of standard development. At present,standard roadmap at home and abroad is mainly determined by industry research, combined with the knowledge and experience of experts. However, the standard roadmaps developed by different subjects differ greatly, and have different guiding effects on practice. Quantitative trend discovery research has become an important direction in the standards field.In this paper, standards in the field of robotics are taken as the research object, and the importance of each standard in the standard cluster is measured and ranked based on the gravity model. On the basis of this, combined with text mining and time series analysis methods, python program is compiled and Citespace tool is used to further study the characteristics of the top 100 gravity-value standards in the dimension of “subject” and “time”. It finds three important trends of robot standards development in China. Firstly, terminology, performance testing, safety and other basic standards play an importance role;secondly, the dependence on foreign countries is still relatively strong;finally, the development of robot standards can be divided into four stages. There are opportunities in special use, service and other subdivision fields. It also puts forward some suggestions on the standardization development of the robot industry.
作者 张正敏 ZHANG Zheng-min(Shanghai Institute of Quality and Standards)
出处 《标准科学》 2022年第9期47-51,共5页 Standard Science
关键词 引力模型 时间序列 标准路线图 知识发现 gravity model time series standard roadmap knowledge discovery
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