文献类型：期刊文献

中文题名：利用局部离群因子算法探测核心技术发展趋势——以中国风能专利数据为例

英文题名：Detecting Core Technology's Development Trend with LOF——Based on Wind Energy Patent Data of China

作者：李佳佳[1];马铁驹[1]

机构：[1]华东理工大学商学院,上海200237

年份：2017

卷号：36

期号：3

起止页码：119

中文期刊名：情报杂志

外文期刊名：Journal of Intelligence

收录：CSTPCD;;国家哲学社会科学学术期刊数据库;北大核心:【北大核心2014】；CSSCI:【CSSCI2017_2018】；

基金：国家杰出青年科学基金项目"技术演化与能源系统分析"(编号:71125002)

语种：中文

中文关键词：局部离群因子算法;专利分析;共现网络;技术预测;中国风能

外文关键词：local outlier factor algorithm patent analysis co-occurrence network technical forecasting China＇s wind energy

摘要：[目的/意义]为验证局部离群因子算法在探测核心技术及核心技术发展趋势的有效性,丰富专利分析领域的研究。[方法/过程]以中外专利数据库服务平台CNIPR作为数据源,分别从局部离群因子算法和社会网络分析方法两个视角对中国风能领域的专利数据进行对比分析,识别中国风能领域的核心技术以及核心技术的发展趋势。[结果/结论]结果显示,局部离群因子算法(LOF)和社会网络分析方法得出的结论基本一致:即中国在风力发电机技术方面一直保持优势,未来的发展潜力集中在风能照明装置及系统,验证了局部离群因子算法在探测核心技术及核心技术发展趋势方面的有效性。
[ Purpose/Significance] The paper aims to verify the effectiveness of LOF algorithm in the detection of the core technology＇s development trend and thus promote the research of patent analysis. [ Method/Process ] Taking patent database CNIPR as patent data source, the paper conducts a contrastive analysis between LOF algorithm and the social network analysis method, identifying the core technology and the core teclmology＇s development trend for wind energy in China. [ Result/Conclusion] The contrastive analysis result shows a consistency of the two different methods of LOF algorithm and the social network analysis method： China always has the advantage in wind motor and will focus on the lighting devices and equipment in the future. The effectiveness of LOF algorithm in the detection of the core technology＇s development tendency is verified.