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数据分析与知识发现  2017, Vol. 1 Issue (7): 2-12     https://doi.org/10.11925/infotech.2096-3467.2017.0546
  首届"数据分析与知识发现"学术研讨会专辑(I) 本期目录 | 过刊浏览 | 高级检索 |
测度技术融合与趋势的数据分析方法研究进展*
李姝影, 方曙()
中国科学院成都文献情报中心 成都 610041
Review of Data Analysis Methods in Measuring Technology Fusion and Trend
Li Shuying, Fang Shu()
Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
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摘要 

目的】对技术会聚/融合相关研究进行较系统的梳理, 期望能够展示该领域的研究进展, 为后续研究提供参考。【文献范围】从Web of Science(WOS)、CNKI等数据库中分别以“技术会聚(Technology Convergence)”、“技术融合(Technology Fusion)”等检索词搜集获得中英文相关文献73篇。【方法】针对技术融合相关概念及内涵、测度技术融合程度与趋势的数据分析方法进行较系统的文献提炼与评析。【结果】近年来有关技术融合的研究越来越丰富; 基于专利数据的技术融合分析方法主要包括评估技术融合度的指标、专利引文网络分析方法探索技术融合演化路径、专利共类识别技术融合轨道等。【局限】混合方法的横向对比研究较少。【结论】已有分析方法尚不完善, 测度技术融合的数据分析方法优化拓展的潜力巨大, 未来具有较大的研究发展空间。

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李姝影
方曙
关键词 技术融合技术会聚专利分析    
Abstract

[Objective] This paper reviews literature on technology convergence/fusion, aiming to explore research progress in the field and provide reference for further studies. [Coverage] We retrieved 73 papers in Chinese and English from the Web of Science (WOS), CNKI and other databases using the keywords “Technology Convergence” or “Technology Fusion”. [Methods] We reviewed the concepts of technology convergence/fusion, and related data analysis methods. [Results] We found the number of research on technology convergence/fusion was increasing. Their data analysis methods used patents as indicators, illustrating evolution path with patent citation network, and establishing fusion track with co-classification analysis. [Limitations] More research is needed to compare the hybrid methods. [Conclusions] The data analysis methods for technology convergence/fusion require much optimization, which leaves many knowledge gaps to be filled.

Key wordsTechnology Fusion    Technology Convergence    Patent Analysis
收稿日期: 2017-06-02      出版日期: 2017-07-26
ZTFLH:  G306.0  
基金资助:*本文系ISTIC-THOMSON科学计量学联合实验室开放基金项目“基于专利计量的产学研协同创新主题识别方法研究”(项目编号: Y6H0951001)的研究成果之一
引用本文:   
李姝影, 方曙. 测度技术融合与趋势的数据分析方法研究进展*[J]. 数据分析与知识发现, 2017, 1(7): 2-12.
Li Shuying,Fang Shu. Review of Data Analysis Methods in Measuring Technology Fusion and Trend. Data Analysis and Knowledge Discovery, 2017, 1(7): 2-12.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.0546      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I7/2
概念 说明
产业 产业会聚
(Industry Convergence)
产业从现有离散分布扩展到一个新的领域的过程。
产业融合
(Industry Fusion)
多个产业开始融合兼并, 形成至少一个共同产业的过程。
技术 技术会聚
(Technology Convergence)
两种以上技术向一个新的方向发展的过程。
技术融合
(Technology Fusion)
两种以上技术在同一个位置合并成一项或多项新的技术。
  产业与技术层面会聚与融合概念区分(整理自文献[9])
  技术融合的过程图[9]
阶段 来源 研究方法 数据使用来源
学科融合 科学
论文
论文合著关系
共词/共类/共被引分析
期刊主题分析
NPL引文分析
SCI/SCOPUS
SciFinder
PASTAT(NPL)
技术融合 专利 专利权人共现
SIC和IPC共现
共引分析
IPC共现
专利局专利
数据
SciFinder
PATSTAT
产品/应用
融合
新闻 产品发布
用户分析
公司数据
专家面谈
产业融合 专利、
公司
与产业
信息
SIC和IPC共现
投入产出分析
产品组合
战略联盟
兼并
专利数据
公司数据
Factiva
新闻媒体
  不同层面融合分析方法与数据来源[15-16]
  技术融合路径识别(整理自文献[42])
维度 指标 研究结论 有效性
引文指标 非专利文献引用(NPL) 对科学文献的引用超过产业学科的增长 不显著
后向引用/前向引用 后向引用: 自引率和他引率超过技术领域的增长;
前向引用: 不显著
后向引用显著
前向引用待定
技术生命周期(TCT) 会聚领域的技术创新率高于企业内部知识产生率;
企业内部产生新知识的周期较快(领域差异)
显著(电子领域)
间接引用 技术领域外的突破性创新呈现增长 显著(电子领域)
学科交叉 学科丰富度 覆盖学科数量 -
平衡性 学科占比的均衡性
差异度 学科间的差异性
核心边缘度 网络中核心地位
网络分派度 凝聚程度
网络密度 联络的紧密程度
网络中心势 网络的集中分布趋势
技术共现 吸收指数 技术集成性 -
扩散指数 技术扩散范围
技术共现强度 -
  评估学科与技术融合的相关指标(整理自文献[15,34,43,46-47])
计量指标 定义 公式
宏观 技术融合度 交叉专利数量(多种技术分类)与专利总量(全领域)的比值。 $\text{技术融合度}=\frac{交叉专利数量}{\text{专利总数}}$
技术融合广度 利用香农熵计算某一特定领域的技术融合广度, F值随时间变化而变化。F值越大, 技术领域融合广度越大。 Fi=$-\sum{_{k}{{P}_{ik}}\ln {{P}_{ik}}}$
Fi: 领域i的熵; Pik: 领域k在领域i中占比。
中观 技术交叉融合广度 交叉技术方向涉及技术的广泛程度和强度。 Wi=$\frac{Ui}{UA}$(iA)
技术交叉融合强度 Ii=$\frac{Pi/Ui}{PA/UA}$(iA)交叉技术i属于产业A, UiPi代表交叉技术i包含的USPC数量和专利数量;UAPA为产业A包含的USPC数量和专利数量。
融合强度
(Convergence Intensity)
交叉专利数量占两个来源技术领域专利数量最小值的比例。 CIA-B=${{N}_{{{P}_{A-B}}}}/Min({{N}_{PA}},{{N}_{PB}})$
CIA-B: 技术领域AB之间的融合强度; ${{N}_{{{P}_{A-B}}}}$: 融合技术领域专利数量。
融合广度
(Convergence Coverage)
涉及共类专利技术的数量以衡量特定技术领域融合过程中的扩散程度。 CC=$C/(M\times N)$
微观 吸收指数
(Originality)
通过一项专利其所在分类之外的专利的情况来测度技术的原创性。 $OI=1-{{\sum\limits_{i=1}^{k}{\left( \frac{{{N}_{i}}}{N} \right)}}^{2}}$
k为观测专利引用文献的专利所属的不同技术领域的个数; Ni为属于i技术领域的被引专利个数; N为被引专利的总数。
扩散指数
(Generality)
基于被引量和被引专利的分类分布情况测度技术的通用性。 $GI=1-{{\sum\limits_{i=1}^{k}{\left( \frac{{{N}_{i}}}{N} \right)}}^{2}}$
k为引用观测专利的专利(施引专利)所属的不同技术领域的个数; Ni为属于i技术领域的施引专利个数; N为施引专利的总数。
  技术融合计量指标(整理自文献[34,44-48])
  基于知识流动的技术融合分析流程[58]
作者(或者机构) 实证领域
OECD[65] 光电子学
Yoffie[25] ICT信息通信技术
Roco和Bainbridge[24] NBIC
Shin[66] 数字技术
No等[42] 纳米生物技术
Kim等[41] 印刷型电子产品
Curran等[3] NFF(保健品和功能性食品)
智能手机产业
Bigliardi等[67] 电信产业
李丫丫等 [59] 生物芯片产业
  技术融合分析方法应用领域
方法 主要思路 应用场景 优势 当前不足 改进方向
引文分析 探索技术融合演化路径 识别科学与技术发展
整体趋势与演化关系
易于分析技术
演化的时间序列
不适用于新兴技术产业
或颠覆性技术
与其他方法相
结合的优化方
法的适用性
专利指标 评价技术融合度 确定主要分类中技术
融合的程度
有助于评估与预测
未来技术融合趋势
无法对技术层面融合的
技术特征以及实际作用
进行分析说明
专利共类 识别技术融合轨道和
技术交叉趋势
界定技术领域的层级结构,
可识别技术之间的关系
快速直观地发现
技术融合现象
IPC覆盖技术领域有限,
技术融合粒度较粗
  基于专利数据的技术融合分析方法对比
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