技术需求弱信号感知模型研究——来自最速曲线思想的跨学科启示

doi:10.3772/j.issn.1000-0135.2026.04.005

情报学报

2026, Vol. 45

Issue (4): 520-533 DOI: 10.3772/j.issn.1000-0135.2026.04.005

情报理论与方法

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技术需求弱信号感知模型研究——来自最速曲线思想的跨学科启示

余辉^1,2, 吴昀璟^1,2,3, 夏文蕾^4,5

1.湖北工业大学经济与管理学院，武汉 430068
2.湖北农村社会管理创新研究中心，武汉 430068
3.武汉大学信息资源研究中心，武汉 430072
4.武汉理工大学创业学院，武汉 430070
5.武汉理工大学数字治理与管理决策创新研究院，武汉 430070

Weak Signal Perception Model of Technical Demand: Interdisciplinary Insights from Brachistochrone Curve

Yu Hui^1,2, Wu Yunjing^1,2,3, Xia Wenlei^4,5

1.School of Economics and Management, Hubei University of Technology, Wuhan 430068
2.Hubei Innovation Research Center of Rural Social Management, Wuhan 430068
3.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
4.School of Entrepreneurship, Wuhan University of Technology, Wuhan 430070
5.Institute of Digital Governance and Management Decision Innovation, Wuhan University of Technology, Wuhan 430070

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摘要技术需求弱信号感知是科技情报中的耳目、尖兵、参谋，对技术发展态势的感知和预测以及技术干预具有重要指导意义。本文基于物理学中的最速曲线原理，构建弱信号感知模型，并对该模型的有效性和稳健性进行验证。首先，在“数量-创新”二维技术地图中，探究技术发展最速曲线，明晰技术发展特征；其次，以技术距离为基础构建多层时序技术空间，进一步展示技术发展状态以及在技术空间中的分布特征，在技术空间中拟合最速曲线，构建最速纺锤场，以感知技术需求弱信号；最后，与其他曲线拟合模型进行对比验证。对现有技术发展数据的实证分析结果表明，本文构建的技术需求弱信号感知模型在有效性和稳健性检验中优于其他模型，能够高效地感知技术需求弱信号。此外，由于弱信号的潜在特征，关于未来技术需求弱信号感知的有效性检验还需要时间验证。本文提出的技术需求弱信号感知模型能够实现对弱信号的高性能感知，初步具备了面向前沿科技情报领域的技术发展态势的决策能力。

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关键词 ：需求弱信号, 技术感知, 技术预测, 最速曲线

收稿日期: 2025-07-25

基金资助:国家自然科学基金青年科学基金项目“科技竞争态势下技术需求弱信号感知研究”（72404081）；教育部人文社会科学研究青年项目“数据要素驱动视角下数据资产的确认计量、信息披露及经济后果研究”（24YJC630239）；湖北农村社会管理创新研究中心2025年度开放基金“AIGC赋能农业全产业链技术需求识别研究”（HNCGLY2503）。

作者简介: 余辉，1993年生，博士，副教授，主要研究领域为科技情报与科技服务；吴昀璟，2000年生，博士研究生，主要研究领域为人工智能；夏文蕾，通信作者，1992年生，博士，讲师，主要研究领域为数据资产、技术创新与企业绩效，E-mail：290149@whut.edu.cn；

引用本文:

余辉, 吴昀璟, 夏文蕾. 技术需求弱信号感知模型研究——来自最速曲线思想的跨学科启示[J]. 情报学报, 2026, 45(4): 520-533.
Yu Hui, Wu Yunjing, Xia Wenlei. Weak Signal Perception Model of Technical Demand: Interdisciplinary Insights from Brachistochrone Curve. 情报学报, 2026, 45(4): 520-533.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2026.04.005 或 https://qbxb.istic.ac.cn/CN/Y2026/V45/I4/520

1 Lee C. A review of data analytics in technological forecasting[J]. Technological Forecasting and Social Change, 2021, 166: 120646.
2 Adamuthe A C, Thampi G T. Technology forecasting: a case study of computational technologies[J]. Technological Forecasting and Social Change, 2019, 143: 181-189.
3 Huenteler J, Schmidt T S, Ossenbrink J, et al. Technology life-cycles in the energy sector—technological characteristics and the role of deployment for innovation[J]. Technological Forecasting and Social Change, 2016, 104: 102-121.
4 Huang Y, Li R N, Zou F, et al. Technology life cycle analysis: from the dynamic perspective of patent citation networks[J]. Technological Forecasting and Social Change, 2022, 181: 121760.
5 Yang X T, Sun B, Liu S L. Study of technology communities and dominant technology lock-in in the Internet of Things domain-based on social network analysis of patent network[J]. Information Processing & Management, 2025, 62(1): 103959.
6 Tseng Y H, Lin C J, Lin Y I. Text mining techniques for patent analysis[J]. Information Processing & Management, 2007, 43(5): 1216-1247.
7 Li X, Xie Q Q, Daim T, et al. Forecasting technology trends using text mining of the gaps between science and technology: the case of perovskite solar cell technology[J]. Technological Forecasting and Social Change, 2019, 146: 432-449.
8 Su H N. Visualization of global science and technology policy research structure[J]. Journal of the American Society for Information Science and Technology, 2012, 63(2): 242-255.
9 Holopainen M, Toivonen M. Weak signals: Ansoff today[J]. Futures, 2012, 44(3): 198-205.
10 Coffman B. Weak signal research, part I: introduction[J]. Journal of Transition Management, 1997, 2(1): 4.
11 沈固朝. 信号分析: 竞争情报研究的又一重要课题[J]. 图书情报工作, 2009, 53(20): 11-14, 59.
12 He X J, Meng X, Wu Y Y, et al. Semantic matching efficiency of supply and demand texts on online technology trading platforms: taking the electronic information of three platforms as an example[J]. Information Processing & Management, 2020, 57(5): 102258.
13 余辉, 吕星仪, 夏文蕾, 等. 科技竞争态势下的技术需求弱信号感知研究[J]. 情报杂志, 2024, 43(12): 106-116.
14 赵动员, 唐中君. 基于改进随机共振模型的需求弱信号演化影响因素研究[J]. 情报理论与实践, 2025, 48(8): 124-134, 148.
15 余辉, 吴昀璟, 夏文蕾, 等. “政府—市场—企业”三维视角下技术需求弱信号感知研究——以新能源汽车领域为例[J]. 情报理论与实践, 2025, 48(3): 106-116.
16 Yu H Q, Liang Y A. A framework for predicting scientific disruption based on graph signal processing[J]. Information Processing & Management, 2024, 61(6): 103863.
17 Ma M, Mao J, Li G. Discovering weak signals of emerging topics with a triple-dimensional framework[J]. Information Processing & Management, 2024, 61(5): 103793.
18 Ebadi A, Auger A, Gauthier Y. Detecting emerging technologies and their evolution using deep learning and weak signal analysis[J]. Journal of Informetrics, 2022, 16(4): 101344.
19 Yan B W, Luo J X. Filtering patent maps for visualization of diversification paths of inventors and organizations[J]. Journal of the Association for Information Science and Technology, 2017, 68(6): 1551-1563.
20 He X J, Meng X, Dong Y B, et al. Demand identification model of potential technology based on SAO structure semantic analysis: the case of new energy and energy saving fields[J]. Technology in Society, 2019, 58: 101116.
21 张慧玲, 许海云, 刘春江, 等. 科技创新弱信号早期感知方法探究与前瞻[J]. 情报学报, 2024, 43(10): 1129-1141.
22 Cheng A C, Chen C J, Chen C Y. A fuzzy multiple criteria comparison of technology forecasting methods for predicting the new materials development[J]. Technological Forecasting and Social Change, 2008, 75(1): 131-141.
23 余辉, 夏文蕾, 黄炜, 等. 基于文本匹配-组态优化的两阶段技术供需匹配方法研究[J]. 情报学报, 2024, 43(3): 261-273.
24 Yoon B, Park Y. A systematic approach for identifying technology opportunities: keyword-based morphology analysis[J]. Technological Forecasting and Social Change, 2005, 72(2): 145-160.
25 Son C, Suh Y, Jeon J, et al. Development of a GTM-based patent map for identifying patent vacuums[J]. Expert Systems with Applications, 2012, 39(3): 2489-2500.
26 Onorato C, Madeu F, Tsakalova M, et al. Navigating the mycelium patent maze: a holistic approach to patent mapping in production technologies[J]. World Patent Information, 2024, 76: 102265.
27 Kim L, Ju J. Can media forecast technological progress? A text-mining approach to the on-line newspaper and blog’s representation of prospective industrial technologies[J]. Information Processing & Management, 2019, 56(4): 1506-1525.
28 Xu S, Hao L Y, Yang G C, et al. A topic models based framework for detecting and forecasting emerging technologies[J]. Technological Forecasting and Social Change, 2021, 162: 120366.
29 Guo F, Yan E H, Zhou H T, et al. Technological life-cycle analysis of ultra-precision machining technology: forecasting perspective directions and tracking the critical transitions with evolution[J]. Advanced Engineering Informatics, 2024, 62: 102805.
30 梁镇涛, 毛进, 李纲. 融合“科学-技术”知识关联的高颠覆性专利预测方法[J]. 情报学报, 2023, 42(6): 649-662.
31 Yuan X D, Cai Y C. Forecasting the development trend of low emission vehicle technologies: based on patent data[J]. Technological Forecasting and Social Change, 2021, 166: 120651.
32 Lin D M, Liu W B, Guo Y X, et al. Using technological entropy to identify technology life cycle[J]. Journal of Informetrics, 2021, 15(2): 101137.
33 Seo W. A patent-based approach to identifying potential technology opportunities realizable from a firm’s internal capabilities[J]. Computers & Industrial Engineering, 2022, 171: 108395.
34 Kumar V, Lai K K, Chang Y H, et al. A structural analysis approach to identify technology innovation and evolution path: a case of m-payment technology ecosystem[J]. Journal of Knowledge Management, 2021, 25(2): 477-499.
35 Li S Y, Garces E, Daim T. Technology forecasting by analogy-based on social network analysis: the case of autonomous vehicles[J]. Technological Forecasting and Social Change, 2019, 148: 119731.
36 Xi X, Zhao J Y, Yu L A, et al. Exploring the potentials of artificial intelligence towards carbon neutrality: technological convergence forecasting through link prediction and community detection[J]. Computers & Industrial Engineering, 2024, 190: 110015.
37 Dosi G, Nelson R R. Technological paradigms and technological trajectories[M]// The Palgrave Encyclopedia of Strategic Management. London: Palgrave Macmillan, 2016: 1-12.
38 Arthur W B. Increasing returns and path dependence in the economy[M]. Ann Arbor: University of Michigan Press, 1994.
39 叶光辉, 彭泽, 李松烨, 等. “能”与“势”: 生态学与物理学交叉视角下的跨学科知识交流动力模型研究[J]. 情报学报, 2024, 43(6): 644-657.
40 马铭, 王超, 许海云, 等. 面向语义信息分析的多层次技术演化轨迹识别方法研究[J]. 图书情报工作, 2022, 66(4): 103-117.
41 Elsgolc L D. Calculus of variations[M]. New York: Dover Publications, 2007.
42 许晖, 卢一斌, 李阳, 等. 国之重器如何实现复杂技术系统整体赶超——中国核电堆芯的纵向案例研究[J]. 经济管理, 2025, 47(1): 24-48.
43 余辉, 周晶, 夏文蕾, 等. 基于“标杆—个性”的企业技术专利研发方向识别——以新能源汽车企业为例[J]. 情报理论与实践, 2024, 47(11): 130-138.
44 Sun X L, Chen N, Ding K. Measuring latent combinational novelty of technology[J]. Expert Systems with Applications, 2022, 210: 118564.
45 Gao L D, Porter A L, Wang J, et al. Technology life cycle analysis method based on patent documents[J]. Technological Forecasting and Social Change, 2013, 80(3): 398-407.
46 Curran C S, Leker J. Patent indicators for monitoring convergence—examples from NFF and ICT[J]. Technological Forecasting and Social Change, 2011, 78(2): 256-273.
47 Han E J, Sohn S Y. Technological convergence in standards for information and communication technologies[J]. Technological Forecasting and Social Change, 2016, 106: 1-10.
48 Yan B W, Luo J X. Measuring technological distance for patent mapping[J]. Journal of the Association for Information Science and Technology, 2017, 68(2): 423-437.
49 von Wartburg I, Teichert T, Rost K. Inventive progress measured by multi-stage patent citation analysis[J]. Research Policy, 2005, 34(10): 1591-1607.
50 Markard J. The life cycle of technological innovation systems[J]. Technological Forecasting and Social Change, 2020, 153: 119407.
51 Matsuda Y, Yamaguchi K. An efficient MDS-based topographic mapping algorithm[J]. Neurocomputing, 2005, 64: 285-299.
52 魏明珠, 郑荣, 高志豪, 等. 融合知识图谱和深度神经网络的产业新兴技术预测模型研究[J]. 情报学报, 2022, 41(11): 1134-1148.
53 冉从敬, 李旺. 校企合作视角下高校高价值专利推荐模型构建——以“新能源汽车”为例[J]. 图书情报工作, 2023, 67(11): 115-126.
54 王丹, 周潇, 赵捧未, 等. 基于离群点视角的颠覆性专利预测研究[J]. 图书情报工作, 2024, 68(5): 74-86.
55 刘立, 苏丽芳, 楼旭明, 等. 基于核心技术驱动的中国太阳能产业技术融合路径及效应研究[J]. 情报学报, 2023, 42(4): 453-464.
56 冯科, 曾德明, 周昕. 技术融合的动态演化路径[J]. 科学学研究, 2019, 37(6): 986-995.
57 Caviggioli F. Technology fusion: identification and analysis of the drivers of technology convergence using patent data[J]. Technovation, 2016, 55: 22-32.