科技创新弱信号早期感知方法探究与前瞻

doi:10.3772/j.issn.1000-0135.2024.10.001

情报学报

2024, Vol. 43

Issue (10): 1129-1141 DOI: 10.3772/j.issn.1000-0135.2024.10.001

情报理论与方法

本期目录 | 过刊浏览 | 高级检索

科技创新弱信号早期感知方法探究与前瞻

张慧玲¹, 许海云², 刘春江³, 陈亮⁴, 王超², 王海燕⁴

1.太原市图书馆，太原 030024
2.山东理工大学管理学院，淄博 255000
3.中国科学院成都文献情报中心，成都 610299
4.中国科学技术信息研究所，北京 100038

Exploring and Anticipating Early Detection Methods for Scientific and Technological Weak Signals

Zhang Huiling¹, Xu Haiyun², Liu Chunjiang³, Chen Liang⁴, Wang Chao², Wang Haiyan⁴

1.Taiyuan City Public Library, Taiyuan 030024
2.Business School, Shandong University of Technology, Zibo 255000
3.National Science Library (Chengdu), Chinese Academy of Sciences, Chengdu 610299
4.Institute of Scientific and Technical Information of China, Beijing 100038

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摘要科技创新弱信号作为未来科技前沿发展的线索，蕴含潜在的创新方向、科技趋势与市场机会。目前，科技预见方法存在挖掘覆盖度低、模型与方法泛化能力弱、可解释度低与主观认知局限等问题。本文系统梳理潜在可行的科技创新弱信号感知模型与方法，以期支撑面向未来技术前瞻预见的智慧情报分析。首先，剖析科技创新弱信号的内涵及其情报感知特征；其次，围绕以弱信号识别、意义建构、预测与响应以及感知效果评估为反馈闭环的科技创新弱信号早期情报感知框架，结合定量与定性、主观与客观、因果与相关三个方法维度，依据科技创新弱信号的特征、适用性与可解释性，探究可用于科技创新弱信号不同感知阶段的潜在方法，解析不同阶段方法的优缺点及组合适用情况；最后，以干细胞领域的案例分析验证了本文提出的感知方法及流程的可行性。未来需要拓展科技创新弱信号的感知模型与溯源方法，扩展科技创新弱信号的特征覆盖度，开发数智驱动的科技创新弱信号感知方法工具库，支撑不确定环境下新兴前沿技术的超前感知。

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	张慧玲
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	王超
	王海燕

关键词 ：科技情报, 新兴技术, 情报感知, 弱信号, 早期信号

收稿日期: 2024-03-11

基金资助:国家自然科学基金项目“基于弱信号时效网络演化分析的变革性科技创新主题早期识别方法研究”（72274113）；国家重点研发计划项目课题“颠覆性技术地平线扫描系统”（2019YFA0707202-01）；山东省泰山学者工程项目“变革性科技创新：动因解析、早期识别与预测”（202103069）。

作者简介: 张慧玲，女，1993年生，硕士研究生，研究领域为科技与产业情报分析；许海云，通信作者，女，1982年生，博士，教授，博士生导师，研究领域为科技与产业情报分析，E-mail：xuhaiyunnemo@gmail.com；刘春江，男，1984年生，博士，高级工程师，硕士生导师，研究领域为科技文献挖掘与知识发现；陈亮，男，1982年生，博士，副研究员，硕士生导师，研究领域为自然语言处理与知识产权管理；王超，男，1988年生，博士，副教授，硕士生导师，研究领域为科技与产业情报分析；王海燕，女，1974年生，博士，副研究员，研究领域为科学计量学；

引用本文:

张慧玲, 许海云, 刘春江, 陈亮, 王超, 王海燕. 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.
Zhang Huiling, Xu Haiyun, Liu Chunjiang, Chen Liang, Wang Chao, Wang Haiyan. Exploring and Anticipating Early Detection Methods for Scientific and Technological Weak Signals. 情报学报, 2024, 43(10): 1129-1141.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2024.10.001 或 https://qbxb.istic.ac.cn/CN/Y2024/V43/I10/1129

1 Ansoff H I, Kipley D, Lewis A O, et al. Implanting strategic management[M]. Cham: Palgrave Macmillan, 2019.
2 徐硕, 王聪聪, 安欣. 新兴技术弱信号扫描预判述评[J]. 情报杂志, 2023, 42(3): 117-122.
3 彭知辉. 基于暗数据视角对情报学的反思[J]. 情报理论与实践, 2024, 47(3): 20-26.
4 左秦. 从VUCA转向BANI, 一场时代迁移[J]. 经理人, 2023(9): 10-12.
5 Xu H Y, Luo R, Winnink J, et al. A methodology for identifying breakthrough topics using structural entropy[J]. Information Processing & Management, 2022, 59(2): 102862.
6 Sebastian Y, Chen C M. The boundary-spanning mechanisms of Nobel Prize winning papers[J]. PLoS One, 2021, 16(8): e0254744.
7 张慧玲, 许海云, 王超, 等. 弱信号环境下情报感知方法框架研究[J]. 情报理论与实践, 2023, 46(11): 9-19.
8 赵柯然, 王延飞. 信息迷雾的情报感知研究[J]. 情报理论与实践, 2021, 44(3): 1-5, 12.
9 赵志耘, 潘云涛, 苏成, 等. 颠覆性技术感知响应系统框架研究[J]. 情报学报, 2021, 40(12): 1245-1252.
10 邓胜利, 林艳青, 王野. 企业竞争弱信号的特征提取与定量识别研究[J]. 图书情报工作, 2016, 60(10): 67-75.
11 Kim J, Park Y, Lee Y. A visual scanning of potential disruptive signals for technology roadmapping: investigating keyword cluster, intensity, and relationship in futuristic data[J]. Technology Analysis & Strategic Management, 2016, 28(10): 1225-1246.
12 Yoon J. Detecting weak signals for long-term business opportunities using text mining of web news[J]. Expert Systems with Applications, 2012, 39(16): 12543-12550.
13 Ansoff H I. Managing strategic surprise by response to weak signals[J]. California Management Review, 1975, 18(2): 21-33.
14 Hiltunen E. The future sign and its three dimensions[J]. Futures, 2008, 40(3): 247-260.
15 刘亚辉, 许海云, 武华维, 等. 基于多元弱关系融合的科学突破主题早期识别研究[J]. 情报学报, 2023, 42(1): 19-30.
16 Xu H Y, Winnink J, Yue Z H, et al. Multidimensional scientometric indicators for the detection of emerging research topics[J]. Technological Forecasting and Social Change, 2021, 163: 120490.
17 Garcia-Nunes P I, da Silva A E A. Using a conceptual system for weak signals classification to detect threats and opportunities from web[J]. Futures, 2019, 107: 1-16.
18 Kaivo-oja J. Weak signals analysis, knowledge management theory and systemic socio-cultural transitions[J]. Futures, 2012, 44(3): 206-217.
19 Endsley M R. Final reflections: situation awareness models and measures[J]. Journal of Cognitive Engineering and Decision Making, 2015, 9(1): 101-111.
20 Kim J, Lee C. Novelty-focused weak signal detection in futuristic data: assessing the rarity and paradigm unrelatedness of signals[J]. Technological Forecasting and Social Change, 2017, 120: 59-76.
21 马铭, 毛进, 李纲. 从弱信号到机会: 关于弱信号的相关研究进展[J]. 图书情报工作, 2023, 67(19): 121-132.
22 刘亚辉, 许海云. 突破性创新早期识别与弱信号分析综述[J]. 图书情报工作, 2021, 65(4): 89-101.
23 Kuosa T. Different approaches of pattern management and strategic intelligence[J]. Technological Forecasting and Social Change, 2011, 78(3): 458-467.
24 董尹, 刘千里, 宋继伟, 等. 弱信号研究综述: 概念、方法和工具[J]. 情报理论与实践, 2018, 41(10): 147-154.
25 唐超, 王延飞. 融入情报流程的情报感知能力研究[J]. 情报理论与实践, 2019, 42(5): 14-18, 22.
26 白如江, 张玉洁, 赵梦梦, 等. 面向关联推理的智慧情报感知: 内涵、组织与路径[J]. 情报理论与实践, 2022, 45(8): 31-37, 67.
27 Kim S, Kim Y E, Bae K J, et al. NEST: a quantitative model for detecting emerging trends using a global monitoring expert network and Bayesian network[J]. Futures, 2013, 52: 59-73.
28 Griol Barres I. Modelling of a system for the detection of weak signals through text mining and NLP. Proposal of improvement by a quantum variational circuit[D]. Valencia: Universitat Politècnica de València, 2022.
29 马雨萌, 黄金霞, 王昉, 等. 融合BERT与多尺度CNN的科技政策内容多标签分类研究[J]. 情报杂志, 2022, 41(11): 157-163.
30 Kwon L N, Park J H, Moon Y H, et al. Weak signal detecting of industry convergence using information of products and services of global listed companies - focusing on growth engine industry in South Korea -[J]. Journal of Open Innovation: Technology, Market, and Complexity, 2018, 4(1): 10.
31 牌艳欣, 李长玲, 徐璐. 弱引文关系视角下跨学科相关知识组合识别方法探讨——以情报学为例[J]. 图书情报工作, 2020, 64(21): 111-119.
32 Adil B, Abdelhadi F. A framework for weak signal detection in competitive intelligence using semantic clustering algorithms[J]. International Journal of Advanced Computer Science and Applications, 2021, 12(12). DOI: 10.14569/ijacsa.2021.0121271.
33 Gutsche T. Automatic weak signal detection and forecasting[D]. Enschede: University of Twente, 2018.
34 Nonaka I, Takeuchi H. The knowledge-creating company[J/OL]. Harvard Business Review, 2007, 85(7/8): 162, 164-171.
35 方微, 邵波. 基于弱信号分析的企业风险识别[J]. 图书情报工作, 2009, 53(14): 80-83.
36 Liu J, Huang H S, Pan W J. Knowledge model based on graphical semantic perception[J]. Cybernetics and Information Technologies, 2015, 15(6): 16-28.
37 Thorleuchter D, van den Poel D. Weak signal identification with semantic web mining[J]. Expert Systems with Applications, 2013, 40(12): 4978-4985.
38 Feldman Y, Indelman V. Spatially-dependent Bayesian semantic perception under model and localization uncertainty[J]. Autonomous Robots, 2020, 44(6): 1091-1119.
39 Shelmanov A O, Kamenskaya M A, Ananyeva M I, et al. Semantic-syntactic analysis for question answering and definition extraction[J]. Scientific and Technical Information Processing, 2017, 44(6): 412-423.
40 Jia S C, Yang F P. Research on intelligent detection method of weak sensing signal based on artificial intelligence[C]// Proceedings of the Conference on Advanced Hybrid Information Processing. Cham: Springer, 2019: 90-98.
41 Griol-Barres I, Milla S, Millet J. System implementation for detection of future weak signals using text mining[J]. Revista Espa?ola de Documentación Científica, 2019, 42(2): e234.
42 Delisle S, Barker K, Copek T, et al. Interactive semantic analysis of technical texts[J]. Computational Intelligence, 1996, 12(2): 273-306.
43 ?wieboda W, Krasuski A, Nguyen H S, et al. Interactive method for semantic document indexing based on explicit semantic analysis[J]. Fundamenta Informaticae, 2014, 132(3): 423-438.
44 赵需要, 郭义钊, 姬祥飞, 等. 政府开放数据生态链上数据要素价值分析及评估模型构建——基于“数据势能” 的方法[J]. 情报理论与实践, 2022, 45(12): 50-59.
45 Jousselme A L, Maupin P, Bossé é. Uncertainty in a situation analysis perspective[C]// Proceedings of the Sixth International Conference of Information Fusion. Piscataway: IEEE, 2003: 1207-1214.
46 Li K, Li H J, Wang H. Situation analysis of relationship in social networks based on link entropy[J]. Modern Physics Letters B, 2015, 29(13): 1550061.
47 Popovich V V, Prokaev A N, Smirnova O V, et al. Combined approach to tactical situations analysis[C]// Proceedings of the 2008 IEEE Military Communications Conference. Piscataway: IEEE, 2008: 1-7.
48 Blasch E P, Pribilski M, Daughtery B, et al. Fusion metrics for dynamic situation analysis[C]// Proceedings of the Signal Processing, Sensor Fusion, and Target Recognition XIII. Bellingham: SPIE, 2004: 428-438.
49 Hempelmann C F, Sakoglu U, Gurupur V P, et al. An entropy-based evaluation method for knowledge bases of medical information systems[J]. Expert Systems with Applications, 2016, 46: 262-273.
50 Agrawal V, Seth N, Dixit J K. A combined AHP-TOPSIS-DEMA approach for evaluating success factors of e-service quality: an experience from Indian banking industry[J]. Electronic Commerce Research, 2020, 22(3): 715-747.
51 Otto C, Puente León F, Schwarzhaupt A. A strategy on situation evaluation for driver assistance systems in commercial vehicles considering pedestrians in urban traffic[C]// Proceedings of the 2011 IEEE Intelligent Vehicles Symposium. Piscataway: IEEE, 2011: 218-223.
52 Rosenbaum P R, Rubin D B. The central role of the propensity score in observational studies for causal effects[J]. Biometrika, 1983, 70(1): 41-55.
53 Yu T, Yu G, Li P Y, et al. Citation impact prediction for scientific papers using stepwise regression analysis[J]. Scientometrics, 2014, 101(2): 1233-1252.
54 Batagelj V, Brandes U. Efficient generation of large random networks[J]. Physical Review E, 2005, 71(3): 036113.
55 Yu F, Liu W, Gao C Y, et al. Network situation prediction based on optimized SVR model[C]// Proceedings of the 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer. Piscataway: IEEE, 2013: 2414-2419.
56 Martínez V, Berzal F, Cubero J C. A survey of link prediction in complex networks[J]. ACM Computing Surveys, 2017, 49(4): Article No.69.
57 Zhang B, Jia M Q, Xu J Z, et al. Network security situation prediction model based on EMD and ELPSO optimized BiGRU neural network[J]. Computational Intelligence and Neuroscience, 2022, 2022: 6031129.
58 史士英, 李涛, 张圣, 等. 模糊逻辑系统的非线性组合预测方法与系统误差分析研究[J]. 计算机工程与科学, 2008, 30(11): 77-79.
59 Andriamamonjy A, Klein R, Saelens D. Automated grey box model implementation using BIM and Modelica[J]. Energy and Buildings, 2019, 188-189: 209-225.
60 Mao Z L, Sun J H. Application of grey-Markov model in forecasting fire accidents[J]. Procedia Engineering, 2011, 11: 314-318.
61 郭艳萍, 高云, 刘寰. 基于灰色+神经网络复合预测模型的研究[J]. 电子技术与软件工程, 2022(14): 196-201.
62 徐天应, 干晓蓉. 基于二元语义与粗糙集的多属性决策方法[J]. 统计与决策, 2014, 30(1): 27-29.
63 Abrishami A, Aliakbary S. Predicting citation counts based on deep neural network learning techniques[J]. Journal of Informetrics, 2019, 13(2): 485-499.
64 冯俊文. 决策分析的情景树方法及其应用[J]. 管理工程学报, 2000, 14(3): 70-72.
65 Bosse E, Jousselme A L, Maupin P. Situation analysis for decision support: a formal approach[C]// Proceedings of the 10th International Conference on Information Fusion. Piscataway: IEEE, 2007: 1801-1803.
66 Kim S K, Choi H J. An ontology model framework for supply of active situation decision service[C]// Proceedings of the Fourth International Conference on Computer Sciences and Convergence Information Technology. Piscataway: IEEE, 2009: 1207-1212.
67 张力伟. “情报—决策”互动为基础的循证决策模式论析[J]. 现代情报, 2021, 41(8): 45-52.
68 王华, 金勇进. 统计数据准确性评估: 方法分类及适用性分析[J]. 统计研究, 2009, 26(1): 32-39.
69 Clermont M, Schaefer J. Identification of outliers in data envelopment analysis[J]. Schmalenbach Business Review, 2019, 71(4): 475-496.
70 De Paolis Kaluza M C, Jain S, Radivojac P. An approach to identifying and quantifying bias in biomedical data[J]. Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, 2023, 28: 311-322.
71 Steffen S, Blandini L, Sobek W. Analysis of the inherent adaptability of basic truss and frame modules by means of an extended method of influence matrices[J]. Engineering Structures, 2022, 266: 114588.
72 Johnsen ?. Does formal strategic planning matter? An analysis of strategic management and perceived usefulness in Norwegian municipalities[J]. International Review of Administrative Sciences, 2021, 87(2): 380-398.
73 Zorlu K. Analysis of individual and organizational factors that have influence on the tacit knowledge sharing by fuzzy logic method[J]. International Refereed Academic Social Sciences Journal, 2015, 6(17): 79.
74 Campos Hebrero A M. Analysing learning behaviour to inform the pedagogical design of e-learning resources: a case study method applied to computer programming courses[D]. Kingston: Kingston University, 2015: 1-5.
75 王芳, 魏中瀚, 连芷萱. 政务智能问答系统评价指标体系构建与测评问题编制[J]. 图书情报知识, 2023, 40(6): 98-111.
76 Logan D C. Known knowns, known unknowns, unknown unknowns and the propagation of scientific enquiry[J]. Journal of Experimental Botany, 2009, 60(3): 712-714.
77 ?ksendal B. Stochastic differential equations: an introduction with applications[M]. 6th ed. Heidelberg: Springer, 2003.
78 任泽裕, 王振超, 柯尊旺, 等. 多模态数据融合综述[J]. 计算机工程与应用, 2021, 57(18): 49-64.