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| Detecting Weak Signals of Frontier Technologies in Future Industries Through Outlier Processing |
| Ye Guanghui1, Tu Kai1, Guo Lu2 |
1.School of Information Management, Central China Normal University, Wuhan 430079
2.Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079 |
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Abstract The industry represents a concrete manifestation of transformations in productive forces. Amid the continuous emergence of technological innovations in future industries, such as artificial intelligence and quantum information, the prospective detection of frontier industrial technologies is essential for understanding the trajectory of scientific and technological development and supporting strategic planning. First, based on the theoretical premise that weak signals tend to emerge as outliers, this study constructed an outlier-feature indicator system for frontier technologies in future industries. From the perspective of signal amplification, the indicator system was integrated with an isolation forest model to filter technologically weak signals at the document level, and SHAP analysis was employed to validate the functional applicability of the model. Second, from a signal attenuation perspective, benchmark experiments involving models such as ChatGPT were designed to identify the optimal knowledge extraction model. By embedding relevant theoretical frameworks, including Hiltunen’s triadic model of future signs and Coffman’s weak signal research model, an intelligent weak signal detection model for frontier technologies in future industries was developed, supported by multiple theoretical underpinnings. Finally, from a signal visualization perspective, to address the issues of semantic deficiency and isolated interpretation, this study integrated approaches, such as social network analysis and topic modeling. Through multi-dimensional signal association, the identified weak technological signals were interpreted, and their evolutionary trajectories were anticipated. In the empirical analysis, the quantum information industry was selected as the domain for technology detection to verify the reliability, validity, and contextual applicability of both the outlier-feature indicator system and the proposed detection model. The results indicated that several technologies, including quantum state tomography, exhibit weak signal characteristics in the micro-level lexical signal dimension. In the macro-level thematic dimension, weak signals in areas such as quantum computing demonstrate stronger evolutionary intensity than those in fields such as quantum theory. These differentiated evolutionary patterns are closely associated with mechanisms such as industrial technological demand, commercialization potential, and disciplinary attributes. The outlier-feature indicator system and weak-signal detection model proposed in this paper not only provide a meaningful extension to existing theoretical frameworks in technology identification but also offer practical implications for proactive technological deployment and the allocation of scientific and technological resources by relevant stakeholders.
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Received: 01 August 2025
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1 钟君. 超前布局建设未来产业[N]. 光明日报, 2024-05-28(5).
2 刘盼盼, 王丽. 战略性新兴产业前沿技术探测研究——以量子计算领域为例[J]. 中国科技论坛, 2024(6): 46-57.
3 邱均平, 宓秦泽, 徐中阳, 等. 我国信息资源管理学科研究赋能新质生产力发展面面观[J]. 图书馆建设, 2025(6): 33-43.
4 陈捷, 吴仲琦, 代涛. 未来技术风险识别框架研究——基于技术经济安全视角[J]. 中国科学院院刊, 2023, 38(4): 570-579.
5 韩盟, 陈悦, 王玉奇, 等. 新兴技术弱信号识别: 理论模型与测度方法[J]. 科学学研究, 2024, 42(11): 2262-2274.
6 Coffman B S. Weak signal research. Part I: introduction[EB/OL]. (1997-01-15) [2025-07-18]. https://legacy.mgtaylor.com/mgtaylor/jotm/winter97/wsrintro.htm.
7 Hiltunen E. The future sign and its three dimensions[J]. Futures, 2008, 40(3): 247-260.
8 唐虎林, 苏成, 李曼迪, 等. 基于弱信号的颠覆性技术早期识别方法研究[J]. 图书情报工作, 2025, 69(10): 42-61.
9 霍朝光, 卢小宾, 杨冠灿, 等. 数据驱动的产业技术情报分析方法体系框架构建[J]. 图书情报知识, 2022, 39(1): 73-83.
10 张百尚, 拓晓瑞. 面向政府决策的战略性新兴产业技术情报研究[J]. 科技管理研究, 2020, 40(14): 38-42.
11 Ebadi A, Auger A, Gauthier Y. Unveiling weak signals of emergence in underwater sensing research trends[J]. Archives of Advanced Engineering Science, 2024. DOI: 10.47852/bonviewAAES42023902.
12 Gutsche T. Automatic weak signal detection and forecasting[D]. Enschede: University of Twente, 2018.
13 Ansoff H I. Implanting strategic management[M]. Englewood Cliffs: Prentice Hall, 1984.
14 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.
15 Ha T, Yang H, Hong S. Automated weak signal detection and prediction using keyword network clustering and graph convolutional network[J]. Futures, 2023, 152: 103202.
16 田雪灿, 王丽. 信号处理视角下的科技前沿弱信号探测研究——以政采合同数据为例[J]. 数据分析与知识发现, 2025, 9(4): 46-56.
17 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.
18 Segev A, Jung S, Choi S. Analysis of technology trends based on diverse data sources[J]. IEEE Transactions on Services Computing, 2015, 8(6): 903-915.
19 Glavackij A, David D P, Mermoud A, et al. Beyond S-curves: recurrent neural networks for technology forecasting[PP/OL]. V1. arXiv (2022-11-28). https://arxiv.org/pdf/2211.15334.
20 Rotolo D, Hicks D, Martin B R. What is an emerging technology?[J]. Research Policy, 2015, 44(10): 1827-1843.
21 韩盟, 陈悦, 王玉奇, 等. 基于异类数据和语义建构的新兴技术弱信号识别研究[J]. 情报学报, 2024, 43(3): 302-312.
22 李秋香, 张淑格, 霍宝锋, 等. 供应链信号传递理论研究动态: 视角、脉络、争鸣与盲区[J]. 中国管理科学, 2026, 34(5): 295-309.
23 Zhao Y, Zhang C Z. A review on the novelty measurements of academic papers[J]. Scientometrics, 2025, 130(2): 727-753.
24 李欣哲, 鲁晓. 合作组织特征如何影响知识成果的新颖性和影响力: 基于计算机科学领域的启示[J]. 数据分析与知识发现, 2025, 9(12): 167-183.
25 李晶, 杨雪, 苏秋丹, 等. 基于知识单元理论的科技成果创新性测度研究述评[J]. 现代情报, 2023, 43(8): 161-177.
26 张玲玲, 林青, 余梦霞, 等. 高质量保护与转化导向下高校专利申请前评估框架体系探索[J]. 科技管理研究, 2024, 44(7): 106-114.
27 Veugelers R, Wang J. Scientific novelty and technological impact[J]. Research Policy, 2019, 48(6): 1362-1372.
28 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.
29 胡志伟, 刘江峰, 裴雷. 融合学科与主题多样性的数字人文领域跨学科性测度研究[J]. 情报理论与实践, 2025, 48(7): 170-178.
30 杨俊浩, 许海云, 王超, 等. 科学突破主题的学科交叉演化特征分析[J]. 信息资源管理学报, 2024, 14(4): 70-85.
31 吕琦, 上官燕红, 李锐. 基于参考文献和文本内容学科分类的跨学科测度研究[J]. 情报学报, 2024, 43(8): 976-991.
32 李晓妍, 马亚雪. 前沿交叉领域识别方法与路径框架——基于相关领域的比较分析[J]. 图书与情报, 2024(6): 10-20.
33 唐虎林, 苏成, 李旺雨. 基于系统思维的颠覆性技术弱信号分析理论研究[J]. 情报学报, 2025, 44(4): 398-413.
34 辛竹琳, 魏凤, 邓阿妹, 等. 基于技术成熟度的技术评价方法研究[J]. 科技管理研究, 2024, 44(11): 80-89.
35 张凯, 吕璐成, 韩涛, 等. “论文—专利”关联视角下的新兴技术识别研究[J]. 情报理论与实践, 2024, 47(9): 183-191.
36 李军凯, 高菲, 龚轶. 构建面向未来产业的创新生态系统: 结构框架与实现路径[J]. 中国科学院院刊, 2023, 38(6): 887-894.
37 刘伟. 加快培育新质生产力 推进实现高质量发展[J]. 经济理论与经济管理, 2024, 44(4): 1-11.
38 Porter A L, Garner J, Carley S F, et al. Emergence scoring to identify frontier R&D topics and key players[J]. Technological Forecasting and Social Change, 2019, 146: 628-643.
39 曹琨, 吴新年, 靳军宝, 等. 基于共词和Node2Vec表示学习的新兴技术识别方法[J]. 数据分析与知识发现, 2023, 7(9): 89-99.
40 Van Houten B A, Phelps J, Barnes M, et al. Evaluating scientific impact[J]. Environmental Health Perspectives, 2000, 108(9): A392-A393.
41 李晶, 邱昕鹏. 融合新颖性和学术影响力特征的论文创新质量测度研究[J]. 情报学报, 2025, 44(2): 143-156.
42 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.
43 陈仕吉, 史丽文, 李冬梅, 等. 论文被引频次标准化方法述评[J]. 现代图书情报技术, 2012(4): 54-60.
44 Yu H Q, Wang Y, Hussain S, et al. Towards a better understanding of Facebook Altmetrics in LIS field: assessing the characteristics of involved paper, user and post[J]. Scientometrics, 2023, 128(5): 3147-3170.
45 余厚强, 章玮, 曹雪婷. 学术成果的脸书提及量分布特征研究[J]. 情报学报, 2021, 40(10): 1079-1091.
46 Thelwall M, Haustein S, Larivière V, et al. Do altmetrics work? Twitter and ten other social web services[J]. PLoS One, 2013, 8(5): e64841.
47 柳美君, 步一, 杨斯杰. 科研团队成员国别差异性的测度、演变及其与团队产出影响力的关系[J]. 情报学报, 2024, 43(7): 818-838.
48 叶光辉, 夏立新. 跨地域科研协作模式分析[J]. 中国图书馆学报, 2019, 45(3): 79-95.
49 叶光辉, 毕崇武. 知识交流视域下的跨地域科研协作发展态势及趋势分析[J]. 情报学报, 2020, 39(5): 500-510.
50 唐旭丽, 李信. 科研团队多样性对学术颠覆性创新的影响研究——以人工智能领域为例[J]. 情报学报, 2023, 42(1): 43-58.
51 AlShebli B K, Rahwan T, Woon W L. The preeminence of ethnic diversity in scientific collaboration[J]. Nature Communications, 2018, 9: Article No.5163.
52 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.
53 Liu F T, Ting K M, Zhou Z H. Isolation forest[C]// Proceedings of 2008 Eighth IEEE International Conference on Data Mining. Piscataway: IEEE, 2008: 413-422.
54 Ounacer S, Ait El Bour H, Oubrahim Y, et al. Using isolation forest in anomaly detection: the case of credit card transactions[J]. Periodicals of Engineering and Natural Sciences, 2018, 6(2): 394-400.
55 Van den Broeck G, Lykov A, Schleich M, et al. On the tractability of SHAP explanations[J]. Journal of Artificial Intelligence Research, 2022, 74: 851-886.
56 Antwarg L, Miller R M, Shapira B, et al. Explaining anomalies detected by autoencoders using SHAP[PP/OL]. V2. arXiv (2020-01-30). https://arxiv.org/pdf/1903.02407.
57 鲍彤, 章成志. ChatGPT中文信息抽取能力测评——以三种典型的抽取任务为例[J]. 数据分析与知识发现, 2023, 7(9): 1-11.
58 田雪灿, 孙蒙鸽, 胡懋地. 基于NE-GraphSAGE和大语言模型的前沿研究热点自动探测研究[J]. 数据分析与知识发现, 2025, 9(8): 32-46.
59 张颖怡, 章成志, 周毅, 等. 基于ChatGPT的多视角学术论文实体识别: 性能测评与可用性研究[J]. 数据分析与知识发现, 2023, 7(9): 12-24.
60 叶光辉, 涂凯, 韩丽, 等. 关键核心技术的关键衍生创新技术弱信号探测模型研究[J]. 情报学报, 2025, 44(12): 1596-1609.
61 杨波, 邵婉婷. 弱信号概念的各领域研究向度和理路构建[J]. 情报杂志, 2023, 42(6): 96-103.
62 韩盟, 陈悦, 王玉奇, 等. 弱信号识别研究综述: 寻找微弱的未来信号[J]. 情报学报, 2023, 42(8): 996-1008.
63 工业和信息化部等七部门关于推动未来产业创新发展的实施意见[EB/OL]. (2024-01-18) [2025-07-28]. https://www.gov.cn/zhengce/zhengceku/202401/content_6929021.htm.
64 习近平在中央政治局第二十四次集体学习时强调 深刻认识推进量子科技发展重大意义 加强量子科技发展战略谋划和系统布局[EB/OL]. (2020-10-17) [2025-07-28]. http://www.moe.gov.cn/jyb_xwfb/s6052/moe_838/202010/t20201019_495479.html.
65 董坤, 许海云, 罗瑞, 等. 科学与技术的关系分析研究综述[J]. 情报学报, 2018, 37(6): 642-652.
66 刘智锋, 吴亚平, 王继民. 科学数据集学术影响力归因研究——基于回归分析与可解释机器学习的双重证据[J]. 科学学研究, 2025, 43(5): 976-987.
67 易明, 姚玉佳, 胡敏. 融合XGBoost与SHAP的政务新媒体公共价值共识可解释性模型——以“今日头条”十大市级政务号为例[J]. 图书情报工作, 2022, 66(16): 36-47.
68 Anello E. Interpretation of isolation forest with SHAP: an easy way to understand the most contributing features for anomaly detection[EB/OL]. (2021-05-30) [2025-07-29]. https://readmedium.com/interpretation-of-isolation-forest-with-shap-d1b6af93ae71.
69 杨金庆, 罗曼, 程秀峰, 等. 融合网络结构特征的学科新兴主题识别方法研究[J]. 情报学报, 2025, 44(6): 645-659.
70 王莉军, 刘洢颖, 郑明, 等. 基于机器阅读理解的科技文献三元组抽取模型研究[J]. 数字图书馆论坛, 2025, 21(4): 21-32.
71 Quantum state tomography for general people[EB/OL]. [2025-07-29]. https://hongyehu.github.io/Hamiltonian-driven-shadow-tomography-page/.
72 Salter A J, Martin B R. The economic benefits of publicly funded basic research: a critical review[J]. Research Policy, 2001, 30(3): 509-532.
73 Alex M. Quantum technologies: a review of the patent landscape[PP/OL]. V1. arXiv (2021-02-03). https://arxiv.org/pdf/2102.04552.
74 国务院关于印发“十三五”国家科技创新规划的通知[EB/OL]. (2016-07-28) [2025-07-18]. https://www.gov.cn/gongbao/content/2016/content_5103134.htm.
75 Jüttner U, Christopher M, Baker S. Demand chain management-integrating marketing and supply chain management[J]. Industrial Marketing Management, 2007, 36(3): 377-392.
76 阳丽娟, 代泛, 邵世龙, 等. 荧光碳量子点在生物医学研究中的前沿进展[J]. 中国激光, 2024, 51(3): 185-196. |
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