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| Identification of Key Common Technologies for Future Industries Based on Augmented Neural Network Entity-Relationship Diagrams |
| Hu Zewen, Xie Shaoke |
| School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044 |
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Abstract The accurate identification of key common technologies in future industries can provide model and intelligence supports for industrial technology prediction and management, policy-making, and the selection of research and development (R&D) and breakthrough directions for enterprises. In this study, we select the humanoid robot field as an example and construct an enhanced neural-network entity-relationship model that integrates a recurrent dilated convolutional neural network and an entity-relationship model. The five-tuple of technical entities and their semantic relationships from the field's patent documents are extracted, and a visual graph analysis is conducted. Then, the commonality of technologies is measured through indicators such as universality, benefit, and correlation, and common technologies are screened out based on the common technology score. Finally, the criticality of technologies is measured through indicators such as technological importance and leadership, thereby accurately and efficiently identifying key common technologies. The research results show that the enhanced neural-network entity-relationship model integrating a recurrent dilated convolutional neural network and five-tuple entity relationship model can effectively extract the five-tuple of technical entities and their semantic relationships from future industry patent documents. The results also reveal that the performance of the entity-recognition and relationship-extraction tasks improves, with the frequency 1 score increasing by 2.95 and 2.57 percentage points, respectively. The precision rate of the added entity-type prediction task in the model exceeds 94%. The constructed model, combined with key common technology-measurement indicators, can effectively identify the key common technologies in future industries covering eight key common technologies in the humanoid robot field such as motion control, joints, motors, heads, legs, sensors, positioning and navigation, and power systems. This model clarifies the technological breakthrough directions in the humanoid robot field. Its innovative achievements can drive the R&D of common technologies and industrial transformation, thereby upgrading the humanoid robot industry.
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Received: 17 June 2025
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1 纪玉山. 未来产业集群谁执牛耳?——基于复杂经济学的理论思考与政策建议[J]. 工业技术经济, 2024, 43(12): 7-13.
2 渠慎宁. 加快发展新质生产力: 时代背景、主要特征、支撑载体与实现路径[J]. 当代世界与社会主义, 2024(2): 39-46.
3 白静. 布局人形机器人赛道 推进智能产业创新发展——解读《人形机器人创新发展指导意见》[J]. 中国科技产业, 2024(1): 30-31.
4 工信部印发《人形机器人创新发展指导意见》[J]. 信息技术与标准化, 2023(11): 5.
5 孔令凯. 基于文本挖掘的产业关键共性技术识别及演化研究[D]. 哈尔滨: 哈尔滨工程大学, 2022.
6 Tassey G. The functions of technology infrastructure in a competitive economy[J]. Research Policy, 1991, 20(4): 345-361.
7 Huber G P. Organizational learning: the contributing processes and the literatures[J]. Organization Science, 1991, 2(1): 88-115.
8 李纪珍. 产业共性技术: 概念、分类与制度供给[J]. 中国科技论坛, 2006(3): 45-47, 55.
9 孙彩虹, 蔡鸿宇, 石进, 等. 专利应用视角下的关键核心技术识别研究[J]. 情报理论与实践, 2025, 48(1): 121-130.
10 马名杰. 政府支持共性技术研究的一般规律与组织[J]. 中国制造业信息化, 2005, 34(7): 14-16.
11 邓程, 杨建君. 关键共性技术创新研究梳理与演化框架构建[J]. 工业技术经济, 2020, 39(9): 3-11.
12 Keenan M. Identifying emerging generic technologies at the national level: the UK experience[J]. Journal of Forecasting, 2003, 22(2-3): 129-160.
13 Bekar C, Carlaw K, Lipsey R. General purpose technologies in theory, application and controversy: a review[J]. Journal of Evolutionary Economics, 2018, 28(5): 1005-1033.
14 栾春娟. 战略性新兴产业共性技术测度指标实证研究[J]. 中国科技论坛, 2012(6): 73-77.
15 Lee C, Kwon O, Kim M, et al. Early identification of emerging technologies: a machine learning approach using multiple patent indicators[J]. Technological Forecasting and Social Change, 2018, 127: 291-303.
16 Appio F P, Martini A, Fantoni G. The light and shade of knowledge recombination: Insights from a general-purpose technology[J]. Technological Forecasting and Social Change, 2017, 125: 154-165.
17 Zhang Y, Wu M J, Miao W, et al. Bi-layer network analytics: a methodology for characterizing emerging general-purpose technologies[J]. Journal of Informetrics, 2021, 15(4): 101202.
18 马永红, 孔令凯, 林超然, 等. 基于专利挖掘的关键共性技术识别研究[J]. 情报学报, 2020, 39(10): 1093-1103.
19 单晓红, 郝秀艳, 刘晓燕. 融合技术社会影响力的关键共性技术识别研究[J]. 情报理论与实践, 2022, 45(11): 115-125, 97.
20 韦景竹, 张桐瑞, 马芷晴. 基于科技报告的关键共性技术识别[J]. 情报杂志, 2025, 44(4): 153-159, 199.
21 胡凯, 谢芬, 杨滨瑜, 等. 基于专利文本挖掘的产业关键共性技术识别与应用研究[J]. 科技管理研究, 2023, 43(8): 21-31.
22 刘辉, 江千军, 桂前进, 等. 实体关系抽取技术研究进展综述[J]. 计算机应用研究, 2020, 37(S2): 1-5.
23 Zheng S C, Hao Y X, Lu D Y, et al. Joint entity and relation extraction based on a hybrid neural network[J]. Neurocomputing, 2017, 257: 59-66.
24 杨冬, 田生伟, 禹龙, 等. 快速联合实体和关系抽取模型[J]. 计算机工程与应用, 2023, 59(13): 164-170.
25 唐瑞雪, 秦永彬, 陈艳平. 融合限定关系和交互信息的实体关系联合抽取模型[J]. 中文信息学报, 2024, 38(10): 106-116.
26 Zheng S C, Wang F, Bao H Y, et al. Joint extraction of entities and relations based on a novel tagging scheme[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 1227-1236.
27 Eberts M, Ulges A. Span-based joint entity and relation extraction with transformer pre-training[C]// Proceedings of the 24th European Conference on Artificial Intelligence. Amsterdam: IOS Press, 2020: 2006-2013.
28 Gupta P, Schütze H, Andrassy B. Table filling multi-task recurrent neural network for joint entity and relation extraction[C]// Proceedings of the 26th International Conference on Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 2537-2547.
29 Zeng X R, Zeng D J, He S Z, et al. Extracting relational facts by an end-to-end neural model with copy mechanism[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018: 506-514.
30 Puccetti G, Giordano V, Spada I, et al. Technology identification from patent texts: a novel named entity recognition method[J]. Technological Forecasting and Social Change, 2023, 186: 122160.
31 Wei Z P, Su J L, Wang Y, et al. A novel cascade binary tagging framework for relational triple extraction[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2020: 1476-1488.
32 成全, 蒋世辉, 李卓卓. 基于改进CasRel实体关系抽取模型的在线健康信息语义发现研究[J]. 数据分析与知识发现, 2024, 8(10): 112-124.
33 Cheng L, Khalitov R, Yu T, et al. Classification of long sequential data using circular dilated convolutional neural networks[J]. Neurocomputing, 2023, 518: 50-59.
34 邓娜, 喻卓群, 但文俊, 等. 一种融合语义特征和多层交叉注意力机制的中药专利文本实体关系联合抽取模型[J]. 数据分析与知识发现, 2025, 9(7): 141-153.
35 王忠义, 谭旭, 夏立新. 共词分析方法的细粒度化与语义化研究[J]. 情报学报, 2014, 33(9): 969-978.
36 江鸿, 石云鸣. 共性技术创新的关键障碍及其应对——基于创新链的分析框架[J]. 经济与管理研究, 2019, 40(5): 74-84.
37 栾春娟. 共性技术测度体系及其应用[M]. 北京: 科学出版社, 2015.
38 黄鲁成, 张静. 基于专利分析的产业共性技术识别概念框架[J]. 技术经济, 2013, 32(12): 1-4.
39 Zhang Y, Qian Y, Huang Y, et al. An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation[J]. Scientometrics, 2017, 111(3): 1925-1946.
40 栾春娟, 王续琨, 刘则渊. 基于《德温特》数据库的核心技术确认方法[J]. 科学学与科学技术管理, 2008, 29(6): 32-34.
41 孙涛涛, 唐小利, 李越. 核心专利的识别方法及其实证研究[J]. 图书情报工作, 2012, 56(4): 80-84.
42 罗天雨. 核心专利判别方法及其在风力发电产业中的应用[J]. 图书情报工作, 2012, 56(24): 96-101.
43 杨武, 杨大飞. 基于专利数据的产业核心技术识别研究——以5G移动通信产业为例[J]. 情报杂志, 2019, 38(3): 39-45, 52.
44 袁润, 钱过. 战略性新兴产业核心专利的识别[J]. 情报杂志, 2013, 32(3): 44-50, 24.
45 杨艳萍, 董瑜, 韩涛. 基于专利共被引聚类和组合分析的产业关键技术识别方法研究——以作物育种技术为例[J]. 图书情报工作, 2016, 60(19): 143-148, 124.
46 Grootendorst M. BERTopic: neural topic modeling with a class-based TF-IDF procedure[PP/OL]. V1. arXiv (2022-03-11) [2026-06-30]. https://doi.org/10.48550/arXiv.2203.05794.
47 Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2019: 4171-4186.
48 南京信息工程大学. 一种关键共性技术实体的识别方法和系统: CN202511056105.3[P]. 2025-09-26.
49 幸福招商行业研究院. 2025人形机器人行业白皮书: 躯体觉醒: 叩响具身智能纪元奇点[R/OL]. (2025-12-29) [2026-01-10]. https://www.fxbaogao.com/detail/5208866.
50 Sheng Q C, Zhou Z X, Li J H, et al. A comprehensive review of humanoid robots[J]. SmartBot, 2025, 1(1): e12008.
51 全国机器人标准化技术委员会. 人形机器人标准化白皮书(2024版)[R/OL]. (2024-12-12) [2026-01-10]. https://www.fxbaogao.com/detail/4727824. |
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