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Article(id=1309226307368607744, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1750348800000, receivedDateStr=2025-06-20, revisedDate=1764086400000, revisedDateStr=2025-11-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1790073475925, onlineDateStr=2026-09-22, pubDate=1781452800000, pubDateStr=2026-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790073475925, onlineIssueDateStr=2026-09-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790073475925, creator=13701087609, updateTime=1790073475925, updator=13701087609, issue=Issue{id=1309226306445865584, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='6', pageStart='1121', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='1781452800000', pubDateStr='2026-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790073475705, creator='13701087609', updateTime=1790073520980, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309226496414286647, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309226496414286648, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1192, endPage=1205, ext={EN=ArticleExt(id=1309226307603488770, articleId=1309226307368607744, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=Research on the AI-enhanced method for technology roadmap construction, columnId=1309226307540574209, journalTitle=Studies in Science of Science, columnName=Theories and Methods, runingTitle=null, highlight=null, articleAbstract=

The technology roadmap holds significant strategic importance for the development of new quality productive forces in China. However, the existing methods for constructing technology roadmap still need to make trade-offs between ease of use and accuracy, and the intelligence level also needs to be improved. Moreover, the accuracy of mining information in technology roadmap based on network data source still needs to be optimized. As artificial intelligence enters a new stage, how to simultaneously enhance the efficiency, accuracy, and intelligence level of technology roadmap construction method based on AI technology is an urgent issue to be solved today.Therefore, given the existing limitations in current research, this study proposes an AI-enhanced framework for technology roadmap construction. Firstly, conduct theme evolution analysis for the three dimensions of "technology" - "product function" - "market" respectively. In the technology dimension, based on the evaluation index system of frontier technologies, the data set is subject to theme clustering through the screening of patents and academic papers, and the theme evolution of the technology dimension is analyzed in combination with the time dimension. In the product function dimension, the text classification of Large Language Model(LLM) is used to screen product news related to the field, and based on this, information extraction of product categories and functions is carried out, and the product theme evolution is analyzed in combination with the time dimension. In the market dimension, for two types of data sources, namely news, scientific and technological reports, the methods of "generative summary by LLM + clustering" and "TRT semantic analysis" are respectively adopted, and the market theme evolution is analyzed in combination with the time dimension. Secondly, the themes among various dimensions are associated through keywords matching. The technical points and market applications in the product function sentences are extracted based on LLM, and are respectively matched with the theme words of the technology dimension and the market dimension to establish the associations among various dimensions. Finally, based on the Retrieval-Augmented Generation (RAG) technology and the interactive dialogue with LLM, the framework implements systematic prediction of technological development.Utilizing the knowledge database built with futuristic data, through the multi-round interaction with the RAG-enhanced LLM, the future application scenarios are comprehensively predicted, and based on these scenarios, the technological development trends in the future are scientifically forecasted, completing the entire technology roadmap construction process. An empirical analysis is conducted in the field of UAV target identification and tracking, constructing a technology roadmap for this field. Three realized technical routes are identified, including radar detection, computer vision, and laser detection. The analysis reveals three characteristics of future technological development in the field of UAV target identification and tracking, namely "systematization," "intelligence," and "modularity". This study provides strong support for the development of the UAV target identification and tracking field and validates the scientificity and feasibility of the proposed framework, which makes the drawing of the technology roadmap more efficient, accurate, and convenient, and enhances the intelligence level of the node construction and prediction process of the technology roadmap.

, authors=Hong MIAO, Hao-tong WANG, Fei-fei WU, authorsList=Hong MIAO, Hao-tong WANG, Fei-fei WU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309226311101538326, articleId=1309226307368607744, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=基于AI增强的技术路线图构建方法研究, columnId=1309226307683180547, journalTitle=科学学研究, columnName=理论与方法, runingTitle=null, highlight=null, articleAbstract=

技术路线图对于我国新质生产力发展具有重要战略意义。为提升技术路线图构建的效率、准确度和智能化水平,研究提出基于AI增强的技术路线图构建框架:首先,针对技术-产品功能-市场三维度的主题演化分析,分别基于技术评价指标体系、大语言模型信息抽取、大语言模型生成式摘要+聚类及技术-关系-技术(Technology-Relation-Technology,TRT)语义分析等方法识别各维度内主题,并分析演化过程。其次,针对维度间关联构建,基于大语言模型提取中间产品功能层的技术点及市场应用,通过主题词匹配构建维度间关联。再次,针对技术预测,以未来数据搭建的知识库为基础,基于检索增强生成(Retrieval-Augmented Generation,RAG)技术增强大语言模型,通过与其交互,预测未来应用场景,并面向场景预测技术发展。最后,以无人机目标识别与跟踪领域为例进行实证分析,验证了方法的科学性与可行性。

, authors=苗红, 王浩桐, 吴菲菲, authorsList=苗红, 王浩桐, 吴菲菲, authorCompany=null, correspAuthors=null, authorNote=

苗红(1977-),女,副教授,博士,E-mail:。

王浩桐(2000-),女,硕士。

吴菲菲(1962-),女,教授,博士。

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苗红(1977-),女,副教授,博士,E-mail:。

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王浩桐(2000-),女,硕士。

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吴菲菲(1962-),女,教授,博士。

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吴菲菲(1962-),女,教授,博士。

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figureFileBig=Fq7R37TXlsvlZKo16ib6xA==, tableContent=null), ArticleFig(id=1309226315845296197, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226307368607744, language=EN, label=Figure 9, caption=Technology roadmap in UAV target identification and tracking, figureFileSmall=JjtoCyO1LtPDfR7dYj57gg==, figureFileBig=H3hne9ZMIUeB26z3qthZqQ==, tableContent=null), ArticleFig(id=1309226315924987974, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226307368607744, language=CN, label=图9, caption=无人机目标识别与跟踪领域技术路线图, figureFileSmall=JjtoCyO1LtPDfR7dYj57gg==, figureFileBig=H3hne9ZMIUeB26z3qthZqQ==, tableContent=null), ArticleFig(id=1309226316046622791, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226307368607744, language=EN, label=Table 1, caption=

Evaluation of the effectiveness of product news information extraction based on LLM

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任务描述 效果评估
判断领域相关新闻文本分类任务 精确率93.57%,召回率97.58%,F1分数95.52%
产品类别、名称、供应商信息挖掘任务 前50项数据中有44项三元组信息全部正确
产品功能信息挖掘任务 前50项数据中有43项提取全面准确
), ArticleFig(id=1309226316126314568, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226307368607744, language=CN, label=表1, caption=

基于大语言模型的产品新闻信息提取任务效果评估

, figureFileSmall=null, figureFileBig=null, tableContent=
任务描述 效果评估
判断领域相关新闻文本分类任务 精确率93.57%,召回率97.58%,F1分数95.52%
产品类别、名称、供应商信息挖掘任务 前50项数据中有44项三元组信息全部正确
产品功能信息挖掘任务 前50项数据中有43项提取全面准确
), ArticleFig(id=1309226316206006345, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226307368607744, language=EN, label=Table 2, caption=

The futuristic data information included in the knowledge base

, figureFileSmall=null, figureFileBig=null, tableContent=
数据源 出版方 出版物类型 数量
政府网站 中华人民共和国中央人民政府 政府新闻 1
政府相关官方组织 北京市人民政府 短期规划 1
美国空军部(Department of the Air Force) 中长期规划 1
美国国防部(U.S. Department of Defense) 中长期规划 1
政府智库 美国国会研究服务部(The Congressional Research Service) 智库报告 1
民间机构
及组织		 麻省理工学院科技评论(MIT Technology Review) 专业人士评论 1
无人争锋(远望智库旗下无人装备领域前沿资讯平台) 公众号文章 2
), ArticleFig(id=1309226316306669642, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226307368607744, language=CN, label=表2, caption=

构建知识库收录的未来数据信息

, figureFileSmall=null, figureFileBig=null, tableContent=
数据源 出版方 出版物类型 数量
政府网站 中华人民共和国中央人民政府 政府新闻 1
政府相关官方组织 北京市人民政府 短期规划 1
美国空军部(Department of the Air Force) 中长期规划 1
美国国防部(U.S. Department of Defense) 中长期规划 1
政府智库 美国国会研究服务部(The Congressional Research Service) 智库报告 1
民间机构
及组织		 麻省理工学院科技评论(MIT Technology Review) 专业人士评论 1
无人争锋(远望智库旗下无人装备领域前沿资讯平台) 公众号文章 2
), ArticleFig(id=1309226316390555723, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226307368607744, language=EN, label=Table 3, caption=

Comparison between the identified existing frontier technical nodes and the predicted technical nodes based on future scenarios

, figureFileSmall=null, figureFileBig=null, tableContent=
识别的现有前沿技术节点 基于未来场景驱动的预测技术节点
雷达传感器探测技术 传感器信息融合技术
信号滤波处理
激光探测与测距技术
视觉系统与图像处理技术
目标识别与跟踪算法 人工智能算法
无线通信技术 通信感知一体化技术
波束成形技术
跟踪路径规划技术
任务规划技术
飞行控制技术 超视距自主飞行技术
系统稳定性控制 抗干扰与自适应技术
/ 实时数据处理技术
/ 载荷模块化
/ 隐形技术
), ArticleFig(id=1309226316474441804, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226307368607744, language=CN, label=表3, caption=

识别的现有前沿技术节点与基于未来场景的预测技术节点对照

, figureFileSmall=null, figureFileBig=null, tableContent=
识别的现有前沿技术节点 基于未来场景驱动的预测技术节点
雷达传感器探测技术 传感器信息融合技术
信号滤波处理
激光探测与测距技术
视觉系统与图像处理技术
目标识别与跟踪算法 人工智能算法
无线通信技术 通信感知一体化技术
波束成形技术
跟踪路径规划技术
任务规划技术
飞行控制技术 超视距自主飞行技术
系统稳定性控制 抗干扰与自适应技术
/ 实时数据处理技术
/ 载荷模块化
/ 隐形技术
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苗红 , 王浩桐 , 吴菲菲
科学学研究 | 理论与方法 2026,44(6): 1192-1205
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科学学研究 |理论与方法 2026 , 44 (6) : 1192 -1205
基于AI增强的技术路线图构建方法研究
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苗红 , 王浩桐, 吴菲菲
作者信息
  • 北京工业大学经济与管理学院, 北京 100124
作者简介:

苗红(1977-),女,副教授,博士,E-mail:。

王浩桐(2000-),女,硕士。

吴菲菲(1962-),女,教授,博士。

Research on the AI-enhanced method for technology roadmap construction
Hong MIAO , Hao-tong WANG, Fei-fei WU
Affiliations
  • School of Economics and Management, Beijing University of Technology, Beijing 100124, China
出版时间: 2026-06-15
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摘要
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技术路线图对于我国新质生产力发展具有重要战略意义。为提升技术路线图构建的效率、准确度和智能化水平,研究提出基于AI增强的技术路线图构建框架:首先,针对技术-产品功能-市场三维度的主题演化分析,分别基于技术评价指标体系、大语言模型信息抽取、大语言模型生成式摘要+聚类及技术-关系-技术(Technology-Relation-Technology,TRT)语义分析等方法识别各维度内主题,并分析演化过程。其次,针对维度间关联构建,基于大语言模型提取中间产品功能层的技术点及市场应用,通过主题词匹配构建维度间关联。再次,针对技术预测,以未来数据搭建的知识库为基础,基于检索增强生成(Retrieval-Augmented Generation,RAG)技术增强大语言模型,通过与其交互,预测未来应用场景,并面向场景预测技术发展。最后,以无人机目标识别与跟踪领域为例进行实证分析,验证了方法的科学性与可行性。

技术路线图  /  人工智能  /  大语言模型  /  检索增强生成  /  无人机

The technology roadmap holds significant strategic importance for the development of new quality productive forces in China. However, the existing methods for constructing technology roadmap still need to make trade-offs between ease of use and accuracy, and the intelligence level also needs to be improved. Moreover, the accuracy of mining information in technology roadmap based on network data source still needs to be optimized. As artificial intelligence enters a new stage, how to simultaneously enhance the efficiency, accuracy, and intelligence level of technology roadmap construction method based on AI technology is an urgent issue to be solved today.Therefore, given the existing limitations in current research, this study proposes an AI-enhanced framework for technology roadmap construction. Firstly, conduct theme evolution analysis for the three dimensions of "technology" - "product function" - "market" respectively. In the technology dimension, based on the evaluation index system of frontier technologies, the data set is subject to theme clustering through the screening of patents and academic papers, and the theme evolution of the technology dimension is analyzed in combination with the time dimension. In the product function dimension, the text classification of Large Language Model(LLM) is used to screen product news related to the field, and based on this, information extraction of product categories and functions is carried out, and the product theme evolution is analyzed in combination with the time dimension. In the market dimension, for two types of data sources, namely news, scientific and technological reports, the methods of "generative summary by LLM + clustering" and "TRT semantic analysis" are respectively adopted, and the market theme evolution is analyzed in combination with the time dimension. Secondly, the themes among various dimensions are associated through keywords matching. The technical points and market applications in the product function sentences are extracted based on LLM, and are respectively matched with the theme words of the technology dimension and the market dimension to establish the associations among various dimensions. Finally, based on the Retrieval-Augmented Generation (RAG) technology and the interactive dialogue with LLM, the framework implements systematic prediction of technological development.Utilizing the knowledge database built with futuristic data, through the multi-round interaction with the RAG-enhanced LLM, the future application scenarios are comprehensively predicted, and based on these scenarios, the technological development trends in the future are scientifically forecasted, completing the entire technology roadmap construction process. An empirical analysis is conducted in the field of UAV target identification and tracking, constructing a technology roadmap for this field. Three realized technical routes are identified, including radar detection, computer vision, and laser detection. The analysis reveals three characteristics of future technological development in the field of UAV target identification and tracking, namely "systematization," "intelligence," and "modularity". This study provides strong support for the development of the UAV target identification and tracking field and validates the scientificity and feasibility of the proposed framework, which makes the drawing of the technology roadmap more efficient, accurate, and convenient, and enhances the intelligence level of the node construction and prediction process of the technology roadmap.

technology roadmap  /  artificial intelligence  /  large language models  /  retrieval-augmented generation  /  UAV
苗红, 王浩桐, 吴菲菲. 基于AI增强的技术路线图构建方法研究. 科学学研究, 2026 , 44 (6) : 1192 -1205 .
Hong MIAO, Hao-tong WANG, Fei-fei WU. Research on the AI-enhanced method for technology roadmap construction[J]. Studies in Science of Science, 2026 , 44 (6) : 1192 -1205 .
正文
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技术路线图是一种技术管理工具,通过结构化方式可逐步展示技术创新过程,探索和交流市场、产品、技术随时间推移不断发展以及其过程中三者之间关系[1],它不仅有助于机构与企业厘清未来技术方向、有效调整科技创新规划布局,更在国家战略层面发挥着至关重要的作用。近年来,各国纷纷在关键技术领域开展技术路线图研究以助力科技政策的制定与实施,例如美国发布的《聚变科学与技术路线图》、英国发布的《英国聚变材料路线图》以及韩国发布的《半导体未来技术路线图》等。这表明,技术路线图的制定对国家明确战略方向与投资重点、塑造技术未来、构建创新生态以及促进创新力量整合等多方面均产生深远影响。
技术路线图构建常涉及维度内节点构建、维度间关联构建和技术预测三个环节,然而现有构建方法的研究在使用便捷性、结果准确度等方面仍待提升。在大数据时代,技术路线图研究更加关注运用各类算法的客观方法,例如链路预测、BERT等深度学习方法[2]。大语言模型相较BERT等模型,在执行特定任务时无需基于标注数据进行微调,仅通过自然语言表达的提示词即可实现较高性能[3]。Kim[2]特别强调了大语言模型等AI技术在构建技术路线图过程中的有效性,但尚未体现对该技术的运用。因此,对技术路线图的研究亟待运用前沿AI技术进行探索。
鉴于此,研究提出基于AI增强的技术路线图构建框架,将大语言模型、RAG技术运用到技术路线图绘制过程中所涉及的维度内节点构建、维度间关联构建及技术发展预测中,以提高技术路线图构建方法的高效性、准确性、智能化程度,旨在为我国运用举国体制优势、高效推进创新合作、提高新质生产力提供方法支持。
1 文献综述
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1.1 技术路线图构建研究
技术路线图的构建方法经历了从主观定性到客观定量分析的演化。定性分析方法[4,5]虽具针对性,但存在成本高、主观性强及可重复性差等问题。随着算法、算力技术的进步与提升,学者们开始利用专利、文献及新闻等客观数据源,通过文本挖掘、主题模型等客观方法进行分析,相较于主观定性方法更为可信、更不受限制[6],利于提升构建过程的科学性与效率。
在维度内节点构建上,现有研究主要采用基于BERT[7]、LDA[8]等主题模型的宏观识别策略直接对原始网络数据展开分析,从而获得层级内节点主题,但其中所包含的引述句等噪声易对主题识别的准确性产生干扰。已有研究针对专利摘要基于TextRank方法抽取关键句以减少不必要信息[7],但在面对长篇网络文本数据效果不佳,易存在信息丢失或冗余的缺点,影响后期聚类结果的精准解读。此外,针对从产品新闻数据中识别产品功能等细粒度信息提取任务,通过现有研究中的方法仍然难以准确得到结果。
在维度间关联构建上,现有研究中针对单一数据源或两个层级,已使用了包括SAO与TRT语义分析[9]、关联规则挖掘[10]、余弦相似度[11]在内的多种客观方法;当延伸至多源数据及多层级的映射中时,多采用相似度方法,但由于数据源内容侧重点不同,仅通过计算文档相似度来判定维度之间的关联,易致关联与实际出现偏差。
在开展技术预测上,现有研究中主要基于链路预测与专家评估[8]、生成对抗网络(Generative adversarial network,GAN)[12]等方法开展技术前景预测,均展现出了一定的客观性与科学性。但前者无法直接准确地预测新节点的出现;后者虽然可生成新节点,但是无法预测与现有技术非连续相关的新技术,以及跨领域结合产生的新技术;同时,也依赖大量现存的技术路线图案例以支持模型训练,难以适用于无法获取足够数量案例的新兴领域。
1.2 基于AI技术的数据挖掘研究
大语言模型具有强大的上下文理解和泛化能力,在执行特定任务时通过自然语言表达的提示词即可在文本分类、信息抽取等数据挖掘中实现较高性能[13,14],在使用便捷性、可操作性上占据优势,有效节省标注数据与算力的准备成本;此外,在摘要生成任务中,特别针对长文本,具有更高准确度[3]。由此,大语言模型可更好地适应绘制技术路线图时产品功能维度的网页数据分析以及市场维度的长篇新闻数据分析需求:一方面,可高效准确地实现产品功能信息从噪声中剥离,另一方面在鲜有数据库为新闻提供摘要的背景下,可有效生成长篇新闻的精准摘要,显著提升了文本处理的效率和准确性。
此外,学者们对于具备专业知识的垂直化大语言模型也开展了一定研究,其中RAG技术是当前快捷有效的方法之一,通过使用来自特定数据源中的信息即可有效实现提升大语言模型生成内容准确性与可靠性[15],有效降低大语言模型出现幻觉的可能性。目前该技术已在医疗等专业领域中展现出强大潜力,基于临床指南、书籍等专业知识,可在临床决策支持、医疗建议输出等方面提供较高准确度的专业建议[16]。由此,将RAG技术运用于大语言模型可实现需要基于既定事实和最新知识进行推演的技术预测任务。
1.3 评述
现有技术路线图构建研究已取得了较大进展,但仍然存在以下不足:
首先,维度内节点构建中对基于网络数据源的节点信息挖掘精准度有待提升。研究中运用可直接使用的算法模型识别各维度主题,在面对具有噪声的网络数据源时,直接进行粗粒度主题挖掘会降低准确度;为了提升效果,现有研究采用了基于人工构建高质量标注数据集的微调或训练等方法,但会在人力、算力及时间上产生成本,目前尚缺乏高效、准确的细粒度数据挖掘方法。
其次,维度间关联构建方法难以真实反映各维度间关联关系。当前,基于语义对多源数据及多维度展开关联构建的研究主要采用直接计算不同数据源间相似度的方法,但由于内容的侧重点不同,相似度测算结果的有效性尚待提高。
最后,现有节点预测方法难以开展更全面、准确的预测。链路预测和GAN方法仅可基于现有节点展开预测,无法体现新发展路径或发展趋势;此外,特别针对GAN方法,其预测质量高度依赖于收集的大量训练数据,难以对现存高质量技术路线图数量不足的技术领域开展预测,在方法的通用性上有所欠缺。
以大语言模型及RAG技术为代表的AI技术,可在自然语言处理领域实现更高效、准确的数据挖掘任务,为技术路线图构建提供便捷、高效、准确的方法。
由此,本文提出基于AI增强的技术路线图构建方法,从以下方面进行创新性探索:一是在维度内节点构建方面,基于大语言模型细粒度信息挖掘以及生成式摘要的方法,以提高基于网络数据源挖掘节点信息的准确度;二是在维度间关联构建方面,基于大语言模型细粒度信息挖掘和关键词匹配,更准确地实现基于多源数据的多层级关系构建;三是在技术预测方面,基于RAG增强大语言模型预测技术路线图未来节点,提高预测准确度与效率。
2 基于AI增强的技术路线图构建框架
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基于AI增强的技术路线图构建框架,包括技术、产品功能、市场三维度主题演化分析和技术路线图绘制与分析两大部分。第一部分基于专利、文献、产品新闻、领域新闻、科技报告等数据,采用指标体系法、大语言模型细粒度信息挖掘及摘要生成、TRT语义分析等方法,分析了技术路线图技术、产品功能、市场三维度的主题演化。第二部分包含两个模块:模块一是基于大语言模型细粒度信息挖掘和关键词匹配,对技术路线图三维度间的节点进行关联,更准确地进行基于多源数据的多层级关系构建;模块二是基于RAG增强大语言模型,预测技术未来的发展,由此提高预测准确度与效率。研究思路如图1所示。
2.1 技术、产品功能、市场三维度主题挖掘
(1)基于语义分析的技术维度主题挖掘
以论文、专利作为数据源,采取“指标体系计量分析+语义分析”法进行前沿技术主题识别。前沿技术是指高技术领域中具有前瞻性、先导性和探索性的重大技术,是未来高技术更新换代和新兴产业发展的重要基础,是国家高技术创新能力的综合体现[17]。基于团队已有成果[18,19],在依据前沿技术指标筛选数据的基础上,运用K-means等语义分析方法进行前沿技术主题识别。
(2)基于大语言模型的产品功能维度主题挖掘
以产品新闻数据为数据源,基于“大语言模型细粒度信息挖掘+语义分析”法对产品类别及产品功能展开细粒度挖掘以识别该维度主题,具体包括数据获取、提示词构建、交互结果获取及评估、主题识别。
首先,获取产品新闻数据。基于对包含功能等详细介绍的技术领域内各企业产品新闻开展分析,可为更好理解相关产品功能与性能上的创新与提升提供支撑。
其次,构建有效提示词。结合思维链提示原则形成产品功能维度信息挖掘任务提示词结构框架(图2),并基于对设计的提示词进行实际测试与迭代修改,以提高提示词的有效性[20]。其中,基于已有学者定义的产品及功能概念[21-23],本文分别将其定义为可从市场采购的技术活动的产品、产品组件或服务,以及产品开发的目的、效果、能力。
最后,基于API调用展开与大语言模型的交互,获得并评估信息挖掘结果,并基于词频统计及聚类结果解读识别产品功能主题。其中,对信息挖掘结果的评估主要采取客观指标与主观判断相结合的方法(图2)。
(3)基于大语言模型和TRT语义分析的市场维度主题挖掘
针对基于领域新闻数据的社会层面及基于科技报告数据的国家层面两方面所关注的主题,分别采取“大语言模型摘要生成+聚类”和“TRT语义分析应用分析+聚类”的方法展开分析。
社会层面市场主题挖掘步骤为:首先,获取领域新闻数据。选择ABI/INFORM COLLECTION商业信息全文数据库,其中出版物类型中“Newspaper”收录包含华尔街日报等主流新闻媒体在内的报道,侧重事件阐述,适合作为市场维度信息的来源。爬取领域新闻数据,进行清洗。其次,基于构建的提示词,通过与大语言模型交互生成领域新闻摘要,并聚类识别主题。其中,评估任务执行效果通过人工抽样评估,判断基于大语言模型生成的摘要反映原文关键要点的情况。
国家层面市场主题挖掘步骤为:首先,获取科技报告数据。该类数据是由政府部门立项资助的科研项目产生的特种文献[24],是政府科技项目的重要产出形式[25],相较于侧重国家长远规划与发展目标考量的政策文本,更能展现基于战略背景与顶层设计下的可实施、具象化的国家需求,为社会发展和产业升级提供宏观方向,与现有研究中在市场维度所使用的新闻等代表社会需求的数据互为补充。本文选择获取包含美国四大报告的“NTRL政府科技报告全文数据库”并清洗数据。其次,使用TRT语义分析方法拆解语句中的名词-介词-名词结构短语挖掘市场主题[19]。
2.2 技术路线图绘制与分析
(1)基于大语言模型的技术-产品功能-市场维度关联挖掘
采取关键词匹配的策略,基于大语言模型对中间产品功能层的技术点与市场应用挖掘得到的关键词分别与技术层和市场层节点主题词进行对比,更准确地构建基于不同数据源的技术-产品功能-市场间的关联关系。
首先,参照前文方法构建提示词,基于大语言模型提取产品功能句对应的技术点及市场应用信息;其次,统计各功能的技术点及市场应用信息词频,筛选排名靠前的词汇作为各功能的技术点及市场应用的主题词;再次,分别基于技术层、市场层主题词,形成技术-产品功能层和产品功能-市场层的两组关键词匹配,若存在交集即记为该技术节点或市场应用节点与该功能节点之间存在关联关系。
(2)基于RAG增强大语言模型的技术发展预测
以场景为导向的技术创新与进步,有助于提升技术的市场适应性,从而促进技术的转化与产业化落地。本部分基于RAG技术增强的大语言模型挖掘未来应用场景,并以此为基础进一步预测与应用场景相关的有发展前景的技术。此处的未来应用场景主要是预测分析未来的市场应用,是对现有市场应用的延续与补充。
首先,构建RAG增强大语言模型。第一步,获取知识库构建数据。本文选择未来数据构建知识库。未来数据(futuristic data)是从在线论坛、社区和报告中提取的面向未来的专家意见的集合[26],是蕴含丰富的预测性信息(包括未来方向、期望等)的数据,因此适于将其作为未来技术预测的数据源[27]。在构建领域未来知识库时,除相关研究中常用的面向未来的网站或技术前瞻网站(如麻省理工学院科技评论等)的未来数据来源之外[28,29],本文还选取公众号文章、短期行动方案、中长期规划等作为未来数据来源的补充,以期更全面地从中挖掘未来信息。第二步,构建知识库。创建知识索引,将未来数据文档预处理并经过文档切割形成若干文本块,其后设定嵌入模型并配置排序设置。第三步,形成RAG增强大语言模型。基于已有大语言模型,通过引入变量等形式在系统消息中配置知识库,完成RAG增强大语言模型的构建。
其次,基于构建的RAG增强大语言模型进行技术发展预测。第一步,挖掘未来应用场景。向RAG大语言模型输入提问,询问该技术领域未来的应用场景,基于大语言模型对知识库检索结果的理解、整合,可生成以所提供未来数据为基础挖掘得到的领域未来应用场景。第二步,基于挖掘得到的未来应用场景继续向大语言模型输入提问,明确实现这些应用场景在未来所需重点发展的技术,并通过进一步交互反向验证上述技术对应的应用场景,以确保前后一致性。
3 基于无人机目标识别与跟踪领域的实证研究
收起
3.1 技术-产品功能-市场维度主题挖掘
(1)数据获取与处理
在技术维度,本研究以苗红等学者所构建的无人机目标识别与跟踪领域检索策略为依据[19],分别在德温特专利数据库(Derwent Innovation,DI)和工程索引文献数据库(The Engineering Index,EI)中进行检索,经数据清洗,获得13319条专利及9082条文献,检索时间为2022年11月30日。
在产品功能维度,以unmanned systems technology网站作为数据来源。该网站于持续发布最新技术动态及业内新闻,是政府机构、国防部、学术机构等主体值得信赖的信息来源,其中“UAV/Drone News”模块涵盖了全球知名无人机企业的产品新闻,蕴含丰富的产品性能或功能信息。基于爬虫对网站的该模块获取,获得2011-2024年6378条无重复的产品新闻数据(2024年数据作为效果评估集),获取时间为2024年10月21日。
在市场维度,针对新闻数据源,在ABI/INFORM数据库对检索表达式“("uav*" OR "drone*")”进行检索,出版物类型选择“Newspaper”,语言选择“英语”,主题选择包含“drones”或“aircraft”或“unmanned aerial vehicles”或“aviation”的数据,出版时间截至2023年12月31日,经数据清洗,获得1980-2023年的14109条数据,检索时间为2024年9月10日。针对科技报告数据源,在“NTRL美国政府科技报告全文数据库”网站中检索“("unmanned aerial vehicle*" OR drone)”,在检索类型中选择科技报告,通过网络爬虫获得结果,经数据清洗,获得3893条数据,检索时间为2023年11月27日。
(2)技术维度主题挖掘
依据前沿技术识别指标体系筛选得到数据集,聚类取并集得到11个前沿技术主题,包括无线通信、雷达传感器探测、激光探测及测距、飞行控制、视觉系统与图像处理、目标识别与跟踪算法、任务规划、跟踪路径规划、系统稳定性控制、波束成形、信号滤波处理。
(3)产品功能维度主题挖掘
选取适用于执行复杂、多步骤任务的阿里云通义千问qwen-max大语言模型,开展产品类别及功能等信息提取的复杂任务。
结合领域相关文献及提示词框架,构建提示词(图3)。基于API与模型交互,其中,为减少输出结果随机性,设置温度为最小值0.1,从6378条产品新闻数据中筛选得到2793条,并对应提取产品类别、名称、功能、供应商信息。
将2024年的418项数据作为效果评估集,对大语言模型的挖掘结果进行评估(表1)。可见,基于构建的有效提示词,使用大语言模型执行产品新闻信息挖掘任务效果良好。
识别得到产品功能类别及其主题(图4)。
(4)市场维度主题挖掘
一方面,针对领域新闻数据,基于大语言模型生成式摘要并聚类得到市场维度主题。以构建的提示词“你是一个擅长对原始新闻文本进行预处理并生成摘要的AI助手,请清洗数据,并基于理解与无人机相关的上下文,生成一段不超过10句的连贯英文摘要”为基础,分别设置温度值、最大响应数为最小值和200,使用API与模型交互得到领域新闻摘要,并通过人工抽样评估判定大语言模型在领域新闻数据的摘要生成任务中具有良好效果。基于对大语言模型生成式摘要的聚类得到领域新闻的主题。另一方面,针对科技报告,基于TRT语义分析识别得到市场应用主题[19],最终得到市场维度主题识别结果。
3.2 技术路线图绘制
(1)技术-产品功能-市场维度关联挖掘
首先,基于qwen-max-latest模型挖掘产品功能句技术点及市场应用。以构建的提示词工程(图5)为基础得到信息挖掘结果。
其次,基于词频分别提取产品功能句对应技术点及市场应用的主题词,并分别与技术层各技术以及市场层各应用主题对应的主题词进行匹配,得到技术层与产品功能层两个维度之间的关联关系(图6)以及产品功能层与市场层两个维度之间的关联关系(图7)。图中左侧的灰、白色节点分别代表该节点与后一层的某些节点存在关联,以及未找到关联。
(2)技术发展预测分析
首先,构建RAG增强大语言模型。收集近三年产出的未来数据,分别构建包含未来数据的中文英文知识库(见表2)。在阿里云百炼中以qwen-max-latest作为基座模型并配置知识库,得到RAG增强大语言模型。
其次,基于RAG增强大语言模型进行技术发展预测。分别对基于中、英文知识库的大语言模型进行人机交互,提问“无人机在未来的市场应用包括哪些?请尽可能全面地列举”,整理响应结果得到基于RAG增强大语言模型在未来数据知识库中挖掘的未来应用场景。
再次,提问“无人机目标识别与跟踪领域需要继续发展哪些技术来支撑以上这些未来的市场应用?”并针对输出的技术点再次提问其未来市场应用,以验证逻辑一致性并明确对应关系(图8),获得基于RAG增强大语言模型的技术发展预测及对应应用场景。
3.3 结果分析
基于挖掘的技术、产品功能、市场维度主题和各维度间关联以及预测的未来节点,绘制无人机目标识别与跟踪领域技术路线图(图9)。
(1)无人机目标识别与跟踪领域现有技术路线分析
技术路线图(图9)显示,无人机目标识别与跟踪领域的技术路线包含三种:
一是雷达探测技术路线(技术路线1),涉及雷达传感器探测技术、波束形成技术、信号处理滤波器,是无人机目标识别与跟踪领域中最早、最传统的一种技术路线,通过雷达传感器探测电磁波的回波强度,从而提取物体的速度、距离、位置等特征以识别、跟踪受关注目标。为提升在复杂杂波背景下的目标检测与跟踪性能,自从20世纪后半叶开始的相关研究主要沿硬件与算法两个路径演进,后者在演进过程中对深度学习等技术的引入,显著提升了对雷达信号与图像的处理能力,有效提升目标状态估计与跟踪的精度。基于雷达探测技术路线的多维度研究进展与突破,无人机在目标感知体系类产品的各项功能以及传感器探测载荷类产品的目标监视、环境感知功能上均展示了较好性能,从而在军事应用、环境观察与态势感知、应急救援等市场应用中表现出色。
二是计算机视觉技术路线(技术路线2),涉及无人机目标识别与跟踪算法、视觉系统与图像处理技术。计算机视觉技术路线起源于21世纪初,通过视觉传感器获取图像信息,进而完成目标识别与跟踪。虽然出现时间较晚,但较雷达技术路线,该技术路线提供的图像特征更为丰富,结果更可靠。2012年后,深度卷积神经网络的兴起推动了目标检测算法的革新,其与2015年前后出现的专用嵌入式计算硬件相结合,共同促使无人机数据计算与处理套件类产品逐步实现了实时自动识别、高精度跟踪及智能决策支持等高级功能。基于计算机视觉技术路线的多维度研究进展与突破,无人机在目标感知体系类产品的任务执行、区域监视功能和数据计算与处理套件类产品决策支持、智能追踪、图像处理功能以及视觉感知载荷的影像采集功能上均展示了较好性能,从而在军事应用、环境观察与态势感知等场景的应用中表现出色。
三是激光探测技术路线(技术路线3),涉及激光探测与测距(Light Detection And Ranging,LiDAR)技术。激光探测技术在无人机目标识别与跟踪领域的应用出现在2011年,晚于雷达探测技术路线,但通过发射激光束并测量反射时间,能够获取高分辨率的目标信息,探测精度更具优势。由于多线LiDAR传感器的逐渐普及,研究人员考虑基于分析获取的点云数据实现目标检测与跟踪。早期主要依赖传统滤波与聚类算法,而随着深度学习的突破,基于神经网络的方法逐渐成为主流,可自动提取点云特征,显著提升了对多类目标的识别准确率。基于激光探测技术路线的多维度研究进展与突破,无人机在高精度大面积远距离测绘、复杂环境下目标监视等功能上均展示了较好性能,从而在军事应用、环境观察与态势感知等场景的应用中表现出色。
在该领域各技术路线的演进过程中,国家、政府、机构以及企业等多元主体的市场行为也发挥着不可或缺的推动作用。鉴于无人机特殊的军事属性,最初主要由政府和国家主导,对无人机相关技术研发予以持续的资金与政策支持。在无人机商业化应用蓬勃兴起后,监管机构的监督管理同步跟进,通过出台一系列针对性的法律法规,确保无人机的商业化应用在安全、可控的范围内,保障公众安全与隐私;同时,在合理管控的基础上适度放开空域限制的举措,也为其商业化应用拓展空间。而众多新兴企业也凭借创新的商业模式和技术在获得投资后持续进行技术创新,为行业注入新鲜血液。
(2)无人机目标识别与跟踪领域未来发展预测分析
由图9可知,无人机目标识别与跟踪领域在未来将会以现有市场所关注的应用为基础,持续深入地运用于物流配送、空中交通、应急救援、智能保障、军事作战五类应用场景中。
为进一步推动无人机在上述应用场景的实现与落地,仍然需从技术维度对传感器信息融合等8项技术进行提升与突破。基于文献查阅及专家知识,对比了识别的现有技术节点与基于未来应用场景预测所需发展的技术节点(表3)。经对比,发现二者存在大量交集。同时,少量预测的技术节点中还包含未出现的技术,但与识别出的现有技术节点之间存在部分联系。
综上,未来无人机目标识别与跟踪领域的发展方向存在以下几个特点:
一是系统化。发展传感器信息融合、通信感知一体化等技术,集成多种技术构建综合框架,可弥补现有雷达传感、计算机视觉、激光探测单一技术路线的缺点,实现多源数据的互补与协同处理,提升目标识别的准确性、鲁棒性以及整体系统效能,以应对如空中交通管理、应急救援等复杂场景下的任务要求。
二是智能化。通过发展人工智能算法以及实时数据分析技术,可使得无人机在某些缺乏人工实时干预的复杂情境下,根据环境变化做出合理的自主决策,并有效减少人为决策的失误,与美国国防部《2017—2042财年无人系统综合路线图》中提到的未来无人系统自主化的发展趋势相吻合。
三是模块化。未来无人机需要完成的任务类型广泛,通过对各类载荷的模块化设计,并进行标准化、通用化处理,提高载荷模块的兼容性。“即插即用”功能系统可支持根据不同任务需求快速更换适配的传感器和任务设备,实现无人机功能的灵活定制,也在一定程度上增强了无人机系统的互操作性,为多无人机系统执行任务时的协同行动提供基础。
4 结论
收起
本文通过在技术路线图构建过程中引入大语言模型、RAG技术等新兴AI技术的运用,使得技术路线图的绘制更高效、准确、简便,提升了技术路线图节点构建与预测过程的智能化程度,并以无人机目标识别与跟踪技术领域展开实证研究,基于构建技术路线图,得到该领域已实现的雷达探测、计算机视觉、激光探测三条技术-产品功能-市场技术路线。
本文的研究贡献在于提出了基于AI增强的技术路线图构建框架:首先,为各领域的个性化技术路线图绘制提供了一个高效、准确的通用框架。基于准确度与使用便捷性较高的AI技术,为各技术领域快速准确地构建技术路线图提供方法支持,为未来基于专家的人类智能与基于机器的人工智能的深度融合奠定基础。其次,为从海量非结构化的网络数据中精确构建技术路线图各维度内节点及维度间关联提供了可行方法,基于大语言模型的细粒度信息挖掘和生成式摘要的方法,减少冗余信息干扰,降低网络数据源的噪声。再次,为技术路线图节点预测提供科学的智能化方法,以单独构建的未来数据知识库为基础,通过与RAG大语言模型的多次交互,可更全面、准确地实现基于应用场景的技术预测,从而推进场景驱动创新与技术产业化落地。
本研究不足为:首先,仅以无人机目标识别与跟踪的单一技术领域为例展开研究,未来可扩展实证领域范围;其次,针对技术维度的主题演化分析仍然基于指标体系方法,未来有待融入更多前沿AI技术;再次,对于未来数据仍以手动的方式获取,未来可梳理相关数据批量获取流程,创建智能体工作流,将更新知识库的过程自动化,更好地维护构建的技术路线图。

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2026年第44卷第6期
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  • 接收时间：2025-06-20
  • 首发时间：2026-09-22
  • 出版时间：2026-06-15
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  • 收稿日期：2025-06-20
  • 修回日期：2025-11-26
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    北京工业大学经济与管理学院, 北京 100124
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