Abstract:

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.

Key words: technology roadmap, artificial intelligence, large language models, retrieval-augmented generation, UAV

摘要：

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。

关键词: 技术路线图, 人工智能, 大语言模型, 检索增强生成, 无人机