Research on intelligent generation and accurate retrieval technologies for traffic engineering electronic archives based on large language models
基于大语言模型的交通工程电子档案智能生成与精准检索技术研究

摘要

人工智能技术的快速发展，尤其是大语言模型（LLM）在自然语言处理领域的突破，为电子档案的智能生成与精准检索提供了新的技术路径。本文以瑞苍高速公路项目为依托，探讨基于大语言模型的交通工程电子档案智能生成与精准检索技术，通过构建领域知识库、优化语义理解算法，实现档案内容的自动生成、合规性审核及高效检索，从而提升档案管理效率，降低碳排放，为交通工程全生命周期数字化管理提供支撑。研究旨在解决档案标准化、技术适配性及数据安全等核心问题，助力绿色智慧交通体系建设。

Abstract

The rapid development of AI, especially the breakthroughs of LLMs in natural language processing, has opened up new technical avenues for the intelligent generation and precise retrieval of electronic archives. This paper, using the Ruicang Highway Project as a basis, explores the intelligent generation and precise retrieval of traffic engineering electronic archives based on LLMs. By building a domain - specific knowledge base and refining semantic understanding algorithms, it aims to automate and ensure compliance in archive content generation while improving retrieval efficiency. These efforts enhance archive management and reduce carbon emissions, thereby supporting the digital management of traffic engineering throughout its entire life cycle. The study focuses on addressing key issues such as archive standardization, technological adaptability, and data security, with the goal of advancing the green and smart transportation system.