中图分类号：TP181文献标志码：ADOI:10.19358/j.issn.2097-1788.2026.04.009 中文引用格式：王亮，张强，魏韵萧. 基于知识库的智能问答系统构建技术研究［J］.网络安全与数据治理，2026，45（4）：68-73. 英文引用格式：Wang Liang，Zhang Qiang，Wei Yunxiao. Research on construction technology of intelligent questionanswering system based on knowledge bases of data center［J］.Cyber Security and Data Governance，2026，45（4）：68-73.
Research on construction technology of intelligent question-answering system based on knowledge bases of data center
Wang Liang，Zhang Qiang，Wei Yunxiao
The 91001 Unit of the Chinese People′s Liberation Army
Abstract： With the rapid development of information technology, data centers have accumulated massive amounts of data. How to efficiently obtain valuable information from this data has become a key issue restricting data centers from exerting their military effectiveness. To address this issue, considering that intelligent question-answering systems, as tools capable of understanding users′ natural language questions and providing accurate answers, have broad application prospects in the field of data centers, this paper proposes a construction technology for intelligent questionanswering systems based on data center knowledge bases. First, focusing on users′ diverse data needs in different application scenarios within the military field, a data retrieval demand model is established. Second, for data retrieval demand models of different dimensions, a diverse knowledge retrieval correlation algorithm is proposed. Parameters such as correlation degree and recommendation degree between retrieval demands and candidate datasets are defined. Based on these parameters, the matching degree between candidate datasets and retrieval demands is calculated to find the most relevant candidate datasets. Then, the generated candidate datasets and users′ retrieval demands are transmitted to the large language model together, and high-quality answers are generated through the retrievalaugmented generation algorithm. Finally, verified through experiments, this technology has shown good performance in answering data center-related questions, which helps improve the comprehensive utilization level and service quality of multisource knowledge bases in data centers.
Key words : data center; multi-source knowledge bases; large language model; intelligent question-answering
引言
数据中心作为数据信息资源存储与处理的核心载体，承载着各业务领域的多源异构知识库，积淀了海量的数据信息资源。在业务应用场景中，针对知识库的信息获取，主要还是使用传统的人工检索文档或数据库的方式，存在着较大的效率瓶颈，难以满足高效信息获取的现实诉求。近年来，大语言模型(Large Language Model, LLM)技术在自然语言处理(Natural Language Processing, NLP)领域取得突破性进展［1-3］。凭借大模型参数规模和强大的语义学习能力，大语言模型能够对海量文本数据进行深度语义理解与知识挖掘，对各类自然语言的任务处理表现优异。如何依托大语言模型相关技术，提升数据中心多源异构知识库的检索效能，提高不同用户群体从海量数据中获取信息的便捷性与实用性，已成为数据中心业务保障中亟待解决的关键问题。智能问答系统的出现为该问题提供了有效的解决方案［4-5］，它通过自然语言交互模式精准理解用户查询意图并快速返回精准答案，显著提升了信息获取效率。
