中圖分類號：TP181文獻標志碼：ADOI:10.19358/j.issn.2097-1788.2026.04.009 中文引用格式：王亮，張強，魏韻蕭. 基于知識庫的智能問答系統構建技術研究［J］.網絡安全與數據治理，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
數據中心作為數據信息資源存儲與處理的核心載體，承載著各業務領域的多源異構知識庫，積淀了海量的數據信息資源。在業務應用場景中，針對知識庫的信息獲取，主要還是使用傳統的人工檢索文檔或數據庫的方式，存在著較大的效率瓶頸，難以滿足高效信息獲取的現實訴求。近年來，大語言模型(Large Language Model, LLM)技術在自然語言處理(Natural Language Processing, NLP)領域取得突破性進展［1-3］。憑借大模型參數規模和強大的語義學習能力，大語言模型能夠對海量文本數據進行深度語義理解與知識挖掘，對各類自然語言的任務處理表現優異。如何依托大語言模型相關技術，提升數據中心多源異構知識庫的檢索效能，提高不同用戶群體從海量數據中獲取信息的便捷性與實用性，已成為數據中心業務保障中亟待解決的關鍵問題。智能問答系統的出現為該問題提供了有效的解決方案［4-5］，它通過自然語言交互模式精準理解用戶查詢意圖并快速返回精準答案，顯著提升了信息獲取效率。
