基于DeepSeek的智能问答驱动型气象装备保障系统

Intelligent Q&A-Driven Meteorological Equipment Support System Based on DeepSeek

摘要: 针对基层气象部门装备保障中存在的知识获取困难、故障诊断效率低等问题，本文提出一种基于DeepSeek大模型的智能问答解决方案。通过建立“提问模板库 + 语义增强引擎”的双层问答架构，结合本地知识库的轻量化部署方案，实现自然语言问题的精准解析与高效响应。研究结果表明，该方案能够有效提升气象装备保障人员的故障诊断和维修能力，显著提高装备保障效率。

Abstract: To address the challenges of difficult knowledge acquisition and low efficiency in fault diagnosis encountered in equipment support at grassroots meteorological departments, this study proposes an intelligent question-answering solution based on the DeepSeek large model. By establishing a dual-layer Q&A architecture comprising a “question template library + semantic enhancement engine” and implementing a lightweight deployment solution for local knowledge bases, the approach achieves accurate parsing and efficient response to natural language queries. The results demonstrate that this solution can effectively enhance maintenance personnel’s capabilities in fault diagnosis and equipment repair, significantly improving operational support efficiency.