基于DeepSeek-7B与LoRA微调的专业产线维修大模型构建与应用

中图分类号：TN92；TP3 文献标志码：A 文章编码：1672-7274（2026）01-0051-03

Construction and Application of a Professional Production Line Maintenance Large Model Based on DeepSeek-7B and LoRA Fine-Tuning

JINRan,LUO Xiwang,TIAN Zhoupeng,WANGPei,MA Guangbin(Inspur Electronic Information Industry Co.,Ltd.,Jinan 25oooo,China)

Abstract: By analyzing the current issues of data quality, computing resources,and model adaptability in the fine-tuning of intelligent maintenance data large models,specific measures such as an improved data preprocessing technique,enhanced model generalization ability,and optimized fine-tuning strategies are proposed.The research is basedon typical application cases to deeply explore the successs and failures in the fine-tuning process The studyshows thatreasonabledatamanagement,optimizedmodel structure,andappropriate fine-tuning strategiescan significantly improve the model accuracyand adaptability.Itisconcluded thatthe fine-tuning technologyof intellgent maintenance large models has broad application prospects in thecontext of the in-depth development of deep learning and big data, but it stillneeds to be continuously optimized and improved in practical applications.

Keywords: intelligent maintenance; fine-tuning of large models; data preprocessing; model optimization: DeepSeek platform

微调技术在提升模型精度和适应性方面发挥着核心作用，但在实践中仍面临一系列挑战：维修历史数据存在录入不完整甚至错误的情况，数据质量参差不齐；同时模型训练对计算资源要求较高，长时间运行消耗较大；此外，模型在面对多样化维修场景时的泛化能力仍有限，需要进一步优化。（剩余5318字）