科技创新与应用2026，Vol.16Issue(23)：31-34,39,5.DOI:10.19981/j.CN23-1581/G3.2026.23.007
思维链微调大语言模型赋能网络故障自动诊断
摘要
Abstract
In order to solve the problems of difficult configuration and unexecutable output of the universal large language model in network fault diagnosis,this paper proposes an automated network fault diagnosis framework based on Chain-of-Thought(CoT).The framework first integrates network topology,device configuration,logs and fault phenomena into LLM-friendly inputs through a unified YAML serialization method;secondly,it builds a thought chain fine-tuning dataset covering 20 typical faults,using QLoRA(Quantized Low-Rank Adaptation)technology fine-tuned the Qwen3-8B model to enable the model to master hierarchical troubleshooting logic and generate executable solutions;finally,a"diagnosis-execution-verification"closed-loop evaluation system linked with the GNS3 network simulation tool was designed and implemented,with functional repair success rate as the core indicator.Experiments show that the model repair success rate after fine-tuning reaches 82.6%,an increase of more than 54 percentage points from the baseline,proving that this framework can effectively improve the ability of large models to solve actual network failures.关键词
网络故障诊断/大语言模型/思维链/参数微调/Qwen3-8BKey words
network fault diagnosis/large language model(LLM)/Chain-of-Thought(CoT)/parameter fine-tuning/Qwen3-8B分类
信息技术与安全科学引用本文复制引用
陈丹,龙星延,黄懿漫,龚仕涛,冉红梅..思维链微调大语言模型赋能网络故障自动诊断[J].科技创新与应用,2026,16(23):31-34,39,5.基金项目
2024年度广西高校中青年教师(科研)基础能力提升项目课题(桂教科研[2024]1号,2024KY1088) （科研）
柳州职业技术大学2025年校级科研课题(2025DZ06) （2025DZ06）
