用智能体系统和微调小模型实现电信网络故障自动诊断
Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting
- 构建多智能体系统，分工协作完成故障诊断全流程
- 在无线接入网和核心网中显著缩短故障处理时间
- 微调小模型生成基于内部文档的可执行修复方案
电信网络规模与复杂性持续增长，管理运维面临巨大挑战。尽管人工智能已应用于多项任务，但现有模型普遍存在适用范围窄、依赖大量标注数据、跨异构部署泛化能力差等问题，故障排查仍高度依赖领域专家手动关联多源数据定位根因。为此，本文提出一种多智能体系统（MAS），利用大语言模型协调多个专用工具，实现网络故障的全自动诊断。当AI/ML监控检测到异常后，系统动态激活编排器、解决方案规划器、执行器、数据检索器和根因分析器等智能体，在短时间内完成诊断并推荐修复策略。关键在于解决方案规划器，其基于内部文档生成修复计划，通过在专有故障排除文档上微调小语言模型（SLM）实现。实验表明，该框架在无线接入网（RAN）和核心网域均显著加速了故障排查自动化进程。
原文摘要 · Abstract (English)
Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging. Although Artificial Intelligence (AI) has been applied to many telecom tasks, existing models are often narrow in scope, require large amounts of labeled data, and struggle to generalize across heterogeneous deployments. Consequently, network troubleshooting continues to rely heavily on Subject Matter Experts (SMEs) to manually correlate various data sources to identify root causes and corrective actions. To address these limitations, we propose a Multi-Agent System (MAS) that employs an agentic workflow, with Large Language Models (LLMs) coordinating multiple specialized tools for fully automated network troubleshooting. Once faults are detected by AI/ML-based monitors, the framework dynamically activates agents such as an orchestrator, solution planner, executor, data retriever, and root-cause analyzer to diagnose issues and recommend remediation strategies within a short time frame. A key component of this system is the solution planner, which generates appropriate remediation plans based on internal documentation. To enable this, we fine-tuned a Small Language Model (SLM) on proprietary troubleshooting documents to produce domain-grounded solution plans. Experimental results demonstrate that the proposed framework significantly accelerates troubleshooting automation across both Radio Access Network (RAN) and Core network domains.
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