NetAutoLLM：基于LLM的自主化网络健康管理

唐枫枭; 王啸楠; 张君健; 赵明

doi:10.12263/DZXB.20250440

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NetAutoLLM：基于LLM的自主化网络健康管理

学术论文 | 更新时间：2026-06-16

- NetAutoLLM：基于LLM的自主化网络健康管理
- NetAutoLLM: Network Autonomous Health Management Based on LLM
- 电子学报 2026年54卷第3期页码：1147-1160
- 作者机构：
  
  中南大学计算机学院，湖南长沙 410083
- 作者简介：
  
  唐枫枭男，1990年6月出生于重庆市。现为中南大学计算机科学与工程学院正教授。主要研究方向为网络智能化、复杂系统数字孪生与故障诊断、网络智能运维、空天地一体化网络运维与优化等。 E-mail: tangfengxiao@csu.edu.cn
  王啸楠男，2000年6月出生于河南省郑州市。现为中南大学计算机科学与工程学院博士研究生。主要研究方向为大语言模型、通信网络健康管理等。 E-mail: blackbug2021@163.com
  张君健男，1994年3月出生于湖南省长沙市。现为中南大学计算机科学与工程学院博士研究生。主要研究方向为数字孪生网络、网络通信和网络故障诊断。 E-mail: 244701030@csu.edu.cn
  赵明男，1975年11月出生于湖南省益阳市。现为中南大学计算机科学与工程学院教授。主要研究方向为网络智能、机器学习、健康评估。 E-mail: meanzhao@csu.edu.cn
- 基金信息：
  
  国家自然科学青年基金(62302527);湖南省自然科学基金(2025JJ90177;2024JJ9173);湖南省青年基金(2023jj40774)
- DOI：10.12263/DZXB.20250440
  中图分类号： TP277;
- 收稿：2025-05-29，
  
  录用：2026-02-26，
  
  纸质出版：2026-03-25
- 稿件说明：
移动端阅览
唐枫枭, 王啸楠, 张君健, 等. NetAutoLLM：基于LLM的自主化网络健康管理[J]. 电子学报, 2026, 54(03): 1147-1160.

TANG Fengxiao, WANG Xiaonan, ZHANG Junjian, et al. NetAutoLLM: Network Autonomous Health Management Based on LLM[J]. Acta Electronica Sinica, 2026, 54(03): 1147-1160.
唐枫枭, 王啸楠, 张君健, 等. NetAutoLLM：基于LLM的自主化网络健康管理[J]. 电子学报, 2026, 54(03): 1147-1160. DOI：10.12263/DZXB.20250440

TANG Fengxiao, WANG Xiaonan, ZHANG Junjian, et al. NetAutoLLM: Network Autonomous Health Management Based on LLM[J]. Acta Electronica Sinica, 2026, 54(03): 1147-1160. DOI：10.12263/DZXB.20250440

摘要

在现代网络运营和维护中，可靠的网络服务与系统健康管理至关重要。传统的网络健康管理方案依赖于规则匹配的简单算法或机器学习模型，难以应对通信状况复杂、信息难以拟合的网络通信环境。大型语言模型（Large Language Model，LLM）因其具备自主的思考与推理能力可以解决这些问题。目前，最先进的基于LLM的算法在通信网络领域取得了显著进展。然而，现有方案通常将LLM作为特征梳理与学习的工具，LLM本身被动接收数据，其自主性和思考能力受到了极大的限制。这导致目前基于LLM的方案无法直接应用于网络运营与维护任务中。为了解决这个问题，本文通过外接行为树（Behavior Trees，BTs）赋予LLM感知与修改网络环境的能力，并构建自主化的网络健康管理框架。此外，该框架通过双池设计保证各专家信息隔离与案例经验更新。同时，通过多专家讨论的方式加固操作安全性，以保证网络运维任务的完成。实验证明：相较于目前流行的模型，基于LLM的自主化网络健康管理（Network Autonomous health management based on LLM，NetAutoLLM）模型在异常检测任务中精度提高了8.44个百分点；在故障溯源任务中，精度提高了21.7个百分点，同时可以自动缓解故障。

Abstract

In modern network operation and maintenance

reliable network services and system health management are paramount. Traditional network health management solutions relying on machine learning models or rule-based algorithms struggle to address complex network communication environments with heterogeneous devices. Large language models (LLMs)

with their reasoning and generalization capabilities

offer potential solutions to these challenges. While state-of-the-art LLM-based algorithms have achieved significant progress in network domains

existing approaches typically utilize LLMs merely as tools for feature extraction and learning

where models passively receive data with severely constrained autonomy and cognitive capabilities. This limitation hinders direct application of current LLM-based solutions to network operation and maintenance tasks. To address this issue

this paper proposes an autonomous network health management framework by integrating external behavior trees (BTs) that empower LLMs with network environment perception and modification capabilities. The framework ensures information authenticity through a dual-pool design distinguishing public and private information

while employing multi-expert discussions to verify action effectiveness and guarantee successful network maintenance. Experimental results demonstrate that compared to conventional models

network autonomous health management based on LLM (NetAutoLLM) achieves an 8.44 percentage points improvement in anomaly detection accuracy over mainstream models

and enhances fault localization precision by 21.7 percentage points

while enabling automated fault mitigation.

关键词

Keywords

references

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