基于GPT网络的日志文本故障关联规则挖掘研究

李冰; 叶庆卫; 王雨晞; 何镇秦; 王晓东

doi:10.11959/j.issn.1000-0801.2025207

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基于GPT网络的日志文本故障关联规则挖掘研究

研究与开发 | 更新时间：2026-08-05

- 基于GPT网络的日志文本故障关联规则挖掘研究
- Research on log text fault association rule mining based on GPT network
- 电信科学 2025年41卷第12期页码：100-115
- 作者机构：
  
  宁波大学信息科学与工程学院，浙江宁波 315211
- 作者简介：
  
  李冰（2001- ），女，宁波大学信息科学与工程学院硕士生，主要研究方向为通信网故障诊断。
  叶庆卫（1970- ），男，宁波大学信息科学与工程学院教授、硕士生导师，主要研究方向为通信信号处理、信号传输与检测、分类器与机器学习等。
  王雨晞（1999- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为通信网智能运维。
  何镇秦（2001- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为通信网智能运维。
  王晓东（1970- ），男，宁波大学信息科学与工程学院教授、硕士生导师，主要研究方向为多媒体信号处理、图像处理等。
- 基金信息：
  
  浙江省产学研合作项目(062400020);大型横向项目网络运维平台研发项目(HK2022000189)
- DOI：10.11959/j.issn.1000-0801.2025207
  中图分类号： TN915
- 收稿：2025-03-24，
  
  修回：2025-05-12，
  
  录用：2025-06-04，
  
  网络首发：2026-01-08，
  
  纸质出版：2025-12-20
- 稿件说明：
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李冰,叶庆卫,王雨晞等.基于GPT网络的日志文本故障关联规则挖掘研究[J].电信科学,2025,41(12):100-115.

LI Bing,YE Qingwei,WANG Yuxi,et al.Research on log text fault association rule mining based on GPT network[J].Telecommunications Science,2025,41(12):100-115.
李冰,叶庆卫,王雨晞等.基于GPT网络的日志文本故障关联规则挖掘研究[J].电信科学,2025,41(12):100-115. DOI： 10.11959/j.issn.1000-0801.2025207.

LI Bing,YE Qingwei,WANG Yuxi,et al.Research on log text fault association rule mining based on GPT network[J].Telecommunications Science,2025,41(12):100-115. DOI： 10.11959/j.issn.1000-0801.2025207.

摘要

日志文本异常检测及故障关联规则挖掘是现代系统运维和故障诊断中的关键环节，对于提升系统可靠性和降低运维成本具有重要意义。针对传统人工监控方式难以应对系统复杂性和规模增长的问题，提出了一种基于深度学习的通信网日志信息关联规则挖掘方法。该方法首先从通信网日志信息中提取主机IP地址、时间和故障类型等关键信息，并采用多种经典关联规则算法进行融合，生成关联规则标签，构建深度学习网络的数据集及标签集。然后，引入逐层学习机制，设计了一种高效的深度学习模型，在构建的数据集上进行训练，获得专门用于挖掘通信网故障事件之间关联关系的深度学习网络模型。在GAIA大型日志数据集上的实验结果表明，新算法展现出显著优势：在准确率方面达到64.2%，较FP-Growth算法（22.1%）提升190.5%，较Eclat算法（43.7%）提升46.9%，较Quant Matrix Miner算法（37.6%）提升70.7%；在运行时间方面仅需要11 s，较FP-Growth算法提速138.2%，较图神经网络（graph neural network，GNN）提速195.0%，同时保持了与Apriori算法相近的处理速度。此外，新算法在支持度（0.42）和置信度（0.75）等关键指标上也表现出良好的均衡性。该方法为故障事件关联规则的智能挖掘提供了新的工具，具有一定的实用价值和应用前景。

Abstract

Log anomaly detection and fault association rule mining are critical components in modern system operation and maintenance

as well as fault diagnosis

playing a significant role in enhancing system reliability and reducing operational costs. To address the challenges posed by the increasing complexity and scale of systems

which traditional manual monitoring methods struggle to handle

a deep learning-based method for mining association rules from communication network logs was proposed. The method first extracted key information such as host IP addresses

timestamps

and fault types from communication network logs. Subsequently

it integrated multiple classic association rule algorithms to generate association rule labels

constructing a dataset and label set for the deep learning network. Subsequently

a hierarchical learning mechanism was introduced

and an efficient deep learning model was designed to train on the constructed dataset

resulting in a specialized deep learning network model for mining association relationships among communication network fault events. The experimental outcomes derived from the comprehensive GAIA log dataset underscore the pronounced superiority of the proposed method. It achieved an accuracy of 64.2%

representing a considerable enhancement of 190.5% over the FP-Growth algorithm’s performance of 22.1%

46.9% over the Eclat algorithm’s 43.7%

and 70.7% over Quant Matrix Miner’s 37.6%. With respect to operational time

the method exhibited remarkable speed

requiring only 11 seconds for execution

thereby outpacing the FP-Growth algorithm by 138.2% and the graph neural network (GNN) by 195.0%

while maintaining a comparable processing velocity to the Apriori algorithm. Furthermore

the method demonstrated a well-balanced performance in key metrics including support (0.42) and confidence (0.75). This method provides a new tool for the intelligent mining of fault event association rules

offering practical value and promising application prospects.

关键词

Keywords

references

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