一个端到端的数据谱系分析框架,涵盖链接模式识别,故障诊断和早期预警
Rongxu Hou1, Shaobo Zhang2, Hongjiang Wang1
1School of Computer Science and Technology, Shenyang Institute of Engineering, Shenyang, 110136, China.
Scientific reports
|January 7, 2026
概括
本研究介绍了一种端到端全链 (EEFL) 智能分析框架,用于实时预测数据链故障. EEFL使用图形神经网络和深度学习来准确检测和分类各种数据故障,改善数据治理.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 数据平台日益复杂,需要实时故障检测.
- 传统方法在复杂的数据谱系和故障追踪方面扎.
研究的目的:
- 提出一个端到端全链 (EEFL) 智能分析框架.
- 为了实现实时预测和追踪数据链路故障.
- 通过智能故障分析来增强数据治理.
主要方法:
- 使用图形神经网络 (GNN) 构建一个动态数据谱系图形模型.
- 使用时间卷积网络 (TCN) 和GNN用于混合故障诊断.
- 使用贝叶斯优化和在线学习实现动态值预警机制.
主要成果:
- 在企业和模拟数据上,EEFL的平均准确率为92.73%.
- 该框架准确地分类数据中断,延迟异常和数据污染.
- 与传统故障检测方法相比,其表现优越.
结论:
- EEFL框架为实时数据链路故障分析提供了有效的解决方案.
- EEFL为数据治理提供智能决策支持.
- 提出的方法大大提高了故障检测的准确性,并减少了错误报警.
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