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对不平衡的 Phi OTDR 事件分类的可控制扩散框架

Bang Zhu¹, Wenkai Cheng¹, Shiting Wen¹

¹the School of Computer Science and Data Engineering, NingboTech University, Ningbo, 315000, China.

Scientific reports

|December 8, 2025

概括

可控制扩散 (ConDiff) 框架通过生成合成异常事件样本,有效地解决不平衡的 Φ-OTDR 数据. 这提高了电网异常分类的准确性,解决了长尾分布的挑战.

科学领域:

电气工程电气工程
数据科学数据科学数据科学
信号处理信号处理

背景情况:

阶段光学时域反射计 (Φ-OTDR) 系统监测电网,检测挖掘或水等异常.
现有的深度学习模型与不平衡的 Φ-OTDR 数据集扎,其中与正常噪音相比,异常事件很少发生.
这种长尾分布显著阻碍了关键电网事件的准确分类.

研究的目的:

引入一个新的框架,可控扩散 (ConDiff),用于生成高质量的合成异常事件数据.
解决 Φ-OTDR 数据分类中的长尾不平衡问题,以加强电网监控.
为了提高在现实世界电网基础设施中分类罕见异常的准确性.

主要方法:

开发了可控制扩散 (ConDiff) 框架,其中包含三个关键组件:反引导的 Φ-OTDR 增强器,高质量样本选择和动态值调整.
利用扩散模型生成合成的 Φ-OTDR 数据,模拟各种异常事件.
实施了反循环,用于动态控制和生成合成样品的质量评估.

主要成果:

与基线方法相比,ConDiff框架显示了BJTU-OTDR-LT数据集的分类准确度的显著改善,从3.7%到7.2%不等.
成功为代表性不足的异常事件类别生成了高质量的合成样本.
有效地缓解了 Φ-OTDR 事件分类中长尾数据分布所带来的挑战.

结论:

拟议的ConDiff框架为处理电网监控中不平衡的Φ-OTDR数据集提供了一个强大的解决方案.
使用ConDiff的合成数据生成提高了用于异常检测的深度学习模型的性能.
这种方法对于通过准确的事件分类来提高电网基础设施的可靠性和安全性至关重要.

关键词:

Φ-OTDR事件的分类.数据增强数据增强深度学习是一种深度学习.扩散模型是一个扩散模型.信号处理信号处理

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