石油和天然气管道入侵事件的识别和分类基于1-D CNN网络的事件
Han Qin1, Xiaoli Huang1, Xingcheng Wang1,1
1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China.
PloS one
|December 23, 2025
概括
这项研究引入了一个新的石油和天然气管道安全系统,使用光纤传感和人工智能以超过95%的准确度检测窃听和挖掘等入侵.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 安全技术 安全技术
背景情况:
- 迫切需要加强石油和天然气管道安全.
- 当前监控系统在检测微妙入侵时的局限性.
研究的目的:
- 为实时管道安全开发一种新的多式联通传感和交互框架.
- 为了提高入侵检测的灵敏度和响应能力.
主要方法:
- 集成基于分发声学传感 (DAS) 的相位敏感光学时域反射计 (φ-OTDR).
- 利用一个优化的一维卷积神经网络 (1-D CNN) 架构.
- 整合了从人类活动数据集中的转移学习,以增强功能概括.
主要成果:
- 对各种事件类型 (敲击,挖掘,脚步) 实现了强大的自动入侵分类.
- 经过证明的分类准确度超过95%.
- 验证了将多式联网传感与深度学习相结合,用于智能基础设施保护的潜力.
结论:
- 拟议的框架在实时管道安全监控方面取得了重大进展.
- 强调深度学习和多式联网传感对于基础设施保护的有效性.
- 未来的工作包括数据集扩展,多传感器融合和自适应交互功能.
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