The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

翻译：人工智能技术的应用极大地增强和巩固了能源管道的安全性，尤其是在抵御外部威胁方面。主要方法涉及集成智能传感器检测外部振动，从而识别事件类型和位置，取代了人工检测方法。然而，实际应用暴露了现有方法的一个局限性——它们准确辨别外部信号空间维度的能力有限，这使得威胁事件的验证变得复杂。我们的研究旨在通过利用深度学习技术实现更精细的识别和定位过程来克服上述问题。这种精细化对于有效识别管道面临的真正威胁至关重要，从而增强能源运输的安全性。本文提出了一种基于分布式光纤传感技术的能源管道径向威胁估计方法。具体而言，我们引入了一种连续多视角、多域特征融合方法，以提取全面的信号特征并构建威胁估计与识别网络。优化了采集到的声学信号数据的利用率，并阐明了其基本原理。此外，我们通过预训练模型融入了迁移学习的概念，既提高了识别精度，又提升了训练效率。从真实场景中获得的经验证据证明了我们方法的有效性，特别是在显著降低误报率和大幅提高识别精度方面。更广泛地说，我们的方法表现出多功能性，并可推广到更广泛的识别任务和场景中。