  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.
