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The application of artificial intelligence technology has greatly enhancedand fortified the safety of energy pipelines, particularly in safeguardingagainst external threats. The predominant methods involve the integration ofintelligent sensors to detect external vibration, enabling the identificationof 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 ofexternal signals, which complicates the authentication of threat events. Ourresearch endeavors to overcome the above issues by harnessing deep learningtechniques to achieve a more fine-grained recognition and localization process.This refinement is crucial in effectively identifying genuine threats topipelines, thus enhancing the safety of energy transportation. This paperproposes a radial threat estimation method for energy pipelines based ondistributed optical fiber sensing technology. Specifically, we introduce acontinuous multi-view and multi-domain feature fusion methodology to extractcomprehensive signal features and construct a threat estimation and recognitionnetwork. The utilization of collected acoustic signal data is optimized, andthe underlying principle is elucidated. Moreover, we incorporate the concept oftransfer learning through a pre-trained model, enhancing both recognitionaccuracy and training efficiency. Empirical evidence gathered from real-worldscenarios underscores the efficacy of our method, notably in its substantialreduction of false alarms and remarkable gains in recognition accuracy. Moregenerally, our method exhibits versatility and can be extrapolated to a broaderspectrum of recognition tasks and scenarios.

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System