MS-ConformerNet: A Multi-Scale Joint Encoding Network for OTDR Signal Analysis

This paper proposes a multi-task deep neural network architecture for optical time-domain reflectometer OTDR signal analysis, enabling end-to-end learning for fiber fault classification and event localization. To address the limitations of traditional methods in complex scenarios, such as insufficient feature representation and conflicts in multi-task optimization, this study designs a multi-scale pooling module to extract cross-scale features, integrates an improved bidirectional feature pyramid network BiFPN to enhance multi-resolution feature fusion, and introduces a Conformer hybrid encoding block that combines self-attention and gated convolution to model both global and local features. Additionally, a task-aware dynamic gating mechanism is proposed to mitigate conflicts in multi-objective optimization. Experimental results demonstrate that the proposed model outperforms traditional methods in classification accuracy and fault localization, providing a high-precision, cost-effective solution for optical network monitoring and maintenance.