国际海道测量组织(International Hydrographic Organization,IHO) S-98标准定义的电子海图与多源水文数据互操作是未来应用趋势，本研究旨在探索融合电子海图（Electronic Navigational Chart, ENC）产品与实时水位数据的动态等深线智能生成技术。 针对S-102测深表面数据不完备情况下ENC产品包含的稀疏水深数据精度难以直接支撑高可靠性的动态水深计算的制约，提出一种基于深度学习超分辨率技术的解决方案，利用改进的TSE-EDSR模型，从海图稀疏水深点中重建高保真度的海底数字高程模型，再结合实时水位数据生成动态等深线。实验表明，本文方法生成的数字高程模型及动态等深线在精度和形态真实性上均显著优于传统插值方法，可为S-98互操作应用提供技术支撑，对推动新一代电子海图的应用与发展具有重要的理论和实践价值。

The IHO S-98 standard defines the interoperability between electronic navigational charts (ENCs) and multi-source hydrographic data as a future application trend. This study aims to explore intelligent techniques for generating dynamic depth contours by integrating ENC products with real-time water level data. In scenarios where S-102 bathymetric surface data are incomplete, the sparse depth data contained in ENC products are insufficient to directly support high-reliability dynamic depth computations. To address this limitation, we propose a deep learning-based super-resolution solution. Specifically, an improved TSE-EDSR model is employed to reconstruct a high-fidelity digital elevation model (DEM) of the seabed from sparse chart soundings. By further integrating real-time water level data, dynamic depth contours are generated. Experimental results demonstrate that the proposed method produces DEMs and dynamic depth contours with significantly higher accuracy and morphological authenticity compared to traditional interpolation approaches. This work provides technical support for S-98 interoperability applications and holds substantial theoretical and practical value for advancing the development and application of next-generation electronic navigational charts.