Intelligent vessel navigation increasingly demands high-density bathymetric data. To resolve the limitations of traditional standards and overcome existing management bottlenecks, this study proposes a novel methodology for high-density bathymetric data modeling and system construction integrated with the S-100 framework. Centered on the International Hydrographic Organization (IHO) S-102 standard, this methodology pioneers a strongly correlated management paradigm for datasets, data, and metadata. Leveraging a relational database architecture and a three-level indexing mechanism, it enables the structured organization and efficient retrieval of data throughout its entire life cycle. At the data production stage, geometric feature constraints based on convex hulls are innovatively incorporated to facilitate the interpolation of high-density water depth data and the generation of grid arrays. A data organization and structured storage model based on the three-tier logical architecture of the Hierarchical Data Format version 5 (HDF5) is proposed, which couples the technologies of block-based storage and refined version control to achieve the synergistic optimization of storage costs and access efficiency for high-density water depth data. Validation via field measurements in selected sea areas of the East China Sea demonstrated that the generated S-102 bathymetric data complied with international specifications and achieved excellent terrain restoration accuracy. Meanwhile, the proposed HDF5-based storage strategy achieves a storage space reduction of 83.6%. This research provides authoritative and efficient data support for scenarios such as intelligent navigation and port digitalization, and contributes to the construction of an intelligent shipping ecosystem.
Luo et al. (Mon,) studied this question.