An underwater digital elevation model construction method, system, computer device and storage medium

Abstract

The application relates to an underwater digital elevation model construction method, system, computer equipment and storage medium. The method comprises the following steps: based on a water body geographical boundary, an adaptive feature line constraint network is constructed; a classification interpolation algorithm is used to give the nodes of the adaptive feature line constraint network elevation information, so that an elevation feature line constraint network is formed; the elevation feature line constraint network is used as a terrain constraint reference, and a differentiated constraint type irregular triangle net is constructed in combination with an effective elevation point data set of an underwater terrain; and the differentiated constraint type irregular triangle net is converted into an underwater digital elevation model. The application can guarantee water area boundary shape retention and terrain internal continuity, and is helpful to improving the precision and fitting degree of the underwater terrain model.

Description

Technical Field

[0001] This invention relates to the field of underwater terrain modeling technology, and more specifically, to a method, system, computer equipment, and storage medium for constructing an underwater digital elevation model. Background Technology

[0002] With the digital and intelligent upgrading of the surveying and mapping geographic information industry, underwater topographic mapping, as a core foundation for water conservancy projects, water resource development, and digital water management, faces continuously increasing demands for technical accuracy and modeling efficiency. Digital elevation models (DEMs), as the core carrier for the digital representation of underwater topography, can accurately reflect the spatial morphology and elevation characteristics of underwater terrain. They are widely used in numerous water development and management scenarios, such as river management, reservoir planning, waterway construction, and aquatic ecological protection, becoming crucial data support for refined underwater spatial control.

[0003] In the process of realizing this invention, the inventors discovered that current underwater DEM construction technology, when faced with sparse and irregularly distributed bathymetric data, has difficulty in effectively balancing the geometric conformity of water boundaries with the morphological continuity of the terrain. This easily leads to problems such as distortion of the land-water boundary and terrain distortion, resulting in a large deviation between the constructed DEM and the actual underwater terrain. This fails to meet the application requirements of high-precision water development and management for underwater terrain models, posing a severe challenge to the accuracy of underwater terrain digital modeling. Summary of the Invention

[0004] Based on this, and to address the aforementioned problems, this invention provides a method, system, computer equipment, and storage medium for constructing an underwater digital elevation model. By constructing an adaptive feature line constraint network, differential elevation interpolation is performed on the feature line nodes. Combined with the differential constraints implemented on the feature lines, a triangular network is constructed and converted into an underwater digital elevation model. This ensures the conformity of the water boundary and the continuity of the terrain interior, solves the DEM distortion problem under sparse sounding points, and helps improve the accuracy and fit of the underwater terrain model.

[0005] In a first aspect, the present invention provides a method for constructing an underwater digital elevation model, the method comprising: constructing an adaptive feature line constraint network based on the geographical boundary of the water body; assigning elevation information to the nodes of the adaptive feature line constraint network using a classification interpolation algorithm to form a feature line constraint network with elevation; using the feature line constraint network with elevation as the terrain constraint benchmark, and constructing a differentiated constraint-type irregular triangular network in combination with an effective elevation point dataset of underwater terrain; and converting the differentiated constraint-type irregular triangular network into an underwater digital elevation model.

[0006] Optionally, in this embodiment of the invention, an adaptive feature line constraint network is constructed based on the geographical boundary of the water body, including: using the geographical boundary of the water body as an initial seed feature line and incorporating it into an initial feature line set; determining the feature line buffer distance according to the preset grid parameters of the underwater digital elevation model to be constructed; creating a buffer zone inwards from the initial seed feature line as a reference, and incorporating the boundary line of the buffer zone as a new seed feature line into the feature line set; performing inward buffering and incorporation of new seed feature lines based on the buffer distance until the width inside the buffer zone can no longer meet the buffer distance requirement; determining whether the water body shape is a long and narrow water body, and if so, extracting the center line of the long and narrow water body and incorporating it into the feature line set; and integrating all feature lines in the feature line set to form an adaptive feature line constraint network. By constructing an adaptive feature line constraint network, the entire process is automated without human intervention, completely replacing the traditional method of manually drawing feature lines. This significantly reduces human subjective errors and workload, improving modeling efficiency. At the same time, it relies on the geographical boundary of the water body and the DEM grid parameters to achieve uniform generation of feature lines, adapting to the natural shape of the water body and specifically supplementing the center line of the narrow water body. The resulting feature line network completely fits the outline of the water body, providing a precise and adaptable terrain constraint benchmark for subsequent elevation interpolation and differentiated constraint network construction, ensuring the boundary conformity and internal terrain continuity of the underwater DEM from the source.

[0007] Optionally, in this embodiment of the invention, a classification interpolation algorithm is used to assign elevation information to the nodes of the adaptive feature line constraint network, including: establishing a spatial index for the effective elevation point dataset of underwater topography; dividing the feature lines in the adaptive feature line constraint network into water body boundary lines and water body interior feature lines; for the water body boundary lines, retrieving single-sided effective elevation points according to the spatial index, and assigning node elevation values ​​using distance weight interpolation; for the water body interior feature lines, retrieving double-sided effective elevation points according to the spatial index, and assigning node elevation values ​​using linear interpolation; and encrypting all feature line nodes that have completed elevation assignment, and supplementing them with elevation values ​​to form a feature line constraint network with elevation information. Efficient retrieval of sounding points is achieved through spatial indexing. Differentiated interpolation strategies are adopted for different types of feature lines. The water body boundary line is adapted to the characteristics of single-sided sounding points, and the internal feature lines are aligned with the distribution of double-sided sounding points, resulting in an assignment result that better matches the actual terrain. Node densification and interpolation further ensure elevation continuity. The resulting network of elevation feature lines provides a high-precision elevation benchmark for subsequent constrained network construction, which not only improves interpolation efficiency and accuracy but also avoids errors caused by manual assignment. From the elevation dimension, it ensures the boundary conformity and internal terrain continuity of the underwater DEM, effectively solving the modeling distortion problem under sparse sounding points and significantly improving the accuracy and fit of the underwater terrain model.

[0008] In the above implementation process, for the water body boundary line, effective elevation points on one side are retrieved based on the spatial index, and the node elevation is assigned using the distance weight interpolation method. This includes: for any node on the water body boundary line, retrieving and obtaining the two nearest effective elevation points located on the water-side from the effective elevation point dataset based on the spatial index; calculating the spatial distance between the two nearest effective elevation points and the node; assigning interpolation weights to the two nearest effective elevation points based on the spatial distance; and calculating the elevation value of the node based on the interpolation weights and the elevation values ​​of the two nearest effective elevation points. By accurately retrieving the nearest effective elevation points on one side of the water body using the spatial index, and combining the spatial distance to assign interpolation weights and calculate the elevation, this method adapts to the terrain characteristics of the water-land boundary where only measurement points are available on one side, making the boundary line node elevation assignment more consistent with the actual underwater terrain. The assignment logic is accurate and the calculation is efficient, effectively compensating for the sparse sounding points at the water boundary, improving the boundary line elevation accuracy, and establishing a solid boundary elevation benchmark for subsequent constraint network construction. This further ensures the conformity of the water-land boundary of the underwater DEM and avoids boundary terrain distortion.

[0009] In the above implementation process, for the feature line inside the water body, effective elevation points on both sides are retrieved according to the spatial index, and the node elevation is assigned using linear interpolation. This includes: for any node on the feature line inside the water body, retrieving and obtaining the two nearest effective elevation points on both sides of the feature line from the effective elevation point dataset according to the spatial index; calculating the spatial distance between the two nearest effective elevation points and the node; determining the linear interpolation coefficients according to the proportional relationship of the spatial distances; and calculating the elevation value of the node based on the linear interpolation coefficients and the elevation values ​​of the two nearest effective elevation points. By quickly retrieving the nearest valid elevation points on both sides of the feature line through spatial indexing, and determining the interpolation coefficients based on the spatial distance ratio, the elevation is linearly calculated. This adapts to the terrain features with measuring points on both sides within the water body, and the assigned values ​​closely match the natural undulation trend of the underwater terrain. The efficient and accurate bilateral interpolation logic compensates for the sparse depth sounding points within the water body, improves the accuracy and continuity of the internal feature line elevation assignment, provides an accurate internal elevation benchmark for subsequent differentiated constraint network construction, ensures the morphological continuity within the underwater DEM terrain, and avoids distortion of the internal terrain.

[0010] Optionally, in this embodiment of the invention, a differentiated constraint-type irregular triangular network is constructed using a feature line constraint network with elevation as the terrain constraint benchmark and combined with an effective elevation point dataset of underwater terrain. This includes: setting the water body boundary lines in the feature line constraint network with elevation as the mandatory constraint edges of the triangular network; using an automatic insertion of densification points to set the internal water body feature lines in the feature line constraint network with elevation as legal edges conforming to the Delaunay triangular network criterion; and constructing a differentiated constraint-type irregular triangular network by combining the effective elevation point dataset of underwater terrain. By setting mandatory constraint edges for the water body boundary lines, the morphology of the land-water boundary is ensured not to be destroyed. For the internal feature lines, legal edges are formed by adapting to the Delaunay criterion through densification points, taking into account the natural morphology of the internal terrain. By constructing the network using the effective elevation point dataset, differentiated modeling with "strong boundary constraints and internal adaptability" is achieved, effectively avoiding the terrain distortion problem of triangular network construction under sparse bathymetry points, providing a high-precision, shape-preserving terrain skeleton for subsequent DEM conversion, and further ensuring the boundary shape preservation and internal continuity of the underwater DEM.

[0011] Optionally, in this embodiment of the invention, converting the differentiated constrained irregular triangular network into an underwater digital elevation model includes: based on a preset regular grid size, rasterizing the differentiated constrained irregular triangular network through interpolation to obtain regular grid data containing elevation information; verifying the accuracy of the regular grid data containing elevation information; if the accuracy verification result meets a preset threshold, outputting the underwater digital elevation model; if not, reconstructing the differentiated constrained irregular triangular network until it passes the accuracy verification. By rasterizing the high-precision constrained triangular network with a preset grid size, the advantages of terrain conformity preservation from the preceding network construction are directly inherited; through the accuracy verification closed-loop mechanism, substandard models are reconstructed and optimized to ensure that the output regular grid DEM retains the terrain accuracy of the triangular network while meeting the grid data format requirements for engineering applications; effectively avoiding the terrain distortion problem that easily occurs in traditional rasterization, the final output underwater DEM has controllable accuracy, closely matches the actual underwater terrain, and meets the application needs of high-precision water area development and management.

[0012] Secondly, the present invention also provides an underwater digital elevation model construction system, characterized in that the system comprises: a feature line constraint network construction module, used to construct an adaptive feature line constraint network based on the geographical boundary of the water body; an elevation assignment module, used to assign elevation information to the nodes of the adaptive feature line constraint network using a classification interpolation algorithm to form a feature line constraint network with elevation; a triangulation network construction module, used to construct a differentiated constraint-type irregular triangulation network using the feature line constraint network with elevation as the terrain constraint benchmark and combined with the effective elevation point dataset of the underwater terrain; and a model generation module, used to convert the differentiated constraint-type irregular triangulation network into an underwater digital elevation model.

[0013] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform the steps in any of the above implementations.

[0014] Fourthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program / instruction, the computer program / instruction being processed by the steps in any of the above implementations.

[0015] This invention provides a method, system, computer equipment, and storage medium for constructing an underwater digital elevation model. By constructing an adaptive feature line constraint network, differential elevation interpolation is performed on the feature line nodes. Combined with the differential constraints of the feature lines, a triangular network is constructed and converted into an underwater digital elevation model. This ensures the conformity of the water boundary and the continuity of the terrain interior, solves the problem of DEM distortion under sparse sounding points, and helps to improve the accuracy and fit of the underwater terrain model. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the underwater digital elevation model construction method provided in the embodiments of the present invention; Figure 2 This is a flowchart of the feature line constraint network construction method provided in the embodiments of the present invention; Figure 3 This is a flowchart of the feature line elevation assignment method provided in the embodiments of the present invention; Figure 4 A flowchart of the elevation assignment method for water body boundary lines provided in the embodiments of the present invention; Figure 5 A flowchart of the elevation assignment method for feature lines inside a water body provided in an embodiment of the present invention; Figure 6 This is a flowchart of the triangular mesh construction method provided in the embodiments of the present invention; Figure 7 This is a flowchart of the model generation method provided in the embodiments of the present invention; Figure 8 This is a schematic diagram illustrating the process of constructing an underwater digital elevation model provided in an embodiment of the present invention. Figure 9 This is a schematic diagram of the construction of the feature line constraint network provided in an embodiment of the present invention; Figure 10 This is a schematic diagram illustrating the assignment of feature line elevation values ​​in an embodiment of the present invention. Figure 11 This is a schematic diagram of a feature line with elevation information provided in an embodiment of the present invention; Figure 12 This is a schematic diagram comparing the results of feature line constrained DEM and unconstrained DEM provided in the embodiments of the present invention. Figure 13 This is a schematic diagram of the underwater digital elevation model construction system provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will now be described with reference to the accompanying drawings. For example, the flowcharts and block diagrams in the drawings illustrate the architecture, functions, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions. In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0019] The digital upgrade of the surveying and mapping geographic information industry has driven the rapid development of underwater topographic mapping technology. Single-beam bathymetry, with its high efficiency and low cost, has become the mainstream acquisition method for underwater topographic surveying in large-scale, dense water bodies. The underwater topographic data it collects is the core foundational data for water development and management work such as river management, reservoir planning, waterway construction, and aquatic ecological protection. In the underwater topographic results production stage of single-beam bathymetry, data processing and the construction of digital elevation models (DEMs) are the key technical challenges. Constructing high-precision, continuous underwater DEMs is crucial for accurately reflecting the spatial morphology of underwater topography and supporting water engineering planning and operation and maintenance management. The industry has placed higher demands on the automation and accuracy of underwater DEM construction using single-beam bathymetry.

[0020] Existing underwater topographic results corresponding to single-beam bathymetry are mostly presented in cross-sectional form. For the construction of continuous underwater DEMs, the mainstream technical solution is based on discrete bathymetry point data, relying on spatial interpolation algorithms combined with triangular network (TIN) network construction technology to achieve modeling, and finally converting it into regular grid DEM results. Its core working principle is as follows: First, the collected single-beam bathymetry points are processed. Then, spatial interpolation algorithms such as Kriging and inverse distance weighting are used to perform elevation interpolation to complete the discrete bathymetry points. Subsequently, an irregular triangular network is constructed using the interpolated elevation data to simulate the spatial structure of underwater topography. Finally, the triangular network model is converted into a regular grid underwater DEM, completing the transformation from discrete measurement points to a continuous topographic model. During the construction process, if it is necessary to set feature lines such as water boundary lines and contour lines to constrain topographic features, the underwater topographic morphology must be manually interpreted based on the bathymetry data, the feature lines must be manually drawn and the constraint features must be established, and then the manually constructed constraint features are integrated into the triangular network construction and interpolation process.

[0021] The inventors discovered that existing underwater DEM construction technologies based on single-beam bathymetry data have many fundamental flaws. They cannot adapt to the modeling requirements of sparse cross-section bathymetry data, and their low level of automation limits modeling efficiency and accuracy, making it difficult to meet the high standards required for aquatic development applications. Specifically, the drawbacks are: First, they require extremely high cross-section density from single-beam bathymetry. If the actual measured cross-sections are sparsely distributed, the DEM data between cross-sections lacks effective constraints, easily leading to distortion and poor accuracy in the constructed DEM, failing to accurately reflect the actual underwater topography. Second, the construction of constraint feature lines heavily relies on manual interpretation and drawing, resulting in extremely low automation. This is not only time-consuming and labor-intensive, but also prone to subjective errors due to differences in human experience, making it difficult to ensure accuracy. The accuracy and consistency of feature line construction restrict the overall modeling accuracy of DEM. Third, the edges of water bodies and the vicinity of feature lines often lack measured depth points. Existing spatial interpolation methods struggle to simultaneously ensure the accuracy of terrain details and the smoothness of the overall model when facing such complex boundaries and sparse measurement points. This results in edge distortion, terrain distortion, and even "holes" and "slopes" that do not match the real terrain in the generated TIN model at the water-land boundary. Fourth, existing technologies do not design differentiated network constraint rules for underwater terrain features. During the construction of triangulation networks, triangles may cross water feature lines, further exacerbating the modeling distortion problem of underwater DEMs. Ultimately, the accuracy and reliability of the generated DEMs fail to meet the needs of practical applications.

[0022] Taking the practical application scenario of underwater DEM construction using single-beam bathymetry in a river area as an example, when conducting underwater topographic mapping of a certain river, due to the large span of the river and high navigation requirements, only sparse measurement sections were deployed using single-beam bathymetry. When constructing the underwater DEM of this river using existing technology, the specific operation is as follows: Technicians first organize the collected sparse section bathymetry points, then manually interpret the topographic features such as the riverbank and thalweg based on the bathymetry section data, and manually draw the water boundary lines and other constraint feature lines. Subsequently, the inverse distance weighting method is used to interpolate the elevation of the blank areas between sections. Combining the manually drawn feature lines and the interpolated data, an irregular triangular network is constructed, which is finally converted into a regular grid underwater DEM. Several problems arose during the construction process: First, due to the sparse sounding sections, the interpolation data between sections lacked effective constraints, resulting in significant topographic distortion at the gaps between sections in the constructed DEM, deviating considerably from the actual underwater topography of the river channel. Second, when manually delineating the river channel boundary lines, the sounding points were only distributed at the sections, leading to subjective errors in shoreline position interpretation. This resulted in the delineated boundary lines not matching the actual land-water boundary, causing numerous "holes" at the boundary during subsequent network construction, failing to accurately reflect the actual contour of the river channel. Third, manually delineating feature lines was time-consuming and labor-intensive, requiring three professional technicians and taking two working days just for feature line delineation, resulting in low overall modeling efficiency. Fourth, no specific constraint rules were set for the manually delineated boundary lines during the triangulation network construction, causing some triangles to directly cross the boundary lines, resulting in steep slopes in the DEM at the river channel boundary, completely inconsistent with the actual underwater topographic undulations. Analysis reveals that the core reasons for the aforementioned problems are: the high dependence of existing technologies on the density of sounding profiles makes them unsuitable for underwater modeling scenarios with sparse sounding points; the manual-driven feature line construction mode inherently suffers from low efficiency and large errors; and existing interpolation and mesh construction technologies are not specifically designed for complex underwater boundaries and sparse sounding points, lack a scientific terrain constraint mechanism, and struggle to balance the boundary conformity and internal continuity of the DEM. Therefore, existing technologies can no longer meet the practical needs of high-precision, automated construction of underwater DEMs under single-beam bathymetry.

[0023] Based on this, the embodiments of this application provide an underwater digital elevation model construction method, system, computer equipment, and storage medium. By constructing an adaptive feature line constraint network, differential elevation interpolation is performed on the feature line nodes. Combined with the feature lines, differential constraints are implemented to construct a triangular network and convert it into an underwater digital elevation model. This ensures the conformity of the water boundary and the continuity of the terrain interior, solves the DEM distortion problem under sparse sounding points, and helps to improve the accuracy and fit of the underwater terrain model.

[0024] Please refer to Figure 1 , Figure 1 The flowchart below shows the underwater digital elevation model construction method provided in this embodiment of the invention; the underwater digital elevation model construction method includes: Step S100: Construct an adaptive feature line constraint network based on the geographical boundary of the water body.

[0025] In step S100 above, the geographical boundary line of the water body is first added to the feature line set and used as the initial seed feature line. Then, according to the preset grid parameters of the underwater digital elevation model to be constructed in the project, the corresponding feature line buffer distance is set. Based on the initial seed feature line, a buffer zone is created inward towards the water body, and the boundary line of this buffer zone is added to the feature line set. At the same time, the boundary line of this buffer zone is used as a new seed feature line. The operation of buffering inward and adding new seed feature lines to the feature line set is continuously iterated until the width inside the buffer zone can no longer meet the preset buffer distance requirement, at which point the iteration stops. Then, the shape of the water body is determined. If the water body is determined to be a long and narrow river, the center line of the long and narrow water body is extracted and added to the feature line set. Finally, all feature lines in the feature line set are integrated to form an adaptive feature line constraint network that adapts to the shape of the water body.

[0026] Step S200: Use a classification interpolation algorithm to assign elevation information to the nodes of the adaptive feature line constraint network to form a feature line constraint network with elevation.

[0027] In step S200 above, water depth data is first acquired using a single-beam echo sounder, and real-time three-dimensional coordinates are obtained through GNSS combined with SHCORS. The original underwater topographic elevation is then calculated through relative position conversion. Subsequently, the original data is preprocessed: parameter corrections such as draft correction and sound velocity correction are performed; gross errors that cannot be corrected, such as signal loss, plane or elevation anomalies, and water surface height anomalies, are identified and removed; and data rationality checks are completed. Finally, a dataset of effective elevation points for underwater topography that has passed quality control and can be used for subsequent modeling is obtained.

[0028] A KD-tree spatial index is established on the preprocessed high-reliability underwater effective elevation point dataset to improve the efficiency of nearest neighbor search for bathymetry points. Then, all feature lines in the adaptive feature line constraint network are traversed and divided into two categories: water body boundary lines and water body interior feature lines, which are processed separately. For water body boundary lines, the two nearest effective elevation points located on the water body interior side are retrieved from the effective elevation point dataset according to the established spatial index for each cross-section. Interpolation weights are calculated using the distance between these two points and their distance to the feature line, and the elevation values ​​of the boundary line nodes are interpolated using the distance-weighted interpolation method. For feature lines within the water body, based on the spatial index, the two nearest valid elevation points located on both sides of the feature line are retrieved from the effective elevation point dataset for each cross-section. These points are located at a distance less than a preset threshold (generally set as the average measurement point spacing). Using the distance from these two points to the feature line, the elevation values ​​of the internal feature line nodes are calculated using linear interpolation. In addition, for all feature lines with completed elevation assignments, the nodes are further densified and elevation values ​​are added between adjacent interpolated nodes according to the grid size of the DEM to be constructed. Finally, these are integrated to form a feature line constraint network with continuous and accurate elevation information.

[0029] Step S300: Using the feature line constraint network with elevation as the terrain constraint benchmark, construct a differentiated constraint type irregular triangular network by combining the effective elevation point dataset of underwater terrain.

[0030] In step S300 above, the feature line constraint network with elevation and the effective elevation point dataset of underwater topography can be used together as input for network construction. First, the water body boundary lines in the network are set as constrained Delaunay conditions, which are then integrated into the network structure as mandatory constraint edges of the triangulation network, preventing triangles from crossing the boundary lines and effectively preserving the abrupt changes in the water-land boundary and underwater topography. Next, the feature lines inside the water body in the network are set as conditions that conform to the Delaunay criterion. The method of automatically inserting densification points on the line segments is adopted to ensure that the entire internal feature line is composed of legal Delaunay edges, ensuring that the entire triangulation network is global and strictly satisfies the empty circumcircle and the maximum and minimum angle criteria, without forcibly locking edges or destroying the overall quality of the triangulation network. Finally, the boundary lines and internal feature lines with set constraint rules are used together with the effective elevation point dataset of underwater topography to participate in the network construction, completing the construction of a differentiated constrained irregular triangulation network.

[0031] Step S400: Convert the differentiated constraint type irregular triangular network into an underwater digital elevation model.

[0032] In step S400 above, based on the preset regular grid size of the project, the constructed differentiated constrained irregular triangular network is rasterized through interpolation, converting the terrain elevation information of the triangular network into regular grid data containing elevation information. Subsequently, the accuracy of the regular grid data is verified. If the accuracy verification result meets the preset accuracy threshold, the regular grid data is directly output as the final underwater digital elevation model. If the accuracy verification result does not reach the preset threshold, the relevant steps for constructing the differentiated constrained irregular triangular network are repeated, the triangular network is optimized and reconstructed, and rasterization and accuracy verification operations are performed again until the accuracy verification result meets the preset threshold requirements, and finally the underwater digital elevation model that meets the accuracy standard is output.

[0033] Therefore, the underwater digital elevation model construction method provided by the embodiments of the present invention constructs an adaptive feature line constraint network, performs differential elevation interpolation on feature line nodes, combines the feature lines to implement differential constraints to construct a triangular network and convert it into an underwater digital elevation model, thereby ensuring the conformity of the water boundary and the continuity of the terrain interior, solving the DEM distortion problem under sparse sounding points, and helping to improve the accuracy and fit of the underwater terrain model.

[0034] Please refer to Figure 2 , Figure 2 This is a flowchart of a feature line constraint network construction method provided in an embodiment of the present invention; the feature line constraint network construction method includes: Step S20: Use the geographical boundary of the water body as the initial seed feature line and include it in the initial feature line set.

[0035] In step S20 above, the actual geographical boundary of the water body to be modeled can be accurately extracted first. This boundary is the real outline of the water-land junction, which can accurately reflect the actual range and external shape of the water body. The geographical boundary of the water body is used as the initial seed feature line for the construction of the entire feature line constraint network. It is the basis for the generation of all subsequent hierarchical feature lines. Then, the initial seed feature line is formally included in the pre-created initial feature line set to complete the initial construction and data collection of the feature line network.

[0036] Step S21: Determine the feature line buffer distance based on the preset grid parameters of the underwater digital elevation model to be constructed.

[0037] In step S21 above, the preset grid size parameters of the underwater digital elevation model to be constructed can be determined according to the actual needs of the project. The grid size is directly used as the basis for setting the feature line buffer distance. For example, if the required underwater DEM grid size is 10m, the corresponding feature line buffer distance parameter is set to 10m. This achieves linkage and adaptation between the feature line density and the final DEM resolution, so that the generated feature line network can accurately match the accuracy requirements of the subsequent DEM construction.

[0038] Step S22: Using the initial seed feature line as a reference, create a buffer zone towards the interior of the water body, and incorporate the boundary line of the buffer zone into the feature line set as a new seed feature line.

[0039] In step S22 above, the initial seed feature line, which is the geographical boundary of the water body included in the initial feature line set, is used as the geometric reference. According to the feature line buffer distance determined above, a buffer zone is geometrically created in the direction of the water body interior. A buffer zone boundary line is generated that is parallel to the initial seed feature line and the spacing is the buffer distance. Then, the newly generated buffer zone boundary line is used as a new seed feature line and included in the feature line set to complete a new round of seed feature line collection.

[0040] Step S23: Perform inward buffering and new seed feature line inclusion operations based on buffer distance until the width inside the buffer can no longer meet the buffer distance requirement.

[0041] In step S23 above, the operation of creating a buffer zone into the water body at a predetermined buffer distance and incorporating the boundary line of the newly generated buffer zone into the feature line set as a new seed feature line can be repeated. This process is carried out iteratively. In each iteration, a buffer zone is built inward based on the latest seed feature line and new seed feature lines are added until the width of the remaining area of ​​the water body without a buffer zone can no longer meet the above-determined feature line buffer distance requirement. At this point, the iterative operation of buffering inward and incorporating new seed feature lines is stopped.

[0042] Step S24: Determine whether the water body is a long and narrow shape. If so, extract the center line of the long and narrow water body and include it in the feature line set.

[0043] In step S24 above, the overall shape of the water body to be modeled can be comprehensively determined to identify whether it is a long and narrow water body of the river type. If the water body is determined to be a long and narrow water body, considering that the central area of ​​such water bodies is prone to missing feature lines, the center line of the long and narrow water body is extracted and added to the feature line set to improve the feature line layout of the long and narrow water body, make up for the feature line gap in its central area, and enable the feature line network to completely fit the topographic shape of the long and narrow water body.

[0044] Step S25: Integrate all feature lines in the feature line set to form an adaptive feature line constraint network.

[0045] In step S25 above, all feature lines in the feature line set are uniformly integrated. This set includes the initial seed feature line, i.e., the geographical boundary of the water body, all buffer boundary lines generated and incorporated in each inward buffering, and the water body center line supplemented for the narrow and elongated water body. By integrating all types of feature lines, a complete, uniformly distributed and highly adapted constraint feature line network that fits the natural shape of the water body to be modeled is formed, i.e., the adaptive feature line constraint network.

[0046] Therefore, the feature line constraint network construction method provided by the embodiments of the present invention, through the adaptive feature line constraint network construction method, completes the entire process automatically without human intervention, completely replacing the traditional method of manually drawing feature lines, greatly reducing human subjective errors and workload, and improving modeling efficiency; at the same time, it relies on the geographical boundary of the water body and the DEM grid parameters to achieve uniform generation of feature lines, adapt to the natural shape of the water body and specifically supplement the center line of the narrow water body, and the formed feature line network completely fits the outline of the water body, providing a precise and adapted terrain constraint benchmark for subsequent elevation interpolation and differentiated constraint network construction, ensuring the boundary shape preservation and terrain internal continuity of the underwater DEM from the source.

[0047] Please refer to Figure 3 , Figure 3 This is a flowchart of a feature line elevation assignment method provided in an embodiment of the present invention; the feature line elevation assignment method includes: Step S30: Establish a spatial index for the effective elevation point dataset of underwater topography.

[0048] In step S30 above, for the effective elevation point dataset of underwater topography obtained after preprocessing, KD tree or other efficient spatial index structure is used to organize the three-dimensional coordinate information of all effective elevation points into an index structure that can be quickly retrieved. This provides efficient nearest-neighbor point retrieval support for the subsequent elevation interpolation calculation of water body boundary lines and internal feature line nodes, thereby improving the efficiency and accuracy of interpolation calculation.

[0049] Step S31: Divide the feature lines in the adaptive feature line constraint network into water body boundary lines and water body interior feature lines.

[0050] In step S31 above, all feature lines in the constructed adaptive feature line constraint network can be fully traversed. Based on the spatial location attributes and terrain features of each feature line, they are classified and divided. Feature lines located at the intersection of land and water and with effective elevation points distributed only on one side of the water body are defined as water body boundary lines. Feature lines located inside the water body and with effective elevation points retrieved on both sides are defined as water body interior feature lines.

[0051] Step S32: For the water body boundary line, retrieve the effective elevation points on one side according to the spatial index, and assign node elevation values ​​using the distance weight interpolation method.

[0052] In step S32 above, based on the KD tree spatial index established for the underwater effective elevation point dataset, a nearest neighbor measurement point search is performed on any node of the water body boundary line for each cross section to accurately obtain the two nearest effective elevation points located on the inner side of the water body; the distance between the two effective elevation points and the spatial distance from the two points to the corresponding node of the boundary line are calculated, and corresponding interpolation weights are assigned according to the obtained spatial distances; based on the assigned interpolation weights and the actual elevation values ​​of the two nearest effective elevation points, the elevation value of the node of the water body boundary line is calculated by the distance weight interpolation method, and the elevation values ​​of all nodes of the water body boundary line are completed in this way.

[0053] Step S33: For the feature lines inside the water body, retrieve the effective elevation points on both sides according to the spatial index, and assign the node elevation values ​​using the linear interpolation method.

[0054] In step S33 above, based on the KD tree spatial index established for the underwater effective elevation point dataset, a nearest neighbor measurement point search can be performed on any node of the feature line inside the water body for each cross section. The two nearest effective elevation points located on both sides of the feature line and with a distance less than a preset threshold (generally set as the average measurement point spacing) can be accurately obtained. The spatial distance from the two effective elevation points to the corresponding node of the internal feature line can be calculated, and the linear interpolation coefficient can be determined according to the proportional relationship of the obtained spatial distance. Based on the interpolation coefficient and the actual elevation values ​​of the two nearest effective elevation points, the elevation value of the node of the feature line inside the water body can be calculated by linear interpolation. The elevation assignment of all nodes of the feature line inside the water body can be completed in this way.

[0055] Step S34: Encrypt all feature line nodes that have been assigned elevation values ​​and insert elevation values ​​to form a feature line constraint network with elevation.

[0056] In step S34 above, for the water body boundary lines and internal feature lines that have completed elevation assignment, the nodes of adjacent assigned nodes on the feature lines can be densified using the grid size of the underwater digital elevation model to be constructed as the standard, and densified nodes can be added; then, based on the elevation information of the adjacent assigned nodes, the newly added densified nodes are subjected to elevation interpolation calculation and assigned corresponding elevation values, so that all feature lines have continuous and accurate elevation information; finally, all feature lines that have completed elevation assignment and densification interpolation are integrated to form a feature line constraint network with elevation.

[0057] Therefore, the feature line elevation assignment method provided by the embodiments of the present invention achieves efficient retrieval of sounding points through spatial indexing, adopts differentiated interpolation strategies for different types of feature lines, adapts water body boundary lines to single-sided sounding point features, and fits internal feature lines to the distribution of double-sided sounding points, resulting in assignment results that better match the actual terrain. Node densification and interpolation further ensure elevation continuity, and the resulting network of feature lines with elevation provides a high-precision elevation benchmark for subsequent constrained network construction, which not only improves interpolation efficiency and accuracy but also avoids manual assignment errors. From the elevation dimension, it ensures the boundary conformity and internal terrain continuity of the underwater DEM, effectively solves the modeling distortion problem under sparse sounding points, and significantly improves the accuracy and fit of the underwater terrain model.

[0058] Please refer to Figure 4 , Figure 4 This is a flowchart of a method for assigning elevation values ​​to water body boundaries provided in an embodiment of the present invention; the method for assigning elevation values ​​to water body boundaries includes: Step S40: For any node on the water body boundary line, retrieve and obtain the two most recent valid elevation points located on the inside side of the water body from the valid elevation point dataset according to the spatial index.

[0059] In step S40 above, the KD tree spatial index pre-established for the underwater effective elevation point dataset is used as the retrieval basis. Any node to be assigned a value on the water body boundary line is selected. With this node as the retrieval center, the nearest neighbor point is retrieved in the effective elevation point dataset according to the corresponding cross-sectional dimension. The effective elevation points located on the inside side of the water body are accurately selected, and the two effective elevation points closest to the node to be assigned a value are extracted from them as the basic data for the elevation interpolation calculation of this node.

[0060] Step S41: Calculate the spatial distance between the two nearest valid elevation points and the node.

[0061] In step S41 above, based on the three-dimensional coordinates of the two nearest valid elevation points on one side of the water body obtained by retrieval, and the planar coordinates of the node to be assigned on the water body boundary line, the spatial distance from the first valid elevation point to the node to be assigned, the spatial distance from the second valid elevation point to the node to be assigned, and the spatial distance between the two valid elevation points can be calculated respectively.

[0062] Step S42: Assign interpolation weights to the two nearest valid elevation points based on spatial distance.

[0063] In step S42 above, interpolation weights can be allocated according to the core principle of distance-weighted interpolation. The closer the effective elevation point is to the node to be assigned, the higher the proportion of interpolation weights it is assigned. The farther the effective elevation point is from the node to be assigned, the lower the proportion of interpolation weights it is assigned. In this way, the interpolation weight values ​​corresponding to the two closest effective elevation points are determined.

[0064] Step S43: Calculate the elevation value of the node based on the interpolation weight and the elevation values ​​of the two nearest valid elevation points.

[0065] In step S43 above, the interpolation weights of the two nearest valid elevation points obtained above can be multiplied by the actual elevation values ​​of the corresponding valid elevation points respectively, and the results of the two multiplications can be summed. The calculated result is the final elevation value of the node to be assigned on the water body boundary line. The elevation of all nodes on the water body boundary line can be solved by this calculation method.

[0066] Therefore, the elevation assignment method for water body boundary lines provided by the embodiments of the present invention accurately retrieves the nearest effective elevation point on one side of the water body through spatial indexing, and calculates the elevation by combining spatial distance with interpolation weights. This method is adapted to the terrain features of only measuring points on one side of the water-land boundary, making the elevation assignment of boundary line nodes more consistent with the actual underwater terrain. The assignment logic is accurate and the calculation is efficient, effectively making up for the deficiency of sparse sounding points at the water boundary, improving the elevation accuracy of the boundary line, and building a solid boundary elevation benchmark for subsequent constraint network construction. This further ensures the conformity of the water-land boundary of the underwater DEM and avoids distortion of the boundary terrain.

[0067] Please refer to Figure 5 , Figure 5 This is a flowchart of a method for assigning elevation values ​​to feature lines within a water body, provided in an embodiment of the present invention. The method for assigning elevation values ​​to feature lines within a water body includes: Step S50: For any node on the feature line inside the water body, retrieve and obtain the two nearest valid elevation points located on both sides of the feature line from the valid elevation point dataset according to the spatial index.

[0068] In step S50 above, the KD tree spatial index pre-built for the underwater effective elevation point dataset is used as the basis for efficient retrieval. Any node to be assigned a value on the feature line inside the water body is selected, and the nearest neighbor measurement point is retrieved according to the cross-sectional dimension corresponding to the node. Effective elevation points located on both sides of the feature line are accurately selected in the effective elevation point dataset, and it is ensured that the distance between the two points and the node is less than a preset threshold (generally set as the average measurement point spacing). The two effective elevation points closest to the node to be assigned a value are extracted from it as the basic data for the linear interpolation calculation of the node.

[0069] Step S51: Calculate the spatial distance between the two nearest valid elevation points and the node.

[0070] In step S51 above, based on the three-dimensional coordinates of the two nearest effective elevation points located on both sides of the feature line obtained above, and the planar coordinates of the current node to be assigned on the feature line inside the water body, the spatial distance from the first effective elevation point to the node to be assigned and the spatial distance from the second effective elevation point to the node to be assigned can be accurately calculated respectively.

[0071] Step S52: Determine the linear interpolation coefficients based on the proportional relationship of spatial distance.

[0072] In step S52 above, the linear interpolation coefficients can be determined according to the inverse proportional relationship of distance. The closer the effective elevation point is to the node to be assigned, the larger its corresponding interpolation coefficient is, and the farther away it is, the smaller the interpolation coefficient is, so as to obtain the coefficient values ​​used for linear interpolation calculation.

[0073] Step S53: Calculate the elevation value of the node based on the linear interpolation coefficients and the elevation values ​​of the two nearest valid elevation points.

[0074] In step S53 above, the elevation values ​​of the two nearest valid elevation points on both sides can be combined with the linear interpolation coefficients and weighted according to the linear interpolation formula. The calculation result is used as the elevation value of the node to be assigned on the feature line inside the water body. The elevation calculation of all nodes is completed in sequence.

[0075] Therefore, the elevation assignment method for feature lines within water bodies provided by this invention quickly retrieves the nearest effective elevation points on both sides of the feature line using spatial indexing, determines the interpolation coefficients based on spatial distance ratios, and linearly calculates the elevation. This method is suitable for terrain features with measuring points on both sides within the water body, and the assignment results closely match the natural undulation trend of the underwater terrain. The efficient and accurate bilateral interpolation logic compensates for the sparseness of sounding points within the water body, improves the accuracy and continuity of elevation assignment for internal feature lines, provides an accurate internal elevation benchmark for subsequent differentiated constraint network construction, ensures the morphological continuity within the underwater DEM terrain, and avoids distortion of the internal terrain.

[0076] Please refer to Figure 6 , Figure 6 This is a flowchart of a triangulation network construction method provided in an embodiment of the present invention; the triangulation network construction method includes: Step S60: Set the water body boundary line in the feature line constraint network with elevation as the forced constraint edge of the triangular network.

[0077] In step S60 above, the water body boundary line in the feature line constraint network with completed elevation assignment can be set as a forced constraint edge when constructing the irregular triangular network. This ensures that the triangular network must strictly follow the direction of the boundary line during the network construction process, prohibiting triangles from crossing the water body boundary line and ensuring that the topography of the water-land boundary does not become distorted.

[0078] Step S61: Using the method of automatically inserting encrypted points, the internal feature lines of the water body in the feature line constraint network with elevation are set as legal edges that conform to the Delaunay triangulation criterion.

[0079] In step S61 above, the feature lines inside the water body can be optimized by automatically inserting densification points. The feature lines inside the water body in the feature line constraint network with elevation are embedded into the triangular network construction process, so that they satisfy the Delaunay triangular network empty circumcircle and maximum and minimum angle criteria, and become legal edges in the constraint triangular network.

[0080] Step S62: Combine the effective elevation point dataset of underwater topography to construct a differentiated constrained irregular triangular network.

[0081] In step S62 above, the water body boundary line set as a mandatory constraint edge and the water body internal feature line conforming to the Delaunay criterion can be used as constraints. Triangulation is performed in combination with the effective elevation point dataset of underwater topography to construct an irregular triangular network that implements differentiated constraints on the boundary and internal feature lines.

[0082] Therefore, the triangulation network construction method provided by the embodiments of the present invention ensures that the water-land boundary morphology is not destroyed by setting mandatory constraint edges for the water body boundary line, and forms legal edges for the internal feature lines by adapting the Delaunay criterion with densified points, taking into account the natural morphology of the internal terrain. By combining the effective elevation point dataset for network construction, it achieves differentiated modeling with "strong boundary constraints and internal adaptability", effectively avoiding the terrain distortion problem of triangulation network construction under sparse sounding points, providing a high-precision and shape-preserving terrain skeleton for subsequent DEM conversion, and further ensuring the boundary shape preservation and internal continuity of the underwater DEM.

[0083] Please refer to Figure 7 , Figure 7 This is a flowchart of a model generation method provided in an embodiment of the present invention; the model generation method includes: Step S70: Based on the preset regular grid size, the differentially constrained irregular triangular network is rasterized through interpolation to obtain regular grid data containing elevation information.

[0084] In step S70 above, grid interpolation can be performed on the basis of the differentiated constraint type irregular triangular network according to the pre-set underwater digital elevation model grid size parameters. The triangular network is converted into a regularly arranged grid data structure by assigning values ​​grid by grid, and finally regular grid data containing corresponding elevation values ​​is generated.

[0085] Step S71: Verify the accuracy of the regular grid data containing elevation information; if the accuracy verification result meets the preset threshold, output the underwater digital elevation model; if it does not meet the threshold, reconstruct the differentiated constrained irregular triangular network until the accuracy verification is passed.

[0086] In step S71 above, the generated regular grid data containing elevation information can be compared and calculated with the measured checkpoint data to complete the model accuracy verification. If the verification error is within the preset threshold range, the final underwater digital elevation model is output. If the error exceeds the preset threshold, the process is returned to re-optimize and reconstruct the differentiated constraint irregular triangular network, and the interpolation rasterization and accuracy verification process is repeated until the accuracy requirements are met.

[0087] Therefore, the model generation method provided by the embodiments of the present invention directly inherits the terrain conformity advantage of the previous grid construction by rasterizing the high-precision constrained triangular mesh through preset grid size; through the accuracy verification closed-loop mechanism, substandard models are re-constructed and optimized to ensure that the output regular grid DEM retains the terrain accuracy of the triangular mesh and meets the grid data format requirements of engineering applications; it effectively avoids the terrain distortion problem that is prone to occur in traditional rasterization, and the final output underwater DEM has controllable accuracy and fits the actual underwater terrain, meeting the application needs of high-precision water area development and management.

[0088] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the underwater digital elevation model (DEM) construction process provided by the embodiments of the present invention. First, in the data acquisition and preprocessing stage, raw data is obtained using single-beam bathymetry, shoreline, and shallow water depth sounders. After data conversion, standardization, and data checking, a reliable dataset of valid elevation points is obtained. Then, in the feature line and DEM construction stage, a constrained feature line network is adaptively generated based on the valid elevation points (including seed line buffering, iterative line construction, and supplementation with the center line of narrow water bodies). The elevation interpolation and densification of feature line nodes are completed by establishing spatial indexes and search rules. Finally, a constrained irregular triangular network (TIN) is constructed by combining the measurement points and constrained feature lines, and a regular underwater digital elevation model (DEM) is generated by rasterization.

[0089] Please refer to Figure 9 , Figure 9 This is a schematic diagram illustrating the construction of the feature line constraint network provided in this embodiment of the invention. Based on the preset grid size of the constructed DEM, a buffer distance is set, and buffer zones are created progressively towards the interior of the water body. Feature lines are iteratively established. For narrow water bodies such as rivers, their center lines are simultaneously included in the feature line set to supplement the features of the central region. Through this process, a complete, uniform feature line constraint network that conforms to the morphology of the water body can be automatically generated.

[0090] Please refer to Figure 10 and Figure 11 , Figure 10 This is a schematic diagram illustrating the assignment of feature line elevation values ​​in an embodiment of the present invention. Figure 11 This is a schematic diagram of the feature lines with elevation information provided in the embodiments of the present invention. By establishing a spatial index to support efficient proximity search, for both boundary lines and feature lines within the boundary, at each cross-section location, the two closest points in the sounding points are searched, and the elevation at that node is calculated based on their distance weight or a linear interpolation method. Subsequently, densification interpolation is performed between adjacent nodes according to the grid size, thereby ensuring that the feature lines have continuous and accurate elevation information. The output is a feature line network with elevation information.

[0091] Please refer to Figure 12 , Figure 12 This is a schematic diagram comparing the results of feature line constrained DEM and unconstrained DEM provided in the embodiments of the present invention; compared with unconstrained DEM, feature line constrained DEM has a more complete data range, more obvious expression in details, and the edges at the bifurcation points are more realistic.

[0092] Please refer to Figure 13 , Figure 13 This is a schematic diagram of the underwater digital elevation model construction system provided in an embodiment of the present invention. The system includes a feature line constraint network construction module 10, an elevation assignment module 20, a triangulation network construction module 30, and a model generation module 40. The feature line constraint network construction module 10 is used to construct an adaptive feature line constraint network based on the geographical boundary of the water body. The elevation assignment module 20 is used to assign elevation information to the nodes of the adaptive feature line constraint network using a classification interpolation algorithm to form a feature line constraint network with elevation. The triangulation network construction module 30 is used to construct a differentiated constraint-type irregular triangulation network using the feature line constraint network with elevation as the terrain constraint benchmark and combining it with a dataset of effective elevation points. The model generation module 40 is used to convert the differentiated constraint-type irregular triangulation network into an underwater digital elevation model.

[0093] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform the steps in any of the above implementations.

[0094] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, the computer program product including a computer program / instruction, the computer program / instruction being processed by the steps in any of the above implementation methods.

[0095] The computer-readable storage medium can be any medium capable of storing program code, such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), or Electrically Erasable Programmable Read-Only Memory (EEPROM). The storage medium stores the program, and the processor executes the program after receiving an execution instruction. The method executed by the electronic terminal according to the process definition disclosed in any embodiment of this invention can be applied to the processor or implemented by the processor.

[0096] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The system implementations described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some communication interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0097] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0098] Furthermore, in the various embodiments of the present invention, the functional modules can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0099] It can be replaced and can be implemented, wholly or partially, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, wholly or partially, in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated.

[0100] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0101] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0102] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing an underwater digital elevation model, characterized in that, The method includes: An adaptive feature line constraint network is constructed based on the geographical boundaries of water bodies. An elevation information is assigned to the nodes of the adaptive feature line constraint network using a classification interpolation algorithm to form a feature line constraint network with elevation information. Using the feature line constraint network with elevation as the terrain constraint benchmark, a differentiated constraint type irregular triangular network is constructed by combining the effective elevation point dataset of underwater terrain. The differentiated constraint type irregular triangular network is transformed into an underwater digital elevation model.

2. The method according to claim 1, characterized in that, The adaptive feature line constraint network constructed based on the geographical boundary of the water body includes: The geographical boundary of the water body is used as the initial seed feature line and included in the initial feature line set; Determine the feature line buffer distance based on the preset grid parameters of the underwater digital elevation model to be constructed; Using the initial seed feature line as a reference, a buffer zone is created towards the interior of the water body, and the boundary line of the buffer zone is included in the feature line set as a new seed feature line. Perform inward buffering and new seed feature line inclusion operations based on the buffer distance until the width inside the buffer can no longer meet the buffer distance requirement; Determine whether the water body is a long and narrow shape. If so, extract the centerline of the long and narrow water body and include it in the set of feature lines. All feature lines in the feature line set are integrated to form the adaptive feature line constraint network.

3. The method according to claim 1, characterized in that, The step of assigning elevation information to the nodes of the adaptive feature line constraint network using a classification interpolation algorithm includes: Establish a spatial index for the effective elevation point dataset of underwater topography; The feature lines in the adaptive feature line constraint network are divided into water body boundary lines and water body interior feature lines. For the water body boundary line, effective elevation points on one side are retrieved according to the spatial index, and the node elevation is assigned using the distance weighted interpolation method. For the feature lines inside the water body, valid elevation points on both sides are retrieved according to the spatial index, and the node elevation is assigned using linear interpolation. All feature line nodes that have been assigned elevation values ​​are encrypted and their elevation values ​​are inserted to form the feature line constraint network with elevation.

4. The method according to claim 3, characterized in that, For the water body boundary line, effective elevation points on one side are retrieved according to the spatial index, and the node elevation is assigned using distance-weighted interpolation, including: For any node on the boundary line of the water body, the two nearest valid elevation points located on the inside side of the water body are retrieved from the valid elevation point dataset according to the spatial index. Calculate the spatial distance between the two nearest valid elevation points and the node; Based on the spatial distance, assign interpolation weights to the two nearest valid elevation points; The elevation value of the node is calculated based on the interpolation weight and the elevation values ​​of the two nearest valid elevation points.

5. The method according to claim 3, characterized in that, For the feature lines inside the water body, valid elevation points on both sides are retrieved according to the spatial index, and nodal elevations are assigned using linear interpolation, including: For any node on the feature line inside the water body, the two nearest valid elevation points located on both sides of the feature line are retrieved from the valid elevation point dataset according to the spatial index. Calculate the spatial distance between the two nearest valid elevation points and the node; Based on the proportional relationship of the spatial distances, determine the linear interpolation coefficients; The elevation value of the node is calculated based on the linear interpolation coefficients and the elevation values ​​of the two nearest valid elevation points.

6. The method according to any one of claims 1 to 5, characterized in that, The process of constructing a differentiated constrained irregular triangular network using the elevation-bound feature line constraint network as the terrain constraint benchmark and combining it with the effective elevation point dataset of underwater terrain includes: Set the water body boundary line in the feature line constraint network with elevation as the triangulation network forced constraint edge; By automatically inserting encryption points, the water body internal feature lines in the feature line constraint network with elevation are set as legal edges that conform to the Delaunay triangulation criterion. The differentially constrained irregular triangular network is constructed by combining the effective elevation point dataset of underwater topography.

7. The method according to any one of claims 1 to 5, characterized in that, The process of converting the differentiated constrained irregular triangular network into an underwater digital elevation model includes: Based on the preset regular grid size, the differentiated constrained irregular triangular network is rasterized through interpolation to obtain regular grid data containing elevation information; The accuracy of the regular grid data containing elevation information is verified; if the accuracy verification result meets the preset threshold, the underwater digital elevation model is output; if it does not meet the threshold, the differentiated constraint irregular triangular network is reconstructed until the accuracy verification is passed.

8. An underwater digital elevation model construction system, characterized in that, The system includes: The feature line constraint network construction module is used to construct an adaptive feature line constraint network based on the geographical boundary of the water body; The elevation assignment module is used to assign elevation information to the nodes of the adaptive feature line constraint network using a classification interpolation algorithm to form a feature line constraint network with elevation. The triangulation construction module is used to construct a differentiated constraint-type irregular triangulation network by using the feature line constraint network with elevation as the terrain constraint reference and combining it with the effective elevation point dataset of underwater terrain. The model generation module is used to convert the differentiated constraint irregular triangular network into an underwater digital elevation model.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, perform the steps of the method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1-7.

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