Multi-source water-land seamless coupling modeling method and system based on river section constraint

Abstract

This invention discloses a multi-source seamless water-land topography coupling modeling method and system based on river cross-section constraints. The method includes: 1. Multi-source data preparation and preprocessing to generate constraint lines; 2. Constructing a TIN model based on topological constraints to eliminate the "step effect"; 3. Performing two-stage seamless elevation coupling on the TIN model to prevent abrupt changes caused by elevation differences. The two stages are an elevation difference interpolation stage for high-precision fusion and an iterative smoothing stage based on curvature to eliminate local abrupt changes; 4. Model output: Outputting the final seamless elevation-coupled TIN model in standard GIS format for hydrodynamic simulation or visualization applications. This invention achieves true seamless coupling by resolving geometric discontinuities through topological constraints (TIN) and addressing elevation differences through two-stage fusion, fundamentally eliminating the "step effect" and "suspended" phenomena at the water-land interface.

Description

Technical Field

[0001] This invention belongs to the fields of geographic information systems, water conservancy engineering, remote sensing and mapping and 3D modeling. In particular, it relates to a method and system for seamless coupling modeling of multi-source water and land terrain based on river cross-section constraints. It is a technology for constructing high-precision, high-fidelity, topologically constrained digital terrain models of the entire water and land area using multi-source heterogeneous data. Background Technology

[0002] Existing methods for coupled water and land topography modeling mainly face the following technical challenges:

[0003] 1. Geometric discontinuity at the land-water interface: Since land topographic data and underwater topographic data come from different sensors and have different accuracies and resolutions, simple grid fusion or general interpolation at the interface can easily cause abrupt elevation changes, which manifest as a local "step effect" or data "hanging" phenomenon, seriously affecting the accuracy and stability of hydrodynamic simulation.

[0004] 2. Insufficient utilization of sparse cross-sectional data: Although the measured cross-sectional data of the river channel are extremely accurate, in traditional methods, they are usually regarded as ordinary points for interpolation or only used for model verification, rather than as mandatory topological constraints to guide the geometric construction of the model, resulting in low geometric fidelity in local areas of the river channel. Summary of the Invention

[0005] The purpose of this invention is to propose a seamless coupling modeling method and system for multi-source water-land topography based on river cross-section constraints. This aims to solve the problem of discontinuous elevation of the water-land boundary line caused by differences in data sources in multi-source water-land topography coupling, as well as the problem that measured high-precision cross-section data is not effectively used as topological constraints.

[0006] To achieve the above objectives, the solution of the present invention is as follows:

[0007] A multi-source water-land terrain seamless coupling modeling method based on river cross-section constraints includes:

[0008] Step 1: Multi-source data preparation and preprocessing to generate constraint lines:

[0009] The preparation of multi-source data involves unifying the coordinate system and elevation datum of all data based on the collected point cloud data of land elevation around the river, point cloud data of underwater water depth in the river, and measured cross-sectional data of the river.

[0010] The preprocessing generates constraint lines: the central axis of the main channel of the river is extracted as the longitudinal constraint line, the preliminary riverbank line is extracted, and the endpoints of the cross-section line are used as constraint points for correction to generate the final riverbank line as the constraint line of the water-land boundary. All measured river cross-section lines are defined as transverse constraint lines.

[0011] Step 2: Constructing the TIN model based on topological constraints: The TIN model is constructed by taking the final riverbank line, central axis, and cross-section line from Step 1 as inputs, so that the geometric topology of the model has continuity and the "step effect" is eliminated.

[0012] Step 3: Perform two-stage seamless elevation coupling on the TIN model to prevent abrupt changes caused by elevation differences. The two stages are the high-precision fusion stage of elevation difference interpolation and the stage of eliminating local abrupt changes based on curvature iterative smoothing.

[0013] Step 4: Model Output: Output the final seamlessly elevation-coupled TIN model in a standard GIS format for hydrodynamic simulation or visualization applications.

[0014] The solution further includes: In step two, the construction of the TIN model based on topological constraints further includes: executing the constrained Delaunay triangulation algorithm, using land elevation point cloud data as nodes, and in generating the TIN model, the sides of the triangles must coincide with all hard boundary lines.

[0015] The solution further includes the following high-precision fusion stage of the elevation difference interpolation: difference calculation, difference interpolation, and final superposition.

[0016] The difference calculation involves calculating the elevation difference relative to the background DEM for all constraint points i within the transition area. :

[0017] ;

[0018] in: This represents the elevation difference of the i-th constraint point, i.e., the difference between the actual elevation and the background DEM elevation; This represents the actual measured elevation value of the i-th constraint point; This represents the elevation value extracted from the background DEM at the location of the i-th constraint point;

[0019] The interpolation of the difference: using As the interpolation variable, interpolation is performed on all TIN nodes j within the transition region to obtain the correction amount. :

[0020] ;

[0021] in: The elevation correction at node j is obtained through inverse distance weighted interpolation; d ij is the horizontal distance between TIN node j and constraint point i; P is the power parameter in the inverse distance weighted interpolation; N is the total number of constraint points participating in the interpolation.

[0022] The final overlay: The correction amount is overlaid onto the background DEM to obtain the final elevation of node j. :

[0023] ;

[0024] in: This is the final, corrected elevation. The original elevation of node j in the background DEM; This is the elevation correction for node j.

[0025] The solution further includes: the curvature-based iterative smoothing and local mutation elimination stage includes: local optimization and hard constraint locking.

[0026] The local optimization involves performing a curvature-based iterative smoothing algorithm on the TIN nodes after elevation calculation in the transition region to eliminate sharp local elevation abrupt changes that may occur during the fusion process. The iterative correction formula for node j is as follows:

[0027] ;

[0028] in: The elevation value obtained by updating node j after the (t+1)th iteration; Let be the elevation value of node j in the t-th iteration; ω is the relaxation coefficient, used to control the strength of smoothing and the convergence speed; N j This represents the number of TIN nodes directly adjacent to node j. Let be the elevation value of the kth adjacent node of node j at the t-th iteration; This represents the average elevation of all adjacent nodes of node j at the t-th iteration.

[0029] The hard constraint locking: Throughout the entire iterative smoothing process, the hard constraint conditions are observed.

[0030] ;

[0031] in: A set of hard boundary lines; Let J be the constraint elevation value for node j; Let be the elevation value of node j in the t-th iteration;

[0032] If node j is located on the hard boundary line, its elevation remains unchanged during the iteration, i.e. .

[0033] A further step in the scheme is that the power parameter in the inverse distance weighted interpolation is taken as the inverse distance squared weighted value of 2, that is: P=2.

[0034] A multi-source seamless coupling modeling system for water and land topography based on river cross-section constraints, which implements the modeling method, includes a data access and preprocessing module, a feature extraction and correction module, a topological constraint TIN construction module, a seamless coupling elevation calculation module, and a result visualization and output module.

[0035] The data access and preprocessing module is responsible for reading multi-source data and unifying coordinate systems and elevation benchmarks.

[0036] The feature extraction and correction module is responsible for extracting feature lines and generating a set of constraint lines based on the cross-sectional data.

[0037] The topological constraint TIN construction module is responsible for executing the constraint Delaunay algorithm to construct the geometric topological constraint TIN.

[0038] The seamlessly coupled elevation calculation module is responsible for executing a two-stage algorithm: elevation difference interpolation (DIF) and curvature-based iterative smoothing.

[0039] The results visualization and output module is responsible for model validation, visualization, and format output.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] 1. Achieve true seamless coupling: Geometric discontinuities are resolved through topological constraints (TIN), and elevation differences are resolved through two-stage fusion, fundamentally eliminating the "step effect" and "suspended" phenomena at the water-land interface.

[0042] 2. High geometric fidelity: By using high-precision measured cross-sectional data as forced geometric anchor points, the geometric fidelity and accuracy of the final model in the riverbed area are greatly improved.

[0043] 3. High numerical stability: By introducing curvature-based iterative smoothing and local optimization of the transition region, the model is ensured to have good computational stability when used for hydrodynamic simulation.

[0044] The invention will be further explained in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0046] Figure 2 This is a schematic diagram of the TIN constraint of the present invention;

[0047] Figure 3 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0048] Example 1:

[0049] This embodiment aims to address the problem of discontinuous elevation at the land-water boundary caused by differences in data sources in multi-source land-water topography coupling, and the problem that measured high-precision cross-sectional data is not effectively utilized as topological constraints; such as Figure 1 and Figure 2 As shown, a multi-source water-land terrain seamless coupling modeling method based on river channel cross-section constraints includes:

[0050] Step 1: Multi-source data preparation and preprocessing to generate constraint lines:

[0051] The preparation of multi-source data involves: strictly unifying the coordinate system and elevation benchmark of all data based on the collected elevation point cloud data of the land surrounding the river, the underwater water depth point cloud data of the river, and the measured high-precision river cross-section data.

[0052] The preprocessing generates constraint lines: the central axis of the main channel of the river is extracted as the longitudinal constraint line, the preliminary riverbank line is extracted, and the endpoints of the cross-section line are used as hard constraint points for correction to generate the final riverbank line as the constraint line of the water-land boundary. All measured river cross-section lines are defined as transverse constraint lines.

[0053] Step 2: Constructing the TIN model based on topological constraints (geometric constraints): The TIN model is constructed by taking the final riverbank line, central axis, and cross-section line from Step 1 as inputs, so that the geometric topology of the model has continuity and the "step effect" is eliminated.

[0054] Step 3: Perform two-stage seamless elevation coupling on the TIN model to prevent abrupt changes caused by elevation differences. The two stages are the high-precision fusion stage of elevation difference interpolation and the stage of eliminating local abrupt changes based on curvature iterative smoothing.

[0055] Step 4: Model Output: Output the final seamlessly elevation-coupled TIN model in a standard GIS format for hydrodynamic simulation or visualization applications.

[0056] The TIN (Triangulated Irregular Network) model is a terrain surface modeling method that uses a network of irregular triangles to describe the terrain surface. This model is commonly used in Geographic Information Systems (GIS), terrain analysis, and earth sciences.

[0057] In step two, the construction of the TIN model based on topological constraints further includes: executing the constrained Delaunay triangulation algorithm, using land elevation point cloud data as nodes, and in generating the TIN model, the sides of the triangles must coincide with all hard boundary lines.

[0058] The high-precision fusion stage of the elevation difference interpolation includes: difference calculation, difference interpolation, and final superposition.

[0059] The difference calculation involves calculating the elevation difference relative to the background DEM for all constraint points i within the transition area. :

[0060] ;

[0061] in: This represents the elevation difference of the i-th constraint point, i.e., the difference between the actual elevation and the background DEM elevation; This represents the actual measured elevation value of the i-th constraint point; This represents the elevation value extracted from the background DEM at the location of the i-th constraint point;

[0062] The interpolation of the difference: using As the interpolation variable, interpolation is performed on all TIN nodes j within the transition region to obtain the correction amount. For example, using the IDW algorithm:

[0063] ;

[0064] in: The elevation correction at node j is obtained through inverse distance weighted interpolation; d ij is the horizontal distance between TIN node j and constraint point i; P is the power parameter in the inverse distance weighted interpolation; N is the total number of constraint points participating in the interpolation.

[0065] The final overlay: The correction amount is overlaid onto the background DEM to obtain the final elevation of node j. :

[0066] ;

[0067] in: This is the final, corrected elevation. The original elevation of node j in the background DEM; This is the elevation correction for node j.

[0068] In this embodiment: the curvature-based iterative smoothing and elimination of local abrupt changes stage includes: local optimization and hard constraint locking.

[0069] The local optimization involves performing a curvature-based iterative smoothing algorithm on the TIN nodes after elevation calculation in the transition region to eliminate sharp local elevation abrupt changes that may occur during the fusion process. The iterative correction formula for node j is as follows:

[0070] ;

[0071] in: The elevation value obtained by updating node j after the (t+1)th iteration; Let be the elevation value of node j in the t-th iteration; ω is the relaxation coefficient, used to control the strength of smoothing and the convergence speed; N j This represents the number of TIN nodes directly adjacent to node j. Let be the elevation value of the kth adjacent node of node j at the t-th iteration; This represents the average elevation of all adjacent nodes of node j at the t-th iteration.

[0072] The hard constraint locking: This enforces adherence to hard constraint conditions throughout the entire iterative smoothing process.

[0073] ;

[0074] in: A set of hard boundary lines; Let J be the constraint elevation value for node j; Let be the elevation value of node j in the t-th iteration;

[0075] If node j is located on the hard boundary line, its elevation remains unchanged during the iteration, i.e. .

[0076] Wherein: the power parameter in the inverse distance weighted interpolation is taken as the inverse distance squared weighted value of 2, that is: P=2;

[0077] This stage ensures that the model has numerical stability without losing the original accuracy of the hard boundary lines.

[0078] Example 2:

[0079] A seamlessly coupled multi-source water and land topography modeling system based on river cross-section constraints, implementing the modeling method described in Embodiment 1, is as follows: Figure 3 As shown, the system consists of an application and interaction layer, a software functional module layer (core layer), and a hardware and data layer (basic layer). The software functional module layer, as the core layer of the system, includes: a data access and preprocessing module, a feature extraction and correction module, a topological constraint TIN construction module, a seamlessly coupled elevation calculation module, and a result visualization and output module.

[0080] The data access and preprocessing module is responsible for reading multi-source data and unifying coordinate systems and elevation benchmarks.

[0081] The feature extraction and correction module is responsible for extracting feature lines and generating a set of constraint lines based on the cross-sectional data.

[0082] The topological constraint TIN construction module is responsible for executing the constraint Delaunay algorithm to construct the geometric topological constraint TIN.

[0083] The seamlessly coupled elevation calculation module is responsible for executing a two-stage algorithm: elevation difference interpolation (DIF) and curvature-based iterative smoothing.

[0084] The results visualization and output module is responsible for model validation, visualization, and format output.

[0085] The multi-source water and land terrain seamless coupling modeling method and system based on river cross-section constraints described in Examples 1 and 2 above have the following characteristics:

[0086] 1. Achieve true seamless coupling: Geometric discontinuities are resolved through topological constraints (TIN), and elevation differences are resolved through two-stage fusion, fundamentally eliminating the "step effect" and "suspended" phenomena at the water-land interface.

[0087] 2. High geometric fidelity: By using high-precision measured cross-sectional data as forced geometric anchor points, the geometric fidelity and accuracy of the final model in the riverbed area are greatly improved.

[0088] 3. High numerical stability: By introducing curvature-based iterative smoothing and local optimization of the transition region, the model is ensured to have good computational stability when used for hydrodynamic simulation.

Claims

1. A multi-source water-land terrain seamless coupling modeling method based on river channel cross-section constraints, characterized in that, The modeling method includes: Step 1: Multi-source data preparation and preprocessing to generate constraint lines: The preparation of multi-source data involves unifying the coordinate system and elevation datum of all data based on the collected point cloud data of land elevation around the river, point cloud data of underwater water depth in the river, and measured cross-sectional data of the river. The preprocessing generates constraint lines: the central axis of the main channel of the river is extracted as the longitudinal constraint line, the preliminary riverbank line is extracted, and the endpoints of the cross-section line are used as constraint points for correction to generate the final riverbank line as the constraint line of the water-land boundary. All measured river cross-section lines are defined as transverse constraint lines. Step 2: Constructing the TIN model based on topological constraints: The TIN model is constructed by taking the final riverbank line, central axis, and cross-section line from Step 1 as inputs, so that the geometric topology of the model has continuity and the "step effect" is eliminated. Step 3: Perform two-stage seamless elevation coupling on the TIN model to prevent abrupt changes caused by elevation differences. The two stages are the high-precision fusion stage of elevation difference interpolation and the stage of eliminating local abrupt changes based on curvature iterative smoothing. Step 4: Model Output: Output the final seamlessly elevation-coupled TIN model in standard GI format for hydrodynamic simulation or visualization applications.

2. The modeling method according to claim 1, characterized in that, In step two, the construction of the TIN model based on topological constraints further includes: executing the constrained Delaunay triangulation algorithm, using land elevation point cloud data as nodes, and ensuring that the sides of the triangles coincide with all hard boundary lines in the generated TIN model.

3. The modeling method according to claim 1, characterized in that, The high-precision fusion stage of the elevation difference interpolation includes: difference calculation, difference interpolation, and final superposition. The difference calculation involves calculating the elevation difference relative to the background DEM for all constraint points i within the transition area. : ； in: This represents the elevation difference of the i-th constraint point, i.e., the difference between the actual elevation and the background DEM elevation; This represents the actual measured elevation value of the i-th constraint point; This represents the elevation value extracted from the background DEM at the location of the i-th constraint point; The interpolation of the difference: using As the interpolation variable, interpolation is performed on all TIN nodes j within the transition region to obtain the correction amount. : ； in: The elevation correction at node j is obtained through inverse distance weighted interpolation; d ij is the horizontal distance between TIN node j and constraint point i; P is the power parameter in the inverse distance weighted interpolation; N is the total number of constraint points participating in the interpolation. The final overlay: The correction amount is overlaid onto the background DEM to obtain the final elevation of node j. : ； in: This is the final, corrected elevation. The original elevation of node j in the background DEM; This is the elevation correction for node j.

4. The modeling method according to claim 1, characterized in that, The curvature-based iterative smoothing and local mutation elimination stage includes: local optimization and hard constraint locking. The local optimization involves performing a curvature-based iterative smoothing algorithm on the TIN nodes after elevation calculation in the transition region to eliminate sharp local elevation abrupt changes that may occur during the fusion process. The iterative correction formula for node j is as follows: ； in: The elevation value obtained by updating node j after the (t+1)th iteration; Let be the elevation value of node j in the t-th iteration; ω is the relaxation coefficient, used to control the strength of smoothing and the convergence speed; N j This represents the number of TIN nodes directly adjacent to node j. Let be the elevation value of the kth adjacent node of node j at the t-th iteration; This represents the average elevation of all adjacent nodes of node j at the t-th iteration. The hard constraint locking: Throughout the entire iterative smoothing process, the hard constraint conditions are observed. ； in: A set of hard boundary lines; Let J be the constraint elevation value for node j; Let be the elevation value of node j in the t-th iteration; If node j is located on the hard boundary line, its elevation remains unchanged during the iteration, i.e. .

5. The modeling method according to claim 3, characterized in that, The power parameter in the inverse distance weighted interpolation is taken as the inverse distance squared weighted value of 2, that is: P=2.

6. A multi-source seamless coupling modeling system for water and land topography based on river cross-section constraints, implementing the modeling method as described in any one of claims 1 to 5, comprising a data access and preprocessing module, a feature extraction and correction module, a topological constraint TIN construction module, a seamless coupling elevation calculation module, and a result visualization and output module; The data access and preprocessing module is responsible for reading multi-source data and unifying coordinate systems and elevation benchmarks. The feature extraction and correction module is responsible for extracting feature lines and generating a set of constraint lines based on the cross-sectional data. The topological constraint TIN construction module is responsible for executing the constraint Delaunay algorithm to construct the geometric topological constraint TIN. The seamlessly coupled elevation calculation module is responsible for executing a two-stage algorithm: elevation difference interpolation (DIF) and curvature-based iterative smoothing. The results visualization and output module is responsible for model validation, visualization, and format output.

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