A method for automatic generation and optimization of underwater depth contours taking river direction into account

Abstract

The present invention discloses a method for automatically generating and optimizing underwater depth contours that takes into account the direction of a river. The method pre-processes underwater measuring points, and uses the pre-processed underwater measuring points and water boundary points to construct a triangulated network that conforms to the direction of the river. The Constrained Delaunay Triangulation method is used as a constraint condition for the river water boundary, so that the generated triangulated network meets actual needs. Underwater depth contours are automatically generated based on the constructed triangulated network, and the nodes of the automatically generated underwater depth contours are optimized using a node thinning method, retaining only the nodes near the cross-section line. The underwater depth contours are redrawn based on the node data after the thinning process to obtain the final optimized underwater depth contours. The present invention obtains more accurate and practical underwater depth contours through measuring point pre-processing and triangulated network optimization that takes into account the direction of the river, and makes the underwater depth contour map more concise and beautiful through node thinning editing. It can reduce the consumption of computing resources and improve data processing speed while ensuring accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater surveying and engineering mapping, and in particular to a method for automatically generating and optimizing underwater depth contours taking into account river directions. Background Art

[0002] Underwater topography surveying technology, as the foundation for river regulation and hydrological sedimentation research, is valuable for understanding the evolution of riverbed topography by acquiring riverbed elevation data. This provides valuable guidance for the planning and implementation of water conservancy projects. Contour lines drawn at different scales from underwater survey points can visually demonstrate the undulations and changes in riverbed topography, river flow direction, riverbed morphology, and the complex features of nearshore terrain. This is crucial for water conservancy project site selection, embankment construction, and flood control and disaster reduction.

[0003] The traditional method uses discrete elevation points to construct a triangulated network to generate depth contours. Although it can maintain the accuracy of the original data, it has significant defects in practical applications: (1) When there is no constraint on the land-water boundary, the triangulated network is prone to sudden elevation changes, such as a steep slope at the junction of the water surface and the bank slope. (2) The original triangulated network constructed is irregular and does not take into account the characteristics of the river itself, resulting in reduced accuracy. (3) The generated depth contours have node redundancy and rough graphics problems, resulting in distortion of terrain features and contradictions between points and lines. Manual intervention is required to clean up the nodes, which greatly reduces the efficiency of drawing. Existing technologies have not yet effectively solved the contradiction between the above-mentioned measurement data characteristics and engineering mapping requirements. It is necessary to develop an intelligent processing method that takes into account both terrain authenticity and mapping display friendliness. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention proposes a method for automatically generating and optimizing underwater depth contours that takes into account the direction of the river. A triangulated network optimization algorithm is constructed based on underwater measurement points and water edge points to automatically generate depth contours, significantly improving measurement accuracy and processing efficiency.

[0005] The present invention provides the following technical solutions:

[0006] A method for automatically generating and optimizing underwater depth contours taking into account river direction includes the following steps:

[0007] S1. Pre-process the underwater measurement points, delete invalid underwater measurement points, and divide the area range according to the geographical location of the underwater measurement points.

[0008] S2. Construct a triangulated network based on the underwater measurement points and water boundary points pre-processed in step S1, and make the triangulated network follow the direction of the river so that the generated triangulated network meets actual needs.

[0009] S3. Automatically generate underwater depth contours based on the triangulated network constructed in step S2.

[0010] S4. Perform node editing and thinning on the underwater isobaths automatically generated in step S3, retaining only the nodes near the section line, and redrawing the underwater isobaths based on the thinned-out node data to obtain the final optimized underwater isobaths.

[0011] Furthermore, in step S1, the underwater measuring points are pre-processed to delete invalid underwater measuring points, specifically:

[0012] S11. Set a vertical distance threshold m between the section line and the underwater measuring point, perform vertical projection on each section line using the vertical distance threshold m, and obtain the adjacent area of each section line.

[0013] S12. Underwater measuring points falling within the vicinity of any section line shall be retained.

[0014] S13. If no underwater measuring point is found in the vicinity of a section line, the area is considered a blank section area, and the underwater measuring point that is closest to the section line in vertical distance is retained.

[0015] S14. The remaining underwater measuring points not retained in step S12 and step S13 are identified as invalid underwater measuring points and deleted.

[0016] Furthermore, in step S2, a triangulated network is constructed for the underwater measurement points and water boundary points preprocessed in step S1, and the triangulated network is made to conform to the direction of the river. The generated triangulated network meets the actual needs, specifically:

[0017] S21. Statistically analyze the pre-processed underwater measurement point elevation data and track the elevation valley point to obtain the river centerline.

[0018] S22. Construct a triangulated network using underwater measurement points and water boundary points. Use the Constrained Delaunay Triangulation (CDT) method as the constraint condition for the river water boundary. Define the water boundary point set as the mandatory constraint edge and the river centerline as the constraint direction. Use the water boundary points and the river center direction as input to ensure that the river water boundary point set is integrated into the triangulated network during the triangulation process. The mesh edge and the river direction are consistent. This ensures that the vertices of the triangulated network not only meet the Delaunay condition but also follow the natural flow direction of the river, ensuring the elevation continuity of the water boundary line and its surrounding area.

[0019] Furthermore, in step S3, underwater depth contours are automatically generated based on the triangulated network constructed in step S2, specifically:

[0020] Determine whether the difference in water depth between the two end points of each triangle edge is greater than the set target value a, and automatically generate underwater isobaths by performing linear interpolation on the end points that meet the judgment conditions.

[0021] Furthermore, in step S4, the underwater depth contour automatically generated in step S3 is subjected to node editing and thinning, and only the nodes near the section line are retained. The underwater depth contour is redrawn according to the node data after thinning processing to obtain the final optimized underwater depth contour, which is specifically:

[0022] S41. Encode and calibrate the section lines within the measurement area.

[0023] S42. Node the underwater isobaths automatically generated in step S3, set a predetermined node limit value n, calculate the vertical distance from each underwater isobath node to each section line, retain the nodes whose minimum vertical distance is less than or equal to the node limit value n, and delete the nodes whose minimum vertical distance is greater than the node limit value n.

[0024] S43. Check each section line to ensure that there is at least one node on or near each section line.

[0025] S44. Redraw the underwater depth contour according to the node data after the thinning process to obtain the final optimized underwater depth contour.

[0026] Furthermore, the method for obtaining the river centerline in step S21 may also be that, for a single river section, the river centerline may be obtained by calculating the median line of the waterside lines on both sides of the river.

[0027] Furthermore, the linear interpolation adopts bilinear interpolation or piecewise linear interpolation.

[0028] Furthermore, the target value a is set according to the slope of the river bed.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. This invention uses an algorithm to dynamically select and optimize underwater measurement points, ensuring a rational distribution of measurement points within the underwater space. This optimized distribution of measurement points better reflects the actual variations in underwater topography, improving the representativeness and accuracy of the data. Furthermore, it significantly reduces the workload of manual screening and data preprocessing, improving data processing efficiency and saving significant time and resources.

[0031] 2. This invention effectively optimizes traditional Delaunay triangulation by introducing the Constrained Delaunay Triangulation (CDT) method, successfully resolving the problem of sudden elevation changes in triangulation at the interface between land and water. Furthermore, the triangulation conforms to the actual river morphology. During triangulation construction, constraints ensure that the distribution of mesh elements aligns with the river's direction, effectively preserving the river's boundaries. This process effectively reflects the natural form of the river channel, enhances the realism of the water terrain, and makes subsequent analysis and modeling more realistic. It also reduces mesh errors in water boundary areas and optimizes data processing accuracy. Furthermore, by introducing constraints on river boundaries, the workload of subsequent manual corrections is reduced, saving significant time and labor costs and improving work efficiency.

[0032] 3. By incorporating a node thinning method, this invention generates simpler and smoother depth contours, avoiding redundant information caused by overcrowded nodes. Furthermore, while ensuring accuracy, it reduces computing resource consumption, improves data processing speed, and reduces the storage burden and processing time for large data volumes. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Schematic diagram of underwater measurement point preprocessing according to the present invention.

[0034] Figure 2 This is a comparison diagram before and after the triangulation network optimization of the present invention.

[0035] Figure 3 This is a comparison chart before and after the node thinning optimization of the present invention. Specific embodiments

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] A method for automatically generating and optimizing underwater depth contours taking into account river direction includes the following steps:

[0038] S1. Pre-process the underwater measurement points, delete invalid underwater measurement points, and divide the area range according to the geographical location of the underwater measurement points.

[0039] S2. Construct a triangulated network based on the underwater measurement points and water boundary points pre-processed in step S1, and make the triangulated network follow the direction of the river so that the generated triangulated network meets actual needs.

[0040] S3. Based on the triangulated network constructed in step S2, determine whether the difference in water depth between the two end points of each triangle edge is greater than the set target value a, and automatically generate underwater isobaths by performing linear interpolation on the end points that meet the judgment conditions.

[0041] S4. Perform node editing and thinning on the underwater isobaths automatically generated in step S3, retaining only the nodes near the section line, and redrawing the underwater isobaths based on the thinned-out node data to obtain the final optimized underwater isobaths.

[0042] Furthermore, in step S1, the underwater measuring points are pre-processed to delete invalid underwater measuring points, specifically:

[0043] S11. Set a vertical distance threshold m between the section line and the underwater measuring point, perform vertical projection on each section line using the vertical distance threshold m, and obtain the adjacent area of each section line.

[0044] S12. Underwater measuring points falling within the vicinity of any section line shall be retained.

[0045] S13. If no underwater measuring point is found in the vicinity of a section line, the area is considered a blank section area, and the underwater measuring point that is closest to the section line in vertical distance is retained.

[0046] S14. The remaining underwater measuring points not retained in step S12 and step S13 are identified as invalid underwater measuring points and deleted.

[0047] Taking the application in actual production as an example, underwater topography measurement was carried out on a certain water area. Figure 1 This is a schematic diagram of underwater measurement point preprocessing. During the data preprocessing stage, the vertical distance threshold m between the section line and the underwater measurement point is set in the drawing system. If the underwater measurement point deviates far from the section line and the vertical distance is greater than the threshold m, the point to be deleted will be displayed with a small red flag. These measurement points can be deleted after confirmation.

[0048] Furthermore, in step S2, a triangulated network is constructed for the underwater measurement points and water boundary points preprocessed in step S1, and the triangulated network is made to conform to the direction of the river. The generated triangulated network meets the actual needs, specifically:

[0049] S21. Statistically analyze the pre-processed underwater measurement point elevation data and track the elevation valley point to obtain the river centerline.

[0050] S22. Construct a triangulated network using underwater measurement points and water boundary points. Use the Constrained Delaunay Triangulation (CDT) method as the constraint condition for the river water boundary. Define the water boundary point set as the mandatory constraint edge and the river centerline as the constraint direction. Use the water boundary points and the river center direction as input to ensure that the river water boundary point set is integrated into the triangulated network during the triangulation process. The mesh edge and the river direction are consistent. This ensures that the vertices of the triangulated network not only meet the Delaunay condition but also follow the natural flow direction of the river, ensuring the elevation continuity of the water boundary line and its surrounding area.

[0051] Furthermore, the method for obtaining the river centerline in step S21 may also be that, for a single river section, the river centerline may be obtained by calculating the median line of the waterside lines on both sides of the river.

[0052] In the actual triangulation process, the traditional Delaunay condition is enhanced to "constrained Delaunay triangulation." This requires that, in addition to requiring every interior angle in the triangulation to be greater than or equal to 60 degrees, certain edges must also be constrained, particularly at the interface between land and water. This constraint ensures that the triangulation is smooth and excellent, while also ensuring that the triangulation boundary perfectly aligns with the water boundary and extends along the river.

[0053] like Figure 2 The following is a comparison of the above triangulated network before and after optimization, where (1) is before optimization and (2) is after optimization. In the triangulated network generation implementation, the water boundary point set is defined as the mandatory constraint edge and the river centerline is defined as the constraint direction. This makes the boundary of the triangulated network completely fit the water boundary and extend along the river direction, which can effectively avoid unreasonable triangle distribution and mutation problems.

[0054] Furthermore, in step S3, underwater depth contours are automatically generated based on the triangulated network constructed in step S2, specifically:

[0055] Determine whether the difference in water depth between the endpoints of each triangle edge is greater than a set target value a. Linear interpolation is performed on endpoints that meet the criteria to automatically generate underwater depth contours. This linear interpolation employs bilinear interpolation or piecewise linear interpolation. The set target value a is determined based on the riverbed slope.

[0056] Furthermore, in step S4, the underwater depth contour automatically generated in step S3 is subjected to node editing and thinning, and only the nodes near the section line are retained. The underwater depth contour is redrawn according to the node data after thinning processing to obtain the final optimized underwater depth contour, which is specifically:

[0057] S41. Encode and calibrate the section lines within the measurement area.

[0058] S42. Node the underwater isobaths automatically generated in step S3, set a predetermined node limit value n, calculate the vertical distance from each underwater isobath node to each section line, retain the nodes whose minimum vertical distance is less than or equal to the node limit value n, and delete the nodes whose minimum vertical distance is greater than the node limit value n.

[0059] S43. Check each section line to ensure that there is at least one node on or near each section line.

[0060] S44. Redraw the underwater depth contour according to the node data after the thinning process to obtain the final optimized underwater depth contour.

[0061] Figure 3 The following is a comparison diagram before and after node thinning optimization, where (1) is before node thinning optimization and (2) is after node thinning optimization, showing the actual effect of underwater depth contour node thinning optimization before and after. As can be seen from the figure, the fewer the curve nodes, the smoother the underwater depth contour. According to the characteristics of the established cross-section measurement in the actual measurement process, the strategy of only retaining key nodes on or near the cross-section line is adopted to avoid the clutter caused by too many details, so that the output is beautiful and smooth while retaining the actual terrain undulations. This method only retains nodes on or near the cross-section line, thereby achieving underwater depth contour smoothing. Since there will be a certain offset between the measuring point and the cross-section line during the actual measurement process, this method sets a distance limit to retain nodes within the specified distance range of the cross-section while identifying the cross-section line code on the map, ensuring that there is at least one node on the cross-section line.

[0062] The above is only an embodiment of the present invention, and common sense such as the specific structure and characteristics of the scheme are not described in detail here. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claim involved.

Claims

1. A method for automatically generating and optimizing underwater depth contours taking into account the direction of the river, characterized in that: The steps include: S1. Preprocess the underwater measurement points, delete invalid underwater measurement points, and divide the area according to the geographical location of the underwater measurement points; S2. Construct a triangulated network of the underwater measurement points and water boundary points preprocessed in step S1, and make the triangulated network conform to the direction of the river, so that the generated triangulated network meets the actual needs; S3. Automatically generate underwater depth contours based on the triangulated network constructed in step S2; S4. Perform node thinning on the underwater depth contours automatically generated in step S3, retaining only the nodes near the cross-section line, and redraw the underwater depth contours based on the thinned node data to obtain the final optimized underwater depth contours; In step S2, a triangulated network is constructed for the underwater measurement points and water boundary points preprocessed in step S1, and the triangulated network is made to follow the direction of the river. The generated triangulated network meets the actual needs, specifically: S21. Statistically analyze the pre-processed underwater measurement point elevation data and track the elevation valley point to obtain the river centerline; S22. Construct a triangulated network using underwater measurement points and water boundary points. Use the Constrained Delaunay Triangulation method as the constraint condition for the river water boundary. Define the water boundary point set as the mandatory constraint edge and the river centerline as the constraint direction. Use the water boundary points and the river center direction as input to ensure that the river water boundary point set is integrated into the triangulated network during the triangulation process. The mesh edge and the river direction are consistent. This ensures that the vertices of the triangulated network not only meet the Delaunay condition but also follow the natural flow direction of the river, ensuring the elevation continuity of the water boundary line and its surrounding area.

2. The method for automatically generating and optimizing underwater depth contours taking into account the direction of the river according to claim 1, characterized in that: Regarding step S1, the underwater measuring points are pre-processed to delete invalid underwater measuring points, specifically: S11. Set a vertical distance threshold m between the cross section line and the underwater measuring point, perform vertical projection on each cross section line using the vertical distance threshold m, and obtain the adjacent area of each cross section line; S12. Retain underwater measurement points that fall within the vicinity of any section line; S13. If no underwater measuring point is found in the vicinity of a section line, the area is considered a blank section area, and the underwater measuring point closest to the section line is retained; S14. The remaining underwater measuring points not retained in step S12 and step S13 are identified as invalid underwater measuring points and deleted.

3. The method for automatically generating and optimizing underwater depth contours taking into account the direction of the river according to claim 1, characterized in that: In step S3, underwater depth contours are automatically generated based on the triangulated network constructed in step S2, specifically: Determine whether the difference in water depth between the two end points of each triangle edge is greater than the set target value a, and automatically generate underwater isobaths by performing linear interpolation on the end points that meet the judgment conditions.

4. The method for automatically generating and optimizing underwater depth contours taking into account the direction of the river according to claim 1, characterized in that: In step S4, the underwater depth contours automatically generated in step S3 are thinned out by node editing, and only the nodes near the section line are retained. The underwater depth contours are redrawn based on the thinned-out node data to obtain the final optimized underwater depth contours, specifically: S41. Encode and calibrate the cross-section lines within the measurement area; S42. Node the underwater depth contours automatically generated in step S3, set a predetermined node limit value n, calculate the vertical distance from each underwater depth contour node to each cross-section line, retain nodes whose minimum vertical distance is less than or equal to the node limit value n, and delete nodes whose minimum vertical distance is greater than the node limit value n; S43. Check each section line to ensure that there is at least one node on or near each section line; S44. Redraw the underwater depth contour according to the node data after the thinning process to obtain the final optimized underwater depth contour.

5. The method for automatically generating and optimizing underwater depth contours taking into account the direction of the river according to claim 1, characterized in that: For a single river section, the method for obtaining the river centerline in step S21 may also be to calculate the center line of the waterside lines on both sides of the river.

6. The method for automatically generating and optimizing underwater depth contours taking into account the direction of the river according to claim 3, characterized in that: The linear interpolation adopts bilinear interpolation or piecewise linear interpolation.

7. The method for automatically generating and optimizing underwater depth contours taking into account the direction of the river according to claim 3, characterized in that: The target value a is set according to the slope of the river bed.

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