Methods, systems, storage media, and electronic devices for generating river topography

Abstract

This invention belongs to the field of water conservancy engineering technology and proposes a method, system, storage medium, and electronic device for generating river topography. The method includes: collecting data required for generating river topography; dividing the river topography into topography above the highest water level, topography below the lowest water level, and topography between the highest and lowest water levels; extracting the waterline, centerline, and flow cross-section; calculating the relative stability index of the flow cross-section; identifying stable river segments in the river channel; dividing stable river segments into meandering river segments and straight river segments; assigning elevation values ​​to the waterline of the meandering and straight river segments to obtain contour lines; constructing the topography above the highest water level, the topography between the lowest and highest water levels, and the topography below the lowest water level; and stitching the three types of topography together to form a complete river topography. This invention achieves contactless generation of river topography, provides a topographical basis for hydrodynamic analysis, and plays an important role in flood simulation, watershed management, and ecological protection.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy engineering technology, and in particular relates to a method, system, storage medium and electronic device for generating river topography. Background Technology

[0002] In watershed management and hydrological research, river topography data is fundamental for hydrodynamic analysis, flood simulation, ecosystem protection, and flood control decision-making. River topography not only determines the path and velocity of water flow but also profoundly impacts habitat formation, sediment transport, and water quality maintenance within river ecosystems. Currently, river topography acquisition primarily relies on manual surveying, topography reconstruction by the Gaofen-7 satellite at low water levels, and calculations using digital elevation models (DEMs). While manual surveying offers high accuracy in certain areas, it requires on-site work and is limited by terrain, climate, and transportation conditions. Especially in remote mountainous and canyon areas, the operational difficulty and cost are high, making it difficult to acquire large-scale, continuous topographic data. Although DEM-based topography generation has a certain degree of wide applicability, it often fails to reflect the fine geomorphological features of the river channel due to resolution limitations, and it struggles to accurately represent the actual river topography during local or dynamic changes in water level within river sections. Summary of the Invention

[0003] This invention proposes a method, system, storage medium, and electronic device for generating river topography.

[0004] A method for generating riverbed topography according to the present invention includes:

[0005] Collect digital elevation model data, remote sensing image data, elevation data, daily water level data and cross-sectional data of the river channel. Use the edge detection method to divide the river channel topography into the topography above the highest water level, the topography below the lowest water level, and the topography between the highest and lowest water levels. Extract the waterline, centerline and flow cross-section.

[0006] The relative stability index of the flow section is calculated based on the centerline and the flow section. The relative stability index of the flow section is used to identify stable river sections in the river channel and to divide the stable river sections into meandering river sections and straight river sections.

[0007] Using the elevation data and the daily water level data, elevation values ​​are assigned to the waterline of the meandering river section and the waterline of the straight river section to obtain contour lines;

[0008] The terrain above the highest water level is constructed using the remote sensing image data;

[0009] Using the elevation data and the daily water level data, the terrain between the lowest and highest water levels is constructed based on the contour lines and the waterline.

[0010] The terrain below the lowest water level is constructed using the daily water level data and the cross-sectional data;

[0011] By piecing together the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest and highest water levels, a complete riverbed terrain is formed.

[0012] Furthermore, the method of using edge detection to divide the riverbed topography into topography above the highest water level, topography below the lowest water level, and topography between the highest and lowest water levels includes:

[0013] The edge detection method is used to extract water surface feature lines from the digital elevation model data;

[0014] The edge detection method includes: noise smoothing; calculating gradient magnitude and gradient direction; performing non-maximum suppression along the gradient direction to obtain edge points; and using a double threshold algorithm to detect and connect edge points to form edge lines.

[0015] The water surface feature lines include the water surface line, the shoreline line, and the valley boundary line;

[0016] The riverbed topography is divided using the water surface feature lines. The riverbed topography below the water surface line is divided into the topography below the lowest water level, the riverbed topography between the water surface line and the bank line is divided into the topography between the highest and lowest water levels, and the riverbed topography between the bank line and the valley boundary line is divided into the topography above the highest water level.

[0017] Furthermore,

[0018] The relative stability index of the flow cross-section is calculated based on the centerline and the flow cross-section. This index is used to identify stable river sections within the river channel, classifying them into meandering and straight sections.

[0019] Obtain the intersection point of the flow cross section and the center line, and record the intersection point of the flow cross section and the flow center line as the second intersection point;

[0020] Select two adjacent time points of the center line, merge the two adjacent time points of the center line, obtain the migration distance of the second intersection point, and record it as the intersection point migration distance. At the same time, obtain the intersection point migration velocity and the intersection point migration acceleration.

[0021] Calculate the relative stability index of the flow cross section;

[0022] A threshold is determined based on the relative stability index of the flow section, and the threshold is used to determine whether the river section is stable.

[0023] The relative stability index of the river section is determined based on the relative stability index of the flow cross section.

[0024] By comparing the relative stability index of the river segment with the threshold, the river segment is divided into stable river segments and oscillating river segments, thereby identifying the stable river segments from the river channel;

[0025] The stable river section is divided into meandering river section and straight river section.

[0026] Furthermore,

[0027] The relative stability index of the flow section is calculated using the following formula:

[0028] RSIRR-G=β1s+β2v+β3a,

[0029] In the formula, RSIRR-G refers to the relative stability index; s refers to the intersection migration distance; v refers to the intersection migration velocity; α refers to the intersection migration acceleration; β1 refers to the coefficient of the intersection migration distance; β2 refers to the coefficient of the intersection migration velocity; and β3 refers to the coefficient of the intersection migration acceleration.

[0030] Furthermore,

[0031] The coefficients of the intersection point migration distance, the point migration velocity, and the intersection point migration acceleration are calculated using cluster analysis, including:

[0032] The cross-sections were grouped using hierarchical clustering, resulting in multiple groups.

[0033] Calculate the average value of the intersection migration distances corresponding to the cross sections contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration distances to obtain the first sum of squares, calculate the sum of squares of errors between the groups using the average value of the intersection migration distances to obtain the first sum of squares of errors, calculate the ratio of the first sum of squares to the first sum of squares of errors to obtain the first ratio, and use the first ratio as the coefficient of the intersection migration distance;

[0034] Calculate the average value of the intersection migration speed corresponding to the cross-section contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration speed to obtain a second sum of squares, calculate the sum of squares of errors between the groups using the average value of the intersection migration speed to obtain a second sum of squares of errors, calculate the ratio of the second sum of squares to the second sum of squares of errors to obtain a second ratio, and use the second ratio as the coefficient of the intersection migration speed;

[0035] Calculate the average value of the intersection migration acceleration corresponding to the cross section contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration acceleration to obtain the third sum of squares, calculate the sum of squares of errors between the groups using the average value of the intersection migration acceleration to obtain the third sum of squares of errors, calculate the ratio of the third sum of squares to the third sum of squares of errors to obtain the third ratio, and use the third ratio as the coefficient of the intersection migration acceleration.

[0036] Furthermore,

[0037] The process of assigning elevation values ​​to the waterline of meandering river sections and straight river sections using elevation data and daily water level data to obtain contour lines includes:

[0038] The elevation data is used to assign a first elevation value to the waterline of the meandering river section.

[0039] A second elevation value is assigned to the waterline of the straight river section using the elevation data;

[0040] Elevation points are interpolated along the waterline of the meandering river section and the waterline of the straight river section to obtain a third elevation value;

[0041] The daily water level data is used to assign a fourth elevation value to the waterline of the meandering river section and the waterline of the straight river section.

[0042] Along the waterline of the meandering river section and the waterline of the straight river section, elevation points with the same elevation value are identified within the range of the first elevation value, the second elevation value, the third elevation value, and the fourth elevation value. The elevation points with the same elevation value are connected to obtain the contour lines.

[0043] Furthermore,

[0044] The construction of the terrain above the highest water level using remote sensing image data includes:

[0045] Acquire stereo image pair data, perform orthorectification and image fusion on the stereo image pair data to generate orthophoto, and extract digital surface model and point cloud data from the orthophoto image data.

[0046] Furthermore,

[0047] The method of constructing the terrain between the lowest and highest water levels using elevation data and daily water level data, based on contour lines and waterline lines, includes:

[0048] Using the elevation data and the daily water level data, based on the contour lines and the waterline, the terrain between the lowest and highest water levels is interpolated with elevation points to form continuous elevation points, thereby forming an elevation surface and obtaining the preliminary terrain construction result;

[0049] Based on the preliminary terrain construction results, a second interpolation is performed to fill the elevation gap between the lowest water level and the highest water level.

[0050] A riverbed topography generation system according to the present invention is used to implement the aforementioned riverbed topography generation method, the system comprising:

[0051] The collection and processing module is used to collect digital elevation model data, remote sensing image data, elevation data, daily water level data and cross-sectional data of the river channel. It uses the edge detection method to divide the river channel topography into the topography above the highest water level, the topography below the lowest water level, and the topography between the highest and lowest water levels, and extracts the water edge line, center line and flow cross section.

[0052] The identification and division module is used to calculate the relative stability index of the flow section based on the centerline and the flow section, and to identify stable river sections in the river channel using the relative stability index of the flow section, and to divide the stable river sections into meandering river sections and straight river sections.

[0053] The contour line generation module uses the elevation data and the daily water level data to assign elevation values ​​to the waterline of the meandering river section and the waterline of the straight river section, thereby obtaining contour lines.

[0054] The first construction module is used to construct the terrain above the highest water level using the remote sensing image data;

[0055] The second construction module is used to construct the terrain between the lowest and highest water levels based on the elevation data and the daily water level data, using the contour lines and the waterline.

[0056] The third construction module uses the daily water level data and the cross-sectional data to construct the terrain below the lowest water level;

[0057] The splicing module is used to splice together the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest and highest water levels to form a complete riverbed terrain.

[0058] The present invention provides a computer-readable storage medium storing a program or instructions that, when executed on a computer, cause the computer to perform the aforementioned riverbed topography generation method.

[0059] An electronic device according to the present invention includes a processor coupled to a memory; the processor is used to read and execute a computer program stored in the memory to implement the aforementioned riverbed topography generation method.

[0060] Compared with the prior art, the beneficial effects of this invention are:

[0061] By extracting water body boundaries using optical imagery data and assigning elevation information through altimetry data, this technology enables contactless generation of river channel topography through interpolation. This not only provides a high-precision topographic foundation for hydrodynamic analysis but also plays a crucial role in flood simulation, watershed management, and ecological protection. Furthermore, the application of remote sensing technology allows for the acquisition of river channel topography beyond geographical and temporal limitations. Especially for river sections lacking supporting data, it enables topographic construction, effectively supporting large-scale hydrological monitoring, disaster early warning, and water resource management, providing a more convenient and accurate solution for hydrological research and environmental management. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart of the river topography generation method of the present invention;

[0064] Figure 2 This is a schematic diagram of the river topography generation system of the present invention;

[0065] Figure 3 This is a schematic diagram of the structure of the electronic device of the present invention.

[0066] Explanation of reference numerals in the attached figures:

[0067] 201-Collection and processing module, 202-Identification and division module, 203-Contour line generation module, 204-First construction module, 205-Second construction module, 206-Third construction module, 207-Splitting module, 301-Processor, 302-Memory. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Figure 1 This is a flowchart of a river topography generation method provided in an embodiment of the present invention. In one embodiment, it specifically includes the following steps:

[0070] S101: Collect digital elevation model data, remote sensing image data, elevation data, daily water level data and cross-sectional data of the river channel. Use edge detection method to divide the river channel topography into the topography above the highest water level, the topography below the lowest water level, and the topography between the highest and lowest water levels. Extract the waterline, centerline and flow cross-section.

[0071] S101-1: Collect digital elevation model data, remote sensing image data, elevation data, daily water level data, and cross-sectional data of the river channel.

[0072] 1. Digital Elevation Model Data

[0073] The full name of the Digital Elevation Model is "Digital Elevation Model", abbreviated as "DEM". DEM data with a resolution of 12.5 meters was acquired for the river channel in the study area at a specified time. The DEM data is raster image data.

[0074] 2. Remote sensing image data

[0075] Remote sensing image data of the river channel in the study area at different times within a specified time range were collected from Landsat, Sentinel and Gaofen satellites.

[0076] 3. Elevation data

[0077] Elevation data of the river channel in the study area at different times within a specified time range were collected from satellites such as TOPEX / Poseidon, Envisat, CryoSat-2, ICESat series, Jason series, Sentinel-3, and Gaofen series.

[0078] Elevation data includes elevation points and elevation values. Within a footprint, multiple elevation points can be formed along the direction of satellite orbital movement, and each elevation point has a corresponding elevation value.

[0079] 4. Daily water level data and cross-sectional data

[0080] Daily water level data and cross-sectional data of the river in the study area were collected at different times within a specified time range. Both the daily water level data and cross-sectional data are actual measured data from hydrological stations.

[0081] S101-2: Use edge detection to extract water surface feature lines from digital elevation model data, and use the water surface feature lines to divide the river topography, dividing the topography above the highest water level, the topography below the lowest water level, and the topography between the highest and lowest water levels.

[0082] The Canny edge detection method is a multi-stage edge detection method, consisting of four steps: noise smoothing, calculating gradient magnitude and direction, non-maximum suppression, and detecting and connecting edges using a double threshold algorithm. The Canny edge detection method was used to extract the water surface feature lines of the river channel in the study area from 12.5-meter DEM data. Specifically, the water surface feature lines of the river channel in the study area were extracted from the image, and the extraction process is as follows:

[0083] Step 1: Noise smoothing.

[0084] A Gaussian filter is an image processing method that uses a Gaussian kernel. It performs a convolution operation on the image using the Gaussian kernel to achieve smoothing. The Gaussian kernel is calculated using a two-dimensional Gaussian function, as shown below.

[0085]

[0086] In the formula, (x,y) are planar coordinates; σ is the standard deviation of the Gaussian distribution, used to control the width of the Gaussian function.

[0087] Different Gaussian kernel sizes yield different processing results. Using a Gaussian kernel of size (2k+1)x(2k+1) to process the image results in a Gaussian kernel with Gaussian distribution characteristics. The calculation formula is as follows.

[0088]

[0089] In the formula, (i,j) are planar coordinates, where i and j are offsets relative to the center point of the Gaussian kernel; σ is the standard deviation of the Gaussian distribution; and k is the radius of the Gaussian kernel.

[0090] To ensure that the sum of the weights of all pixels in the Gaussian kernel is 1, the Gaussian kernel, which has Gaussian distribution characteristics, needs to be normalized. The normalization formula is as follows:

[0091]

[0092] In the formula, K is the normalization constant; H(i,j) is a Gaussian kernel with Gaussian distribution characteristics.

[0093]

[0094] In the formula, G(i,j) is the normalized Gaussian kernel; H(i,j) is the Gaussian kernel with Gaussian distribution characteristics; and K is the normalization constant.

[0095] After smoothing the image, image noise is reduced, ensuring the accuracy of edge detection.

[0096] Step 2: Calculate the gradient magnitude and gradient direction.

[0097] The Sobe l operator is used to calculate the gradient and orientation of pixels in the image in the horizontal and vertical directions, respectively. The calculation formulas are as follows.

[0098]

[0099] In the formula, G is the gradient magnitude; G x G is the Sobel operator in the horizontal direction; y For the vertical Sobel operator.

[0100] θ = arc tan(G) y / G x (6),

[0101] In the formula, θ is the gradient direction; G x G is the Sobel operator in the horizontal direction; y The Sobel operator is for the vertical direction.

[0102] In equations (5) and (6), the horizontal Sobel operator G x And the Sobel operator G in the vertical direction y They are respectively:

[0103]

[0104]

[0105] This step quantizes the gradient direction into one of eight directions, which facilitates subsequent nonmaximum suppression.

[0106] Step 3: Perform non-maximum suppression along the gradient direction to obtain edge points.

[0107] By traversing the pixels in the image along the gradient direction, each pixel in the image and its two adjacent pixels are divided into a local region, thereby obtaining multiple local regions.

[0108] Compare the gradient magnitude of each pixel within each local range, and obtain the pixel with the largest gradient magnitude within the local range, which is recorded as the edge point.

[0109] Eliminate non-edge points and retain only edge points.

[0110] This step makes the image edges relatively sharper.

[0111] Step 4: Use a double threshold algorithm to detect and connect edge points to form edge lines.

[0112] Two thresholds, a high threshold and a low threshold, are set. The gradient of each edge point is compared with the high threshold and the low threshold, respectively. Edge points above the high threshold are marked as strong boundary points, those below the low threshold are marked as non-boundary points, and those less than or equal to the high threshold and greater than or equal to the low threshold are marked as weak boundary points. For weak boundary points, their type is further determined using 8-connected regions. Specifically, if a weak boundary point is contained within the 8-neighborhood of a strong boundary, it is changed to a strong boundary point. Similarly, if a weak boundary point is contained within the 8-neighborhood of a non-boundary point, it is changed to a non-boundary point.

[0113] Connect all strong boundary points to form a complete edge line.

[0114] The above method can be used to extract water surface feature lines at different water levels. These water surface feature lines include: water surface line, bank line, and valley boundary line. The river topography can be divided using these water surface feature lines. The river topography below the water surface line is classified as the topography below the lowest water level. The river topography between the water surface line and the bank line is classified as the topography between the highest and lowest water levels. The river topography between the bank line and the valley boundary line is classified as the topography above the highest water level.

[0115] S101-3: Processing remote sensing image data and elevation data.

[0116] Radiometric correction and atmospheric correction are performed on remote sensing image data. Radiometric correction can correct the pixel values ​​of remote sensing images to the actual reflectance or brightness, eliminating the influence of sensor differences. Atmospheric correction can eliminate the influence of atmospheric scattering and absorption on the image, ensuring that the spectral information of the image can accurately reflect the true condition of the surface.

[0117] Align the collection times of the two types of data, remote sensing image data and elevation data, to ensure that the collection times of the two types of data are consistent; align the spatial locations of the two types of data, remote sensing image data and elevation data, to ensure that the elevation points in the elevation data are located within the remote sensing image data.

[0118] S101-4: Extract the waterline, centerline, and water width from remote sensing image data.

[0119] 1. Waterline

[0120] River water morphology information is extracted from remote sensing image data. Using different bands of the remote sensing image data, the water normalization index is calculated using the following formula.

[0121]

[0122] In the formula, NDWI refers to the normalized water index; Green refers to the green light band; and NIR refers to the near-infrared band.

[0123] Based on the water body normalization index, multiple water boundary lines at different times (i.e., at different water levels) were extracted using geographic information system tools.

[0124] 2. Centerline

[0125] Geographic Information System (GIS) tools were used to extract multiple centerlines from remote sensing image data at different times (i.e., at different water levels).

[0126] 3. Flow cross-section and water surface width

[0127] Using geographic information system tools, a flow cross section perpendicular to the centerline is created. The flow cross section intersects with the water edge line, and the intersection point of the flow cross section and the water edge line is obtained, which is recorded as the first intersection point. There are two first intersection points, and the straight-line distance between the two first intersection points is the water surface width. Finally, multiple flow cross sections and water surface widths at different times (i.e., at different water levels) are obtained.

[0128] S102: Calculate the relative stability index of the flow section based on the centerline and the flow section, and use the relative stability index of the flow section to identify stable river sections in the river channel, and divide the stable river sections into meandering river sections and straight river sections.

[0129] The specific process is as follows:

[0130] S102-1: Use geographic information system tools to obtain the intersection point of the flow section and the centerline, and record it as the second intersection point.

[0131] S102-2: Select two adjacent time centers as needed, merge the two adjacent time centers, and obtain the migration distance of the second intersection point, which is called the "intersection migration distance". The intersection migration distance is denoted as s, and the corresponding intersection migration velocity and intersection migration acceleration are denoted as v and a, respectively.

[0132] S102-3: Calculate the relative stability index of the flow section.

[0133] The calculation formula is as follows:

[0134] RSIRR-G=β1s+β2v+β3a (8),

[0135] In the formula, RSIRR-G refers to the relative stability index; s refers to the intersection migration distance; v refers to the intersection migration velocity; α refers to the intersection migration acceleration; β1 refers to the coefficient of the intersection migration distance; β2 refers to the coefficient of the intersection migration velocity; and β3 refers to the coefficient of the intersection migration acceleration.

[0136] The values ​​of β1, β2, and β3 in formula (8) are determined by cluster analysis, as follows:

[0137] Step 1: Use hierarchical clustering to group the flow cross sections, resulting in multiple groups.

[0138] When grouping flow sections, the number of flow sections and the number of groups are first preset, and then the flow sections are grouped according to the preset number of groups. Specifically, the number of flow sections is N, and the number of groups is g. Preferably, g = 3.

[0139] Step 2: Calculate β1, β2 and β3 using the following formula.

[0140] The calculation formula is as follows:

[0141]

[0142]

[0143]

[0144] In the formula, F represents the ratio of the sum of squares among groups to the sum of squares of errors among groups; SS(TR) represents the sum of squares among groups; SSE represents the sum of squares of errors among groups; g represents the number of groups; n i This refers to the number of flow sections contained in a single group; The average index value of the flow sections contained in a single group; This refers to the average index value of the flow sections contained in all groups; This refers to the index value of a specific flow section within a single group.

[0145] Specifically, the average index value refers to the average value of the intersection migration distance, the average value of the intersection migration velocity, or the average value of the intersection migration acceleration. The average values ​​of the intersection migration distance, the average value of the intersection migration velocity, and the average value of the intersection migration acceleration are all calculated by averaging.

[0146] Based on formulas (9), (10), and (11), the following processing is performed:

[0147] Substitute the average value of the intersection migration distance into formula (10) to calculate the sum of squares between groups and obtain the first sum of squares. Substitute the average value of the intersection migration distance into formula (11) to calculate the sum of squares of errors between groups and obtain the first sum of squares of errors. Calculate the ratio of the first sum of squares to the first sum of squares of errors according to formula (9) to obtain the first ratio F1. Use the first ratio F1 as the coefficient β1 of the intersection migration distance.

[0148] Substitute the average value of the intersection migration speed into formula (10) to calculate the sum of squares between groups and obtain the second sum of squares. Substitute the average value of the intersection migration speed into formula (11) to calculate the sum of squares of errors between groups and obtain the second sum of squares of errors. Calculate the ratio of the second sum of squares to the second sum of squares of errors according to formula (9) to obtain the second ratio F2. Use the second ratio F2 as the coefficient β2 of the intersection migration speed.

[0149] Substitute the average value of the intersection migration acceleration into formula (10) to calculate the sum of squares between groups, and obtain the third sum of squares. Substitute the average value of the intersection migration acceleration into formula (11) to calculate the sum of squares of errors between groups, and obtain the third sum of squares of errors. Calculate the ratio of the third sum of squares to the third sum of squares of errors according to formula (9), and obtain the third ratio F3. Use the third ratio F3 as the coefficient β3 of the intersection migration acceleration.

[0150] After calculating β1, β2 and β3, β1, β2 and β3 are substituted into formula (8) to calculate the relative stability index of the flow section. Since the migration distance, migration velocity and acceleration of the intersection point are different for each flow section, the relative stability index of N flow sections is finally calculated.

[0151] S102-4: Determine the threshold based on the relative stability index of the flow section. The threshold is used to determine whether the river section is stable.

[0152] Step S103-3 yields the relative stability indices for N flow sections. Therefore, for each of the g groups, the maximum and minimum relative stability indices can be determined, denoted as RSIRR-G respectively. max,i and RSIRR-G min,i , i = 1, 2, ..., g.

[0153] A specific value is manually selected as the threshold between two adjacent groups; specifically, the threshold is within the interval [RSIRR-G]. max,i RSIRR-G min,i+1 The selection is made within a certain range. If the range is small, a constant that is similar to a specific value within the range is manually selected as the threshold.

[0154] S102-5: Determine the relative stability index of the river section based on the relative stability index of the flow cross section.

[0155] A river channel comprises multiple river segments, and each river segment comprises multiple flow cross-sections. The average relative stability index of the flow cross-sections within a given river segment is calculated using the following formula:

[0156]

[0157] In the formula, RSIRR-G refers to the average value of the relative stability index of the cross-sections passing through a certain river segment; n refers to the number of cross-sections passing through a certain river segment; RSIRR-G m It refers to the relative stability index of a certain flow section.

[0158] S102-6: By comparing the relative stability index of a river segment with the threshold, river segments are divided into stable river segments and oscillating river segments.

[0159] The average relative stability index of all cross-sections in a river segment is used as the relative stability index of that river segment. The relative stability index of the river segment is compared with the threshold. If the relative stability index of the river segment is greater than or equal to the threshold, the river segment is considered to be a swinging river segment; if the relative stability index of the river segment is less than the threshold, the river segment is considered to be a stable river segment.

[0160] S102-7: Divide stable river sections into meandering river sections and straight river sections.

[0161] Obtain the curve length and straight length of the river segment, calculate the meandering degree, and classify stable river segments into meandering and straight segments based on the meandering degree. The calculation formula is as follows.

[0162]

[0163] In the formula, r refers to the meandering degree of the river section; L refers to the curve length of the river section, in meters; and l refers to the straight length of the river section, in meters.

[0164] Based on the meandering classification system, stable river sections are divided into the following categories: if r ≤ 1, it is a straight river section; if 1 < r ≤ 1.2, it is a low meandering river section; if 1.2 < r ≤ 1.5, it is a medium meandering river section; and if 1.5 < r, it is a high meandering river section. Low, medium, and high meandering river sections are all considered meandering river sections.

[0165] S103: Using elevation data and daily water level data, assign elevation values ​​to the waterline of the meandering river section and the waterline of the straight river section to obtain contour lines.

[0166] S103-1: Using elevation data and daily water level data, assign elevation values ​​to the waterline of the meandering river section and the waterline of the straight river section to obtain the waterline elevation value.

[0167] The process of assigning elevation values ​​to straight and meandering river sections is described separately:

[0168] 1. Straight river section

[0169] Set a fixed interval, and draw a perpendicular line through the center line at the fixed interval, intersecting the waterline. The intersection point of the perpendicular line and the waterline is recorded as the third intersection point. The elevation value corresponding to each third intersection point is the same as the elevation value corresponding to the elevation point falling on the perpendicular line. Based on this conclusion, the point where the third intersection point and the elevation point overlap is recorded as the first overlap point. The first overlap point is assigned the corresponding elevation value to this elevation point, thus obtaining the first elevation value. Since the first overlap point is located on the waterline, assigning values ​​to multiple first overlap points indirectly assigns the first elevation value to the waterline.

[0170] 2. Meandering river sections

[0171] Because the elevation values ​​of the waterline and the centerline may differ in a meandering river section, a linear fit is performed on multiple elevation points along a direction perpendicular to the centerline to obtain the continuous range of elevation variation. Simultaneously, points where the elevation points overlap with the waterline are identified and designated as second overlap points. Elevation values ​​are assigned to these second overlap points using the linear fit method, yielding second elevation values. Similarly, assigning values ​​to multiple second overlap points indirectly assigns second elevation values ​​to the waterline.

[0172] After assigning a first elevation value to the waterline of straight river sections and a second elevation value to the waterline of meandering river sections, to ensure the integrity and uniformity of the elevation values, multiple waterline ranges are defined for both straight and meandering river sections. Each waterline range is required to include at least two elevation values. Elevation points are then interpolated along the waterline at fixed intervals within each range to obtain a third elevation value. This method effectively fills the gaps in elevation points.

[0173] Furthermore, to compensate for the discontinuity of elevation data over time, a fourth elevation value is assigned to the portion of the waterline that has not been assigned an elevation value, using daily water level data and following the method described in S103-1.

[0174] S103-2: Extracting contour lines based on waterline elevation values.

[0175] Along the waterline of both the meandering and straight sections of the river, elevation points with the same elevation value are identified within the range of the first, second, third, and fourth elevation values. These elevation points with the same elevation value are called contour points. Contour points are connected to generate contour lines, and finally, multiple contour lines are generated.

[0176] Furthermore, the contour lines are smoothed to ensure their fluidity.

[0177] S104: Construct the terrain above the highest water level using remote sensing image data.

[0178] We acquired Gaofen-7 stereo image pairs and performed orthorectification and image fusion on them using common remote sensing image processing tools to generate high-resolution orthophoto data. We then extracted digital surface model (DSM) and point cloud data from the high-resolution orthophoto data using common remote sensing image processing tools. The digital surface model reflects the undulation of the river channel.

[0179] By using orthophoto data, digital surface models, and point cloud data, the complete topography above the highest water level can be obtained.

[0180] S105: Using elevation data and daily water level data, construct the terrain between the lowest and highest water levels based on contour lines and waterline lines.

[0181] Using elevation data and daily water level data, based on contour lines and waterline lines, elevation points are interpolated on the terrain between the lowest and highest water levels to form continuous elevation points, thereby forming an elevation surface and obtaining the preliminary terrain construction results.

[0182] Based on the preliminary terrain construction results, secondary interpolation is performed using TIN interpolation or Kriging interpolation to fill the elevation gaps between the lowest and highest water levels, ensuring a smooth transition of the boundary area and the integrity of the terrain.

[0183] S106: Construct the terrain below the lowest water level using daily water level data and cross-sectional data.

[0184] S106-1: The cross-sections in the large cross-section data are generalized into triangular cross-sections, and the water depth calculation model is determined based on the daily water level data.

[0185] To simplify calculations and analysis, the cross-section during the transition from the lowest to the highest water level is generalized into a triangular cross-section. The generalized triangular cross-section can better reflect the changes in underwater topography.

[0186] The triangular cross-section at the lowest water level and the triangular cross-section at the highest water level are generalized similar triangles, with the same geometric shape but different scales.

[0187] Analysis of the generalized triangular cross-section reveals that during the transition from the lowest to the highest water level, as the water surface width increases, the centerline offset increases, the water depth increases, and the corresponding eccentric angle of the centerline also increases. Therefore, it can be determined that the centerline offset, the corresponding eccentric angle, and the water depth are directly proportional to the water surface width.

[0188] Large-section data and daily water level data during the transition from the lowest to the highest water level were selected to obtain the water surface width at different water levels, calculate the cross-sectional area at different water levels, and calculate the water depth at different water levels under the generalized triangular cross-sectional shape. The maximum water surface width and maximum centerline offset at the highest water level were obtained through calculation. The centerline offset is closely related to the river morphology and hydrodynamics, and the eccentricity angle can reflect the centerline offset.

[0189] Based on the cross-sectional area and maximum water surface width at the highest water level, as well as the cross-sectional area and water surface width at different water levels during the transition from the lowest to the highest water level, the offset of the centerline of the triangular cross-section is analyzed to determine the relationship between the water depth, water surface width, and eccentricity angle at different water levels during the transition from the lowest to the highest water level. This relationship expression is the water depth calculation model.

[0190] Based on the cross-sectional area and maximum water surface width at the highest water level, as well as the cross-sectional area and water surface width at different water levels during the transition from the lowest to the highest water level, determine the offset angles corresponding to the multiple centerline offsets at the highest water level and at different water levels during the transition from the lowest to the highest water level.

[0191] Since the cross-section is a generalized triangular cross-section, the cross-sectional area and maximum water surface width at the highest water level can be calculated using CAD and other software, as well as the cross-sectional area and water surface width at different water levels during the transition from the lowest to the highest water level. Then, the water depth h at the highest water level can be calculated using the following formula. xun and the lowest water level to the highest

[0192]

[0193]

[0194] In the formula, h guo A represents the water depth at different water levels during the transition from the lowest to the highest water level. guo The cross-sectional area refers to the area at different water levels during the transition from the lowest to the highest water level; W guo h represents the width of the water surface at different water levels during the transition from the lowest to the highest water level. xun The water depth at the highest water level; A xun W is the cross-sectional area at the highest water level. xun This represents the maximum width of the water surface at the highest water level.

[0195] The offset of the river centerline is calculated using the following formula, based on the maximum water surface width at the highest water level and the water surface width at different water levels during the transition from the lowest to the highest water level:

[0196]

[0197] In the formula, a and Δw are the offsets of the center line; W xun W represents the maximum width of the water surface at the highest water level. guo This refers to the width of the water surface at different water levels during the transition from the lowest to the highest water level.

[0198] Based on the similarity between the centerline offset and the triangular cross-section, the eccentricity angle corresponding to the centerline offset at different water levels during the transition from the lowest to the highest water level is determined by the following formula:

[0199]

[0200]

[0201] In the formula, h xun The water depth at the highest water level; h guo W represents the water depth at different water levels during the transition from the lowest to the highest water level. guo W represents the width of the water surface at different water levels during the transition from the lowest to the highest water level. xun Δw is the maximum water surface width at the highest water level; Δw is the centerline offset; tanα is the eccentricity angle corresponding to the centerline offset.

[0202] The average of the offset angles corresponding to multiple centerline offsets is taken, and this average offset angle is determined as a fixed offset angle, as shown in the following formula.

[0203]

[0204] In the formula, It is a fixed offset angle.

[0205] The relationship between water depth, water surface width, and fixed offset angle at different water levels during the transition from the lowest to the highest water level is determined as a water depth calculation model, as shown in the following formula.

[0206]

[0207] In the formula, h α-guo To determine the water depth at different water levels corresponding to a fixed eccentricity angle; W xun W represents the maximum width of the water surface at the highest water level. guo Δw represents the width of the water surface at different water levels during the transition from the lowest to the highest water level; tanα represents the centerline offset; and tanα represents the eccentricity angle corresponding to the centerline offset.

[0208] S106-2: Based on the water surface width, a water depth calculation model is used to reconstruct the water depth of the underwater topography of the river channel.

[0209] The maximum water surface width at the highest water level and the water surface width at different water levels during the transition from the lowest to the highest water level are obtained. A water depth calculation model is used to determine the water depth at different water levels corresponding to a fixed eccentric angle.

[0210] Based on the water depth at the lowest water level, the water depth of the terrain below the lowest water level is reconstructed, thereby completing the reconstruction of the terrain below the lowest water level.

[0211] S107: Combine the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest and highest water levels to form a complete riverbed topography.

[0212] TIN interpolation or Kriging interpolation is used to stitch together the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest and highest water levels to ensure a smooth transition between the three.

[0213] Embodiments of the present invention also provide a riverbed topography generation system, such as Figure 2 As shown, it includes:

[0214] The collection and processing module 201 is used to collect digital elevation model data, remote sensing image data, elevation data, daily water level data and cross-sectional data of the river channel. It uses the edge detection method to divide the river channel topography into the topography above the highest water level, the topography below the lowest water level, and the topography between the highest and lowest water levels, and extracts the waterline, centerline and flow cross-section.

[0215] The identification and division module 202 is used to calculate the relative stability index of the flow section based on the centerline and the flow, and to identify stable river sections in the river channel using the relative stability index of the flow section, and to divide the stable river sections into meandering river sections and straight river sections.

[0216] The contour line generation module 203 is used to assign elevation values ​​to the waterline of the meandering river section and the waterline of the straight river section using elevation data and daily water level data, so as to obtain contour lines.

[0217] The first building module 204 is used to construct the terrain above the highest water level using remote sensing image data.

[0218] The second construction module 205 is used to construct the terrain between the lowest and highest water levels based on contour lines and waterline lines, using elevation data and daily water level data.

[0219] The third construction module 206 is used to construct the terrain below the lowest water level using daily water level data and cross-sectional data.

[0220] The splicing module 207 is used to splice together the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest and highest water levels to form a complete riverbed terrain.

[0221] It should be noted that the above-mentioned collection and processing module 201, identification and division module 202, contour line generation module 203, first construction module 204, second construction module 205, third construction module 206 and splicing module 207 correspond to steps S101 to S107 in the embodiment of the river terrain generation method. The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiment.

[0222] Embodiments of the present invention also provide a computer-readable storage medium storing a program or instructions that, when executed on a computer, cause the computer to perform the river terrain generation method as described in the above method embodiments.

[0223] like Figure 3 As shown, an embodiment of the present invention also provides an electronic device, including: a processor 301, the processor 301 being coupled to a memory 302, the processor 301 being used to read and execute a computer program stored in the memory 302 to implement a riverbed terrain generation method as described in the above method embodiment.

[0224] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating river channel topography, characterized in that, include: Collect digital elevation model data, remote sensing image data, elevation data, daily water level data and cross-sectional data of the river channel; use edge detection method to divide the river channel topography into the topography above the highest water level, the topography below the lowest water level, and the topography between the highest and lowest water levels, and extract the waterline, centerline and flow cross-section. The relative stability index of the flow section is calculated based on the centerline and the flow section. The relative stability index of the flow section is used to identify stable river sections in the river channel and to divide the stable river sections into meandering river sections and straight river sections. It includes: Obtain the intersection point of the flow section and the centerline; Select two adjacent time points of the center line, merge the two adjacent time points of the center line, and obtain the migration distance, migration speed, and migration acceleration of the intersection point, which are respectively denoted as intersection point migration distance, intersection point migration speed, and intersection point migration acceleration; Calculate the relative stability index of the flow cross section; A threshold is determined based on the relative stability index of the flow section, and the threshold is used to determine whether the river section is stable. The relative stability index of the river section is determined based on the relative stability index of the flow cross section. By comparing the relative stability index of the river segment with the threshold, the river segment is divided into stable river segments and oscillating river segments, thereby identifying the stable river segments from the river channel; The stable river section is divided into meandering river sections and straight river sections; The relative stability index of the flow section is calculated using the following formula: ， In the formula, Indicators of relative stability; The distance of the intersection point migration; The speed of intersection migration; Intersection point migration acceleration; The coefficient for the migration distance at the intersection point; The coefficient of the intersection point migration speed; The coefficient of the acceleration during the intersection point migration; The coefficients of the intersection point migration distance, the point migration velocity, and the intersection point migration acceleration are calculated using cluster analysis, including: The cross-sections were grouped using hierarchical clustering, resulting in multiple groups. Calculate the average value of the intersection migration distances corresponding to the cross sections contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration distances to obtain the first sum of squares, calculate the sum of squares of errors between the groups using the average value of the intersection migration distances to obtain the first sum of squares of errors, calculate the ratio of the first sum of squares to the first sum of squares of errors to obtain the first ratio, and use the first ratio as the coefficient of the intersection migration distance; Calculate the average value of the intersection migration speed corresponding to the cross-section contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration speed to obtain a second sum of squares, calculate the sum of squares of errors between the groups using the average value of the intersection migration speed to obtain a second sum of squares of errors, calculate the ratio of the second sum of squares to the second sum of squares of errors to obtain a second ratio, and use the second ratio as the coefficient of the intersection migration speed; Calculate the average value of the intersection migration acceleration corresponding to the cross-section contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration acceleration to obtain the third sum of squares, calculate the sum of squares of error between the groups using the average value of the intersection migration acceleration to obtain the third sum of squares of error, calculate the ratio of the third sum of squares to the third sum of squares of error to obtain the third ratio, and use the third ratio as the coefficient of the intersection migration acceleration; Using the elevation data and the daily water level data, elevation values ​​are assigned to the waterline of the meandering river section and the waterline of the straight river section to obtain contour lines; The terrain above the highest water level is constructed using the remote sensing image data; Using the elevation data and the daily water level data, the terrain between the lowest and highest water levels is constructed based on the contour lines and the waterline. The terrain below the lowest water level is constructed using the daily water level data and the cross-sectional data; By piecing together the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest and highest water levels, a complete riverbed terrain is formed.

2. The method according to claim 1, characterized in that, The method of using edge detection to divide the riverbed topography into topography above the highest water level, topography below the lowest water level, and topography between the highest and lowest water levels includes: The edge detection method is used to extract water surface feature lines from the digital elevation model data; The edge detection method includes: noise smoothing; calculating gradient magnitude and gradient direction; performing non-maximum suppression along the gradient direction to obtain edge points; and using a double threshold algorithm to detect and connect edge points to form edge lines. The water surface feature lines include the water surface line, the shoreline line, and the valley boundary line; The riverbed topography is divided using the water surface feature lines. The riverbed topography below the water surface line is divided into the topography below the lowest water level, the riverbed topography between the water surface line and the bank line is divided into the topography between the highest and lowest water levels, and the riverbed topography between the bank line and the valley boundary line is divided into the topography above the highest water level.

3. The method according to claim 1, characterized in that, The process of assigning elevation values ​​to the waterline of meandering river sections and straight river sections using elevation data and daily water level data to obtain contour lines includes: The elevation data is used to assign a first elevation value to the waterline of the meandering river section. A second elevation value is assigned to the waterline of the straight river section using the elevation data; Elevation points are interpolated along the waterline of the meandering river section and the waterline of the straight river section to obtain a third elevation value; The daily water level data is used to assign a fourth elevation value to the waterline of the meandering river section and the waterline of the straight river section. Along the waterline of the meandering river section and the waterline of the straight river section, elevation points with the same elevation value are identified within the range of the first elevation value, the second elevation value, the third elevation value, and the fourth elevation value. The elevation points with the same elevation value are connected to obtain the contour lines.

4. The method according to claim 1, characterized in that, The construction of the terrain above the highest water level using remote sensing image data includes: Acquire stereo image pair data, perform orthorectification and image fusion on the stereo image pair data to generate orthophoto, and extract digital surface model and point cloud data from the orthophoto image data.

5. The method according to claim 1, characterized in that, The method of constructing the terrain between the lowest and highest water levels using elevation data and daily water level data, based on contour lines and waterline lines, includes: Using the elevation data and the daily water level data, based on the contour lines and the waterline, the terrain between the lowest and highest water levels is interpolated with elevation points to form continuous elevation points, thereby forming an elevation surface and obtaining the preliminary terrain construction result; Based on the preliminary terrain construction results, a second interpolation is performed to fill the elevation gap between the lowest water level and the highest water level.

6. A river channel topography generation system, characterized in that, include: The collection and processing module is used to collect digital elevation model data, remote sensing image data, elevation data, daily water level data and cross-sectional data of the river channel. It uses the edge detection method to divide the river channel topography into the topography above the highest water level, the topography below the lowest water level, and the topography between the highest and lowest water levels, and extracts the water edge line, center line and flow cross section. The identification and division module is used to calculate the relative stability index of the flow section based on the centerline and the flow section, and to identify stable river sections in the river channel using the relative stability index of the flow section, and to divide the stable river sections into meandering river sections and straight river sections. It includes: Obtain the intersection point of the flow section and the centerline; Select two adjacent time points of the center line, merge the two adjacent time points of the center line, and obtain the migration distance, migration speed, and migration acceleration of the intersection point, which are respectively denoted as intersection point migration distance, intersection point migration speed, and intersection point migration acceleration; Calculate the relative stability index of the flow cross section; A threshold is determined based on the relative stability index of the flow section, and the threshold is used to determine whether the river section is stable. The relative stability index of the river section is determined based on the relative stability index of the flow cross section. By comparing the relative stability index of the river segment with the threshold, the river segment is divided into stable river segments and oscillating river segments, thereby identifying the stable river segments from the river channel; The stable river section is divided into meandering river sections and straight river sections; The relative stability index of the flow section is calculated using the following formula: ， In the formula, Indicators of relative stability; The distance of the intersection point migration; The speed of intersection migration; Intersection point migration acceleration; The coefficient for the migration distance at the intersection point; The coefficient of the intersection point migration speed; The coefficient of the acceleration during the intersection point migration; The coefficients of the intersection point migration distance, the point migration velocity, and the intersection point migration acceleration are calculated using cluster analysis, including: The cross-sections were grouped using hierarchical clustering, resulting in multiple groups. Calculate the average value of the intersection migration distances corresponding to the cross sections contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration distances to obtain the first sum of squares, calculate the sum of squares of errors between the groups using the average value of the intersection migration distances to obtain the first sum of squares of errors, calculate the ratio of the first sum of squares to the first sum of squares of errors to obtain the first ratio, and use the first ratio as the coefficient of the intersection migration distance; Calculate the average value of the intersection migration speed corresponding to the cross-section contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration speed to obtain a second sum of squares, calculate the sum of squares of errors between the groups using the average value of the intersection migration speed to obtain a second sum of squares of errors, calculate the ratio of the second sum of squares to the second sum of squares of errors to obtain a second ratio, and use the second ratio as the coefficient of the intersection migration speed; Calculate the average value of the intersection migration acceleration corresponding to the cross-section contained in each group, calculate the sum of squares between the groups using the average value of the intersection migration acceleration to obtain the third sum of squares, calculate the sum of squares of error between the groups using the average value of the intersection migration acceleration to obtain the third sum of squares of error, calculate the ratio of the third sum of squares to the third sum of squares of error to obtain the third ratio, and use the third ratio as the coefficient of the intersection migration acceleration; The contour line generation module uses the elevation data and the daily water level data to assign elevation values ​​to the waterline of the meandering river section and the waterline of the straight river section, thereby obtaining contour lines. The first construction module is used to construct the terrain above the highest water level using the remote sensing image data; The second construction module is used to construct the terrain between the lowest and highest water levels based on the elevation data and the daily water level data, using the contour lines and the waterline. The third construction module uses the daily water level data and the cross-sectional data to construct the terrain below the lowest water level; The splicing module is used to splice together the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest and highest water levels to form a complete riverbed terrain.

7. A computer-readable storage medium, characterized in that, The system stores a program or instructions that, when executed on a computer, cause the computer to perform the river terrain generation method as described in any one of claims 1-5.

8. An electronic device, characterized in that, Includes a processor, which is coupled to a memory; The processor is used to read and execute the computer program stored in the memory to implement the river terrain generation method as described in any one of claims 1-5.

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