Automatic drawing method for precise thalweg of riverway

Abstract

The invention relates to the technical field of thalweg drawing, and discloses a riverway precise thalweg automatic drawing method, which comprises the following steps: obtaining riverway vector data from a riverway management mechanism, scanning the underwater terrain of a riverway by a multi-beam depth finder to obtain river bottom terrain data, and drawing the riverway vector data according to the river bottom terrain data. Performing cloud thinning and noise reduction on the river bottom topographic data points to obtain river bottom total data, extracting a river channel center line in the river channel vector data, converting the river channel center line into river channel turning point coordinate data, and performing extraction fusion on the river bottom total data and the river channel turning point coordinate data so as to determine a plurality of river channel section line data; and connecting the minimum values of the coordinates z in the plurality of pieces of river channel section line data into a line to obtain the river channel thalweg. According to the method, the river bottom topographic data are collected through the multi-beam sounding system, and the river basic data and the river bottom topographic data are thinned and denoised by using an intelligent algorithm, so that the data accuracy is improved, and the thalweg is automatically generated.

Description

Technical Field

[0001] The present invention relates to the field of channel depth line drawing, and in particular to a method for automatically drawing accurate channel depth lines of a river. Background Art

[0002] The deep water line, also known as the stream line, is the line connecting the maximum water depth along the river direction. The section along this line is the longitudinal section of the river. The deep water line is often misunderstood as the middle water line in engineering hydrology, but in fact there is a clear difference between the two. The middle water line refers to the line connecting the maximum flow velocity points on the surface of each cross-section of the river, while the deep water line is the line connecting the maximum water depth points of each cross-section.

[0003] The existing river channel deep-water lines are all drawn using single-beam bathymetry technology to obtain river sections with intervals of more than 20 meters. The deep-water lines are also mostly drawn by manually extracting deep-water points and then using mapping software, or by custom development based on geographic information systems, which fuses the lowest points of discrete sections to generate deep-water lines. The deep-water lines are generated by fusion of multiple data, and there are errors between the data. Therefore, the deep-water lines generated by fitting are partially deviated from the real deep-water lines, and the drawing efficiency is low. Summary of the invention

[0004] The present invention provides a method for automatically drawing accurate channel depth lines in a river, which solves the problems mentioned in the above background technology that the existing channel depth line drawing method is a fusion of multiple data, there are errors between the data, the local fitted channel depth line has a large deviation from the real channel depth line, and the drawing efficiency is low.

[0005] The present invention provides the following technical solution: A method for automatically drawing a precise river channel depth line. A method for automatically drawing a precise river channel depth line comprises the following steps: River vector data, obtaining the required river vector data from the river management agency; S1. The multi-beam echo sounder scans the underwater terrain of the river channel to obtain riverbed terrain data points, which include structured points X n , structured point Y n and the structured point Z n ; S2, perform cloud thinning and noise reduction on the riverbed topographic data points obtained in S1 to obtain the full riverbed data; S3, extracting the river centerline from the river vector data, and converting the river centerline into river turning point coordinate data; S4, extracting and fusing the riverbed full data obtained in S2 and the river channel turning point coordinate data obtained in S3, thereby determining a number of river channel cross-section line data points, wherein the river channel cross-section line data points include coordinates X, Y and Z; S5. Connect the minimum Z coordinate values ​​in the river section line data into a line to obtain the river depth line.

[0006] As an optional solution of the method for automatically drawing the precise channel depth line of the present invention, wherein: a plurality of structured points X n , structured point Y n and the structured point Z n Construct the first KD tree; For the structured point X n , structured point Y n and the structured point Z n Perform variance operations respectively to select dimensions and divide the data according to the selected dimensions; performing variance calculation on the selected divided data to obtain a plurality of first point cloud data, dividing the plurality of first point cloud data into a first point cloud data set, The variance calculation formula is, , Where N represents the number of data sets, Di represents the value of the point in the data set, and μ represents the mean value of the data set; A first distance threshold is set, point cloud data greater than the set first distance threshold is removed, point cloud data less than the set first distance threshold is retained, data point cloud thinning is completed, and the retained point cloud data constitutes a second KD tree structure.

[0007] As an optional solution of the method for automatically drawing the precise channel depth line of the river channel according to the present invention, wherein: the Euclidean operation formula, the point cloud data is obtained by the Euclidean operation formula to obtain the spacing of the reserved points in the point cloud data, and the Euclidean operation formula is: ; When the distance between the reserved points is less than the distance threshold, the redundant points are determined and the reserved points are thinned out; When the distance between the reserved points is greater than the distance threshold, it is determined to be a key node and the reserved point is retained; The key nodes are merged to obtain a point cloud dataset, and the cloud dataset is converted into an output structured point X m , output structured point Y m And output structured point Z m .

[0008] As an optional solution of the method for automatically drawing the precise channel depth line of the present invention, wherein: by calculating the output structured point X m , output structured point Y m And output structured point Z m Obtain the distance data set C; The distance data set C is calculated to obtain the average distance D in the distance data set; The median of the average distance meanD is obtained by calculating the average distance D in the distance data set; The variance S is obtained by calculating the average distance D in the distance data set; By comparing the average distance D with MaxD, the MaxD calculation formula is: MaxD=meanD+k*S; meanD represents the median of the data set, k represents the standard deviation multiple, and S represents the standard deviation of the data set; When the average distance D is greater than the value of MaxD, the output structured point is considered to be a noise point and is removed; When the average distance D is less than the value of MaxD, the output structured point is considered to be a pointing point and is retained.

[0009] As an optional solution of the method for automatically drawing the precise deep-water line of a river channel according to the present invention, the output structured point X m , output structured point Y m And output structured point Z m Dividing the data according to the selected dimension to obtain a plurality of second point cloud data, and dividing the plurality of second point cloud data into a second point cloud data set; The second point cloud dataset includes (X1, Y1, Z1), (X2, Y2, Z2), …, (X 10 ,Y 10 ,Z 10 ) basis points; Calculate the distance C from the neighboring point in the base point data set to the calculation base point. The calculation formula is: ; Get distance data sets C1, C2, C3...C9.

[0010] Calculate the average distance D in the distance data set. The calculation formula is: , Get average distance data sets D1, D2, D3...D9; In the formula, N represents the number of data sets.

[0011] As an optional solution of the method for automatically drawing the precise channel depth line of the present invention, the median meanD of the average distance is calculated using the following formula: meanD=median(D1,D2,…,Dn) Calculate the variance S of the average distance using the formula: , In the formula, N represents the number of data sets, and μ represents the average value of the data set; The MaxD calculation formula is: MaxD = meanD+k*S; In the formula, meanD represents the median of the data set, k represents the standard deviation multiple, and S represents the standard deviation of the data set.

[0012] As an optional solution of the method for automatically drawing the precise channel depth line of the present invention, the method comprises: converting the channel turning point coordinate data into a plurality of serialized cross sections; The pointing points are distributed on a number of serialized cross sections, and the pointing points that are not distributed on the serialized cross sections are removed.

[0013] As an optional solution of the method for automatically drawing the precise deep-sea line of a river channel according to the present invention, wherein: a range value F is set, and the set range value F is used to determine the pointing point on the serialized cross section; Automatically select the appropriate pointing points ie, xe, ye and ze.

[0014] As an optional solution of the method for automatically drawing the precise deep-water line of a river channel according to the present invention, JavaScript is used to identify the pointing points ie, xe, ye and ze and perform segmentation processing; The split processing array format is xs, ys and zs; Merge xs, ys and zs under the same ID, with the first element as ID and the remaining elements as coordinate values. For the coordinate value array under each ID, filter out the third value, and take the smallest value as the final result, which is the deep point data.

[0015] As an optional solution to the method for automatically drawing accurate deep-channel fen lines in the river channel described in the present invention, a longitudinal profile change diagram of the deep-channel fen line and a planar distribution diagram of the deep-channel fen line are automatically drawn through the deep-channel point data.

[0016] The present invention has the following beneficial effects: 1. This method for automatically drawing precise channel depth lines collects riverbed topography data through a multi-beam bathymetric system, and uses intelligent algorithms to thin and denoise the basic channel data and riverbed topography data, thereby improving data accuracy and automatically generating depth lines.

[0017] 2. This method of automatically drawing precise river channel deep-water lines serializes and extracts the basic river channel data and riverbed topography data to further improve data accuracy and significantly remove noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a step diagram of the method of the present invention.

[0019] Figure 2 It is the three-dimensional map of underwater terrain of the present invention.

[0020] Figure 3 This is a schematic diagram of the center turning point of the river channel of the present invention.

[0021] Figure 4 It is a schematic diagram of the serialized cross-section line of the river channel of the present invention.

[0022] Figure 5 This is a schematic diagram of river section point extraction according to the present invention.

[0023] Figure 6 This is a graph showing changes in the longitudinal profile data of the deep sea line of the present invention.

[0024] Figure 7 This is the plane distribution diagram of the deep line of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0026] Example 1 See also Figure 1-2 A method for automatically drawing a precise river channel depth line comprises the following steps: River vector data: obtain the required river vector data from the river management agency; S1. The multi-beam echo sounder scans the underwater terrain of the river channel to obtain riverbed terrain data points, which include structured points X n , structured point Y n and the structured point Z n ; S2, perform cloud thinning and noise reduction on the riverbed topographic data points obtained in S1 to obtain the full riverbed data; S3, extracting the river centerline from the river vector data, and converting the river centerline into river turning point coordinate data; S4, extracting and fusing the riverbed full data obtained in S2 and the river channel turning point coordinate data obtained in S3, thereby determining a number of river channel cross-section line data points, wherein the river channel cross-section line data points include coordinates X, Y and Z; S5. Connect the minimum Z coordinate values ​​in several river section line data into a line to obtain the river channel depth line.

[0027] An unmanned boat equipped with a multi-beam echo sounder was used to conduct a preliminary scan of the underwater topography of the river channel to obtain preliminary scanned three-dimensional topographic data of the riverbed. Let several structured points X n , structured point Y n and the structured point Z n Construct the first KD tree; For the structured point X n , structured point Y n and the structured point Z n Perform variance operations respectively to select dimensions and divide the data according to the selected dimensions; A variance operation is performed on the selected divided data to obtain a plurality of first point cloud data, and the plurality of first point cloud data are divided into a first point cloud data set, which is composed of a structured point cloud X n , Y n and Z n The point cloud spacing is 0.25m, the total number of point clouds is 526352, the coordinate system uses CGCS2000 plane coordinates, the elevation uses the geodetic elevation system, and the required river vector data is obtained from the river management agency; It should be noted that the river vector data format is shp, which is convenient for extracting the river centerline; The KD tree and Euclidean algorithm are used to thin the point cloud of the riverbed three-dimensional terrain data obtained in step 1. Thinning can reduce the amount of data, thereby improving the processing efficiency of subsequent steps; Since the point cloud data collected by multi-beam has the characteristics of high density, in order to reduce the amount of calculation, the structured point X n , structured point Y n and the structured point Z n Construct the first KD tree for the three-dimensional point cloud dataset; When k=3, the structured point X n , structured point Y n and the structured point Z n They represent the coordinate values ​​of a point in three-dimensional space, X represents the first dimension, Y represents the second dimension, and Z represents the third dimension; Select the data to be divided according to the variance size on each dimension. The larger the variance, the more dispersed the data is on that dimension. The latitude with large variance is selected for division. The variance calculation formula is, , Where N represents the number of data sets, Di represents the value of the point in the data set, and μ represents the mean value of the data set; After calculation, the variance of the first dimension of x is 2951.86, the variance of the second dimension of y is 6121.16, and the variance of the third dimension of z is 9.90, that is, the second dimension of y is selected to divide the data.

[0028] For the selected partition dimension y, get the median m=3432185.12 of the data set on this dimension (when the number of data points is even, the median is the average of the two middle numbers), and then divide the data set into two subsets: Pl={pi|pi≤m}, that is, divide the points whose coordinate values ​​on the second dimension y are less than or equal to the median m into subsets; Pt={pt|pt>m}, that is, to divide the points whose coordinate values ​​on the second dimension y are greater than the median m into subsets; Finally, recursively build the second KD tree on Pl and Pt according to the above steps until the number of data points in the subset is less than the predetermined threshold. The threshold is set to 30 this time, and finally a complete KD tree structure is formed; It should be noted that for point cloud data containing millions of points, the threshold can be set to 10-100; Set the distance threshold B = 0.5m. This threshold determines the minimum distance between point clouds during the thinning process. The minimum distance between any two points in the thinned point cloud in three dimensions will not be less than this value. The larger the value is, the fewer points are retained. For each node in the KD tree structure, calculate the distance between the points (30) in the area represented by the node and the selected retained points. For two points p (X a ,Y a ,Z a ) and q(X b ,Y b ,Z b ); Euclidean operation formula: The point cloud data uses the Euclidean operation formula to obtain the spacing of the retained points in the point cloud data. The Euclidean operation formula is: ; If the distance d from a point to a reserved point is less than or equal to the distance threshold B, the point is considered to be a redundant point and can be thinned out. If the distance d is greater than the threshold B, the point is retained as a key node. Merge all the reserved point cloud data to obtain the thinned point cloud dataset and output the result. Output the structured point cloud {(X m ,Y m ,Z m )}, the point cloud spacing is 0.5m, and the total number of point clouds is 112171; During the acquisition process, multi-beam point cloud data is affected by various factors such as signals, ship measurement environment, and sound speed, and will have varying degrees of noise. Therefore, denoising is required to improve the quality and availability of the data. When the distance between the reserved points is less than the distance threshold, the redundant points are determined and the reserved points are thinned out; When the distance between the reserved points is greater than the distance threshold, it is determined to be a key node and the reserved point is retained; Merge the key nodes to obtain a point cloud dataset, and convert the cloud dataset into an output structured point X m , output structured point Y m And output structured point Z m ; {(X m ,Y m ,Z m )} Divide the data according to the second dimension of Y, set the threshold to 10, and re-form the complete KD tree structure.

[0029] Take the subset {(X1,Y1,Z1), (X2,Y2,Z2), …, (X 10 ,Y 10 ,Z 10 )}, for example, each point in the subset {e.g. (X1, Y1, Z1)} is used as the calculation base point in turn, and the distance C from the neighboring point in the data set to the calculation base point is calculated; Calculate the distance C from the neighboring point in the base point data set to the calculation base point. The calculation formula is: ; Get distance data sets C1, C2, C3...C9; The distance data set C is calculated to obtain the average distance D in the distance data set; Calculate the average distance D in the distance data set. The calculation formula is: , Get average distance data sets D1, D2, D3...D9; The median of the average distance meanD is obtained by calculating the average distance D in the distance data set; The MaxD calculation formula is: MaxD=meanD+k*S; In the formula, meanD represents the median of the data set, k represents the standard deviation multiple, and S represents the standard deviation of the data set; The variance S is obtained by calculating the average distance D in the distance data set; By comparing the average distance D with MaxD, the MaxD calculation formula is: MaxD=meanD+k*S; meanD represents the median of the data set, k represents the standard deviation multiple, and S represents the standard deviation of the data set; When the average distance D is greater than the value of MaxD, the output structured point is considered to be a noise point and is removed; When the average distance D is less than the value of MaxD, the output structured point is considered to be a pointing point and the output structured point is retained; The output structured point X m , output structured point Y m And output structured point Z m Dividing the data according to the selected dimension to obtain a number of second point cloud data, and dividing the number of second point cloud data into a second point cloud data set; The second point cloud dataset includes (X1, Y1, Z1), (X2, Y2, Z2), …, (X 10 ,Y 10 ,Z 10 ) basis points; In the formula, N represents the number of data sets.

[0030] Calculate the median of the average distance meanD, the calculation formula is, meanD=median(D1,D2,…,Dn) Calculate the variance S of the average distance using the formula: , In the formula, N represents the number of data sets, and μ represents the average value of the data set; The river vector data is used to extract the river centerline using the "rcScan" tool in the ArcGIS geographic information system. The obtained river centerline is connected by turning points. The greater the river curvature coefficient, the more turning points there are. After extracting the river centerline, the CGCS2000 plane coordinates are used to obtain the turning point coordinate data. like Figure 3 As shown, the coordinate data of the turning point is as follows: X1, Y1; X2, Y2; X3, Y3; ……; X P ,Y P ; After obtaining the coordinate data of the centerline of the river, the straight line L1 formed by the two adjacent points (X1, Y1) (X2, Y2) The formula of the serialized cross-section line of straight line L1 is expressed as: , in, , In the formula, k represents the slope of line L1; Q represents the spacing between the section of the layout line. The larger the value, the fewer serialized sections are generated. In order to achieve an accuracy of 1m, Q is set to 1m. Q0 represents that the vertical distance of the section line remains at Q. i represents the section line number. When the section line is drawn starting from the turning point (X1, Y1), i=0. B represents the intercept of the layout line on the y-axis.

[0031] The section number i conforms to the following formula: , After calculation, the serialized cross section of the straight line formed by the turning point (X1, Y1) and the turning point (X2, Y2) is as follows Figure 4 As shown; Convert the river channel turning point coordinate data into a number of serialized cross sections; The pointing points are distributed on a number of serialized cross sections, and the pointing points that are not distributed on the serialized cross sections are removed; After the serialized cross section line is formed, due to the scattered characteristics of the point cloud, it is impossible to ensure that all the point clouds are on the cross section line. The range value F is set. The range value F is used to determine the pointing point on the serialized cross section; Set the range value F=0.25: The calculation process is as follows Figure 5 As shown, the range value F is used to include points adjacent to the cross section line. The formula can be expressed as follows based on step 3: , The range value F meets the following formula: , Automatically select the pointing points ie, xe, ye and ze that meet the formula, and output the format as section line number i, coordinate x, coordinate y, coordinate z; Automatically select the appropriate pointing points ie, xe, ye and ze; Use JavaScript to identify the pointing points ie, xe, ye and ze and perform segmentation processing; The split processing array format is xs, ys and zs; Take xs, ys and zs, the first element as ID, and the rest as coordinate values, and merge them under the same ID. For each coordinate value array under each ID, filter out the third value, and take the smallest value as the final result. The smallest value is the deep point data. Automatically draw the change diagram of the longitudinal section of the deep-sea line and the plane distribution diagram of the deep-sea line through the deep-sea point data. The change diagram of the longitudinal section of the deep-sea line can intuitively display the elevation information of the deep-sea line, and the plane distribution diagram of the deep-sea line can intuitively display the plane information of the deep-sea line. In Echarts, the data xs, ys, zmin output in step 4 are used to draw a vertical profile change diagram of the deep-sea line. In Echarts, first specify the chart type as a line chart 'line'; like Figure 6 As shown, specify the data source of the line chart as xs, ys, and zmin, each data point corresponds to a point on the line chart, set the line of the line chart to a smooth curve, connect the data points through the smooth curve, set the size of the inflection point to 10 pixels, define the style of the coordinate axis, set the color of the coordinate axis to black, set the width of the coordinate axis to 1 pixel, and set the transparency of the coordinate axis to 1 (completely opaque); The x-axis represents the straight-line distance between the deep points, and the y-axis represents zmin in is: [xs, ys, zmin].

[0032] like Figure 7As shown, by calling the Proj4js library to convert xs, ys in xs, ys, zmin into longitude and latitude format, new data Bj, Lj are obtained, and the Cesium.Entity class of Cesium is used to create the deep line. The polyline representation of the deep line is set through the polygon attribute. The positions attribute is used to specify the vertex positions (xj, yj) of the polyline. The width attribute of the polyline is set to control the width of the line to 5.0 pixels. By calling the add method of the entities attribute of the CesiumViewer instance, the deep line object is added to the Amap image scene. The view control functions of Cesium, such as zooming, panning, and rotating, can be used to observe the three-dimensional shape and positional relationship of the deep line.

[0033] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0034] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for automatically drawing accurate river channel depth lines, characterized in that: The following steps are involved: River vector data, obtaining the required river vector data from the river management agency; S1. The multi-beam echo sounder scans the underwater terrain of the river channel to obtain riverbed terrain data points, which include structured points X n , structured point Y n and the structured point Z n ; S2, perform cloud thinning and noise reduction on the riverbed topographic data points obtained in S1 to obtain the full riverbed data; S3, extracting the river centerline from the river vector data, and converting the river centerline into river turning point coordinate data; S4, extracting and fusing the riverbed full data obtained in S2 and the river channel turning point coordinate data obtained in S3, thereby determining a number of river channel cross-section line data, wherein the river channel cross-section line data points include coordinates X, Y and Z; S5. Connect the minimum Z coordinate values ​​in the river section line data into a line to obtain the river depth line.

2. The method for automatically drawing accurate river channel depth lines according to claim 1, characterized in that: Let several structured points X n , structured point Y n and the structured point Z n Construct the first KD tree; For the structured point X n , structured point Y n and the structured point Z n Perform variance operations respectively to select dimensions and divide the data according to the selected dimensions; performing variance calculation on the selected divided data to obtain a plurality of first point cloud data, dividing the plurality of first point cloud data into a first point cloud data set, The variance calculation formula is, , Where N represents the number of data sets, Di represents the value of the point in the data set, and μ represents the mean value of the data set; A first distance threshold is set, point cloud data greater than the set first distance threshold is removed, point cloud data less than the set first distance threshold is retained, data point cloud thinning is completed, and the retained point cloud data constitutes a second KD tree structure.

3. The method for automatically drawing accurate river channel depth lines according to claim 2, characterized in that: Euclidean operation formula: the point cloud data is obtained by Euclidean operation to obtain the spacing of the reserved points in the point cloud data. The Euclidean operation formula is: ; When the distance between the reserved points is less than the distance threshold, the redundant points are determined and the reserved points are thinned out; When the distance between the reserved points is greater than the distance threshold, it is determined to be a key node and the reserved point is retained; The key nodes are merged to obtain a point cloud dataset, and the cloud dataset is converted into an output structured point X m , output structured point Y m And output structured point Z m .

4. The method for automatically drawing a precise river channel depth line according to claim 3, characterized in that: By operating the output structure point X m , output structured point Y m And output structured point Z m Obtain the distance data set C; The distance data set C is calculated to obtain the average distance D in the distance data set; The median of the average distance meanD is obtained by calculating the average distance D in the distance data set; The variance S is obtained by calculating the average distance D in the distance data set; By comparing the mean distance D with MaxD, the MaxD calculation formula is: MaxD=meanD+k*S; meanD represents the median of the data set, k represents the standard deviation multiple, and S represents the standard deviation of the data set; When the average distance D is greater than the value of MaxD, the output structured point is considered to be a noise point and is removed; When the average distance D is less than the value of MaxD, the output structured point is considered to be a pointing point and is retained.

5. The method for automatically drawing accurate river channel depth lines according to claim 4, characterized in that: The output structured point X m , output structured point Y m And output structured point Z m Dividing the data according to the selected dimension to obtain a plurality of second point cloud data, and dividing the plurality of second point cloud data into a second point cloud data set; The second point cloud dataset includes several (X n ',Y n ',Z n '); Calculate the distance C from the neighboring point in the base point data set to the calculation base point. The calculation formula is: , Get several distance data sets C; Calculate the average distance D in the distance data set. The calculation formula is: , Obtain several average distance data sets D; In the formula, N represents the number of data sets.

6. The method for automatically drawing accurate river channel depth lines according to claim 5, characterized in that: Calculate the median of the average distance meanD, the calculation formula is, meanD=median Calculate the variance S of the average distance using the formula: , In the formula, N represents the number of data sets, and μ represents the average value of the data set; The MaxD calculation formula is: MaxD = meanD+k*S; In the formula, meanD represents the median of the data set, k represents the standard deviation multiple, and S represents the standard deviation of the data set.

7. The method for automatically drawing accurate river channel depth lines according to claim 4, characterized in that: Converting the river channel turning point coordinate data into a number of serialized cross sections; The pointing points are distributed on a number of serialized cross sections, and the pointing points that are not distributed on the serialized cross sections are removed.

8. The method for automatically drawing accurate river channel depth lines according to claim 7, characterized in that: Setting a range value F, wherein the set range value F is used to determine a pointing point on a serialized cross section; Automatically select the appropriate pointing points ie, xe, ye and ze.

9. The method for automatically drawing accurate river channel depth lines according to claim 8, characterized in that: Use JavaScript to identify the pointing points ie, xe, ye and ze and perform segmentation processing; The split processing array format is xs, ys and zs; Merge xs, ys and zs under the same ID, with the first element as ID and the remaining elements as coordinate values. For the coordinate value array under each ID, filter out the third value, and take the smallest value as the final result, which is the deep point data.

10. The method for automatically drawing accurate river channel depth lines according to claim 9, characterized in that: Through the deep-water point data, a longitudinal profile change diagram of the deep-water line and a planar distribution diagram of the deep-water line are automatically drawn.

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