A method for generating reservoir river cross sections by fusing multi-source data and performing step-by-step encryption

Abstract

The present invention discloses a method for fusing multi-source data and encrypting it step by step to generate a reservoir river channel cross section, which relates to the field of hydropower engineering measurement. The method includes obtaining multi-source data of the reservoir river channel section, fusing the multi-source data to obtain a point cloud set Q; laying out section line segments, and determining an initial section data set A based on the laid out section line segments; obtaining terrain feature points on the section line segments to form a new section data set B; obtaining a new section data set C; forming a new section data set D; converting the section data set D into a mileage file of the section; drawing the mileage file into a section map, and then generating a reservoir river channel cross section. The multi-source data in the present invention is fused into point cloud data for the entire reservoir bank area. The data source includes true color airborne laser point cloud data of the shore part, elevation point data collected by underwater unmanned boats, and elevation point data manually collected near the shore and in shallow water areas of the reservoir, ensuring the high accuracy and comprehensiveness of the data source.

Description

Technical Field

[0001] The present invention relates to the field of hydropower engineering surveying, and in particular to a method for fusing multi-source data and generating a reservoir river cross section by step-by-step encryption. Background Art

[0002] In the field of river or reservoir cross-section measurement in hydropower engineering surveys, current cross-section measurement methods primarily include traditional manual field measurement and point cloud extraction. Traditional field measurement offers the advantages of high accuracy but low efficiency. Point cloud extraction, which primarily extracts cross-section data by generating DEMs or interpolating elevation points using an irregular triangulated network (TIN), offers the advantages of high efficiency but suffers from low accuracy in water areas. Therefore, a river cross-section generation method that can improve data accuracy is needed. Summary of the Invention

[0003] In view of the problems existing in the prior art, the present invention provides a method for fusing multi-source data and generating a reservoir river cross section by step-by-step encryption.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] A method for fusing multi-source data and encrypting step by step to generate a reservoir river cross section comprises the following steps: step S1, obtaining multi-source data of the reservoir river section, and fusing the multi-source data to obtain a point cloud set Q; step S2, laying out section line segments, and determining an initial section data set A based on the laid out section line segments, wherein the data set A includes the start and end point data of the section; step S3, obtaining terrain feature points on the section line segments, and adding them to the section data set A to form a new section data set B; step S4, obtaining the endpoints of the ground feature contours on the section line segments based on the colored point cloud set Q, adding them to the section data set B, and adding ground feature attribute labels to the section points to obtain a new section data set C; step S5, encrypting the section points according to the section spacing requirements of different scales required by the hydropower engineering survey specification to form a new section data set D; step S6, converting the section data set D into a section mileage file, that is, each point D on the line segment is a mileage file of the section. i (X, Y, Z) is converted into (mileage value, elevation value, attribute value); step S7, the mileage file is drawn as a cross-section diagram, and then the river cross section of the reservoir area is generated.

[0006] Based on the above technical solution, further, in step S1, the multi-source data includes point cloud data collected by airborne lidar, river underwater elevation point data collected by unmanned boats, and artificial elevation point data collected by RTK.

[0007] Based on the above technical solution, further, in step S1, the fusion processing process is: coloring, denoising, classification, and coordinate conversion processing are performed on the point cloud data collected by the airborne lidar to obtain a ground point cloud set R with RGB color in the engineering coordinate system; the river underwater elevation point data collected by the unmanned boat and the artificial elevation point data collected by RTK in the obtained multi-source data are converted into the engineering coordinate system and converted into a point cloud file in las format, and merged into the point cloud set R to form a new point cloud set Q.

[0008] Based on the above technical solution, further, the specific method of converting to the las format is: first organize the river underwater elevation point data and artificial elevation point data into a fixed format of an Excel table, where the first column is the point number, the second column is the X coordinate under the engineering coordinate system, the third column is the Y coordinate under the engineering coordinate system, and the fourth column is the Z coordinate of the elevation value; after organizing, save the file as csv format, and then use the LasWrite function in MATLAB to write the las file.

[0009] Based on the above technical solution, further, in step S3, the process of obtaining terrain feature points includes the following steps: step S31, calculating the plane coordinates of the interpolation point Mn of the cross-section line segment: interpolating a point every λ from the starting point to the end point of the cross-section line segment, and the coordinates of the interpolation point are (Xn, Yn); step S32, using the KD tree search method to search for the four points closest to the interpolation point Mn in the point cloud set Q, and calculating the elevation value Zn of the target point according to the inverse distance weighted interpolation method; step S33, determining the terrain feature points on the cross-section line segment according to the change in terrain slope.

[0010] Based on the above technical solution, further, in step S31, the method for calculating the plane coordinates of the interpolation point is:

[0011] ;

[0012] ;

[0013] ;

[0014] Where L is the length of the cross-section segment, (X1, Y1) is the starting coordinate of the cross-section segment, and (X2, Y2) is the ending coordinate of the cross-section segment.

[0015] Based on the above technical solution, further, in step S32, the calculation process of the elevation value Zn includes the following steps: step S321, using the KD tree search method to search for the four coordinates of the closest point to the point Mn in the point cloud set Q, which are Q1 (x1, y1, z1), Q2 (x2, y2, z2), Q3 (x3, y3, z3), and Q4 (x4, y4, z4); step S322, calculating the Euclidean distance from the point Mn to the four points: ; ; ; ; Step S323, set the weight index p and calculate the weight; where p=2; ; ; ; ;

[0016] Step S324: Calculate the elevation value Zn of the target point: .

[0017] Based on the above technical solution, further, in step S33, the determination process is: obtain two adjacent points M i 、M i+1 , and calculate the adjacent points M i , M i+1 Interval slope θ i , the slope θ i Compare with the set slope threshold θ, if the slopes of two adjacent points θ i If the slope is greater than the threshold value θ, then both points are recorded as terrain feature points, and the terrain feature points, the starting point, and the end point together constitute a new cross-section dataset B; , where ( , ) is M i The coordinates of ( ) is M i+1 's coordinates.

[0018] Based on the above technical solution, further, in step S6, the mileage value calculation process is as follows: along the direction of the downstream of the river, the mileage value of the left endpoint of the line segment is set to 0, and the attribute value left endpoint is marked; the mileage is accumulated to the right endpoint, the mileage value of the end point of the cross-section line segment is the length of the line segment, the attribute value right endpoint is marked, and the mileage value S of any point in the middle is i : .

[0019] Based on the above technical solution, further, in step S7, the drawing process is: take the left endpoint of the section line segment as the origin, the direction of the section line segment as the X-axis, the elevation value as the Z-axis, and mark the mileage value, elevation value, attribute value, and section name information.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The multi-source data in the present invention is fused into point cloud data for the entire reservoir bank area. The data sources include true-color airborne laser point cloud data on the shore, elevation point data collected by underwater unmanned boats, and elevation point data manually collected near the shore and in shallow water areas of the reservoir. This ensures the high precision and comprehensiveness of the data sources. After being uniformly converted to the engineering coordinate system, they are fused into a point cloud las data format to extract cross-section points, seamlessly meeting engineering needs. In addition, four sets AD are set up, and a step-by-step encryption method is used to generate cross-section point sets with high precision, comprehensive terrain features, and no data redundancy. Set A collects the starting point and end point data, set B collects the starting point, terrain feature points, and end point data, set C collects the starting point, terrain feature points, feature contour points, and end point data, and set D collects the starting point, terrain feature points, feature contour points, minimum spacing point encryption, and end point data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention may be combined accordingly, provided that there is no conflict between them.

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more clearly understood, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in the various embodiments of the present invention can be combined accordingly without conflicting with each other.

[0025] Example 1

[0026] Combine Figure 1 As shown, this embodiment provides a method for fusing multi-source data and encrypting them step by step to generate a river cross-section in a reservoir area. It proposes to use unmanned boats to measure part of the water area data, and to add manual measurement data on the shore and nearby areas that cannot be measured by unmanned boats. The unmanned boat data and manual measurement data are converted into point cloud format, and are fused with airborne laser point cloud data. A cross-section data set is formed through step-by-step encryption, which can fuse the advantages of multi-source data and has the advantages of high precision and comprehensive data.

[0027] The specific steps include:

[0028] Step S1: Acquire multi-source data related to the river section in the reservoir area and fuse the multi-source data to obtain a point cloud set Q. The acquired relevant data includes point cloud data collected by airborne lidar, underwater river elevation point data collected by unmanned boats, and artificial elevation point data collected by RTK.

[0029] In this embodiment, the processing process includes the following steps:

[0030] The point cloud data collected by the airborne lidar is processed by colorization, denoising, classification, and coordinate conversion using point cloud related professional processing software to obtain a point cloud set R of the ground with RGB color in the engineering coordinate system.

[0031] The multi-source data, including underwater river elevation point data collected by unmanned vessels and artificial elevation point data collected by RTK, is converted into an engineering coordinate system and a point cloud file in LAS format. This data is then merged into point cloud set R to form a new point cloud set Q. Given the actual data, manually collected engineering points are primarily near-shore data, which cannot be collected by unmanned vessels. In other words, point cloud set R is the airborne laser point cloud data set, while point cloud set Q is the point cloud data set that integrates the unmanned vessel and manual measurement points.

[0032] Specifically, the method for converting data into LAS format is to first organize the river underwater elevation point data and artificial elevation point data into a fixed Excel spreadsheet format, where the first column contains the point number, the second column contains the X coordinate in the engineering coordinate system, the third column contains the Y coordinate in the engineering coordinate system, and the fourth column contains the Z elevation value. After organizing the data, save the file as CSV format and then use the LasWrite function in MATLAB to write the LAS file.

[0033] Step S2: Determine the initial cross-section dataset A based on the laid-out cross-section segments. Dataset A contains the cross-section start and end point data. The plane coordinates of the cross-section start point A1 (X1, Y1) and end point An (X2, Y2) are known. It should be noted that cross-section segments are determined based on actual project requirements. Data acquisition determines the flight range and elevation point collection range based on the cross-section segments to ensure that the data source covers all cross-section segments. Furthermore, the cross-section segment layout is primarily based on actual project requirements and in accordance with hydropower engineering surveying specifications.

[0034] Step S3: Obtain terrain feature points on the cross-section line segment and add them to the cross-section dataset A to form a new cross-section dataset B.

[0035] In this embodiment, the process of obtaining terrain feature points includes the following steps:

[0036] Step S31, calculate the plane coordinates of the interpolation point Mn of the cross-section line segment: interpolate a point Mn at intervals of λ from the starting point to the end point of the cross-section line segment. The coordinates of the interpolation point are (Xn, Yn), where the value of λ is set according to the grid spacing of the digital elevation model in the hydropower engineering measurement specification. Specifically, the method for calculating the plane coordinates of the interpolation point is:

[0037] ;

[0038] ;

[0039] ;

[0040] Where L is the length of the cross-section segment, (X a 、Y a ) is the starting coordinate of the cross-section line segment, (X b 、Y b ) is the coordinate of the end point of the cross-section segment; n takes values ​​of 1, 2, 3, etc.

[0041] Step S32: Use the KD tree search method to search for the four points closest to the interpolation point Mn in the point cloud set Q, and calculate the elevation value Zn of the target point according to the inverse distance weighted interpolation method. The specific calculation method includes the following steps:

[0042] Step S321: Use the KD tree search method to search for the four coordinates of the points closest to point Mn in the point cloud set Q, which are Q1 (x1, y1, z1), Q2 (x2, y2, z2), Q3 (x3, y3, z3), and Q4 (x4, y4, z4).

[0043] Step S322: Calculate the Euclidean distance from point Mn to the four points:

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] Step S3: Calculate the weight and set a weight index p. The larger the p value, the smaller the influence of distant points on the interpolation result. The weight value can be adjusted according to the density of the point cloud. Usually, p=2 is used. The specific calculation method is:

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] Step S324: Calculate the elevation value Zn of the target point:

[0054] .

[0055] Step S33: Determine the terrain feature points on the cross-section line segment according to the change in terrain slope.

[0056] The specific determination method includes: obtaining two adjacent points M i 、M i+1 , and calculate any two adjacent points M on the cross-section segment i 、M i+1 The slope θ between i , the slope θ i Compare with the set slope threshold θ, if the slopes of two adjacent points θ i If the slope is greater than the slope threshold θ, both points are recorded as terrain feature points, and the terrain feature points, the start point, and the end point together constitute a new cross-section dataset B.

[0057] in, , where ( , ) is M i The coordinates of ( ) is M i+1 's coordinates.

[0058] Step S4: Obtain the endpoints of the feature contours on the cross-section line segments from the colored point cloud set Q and add them to the cross-section dataset B. Feature attribute labels are then added to the cross-section points to obtain a new cross-section dataset C. Specifically, the process for obtaining cross-section dataset B is as follows: After the point cloud is colored, the coordinates (X, Y, Z) of the feature contour points can be directly selected and recorded using the point cloud software.

[0059] Step S5: Encrypt the adjacent points that do not meet the requirements according to the different scale requirements of the hydropower engineering survey specification and add them to the cross-section dataset C to form a new cross-section dataset D. The recorded cross-section dataset D includes point numbers and coordinate information (X, Y, Z).

[0060] Step S6: Convert the final cross-section data set D into the cross-section mileage file, i.e., each point D on the cross-section line segment i (X, Y, Z) is converted to (mileage value, elevation value, attribute value). The mileage value is calculated as follows: looking toward the downstream of the river, the mileage value of the left endpoint of the line segment is 0, and the attribute value of the left endpoint is marked; the mileage is accumulated toward the right endpoint, and the mileage value of the end point of the cross-section line segment is the length of the line segment, and the attribute value of the right endpoint is marked. The mileage value of any point in the middle is :

[0061] , where i takes values ​​of 1, 2, 3, etc.

[0062] Step S7: Draw the mileage file as a cross-section diagram to generate a reservoir river cross section. For example, the left endpoint of the cross-section line is used as the origin, the cross-section line direction is the X-axis, and the elevation value is the Z-axis. The mileage value, elevation value, attribute value, cross-section name, and other information are annotated. If the length or relative height of cross sections in the same survey area varies significantly, different scales can be used.

[0063] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for generating a reservoir river cross section by fusing multi-source data and gradually encrypting it, characterized in that: The following steps are involved: Step S1: Acquire multi-source data of the river section in the reservoir area, and fuse the multi-source data to obtain a point cloud set Q; Step S2: Laying out cross-section line segments, and determining an initial cross-section data set A based on the laid-out cross-section line segments, wherein the data set A includes the start and end point data of the cross section; Step S3: Obtain terrain feature points on the cross-section line segment and add them to the cross-section dataset A to form a new cross-section dataset B. In step S3, the process of obtaining terrain feature points includes the following steps: Step S31, calculate the interpolation point M of the cross-section line segment n Plane coordinates: interpolate a point every λ from the starting point to the end point of the cross-section line segment. The coordinates of the interpolation point are (X n ,Y n ); Step S32: Use KD tree search method to search for interpolation point M in point cloud set Q n The four nearest points are used to calculate the elevation value Z of the target point using the inverse distance weighted interpolation method. n ; Step S33: Determine the terrain feature points on the cross-section line segment according to the change in terrain slope. The determination process is as follows: Get two adjacent points M i 、M i+1 , and calculate the adjacent points M i 、M i+1 Interval slope θ i , the slope θ i Compare with the set slope threshold θ, if the slopes of two adjacent points θ i If the slope is greater than the slope threshold θ, both points are recorded as terrain feature points, and the terrain feature points, the starting point, and the end point together constitute a new cross-section dataset B; Step S4: Obtain the endpoints of the feature contours on the cross-section line segment according to the colored point cloud set Q and add them to the cross-section dataset B, and add feature attribute labels to the cross-section points to obtain a new cross-section dataset C; Step S5: Encrypt the cross-section points according to the cross-section spacing requirements of different scales required by the hydropower engineering survey specification to form a new cross-section dataset D; Step S6: Convert the cross-section dataset D into a cross-section mileage file, i.e., each point D on the line segment i (X, Y, Z) is converted to (mileage value, elevation value, attribute value); Step S7: Draw the mileage file as a cross-section diagram to generate a river cross-section in the reservoir area.

2. The method for generating a reservoir river cross section by fusing multi-source data and gradually encrypting it according to claim 1 is characterized in that: In step S1, the multi-source data includes point cloud data collected by airborne lidar, river underwater elevation point data collected by unmanned boats, and artificial elevation point data collected by RTK.

3. The method for generating a reservoir river cross section by fusing multi-source data and gradually encrypting it according to claim 2 is characterized in that: In step S1, the fusion process is as follows: The point cloud data collected by the airborne lidar is processed by color addition, denoising, classification, and coordinate conversion to obtain the ground point cloud set R with RGB color in the engineering coordinate system; The river underwater elevation point data collected by unmanned boats and the artificial elevation point data collected by RTK in the acquired multi-source data are converted into the engineering coordinate system and converted into point cloud files in LAS format, and merged into the point cloud set R to form a new point cloud set Q.

4. The method for generating a reservoir river cross section by fusing multi-source data and gradually encrypting it according to claim 3 is characterized in that: The specific method of converting to las format is: first organize the river underwater elevation point data and artificial elevation point data into Excel table format, where the first column is the point number, the second column is the X coordinate under the engineering coordinate system, the third column is the Y coordinate under the engineering coordinate system, and the fourth column is the Z coordinate of the elevation value; after organizing, save the file as csv format, and then use the LasWrite function in MATLAB to write the las file.

5. The method for generating a reservoir river cross section by fusing multi-source data and performing step-by-step encryption according to claim 4 is characterized in that: In step S31, the method for calculating the plane coordinates of the interpolation point is: ； ； ； Where n represents the number of interpolation points, which can be 1, 2, 3, etc.; L is the length of the cross-section segment; (X a ,Y a ) is the starting coordinate of the cross-section line segment; (X b ,Y b ) are the coordinates of the end point of the section segment.

6. The method for generating a reservoir river cross section by fusing multi-source data and performing step-by-step encryption according to claim 5 is characterized in that: In step S32, the elevation value Z n The calculation process includes the following steps: Step S321: Use KD tree search method to search for the distance M in the point cloud set Q. n The four nearest coordinate points are Q1 (x1, y1, z1), Q2 (x2, y2, z2), Q3 (x3, y3, z3), and Q4 (x4, y4, z4). Step S322: Calculate M n The Euclidean distance from a point to four coordinate points: ； ； ； ； Step S323: Set the weight index p and calculate the weight; where p=2; ； ； ； ； Step S324: Calculate the elevation value Z of the target point n : 。 7. The method for generating a reservoir river cross section by fusing multi-source data and performing step-by-step encryption according to claim 6 is characterized in that: In step S33, , where ( , ) is M i The coordinates of ( ) is M i+1 's coordinates.

8. The method for generating a reservoir river cross section by fusing multi-source data and performing step-by-step encryption according to claim 7 is characterized in that: In step S6, the mileage value is calculated as follows: Looking towards the downstream of the river, set the mileage value of the left endpoint of the cross-section line segment to 0, and mark the left endpoint of the attribute value; the mileage is accumulated to the right endpoint, and the mileage value of the end point of the cross-section line segment is the length of the line segment, and the right endpoint of the attribute value is marked. The mileage value S of any point in the middle i : 。 9. The method for generating a reservoir river cross section by fusing multi-source data and performing step-by-step encryption according to claim 1 is characterized in that: In step S7, the drawing process is: take the left end point of the section line segment as the origin, the section line segment direction as the X axis, the elevation value as the Z axis, and mark the mileage value, elevation value, attribute value, and section name information.

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