Point cloud-based slope excavation engineering quantity calculation system and method

Abstract

The invention provides a slope excavation engineering quantity calculation system and method based on point cloud, and the method comprises the steps: reading the slope geological data of hydropower engineering to construct an initial three-dimensional model, and collecting the multi-stage point cloud data through a three-dimensional laser scanner; performing equidistant gridding on the point clouds of the model region according to an X / Y axis, calculating the spatial similarity and color similarity of the point clouds in grids, and dividing sub-data sets according to threshold values; fitting an excavation surface through a Delaunay triangulation network, calculating an XY plane projection area, and calculating an average Z-direction distance from a subset to the surface of the initial model; the node weight of the triangulation network is corrected by fusing rock stratum interface parameters, finally, the excavation engineering amount is dynamically output through summation of the product of the projection area of each subset and the average distance, and when the deviation of adjacent stages exceeds 10%, cross validation is conducted in combination with a profile method. According to the method, the precision and the real-time performance of slope excavation engineering quantity calculation can be improved, and automatic integration of lithologic characteristics and mechanical attributes is realized.

Description

Technical Field

[0001] This invention relates to the field of digital construction technology for water conservancy and hydropower projects, and more specifically, to a system and method for calculating slope excavation quantities based on point clouds. Background Technology

[0002] In slope excavation engineering, accurate calculation of quantities is crucial for project schedule control, cost accounting, and construction safety management. Traditional methods for calculating slope excavation quantities primarily rely on manual measurement and the interpretation of two-dimensional drawings. Construction workers need to conduct extensive on-site measurements to obtain geometric parameters such as slope height, width, and gradient, and then perform calculations based on these parameters on two-dimensional drawings. This method is not only inefficient but also susceptible to human error, leading to significant measurement inaccuracies.

[0003] Furthermore, due to the complex geometry of slopes, especially irregular slopes, two-dimensional drawings cannot accurately reflect their actual shape, resulting in inaccurate calculation results. With the development of three-dimensional laser scanning technology, its application in slope engineering has gradually gained attention. Three-dimensional laser scanners can quickly and accurately acquire point cloud data of slope surfaces, which contains rich geometric and textural information. However, current methods for calculating slope excavation quantities based on point cloud data still have some shortcomings. For example, some methods only utilize the geometric information of the point cloud data for calculation, ignoring other useful information such as color information, leading to limited calculation accuracy. In addition, for processing point cloud data of complex slopes, existing methods often suffer from low computational efficiency and insufficient accuracy when dividing regions, fitting surfaces, and calculating quantities.

[0004] In implementing the embodiments of the present invention, the prior art has at least the following problems or defects: First, traditional methods rely on manual measurement and two-dimensional drawing analysis, which is inefficient and has large errors; second, existing point cloud data-based methods do not make full use of information such as color, resulting in limited calculation accuracy; and third, when processing point cloud data of complex slopes, existing methods have problems such as low calculation efficiency and insufficient accuracy. Summary of the Invention

[0005] This invention provides a system and method for calculating slope excavation quantities based on point clouds.

[0006] In a first aspect of the present invention, a method for calculating the quantity of slope excavation work based on point clouds is provided, comprising: Read the early geological data of the hydropower project slope and construct the initial three-dimensional geological model of the slope; according to the slope excavation construction progress, use a three-dimensional laser scanner to collect point cloud data of the slope surface and form a multi-stage point cloud dataset containing coordinate values ​​and color values. The point cloud of the initial geological 3D model region is divided into i parts at equal intervals along the X-axis and j parts at equal intervals along the Y-axis to generate a gridded point cloud dataset; Calculate the spatial and color similarity between point clouds within the grid, and divide the point clouds into several subsets based on the similarity threshold; The excavation face was fitted and the projected area was calculated using the Delaunay triangulation generation algorithm. At the same time, the average projected distance from each subset to the surface of the initial geological model was calculated. The slope excavation volume is dynamically output by summing the products of the projected area and the average projected distance.

[0007] Furthermore, the spatial similarity and color similarity between point clouds within the computational grid include: Neighboring points in a grid point cloud dataset and Spatial similarity calculation satisfies:

[0008] in, For point The three-dimensional spatial coordinates, For point The three-dimensional spatial coordinates, Let be the spatial similarity value between two points. For color similarity calculation, the following conditions must be met:

[0009] in, For point RGB color values, For point RGB color values, The color similarity value between the two points.

[0010] When the spatial similarity value is greater than 0.7 and the color similarity value is greater than 0.7, the two points are considered similar and are assigned to the same subset.

[0011] Furthermore, it also includes: Extract the rock strata interface parameters from the geological model and match them with the spatial distribution of the point cloud sub-dataset; When the spatial distribution of the point cloud sub-dataset differs from the curvature of the rock stratum interface by less than 5%, the physical and mechanical parameters of the corresponding rock stratum are assigned to the sub-dataset. The node weights of the Delaunay triangulation are adjusted based on the assigned physical and mechanical parameters.

[0012] Furthermore, the fitting of the excavation face using the Delaunay triangulation generation algorithm includes: For each point cloud subset Generate a triangular mesh surface; Project the triangular mesh surface onto the XY plane and calculate the area of ​​the projected polygon. ; Compute subdataset The average distance of all points from the surface of the initial geological model along the Z-axis .

[0013] in, For the t-th subset of data, The projected area of ​​the triangular mesh surface generated for the t-th subset of data onto the XY plane. Let be the average projected distance from the t-th subset of data to the surface of the initial geological model.

[0014] Furthermore, it also includes: Along the X-axis of the initial geological 3D model of the slope, set m vertical profiles at intervals d; Centered on each profile, the point cloud dataset is divided into strip-shaped subsets of thickness d; the strip-shaped subsets are projected onto the corresponding profiles and fitted as boundary curves.

[0015] Where d is the section spacing and m is the number of sections.

[0016] Further, the calculation of the projected area includes: cross section The curve is fitted using the least squares method with the projection point set on the surface. ; Calculate the fitted curve Area of ​​the closed region at the boundary G_i of the initial geological model profile: When the normal direction of the profile is the X-axis:

[0017] When the normal direction of the profile is the Y-axis:

[0018] in The coordinates of the intersection points of the curves, For the first A cross-section, For the first The curve fitted on a cross section, For the initial geological model in the first The boundary curve of a cross section, This is the boundary curve function of the initial geological model profile. For fitting curve functions, For the first The excavation projection area of ​​each cross section.

[0019] Furthermore, the dynamically output slope excavation quantities include: After each scan is completed, the current quantity calculation result is compared with the result of the previous stage; When the deviation in the amount of work between adjacent stages exceeds 10%, the profile method is triggered to cross-validate the grid-divided area. The final quantities of the work are output using a weighted fusion formula:

[0020] in, The results are from the mesh method calculation. The results are from the profile method. This represents the final output of the slope excavation work volume. Calculate the weights using the grid method. Calculate the weights using the profile method.

[0021] Furthermore, when constructing the initial three-dimensional geological model of the slope: By integrating geological borehole data, core compressive strength test data, and seismic wave velocity detection data, a radial basis function interpolation algorithm is used to generate continuous rock strata interfaces. Lithology types are mapped to color-coded values, with sandstone mapped to a red channel value of 255, shale to a green channel value of 255, and limestone to a blue channel value of 255.

[0022] Furthermore, it also includes: Point cloud of excavation trajectory is obtained through the positioning system of construction machinery; ICP registration was performed between the mechanical trajectory point cloud and the 3D laser scan point cloud. When the registration error is less than 0.1 meters, the point cloud of the mechanical working surface is added to the scanned point cloud dataset.

[0023] In a second aspect of the invention, a slope excavation quantity calculation system based on similarity and point cloud is provided, comprising: The initial geological 3D model construction module is used to read the early geological data of hydropower project slopes and construct the initial geological 3D model of the slopes. The point cloud acquisition and processing module is used to acquire multi-stage point cloud datasets containing coordinate and color values ​​according to the progress of slope excavation construction. The mesh generation module is used to divide the point cloud of the initial geological 3D model area into i equal parts along the X-axis and j equal parts along the Y-axis; The similarity calculation module is used to calculate the spatial and color similarity of point clouds within the grid and to divide the dataset into sub-datasets; The surface fitting module is used to fit the excavation surface using the Delaunay triangulation generation algorithm and calculate the projected area and average projected distance. The quantity calculation module is used to sum and output the quantity of slope excavation work based on the product of the projected area and the average projected distance.

[0024] The embodiments of the present invention have at least the following beneficial effects: 1. By integrating 3D laser scanning point cloud data with the initial geological model and combining spatial similarity and color similarity as dual criteria for intelligent point cloud partitioning, the problem of inaccurate identification of complex rock layer boundaries by traditional methods is effectively solved, the accuracy of excavation face feature extraction is improved, and the matching between engineering quantity calculation and geological structure is ensured.

[0025] 2. By dynamically fitting the excavation face using Delaunay triangulation and correcting the node weights using rock strata interface parameters, the shortcomings of conventional volumetric algorithms in adapting to irregular excavation faces are overcome. This enables high-precision surface integral calculation of slope excavation volume, and is especially suitable for calculating engineering quantities in areas with multiple rock strata.

[0026] 3. By using a cross-validation mechanism combining grid and profile methods, the deviation between the scanning data and the geological model is compared in real time during construction. This solves the problem that traditional static calculation methods cannot dynamically track the excavation progress, providing an immediate quantitative basis for project adjustments, while ensuring the continuity and reliability of multi-stage engineering quantity calculations. Attached Figure Description

[0027] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a flowchart illustrating a method for calculating slope excavation quantities based on similarity and point clouds, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a slope excavation engineering quantity calculation system based on similarity and point cloud provided in an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0028] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0029] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0030] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0031] The following is for reference. Figure 1 , Figure 1 This is a flowchart illustrating a point cloud-based method for calculating slope excavation quantities according to an embodiment of the present invention. Figure 1 As shown, a method for calculating slope excavation quantities based on point clouds includes: S1. Read the preliminary geological data of the hydropower project slope and construct the initial three-dimensional geological model of the slope; according to the slope excavation construction progress, use a three-dimensional laser scanner to collect point cloud data of the slope surface and form a multi-stage point cloud dataset containing coordinate values ​​and color values. S2. Divide the point cloud of the initial geological 3D model region into i equal parts along the X-axis and j equal parts along the Y-axis to generate a gridded point cloud dataset; S3. Calculate the spatial similarity and color similarity between point clouds within the grid, and divide the point cloud into several subsets based on the similarity threshold. S4. Fit the excavation face and calculate the projected area using the Delaunay triangulation generation algorithm, and at the same time calculate the average projected distance of each subset to the surface of the initial geological model. S5. Calculate the sum of the products of the projected area and the average projected distance to dynamically output the slope excavation volume.

[0032] It should be noted that this invention proposes a method for calculating slope excavation quantities based on point clouds. The initial 3D geological model of the slope refers to a 3D digital model of the slope constructed using prior geological data, which accurately reflects the initial geological structure and morphology of the slope. Point cloud data refers to a set of points on the slope surface acquired by a 3D laser scanner. Each point contains coordinate values ​​and color values; the coordinate values ​​determine the point's spatial location, and the color values ​​reflect the point's surface color information. This method analyzes the similarity between these point cloud data and the initial geological model to divide the data into different subsets, thereby more accurately calculating the slope excavation quantities.

[0033] Specifically, the initial 3D geological model was constructed by integrating geological borehole data, core compressive strength test data, and seismic wave velocity detection data. A radial basis function interpolation algorithm was used to generate continuous rock strata interfaces, and lithology types were mapped to color-coded values. For example, sandstone was mapped to a red channel value of 255, shale to a green channel value of 255, and limestone to a blue channel value of 255. The point cloud dataset was collected by a 3D laser scanner at different stages of slope excavation, containing detailed geometric and color information of the slope surface. When dividing the grid, it was divided into i equal parts along the X-axis and j equal parts along the Y-axis to generate a gridded point cloud dataset. Here, i and j are pre-set parameters based on the size and accuracy requirements of the slope, used to determine the size and number of grids. When calculating similarity, the formulas for spatial similarity and color similarity are based on the 3D spatial coordinates and RGB color values ​​of the points. When both the spatial similarity and color similarity values ​​are greater than 0.7, the two points are considered similar and assigned to the same subset of the dataset.

[0034] Preferably, when constructing the initial 3D geological model of the slope, geological borehole data, core compressive strength test data, and seismic wave velocity detection data can be comprehensively analyzed. Using professional geological modeling software, after inputting the above data, a radial basis function interpolation algorithm is used to generate continuous rock layer interfaces. For point cloud data acquisition, a suitable 3D laser scanner can be selected based on the scale and complexity of the slope, and appropriate scanning resolution and angle can be set to ensure that the acquired point cloud data is both detailed and accurate. When dividing the grid, the specific values ​​of i and j can be determined based on the length and width of the slope and the required accuracy. For example, for a slope with a length of 100 meters and a width of 50 meters, if an accuracy of 1 meter is required, i can be set to 100 and j to 50. When calculating similarity, spatial similarity and color similarity can be calculated using corresponding algorithms. The algorithm calculates the similarity between each pair of points according to the above formula based on the coordinates and color values ​​of the points, thereby obtaining the similarity value between each pair of points and dividing the point cloud into different subsets accordingly.

[0035] In some embodiments, the spatial similarity and color similarity between point clouds within the computational grid include: Neighboring points in a grid point cloud dataset and Spatial similarity calculation satisfies:

[0036] in, For point The three-dimensional spatial coordinates, For point The three-dimensional spatial coordinates, Let be the spatial similarity value between two points. For color similarity calculation, the following conditions must be met:

[0037] in, For point RGB color values, For point RGB color values, The color similarity value between the two points.

[0038] When the spatial similarity value is greater than 0.7 and the color similarity value is greater than 0.7, the two points are considered similar and are assigned to the same subset.

[0039] It should be noted that the spatial and color similarity between point clouds within the computational grid mentioned in this invention are for the purpose of more accurately identifying and classifying similar regions in the point cloud data. Spatial similarity is calculated based on the three-dimensional spatial coordinates of each point in the point cloud data, reflecting the degree of proximity between points in spatial location. Color similarity, on the other hand, is calculated based on the RGB color values ​​of each point in the point cloud data, reflecting the degree of similarity between points in color. By setting a similarity threshold, points with similar spatial locations and colors can be grouped into the same subset, thereby providing a more accurate data foundation for subsequent excavation face fitting and engineering quantity calculation.

[0040] Specifically, spatial similarity is calculated by comparing the three-dimensional spatial coordinates of two points. These two points refer to two adjacent points in the gridded point cloud dataset, and their three-dimensional spatial coordinates are represented by three values, corresponding to their positions on the X, Y, and Z axes, respectively. Color similarity is calculated by comparing the RGB color values ​​of two points. Each point's RGB color value also consists of three values, corresponding to the intensity of the red, green, and blue color channels, respectively. Both similarity calculations use a specific mathematical method to measure the degree of similarity between two points. When both the spatial similarity value and the color similarity value are greater than 0.7, the two points are considered similar and assigned to the same subset of the dataset. The 0.7 value is a threshold parameter that can be adjusted according to actual engineering needs and the characteristics of the point cloud data to achieve the best partitioning effect.

[0041] Preferably, when calculating spatial and color similarity, the point cloud data can be preprocessed, such as by removing noise points and outliers, to improve the accuracy of the calculation. In practical applications, the optimal similarity threshold can be determined through experiments and experience based on the specific conditions of the slope and engineering requirements. For example, for a slope with complex color variations, the color similarity threshold needs to be set higher to ensure that only points with very similar colors are grouped into the same subset. Simultaneously, to improve computational efficiency, optimized algorithms, such as block-based or parallel computing, can be used to process large-scale point cloud data. During the calculation process, each point is compared with its surrounding points for similarity; only when two points meet the similarity thresholds in both spatial and color aspects are they grouped into the same subset.

[0042] In some embodiments, it also includes: Extract the rock strata interface parameters from the geological model and match them with the spatial distribution of the point cloud sub-dataset; When the spatial distribution of the point cloud sub-dataset differs from the curvature of the rock stratum interface by less than 5%, the physical and mechanical parameters of the corresponding rock stratum are assigned to the sub-dataset. The node weights of the Delaunay triangulation are adjusted based on the assigned physical and mechanical parameters.

[0043] It should be noted that the extraction of rock strata interface parameters from the geological model and matching them with the spatial distribution of the point cloud subset, mentioned in this invention, is to more accurately reflect the geological structure of the slope. Rock strata interface parameters refer to the geometric and physical properties defining the boundaries between rock strata in the geological model. These parameters include the thickness, dip angle, and strike of the rock strata. By matching these parameters with the spatial distribution of the point cloud subset, it is possible to determine which parts of the point cloud data correspond to specific rock strata. When the curvature difference between the spatial distribution of the point cloud subset and the rock strata interface is less than 5%, the physical and mechanical parameters of the corresponding rock strata are assigned to that subset. This helps in the subsequent accurate fitting of the excavation face and the calculation of engineering quantities.

[0044] Specifically, rock strata interface parameters refer to the geometric and physical properties defining the boundaries between rock strata in a geological model. These parameters include the thickness, dip angle, and strike of the rock strata. Physical and mechanical parameters refer to the physical and mechanical properties of the rock strata, such as density, compressive strength, and elastic modulus. These parameters are typically obtained through geological exploration and laboratory testing. The spatial distribution of the point cloud subset refers to the geometric arrangement and distribution of the point cloud data in three-dimensional space. Curvature difference refers to the degree of difference between the geometry of the point cloud subset and the geometry of the rock strata interface, measured by calculating the curvature between the two. When this difference is less than 5%, it means that the point cloud subset matches the rock strata interface well, and the point cloud subset can be considered to accurately reflect the geometric characteristics of the rock strata.

[0045] Preferably, when extracting parameters of the rock strata interface, specialized geological modeling software can be used. Geological exploration data, such as borehole data and seismic wave velocity data, can be input, and a three-dimensional model of the rock strata interface can be generated through numerical simulation and interpolation algorithms. During the matching process, computer vision and pattern recognition technologies can be used to preprocess the point cloud data, such as filtering and denoising, to improve matching accuracy. For calculating curvature differences, the curvature of the point cloud data can be approximated using the difference method or fitting method and compared with the curvature of the rock strata interface. After successful matching, the physical and mechanical parameters of the rock strata are assigned to the point cloud subset. These parameters can be used to correct the node weights of the Delaunay triangulation, thereby more accurately reflecting the physical properties of the rock strata and improving the accuracy of the excavation face fitting.

[0046] In some embodiments, fitting the excavation face using the Delaunay triangulation generation algorithm includes: For each point cloud subset Generate a triangular mesh surface; Project the triangular mesh surface onto the XY plane and calculate the area of ​​the projected polygon. ; Compute subdataset The average distance of all points from the surface of the initial geological model along the Z-axis .

[0047] in, For the t-th subset of data, The projected area of ​​the triangular mesh surface generated for the t-th subset of data onto the XY plane. Let be the average projected distance from the t-th subset of data to the surface of the initial geological model.

[0048] It should be noted that the Delaunay triangulation algorithm used in this invention to fit the excavation face is for the purpose of accurately calculating the amount of slope excavation work. Delaunay triangulation is a triangular mesh generated from point cloud data, capable of effectively fitting complex terrain surfaces. This algorithm constructs a geometric model of the excavation face by connecting points in the point cloud to form a series of non-overlapping triangles. The projected area refers to the area of ​​the polygon formed by projecting these triangles onto the XY plane, while the average projected distance is the average distance along the Z-axis from all points in the point cloud subset to the surface of the initial geological model. By calculating these parameters, the amount of slope excavation work can be determined more accurately.

[0049] Specifically, the Delaunay triangulation generation algorithm is a geometric modeling method based on point cloud data. It connects points in the point cloud to form a series of non-overlapping triangles, which can effectively fit complex terrain surfaces. The projected area refers to the area of ​​the polygon formed by projecting these triangles onto the XY plane. Calculation requires determining the vertex coordinates of each triangle and obtaining its projected area on the XY plane through geometric calculations. The average projected distance refers to the average distance along the Z-axis from all points in the point cloud subset to the surface of the initial geological model. Calculation requires determining the perpendicular distance from each point to the surface of the initial geological model and calculating its average value. The calculation of these parameters is based on the geometric information of the point cloud data and is achieved through specific algorithms and mathematical methods.

[0050] Preferably, when generating the Delaunay triangulation, professional 3D modeling software or programming tools can be used. After inputting point cloud data, the triangulation is automatically constructed through an algorithm. When calculating the projected area, a numerical integration method can be used to sum the projected areas of each triangle to obtain the total projected area. The average projected distance can be calculated by taking the vertical distance from each point to the surface of the initial geological model and then averaging these distances.

[0051] Furthermore, in practical applications, the optimal calculation parameters can be determined through experiments and experience based on the specific conditions of the slope and engineering requirements, thereby improving the accuracy and efficiency of the calculations. For example, for a slope with complex terrain, preprocessing of the point cloud data, such as filtering and denoising, is required to improve the accuracy of the triangulation network generation. Simultaneously, to improve computational efficiency, optimized algorithms, such as block-based computation or parallel computation, can be employed to process large-scale point cloud data.

[0052] In some embodiments, it also includes: Along the X-axis of the initial geological 3D model of the slope, set m vertical profiles at intervals d; Centered on each profile, the point cloud dataset is divided into strip-shaped subsets of thickness d; the strip-shaped subsets are projected onto the corresponding profiles and fitted as boundary curves.

[0053] Where d is the section spacing and m is the number of sections.

[0054] It should be noted that the vertical profiles set at intervals d along the X-axis of the initial geological 3D model of the slope mentioned in this invention are for more accurate processing of point cloud data of complex terrain. A vertical profile refers to a plane perpendicular to a certain direction of the slope, specifically the X-axis direction. These profiles divide the slope into multiple strip-shaped regions. By segmenting the point cloud dataset into strip-shaped subsets of thickness d, projecting these subsets onto the corresponding profiles, and fitting them to boundary curves, the geometry of the slope can be more accurately reflected, thereby improving the accuracy of engineering quantity calculations.

[0055] Specifically, vertical profiles are set along the X-axis of the slope, dividing it into multiple strip-shaped regions, each with a thickness of 'd'. Here, 'd' is the profile spacing, a pre-set parameter based on the slope's dimensions and accuracy requirements, used to determine the width of each strip-shaped subset. 'm' is the number of profiles, also a pre-set parameter based on the slope's length and accuracy requirements, used to determine the number of vertical profiles. A strip-shaped subset refers to a subset of the point cloud dataset with a thickness of 'd', into which these subsets are projected onto the corresponding profile and fitted with boundary curves. The boundary curve is the curve fitted onto the profile, used to describe the slope's geometry on that profile.

[0056] Preferably, when setting vertical profiles, the profile spacing *d* and the number of profiles *m* can be determined based on the specific dimensions and complexity of the slope. For example, for a slope 100 meters long, if a precision of 1 meter is required, the profile spacing *d* can be set to 1 meter, resulting in 100 vertical profiles. When segmenting the point cloud dataset into strip-shaped subsets, professional 3D modeling software or programming tools can be used. After inputting the point cloud data and profile parameters, the algorithm automatically segments and projects the data. When fitting the boundary curve, mathematical methods such as the least squares method can be used to fit a curve that best approximates the actual slope shape based on the projected point set.

[0057] Furthermore, in practical applications, the optimal profile spacing and number can be determined through experiments and experience based on the specific conditions of the slope and engineering requirements, thereby improving the accuracy and efficiency of calculations. For example, for a slope with complex terrain, a smaller profile spacing is required to more accurately reflect the slope's geometry.

[0058] In some embodiments, calculating the projected area includes: cross section The curve is fitted using the least squares method with the projection point set on the surface. ; Calculate the fitted curve Area of ​​the closed region at the boundary G_i of the initial geological model profile: When the normal direction of the profile is the X-axis:

[0059] When the normal direction of the profile is the Y-axis:

[0060] in The coordinates of the intersection points of the curves, For the first A cross-section, For the first The curve fitted on a cross section, For the initial geological model in the first The boundary curve of a cross section, This is the boundary curve function of the initial geological model profile. For fitting curve functions, For the first The excavation projection area of ​​each cross section.

[0061] It should be noted that the method for calculating the projected area mentioned in this invention is for the purpose of more accurately calculating the excavation volume of the slope. Specifically, for the set of projected points on each profile, a curve is fitted using the least squares method, and then the area of ​​the closed region between the fitted curve and the boundary of the initial geological model profile is calculated. This method, through mathematical fitting and area calculation, can more accurately reflect the actual excavation area of ​​the slope on each profile, thereby improving the accuracy of the volume calculation.

[0062] Specifically, the least squares method is a mathematical fitting method used to fit a curve that best approximates a set of data points. In this invention, the least squares method is used to fit the set of projected points on a profile to generate a curve. This curve can better reflect the actual shape of the slope on the profile. The initial geological model profile boundary refers to the boundary curve cut along the profile direction in the initial three-dimensional geological model. The area of ​​the closed region refers to the area between the fitted curve and the initial geological model profile boundary. To calculate this area, it is necessary to determine the coordinates of the intersection points of the fitted curve and the initial geological model profile boundary, and calculate the area of ​​the closed region based on these coordinates. When the profile normal is the X-axis or the Y-axis, different integration methods are used to calculate the area.

[0063] Preferably, when calculating the projected area, specialized mathematical software or programming tools can be used to perform least squares fitting and area calculation. First, the set of projected points on the profile is input, and then a curve is fitted using the least squares method. Next, the coordinates of the intersection points between the fitted curve and the boundary of the initial geological model profile are determined. Based on these coordinates, the area of ​​the closed region is calculated using numerical integration.

[0064] Furthermore, in practical applications, the optimal fitting parameters and integration methods can be determined through experiments and experience based on the specific conditions of the slope and engineering requirements, thereby improving the accuracy and efficiency of the calculations. For example, for a slope with complex terrain, a higher-order fitting method is needed to more accurately reflect the slope's geometry. Simultaneously, to improve computational efficiency, optimization algorithms, such as block-based or parallel computing, can be employed to process large-scale data.

[0065] In some embodiments, the dynamic output of slope excavation quantities includes: After each scan is completed, the current quantity calculation result is compared with the result of the previous stage; When the deviation in the amount of work between adjacent stages exceeds 10%, the profile method is triggered to cross-validate the grid-divided area. The final quantities of the work are output using a weighted fusion formula:

[0066] in, The results are from the mesh method calculation. The results are from the profile method. This represents the final output of the slope excavation work volume. Calculate the weights using the grid method. The weights are calculated using the profile method. It should be noted that the method for dynamically outputting slope excavation quantities mentioned in this invention aims to more accurately reflect the actual changes in quantities during the slope excavation process. This method compares the quantity calculation results after each scan with the results of the previous stage. When the deviation between quantities in adjacent stages exceeds 10%, the profile method is triggered to cross-validate the gridded region. This method combines the advantages of the grid method and the profile method, outputting the final quantity through a weighted fusion formula, thereby improving the accuracy and reliability of the calculation results.

[0067] Specifically, dynamic output refers to the continuous updating and output of the calculated quantities of slope excavation work as construction progresses during the slope excavation process. After each scan, the current stage's quantities are calculated and compared with the results of the previous stage. Quantity deviation refers to the difference between the current stage's quantities and the previous stage's quantities. When this difference exceeds 10%, it is considered that there may be errors or construction changes, requiring cross-validation. The profile method is a method that calculates the excavation area on each profile by setting multiple profiles along the slope, thereby obtaining the total quantities. The grid method is a method that divides the slope into multiple grids and calculates the excavation volume within each grid, thereby obtaining the total quantities. The weighted fusion formula is a formula that combines the calculation results of two methods, assigning different weights to integrate the advantages of both methods to obtain more accurate quantity results.

[0068] Preferably, when dynamically outputting the slope excavation volume, specialized engineering quantity calculation software or programming tools can be used. First, after each scan, new point cloud data is input, and the engineering quantity for the current stage is calculated. Then, the engineering quantity for the current stage is compared with that of the previous stage to calculate the deviation. If the deviation exceeds 10%, the profile method is triggered to cross-validate the grid-divided area. During cross-validation, the excavation area on the profile can be recalculated and compared with the result of the grid method. Finally, a weighted fusion formula is used to combine the calculation results of the grid method and the profile method to obtain the final engineering quantity. The weight for the grid method can be 0.5-0.7, and the weight for the profile method can be 0.3-0.5.

[0069] Furthermore, in practical applications, the optimal weighting parameters can be determined through experiments and experience based on the specific conditions of the slope and engineering requirements to improve the accuracy and efficiency of the calculation. For example, for a slope with significant topographical variations, the profile method needs to be assigned higher weights to more accurately reflect the actual excavation situation. Simultaneously, to improve computational efficiency, optimization algorithms, such as block-based or parallel computing, can be employed to process large-scale data.

[0070] In some embodiments, when constructing the initial three-dimensional geological model of the slope: By integrating geological borehole data, core compressive strength test data, and seismic wave velocity detection data, a radial basis function interpolation algorithm is used to generate continuous rock strata interfaces. Lithology types are mapped to color-coded values, with sandstone mapped to a red channel value of 255, shale to a green channel value of 255, and limestone to a blue channel value of 255.

[0071] It should be noted that the method for constructing an initial three-dimensional geological model of a slope mentioned in this invention is intended to more accurately reflect the initial geological structure of the slope. This method integrates various geological data, including geological borehole data, core compressive strength test data, and seismic wave velocity detection data, and uses a radial basis function interpolation algorithm to generate continuous rock layer interfaces. By mapping lithology types to color-coded values, the distribution of different rock layers can be represented more intuitively, thus providing a more accurate basic model for subsequent excavation quantity calculations.

[0072] Specifically, geological borehole data refers to geological information obtained through drilling, including the depth, thickness, and lithology of rock strata. Core compressive strength test data refers to data obtained by testing the compressive strength of core samples obtained through drilling, used to assess the mechanical properties of rock strata. Seismic wave velocity detection data refers to geological data obtained through seismic wave detection technology, used to determine the distribution and interfaces of rock strata. Radial basis function interpolation algorithm is a mathematical algorithm used to generate continuous surfaces or interfaces based on known data points. Lithology type refers to different types of rocks, such as sandstone, shale, and limestone. Color-coded values ​​are a method of mapping lithology types to color values, used to visually represent the distribution of different rock strata in a 3D model. For example, sandstone is mapped to a red channel value of 255, shale to a green channel value of 255, and limestone to a blue channel value of 255.

[0073] Preferably, when constructing the initial 3D geological model of the slope, professional geological modeling software can be used, inputting geological borehole data, core compressive strength test data, and seismic wave velocity detection data. Using this data, a continuous rock stratum interface is generated using a radial basis function interpolation algorithm. When mapping lithology types as color-coded values, appropriate color values ​​can be selected according to actual needs. For example, sandstone can be selected as red with a red channel value of 255, shale as green with a green channel value of 255, and limestone as blue with a blue channel value of 255. In practical applications, the optimal data processing and model construction parameters can be determined through experiments and experience based on the specific conditions of the slope and engineering requirements to improve the accuracy and reliability of the model. For example, for a slope with complex geological conditions, more detailed data and more complex algorithms are needed to generate accurate rock stratum interfaces. Simultaneously, to improve the model's intuitiveness and ease of use, 3D visualization technology can be used to display the model in an intuitive way, facilitating analysis and decision-making by engineers.

[0074] In some embodiments, it also includes: Point cloud of excavation trajectory is obtained through the positioning system of construction machinery; ICP registration was performed between the mechanical trajectory point cloud and the 3D laser scan point cloud. When the registration error is less than 0.1 meters, the point cloud of the mechanical working surface is added to the scanned point cloud dataset.

[0075] It should be noted that the point cloud of the excavation trajectory obtained through the construction machinery positioning system mentioned in this invention, and the ICP registration of the machinery trajectory point cloud with the 3D laser scanning point cloud, are intended to more accurately reflect the actual excavation situation during construction. ICP registration is a point cloud data alignment technology. By registering the point cloud of the excavation trajectory of the construction machinery with the 3D laser scanning point cloud, the point cloud of the machinery's working face can be supplemented into the scanned point cloud dataset, thereby improving the accuracy and completeness of the engineering quantity calculation.

[0076] Specifically, a construction machinery positioning system refers to a system used to track and record the position and movement trajectory of construction machinery in real time, providing excavation trajectory point cloud data. The excavation trajectory point cloud refers to the point cloud data formed by the construction machinery during the excavation process, recording the machinery's movement trajectory and excavation range. ICP registration (Iterative Closest Point registration) is an iterative algorithm used to align two sets of point cloud data, continuously optimizing the matching degree between the point clouds until a certain accuracy requirement is met. Registration error refers to the degree of difference between the two sets of point clouds after registration; when the registration error is less than 0.1 meters, the registration result is considered acceptable. The machinery working face point cloud refers to the point cloud data of the area actually excavated by the construction machinery. After these data are added to the scanned point cloud dataset, they can more comprehensively reflect the actual excavation situation of the slope.

[0077] Preferably, when acquiring the excavation trajectory point cloud, a high-precision construction machinery positioning system, such as GPS or RTK, can be used. These systems can record the position and movement trajectory of the construction machinery in real time. During ICP registration, professional point cloud processing software can be used, inputting the excavation trajectory point cloud and the 3D laser scan point cloud, and setting initial registration parameters, such as the maximum number of iterations and the convergence threshold.

[0078] Furthermore, in practical applications, optimal registration parameters can be determined through experiments and experience based on the type of construction machinery and the construction environment to improve the accuracy and efficiency of registration. For example, large excavators require higher positioning accuracy and more complex registration algorithms. Simultaneously, to improve data processing efficiency, optimized algorithms, such as block computing or parallel computing, can be employed to process large-scale point cloud data. After registration, supplementing the point cloud of the machinery's working face into the scanned point cloud dataset provides a more comprehensive reflection of the actual slope excavation, thus providing a more accurate data foundation for quantity calculations.

[0079] The above embodiments of the present invention have the following beneficial effects: 1. By integrating 3D laser scanning point cloud data with the initial geological model and combining spatial similarity and color similarity as dual criteria for intelligent point cloud partitioning, the problem of inaccurate identification of complex rock layer boundaries by traditional methods is effectively solved, the accuracy of excavation face feature extraction is improved, and the matching between engineering quantity calculation and geological structure is ensured.

[0080] 2. By dynamically fitting the excavation face using Delaunay triangulation and correcting the node weights using rock strata interface parameters, the shortcomings of conventional volumetric algorithms in adapting to irregular excavation faces are overcome. This enables high-precision surface integral calculation of slope excavation volume, and is especially suitable for calculating engineering quantities in areas with multiple rock strata.

[0081] 3. By using a cross-validation mechanism combining grid and profile methods, the deviation between the scanning data and the geological model is compared in real time during construction. This solves the problem that traditional static calculation methods cannot dynamically track the excavation progress, providing an immediate quantitative basis for project adjustments, while ensuring the continuity and reliability of multi-stage engineering quantity calculations.

[0082] like Figure 2 As shown in some embodiments, a slope excavation quantity calculation system based on point cloud is provided. The system includes: The initial geological 3D model construction module 201 is used to read the early geological data of the hydropower project slope and construct the initial geological 3D model of the slope. The point cloud acquisition and processing module 202 is used to acquire a multi-stage point cloud dataset containing coordinate values ​​and color values ​​according to the slope excavation construction progress. The mesh generation module 203 is used to divide the point cloud of the initial geological 3D model area into i parts at equal intervals along the X-axis and j parts at equal intervals along the Y-axis; The similarity calculation module 204 is used to calculate the spatial similarity and color similarity of point clouds within the grid and to divide the dataset into sub-datasets; The surface fitting module 205 is used to fit the excavation surface using the Delaunay triangulation generation algorithm and calculate the projected area and average projected distance. The quantity calculation module 206 is used to output the quantity of slope excavation work by summing the product of the projected area and the average projected distance.

[0083] It is understandable that the modules recorded in this point cloud-based slope excavation quantity calculation system are similar to those in the reference system. Figure 1 The steps described in the point cloud-based slope excavation quantity calculation method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the point cloud-based slope excavation quantity calculation method are also applicable to the point cloud-based slope excavation quantity calculation system and its included modules, and will not be repeated here.

[0084] The following is for reference. Figure 3 The diagram illustrates a structural schematic of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0085] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0086] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0087] Furthermore, the storage medium in the embodiments of this application stores program instructions capable of implementing all the above methods. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0088] The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for calculating slope excavation quantities based on point clouds, characterized in that, include: Read the preliminary geological data of the slope of the hydropower project and construct the initial three-dimensional geological model of the slope; Based on the slope excavation construction progress, a 3D laser scanner is used to collect point cloud data of the slope surface to form a multi-stage point cloud dataset containing coordinate values ​​and color values. The point cloud of the initial geological 3D model region is divided into i parts at equal intervals along the X-axis and j parts at equal intervals along the Y-axis to generate a gridded point cloud dataset; Calculate the spatial and color similarity between point clouds within the grid, and divide the point clouds into several subsets based on the similarity threshold; The excavation face was fitted and the projected area was calculated using the Delaunay triangulation generation algorithm. At the same time, the average projected distance from each subset to the surface of the initial geological model was calculated. The slope excavation volume is dynamically output by summing the products of the projected area and the average projected distance.

2. The method according to claim 1, characterized in that, The spatial similarity and color similarity between point clouds within the computational grid include: Neighboring points in a grid point cloud dataset and Spatial similarity calculation satisfies: in, For point The three-dimensional spatial coordinates, For point The three-dimensional spatial coordinates, This represents the spatial similarity value between two points; The color similarity calculation satisfies: in, For point RGB color values, For point RGB color values, The color similarity value between the two points; When the spatial similarity value is greater than 0.7 and the color similarity value is greater than 0.7, the two points are considered similar and are assigned to the same subset.

3. The method according to claim 1, characterized in that, Also includes: Extract the rock strata interface parameters from the geological model and match them with the spatial distribution of the point cloud sub-dataset; When the spatial distribution of the point cloud sub-dataset differs from the curvature of the rock stratum interface by less than 5%, the physical and mechanical parameters of the corresponding rock stratum are assigned to the sub-dataset. The node weights of the Delaunay triangulation are adjusted based on the assigned physical and mechanical parameters.

4. The method according to claim 1, characterized in that, The fitting of the excavation surface using the Delaunay triangulation generation algorithm includes: For each point cloud subset Generate a triangular mesh surface; Project the triangular mesh surface onto the XY plane and calculate the area of ​​the projected polygon. ; Compute subdataset The average distance of all points from the surface of the initial geological model along the Z-axis .

5. The method according to claim 1, characterized in that, Also includes: Along the X-axis of the initial geological 3D model of the slope, set m vertical profiles at intervals d; Centered on each profile, the point cloud dataset is divided into strip-shaped subsets of thickness d; The strip-shaped subset is projected onto the corresponding profile and fitted as a boundary curve.

6. The method according to claim 5, characterized in that, The calculation of the projected area includes: cross section The projection point set on the curve is fitted using the least squares method. ; Calculate the fitted curve Boundary of the initial geological model profile Area of ​​the closed region: When the normal direction of the profile is the X-axis: When the normal direction of the profile is the Y-axis: in The coordinates of the intersection points of the curves, For the first A cross-section, For the first The curve fitted on each cross section, For the initial geological model in the first The boundary curve of each cross section, This is the boundary curve function of the initial geological model profile. For fitting curve functions, For the first The excavation projection area of ​​each cross section.

7. The method according to claim 1, characterized in that, The dynamically outputted slope excavation work volume includes: After each scan is completed, the current quantity calculation result is compared with the result of the previous stage; When the deviation in the amount of work between adjacent stages exceeds 10%, the profile method is triggered to cross-validate the grid-divided area. The final quantities of the work are output using a weighted fusion formula: in, The results are from the mesh method calculation. The results are from the profile method. This represents the final output of the slope excavation work volume. Calculate the weights using the grid method. Calculate the weights using the profile method.

8. The method according to claim 1, characterized in that, When constructing the initial three-dimensional geological model of the slope: Integrating geological borehole data, core compressive strength test data, and seismic wave velocity detection data; The radial basis function interpolation algorithm is used to generate continuous rock strata interfaces; Lithology types are mapped to color-coded values, with sandstone mapped to a red channel value of 255, shale to a green channel value of 255, and limestone to a blue channel value of 255.

9. The method according to claim 1, characterized in that, Also includes: Point cloud of excavation trajectory is obtained through the positioning system of construction machinery; ICP registration was performed between the mechanical trajectory point cloud and the 3D laser scan point cloud. When the registration error is less than 0.1 meters, the point cloud of the mechanical working surface is added to the scanned point cloud dataset.

10. A slope excavation quantity calculation system based on point cloud, characterized in that, include: The initial geological 3D model construction module is used to read the early geological data of hydropower project slopes and construct the initial geological 3D model of the slopes. The point cloud acquisition and processing module is used to acquire multi-stage point cloud datasets containing coordinate and color values ​​according to the progress of slope excavation construction. The mesh generation module is used to divide the point cloud of the initial geological 3D model area into i equal parts along the X-axis and j equal parts along the Y-axis; The similarity calculation module is used to calculate the spatial and color similarity of point clouds within the grid and to divide the dataset into sub-datasets; The surface fitting module is used to fit the excavation surface using the Delaunay triangulation generation algorithm and calculate the projected area and average projected distance. The quantity calculation module is used to sum and output the quantity of slope excavation work based on the product of the projected area and the average projected distance.

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