CN116012613B - Method and system for measuring and calculating earthwork variation of strip mine based on laser point cloud - Google Patents
Method and system for measuring and calculating earthwork variation of strip mine based on laser point cloud Download PDFInfo
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Abstract
The invention discloses a method and a system for measuring and calculating the earthwork variation of a strip mine based on laser point cloud, wherein the method for measuring and calculating the earthwork variation of the strip mine comprises the following steps: scanning a target strip mine area by using a three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area; preprocessing front and rear two-stage point cloud data according to a point cloud preprocessing algorithm to obtain denoised front and rear two-stage point cloud data; according to the three-dimensional distance transformation diagrams respectively corresponding to the front and rear phase point cloud data, calculating to obtain a change area point cloud set with the chamfering distance larger than a preset distance threshold value; performing rapid octree clustering on the change area point cloud set to obtain a change area boundary corresponding to the change area point cloud set; and constructing a triangular net at the boundary of the change area, and calculating the filling and excavation quantity of the triangular net to obtain the filling and excavation quantity corresponding to the soil quantity change area. The technical scheme of the invention can solve the problems of large workload, high risk, low efficiency and poor calculation accuracy of the earthwork volume in the prior art.
Description
Technical Field
The invention relates to the technical field of mineral exploitation, in particular to a method and a system for measuring and calculating the earthwork variation of an open pit mine based on laser point cloud.
Background
Mines are classified into strip mines and underground mine work and mines. In the mining of strip mines, a large amount of stripping (the volume of earth and stone that is required to strip each ton of useful ore) must occur during the extraction of the useful ore. Different geological conditions of mines and occurrence conditions of mineral deposits are different. In actual production, it is necessary to calculate not only the amount of ore extracted but also the amount of stripping. Therefore, the stripping ratio (stripping amount/extraction amount) is an important index reflecting economic benefits in mining production, and directly determines the cost of mining production.
In order to accurately calculate the stripping ratio of the stripping construction work quantity, budget and planning a stripping technical plan, reasonably arrange the mining progress and configure the collection equipment, and avoid disputes caused by the accuracy problem of the calculation of the earthwork quantity, a method capable of accurately measuring the earthwork quantity is necessary. For calculation of the change of the earthwork quantity at different periods, the key is the calculation of the "change area" because this is not only the calculation of the earthwork quantity of the single period data only, but also the analysis of the change between the multi-period data.
The existing method for calculating the earthwork of the strip mine mainly comprises the following steps: cross section, grid, and trigonometry. Among them, the cross-section method can be selected when the topography is complex, the undulation is large, or the section is long and narrow and the depth is large. The section method is to measure the vertical section data according to the landform of the mine area, and then make the surface section map one by one. The method can obtain the sectional view of two-stage data, then superimpose and compare the lines of the two-stage sections, outline the changed area, and then multiply the section distance L to obtain the earthwork change quantity, namely the stripping quantity. This is also a traditional method used in mine measurements.
The method needs to perform field operation, and elevation data of the terrain is obtained by using RTK, total station and step measurement method. The method has the advantages of large workload, high risk, very complex operation process and low efficiency. In addition, the factors influencing the measurement accuracy are very many, and in order to reduce the calculation amount, the section distance needs to be increased, so that the accuracy of the calculation of the earthwork amount is greatly influenced. In addition, this method does not accurately define the edges of the varying regions.
Disclosure of Invention
The invention provides a scheme for measuring and calculating the earthwork of an open pit based on laser point cloud, and aims to solve the problems of large workload, high risk, low efficiency and poor earthwork calculation precision of the open pit earthwork calculation method provided by the prior art.
To achieve the above object, according to a first aspect of the present invention, there is provided a method for measuring and calculating an earthwork variation of a strip mine based on a laser point cloud, including:
scanning a target strip mine area by using a three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area;
preprocessing front and rear two-stage point cloud data according to a point cloud preprocessing algorithm to obtain denoised front and rear two-stage point cloud data;
according to the three-dimensional distance transformation diagrams respectively corresponding to the front and rear phase point cloud data, calculating to obtain a change area point cloud set with the chamfering distance larger than a preset distance threshold value;
performing rapid octree clustering on the change area point cloud set to obtain a change area boundary corresponding to the change area point cloud set;
and constructing a triangular net at the boundary of the change area, and calculating the filling and excavation quantity of the triangular net to obtain the filling and excavation quantity corresponding to the soil quantity change area.
Preferably, in the method for measuring and calculating the earthwork variation of the strip mine, the step of scanning the target strip mine area by using the three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area comprises the following steps:
Scanning the surrounding environment containing the target strip mine area by using a three-dimensional laser radar, and acquiring front and rear original point cloud data of the surrounding environment;
classifying the front and rear original point cloud data according to the characteristics of the target strip mine, and extracting to obtain the front and rear point cloud data of the target strip mine region.
Preferably, in the method for measuring and calculating the change of the earthwork of the strip mine, preprocessing the front and rear two-stage point cloud data according to a point cloud preprocessing algorithm to obtain the denoised front and rear two-stage point cloud data comprises the following steps:
registering the front and rear two-stage point cloud data, and setting the front and rear two-stage point cloud data under the same coordinate system;
thinning front and back phase point cloud data under the same coordinate system by using a voxel filter;
and denoising the front and rear two-stage point cloud data after the thinning by using a statistical analysis filter to obtain the front and rear two-stage point cloud data after denoising.
Preferably, in the method for measuring and calculating the earthwork change of the strip mine, the step of calculating the change area point cloud set with the chamfer distance greater than the predetermined distance threshold value according to the three-dimensional distance transformation graphs respectively corresponding to the front and rear phase point cloud data comprises the following steps:
voxel processing is carried out on the previous-period point cloud data in the previous-period point cloud data and the next-period point cloud data;
According to voxels in the point cloud data of the previous period, a three-dimensional distance transformation diagram corresponding to the point cloud data of the previous period is obtained through transformation;
voxel processing is carried out on later-period point cloud data in the front-period point cloud data and the later-period point cloud data;
according to voxels in the point cloud data of the later period, a three-dimensional distance transformation diagram corresponding to the point cloud data of the later period is obtained through transformation;
overlapping and comparing the three-dimensional distance transformation graph of the point cloud data of the later period with the three-dimensional distance transformation graph of the point cloud data of the previous period;
according to the comparison result of the three-dimensional distance transformation graph, calculating and obtaining the chamfering distance of the later-period point cloud data relative to the earlier-period point cloud data;
and selecting points with the chamfer distance larger than a preset distance threshold value from the later-period point cloud data, and combining to obtain a change area point cloud set.
Preferably, in the method for measuring and calculating the earthwork variation of the strip mine, the step of performing fast octree clustering on the variation region point cloud set to obtain the variation region boundary corresponding to the variation region point cloud set includes:
decomposing the point cloud set of the change area into a plurality of point cloud areas, and performing European clustering on each point cloud area by using a first clustering threshold to obtain a rough clustered point cloud area;
finely clustering the point cloud areas of each coarse cluster by using a second clustering threshold to obtain clustered point clouds corresponding to the point cloud areas;
And carrying out boundary extraction on each clustered point cloud by using a preset boundary extraction algorithm to obtain a change region boundary corresponding to the change region point cloud set.
Preferably, in the method for measuring and calculating the earthwork variation of the strip mine, the step of performing the european clustering on each point cloud area by using the first clustering threshold value to obtain the rough clustered point cloud area includes:
acquiring a maximum bounding box of each point cloud area, and calculating the side length of the point cloud area by using the maximum bounding box;
and layering the point cloud area by using the pyramid structure of the octree according to the side length of the point cloud area until the bottom side length of the pyramid structure is smaller than a first clustering threshold.
Preferably, in the method for measuring and calculating the change of the earthwork of the strip mine, constructing a triangular net of a change area boundary, and calculating the filling amount of the triangular net comprises the following steps:
constructing a triangular net corresponding to the boundary of the change area by using a point-by-point insertion method;
traversing triangular nets corresponding to the boundaries of the change areas, and cutting the triangular nets according to the boundary center point of each triangular net;
and calculating the filling and excavating amount corresponding to each triangle in the cut triangular net, and accumulating the filling and excavating amounts corresponding to the triangles to obtain the filling and excavating amounts corresponding to the soil amount change areas.
According to a second aspect of the present invention, there is provided a system for measuring and calculating the change of the earthwork of a strip mine based on a laser point cloud, comprising:
the radar scanning module is used for scanning the target strip mine area by using a three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area;
the data processing module is used for preprocessing the front and rear two-stage point cloud data according to a point cloud preprocessing algorithm to obtain denoised front and rear two-stage point cloud data;
the point cloud computing module is used for computing and obtaining a change area point cloud set with the chamfer distance larger than a preset distance threshold according to the three-dimensional distance transformation graphs respectively corresponding to the front and rear phase point cloud data;
the point cloud clustering module is used for carrying out rapid octree clustering on the change area point cloud set to obtain a change area boundary corresponding to the change area point cloud set;
and the filling amount calculation module is used for constructing a triangular net at the boundary of the change area, and calculating the filling amount of the triangular net to obtain the filling amount corresponding to the change area of the soil amount.
Preferably, in the above system for measuring and calculating the change of the earthwork of an open pit mine, the point cloud computing module includes:
the first voxel processing sub-module is used for voxelizing the previous-period point cloud data in the previous-period point cloud data and the next-period point cloud data;
The first image transformation submodule is used for transforming to obtain a three-dimensional distance transformation diagram corresponding to the point cloud data of the previous period according to voxels in the point cloud data of the previous period;
the second voxel processing sub-module is used for voxelizing the later-period point cloud data in the front-period point cloud data and the later-period point cloud data;
the second image transformation submodule is used for transforming to obtain a three-dimensional distance transformation diagram corresponding to the later-period point cloud data according to voxels in the later-period point cloud data;
the overlapping comparison sub-module is used for overlapping and comparing the three-dimensional distance transformation graph of the point cloud data of the later period with the three-dimensional distance transformation graph of the point cloud data of the former period;
the chamfering distance calculation sub-module is used for calculating the chamfering distance of the later-period point cloud data relative to the earlier-period point cloud data according to the comparison result of the three-dimensional distance transformation graph;
and the point cloud set combination sub-module is used for selecting points with the chamfer distance larger than a preset distance threshold value from the point cloud data in the later period and combining the points to obtain a point cloud set of the change area.
Preferably, in the above system for measuring and calculating the change of the earthwork of an open pit mine, the point cloud clustering module includes:
the coarse clustering sub-module is used for decomposing the point cloud set of the change area into a plurality of point cloud areas, and performing European clustering on each point cloud area by using a first clustering threshold value to obtain a coarse clustered point cloud area;
The fine clustering sub-module is used for carrying out fine clustering on the point cloud areas of each coarse cluster by using a second clustering threshold value to obtain clustered point clouds corresponding to the point cloud areas;
and the boundary extraction sub-module is used for carrying out boundary extraction on each clustered point cloud by using a preset boundary extraction algorithm to obtain a change area boundary corresponding to the change area point cloud set.
In summary, according to the scheme for measuring and calculating the earthwork variation of the strip mine based on the laser point cloud, the three-dimensional laser radar is used for scanning a target strip mine area to obtain front and rear two-stage point cloud data of the target strip mine area, then the front and rear two-stage point cloud data are preprocessed according to a point cloud preprocessing algorithm, the front and rear two-stage point cloud data after denoising can be obtained, and then the front and rear two-stage point cloud data are processed according to a three-dimensional distance transformation algorithm to obtain three-dimensional distance transformation diagrams corresponding to the front and rear two-stage point cloud data respectively, so that automatic extraction of the two-stage strip mine point cloud data variation area is realized, and a point cloud set of the variation area is obtained; and then, carrying out quick octree distance on the change area point cloud set to respectively obtain a change area boundary corresponding to the change area point cloud set, and then constructing a triangular net of the change area boundary, so that the calculation of the filling amount can be carried out on any plane except a horizontal plane as a reference plane to obtain the filling amount corresponding to the earthwork change area, thereby solving the problems of large workload, high risk, complex operation process, low operation efficiency and poor earthwork calculation precision of the open-pit mine earthwork calculation method provided by the prior art.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for measuring and calculating earthwork variation of a strip mine based on laser point clouds, which is provided by the embodiment of the invention;
FIG. 2 is a flow chart of a method for scanning a target strip mine area provided by the embodiment of FIG. 1;
FIG. 3 is a schematic flow chart of a method for preprocessing point cloud data according to the embodiment shown in FIG. 1;
FIG. 4 is a flowchart illustrating a method for computing a point cloud set of a change area according to the embodiment shown in FIG. 1;
FIG. 5 is a flowchart of a method for clustering a point cloud set in a change area according to the embodiment shown in FIG. 1;
fig. 6 is a schematic flow chart of an european style clustering method for a point cloud area provided by the embodiment shown in fig. 5;
FIG. 7 is a flow chart of a method for calculating the filling rate of a triangular net according to the embodiment shown in FIG. 1;
FIG. 8 is a schematic structural diagram of a first distance map according to an embodiment of the present invention;
FIG. 9 is a schematic structural diagram of a second distance map according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of a pyramid structure of an octree according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of a structure of a boundary of a variation area according to an embodiment of the present invention;
fig. 12 is a schematic structural diagram of a triangle mesh corresponding to a boundary of a change area according to an embodiment of the present invention;
FIG. 13 is a schematic diagram of a triangular fill-out square according to an embodiment of the present invention;
fig. 14 is a schematic structural diagram of a system for measuring and calculating the earthwork variation of a strip mine based on laser point clouds according to an embodiment of the present invention;
FIG. 15 is a schematic diagram of a point cloud computing module according to the embodiment shown in FIG. 14;
fig. 16 is a schematic structural diagram of a point cloud clustering module provided in the embodiment shown in fig. 14.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The technical problems mainly solved by the embodiment of the invention are as follows:
the existing method for calculating the earthwork of the strip mine mainly comprises the following steps:
1. and (5) a section method. The profile method can be selected under the conditions of complex terrain, large fluctuation or long and narrow section and large depth. In short, the section data is measured vertically according to the landform of the mine area, and the surface section diagrams are made one by one. After the sectional diagrams of the two-stage data are obtained by the method, the two-stage section lines are overlapped and compared to outline the changed area, and then the area is multiplied by the section distance L, namely the earthwork variable quantity is the stripping quantity.
2. Grid process (DEM process). The method still needs field operation and has lower precision than a section method.
Dtm method or triangulated method. The triangular mesh DTM is firstly generated according to the field breaking point or the laser point cloud, and then the filling and excavation amount of each triangular prism is calculated through the triangular mesh. Finally, the change condition of the earth and stone in the same area in the two-stage diagram is obtained.
The several methods described above have more or less the following problems:
(1) And the manpower and material resources consumption is huge, and the efficiency is low.
(2) The accuracy is not high. The grid method and the section method cannot simulate the terrain with high precision, the calculated filling and digging amount has a certain error compared with the real filling and digging amount, and the error for the section method mainly comes from the section step length, which is limited by an artificial field measurement mode; the grid method error mainly comes from the grid size and the calculation mode of the filling and digging direction inside the grid. Theoretically, when the grid is infinite hours the error is zero, but this is also limited to field harvesting. Even if the laser radar is used for high-density acquisition, the saw teeth brought by the grid method at the edge of the measuring area can bring calculation errors, and the errors can be increased sharply along with the increase of the range of the measuring area and the increase of the grid edge length.
(3) The edges of the zone for the variation cannot be defined accurately. Although the triangular mesh method (DTM method) can accurately calculate the filling and digging amount relative to the section method and the grid method, the DTM is a convex hull after construction, and cannot well represent a region with rugged range, which requires cutting the triangular mesh, and the cutting range is often defined by human beings. This also gives errors in the final calculation of the amount of earth. As shown in fig. two, assuming original data on the left and DTM data on the right, the DTM data interpolates and fills the concave boundary that would have existed, and taking this part of the filling party into account would result in errors.
In order to solve the problems, according to the scheme for measuring and calculating the earthwork change of the strip mine based on the laser point cloud, which is provided by the embodiment of the invention, through acquiring front and rear two-stage point cloud data of a target strip mine area, preprocessing the front and rear two-stage point cloud data, processing the laser point cloud by using a three-dimensional distance conversion algorithm to realize automatic extraction of a change area in the two-stage point cloud data, automatically processing the point cloud of the change area by using a rapid octree distance algorithm, and finally calculating the filling and excavation amount by using a triangular net conversion mode to obtain the filling and excavation amount corresponding to the accurate earthwork change area, so that the problems of large workload, high risk, complex operation process, low operation efficiency and poor earthwork calculation accuracy of the strip mine earthwork change calculation method provided by the prior art are solved.
In order to achieve the above objective, referring to fig. 1, fig. 1 is a flow chart of a method for measuring and calculating the earthwork variation of a strip mine based on laser point cloud according to an embodiment of the present invention. As shown in fig. 1, the method for measuring and calculating the change of the earth volume comprises the following steps:
s110: and scanning the target strip mine area by using a three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area. According to the method and the device, the three-dimensional laser is adopted to scan the target strip mine area, so that the point cloud data of the target strip mine area in the front and rear periods in time are obtained.
As a preferred embodiment, as shown in fig. 2, the step of scanning the target strip mine area with the three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area includes:
s111: and scanning the surrounding environment containing the target strip mine area by using a three-dimensional laser radar, and acquiring front and rear original point cloud data of the surrounding environment.
S112: classifying the front and rear original point cloud data according to the characteristics of the target strip mine, and extracting to obtain the front and rear point cloud data of the target strip mine region.
According to the method, the device and the system, the three-dimensional laser radar is used for scanning the surrounding environment, original point cloud data of the surrounding environment are collected, and then the original point cloud data of the surrounding environment are classified, so that front-stage point cloud data and rear-stage point cloud data of a target strip mine area can be automatically extracted. Specifically, the laser point cloud refers to describing an actual object by using points distributed in space, that is, describing an absolute spatial position of the object on the earth by using the laser point cloud; these points include all objects of the scanned area, including available and unavailable portions, and even noise points (i.e., the original point cloud data). In order to eliminate unusable portions and noise in the front and rear phase point cloud data, preprocessing is required to be performed on the front and rear phase point cloud data, including registration, voxel thinning, denoising and other processes.
In order to achieve the above object, after obtaining the front and rear phase point cloud data of the target strip mine area, the method for measuring and calculating the earthwork variation according to the embodiment shown in fig. 1 further includes:
s120: and preprocessing the front and rear two-stage point cloud data according to a point cloud preprocessing algorithm to obtain the denoised front and rear two-stage point cloud data. The preprocessing process comprises the processes of registering two-stage point cloud data, voxel thinning, denoising and the like, and front and rear two-stage point cloud data with noise and useless parts removed can be obtained through the preprocessing process.
Specifically, as a preferred embodiment, as shown in fig. 3, the step of preprocessing front and rear two-stage point cloud data according to the point cloud preprocessing algorithm to obtain denoised front and rear two-stage point cloud data includes:
s121: and registering the front and rear phase point cloud data, and setting the front and rear phase point cloud data under the same coordinate system.
S122: and thinning front and back phase point cloud data under the same coordinate system by using a voxel filter.
S123: and denoising the front and rear two-stage point cloud data after the thinning by using a statistical analysis filter to obtain the front and rear two-stage point cloud data after denoising.
According to the technical scheme provided by the embodiment of the application, the two-period point cloud data are registered in a mode of selecting the control point pairs, so that the coordinates of the two point clouds are in a unified coordinate system. Then, thinning the two-period point cloud data by using a voxel filter with a side length of 0.02m, wherein the average distance between every two obtained two-period point cloud data is not more than 0.02m; the reason why the 0.02m voxel filter is used is that the number of point clouds can be reduced without affecting the accuracy, and the calculation load of the subsequent processing process is reduced. And finally, denoising the front and rear phase point cloud data after the thinning by using a statistical analysis filter to remove discrete points in the front and rear phase point cloud data, wherein the influence of the discrete points on the calculation accuracy is definitely huge, so that the discrete points need to be removed to obtain the front and rear phase point cloud data after the denoising.
After the denoised front and rear two-stage point cloud data are obtained, the point cloud of the change area in the two-stage point cloud data needs to be extracted. In the prior art, a manual demarcation method is generally adopted to determine a region, then the filling and excavation amount of the region is calculated, the calculation error caused by the method can be increased along with the increase of the region area, and a lot of workload is brought to the inner industry personnel due to the fact that the region concerned is more.
In order to solve the above problem, the method for measuring and calculating the change of the earthwork provided in the embodiment shown in fig. 1 further includes:
s130: and calculating to obtain a change area point cloud set with the chamfer distance larger than a preset distance threshold according to the three-dimensional distance transformation graphs respectively corresponding to the front and rear phase point cloud data.
The embodiment of the application specifically adopts a three-dimensional distance transformation algorithm to convert the front and rear two-stage point cloud data into a three-dimensional distance transformation graph, and then obtains a change area point cloud set with the chamfer distance larger than a preset distance threshold according to the three-dimensional distance transformation graph. By extracting the point cloud set of the change region with the chamfer distance larger than the preset distance threshold value, the change region in the two-stage point cloud data can be clearly and accurately determined and extracted.
The three-dimensional distance transformation is to perform three-dimensional distance change on the previous-period point cloud data, namely, calculate the space chamfering distance of the previous-period point cloud data. The three-dimensional space chamfering distance is an extension of the two-dimensional chamfering distance, and the two-dimensional chamfering distance calculation is commonly used for image recognition or measuring the similarity degree of two images. The so-called distance transformation, i.e. solving for the distance of each point to the nearest feature point. As shown in fig. 8, the left side is a pixel picture of letter F, and the right side is a distance conversion map thereof. The number in the distance map represents the pixel distance of the pixel to letter F. This is also illustrated by the two-dimensional picture feature to distance transformation in fig. 9.
Specifically, as a preferred embodiment, as shown in fig. 4, the step of calculating a change area point cloud set with a chamfer distance greater than a predetermined distance threshold according to three-dimensional distance transformation maps corresponding to the front and rear phase point cloud data respectively includes:
s131: and voxelization processing is carried out on the previous-period point cloud data in the previous-period point cloud data and the next-period point cloud data.
S132: and transforming according to voxels in the point cloud data of the previous period to obtain a three-dimensional distance transformation graph corresponding to the point cloud data of the previous period.
The voxel size can be set to s=0.02, as shown in fig. 8, in the embodiment of the present application, the voxel is traversed for the first time, if the voxel contains a point cloud, the voxel is marked as 0, otherwise, the voxel is marked as-1; then, a second traversal is performed, and the distance from the calculator to the nearest 0-mark neighborhood (i.e. the sum of the grid distances in the x, y and z directions) is filled into the corresponding grids (the distance must be an integer value), so that a three-dimensional distance transformation graph is formed. As shown in fig. 9, the distance can be calculated by Dijk shortest path algorithm.
S133: and voxelization processing is carried out on the later-period point cloud data in the front-period point cloud data and the later-period point cloud data.
S134: and transforming according to the voxels in the later-period point cloud data to obtain a three-dimensional distance transformation graph corresponding to the later-period point cloud data.
In order to obtain a change area, the later-period point cloud data is subjected to voxelization in the same way, the voxels containing the point cloud are marked as 0, and then the change area can be obtained by overlapping and comparing the voxels with the distance transformation graph of the first-period data. The voxelization process and the three-dimensional distance transformation process of the later-period point cloud data are the same as those of the earlier-period point cloud data.
S135: and overlapping and comparing the three-dimensional distance transformation graph of the later-period point cloud data with the three-dimensional distance transformation graph of the previous-period point cloud data.
S136: and according to the comparison result of the three-dimensional distance transformation graph, calculating and obtaining the chamfering distance of the later-period point cloud data relative to the earlier-period point cloud data.
After overlapping and comparing the three-dimensional distance transformation graph of the later-period point cloud data with the three-dimensional distance transformation graph of the previous-period point cloud data, the method can find that each voxel with the distance value of 0 in the later-period point cloud data (namely, a grid containing the point cloud) can find the corresponding distance (namely, the distance value in the grid) in the three-dimensional distance transformation graph of the previous-period point cloud data. The distance is the chamfering distance of the later-period point cloud data relative to the previous-period point cloud data.
S137: and selecting points with the chamfer distance larger than a preset distance threshold value from the later-period point cloud data, and combining to obtain a change area point cloud set. In the embodiment of the application, given a predetermined distance threshold t, only a point cloud set P with a grid chamfering distance greater than t of the point cloud data of the later period is taken.
According to the technical scheme provided by the embodiment of the invention, the two-dimensional chamfering distance is extended to be three-dimensional. Two-dimensional chamfer distance calculation is often used for image recognition or to measure the degree of similarity of two images. And the so-called distance transformation, i.e. solving for the distance of each point to the nearest feature point. Specifically, the previous-period point cloud data is voxelized first, the voxel size can be set to s=0.02, the voxel is traversed for the first time, if the voxel contains a point cloud, the voxel is marked as 0, otherwise, the voxel is marked as-1. Then a second pass is made to calculate the neighborhood distance to the nearest 0 marker (i.e. the sum of the grid distances in the x, y, z directions) for all marker-1 grids in the space and fill that distance into the corresponding grid (which must be an integer value). Thus, a three-dimensional distance transformation map is formed. The calculation of the distance can be accomplished by using the Dijk shortest path algorithm, which is also a more widely used shortest path algorithm. In order to acquire a change area, the later-stage point cloud data is subjected to voxelization, voxels containing the point cloud are marked as 0, and then the voxels are overlapped and compared with a distance transformation graph of the previous-stage point cloud data, so that corresponding distances (distance values in a grid) can be found in the distance transformation graph of the previous stage for each voxel grid (namely, the grid containing the point cloud) with 0 in the later-stage point cloud data, and the distances are chamfering distances of the later-stage point cloud data relative to the previous-stage point cloud data. Given a threshold t, only the point cloud set P with the second stage grid chamfer distance greater than t is taken.
After the change area point cloud set is obtained, the change area point cloud set is required to be subjected to quick distance, so that each discrete change area boundary is obtained. The method for measuring and calculating the change of the earthwork provided by the embodiment shown in fig. 1 further comprises the following steps:
s140: and carrying out rapid octree clustering on the change area point cloud set to obtain a change area boundary corresponding to the change area point cloud set.
For the above-described change region point cloud set P, the boundaries of each discrete change region need to be accurately acquired. Because the point cloud is treated as a region without clustering the point cloud, the accuracy of the filling and digging amount is greatly affected by the blank region and the registration accuracy. The larger the area of the region, the greater the influence of the arrangement accuracy. The clustering has the advantages that the boundary of each discrete area can be accurately obtained, and meanwhile, calculation errors of the filling and digging amount caused by point cloud registration errors are eliminated to a great extent.
Because the common European clustering method is performed by constructing octree and neighborhood searching. When the cluster radius of the European cluster increases, the cluster efficiency thereof decreases. Therefore, the embodiment of the invention adopts a rapid octree clustering mode to improve the change region point cloud set, and the clustering mode is divided into coarse clustering and fine clustering.
Specifically, as a preferred embodiment, as shown in fig. 5, in the above method for measuring and calculating the change of the earthwork of the strip mine, step S140: the step of carrying out rapid octree clustering on the change area point cloud set to obtain the change area boundary corresponding to the change area point cloud set comprises the following steps:
s141: and decomposing the point cloud set of the change area into a plurality of point cloud areas, and performing European clustering on each point cloud area by using a first clustering threshold to obtain a rough clustered point cloud area.
The coarse clustering method is to quickly and roughly decompose the point cloud into a plurality of point cloud areas, and then European clustering is carried out in each point cloud area. Referring to fig. 10, since the octree itself has pyramid levels, the voxel size of each pyramid level is twice as large as that of the upper pyramid level. Thus, the point cloud set of the change area in the octree form can be decomposed into a plurality of pyramid levels, and a coarse clustering point cloud area is obtained. The coarse clustering is based on an octree structure, and although a final result cannot be obtained, an initial clustering effect can be provided, and the clustering speed is high.
S142: and performing fine clustering on the point cloud areas of each coarse cluster by using a second clustering threshold to obtain clustered point clouds corresponding to the point cloud areas. The clustering threshold value of the fine clustering is smaller than the distance threshold value of the coarse clustering, namely the second clustering threshold value is smaller than the first clustering threshold value. After the clustered point cloud areas are obtained rapidly through coarse clustering, fine clustering is conducted on the point cloud areas, so that the clustering speed can be increased, and clustered point clouds corresponding to the point cloud areas are obtained.
S143: and carrying out boundary extraction on each clustered point cloud by using a preset boundary extraction algorithm to obtain a change region boundary corresponding to the change region point cloud set. Regarding boundary extraction, for each cluster point cloud obtained by coarse clustering and fine clustering in the above embodiment, a boundary extraction algorithm (for example, alpha-shape algorithm) is adopted to perform boundary extraction, and the Alpha-shape algorithm is a common concave-bag range extraction algorithm and is not described herein. The change region boundaries extracted by the predetermined boundary extraction algorithm are shown in fig. 11.
In the technical scheme provided by the embodiment of the application, the clustering mode is divided into two modes of coarse clustering and fine clustering. The coarse clustering method is to quickly and roughly decompose the point cloud into a plurality of point cloud areas, and then perform European clustering in each point cloud area. Because the octree has pyramid levels, the size of each pyramid voxel is twice that of the pyramid of the upper level, and thus a larger clustering threshold value, namely the first clustering threshold value, is set, and a coarse clustering point cloud area can be obtained. And after the coarsely clustered point cloud areas are obtained, finely clustering the coarsely clustered point cloud areas by using a second clustering threshold value, so that the corresponding clustered point cloud can be obtained. The clustering manner of fine clustering is similar to coarse clustering, but the second clustering threshold is far smaller than the first clustering threshold of coarse clustering. After the finely clustered point clouds are obtained, boundary extraction is carried out on each clustered point cloud by using a preset boundary extraction algorithm, and the boundary of a change area corresponding to the change area point cloud set can be obtained, so that open pit mines with the change of earthwork in the later-period point cloud data can be accurately searched.
In a preferred embodiment, as shown in fig. 6, in the method for measuring and calculating the earthwork variation of the strip mine, the step of performing the euclidean clustering on each point cloud area by using the first clustering threshold value to obtain the point cloud area of the coarse cluster includes:
s1411: and acquiring a maximum bounding box of each point cloud area, and calculating the side length of the point cloud area by using the maximum bounding box.
S1412: and layering the point cloud area by using the pyramid structure of the octree according to the side length of the point cloud area until the bottom side length of the pyramid structure is smaller than a first clustering threshold.
According to the technical scheme provided by the embodiment of the invention, the maximum bounding box of each point cloud area is obtained, wherein the maximum bounding box is the maximum boundary of the pointing cloud in the xy direction, namely X max -X min And Y max -Y min The maximum value of (2) is taken as the square side length S. The side length S of the cube is the side length of the point cloud area, the side length is taken as the side length of the first layer of the pyramid structure of the octree, the side length of the second layer is S/2 … … until the S/n of the last layer is smaller than the first clustering threshold value c, the n-1 layer is used for quick coarse clustering,the point cloud area of the coarse clustering can be obtained, and then the fine clustering is performed again, so that the acceleration effect can be achieved. The pyramidal structure of the octree is seen in particular in fig. 10.
The structural features of the octree itself determine that it can be used directly for fast neighborhood searching, because the nodes and neighborhood of the octree can be easily stored for a computer. The clustering speed of octree is fast. The above procedure is called coarse clustering because if there is an octree, its 0-layer cube side length is 10, and its respective layer side lengths are reduced by half in order, 5, 2.5, 1.25, and 0.625. Then now the fine clusters need a threshold of 1.3, but the octree levels do not contain cubes with side lengths of 1.3m, so only coarse clusters of 2.5m can be made as initial values, and then each cluster is subdivided by a threshold of 1.3 m.
After obtaining the boundary of the change area corresponding to the change area point cloud set, the method for measuring and calculating the change of the earthwork provided by the embodiment shown in fig. 1 further includes the following steps:
s150: and constructing a triangular net at the boundary of the change area, and calculating the filling and excavation quantity of the triangular net to obtain the filling and excavation quantity corresponding to the soil quantity change area.
The boundary line of each point cloud area is already obtained, and the filling amount of the area needs to be obtained. Specifically, a Dirony triangular net, triangular net cutting and filling and excavating amount calculation can be constructed, so that the filling and excavating amount corresponding to the earthwork amount change area is obtained.
Specifically, as a preferred embodiment, as shown in fig. 7, the step of constructing the triangle mesh of the change area point cloud set and the change area boundary, and performing the filling square calculation on the triangle mesh includes:
s151: and constructing a triangular net corresponding to the boundary of the change area by using a point-by-point insertion method. Specifically, the embodiment of the application can construct the Dirony triangulation network, and the Dirony triangulation network is constructed by adopting a point-by-point insertion method. This step is one of the basic algorithms commonly used in the industry. Fig. 12 is a dironi triangle mesh constructed for one of the regions. It can be seen from fig. 12 that the triangle net construction result does not coincide with the region of interest, and therefore it is necessary to reject the triangle net outside the boundary.
S152: traversing the triangular nets corresponding to the boundaries of the change areas, and cutting the triangular nets according to the boundary center point of each triangular net. Traversing the triangle network, and calculating the midpoints C1, C2 and C3 of three sides of each triangle in the triangle network. In C1, C2 and C3, the triangle mesh is rejected as long as the midpoint of one edge is outside the outermost contour boundary.
S153: and calculating the filling and excavating amount corresponding to each triangle in the cut triangular net, and accumulating the filling and excavating amounts corresponding to the triangles to obtain the filling and excavating amounts corresponding to the soil amount change areas.
Regarding the calculation of the filling and excavating amount, in the embodiment of the application, the filling and excavating amount of each triangular prism is calculated by adopting a triangular net, and finally the earth amount change in the same area of two-period data can be obtained by accumulation.
The filling and excavation amount for each triangle in the triangle net can be classified into three cases as shown in fig. 13. In these three cases, the reference plane may be a horizontal plane, or may be any plane other than vertical. Namely, the method can be applied to calculation of the filling and excavation amount of any plane.
Specifically, the calculation formula of tetrahedral volume is known first: assuming that the four vertices of the tetrahedron are a, b, c, d, the tetrahedron volume V is:
the three fill volume calculations shown in fig. 13 can all be broken down into tetrahedral volume calculations.
In case 1 shown in fig. 13, S is set as a reference plane. All fill or all dig for the triangle. Auxiliary lines FB and FA can be made to decompose the wedge into tetrahedrons CAFB and rectangular pyramids F-abod. The rectangular pyramid F-ABED can also be decomposed into two tetrahedrons for calculation.
In case 2 shown in fig. 13, the reference plane is a plane formed by AGH, and the above-plane BCHGEF is a hollowed volume, and can be used as auxiliary lines BH and BF, and decomposed into tetrahedrons F-BCH and rectangular pyramids B-GHFE. The rectangular pyramid B-GHTE can also be decomposed into two tetrahedrons for calculation; the tetrahedron A-DCB below the plane is the filling volume, and can be directly calculated.
In case 3 shown in fig. 13, the calculation method is substantially the same as that in case 2.
After the step S150, the filling and excavation amounts of the triangular nets are accumulated, so that the filling and excavation amounts corresponding to the final soil amount change area can be calculated. And respectively carrying out filling and excavation amount calculation on the previous-period point cloud data and the later-period point cloud data, and then subtracting the calculated filling and excavation amounts of the final change areas.
According to actual verification, the earth volume change measuring and calculating method of the strip mine, provided by the embodiment of the invention, has the difference between the obtained filling volume and the true value within one percent, and can meet the actual precision requirement.
In summary, according to the method for measuring and calculating the earthwork variation of the strip mine based on the laser point cloud provided by the embodiment of the invention, the three-dimensional laser radar is used for scanning the target strip mine area to obtain front and rear two-stage point cloud data of the target strip mine area, then the front and rear two-stage point cloud data are preprocessed according to the point cloud preprocessing algorithm, the front and rear two-stage point cloud data after denoising can be obtained, and further the front and rear two-stage point cloud data are processed according to the three-dimensional distance transformation algorithm to obtain three-dimensional distance transformation diagrams corresponding to the front and rear two-stage point cloud data respectively, so that automatic extraction of the two-stage strip mine point cloud data change area is realized, and a point cloud set of the change area is obtained; and then, carrying out quick octree distance on the change area point cloud set to respectively obtain a change area boundary corresponding to the change area point cloud set, and then constructing a triangular net of the change area boundary, so that the calculation of the filling amount can be carried out on any plane except a horizontal plane as a reference plane to obtain the filling amount corresponding to the earthwork change area, thereby solving the problems of large workload, high risk, complex operation process, low operation efficiency and poor earthwork calculation precision of the open-pit mine earthwork calculation method provided by the prior art.
In addition, based on the same concept of the above method embodiment, the embodiment of the present invention further provides a system for measuring and calculating the earthwork variation of the strip mine based on the laser point cloud, so that the principle of solving the problem in the system embodiment is similar to that in the method, and therefore, the system at least has all the beneficial effects brought by the technical solution of the above embodiment, which are not described in detail herein.
Referring to fig. 14, fig. 14 is a schematic structural diagram of a system for measuring and calculating the earthwork variation of a strip mine based on laser point cloud according to an embodiment of the present invention. As shown in fig. 14, the system for measuring and calculating the change in the earthwork of the strip mine comprises:
the radar scanning module 110 is configured to scan the target strip mine area by using a three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area;
the data processing module 120 is configured to pre-process the front and rear two-stage point cloud data according to a point cloud pre-processing algorithm, so as to obtain denoised front and rear two-stage point cloud data;
the point cloud computing module 130 is configured to compute a change area point cloud set with a chamfer distance greater than a predetermined distance threshold according to three-dimensional distance transformation maps corresponding to the front and rear two-stage point cloud data respectively;
The point cloud clustering module 140 is configured to perform fast octree clustering on the change region point cloud set to obtain a change region boundary corresponding to the change region point cloud set;
the filling amount calculation module 150 is configured to construct a triangle network of the change area point cloud set and the change area boundary, and calculate the filling amount of the triangle network to obtain the filling amount corresponding to the change area of the earth volume.
As a preferred embodiment, as shown in fig. 15, in the above system for measuring and calculating the earthwork variation of a strip mine, the point cloud computing module 130 includes:
the first voxel processing sub-module 131 is configured to voxel processing the previous-period point cloud data in the previous-period and subsequent-period point cloud data;
the first image transformation sub-module 132 is configured to transform to obtain a three-dimensional distance transformation map corresponding to the previous-period point cloud data according to voxels in the previous-period point cloud data;
the second voxel processing sub-module 133 is configured to voxel processing the later-period point cloud data in the front-period point cloud data and the later-period point cloud data;
a second image transformation sub-module 134, configured to transform the three-dimensional distance transformation map corresponding to the later-period point cloud data according to the voxels in the later-period point cloud data;
the overlap comparison sub-module 135 is configured to overlap and compare the three-dimensional distance transformation map of the later-period point cloud data with the three-dimensional distance transformation map of the previous-period point cloud data;
The chamfer distance calculating sub-module 136 is configured to calculate a chamfer distance of the later-period point cloud data relative to the earlier-period point cloud data according to a comparison result of the three-dimensional distance transformation graph;
and the point cloud set combining sub-module 137 is configured to select points with a chamfer distance greater than a predetermined distance threshold from the point cloud data in the later period, and combine the points to obtain a point cloud set of the change area.
As a preferred embodiment, as shown in fig. 16, the point cloud clustering module 140 includes:
the coarse clustering sub-module 141 is configured to decompose the point cloud set of the change area into a plurality of point cloud areas, perform euclidean clustering on each point cloud area by using a first clustering threshold value, and obtain a coarse clustered point cloud area;
the fine clustering sub-module 142 is configured to perform fine clustering on the point cloud regions of each coarse cluster by using a second clustering threshold to obtain clustered point clouds corresponding to the point cloud regions;
the boundary extraction sub-module 143 is configured to perform boundary extraction on each clustered point cloud by using a predetermined boundary extraction algorithm, so as to obtain a change region boundary corresponding to the change region point cloud set.
In summary, compared with the mode of the prior art, the technical scheme provided by the embodiment of the invention has the following advantages:
1. according to the technical scheme provided by the invention, the laser point cloud is processed through the three-dimensional distance transformation algorithm, so that the automatic extraction of the point cloud in the two-phase strip mine point cloud data change area can be realized.
2. And automatically processing the point cloud of the change region by combining a rapid octree clustering method with an Alpha-shape algorithm to respectively obtain the boundary of the region of interest. And obtaining effective triangle network data in the boundary by cutting the Delaunay triangle network by the boundary.
3. The triangle net data is calculated as the filling amount, and the filling amount can be calculated with respect to an arbitrary plane other than the horizontal plane as the reference plane.
4. The problems of low efficiency and low precision of field manual treatment in the traditional filling and excavating square measurement are solved. The method is easy to write as a computer program, and has high automation degree and high processing precision.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (4)
1. The method for measuring and calculating the earthwork variation of the strip mine based on the laser point cloud is characterized by comprising the following steps of:
scanning a target strip mine area by using a three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area;
preprocessing the front and rear two-stage point cloud data according to a point cloud preprocessing algorithm to obtain denoised front and rear two-stage point cloud data;
according to the three-dimensional distance transformation graphs respectively corresponding to the front and rear phase point cloud data, calculating to obtain a change area point cloud set with the chamfer distance larger than a preset distance threshold value;
Performing rapid octree clustering on the change area point cloud set to obtain a change area boundary corresponding to the change area point cloud set;
constructing a triangular net at the boundary of the change area, and calculating the filling and excavation amount of the triangular net to obtain the filling and excavation amount corresponding to the change area of the soil amount;
the step of preprocessing the front and rear two-stage point cloud data according to the point cloud preprocessing algorithm to obtain denoised front and rear two-stage point cloud data comprises the following steps:
registering the front and rear two-stage point cloud data, and setting the front and rear two-stage point cloud data under the same coordinate system;
thinning front and back phase point cloud data under the same coordinate system by using a voxel filter;
denoising the front and rear two-stage point cloud data after the thinning by using a statistical analysis filter to obtain the front and rear two-stage point cloud data after the denoising;
the step of calculating a change area point cloud set with a chamfer distance greater than a preset distance threshold according to the three-dimensional distance transformation graphs respectively corresponding to the front and rear phase point cloud data comprises the following steps:
voxel processing is carried out on the previous-period point cloud data in the previous-period point cloud data and the next-period point cloud data;
According to voxels in the previous-period point cloud data, a three-dimensional distance transformation diagram corresponding to the previous-period point cloud data is obtained through transformation;
voxel processing is carried out on the later-period point cloud data in the front-period point cloud data and the later-period point cloud data;
according to the voxels in the later-period point cloud data, a three-dimensional distance transformation diagram corresponding to the later-period point cloud data is obtained through transformation;
overlapping and comparing the three-dimensional distance transformation graph of the later-period point cloud data with the three-dimensional distance transformation graph of the previous-period point cloud data;
according to the comparison result of the three-dimensional distance transformation graph, calculating and obtaining the chamfering distance of the later-period point cloud data relative to the earlier-period point cloud data;
selecting points with the chamfer distance larger than a preset distance threshold value from the later-period point cloud data, and combining to obtain the change area point cloud set;
the step of performing fast octree clustering on the change area point cloud set to obtain a change area boundary corresponding to the change area point cloud set comprises the following steps:
decomposing the point cloud set of the change area into a plurality of point cloud areas, and performing European clustering on each point cloud area by using a first clustering threshold to obtain a rough clustered point cloud area;
Fine clustering is carried out on each of the coarsely clustered point cloud areas by using a second clustering threshold to obtain clustered point clouds corresponding to the point cloud areas;
performing boundary extraction on each clustered point cloud by using a preset boundary extraction algorithm to obtain a change region boundary corresponding to the change region point cloud set;
the step of constructing the triangular net of the boundary of the change area and calculating the filling amount of the triangular net comprises the following steps:
constructing a triangular net corresponding to the boundary of the change area by using a point-by-point insertion method;
traversing triangular nets corresponding to the boundaries of the change areas, and cutting the triangular nets according to the boundary center point of each triangular net;
and calculating the filling and excavating amount corresponding to each triangle in the cut triangular net, and accumulating the filling and excavating amount corresponding to the triangle to obtain the filling and excavating amount corresponding to the soil volume change area.
2. The method for measuring and calculating the change of the earthwork of the strip mine according to claim 1, wherein the step of scanning the target strip mine area by using the three-dimensional laser radar to obtain the front and rear two-stage point cloud data of the target strip mine area comprises the following steps:
scanning the surrounding environment containing the target strip mine area by using a three-dimensional laser radar, and acquiring front and rear two-stage original point cloud data of the surrounding environment;
Classifying the front and rear original point cloud data according to the characteristics of the target strip mine, and extracting to obtain the front and rear point cloud data of the target strip mine region.
3. The method for measuring and calculating the earthwork variation of the strip mine as set forth in claim 1, wherein the step of performing the euclidean clustering on each of the point cloud areas using the first clustering threshold to obtain the rough clustered point cloud areas includes:
acquiring a maximum bounding box of each point cloud area, and calculating the side length of the point cloud area by using the maximum bounding box;
and layering the point cloud area by using a pyramid structure of an octree according to the side length of the point cloud area until the side length of the bottommost layer of the pyramid structure is smaller than the first clustering threshold value.
4. An earthwork variation measuring and calculating system of a strip mine based on laser point cloud, which is characterized by comprising:
the radar scanning module is used for scanning a target strip mine area by using a three-dimensional laser radar to obtain front and rear phase point cloud data of the target strip mine area;
the data processing module is used for preprocessing the front and rear two-stage point cloud data according to a point cloud preprocessing algorithm to obtain denoised front and rear two-stage point cloud data;
The point cloud computing module is used for computing and obtaining a change area point cloud set with the chamfer distance larger than a preset distance threshold according to the three-dimensional distance transformation graphs respectively corresponding to the front and rear two-stage point cloud data;
the point cloud clustering module is used for carrying out rapid octree clustering on the change area point cloud set to obtain a change area boundary corresponding to the change area point cloud set;
the filling amount calculation module is used for constructing a triangular net at the boundary of the change area, and calculating the filling amount of the triangular net to obtain the filling amount corresponding to the change area of the earthwork amount;
the data processing module is specifically configured to register the front and rear two-stage point cloud data, and set the front and rear two-stage point cloud data under the same coordinate system; thinning front and back phase point cloud data under the same coordinate system by using a voxel filter; denoising the front and rear two-stage point cloud data after the thinning by using a statistical analysis filter to obtain the front and rear two-stage point cloud data after the denoising;
the point cloud computing module comprises:
the first voxel processing sub-module is used for voxelizing the previous-period point cloud data in the previous-period point cloud data and the next-period point cloud data;
The first image transformation submodule is used for transforming to obtain a three-dimensional distance transformation diagram corresponding to the previous period point cloud data according to voxels in the previous period point cloud data;
the second voxel processing sub-module is used for voxelizing the later-period point cloud data in the front-period point cloud data and the later-period point cloud data;
the second image transformation submodule is used for transforming to obtain a three-dimensional distance transformation diagram corresponding to the later-period point cloud data according to voxels in the later-period point cloud data;
the overlapping comparison sub-module is used for overlapping and comparing the three-dimensional distance transformation graph of the later-period point cloud data with the three-dimensional distance transformation graph of the previous-period point cloud data;
the chamfering distance calculation sub-module is used for calculating the chamfering distance of the later-period point cloud data relative to the previous-period point cloud data according to the comparison result of the three-dimensional distance transformation graph;
the point cloud set combining sub-module is used for selecting points with the chamfer distance larger than a preset distance threshold value from the later-period point cloud data and combining the points to obtain the point cloud set of the change area;
the point cloud clustering module comprises:
the coarse clustering sub-module is used for decomposing the point cloud set of the change area into a plurality of point cloud areas, and performing European clustering on each point cloud area by using a first clustering threshold value to obtain a coarse clustered point cloud area;
The fine clustering sub-module is used for carrying out fine clustering on the point cloud areas of each coarse cluster by using a second clustering threshold value to obtain clustered point clouds corresponding to the point cloud areas;
the boundary extraction sub-module is used for carrying out boundary extraction on each clustered point cloud by using a preset boundary extraction algorithm to obtain a change area boundary corresponding to the change area point cloud set;
the filling-in square quantity calculating module is specifically used for constructing a triangular net corresponding to the boundary of the change area by using a point-by-point insertion method; traversing triangular nets corresponding to the boundaries of the change areas, and cutting the triangular nets according to the boundary center point of each triangular net; and calculating the filling and excavating amount corresponding to each triangle in the cut triangular net, and accumulating the filling and excavating amount corresponding to the triangle to obtain the filling and excavating amount corresponding to the soil volume change area.
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