CN114608476B - Intelligent analysis and extraction method for three-dimensional point cloud structural plane of complex rock mass - Google Patents
Intelligent analysis and extraction method for three-dimensional point cloud structural plane of complex rock mass Download PDFInfo
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Abstract
The invention discloses an intelligent analysis and extraction method for a three-dimensional point cloud structural plane of a complex rock mass, and relates to the field of rock mass surface feature recognition; on the basis of acquiring three-dimensional point cloud data of the rock mass, the method can realize semi-automatic extraction of a designated structural surface through simple manual interaction, and can automatically extract the structural surface of a complex rock mass; different structural surface extraction modes can be more comprehensively applied to rock slope structural surface extraction in complex scenes such as mines; meanwhile, the rock mass quality grade is intelligently analyzed through rock mass index calculation, and in practical application, data support can be provided for the stability of the rock mass slope; the invention has simple realization, flexible operation and obvious effect, and meets the application requirement.
Description
Technical Field
The invention relates to the field of rock mass surface feature recognition, in particular to an intelligent analysis and extraction method for a three-dimensional point cloud structural plane of a complex rock mass.
Background
In the process of rock mass quality grading and rock mass structural stability analysis, the extracted refinement degree of a rock mass structural surface is an important reference index, and has important reference significance in geological investigation and disaster evaluation. Until now, researchers have studied the structural surface acquisition to a certain extent, and the rock structural surface can be acquired in a semi-automatic or automatic mode. The main research methods adopted include surface reconstruction, neighborhood search, normal vector calculation, RANSAC, least squares fitting, voxel segmentation, k-means clustering, region growing algorithm, zhang Liang voting and the like. Meanwhile, a part of scholars can acquire the structural plane through various processing software. In the existing research, the research object is usually a natural outcrop side slope, and the characteristics of the research object are that the research object is not destroyed, original structures are kept complete, the exposed structures are more, the structure characteristics are obvious, the surface curvature change is large, and the like. The feature extraction of such rock mass constructions can be solved by existing technical means. However, for surface mines, the exposed characteristics are severely damaged by the surface mining process, the surface structure characteristics are not obvious, and the surface curvature is not obvious due to the nearly horizontal or vertical lamellar structure. However, the existing technical means have low accuracy, poor effect and low speed in the application of the slope characteristic identification of the strip mine, and are difficult to play a role. The higher the fineness degree of the extraction of the structural surface of the complex rock mass is, the more favorable is for geological staff to evaluate and analyze the rock mass, but the accurate extraction of the structural surface of the complex rock mass needs more accurate technical means to support. Accordingly, aiming at the problems, it is necessary to provide an intelligent analysis and extraction method for the three-dimensional point cloud structural plane of the complex rock mass.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides the intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass, which can improve the extraction precision of the structural surface of the complex rock mass and obtain the structural surface which accords with the characteristics of the real rock mass.
The technical scheme of the invention is as follows:
a method for intelligently analyzing and extracting a three-dimensional point cloud structural surface of a complex rock mass is characterized by comprising the following steps of: comprising the following steps:
step 1: acquiring three-dimensional point cloud data of different rock masses, and establishing an original three-dimensional point cloud data set D;
step 2: fully or semi-automatically extracting the structural surface of the complex rock mass;
step 3: analyzing the rock mass; calculating to obtain the structure surface distance; calculating according to the structure surface distance to obtain a rock volume management number; calculating according to the rock volume conditioning number to obtain rock structure grade; and analyzing the rock mass according to the rock mass structural grade.
The specific process of the step 1 is as follows:
the unmanned aerial vehicle is used for data acquisition, the flight height of the unmanned aerial vehicle is set according to the precision required by practical application, the flight route of the unmanned aerial vehicle is set according to the size of a target area, the course overlapping degree and the side overlapping degree of the unmanned aerial vehicle meeting the requirements are set, the image data of a target rock mass are obtained, and the three-dimensional point cloud data are obtained through a motion reconstruction technology;
or a three-dimensional laser scanner is adopted to acquire the three-dimensional point cloud data of the target rock mass.
The semi-automatic extraction of the rock mass structural surface is as follows: picking up a plurality of points on any structural surface through manual interaction, and calculating the reference direction of the structural surface; calculating normal vectors of all point clouds by using a principal component analysis method; carrying out direction adjustment on the point cloud normal vector by using a three-dimensional point cloud normal normalization method based on a moving ball; judging an included angle between a point cloud normal direction and a structural surface reference direction, and obtaining a potential structural surface point set; and automatically acquiring the same group of structural surfaces by utilizing an improved hyper-body clustering algorithm.
The rock mass structural plane is automatically extracted as follows: establishing an octree index of point cloud data, and obtaining voxels containing the point cloud data; fitting the point cloud in the voxels by using a principal component analysis method, and calculating the inclination angle of each voxel; calculating a corresponding pole of the inclination angle of each voxel in the polar-radial-mean-square projection, and analyzing the density distribution of each pole; the cluster analysis is utilized to automatically determine the number of structural surface groups and dominant yield.
The specific process for semi-automatically extracting the rock mass structural surface comprises the following steps of:
step 2.1: calculating the reference direction of the structural surface; picking up a plurality of points on any structural surface of the point cloud surface, and creating a point set D p Calculating the normal direction of the structural surface by using a principal component analysis method, taking the normal direction as the reference direction of the structural surface of the same group, and marking the normal direction as the reference direction of the structural surface of the same group
Step 2.2: calculating the normal vector of the point cloud; establishing local neighborhood search at each point, wherein the neighborhood radius is 3 times of the average distance of all point clouds, calculating the normal vector of a neighborhood coplane point set by using a principal component analysis method, and defining as
Step 2.3: normal adjustment of point cloud data; the normal vector of the neighborhood coplanar point set calculated in the step 2.2 is adjusted, the normal vector is pointed in the same direction of the rock mass slope, and the direction of the point cloud normal vector is adjusted by utilizing a three-dimensional point cloud normal normalization method based on a moving ball, so that the point cloud normal vector is pointed in the same direction;
step 2.4: normal consistency analysis; calculating an included angle theta between a normal vector of the point cloud and a reference direction of the structural surface, wherein the included angle theta is shown in a formula (1):
wherein ,
for each point normal vector, storing the point to a potential structural plane point set D when θ is less than a set threshold q As a set of potential points for the structural face;
step 2.5: the method for automatically acquiring the same group of structural surfaces by utilizing the improved hyper-body clustering algorithm comprises the following steps:
step 2.5.1: the structural surface point set D q The connection attribute of the midpoint is set to false;
step 2.5.2: in the set of points D q The method comprises the steps of (1) taking a point with a false connection attribute as an initial crystal nucleus, establishing a neighborhood search, changing the connection attribute into true, enabling the neighborhood radius to be a set multiple of the average distance between all point clouds, absorbing all points in the neighborhood, and storing the points in a structural surface classification cluster list Clusterlist;
step 2.5.3: taking the first point in ClusterList as the next crystal nucleus, and repeating the step 2.5.2;
step 2.5.4: traversing all point sets in the ClusterList until the connection attribute of the points in the ClusterList is true, and obtaining a first group of classification clusters at the moment;
step 2.5.5: continuing to judge point set D q Repeating the steps 2.5.2-2.5.4 until D q The connection attribute of the midpoints is true.
In the step 2.2, normal vectors of all points are calculated, and the operation efficiency is improved by adopting a parallel operation mode.
The specific process for automatically extracting the rock mass structural surface comprises the following steps of:
step S2.1: establishing a point cloud data octree index; the original point cloud is subjected to octree segmentation, so that in order to improve the operation efficiency, only voxels containing point cloud data are left, as shown in the formula (2):
D v ＝{P 1 (x 1 ,y 1 ,z 1 ),P 2 (x 2 ,y 2 ,z 2 ),…,P n (x n ,y n ,z n )},n∈Z (2)
wherein ,P1 (x 1 ,y 1 ,z 1 ) Coordinates for each point;
step S2.2: calculating the occurrence of the target point cloud in each voxel; point set D within each voxel using principal component analysis v Performing plane fitting, and calculating the occurrence of each fitting plane, including inclination and inclination;
step S2.3: performing polar-ray barefoot projection density analysis; calculating a corresponding pole in polar-radial barefoot projection according to the inclination angle of the fitting plane of the point set in each voxel, and then carrying out density statistics on the pole, wherein the density statistics is shown as a formula (3):
wherein ,
for the neighborhood radius of each pole, +.>
Total num (P pole ) The total number of poles; obtaining a polar-jet red flat projection density map;
step S2.4: automatically determining the number of structural plane groups and dominant yield;
when the grouping number is determined, poles with the density lower than the set maximum density percentage are removed, the remaining poles are classified through density clustering, the grouping number of the poles is obtained, and the grouping number of the poles is equal to the grouping number of the structural surface; after poles are grouped, poles with the maximum density exist in each group of poles, and the inclined dip angle of the voxel corresponding to the maximum density pole is the dominant occurrence of each group of structural surfaces;
step S2.5: automatically acquiring a structural surface by utilizing super-body clustering; and automatically acquiring each group of structural surfaces by using a super-body clustering algorithm according to the acquired structural surface group number and the dominant yield.
The specific process of the step 3 comprises the following steps:
step 3.1: calculating the spacing between the structural surfaces; calculating the structural surface method phase spacing of the continuous rock mass, as shown in formula (4):
wherein m represents the number of discontinuous classification clusters of each group, D i Represents a discontinuity classification cluster, ||D i ,D i+1 The i represents the euclidean distance of the neighboring cluster in the normal direction;
step 3.2: calculating the volume rational number of the rock mass; according to the calculated rock mass structural plane spacing, the method is as shown in the formula (5):
step 3.3: calculating the structural grade of the rock mass; the functional relation between the rock volume rational number and the rock structure level is shown as a formula (6):
SR＝264.6×sin(0.09×log(J v )+2.859)+13.93×sin(1.644×log(J v )+1.574)+3.128×sin(3.738×log(J v )-0.838) (6)
the SR represents the rock mass structural grade, and the SR epsilon (0, 100) can calculate the rock mass structural grade;
step 3.4: analyzing the rock mass; and (3) analyzing the rock mass according to the rock mass structure grade SR obtained by calculation in the step (3.3) and combining a rock mass structure comparison table.
And setting the set multiple of the average distance of all the point clouds to be 3 times.
The set maximum density percentage was set at 20%.
Beneficial technical effects
According to the method, on the basis of acquiring the three-dimensional point cloud data of the rock mass, the designated structural surface can be extracted semi-automatically through simple manual interaction, and the structural surface of the complex rock mass can be extracted automatically; different structural surface extraction modes can be more comprehensively applied to rock slope structural surface extraction in complex scenes such as mines; meanwhile, the rock mass quality grade is intelligently analyzed through rock mass index calculation, and in practical application, data support can be provided for the stability of the rock mass slope; the invention has simple realization and obvious effect, and meets the application requirement.
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FIG. 1 is a flow chart of an intelligent analysis and extraction method for a three-dimensional point cloud structural plane of a complex rock mass, which is provided by the embodiment of the invention;
FIG. 2 is a flowchart of an improved ultra-volume clustering algorithm in an intelligent analysis and extraction method for a three-dimensional point cloud structural plane of a complex rock mass, which is provided by the embodiment of the invention;
fig. 3 is a practical application effect diagram of the intelligent analysis and extraction method for the three-dimensional point cloud structural plane of the complex rock mass.
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings and examples:
in this embodiment, an intelligent analysis and extraction method for a three-dimensional point cloud structural plane of a complex rock mass is provided, as shown in fig. 1, and includes the following steps:
step 1: acquiring three-dimensional point cloud data of different rock masses, and establishing an original three-dimensional point cloud data set D;
step 1.1: in the embodiment, a Dajiang eidolon 4RTK single-lens unmanned aerial vehicle is used for data acquisition, the flight height of the unmanned aerial vehicle is set according to the precision required by practical application, the flight route of the unmanned aerial vehicle is set according to the size of a target area, the course overlapping degree and the side overlapping degree of the unmanned aerial vehicle meeting the requirements are set, the image data of a target rock mass are obtained, and the three-dimensional point cloud data of the target rock mass are obtained through a motion reconstruction technology;
step 1.2: directly acquiring three-dimensional point cloud data of a target rock mass by using a three-dimensional laser scanner;
step 2: automatic or semi-automatic extraction of the structural surface of the complex rock mass;
the method comprises the following steps of:
step 2.1: calculating the reference direction of the structural surface; picking up a plurality of points on any structural surface of the point cloud surface, creating a point set Dp, calculating the normal direction of the structural surface by using a principal component analysis method, taking the normal direction as the reference direction of the structural surface of the same group, and marking as
Step 2.2: calculating the normal vector of the point cloud; establishing local neighborhood search at each point, wherein the neighborhood radius is 3 times of the average distance of all point clouds, calculating the normal vector of a neighborhood coplane point set by using a principal component analysis method, and defining as
Step 2.3: normal adjustment of point cloud data; the normal vector of the neighborhood coplanar point set calculated in the step 2.2 is adjusted, the normal vector is pointed in the same direction of the rock mass slope, and the direction of the point cloud normal vector is adjusted by utilizing a three-dimensional point cloud normal normalization method based on a moving ball, so that the point cloud normal vector is pointed in the same direction;
step 2.4: normal consistency analysis; calculating an included angle theta between a normal vector of the point cloud and a reference direction of the structural surface, wherein the included angle theta is shown in a formula (1):
wherein ,
for each point normal vector, storing the point to a potential structural plane point set D when θ is less than a set threshold q As a set of potential points for the structural face;
step 2.5: the improved hyper-body clustering algorithm is utilized to automatically acquire the same group of structural surfaces, as shown in fig. 2, and the method comprises the following steps:
step 2.5.1: the structural surface point set D q The connection attribute of the midpoint is set tofalse；
Step 2.5.2: in the set of points D q The method comprises the steps of (1) taking a point with any connection attribute of false as an initial crystal nucleus, establishing neighborhood search, changing the connection attribute into true, enabling the neighborhood radius to be 3 times of the average distance between all point clouds, absorbing all points in the neighborhood, and storing the points in a structural surface classification cluster list Clusterlist;
step 2.5.3: taking the first point in ClusterList as the next crystal nucleus, and repeating the step 2.5.2;
step 2.5.4: traversing all point sets in the ClusterList until the connection attribute of the points in the ClusterList is true, and obtaining a first group of classification clusters at the moment;
step 2.5.5: continuing to judge point set D q Repeating the steps 2.5.2-2.5.4 until D q The connection attribute of the midpoints is true.
The rock mass structural plane is automatically extracted, and the method comprises the following steps:
step S2.1: establishing a point cloud data octree index; the original point cloud is subjected to octree segmentation, so that in order to improve the operation efficiency, only voxels containing point cloud data are left, as shown in the formula (2):
D v ＝{P 1 (x 1 ,y 1 ,z 1 ),P 2 (x 2 ,y 2 ,z 2 ),…,P n (x n ,y n ,z n )},n∈Z (2)
wherein ,P1 (x 1 ,y 1 ,z 1 ) Coordinates for each point;
step S2.2: calculating the occurrence of the target point cloud in each voxel; point set D within each voxel using principal component analysis v Performing plane fitting, and calculating the occurrence of each fitting plane, including inclination and inclination;
step S2.3: performing polar-ray barefoot projection density analysis; calculating a corresponding pole in polar-radial barefoot projection according to the inclination angle of the fitting plane of the point set in each voxel, and then carrying out density statistics on the pole, wherein the density statistics is shown as a formula (3):
wherein ,
for the neighborhood radius of each pole, +.>
Total num (P pole ) The total number of poles; obtaining a polar-jet red flat projection density map;
step S2.4: automatically determining the number of structural plane groups and dominant yield;
when the grouping number is determined, poles with the density lower than 20% of the maximum density are removed, the remaining poles are classified through density clustering, the grouping number of the poles is obtained, and the grouping number of the poles is equal to the grouping number of the structural surface; after poles are grouped, poles with the maximum density exist in each group of poles, and the inclined dip angle of the voxel corresponding to the maximum density pole is the dominant occurrence of each group of structural surfaces;
step S2.5: automatically acquiring a structural surface by utilizing super-body clustering; and automatically acquiring each group of structural surfaces by using a super-body clustering algorithm according to the acquired structural surface group number and the dominant yield.
Step 3: rock mass analysis, comprising the steps of:
step 3.1: calculating the spacing between the structural surfaces; calculating the structural surface method phase spacing of the continuous rock mass, as shown in formula (4):
wherein m represents the number of discontinuous classification clusters of each group, D i Represents a discontinuity classification cluster, ||D i ,D i+1 The i represents the euclidean distance of the neighboring cluster in the normal direction;
step 3.2: calculating the volume rational number of the rock mass; according to the calculated rock mass structural plane spacing, the method is as shown in the formula (5):
step 3.3: calculating the structural grade of the rock mass; the functional relation between the rock volume rational number and the rock structure level is shown as a formula (6):
SR＝264.6×sin(0.09×log(J v )+2.859)+13.93×sin(1.644×log(J v )+1.574)+3.128×sin(3.738×log(J v )-0.838) (6)
the SR represents the rock mass structural grade, and the SR epsilon (0, 100) can calculate the rock mass structural grade;
step 3.4: and (3) analyzing the rock mass, and analyzing the rock mass according to the rock mass structure grade SR calculated in the step (3.3) and combining a rock mass structure comparison table.
As shown in table 1, in the actual application data processing results, the method of the invention is compared with the existing method (extracting structural surface through firefly clustering) and the existing mature software DSE processing results;
TABLE 1
As can be seen from Table 1 in combination with FIG. 3, the existing DSE software has relatively high precision and accuracy in identifying the structural surface, but has a very low speed; the efficiency of extracting the structural surface by firefly clustering is improved, but the accuracy is relatively poor; the invention can provide data support for the stability of the rock slope; the method is simple to implement, flexible to operate and remarkable in effect, and meets the application requirements.
Claims (4)
1. A method for intelligently analyzing and extracting a three-dimensional point cloud structural surface of a complex rock mass is characterized by comprising the following steps of: comprising the following steps:
step 1: acquiring three-dimensional point cloud data of different rock masses, and establishing an original three-dimensional point cloud data set D;
step 2: fully-automatic extraction of the structural surface of the complex rock mass; establishing an octree index of point cloud data, and obtaining voxels containing the point cloud data; fitting the point cloud in the voxels by using a principal component analysis method, and calculating the inclination angle of each voxel; drawing a three-dimensional polar-projection-level projection density map according to the inclination angle of the voxels, analyzing the density distribution of each pole, and automatically determining the grouping number and the dominant yield of the structural surface; obtaining the same group of structural surfaces by utilizing improved super-body clustering;
step 3: analyzing the rock mass; calculating to obtain the structure surface distance; calculating according to the structure surface distance to obtain a rock volume management number; calculating according to the rock volume conditioning number to obtain rock structure grade; analyzing the rock mass according to the rock mass structural grade;
the specific process of the step 1 is as follows:
acquiring three-dimensional point cloud data by using an unmanned aerial vehicle, setting the flight height of the unmanned aerial vehicle according to the precision required by practical application, setting the flight route of the unmanned aerial vehicle according to the size of a target area, setting the course overlapping degree and the side overlapping degree of the unmanned aerial vehicle which meet the requirements, acquiring the image data of a target rock mass, and acquiring the three-dimensional point cloud data by using a motion reconstruction technology;
or a three-dimensional laser scanner is adopted to acquire three-dimensional point cloud data of the target rock mass;
the step 2 further includes: semi-automatic extraction of a rock structural surface, picking up a plurality of points on any structural surface through manual interaction, and calculating a reference direction of the structural surface; calculating normal vectors of all point clouds by using a principal component analysis method; carrying out direction adjustment on the point cloud normal vector by using a three-dimensional point cloud normal normalization method based on a moving ball; judging an included angle between a point cloud normal direction and a structural surface reference direction, and obtaining a potential structural surface point set; automatically acquiring the same group of structural surfaces by utilizing an improved hyper-body clustering algorithm;
the specific process for semi-automatically extracting the rock mass structural surface comprises the following steps of:
step 2.1: calculating the reference direction of the structural surface; picking up a plurality of points on any structural surface of the point cloud surface, and creating a point set D p Calculating the normal direction of the structural surface by using a principal component analysis method, taking the normal direction as the reference direction of the structural surface of the same group, and marking the normal direction as the reference direction of the structural surface of the same group
Step 2.2: calculating the normal vector of the point cloud; establishing local neighborhood search at each point, wherein the neighborhood radius is 3 times of the average distance of all point clouds, calculating the normal vector of a neighborhood coplane point set by using a principal component analysis method, and defining as
Step 2.3: normal adjustment of point cloud data; the normal vector of the neighborhood coplanar point set calculated in the step 2.2 is adjusted, the normal vector is pointed in the same direction of the rock mass slope, and the direction of the point cloud normal vector is adjusted by utilizing a three-dimensional point cloud normal normalization method based on a moving ball, so that the point cloud normal vector is pointed in the same direction;
step 2.4: normal consistency analysis; calculating an included angle theta between a normal vector of the point cloud and a reference direction of the structural surface, wherein the included angle theta is shown in a formula (1):
wherein ,
for each point normal vector, storing the point to a potential structural plane point set D when θ is less than a set threshold q As a set of potential points for the structural face;
step 2.5: automatically acquiring the same group of structural surfaces by utilizing an improved hyper-body clustering algorithm;
in the step 2.2, normal vectors of all points are calculated, and the operation efficiency is improved by adopting a parallel operation mode;
the step 2.5 utilizes an improved super-body clustering algorithm to automatically acquire the concrete process of the same group of structural faces, and comprises the following steps:
step 2.5.1: the structural surface point set D q The connection attribute of the midpoint is set to false;
step 2.5.2: in the set of points D q Any connection of (3)The method comprises the steps of taking a point with the attribute of false as an initial crystal nucleus, establishing neighborhood search, changing the connection attribute of the point into true, taking the neighborhood radius as a set multiple of the average distance between all point clouds, absorbing all points in the neighborhood, and storing the points in a structural surface classification cluster list Clusterlist;
step 2.5.3: taking the first point in ClusterList as the next crystal nucleus, and repeating the step 2.5.2;
step 2.5.4: traversing all point sets in the ClusterList until the connection attribute of the points in the ClusterList is true, and obtaining a first group of classification clusters at the moment;
step 2.5.5: continuing to judge point set D q Repeating the steps 2.5.2-2.5.4 until D q The connection attribute of the midpoint is true;
the specific process for automatically extracting the rock mass structural face in the step 2 comprises the following steps:
step S2.1: establishing a point cloud data octree index; the original point cloud is subjected to octree segmentation, so that in order to improve the operation efficiency, only voxels containing point cloud data are left, as shown in the formula (2):
D v ＝{P 1 (x 1 ,y 1 ,z 1 ),P 2 (x 2 ,y 2 ,z 2 ),…,P n (x n ,y n ,z n )},n∈Z (2)
wherein ,P1 (x 1 ,y 1 ,z 1 ),P 2 (x 2 ,y 2 ,z 2 ),…,P n (x n ,y n ,z n ) Coordinates for each point;
step S2.2: calculating the occurrence of the target point cloud in each voxel; point set D within each voxel using principal component analysis v Performing plane fitting, and calculating the occurrence of each fitting plane, including inclination and inclination;
step S2.3: three-dimensional polar ray barefoot projection density analysis; calculating a corresponding pole in polar-radial barefoot projection according to the inclination angle of the fitting plane of the point set in each voxel, and then carrying out density statistics on the pole, wherein the density statistics is shown as a formula (3):
wherein ,
for the neighborhood radius of each pole, +.>
Total num (P pole ) The total number of poles; obtaining a polar-jet red flat projection density map;
step S2.4: automatically determining the number of structural plane groups and dominant yield;
when the grouping number is determined, poles with the density lower than the set maximum density percentage are removed, the remaining poles are classified through density clustering, the grouping number of the poles is obtained, and the grouping number of the poles is equal to the grouping number of the structural surface; after poles are grouped, poles with the maximum density exist in each group of poles, and the inclined dip angle of the voxel corresponding to the maximum density pole is the dominant occurrence of each group of structural surfaces;
step S2.5: automatically acquiring a structural surface by utilizing improved super-body clustering; and automatically acquiring each group of structural surfaces by utilizing an improved super-body clustering algorithm according to the acquired structural surface group number and the dominant yield.
2. The intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass is characterized by comprising the following steps of: the specific process of the step 3 comprises the following steps:
step 3.1: calculating the spacing between the structural surfaces; calculating the structural surface method phase spacing of the continuous rock mass, as shown in formula (4):
wherein m represents the number of discontinuous classification clusters of each group, D i Represents a discontinuity classification cluster, ||D i ,D i+1 The i represents the euclidean distance of the neighboring cluster in the normal direction;
step 3.2: calculating the volume rational number of the rock mass; according to the calculated rock mass structural plane spacing, the method is as shown in the formula (5):
step 3.3: calculating the structural grade of the rock mass; the functional relation between the rock volume rational number and the rock structure level is shown as a formula (6):
SR＝264.6×sin(0.09×log(J v )+2.859)+13.93×sin(1.644×log(J v )+1.574)+3.128×
sin(3.738×log(J v ) -0.838) (6) wherein SR represents the rock mass structural grade, SR e (0, 100) being the rock mass structural grade calculated;
step 3.4: analyzing the rock mass; and (3) analyzing the rock mass according to the rock mass structure grade SR obtained by calculation in the step (3.3) and combining a rock mass structure comparison table.
3. The intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass is characterized by comprising the following steps of: and setting the set multiple of the average distance of all the point clouds to be 3 times.
4. The intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass is characterized by comprising the following steps of: the maximum density percentage was set at 20%.
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