CN114608476A - Intelligent analysis and extraction method for three-dimensional point cloud structural surface of complex rock mass - Google Patents
Intelligent analysis and extraction method for three-dimensional point cloud structural surface 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 identification; on the basis of obtaining three-dimensional point cloud data of a rock mass, not only can semi-automatically extracting an appointed structural surface be realized through simple manual interaction, but also the structural surface of a complex rock mass can be automatically extracted; different structural plane extraction modes can be more comprehensively applied to extracting the rock slope structural plane in complex scenes such as mines; meanwhile, the quality grade of the rock mass is intelligently analyzed through rock mass index calculation, and data support can be provided for the stability of the rock slope in practical application; 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 identification, in particular to an intelligent analysis and extraction method for a three-dimensional point cloud structural surface of a complex rock mass.
Background
In the process of rock mass quality grading and rock mass structure stability analysis, the refinement degree of rock mass structure face extraction is an important reference index, and the method has important reference significance in geological exploration and disaster assessment. Until now, researchers have conducted some research on the acquisition of structural planes, and rock structural planes can be acquired in a semi-automatic or automatic manner. The main research methods adopted in the method comprise surface reconstruction, neighborhood search, normal vector calculation, RANSAC, least square fitting, voxel segmentation, k-means clustering, a region growing algorithm, Zhang voting and the like. Meanwhile, a part of scholars can obtain the structural plane through various processing software. In the existing research, a research object is usually a natural outcrop slope and is characterized by being not damaged, intact original structure, more exposed structures, obvious structural characteristics, large surface curvature change and the like. The characteristic extraction of the rock mass structure can be solved by the existing technical means. However, in the case of surface mines, the exposed surface features are damaged seriously by the surface mining process, the surface structure features are not obvious, the surface structures are almost horizontal or vertical lamellar structures, and the surface curvature is not obvious. However, the existing technical means has low accuracy, poor effect and slow speed in the application of strip mine slope feature identification, and is difficult to play a role. The more precise the extraction of the complex rock mass structural plane, the more beneficial the geologist to evaluate and analyze the rock mass, but the more precise the extraction of the complex rock mass structural plane needs to be supported by more precise technical means. Therefore, in order to solve the above problems, it is necessary to provide an intelligent analysis and extraction method for a three-dimensional point cloud structural surface of a complex rock mass.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent analysis and extraction method for a three-dimensional point cloud structural surface of a complex rock mass, which can improve the extraction precision of the structural surface of the complex rock mass and obtain a structural surface conforming to the characteristics of a real rock mass.
The technical scheme of the invention is as follows:
an intelligent analysis and extraction method for a three-dimensional point cloud structural surface of a complex rock mass is characterized by comprising the following steps: the method comprises 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-automatically or semi-automatically extracting a complex rock structural plane;
and step 3: analyzing the quality of the rock mass; calculating to obtain the space of the structural surface; calculating to obtain a rock volume regulating number according to the structural plane interval; calculating to obtain rock mass structure grade according to the rock mass volume joint number; and analyzing the rock mass according to the structural grade of the rock mass.
The specific process of the step 1 is as follows:
the method comprises the steps that an unmanned aerial vehicle is used for data acquisition, the flying height of the unmanned aerial vehicle is set according to the precision required by practical application, the flying 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 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;
or acquiring three-dimensional point cloud data of the target rock mass by adopting a three-dimensional laser scanner.
The semi-automatic extraction of the rock mass structural plane comprises the following steps: 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; utilizing a three-dimensional point cloud normal normalization method based on a moving ball to adjust the direction of a point cloud normal vector; judging an included angle between the normal direction of the point cloud and the reference direction of the structural surface to obtain a potential structural surface point set; and automatically acquiring the same group of structural planes by using an improved super-body clustering algorithm.
The automatic extraction of the rock mass structural plane comprises the following steps: establishing a point cloud data octree index, and acquiring a voxel containing point cloud data; fitting point clouds in the voxels by using a principal component analysis method, and calculating the inclination angle of each voxel; calculating corresponding poles of the inclined dip angle of each voxel in polar emission bathymetric projection, and analyzing the density distribution of each pole; and (4) automatically determining the grouping number and the advantage occurrence of the structural plane by utilizing cluster analysis.
The concrete process of semi-automatic extraction of the rock mass structural plane comprises the following steps:
step 2.1: calculating the reference direction of the structural plane; picking up a plurality of points on any structural surface of the point cloud surface, and creating a point set DpBy usingCalculating the normal direction of the structural plane by principal component analysis, and recording the normal direction as the reference direction of the same group of structural planes
Step 2.2: calculating a normal vector of the point cloud; establishing local neighborhood search at each point, wherein the radius of the neighborhood is 3 times of the average distance of all point clouds, and calculating the normal vector of a neighborhood coplanar point set by using a principal component analysis method, wherein the normal vector is defined as
Step 2.3: normal adjustment of point cloud data; adjusting the normal vector of the neighborhood coplanar point set calculated in the step 2.2, pointing the normal vector to the same direction of the rock slope, and adjusting the direction of the normal vector of the point cloud by using a three-dimensional point cloud normal normalization method based on a moving ball to point the normal vector of the point cloud to the same direction;
step 2.4: analyzing normal consistency; calculating an included angle theta between a normal vector of the point cloud and a structural plane reference direction, wherein the included angle theta is shown in formula (1):
wherein ,
storing the point to a set D of potential structural surface points when theta is smaller than a set threshold value for the normal vector of each pointqAs a set of potential points for the structural surface;
step 2.5: the method for automatically acquiring the same group of structural planes by utilizing the improved super-clustering algorithm comprises the following steps:
step 2.5.1: collecting structural surface points DqThe connection attribute of the midpoint is set to false;
step 2.5.2: set of points DqIn the method, any point with false connection attribute is used as an initial 'crystal nucleus', neighborhood search is established, the connection attribute is changed into true, and the neighborhood radius is equal to the average of all point cloudsAbsorbing all points in the neighborhood by a set multiple of the average distance, and storing the points into a structure surface classification cluster list ClusterList;
step 2.5.3: repeating the step 2.5.2 by taking the first point in the ClusterList as the next 'crystal nucleus';
step 2.5.4: traversing all point sets in the ClusterList until the connection attributes of the points in the ClusterList are true, and obtaining a first group of classification clusters;
step 2.5.5: continue judging point set DqRepeating the step 2.5.2 to the step 2.5.4 until the residual points in the sequence DqThe connection attribute of the midpoint is true.
In the step 2.2, normal vectors of all the points are calculated, and the operation efficiency is improved by adopting a parallel operation mode.
The concrete process for automatically extracting the rock mass structural plane comprises the following steps:
step S2.1: establishing a point cloud data octree index; carrying out octree segmentation on the original point cloud, and only leaving voxels containing point cloud data for improving the operation efficiency, as shown in formula (2):
Dv＝{P1(x1,y1,z1),P2(x2,y2,z2),…,Pn(xn,yn,zn)},n∈Z (2)
wherein ,P1(x1,y1,z1) Coordinates for each point;
step S2.2: calculating the occurrence of the target point cloud in each voxel; using principal component analysis to set D of points in each voxelvPerforming plane fitting, and calculating the occurrence of each fitting plane, including inclination and dip angle;
step S2.3: analyzing the intensity of the polar emission red projection; calculating the corresponding pole in the polar emission red plane projection according to the dip angle of the fitted plane of the point set in each voxel, and then carrying out density statistics on the pole, as shown in formula (3):
wherein ,
for the neighborhood radius of each of the poles,
totalNum (P), the number of poles in a unit circlepole) The total number of poles; obtaining a polar ray bathochromic projection density map;
step S2.4: automatically determining the grouping number and the advantage occurrence of the structural plane;
when the grouping number is determined, the poles with the density lower than the set maximum density percentage are removed, the remaining poles are classified through density clustering, and the grouping number of the poles is obtained and is equal to the grouping number of the structural surface; after the poles are grouped, the poles with the maximum density exist in each group of poles, and the dip angle of the voxel corresponding to the maximum density pole is the advantage occurrence of each group of structural planes;
step S2.5: utilizing super-clustering to automatically obtain a structural plane; and automatically acquiring each group of structural surfaces by using a hyper-clustering algorithm according to the acquired group number and the advantage occurrence.
The specific process of the step 3 comprises the following steps:
step 3.1: calculating the space between the structural surfaces; calculating the spacing of the continuous rock mass structural plane method, as shown in formula (4):
where m represents the number of non-continuous clusters per group, DiRepresenting discontinuous clusters, | Di,Di+1| | represents the euclidean distance of adjacent clusters in the normal direction;
step 3.2: calculating a rock volume adjustment number; according to the calculated distance between the rock mass structural planes, the formula (5) shows:
step 3.3: calculating the rock mass structure degree; the method utilizes the functional relation between the rock mass volume rational number and the rock mass structure degree, and is shown in formula (6):
SR＝264.6×sin(0.09×log(Jv)+2.859)+13.93×sin(1.644×log(Jv)+1.574)+3.128×sin(3.738×log(Jv)-0.838) (6)
wherein SR represents the rock mass structure level, and SR belongs to (0,100), and the rock mass structure level can be calculated;
step 3.4: analyzing the mass of the rock mass; and (4) analyzing the rock mass quality by combining a rock mass structure comparison table according to the rock mass structure level SR obtained by calculation in the step 3.3.
And setting the set multiple of the average distance of all the point clouds to be 3 times.
The set maximum density percentage is set to 20%.
Advantageous technical effects
On the basis of obtaining three-dimensional point cloud data of a rock mass, the method can realize semi-automatic extraction of the specified structural plane through simple manual interaction, and can also automatically extract the structural plane of the complex rock mass; different structural plane extraction modes can be more comprehensively applied to extracting the rock slope structural plane in complex scenes such as mines; meanwhile, the quality grade of the rock mass is intelligently analyzed through rock mass index calculation, and data support can be provided for the stability of the rock slope in practical application; the invention is simple to realize, has 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 according to an embodiment of the invention;
FIG. 2 is a flow chart of an improved super-volume clustering algorithm in the intelligent analysis and extraction method for the complex rock mass three-dimensional point cloud structural plane provided by the embodiment of the invention;
FIG. 3 is a diagram of the practical application effect of the intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass provided by the embodiment of the invention.
Detailed Description
The invention is explained in further detail with reference to the drawings and examples:
in the embodiment, an intelligent analysis and extraction method for a three-dimensional point cloud structural surface of a complex rock mass is provided, as shown in fig. 1, the method comprises 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 4RTK single-lens unmanned aerial vehicle of Xinjiang eidolon is used for data acquisition, the flying height of the unmanned aerial vehicle is set according to the precision required by practical application, the flying route of the unmanned aerial vehicle is set according to the size of a target area, the course overlapping degree and the lateral overlapping degree of the unmanned aerial vehicle meeting the requirement are set, the image data of a target rock mass is obtained, and the three-dimensional point cloud data of the target rock mass is 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: automatically or semi-automatically extracting a complex rock structural plane;
wherein, semi-automatic extraction of rock mass structural plane includes following step:
step 2.1: calculating the reference direction of the structural plane; 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 recording the normal direction as the reference direction of the structural surface of the same group
Step 2.2: calculating a normal vector of the point cloud; establishing local neighborhood search at each point, wherein the radius of the neighborhood is 3 times of the average distance of all point clouds, and calculating the normal vector of a neighborhood coplanar point set by using a principal component analysis method, wherein the normal vector is defined as
Step 2.3: normal adjustment of point cloud data; adjusting the normal vector of the neighborhood coplanar point set calculated in the step 2.2, pointing the normal vector to the same direction of the rock slope, and adjusting the direction of the normal vector of the point cloud by using a three-dimensional point cloud normal normalization method based on a moving ball to point the normal vector of the point cloud to the same direction;
step 2.4: analyzing normal consistency; calculating an included angle theta between a normal vector of the point cloud and the reference direction of the structural plane, wherein the included angle theta is expressed as formula (1):
wherein ,
storing the point to a set D of potential structural surface points when theta is smaller than a set threshold value for the normal vector of each pointqAs a set of potential points for the structural plane;
step 2.5: the same group of structural planes are automatically obtained by utilizing an improved super-clustering algorithm, as shown in fig. 2, the method comprises the following steps:
step 2.5.1: collecting structural surface points DqThe connection attribute of the midpoint is set to false;
step 2.5.2: set of points DqTaking any point with false connection attribute as an initial 'crystal nucleus', establishing neighborhood search, changing the connection attribute of the point into true, enabling the neighborhood radius to be 3 times of the average distance of all point clouds, absorbing all points in the neighborhood, and storing the points in a structural surface classification cluster list;
step 2.5.3: repeating the step 2.5.2 by taking the first point in the ClusterList as the next 'crystal nucleus';
step 2.5.4: traversing all point sets in the ClusterList until the connection attributes of the points in the ClusterList are true, and obtaining a first group of classification clusters;
step 2.5.5: continue judging point set DqRepeating the step 2.5.2 to the step 2.5.4 until DqThe connection attribute of the midpoint is true.
The automatic extraction of the rock mass structural plane comprises the following steps:
step S2.1: establishing a point cloud data octree index; carrying out octree segmentation on the original point cloud, and only leaving voxels containing point cloud data for improving the operation efficiency, as shown in formula (2):
Dv＝{P1(x1,y1,z1),P2(x2,y2,z2),…,Pn(xn,yn,zn)},n∈Z (2)
wherein ,P1(x1,y1,z1) Coordinates for each point;
step S2.2: calculating the occurrence of the target point cloud in each voxel; using principal component analysis to set D of points in each voxelvPerforming plane fitting, and calculating the occurrence of each fitting plane, including inclination and dip angle;
step S2.3: analyzing the intensity of the polar emission red projection; calculating the corresponding pole in the polar emission red plane projection according to the dip angle of the fitted plane of the point set in each voxel, and then carrying out density statistics on the pole, as shown in formula (3):
wherein ,
for the neighborhood radius of each pole point,
totalNum (P), the number of poles in a unit circlepole) The total number of poles; obtaining a polar ray bathochromic projection density map;
step S2.4: automatically determining the grouping number and the advantage occurrence of the structural plane;
when the grouping number is determined, poles with the density lower than 20% of the maximum density are removed, and the remaining poles are classified through density clustering to obtain the grouping number of the poles, wherein the grouping number of the poles is equal to the grouping number of the structural plane; after the poles are grouped, the poles with the maximum density exist in each group of poles, and the dip angle of the voxel corresponding to the maximum density pole is the advantage occurrence of each group of structural planes;
step S2.5: utilizing super-clustering to automatically obtain a structural plane; and automatically acquiring each group of structural surfaces by using a hyper-clustering algorithm according to the acquired group number and the advantage occurrence.
And step 3: rock mass analysis, comprising the following steps:
step 3.1: calculating the space between the structural surfaces; calculating the spacing of the continuous rock mass structural plane method, as shown in formula (4):
where m represents the number of non-continuous clusters per group, DiRepresenting discontinuous clusters, | Di,Di+1| | represents the euclidean distance of adjacent clusters in the normal direction;
step 3.2: calculating a rock volume adjustment number; according to the calculated distance between the rock mass structural planes, the formula (5) shows:
step 3.3: calculating the rock mass structure degree; the method utilizes the functional relation between the rock mass volume rational number and the rock mass structure degree, and is shown in formula (6):
SR＝264.6×sin(0.09×log(Jv)+2.859)+13.93×sin(1.644×log(Jv)+1.574)+3.128×sin(3.738×log(Jv)-0.838) (6)
wherein SR represents the rock mass structure level, and SR belongs to (0,100), and the rock mass structure level can be calculated;
step 3.4: and (4) analyzing the rock mass, and analyzing the rock mass by combining a rock mass structure comparison table according to the rock mass structure level SR obtained by calculation in the step 3.3.
As shown in Table 1, in the practical application data processing results, the method of the invention compares the DSE processing results of the existing method (extracting the structural plane by firefly clustering) and the existing mature software;
TABLE 1
As can be seen from table 1 in conjunction with fig. 3, the existing DSE software has relatively high accuracy and precision for identifying structural planes, but has a slow speed; the method for extracting the structural plane by firefly clustering is improved in efficiency, 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 (10)
1. An intelligent analysis and extraction method for a three-dimensional point cloud structural plane of a complex rock mass is characterized by comprising the following steps: the method comprises 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-automatically or semi-automatically extracting a complex rock structural plane;
and step 3: analyzing the mass of the rock mass; calculating to obtain the space of the structural surface; calculating to obtain a rock volume joint number according to the structural plane interval; calculating to obtain rock mass structure grade according to the rock mass volume joint number; and analyzing the rock mass according to the structural grade of the rock mass.
2. The intelligent analysis and extraction method for the complex rock mass three-dimensional point cloud structural surface as claimed in claim 1, characterized in that: 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 acquiring three-dimensional point cloud data of the target rock mass by adopting a three-dimensional laser scanner.
3. The intelligent analysis and extraction method for the complex rock mass three-dimensional point cloud structural surface as claimed in claim 1, characterized in that: the semi-automatic extraction of the rock mass structural plane comprises the following steps: 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; utilizing a three-dimensional point cloud normal normalization method based on a moving ball to adjust the direction of a point cloud normal vector; judging an included angle between the normal direction of the point cloud and the reference direction of the structural surface to obtain a potential structural surface point set; and automatically acquiring the same group of structural planes by using an improved super-body clustering algorithm.
4. The intelligent analysis and extraction method for the complex rock mass three-dimensional point cloud structural surface as claimed in claim 1, characterized in that: the rock mass structural plane is automatically extracted: establishing a point cloud data octree index, and acquiring a voxel containing point cloud data; fitting point clouds in the voxels by using a principal component analysis method, and calculating the inclination angle of each voxel; calculating corresponding poles of the inclined dip angle of each voxel in polar emission bathymetric projection, and analyzing the density distribution of each pole; and (4) automatically determining the grouping number and the advantage occurrence of the structural plane by utilizing cluster analysis.
5. The intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass according to claim 3, characterized by comprising the following steps: the concrete process of semi-automatic extraction of the rock mass structural plane comprises the following steps:
step 2.1: calculating the reference direction of the structural plane; picking up a plurality of points on any structural surface of the point cloud surface, and creating a point set DpCalculating the normal direction of the structural plane by principal component analysis, and recording the normal direction as the reference direction of the same group of structural planes
Step 2.2: calculating a normal vector of the point cloud; establishing local neighborhood search at each point, wherein the radius of the neighborhood is 3 times of the average distance of all point clouds, and calculating the normal vector of a neighborhood coplanar point set by using a principal component analysis method, wherein the normal vector is defined as
Step 2.3: normal adjustment of point cloud data; adjusting the normal vector of the neighborhood coplanar point set calculated in the step 2.2, pointing the normal vector to the same direction of the rock slope, and adjusting the direction of the normal vector of the point cloud by using a three-dimensional point cloud normal normalization method based on a moving ball to point the normal vector of the point cloud to the same direction;
step 2.4: analyzing normal consistency; calculating an included angle theta between a normal vector of the point cloud and the reference direction of the structural plane, wherein the included angle theta is expressed as formula (1):
wherein ,
storing the point to a set D of potential structural surface points when theta is smaller than a set threshold value for the normal vector of each pointqAs a set of potential points for the structural surface;
step 2.5: the method for automatically acquiring the same group of structural planes by utilizing the improved super-clustering algorithm comprises the following steps:
step 2.5.1: set the points of the structural surface DqThe connection attribute of the midpoint is set to false;
step 2.5.2: set of points DqTaking any point with false connection attribute as an initial 'crystal nucleus', establishing neighborhood search, changing the connection attribute of the point into true, enabling the neighborhood radius to be a set multiple of the average distance of all point clouds, absorbing all points in the neighborhood, and storing the points in a structural surface classification cluster list;
step 2.5.3: repeating the step 2.5.2 by taking the first point in the ClusterList as the next 'crystal nucleus';
step 2.5.4: traversing all point sets in the ClusterList until the connection attributes of the points in the ClusterList are true, and obtaining a first group of classification clusters;
step 2.5.5: continuous judgment pointCollection DqRepeating the step 2.5.2 to the step 2.5.4 until DqThe connection attribute of the midpoint is true.
6. The intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass according to claim 5, characterized by comprising the following steps: in the step 2.2, normal vectors of all the points are calculated, and the operation efficiency is improved by adopting a parallel operation mode.
7. The intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass according to claim 4, characterized by comprising the following steps: the concrete process for automatically extracting the rock mass structural plane comprises the following steps:
step S2.1: establishing a point cloud data octree index; carrying out octree segmentation on the original point cloud, and only leaving voxels containing point cloud data for improving the operation efficiency, as shown in formula (2):
Dv＝{P1(x1,y1,z1),P2(x2,y2,z2),…,Pn(xn,yn,zn)},n∈Z (2)
wherein ,P1(x1,y1,z1) Coordinates for each point;
step S2.2: calculating the occurrence of the target point cloud in each voxel; using principal component analysis to set D of points in each voxelvPerforming plane fitting, and calculating the occurrence of each fitting plane, including inclination and dip angle;
step S2.3: analyzing the intensity of polar emission red plane projection; calculating the corresponding pole in the polar emission red plane projection according to the dip angle of the fitted plane of the point set in each voxel, and then carrying out density statistics on the pole, as shown in formula (3):
wherein ,
for the neighborhood radius of each pole point,
totalNum (P), the number of poles in a unit circlepole) The total number of poles; obtaining a polar ray bathochromic projection density map;
step S2.4: automatically determining the grouping number and the advantage occurrence of the structural plane;
when the grouping number is determined, the poles with the density lower than the set maximum density percentage are removed, the remaining poles are classified through density clustering, and the grouping number of the poles is obtained, wherein the grouping number of the poles is equal to the grouping number of the structural plane; after the poles are grouped, the poles with the maximum density exist in each group of poles, and the dip angle of the voxel corresponding to the maximum density pole is the advantage occurrence of each group of structural planes;
step S2.5: utilizing super-clustering to automatically obtain a structural plane; and automatically acquiring each group of structural surfaces by using a hyper-clustering algorithm according to the acquired group number and the advantage occurrence.
8. The intelligent analysis and extraction method for the complex rock mass three-dimensional point cloud structural surface as claimed in claim 1, characterized in that: the specific process of the step 3 comprises the following steps:
step 3.1: calculating the space between the structural surfaces; calculating the spacing of the continuous rock mass structural plane method, as shown in formula (4):
where m represents the number of non-continuous clusters per group, DiRepresenting discontinuous clusters, | Di,Di+1| | represents the euclidean distance of adjacent clusters in the normal direction;
step 3.2: calculating a rock volume adjustment number; according to the calculated distance between the rock mass structural planes, the formula (5) shows:
step 3.3: calculating the rock mass structure degree; the method utilizes the functional relation between the rock mass volume rational number and the rock mass structure degree, and is shown in formula (6):
SR＝264.6×sin(0.09×log(Jv)+2.859)+13.93×sin(1.644×log(Jv)+1.574)+3.128×sin(3.738×log(Jv)-0.838) (6)
wherein SR represents the rock mass structure level, and SR belongs to (0,100), and the rock mass structure level can be calculated;
step 3.4: analyzing the mass of the rock mass; and (4) analyzing the rock mass quality by combining a rock mass structure comparison table according to the rock mass structure level SR obtained by calculation in the step 3.3.
9. The intelligent analysis and extraction method for the complex rock mass three-dimensional point cloud structural plane according to claim 5, characterized by comprising the following steps: and the set multiple of the average distance of all the point clouds is set to be 3 times.
10. The intelligent analysis and extraction method for the three-dimensional point cloud structural surface of the complex rock mass according to claim 7, characterized by comprising the following steps: the set maximum density percentage was set to 20%.
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