CN112446114B - Three-dimensional model comparison-based power transmission line engineering construction progress monitoring method - Google Patents
Three-dimensional model comparison-based power transmission line engineering construction progress monitoring method Download PDFInfo
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Abstract
The invention discloses a three-dimensional model comparison-based power transmission line engineering construction progress monitoring method, which comprises the steps of firstly removing noise points by performing point cloud filtering on LAS format laser point cloud data obtained by scanning; then converting the GIM design model into a GIM point cloud model, and providing a data base for matching and comparison analysis among subsequent models; then, a point cloud registration algorithm is adopted to realize accurate superposition between the GIM point cloud model and the laser point cloud model of the power transmission line; and finally, monitoring the construction progress of the power transmission line engineering according to the difference analysis between the GIM point cloud model and the laser point cloud model of the power transmission line. The invention effectively overcomes the defects of high operation intensity, high error probability and the like in the current construction progress monitoring, has the advantages of three-dimensional visualization, high automation degree and the like, and promotes the fusion application of the BIM technology and the three-dimensional laser point cloud technology in the field of power transmission and transformation engineering construction.
Description
Technical Field
The invention relates to the field of power transmission and transformation engineering construction, in particular to a power transmission line engineering construction progress monitoring method based on three-dimensional model comparison.
Background
Along with the annual increase of the construction scale of the power transmission line, the requirements of China on the quality and the efficiency of the construction of the power transmission line project are higher and higher, and the effective construction progress monitoring method is an important way for improving the quality and the efficiency of the construction of the power transmission line project. At present, the construction progress monitoring of the power transmission line engineering is mainly based on manual monitoring, a worker firstly searches 2D drawing information of a corresponding position, builds a corresponding three-dimensional model in the brain, and then superimposes the model on an engineering site seen by the worker at present, so that the current construction progress is determined through the difference of drawing design and site construction. The construction progress monitoring method not only consumes a great deal of energy of the staff, but also is extremely easy to cause the situation of error in subjective analysis of the staff, so that a new method and a new technology are needed to be introduced to improve the current situation of the construction progress monitoring of the power transmission line engineering.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a three-dimensional model comparison-based power transmission line engineering construction progress monitoring method, which achieves the purpose of power transmission line engineering construction progress monitoring by realizing the difference analysis between a GIM design model and a three-dimensional laser point cloud model.
The invention aims to provide a three-dimensional model comparison-based power transmission line engineering construction progress monitoring method, which considers that a national power grid limited company follows the interaction specification of a three-dimensional design model of a power transmission and transformation project, under the specification, the standard format of the three-dimensional design model of the power transmission and transformation project is GIM, therefore, the invention selects the GIM model as a research object, realizes construction progress monitoring through comparison analysis between the GIM design model and a laser point cloud model, has the advantages of high visualization and automation degree, and adopts the following technical scheme to achieve the purposes:
the power transmission line engineering construction progress monitoring method based on three-dimensional model comparison comprises the following steps:
s1, filtering LAS format laser point cloud data obtained through scanning to remove noise points, filtering ground feature points through point cloud classification, and extracting a laser point cloud model of a power transmission line;
s2, converting the GIM design model into a GIM point cloud model;
s3, superposing a GIM point cloud model and a laser point cloud model of the power transmission line by adopting a point cloud registration algorithm;
s4, analyzing the difference between the GIM point cloud model and the laser point cloud model of the power transmission line, and monitoring the construction progress of the power transmission line engineering.
Preferably, the step S1 includes the following steps:
s1-1, filtering outliers from LAS format laser point cloud data obtained by scanning by adopting a statistical analysis method;
s1-2, filtering out ground object points in LAS format laser point cloud data processed by the S1-1;
s1-3, dividing a tower point cloud and a power line point cloud according to the change characteristics of the tower in the elevation direction;
s1-4, extracting a single power line point cloud by using a power line point cloud and adopting a K-MEANS clustering algorithm;
s1-5, marking the extracted tower point cloud and the single power line point cloud by semantic information;
s1-6, forming a laser point cloud model A by each classified sub-point cloud containing semantic information, and storing the laser point cloud model A and each classified sub-point cloud in an LAS format.
Preferably, the step S2 includes the following steps:
s2-1, reading an entry file project. CBM, traversing a CBM file, a DEV file, a PHM file and a FAM file in a GIM model step by step, and acquiring longitude and latitude, altitude and transformation matrix information of each power device;
s2-2, reading an MOD file or an STL file under the PHM file, acquiring three-dimensional model data information of the sub-equipment, filling the three-dimensional model surface with uniform point clouds, constructing a point cloud model of each sub-equipment, and carrying out point cloud model P on each sub-equipment i Marking by semantic information, wherein i=1..M, M is the total number of point clouds of the sub-equipment;
s2-3, converting the point cloud model of the sub-equipment constructed in S2-2 into the same reference coordinate system by using the longitude and latitude, the altitude and the transformation matrix information read in S2-1, and establishing a complete GIM point cloud model B;
s2-4, the built GIM point cloud model B and the point cloud model P of each piece of sub-equipment are obtained i Are stored in LAS format.
Preferably, the semantic information is marked in the following format:
{ class, point cloud pointer for each class point cloud }.
Preferably, the step S3 includes the following steps:
s3-1, calculating each classified sub-point cloud Q in the laser point cloud model A j Centroid C of (C) 1j (c 1x ,c 1y ,c 1z ) Centroid C 1j (c 1x ,c 1y ,c 1z ) The calculation formula of (2) is as follows:
where n is each classified sub-point cloud Q j Size, x k 、y k And z k Respectively, are point clouds Q j Each point inX, y and z direction coordinates of (c);
above-mentioned classified sub-point cloud Q j Form a point set c1= { C 1j J=1..n }, j=1..n, N is the number of classified point clouds in the laser point cloud model;
s3-2, sequentially selecting sub-point clouds Q of each category in S3-1 in the GIM point cloud model B j Corresponding sub-device point cloud model P j And calculates the point cloud model P of each piece of sub equipment j Centroid C of (C) 2j (c 2x ,c 2y ,c 2z ) Form the point set c2= { C 2j ,j＝1...N}；
S3-3, the point set C1 and the point set C2 form a corresponding point set, wherein the point C in the point set C1 1j And point C in point set C2 2j The two points are corresponding points, and a SVD decomposition method is adopted to solve a transformation matrix t;
s3-4, taking the transformation matrix T as an initial value of an NDT algorithm, and registering the laser point cloud model A and the GIM point cloud model B by adopting the NDT algorithm to obtain a final transformation matrix T;
s3-5, converting the laser point cloud model A into a laser point cloud model A 'by using a conversion matrix T, and superposing the laser point cloud model A' and the GIM point cloud model B at the moment.
Preferably, the step S4 includes the following steps:
s4-1, carrying out voxel grid division on the laser point cloud model A', and calculating the centroid of each voxel grid;
s4-2, using each piece of sub-equipment point cloud P in the GIM point cloud model B i Traversing each sub-device point cloud P as a unit i Each point m of (3) l And searching the laser point cloud model A' for the point m l Centroid c closest to r Where l=1..l, L is the child device point cloud P i Is of a size of (2);
s4-3, at centroid c r Judging whether a point n exists in the laser point cloud model A' in the belonging voxel grid l And point m l The distance between the two points is smaller than the threshold delta, if the two points exist, the two points are called point m l Covering; otherwise, call point m l Uncovered;
s4-4, calculating point clouds P of all the sub-equipment i Coverage η of (2) i ；
S4-5, judging whether each piece of sub-equipment is built or not according to the point cloud coverage rate of each piece of sub-equipment;
s4-6, marking the built sub-equipment point clouds and the sub-equipment point clouds which are not built or are under construction according to the construction conditions of the sub-equipment, and visually displaying the sub-equipment point clouds in a three-dimensional point cloud model mode to monitor the construction progress of the power transmission line engineering.
Preferably, the point cloud model P of each sub-device i Marked with the following semantic information:
{ name of sub-device, model of sub-device, longitude and latitude information of sub-device, point cloud pointer of sub-device }.
Preferably, in S4-1, when the voxel grid division is performed on the laser point cloud model A', the side length of each grid is 0.2m-0.5m; in S4-3, the threshold δ=0.03 m.
Preferably, the coverage η i The calculation formula is as follows:
η i ＝s m /L
wherein: s is(s) m Point cloud P for child device i Is a covered point.
Preferably, in S4-5, the basis for judging whether each piece of sub-equipment is built is as follows:
the invention has the following beneficial effects:
according to the transmission line engineering construction progress monitoring method based on three-dimensional model comparison, the current construction progress of the transmission line engineering site is obtained through comparison analysis between the three-dimensional laser point cloud model representing the construction site and the GIM design model representing the drawing design. The method has the advantages of high visualization and automation degree, overcomes the defects of high operation intensity, high possibility of error and the like of the existing construction progress monitoring method to a certain extent, effectively improves the quality and efficiency of power transmission line engineering construction, provides guarantee for realizing zero defect operation of the power transmission line engineering, and has good application prospect.
Drawings
Fig. 1 is a flow chart of a method for monitoring the construction progress of a power transmission line project based on three-dimensional model comparison.
FIG. 2 is a flowchart of the algorithm of step (1) according to the embodiment of the present invention.
FIG. 3 is a flowchart of the algorithm of step (2) according to the embodiment of the present invention.
FIG. 4 is an exemplary diagram of a GIM design model converted to a GIM point cloud model according to an embodiment of the invention.
FIG. 5 is a block diagram of an algorithm according to step (3) of the present invention.
FIG. 6 is a flowchart of the algorithm of step (4) according to the embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the drawings and examples.
Referring to fig. 1, the method for monitoring the construction progress of the power transmission line engineering based on the three-dimensional model comparison provided by the invention comprises the following steps:
(1) Firstly removing noise points by point cloud filtering on laser point cloud data in an LAS format obtained by scanning, and then filtering ground feature points by point cloud classification to realize extraction of a laser point cloud model of a power transmission line;
(2) Converting the GIM design model into a GIM point cloud model, and providing a data base for registration between the models in the step (3) and comparison between the models in the step (4);
(3) The accurate superposition between the GIM point cloud model and the laser point cloud model of the power transmission line is realized by adopting a point cloud registration algorithm;
(4) And realizing the monitoring of the construction progress of the power transmission line engineering through the difference analysis between the GIM point cloud model and the laser point cloud model of the power transmission line.
Wherein, the step (1) comprises the following steps:
1-1) adopting a ground type or airborne type three-dimensional laser scanner to scan and acquire the original point cloud data of the power transmission line construction site in an LAS format, and adopting a statistical analysis method to filter outliers (prevent noise points from affecting subsequent steps);
1-2) according to LAS standards issued by the American photogrammetry and remote sensing society, in the point cloud of the LAS format, the classification number of the ground points is 2, the classification numbers of the vegetation and building points are 3-9, and accordingly, the points with the classification numbers of 2-9 can be removed to filter the ground feature points;
1-3) dividing the towers and the power line point clouds according to the characteristic that the towers have large change in the elevation direction, and respectively storing each tower and each section of power line point clouds;
1-4) inputting each section of power line point cloud, and extracting a single power line point cloud by adopting a K-MEANS clustering algorithm;
1-5) marking each classified sub-point cloud (comprising a tower point cloud and a power line point cloud) with semantic information, wherein the marking format is as follows:
{ classification category, point cloud pointer of Point cloud for Classification }
1-6) forming a laser point cloud model A by each classified sub-point cloud containing semantic information, and storing the laser point cloud model A and each classified sub-point cloud in an LAS format.
The step (2) comprises the following steps:
2-1) reading an entry file 'project. CBM', obtaining the F1System file name, and then traversing CBM files, DEV files, PHM files and FAM files in the GIM model step by step to obtain longitude and latitude, elevation and transformation matrix information of each power device;
2-2) reading an MOD file or an STL file under the PHM file, obtaining three-dimensional model data information of the sub-devices, filling the model surface with uniform point clouds (if the MOD file is used, constructing a point cloud model according to the obtained model type, model shape, model size and node information, if the STL file is used, filling all triangular patches in the file with uniform point sets), thereby constructing a point cloud model of each sub-device, and constructing a point cloud model P of each sub-device i (i=1..m, M is the total number of child device point clouds) is labeled with the following semantic information:
{ name of sub-device, model number of sub-device, longitude and latitude information of sub-device, point cloud pointer of sub-device }
2-3) converting the sub-equipment point cloud constructed in the step 2-2) to the same reference coordinate system by using the longitude and latitude, the altitude and the transformation matrix information read in the step 2-1), and establishing a complete GIM point cloud model B;
2-4) the built GIM point cloud model B and the point cloud model P of each piece of sub-equipment i Are stored in LAS format.
The step (3) comprises the following steps:
3-1) inputting the classified laser point cloud model A in the step (1), and calculating each classified sub-point cloud Q in the laser point cloud model A j Centroid C (j=1..n, N is the number of classified point clouds in the laser point cloud model) 1j (c 1x ,c 1y ,c 1z ) Centroid C 1j (c 1x ,c 1y ,c 1z ) The calculation formula is as follows:
wherein n is point cloud Q j Size, x k ，y k And z k Respectively, are point clouds Q j X, y and z coordinates of each point in (c).
Above-mentioned classified sub-point cloud Q j Form a point set c1= { C 1j ,j＝1...N}；
3-2) inputting the GIM point cloud model B in the step (2), and sequentially selecting the sub-point clouds Q in each category in the step 3-1) from the GIM point cloud model B j Corresponding sub-device point cloud model P j And calculates the point cloud model P of each piece of sub equipment j Centroid C of (C) 2j (c 2x ,c 2y ,c 2z ) Form the point set c2= { C 2j ,j＝1...N}；
3-3) Point set C1 and Point set C2 constitute a corresponding Point set, i.e., point C in Point set C1 1j And point C in point set C2 2j Corresponding points are mutually corresponding points, and SVD decomposition method is adoptedSolving a transformation matrix t which is a fourth-order matrix;
3-4) taking the transformation matrix T as an initial value of an NDT algorithm, adopting the NDT algorithm to register the laser point cloud model A and the GIM point cloud model B (in order to improve registration accuracy, voxel grids divided by the NDT algorithm are less than 0.5 m) and obtaining a final transformation matrix T;
3-5) transforming the laser point cloud model a into a laser point cloud model a 'using the transformation matrix T, at which point the laser point cloud model a' and the GIM point cloud model B have been precisely superimposed.
The step (4) comprises the following steps:
4-1) inputting a laser point cloud model A ', carrying out voxel grid division on the laser point cloud model A' (dividing voxel grids is used for improving the searching efficiency in the step 4-3), and calculating the centroid of each voxel grid, wherein the side length of each grid is 0.2m-0.5 m.
4-2) inputting the GIM point cloud model B, and using the point clouds P of each piece of sub-equipment in the GIM point cloud model B i Traversing each sub-device point cloud P as a unit i Each point m of (3) l (l=1..l, L is a child device point cloud P i Is determined) and the sum point m is searched in the laser point cloud model A' l Centroid c closest to r I.e. m l Is of the centroid c r A voxel grid;
4-3) at centroid c r Searching whether a point n exists in the laser point cloud model A' in the belonging voxel grid l And point m l The distance between the two is smaller than a threshold delta (the measurement error of the airborne three-dimensional laser scanner is generally within 3cm, so delta is set to be 0.03 m), and if the three-dimensional laser scanner exists, the three-dimensional laser scanner is called a point m l Covering; otherwise, call point m l Is not covered.
4-4) calculating the point cloud P of each piece of sub-equipment i Coverage η of (2) i The calculation formula is as follows:
η i ＝s m /L
wherein: s is(s) m Point cloud P for child device i The number of points covered in (a);
4-5) judging whether each piece of sub-equipment is built according to the point cloud coverage rate of each piece of sub-equipment, wherein the judging basis is as follows:
4-6) marking the built sub-equipment point cloud as green according to the construction condition of each sub-equipment, marking the sub-equipment point cloud which is not built or is being constructed as red, visually displaying the sub-equipment point cloud in a three-dimensional point cloud model form, and generating a construction progress monitoring report, wherein the report content comprises the name, the model, the position information, the construction completion condition and the like of each sub-equipment.
According to the method, the construction progress of a power transmission line engineering site is obtained according to comparison analysis results of a GIM design model and a three-dimensional laser point cloud model, on one hand, original three-dimensional laser point cloud data obtained through scanning are filtered and classified, ground object points are removed, a three-dimensional laser point cloud model is built, on the other hand, the power transmission line GIM design model is converted into the GIM point cloud model, then, accurate superposition between the three-dimensional laser point cloud model and the GIM point cloud model is achieved through a point cloud registration algorithm, finally, whether all electric equipment is built is confirmed through difference detection between the laser point cloud model and the GIM point cloud model, and a construction progress monitoring report is generated.
Examples
As shown in fig. 1, the method for monitoring the construction progress of the power transmission line engineering based on the three-dimensional model comparison in the embodiment comprises the following steps:
(1) The LAS format laser point cloud data obtained through scanning needs to be subjected to point cloud filtering to remove noise points, then ground feature points are filtered through point cloud classification, extraction of a power transmission line laser point cloud model is achieved, a flow is shown in fig. 2, and step (1) comprises the following steps:
1-1) inputting LAS-format power transmission line construction site original point cloud data, and filtering outliers (preventing noise points from affecting the accuracy of the data) by adopting a statistical analysis method;
1-2) according to LAS standards issued by the American photogrammetry and remote sensing society, in the point cloud of the LAS format, the classification number of the ground points is 2, the classification numbers of vegetation and building points are 3-9, and therefore the ground feature points in the original point cloud data can be filtered (the points with the classification numbers of 2-9 are filtered in the embodiment);
1-3) dividing the tower and the power line point cloud according to the characteristic that the tower has larger change in the elevation direction;
1-4) inputting each section of power line point cloud, and extracting a single power line point cloud by adopting a K-MEANS clustering algorithm;
1-5) marking the pole tower point cloud and the power line point cloud with semantic information, wherein the marking format is as follows:
{ classification category, point cloud pointer of Point cloud for Classification }
1-6) forming a laser point cloud model A by each classified sub-point cloud containing semantic information, and storing the laser point cloud model A and each classified sub-point cloud in an LAS format.
(2) The GIM design model is converted into a GIM point cloud model, a data base is provided for registration between the models in the step (3) and comparison between the models in the step (4), the flow is shown in the figure 3, and the step (2) comprises the following steps:
2-1) reading an inlet file 'project. CBM', obtaining an F1System file name, and then traversing CBM files, DEV files, PHM files and FAM files in a GIM model step by step (the GIM files of an overhead transmission line are divided into 5 stages altogether, wherein a first-stage whole line of the F1System comprises a plurality of second-stage segments, a second-stage segment of the F2System comprises a plurality of third-stage strain segments, a third-stage strain segment of the F3System comprises a plurality of fourth-stage equipment groups, and a fourth-stage equipment group of the F4System comprises a plurality of materials, equipment and facilities), so as to obtain longitude and latitude, altitude and transformation matrix information of each power equipment;
2-2) reading an MOD file or an STL file under the PHM file, obtaining three-dimensional model data information of the sub-equipment, filling the model surface with uniform point clouds, thereby constructing a point cloud model of each sub-equipment, and a point cloud model P of each sub-equipment i (i=1..m, M is the total number of sub-device point clouds) is labeled with the following semantic information (labeling with semantic information may facilitate fast search of each sub-device point cloud in subsequent steps):
{ name of sub-device, model number of sub-device, longitude and latitude information of sub-device, point cloud pointer of sub-device }
2-3) converting the sub-equipment point cloud constructed in the step 2-2) to the same reference coordinate system by using the longitude and latitude, the altitude and the transformation matrix information read in the step 2-1), and establishing a complete GIM point cloud model B, wherein the GIM design model of the 220kV power transmission line with two tension resistant sections is converted into a GIM point cloud model effect diagram as shown in fig. 4;
2-4) the built GIM point cloud model B and the point cloud model P of each piece of sub-equipment i Are stored in LAS format.
(3) The accurate superposition between the GIM point cloud model and the laser point cloud model of the power transmission line is realized by adopting a point cloud registration algorithm, an algorithm frame is shown in fig. 5, and the step (3) comprises the following steps:
3-1) inputting the classified laser point cloud model A in the step (1), and calculating each classified sub-point cloud Q in the laser point cloud model A j (j=1..n, N is the number of classified point clouds in the laser point cloud model, mainly the centroid C of the tower point cloud and the power line point cloud 1j (c 1x ,c 1y ,c 1z ) The calculation formula is as follows:
wherein n is point cloud Q j Size, x k 、y k And z k Respectively, are point clouds Q j X, y and z coordinates of each point in (c).
Above-mentioned classified sub-point cloud Q j Form a point set c1= { C 1j ,j＝1...N}；
3-2) inputting the GIM point cloud model B in the step (2), and sequentially selecting the sub-point clouds Q in each category in the step 3-1) from the GIM point cloud model B j Corresponding sub-device point cloud model P j (i.e., AND Q in GIM Point cloud model B) j Corresponding pole tower and power line point cloud), and calculates a point cloud model P of each sub-device j Centroid C of (C) 2j (c 2x ,c 2y ,c 2z ) Form the point set c2= { C 2j ,j＝1...N}；
3-3) Point set C1 and Point set C2 constitute a corresponding Point set, i.e., point C in Point set C1 1j And point C in point set C2 2j Points corresponding to each other (due to C 1j And C 2j Respectively solving a transformation matrix t which is a fourth-order matrix by adopting an SVD decomposition method for centroids of corresponding classified sub-point clouds in the laser point cloud model A and the GIM point cloud model B;
3-4) taking the transformation matrix T as an initial value of an NDT algorithm, and registering the laser point cloud model A and the GIM point cloud model B by adopting the NDT algorithm to obtain a final transformation matrix T;
3-5) transforming the laser point cloud model a into a laser point cloud model a 'using the transformation matrix T, at which point the laser point cloud model a' and the GIM point cloud model B have been precisely superimposed.
(4) The power transmission line engineering construction progress monitoring is realized through the difference analysis between the power transmission line GIM point cloud model and the laser point cloud model, the algorithm flow is shown in fig. 6, and the step (4) comprises the following steps:
4-1) inputting a laser point cloud model A ', carrying out voxel grid division on the laser point cloud model A', and calculating the centroid of each voxel grid, wherein the side length of each grid is 0.25 m.
4-2) inputting the GIM point cloud model B, and using the point clouds P of each piece of sub-equipment in the GIM point cloud model B i Traversing each sub-device point cloud P as a unit i Each point m of (3) l (l=1..l, L is a child device point cloud P i Is determined) and the sum point m is searched in the laser point cloud model A' l Centroid c closest to r I.e. m l Is of the centroid c r Located voxel grid (dividing the voxel grid first and judging the point m l The voxel grids which belong to the voxel grids are searched in the step 4-3), so that the searching efficiency can be improved;
4-3) at centroid c r Searching whether a point n exists in the laser point cloud model A' in the belonging voxel grid l And point m l The distance between the two is smaller than a threshold delta (the measurement error according to the airborne three-dimensional laser scanner is generally within 3cm, the error of the ground three-dimensional laser scanner is within 1cm, delta is set to be 0.03 m), and if the three-dimensional laser scanner exists, the three-dimensional laser scanner is called a point m l Covering; otherwise, call point m l Is not covered.
4-4) calculating the point cloud P of each piece of sub-equipment i Coverage η of (2) i The calculation formula is as follows:
η i ＝s m /L (2)
wherein: s is(s) m Point cloud P for child device i The number of points covered in (a);
4-5) judging whether each piece of sub-equipment is built according to the point cloud coverage rate of each piece of sub-equipment, wherein the judging basis is as follows:
4-6) marking the built sub-equipment point cloud as green, marking the sub-equipment point cloud which is not built or is being constructed as red according to the construction condition of each sub-equipment, visually displaying the sub-equipment point cloud in a three-dimensional point cloud model, and generating a construction progress monitoring report.
The invention effectively overcomes the defects of high operation intensity, high error probability and the like in the current construction progress monitoring, has the advantages of three-dimensional visualization, high automation degree and the like, and promotes the fusion application of the BIM technology and the three-dimensional laser point cloud technology in the field of power transmission and transformation engineering construction.
Claims (8)
1. The power transmission line engineering construction progress monitoring method based on three-dimensional model comparison is characterized by comprising the following steps of:
s1, filtering LAS format laser point cloud data obtained through scanning to remove noise points, filtering ground feature points through point cloud classification, and extracting a laser point cloud model of a power transmission line;
s2, converting the GIM design model into a GIM point cloud model;
s3, superposing a GIM point cloud model and a laser point cloud model of the power transmission line by adopting a point cloud registration algorithm;
s4, analyzing the difference between the GIM point cloud model and the laser point cloud model of the power transmission line, and monitoring the construction progress of the power transmission line engineering;
the step S3 comprises the following steps:
s3-1, calculating a laser point cloud modelASub-point cloud of each category in (3)Is->Centroid of shapeThe calculation formula of (2) is as follows:
in the middle ofnSub-point cloud for each categorySize of->、/>And->Point clouds->X, y and z coordinates of each point in (a);
above-mentioned classified sub-point cloudsForm a set of points->，/>，NThe number of classified point clouds in the laser point cloud model;
s3-2, in GIM point cloud modelBSequentially selecting sub-point clouds of each category in S3-1Corresponding sub-equipment point cloud modelAnd calculating the point cloud model of each piece of sub-equipment>Is->Constitutes a dot set->；
S3-3, point setAnd Point set->Form a corresponding point set, wherein the point set +.>Points of->And Point set->Points of->Corresponding points are mutually used for solving the transformation matrix by adopting SVD decomposition method>；
S3-4, transform matrixAs an initial value of the NDT algorithm, the NDT algorithm is adoptedMethod registration laser point cloud modelAAnd GIM point cloud modelBObtaining a final transformation matrix->；
S3-5, using a transformation matrixModeling laser point cloudAConversion into a laser point cloud model->At this time, laser point cloud model +.>And GIM point cloud modelBStacking;
the step S4 comprises the following steps:
s4-1, for laser point cloud modelDividing voxel grids, and calculating the centroid of each voxel grid;
s4-2, using GIM point cloud modelBEach sub-device point cloud in (a)Traversing each sub-device point cloud as a unit +.>Each point in (a)And in the laser point cloud model->Search and Point->The centroid nearest to>Wherein->Point cloud for sub-deviceIs of a size of (2);
s4-3, at the centroidJudging the laser point cloud model in the belonging voxel grid>Whether or not there is a dot->And (4) point->The distance between them is smaller than the threshold +.>If present, the point is->Covering; otherwise, call point->Uncovered;
s4-4, calculating point clouds of all the sub-equipmentCoverage of->；
S4-5, judging whether each piece of sub-equipment is built or not according to the point cloud coverage rate of each piece of sub-equipment;
s4-6, marking the built sub-equipment point clouds and the sub-equipment point clouds which are not built or are under construction according to the construction conditions of the sub-equipment, and visually displaying the sub-equipment point clouds in a three-dimensional point cloud model mode to monitor the construction progress of the power transmission line engineering.
2. The method for monitoring the construction progress of the power transmission line project based on the three-dimensional model comparison according to claim 1, wherein the step S1 comprises the following steps:
s1-1, filtering outliers from LAS format laser point cloud data obtained by scanning by adopting a statistical analysis method;
s1-2, filtering out ground object points in LAS format laser point cloud data processed by the S1-1;
s1-3, dividing a tower point cloud and a power line point cloud according to the change characteristics of the tower in the elevation direction;
s1-4, extracting a single power line point cloud by using a power line point cloud and adopting a K-MEANS clustering algorithm;
s1-5, marking the extracted tower point cloud and the single power line point cloud by semantic information;
s1-6, forming a laser point cloud model by each classified sub-point cloud containing semantic informationAAnd model the laser point cloudAAnd storing each classified sub-point cloud in an LAS format.
3. The method for monitoring the construction progress of the power transmission line project based on the three-dimensional model comparison according to claim 2, wherein the step S2 comprises the following steps:
s2-1, reading an entry file project. CBM, traversing CBM files, DEV files, PHM files and FAM files in the GIM model step by step, and obtaining longitude and latitude, altitude and transformation matrix information of each power device
S2-2, reading an MOD file or an STL file under the PHM file, obtaining three-dimensional model data information of the sub-equipment, filling the three-dimensional model surface with uniform point clouds, constructing a point cloud model of each sub-equipment, and obtaining the point cloud model of each sub-equipmentMarking by semantic information, and (E) is (are) added>，MThe total number of point clouds of the sub-equipment;
s2-3, converting the point cloud model of the sub-equipment constructed in S2-2 into the same reference coordinate system by using the longitude and latitude, the altitude and the transformation matrix information read in S2-1, and establishing a complete GIM point cloud modelB；
S2-4, constructing the GIM point cloud modelBAnd point cloud model of each piece of sub-equipmentAre stored in LAS format.
4. The three-dimensional model comparison-based power transmission line engineering construction progress monitoring method according to claim 2 or 3, wherein the semantic information is marked in the following format:
{ class, point cloud pointer for each class point cloud }.
5. The three-dimensional model comparison-based power transmission line engineering construction progress monitoring method according to claim 1 or 3, wherein the point cloud model of each piece of sub-equipmentMarked with the following semantic information:
{ name of sub-device, model of sub-device, longitude and latitude information of sub-device, point cloud pointer of sub-device }.
6. The three-dimensional model comparison-based power transmission line engineering construction progress monitoring method according to claim 1, wherein in S4-1, a laser point cloud model is obtainedWhen voxel grid division is carried out, the side length of each grid is 0.2m-0.5m; in S4-3, threshold->。
7. The three-dimensional model comparison-based power transmission line engineering construction progress monitoring method according to claim 1, wherein coverage rate isThe calculation formula is as follows:
wherein:point cloud for child device>Is a covered point.
8. The three-dimensional model comparison-based power transmission line engineering construction progress monitoring method according to claim 1, wherein in S4-5, the basis for judging whether each piece of equipment is built is as follows:
。
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