A multi-objective optimization method for the spatial layout of multiple targets at 3D laser scanning stations with complex structures

Abstract

This application relates to the field of three-dimensional laser scanning engineering surveying technology, and discloses a multi-objective optimization method for the spatial layout of three-dimensional laser scanning stations with complex structures, including the following steps: identifying and extracting the boundary point cloud of the target; solving the coordinates of the center point of the target; calculating the station monitoring accuracy model for each station layout; establishing a point cloud overlap model; establishing a measurement cost consumption model for each station layout; obtaining a three-dimensional laser scanning station layout optimization mathematical model; using the normalization method to transform the three-dimensional laser scanning station layout optimization problem into a single-objective optimization problem to solve for the optimal distribution of stations; defining the objective function for station layout optimization and performing iterative calculations; obtaining the optimal spatial layout of stations with a relatively high value of a certain objective function under a given spatial measurement area. This application realizes an optimal planning method for the station layout path based on multi-objective optimization analysis of station accuracy, point cloud overlap, and measurement cost, and comprehensively considers multiple influencing parameters.

Description

Technical Field

[0001] The present application relates to the technical field of three-dimensional laser scanning engineering surveying, and particularly relates to a multi-objective optimization method for the spatial layout of survey stations in three-dimensional laser scanning of complex structures. Background Art

[0002] Three-dimensional laser scanning technology can quickly and completely obtain the spatial three-dimensional information of the above-mentioned monitoring objects, and has been widely applied in complex structure engineering. When three-dimensional laser scanning measures complex engineering structures, based on the collaborative effect of multiple stations to obtain complete point cloud data of the structure, as the number of survey stations increases, the measurement range will expand, inevitably causing point cloud data overlap, resulting in redundant point cloud data, leading to non-uniformity in the spatial distribution of point cloud data on the surface of the structure, and at the same time, the measurement cost and data processing time will also increase. And different layouts of survey stations also have a certain impact on the measurement accuracy of three-dimensional laser scanning. Therefore, the optimization of the spatial layout of survey stations of three-dimensional laser scanners is an important problem faced during its application.

[0003] In order to make the spatial layout of survey stations of three-dimensional laser scanners more reasonable, usually, more experienced surveyors make the spatial layout of survey stations more reasonable according to their own measurement experience, but the subjectivity is strong and the measurement quality cannot be guaranteed.

[0004] Therefore, relevant optimization research has been carried out on the layout of survey stations for three-dimensional laser scanning. At present, only the point cloud overlap degree is used as the optimization target for the layout of survey stations. This method can only ensure fewer survey stations and less measurement time, but cannot quantitatively evaluate the measurement accuracy of each survey station, and the accuracy of the collected point cloud cannot be guaranteed. Therefore, how to achieve high-precision, complete and efficient point cloud data acquisition for engineering structures with complex spatial characteristics is an urgent problem to be solved at present. Summary of the Invention

[0005] By providing a multi-objective optimization method for the spatial layout of survey stations in three-dimensional laser scanning of complex structures in the embodiments of the present application, the technical problem in the prior art that only the point cloud overlap degree is used as the optimization target for the layout of survey stations, and the measurement accuracy of each survey station cannot be quantitatively evaluated, and the accuracy of the collected point cloud cannot be guaranteed is solved. An optimal planning method for the layout of survey stations based on multi-objective optimization analysis of survey station accuracy, point cloud overlap degree and measurement cost is realized, comprehensive consideration of multiple influencing parameters is achieved, and rapid positioning of the optimal survey station is realized.

[0006] The embodiment of the present application provides a multi-target optimization method for the spatial layout of a three-dimensional laser scanning station with a complex structure, including the following steps: obtaining the original point cloud of the target at several important monitoring parts under the same station, and identifying and extracting the boundary point cloud of the target; solving the coordinates of the target center point by using a weighted least-squares circle curve fitting method based on the non-uniform distribution characteristics of the target point cloud; calculating the distance mean error of several target center points under any station, and calculating the station monitoring accuracy model for each station layout; arbitrarily selecting stations from all stations for free combination, and calculating the point cloud overlap degree for each station layout on the premise of ensuring the integrity of the structural point cloud, and establishing a point cloud overlap degree model; establishing a measurement cost consumption model for each station layout based on the number of stations and the amount of point cloud data; establishing a target mathematical relationship model composed of station monitoring accuracy, point cloud overlap degree, and measurement cost consumption, and obtaining an optimized mathematical model for the three-dimensional laser scanning station layout; using the normalization method to transform the three-dimensional laser scanning station layout optimization problem into a single-target optimization problem to solve the optimal distribution of the station; assigning a weight to each target, defining an objective function for the station layout optimization, and performing iterative calculations; obtaining an optimal spatial layout of the station with a higher objective function value under a given spatial measurement area.

[0007] Further, the step of identifying and extracting the boundary point cloud of the target includes: calculating the initial value D0 of the circumscribed circle diameter of the target point cloud based on the fact that the circumscribed circle diameter is related to the point cloud density of the recognition point area; selecting a target original point cloud A from the original point clouds of the targets at m important monitoring parts i , selecting any two points k1(x1,y1) and k2(x2,y2) from the point cloud set, generating a rolling circle with a diameter of the rolling circle D0 passing through points k1 and k2, and the rolling circle with a diameter as the initial value of the circumscribed circle diameter of the target point cloud passes through point l n and point k m and circles O1 and O2 with a diameter of D0; calculating the relative positions of the remaining points with respect to the above circles O1 and O2 one by one. If there is point cloud on the edge or inside of circles O1 and O2, then points k1 and k2 are non-crack boundary points. Conversely, if there are no other points on the edge and inside of at least one of circles O1 and O2, then points k1 and k2 are crack boundary points, and finally obtaining the set of target boundary point clouds.

[0008] Further, the steps of solving the target center point coordinates by the weighted least squares circle curve fitting method based on the non-uniform distribution characteristics of the target point cloud include: finding the target position in the point cloud data of the structure surface, intercepting the point cloud data of the target position and its surrounding small area, and exporting the coordinate points of the target and its surrounding small area; using the corresponding point cloud filtering algorithm to denoise the point cloud in this area; using the outlier detection algorithm to remove the edge peripheral point cloud different from the target reflection characteristics in this area, and only leaving the target surface point cloud data; using the principal component analysis method to transform the three-dimensional point cloud data of the target surface in the space coordinate system into the plane where the target is located; proposing the edge contour line of the target point cloud, and fitting a circle based on the weighted least squares circle curve fitting method of non-uniform sampling of the target point cloud to solve the center coordinate of the plane circle; using the principal component analysis method to transform the center coordinate of the plane circle into the initial three-dimensional coordinate system.

[0009] Further, the edge contour line of the target point cloud is proposed using the point cluster edge detection function in Matlab.

[0010] Further, the method further includes deleting invalid observation points before freely combining any selected stations from all stations.

[0011] Further, the steps of deleting invalid observation points include: obtaining the point cloud data sets of each trial measurement station, denoted as S j (j = 1, 2, 3…n); denoting the sample points in the point cloud data set S j as

[0012] p k {(x k , y k , z k ), k = 1, 2, 3…m}, when simultaneously satisfying x min ≤x k ≤x max , y min ≤y k ≤y max and z min ≤z k ≤z max the three conditions, the sample point P k (x k , y k , z k ) is retained as a valid point, otherwise, if at least one of the above three conditions is not satisfied, the sample point P k (x k , y k , z k ) is regarded as an invalid point and screened out.

[0013] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: Analyze the point cloud overlap degree and measurement cost, respectively construct a point cloud overlap degree model and a measurement cost model for each station layout, and finally establish a multi-objective mathematical optimization model composed of station accuracy, point cloud overlap degree, and measurement cost from three levels of influencing factors, optimization objectives, and optimization means. By assigning certain weights to each objective, an optimal layout of the station space with a higher objective function value can be obtained for a given spatial measurement area, solving problems such as point cloud redundancy and high measurement cost caused by multi-station collaborative work. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flowchart of a multi-objective optimization method for the spatial layout of a three-dimensional laser scanning station for a complex structure in the embodiments of the present application;

[0015] Figure 2 It is a schematic diagram of the original point cloud distribution of the target in the embodiments of the present application;

[0016] Figure 3 It is a schematic diagram of the identification and extraction of the edge point cloud of the target in the embodiments of the present application;

[0017] Figure 4 It is a schematic diagram of calculating the weight factor of the sampling points with non-uniform distribution of the edge point cloud of the target in the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The overall idea of the technical solution provided by the present application is as follows:

[0019] The embodiments of the present application provide a multi-objective optimization method for the spatial layout of a three-dimensional laser scanning station for a complex structure. The method includes: obtaining the original point cloud of the target at several important monitoring parts under the same station, identifying and extracting the boundary point cloud of the target; solving the coordinates of the target center point by using the weighted least squares circle curve fitting method based on the non-uniform distribution characteristics of the target point cloud; calculating the distance mean error of several target center points under any station, and calculating the station monitoring accuracy model for each station layout; freely combining stations selected from all stations, and calculating the point cloud overlap degree of each station layout on the premise of ensuring the integrity of the structural point cloud, and establishing a point cloud overlap degree model; establishing a measurement cost consumption model for each station layout based on the number of stations and the amount of point cloud data; establishing an objective mathematical relationship model composed of station monitoring accuracy, point cloud overlap degree, and measurement cost consumption to obtain an optimized mathematical model for the three-dimensional laser scanning station layout; using the normalization method to transform the three-dimensional laser scanning station layout optimization problem into a single-objective optimization problem to solve the optimal distribution of the stations; assigning a weight to each objective, defining an objective function for the station layout optimization, and performing iterative calculations; obtaining an optimal layout of the station space with a higher objective function value for a given spatial measurement area.

[0020] To make the above basic method of the embodiments of the present application more obvious and understandable, the following will give a detailed description of the specific embodiments of the present application with reference to the accompanying drawings.

[0021] Figure 1 It is a multi-target optimization method for the spatial layout of a complex structure three-dimensional laser scanning station in the embodiments of the present application, which will be described in detail through the following specific steps.

[0022] S1. Obtain the original point clouds of the targets at several important monitoring parts under the same station (as shown in Figure 2 ), and identify and extract the boundary point clouds of the targets (as shown in Figure 3 ).

[0023] In specific implementation, extract the original point clouds of the targets at m important monitoring parts under the same station, denoted as P i (i = 1, 2, 3,..., m). Based on the AlphaShapes edge detection algorithm, identify and extract the boundary point clouds of the targets, and denote the set of the boundary point clouds of the targets as P′ i .

[0024] The steps of identifying and extracting the boundary point clouds of the targets include:

[0025] S11. Based on that the diameter of the circumscribed circle is related to the point cloud density of the recognition point area, calculate the initial value D0 of the diameter of the circumscribed circle of the target point cloud.

[0026] Define the calculation formula for the initial value D0 of the diameter of the circumscribed circle of the target point cloud as:

[0027]

[0028] Among them, S Target is the surface area of the circular target, P Target is the total number of point clouds of the sample point cloud in the circular target area, is an empirical value obtained through experiments.

[0029] S12. Select an original point cloud A of a target from the original point clouds of the targets at m important monitoring parts i . Select any two points k1(x1, y1) and k2(x2, y2) in the point cloud set, and generate a rolling circle with a diameter of D0, which is the rolling circle of the circumscribed circle of the target point cloud, passing through point l n and point k m and circles O1 and O2 with a diameter of D0.

[0030] The equations of circles O1 and O2 passing through point l n and point k m and with a diameter of D0 are:

[0031]

[0032]

[0033] Wherein, (x o , y o ) is the coordinate of the circumcenter of the circle passing through points k1(x1, y1) and k2(x2, y2), and there are two circumcircles passing through the two points.

[0034] S13. Calculate the relative positions of the remaining points and the above-mentioned circles O1 and O2 one by one. If there are point clouds on the edge or inside of circles O1 and O2, then points k1 and k2 are non-crack boundary points. On the contrary, if there are no other points on the edge and inside of at least one of circles O1 and O2, then points k1 and k2 are crack boundary points, and finally the set of target boundary point clouds is obtained.

[0035] Identify and extract the set of circular target boundary point clouds P′ i {(x i , y i ), k = 1, 2, 3, …, m′}.

[0036] S2. Solve the coordinates of the target center point by using the weighted least squares circle curve fitting method based on the non-uniform distribution characteristics of the target point cloud.

[0037] In a specific implementation, the weighted least squares circle curve fitting method based on the non-uniform distribution characteristics of the target point cloud includes:

[0038] First, for the set of circular target boundary point clouds P′ i {(x i , y i ), k = 1, 2, 3, …, m′}, construct a weighted error objective function to seek the best expression of the fitting circle curve, so that the sum of the distances from all points in the sample set to the circle curve is the smallest, that is, find the center coordinates (a, b) and the radius R to make the error objective function take the minimum. At the same time, in order to reflect the influence of the non-uniform characteristics of the target edge point cloud on the minimum error objective function, introduce the non-uniformity weight factor λ of the target edge point cloud. The optimized error objective function G can be expressed as:

[0039]

[0040] Wherein, λ i is the non-uniformity weight factor of each point on the target edge, and the center coordinates (a, b) are:

[0041]

[0042] The circle radius is:

[0043]

[0044] Simultaneously satisfy B 2 +C 2 -4AD = 1 error objective function constraint condition.

[0045] Then, determine the non-uniformity weight factor λ of the target edge point cloud.

[0046] Determining the non-uniform distribution weight factor of the target reflector circumferential point cloud is the key to fitting the circular curve equation based on the weighted Pratt method. For example, Figure 4 It is a schematic diagram for calculating the weight factor of the sampling points of the non-uniform distribution of the target edge point cloud.

[0047] The method for confirming the non-uniform distribution weight factor of the target reflector circumferential point cloud is as follows:

[0048] ① When m' edge point clouds in the target point cloud data are completely uniformly distributed along its circular contour, the weight factor of the sampling point is defined as λ1 = λ2 =... = λ m′ = 1.

[0049] ② When there are multiple overlapping point clouds at the same position of the target edge point cloud, that is, when there are multiple point cloud coordinate values that are equal, the weight factor values of each point are the same.

[0050] ③ When m' edge points in the target point cloud data are non-uniformly distributed, corresponding weight factors are assigned to each point, and the weight factor calculation formula is defined as follows:

[0051]

[0052] In the formula, m' is the total number of edge point clouds, and α i is the central angle corresponding to point i, which can be calculated by the following formula:

[0053]

[0054] In the formula, α i-1,i , α i,i+1 are the central angles formed by point i and its two adjacent points on both sides with the center of the circle.

[0055] Next, solve the least squares error objective function improved based on the non-uniform distribution of the target point cloud

[0056] Rewrite the optimized error objective function G into matrix form:

[0057] G = P T (M T M)P

[0058] In the formula:

[0059] P = [A, B, C, D] T

[0060]

[0061] Rewrite the constraint condition B of the error objective function 2 +C 2 -4AD = 1 into matrix form:

[0062]

[0063] Finally, introduce the Lagrange multiplier to solve the problem of finding the minimum value of the error objective function. The error objective function with the Lagrange multiplier introduced can be expressed as:

[0064] G(A, η) = P T (M T M)P - η(P T QP - 1)

[0065] Finally, solve the minimum value problem of the above matrix.

[0066] In a specific implementation, the steps of solving the target center point coordinates by the weighted least squares circle curve fitting method based on the non-uniform distribution characteristics of the target point cloud include the following steps:

[0067] S21. Find the target position in the point cloud data on the surface of the structure, intercept the point cloud data of the target position and its surrounding small area, and export the coordinate points of the target and its surrounding small area.

[0068] S22. Use the corresponding point cloud filtering algorithm to perform noise reduction processing on the point cloud in this area.

[0069] S23. Use the outlier detection algorithm to remove the edge peripheral point cloud that is different from the reflection characteristics of the target in this area, and only leave the point cloud data on the surface of the target.

[0070] S24. Use the principal component analysis method to transform the three-dimensional point cloud data on the surface of the target in the space coordinate system into the plane where the target is located.

[0071] S25. Use the point cluster edge detection function in Matlab to extract the edge contour line of the target point cloud, and fit a circle based on the weighted least squares circle curve fitting method of non-uniform sampling of the target point cloud, and solve the center coordinate of the plane circle.

[0072] S26. Use the principal component analysis method to transform the center coordinate of the plane circle into the initial three-dimensional coordinate system.

[0073] To quantitatively express the station accuracy based on the target center coordinates, assume that there are m targets arranged under the same station, and the spatial absolute coordinates of the target center points extracted and calculated from the point cloud data on the surface of the structure measured at the same station are (a i , b i,c i ) i = 1, 2, 3, …, m. At the same time, set up a total station on the geoid to measure the absolute coordinates of the center positions of each target as (a′ i , b′ i , c′ i ) i = 1, 2, 3, …, m. Then the mean square error of distances of the m target center points under this measuring station is defined as:

[0074]

[0075] Assume that the number of measuring stations under each measuring station layout is t. Then the monitoring accuracy under this measuring station layout is defined as:

[0076]

[0077] Define the expression of the normalized accuracy of the measuring station based on the target center coordinates as:

[0078]

[0079] In the formula: κ is the mean square error of distances of the target center points of the measuring station, κ lim is the required value of engineering survey accuracy, which is determined according to the "Code for Engineering Survey".

[0080] S3, calculate the mean square error of distances of several target center points under any measuring station, and calculate the measuring station monitoring accuracy model under each measuring station layout.

[0081] S4, arbitrarily select measuring stations from all measuring stations for free combination, and calculate the point cloud overlap degree under each measuring station layout on the premise of ensuring the integrity of the structural point cloud, and establish a point cloud overlap degree model.

[0082] In the specific implementation, based on the spatial configuration of the measured foundation pit structure, obtain the set of areas to be scanned. For this set, list the following optimization methods for reducing the point cloud overlap degree of the measuring stations:

[0083] (1) Determine the set of spatial areas S0 to be measured for the foundation pit.

[0084] Set the set of areas projected by the foundation pit in the xoy plane, and then determine the elevation interval according to the depth of the foundation pit and the height of surrounding buildings, so as to determine the set of spatial areas of the structure of the foundation pit to be measured, denoted as S0. Any point Pi(x i , y i , z i ) in the set S0 satisfies:

[0085]

[0086] In the formula, x min , x max , y min , ymax 、z min 、z max The scope is determined by the local space of the foundation pit.

[0087] (2) Invalid observation points are deleted.

[0088] Since the range of the 3D laser scanner is larger than that of the foundation pit area, it is inevitable that there will be many invalid points in the measured point cloud data that deviate from the main object to be measured. These invalid observation points need to be eliminated to reduce the impact on the accuracy of the subsequent foundation pit point cloud model reconstruction. The specific operation is as follows:

[0089] Obtain the cloud data set of each test site, denoted as S j (j=1,2,3…n), for each test site cloud data set S j The data is pre-processed to filter out the point cloud data outside the foundation pit structure space area. The specific screening method is as follows:

[0090] The sample points in the test site cloud dataset Sj are recorded as:

[0091] p k {(x k ,y k ,z k ),k=1,2,3…m},

[0092] When x min ≤x k ≤x max 、y min ≤y k ≤y max and z min ≤z k ≤z max When the three conditions are met, the sample point P k (x k ,y k , z k ) is a valid point and is retained. On the contrary, if at least one of the above three conditions is not met, then the sample point P k (x k ,y k , z k ) are considered as invalid points and then filtered out. The pre-processed point cloud dataset S is obtained. j ′.

[0093] (3) Cloud data stitching from multiple measurement sites.

[0094] Select t points from the n test site cloud data sets for point cloud data splicing, where t≤n, and obtain the spliced point cloud data set S l ′, if S lIf ′≥S0, the requirement is met; otherwise, it is not met and discarded. Denote the set of test stations that meet the conditions as Q. Calculate the point cloud data set S after splicing. l Calculate the point cloud overlap degree in S l ′. Denote the total number of coordinate points in S l ′ as λ, and the number of coordinate points with overlapping coordinate values as λ

[0095]

[0096] S5. Based on the number of stations and the amount of point cloud data, establish a measurement cost consumption model for each station layout.

[0097] The measurement cost consumption model for each layout is shown in the following formula:

[0098]

[0099] In the formula, N act is the actual number of stations used in a certain station layout, N max is the maximum number of stations when completing the measurement task, T act and T max are the times corresponding to N act and N max respectively.

[0100] S6. Establish a target mathematical relationship model composed of station monitoring accuracy, point cloud overlap degree, and measurement cost consumption, and obtain a three-dimensional laser scanning station layout optimization mathematical model.

[0101] S7. Use the normalization method to transform the three-dimensional laser scanning station layout optimization problem into a single-objective optimization problem to solve for the optimal distribution of stations.

[0102] S8. Assign a weight to each target, define the objective function for station layout optimization, and perform iterative calculations.

[0103] The objective function for station layout optimization can be defined as:

[0104] f = K₁O₁ + K₂O₂ + K₃O₃

[0105] In the formula, K₁, K₂, and K₃ respectively represent the weights of each target, and satisfy K₁ + K₂ + K₃ = 0.

[0106] S9. Obtain an optimal spatial layout of stations with a relatively high objective function value in a given spatial measurement area.

[0107] In summary, through the above specific implementation methods, the boundary point cloud of the circular target is first identified and extracted, and a weighted least squares circle curve fitting method based on the non-uniform distribution characteristics of the target point cloud is proposed. By considering the non-uniform distribution characteristics of the actual engineering target point cloud, the non-uniformity weight factor λ of the target edge point cloud is defined, and the three-dimensional coordinates of the center point of the circular target are obtained by solving. Then, it is compared with the target center coordinates measured by the total station, the measurement accuracy under each station layout of the three-dimensional laser scanning is calculated, and the station accuracy model under each station layout is established.

[0108] Then, the point cloud overlap degree and measurement cost are analyzed, and the point cloud overlap degree model and measurement cost model under each station layout are constructed respectively. Finally, a multi-objective mathematical optimization model composed of station accuracy, point cloud overlap degree and measurement cost is established from three levels of influencing factors, optimization objectives and optimization means. By assigning a certain weight to each objective, an optimal layout of the station space with a higher objective function value is obtained for a given spatial measurement area, so as to solve the problems of point cloud redundancy and high measurement cost caused by multi-station collaborative work.

[0109] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a system for the functions specified in one block or multiple blocks.

[0111] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction system, and the instruction system implements in the process Figure 1 one process or multiple processes and / or blocksFigure 1 The functions specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one or more processes and / or boxes Figure 1 one or more processes and / or boxes Figure 1 one or more boxes. Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0113] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A multi-objective optimization method for the spatial layout of 3D laser scanning stations with complex structures, characterized in that, It includes the following steps: Obtain the original point clouds of the targets at several important monitoring positions under the same measuring station, and identify and extract the boundary point clouds of the targets; Solve the coordinates of the target center point by using the weighted least squares circle curve fitting method based on the non-uniform distribution characteristics of the target point clouds; Calculate the mean square error of the distances between several target center points under any measuring station, and calculate the measuring station monitoring accuracy model for each measuring station layout; Arbitrarily select measuring stations from all measuring stations for free combination, and calculate the point cloud overlap degree for each measuring station layout on the premise of ensuring the integrity of the structural point clouds, and establish a point cloud overlap degree model; Establish a measurement cost consumption model for each measuring station layout based on the number of measuring stations and the amount of point cloud data; Establish a target mathematical relationship model composed of the measuring station monitoring accuracy, the point cloud overlap degree, and the measurement cost consumption, and obtain the three-dimensional laser scanning measuring station layout optimization mathematical model; Use the normalization method to transform the three-dimensional laser scanning measuring station layout optimization problem into a single-objective optimization problem to solve the optimal distribution of the measuring stations; Assign a weight to each target, define the measuring station layout optimization objective function, and perform iterative calculations; Obtain the optimal spatial layout of the measuring stations with a relatively high value of a certain objective function under the given spatial measurement area.

2. The multi-objective optimization method for the spatial layout of multiple targets at a 3D laser scanning station with a complex structure as claimed in claim 1, wherein, The steps of identifying and extracting the boundary point clouds of the targets include: Based on the fact that the circumscribed circle diameter is related to the point cloud density of the identified point area, calculate the initial value D0 of the circumscribed circle diameter of the target point cloud; Select a target original point cloud A from the target original point clouds at m important monitoring positions i , select any two points k1(x1, y1) and k2(x2, y2) from the point cloud set, generate a circle with a diameter of D0 passing through points k1 and k2, and pass through point l n (x m， y m ) and point k m (x m， y m ) and circles O1 and O2 with a diameter of D0; Calculate the relative positions of the remaining points and the above circles O1 and O2 one by one. If there are point clouds on the edges or inside of circles O1 and O2, then points k1 and k2 are non-crack boundary points; otherwise, if there are no other points on the edges and inside of at least one of circles O1 and O2, then points k1 and k2 are crack boundary points, and finally obtain the set of target boundary point clouds.

3. The multi-objective optimization method for spatial layout of multiple targets at a 3D laser scanning station with a complex structure as described in claim 1, characterized in that The steps of solving the coordinates of the target center point by using the weighted least squares circle curve fitting method based on the non-uniform distribution characteristics of the target point clouds include: Find the position of the target in the point cloud data of the structure surface, intercept the point cloud data of the position where the target is located and its surrounding small area, and export the coordinate points of the target and its surrounding small area; Use the corresponding point cloud filtering algorithm to denoise the point cloud in this area; Use the outlier detection algorithm to remove the edge peripheral point clouds different from the reflection characteristics of the target in this area, and only leave the point cloud data on the target surface; Use the principal component analysis method to transform the three-dimensional point cloud data on the target surface in the space coordinate system into the plane where the target is located; Propose the edge contour line of the target point cloud, and fit a circle by using the weighted least squares circle curve fitting method based on the non-uniform sampling of the target point cloud, and solve the center coordinates of the plane circle; Use the principal component analysis method to transform the center coordinates of the plane circle into the initial three-dimensional coordinate system.

4. The multi-objective optimization method for the spatial layout of a complex structure three-dimensional laser scanning station according to claim 3, characterized in that, Use the point cluster edge detection function in Matlab to propose the edge contour line of the target point cloud.

5. A multi-objective optimization method for the spatial layout of multiple targets at a 3D laser scanning station with a complex structure as claimed in claim 1, characterized in that, The method also includes deleting invalid observation points before arbitrarily selecting measuring stations from all measuring stations for free combination.

6. The multi-objective optimization method for spatial layout of multiple targets at a 3D laser scanning station with a complex structure as described in claim 5, characterized in that, The steps of deleting invalid observation points include: Obtain the point cloud data sets of each test site, denoted as S j (j = 1, 2, 3…n); Record the trial measurement site cloud data set S j The sample points in it are p k {(x k ,y k ,z k ), k = 1, 2, 3…m}. When the three conditions of x min ≤x k ≤x max , y min ≤y k ≤y max and z min ≤z k ≤z max are satisfied simultaneously, the sample point P k (x k ,y k ,z k ) is retained as a valid point. Conversely, if at least one of the above three conditions is not satisfied, the sample point P k (x k ,y k ,z k ) is regarded as an invalid point and screened out.

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