Internal batch denoising method and system for shield tunnel point cloud model

Abstract

The invention discloses an internal batch denoising method and system for a shield tunnel point cloud model, and relates to the technical field of shield tunnel forming quality detection.The method comprises the steps that point cloud data of different scanning positions of a tunnel are obtained, the point cloud data obtained at the different scanning positions are integrated into the same coordinate system, and control points are selected and registered; based on the same coordinate system, a curve element method is adopted to solve a shield tunnel center line, and a solved center line is obtained; establishing a tunnel cross section coordinate system on the basis of the calculated center line on the basis of the cross section graph of the interval tunnel, and splicing to generate a tunnel design section; performing batch de-noising processing on the interior of the point cloud model by taking the solved center line, the established tunnel cross section coordinate system and the tunnel design cross section as references; and performing point cloud format conversion after de-noising is completed, and outputting standard data. According to the method, the spherical target is used as a registration control point between different observation stations, the observation precision is improved, and the influence of accidental errors on a coefficient matrix is fully considered.

Description

Technical Field

[0001] This invention relates to the field of shield tunnel forming quality inspection technology, and more specifically to a method and system for batch noise reduction within a point cloud model of a shield tunnel. Background Technology

[0002] With the innovative development of new surveying and mapping technologies, 3D laser scanning technology is being used more and more frequently in the acquisition of point cloud data for shield tunnels. However, in order to ensure the handover of the completed tunnel section and the quality of the section results, it is usually necessary to immediately acquire the 3D point cloud model of the tunnel after the tunnel is completed. During this period, the ventilation ducts, walkway slabs, water inlet and outlet pipes, construction equipment for connecting passages, and reinforcing steel segments inside the tunnel are not completely removed in time, resulting in a lot of impurities inside the tunnel point cloud model. The noise reduction work is extremely cumbersome and cannot accurately and efficiently grasp the quality of tunnel formation or analyze the deformation of the tunnel section and tunnel convergence.

[0003] Therefore, in view of the shortcomings of the existing technology, how to provide a method and system for batch denoising inside the point cloud model of shield tunnel is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for batch denoising of the internal point cloud model of a shield tunnel. A spherical target is used as the registration control point between different stations. In order to improve the observation accuracy, the overall least squares method is used for fitting and positioning. The influence of random errors on the coefficient matrix is ​​fully considered. The design is based on the tunnel centerline (calculated by the curve element method) and the tunnel cross section coordinate system to perform batch denoising of the internal point cloud model of the tunnel.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a batch denoising method for the internal point cloud model of a shield tunnel, comprising:

[0006] Obtain point cloud data from different scanning locations in the tunnel, integrate the point cloud data from different scanning locations into the same coordinate system, select control points and perform registration;

[0007] Based on the same coordinate system, the centerline of the shield tunnel is calculated using the curve element method, and the calculated centerline is obtained.

[0008] Based on the cross-sectional diagram of the tunnel section, a cross-sectional coordinate system for the tunnel is established on the basis of the calculated centerline, and the tunnel design cross-section is generated by splicing the coordinates.

[0009] Based on the calculated centerline, the established tunnel cross-section coordinate system, and the tunnel design cross-section, batch denoising processing is performed on the point cloud model.

[0010] After denoising is completed, the point cloud format is converted and standard data is output.

[0011] Preferably, the step of selecting control points and registering them includes: using the coordinates of the center of the spherical target as the control points;

[0012] A mathematical model is established based on the spatial spherical equation. The overall weighted least squares method is used to solve the fitted hemispherical point cloud data. A spherical fitting program is written to calculate the three-dimensional coordinates of the center of the spherical target.

[0013] The three-dimensional coordinates are verified for correctness. The coordinates of the center of the common spherical target at different scanning points are used as the control points for registration. After coordinate rotation, the registration is completed.

[0014] Preferably, a mathematical model is established based on the equation of a sphere in space, including:

[0015] The equation of the spatial sphere of the spherical target is defined as follows:

[0016] (x-x0) 2 +(y-y0) 2 +(z-z0) 2 =r0 2 ;

[0017] Where (x0, y0, z0) are the three-dimensional coordinates of the center of the spherical target, r0 is the radius of the sphere, and (x, y, z) are the hemispherical observation data obtained by the three-dimensional laser scanner.

[0018] Based on the spatial spherical equation of the spherical target, a multiple linear regression model is established to estimate the parameters:

[0019] Y = KB + K0;

[0020] Where K0 represents the random error during scanning observation, and Y and K are the matrices of the new observation values, respectively. B represents the new parameter to be estimated.

[0021] Preferably, the fitted hemispherical point cloud data is solved using the overall weighted least squares method, including:

[0022] Based on the influence of observation error on the coefficient matrix K, an error correction model is established:

[0023] V = BδA - L;

[0024] Where V is the correction vector, B is the coefficient matrix, L is the constant term vector, and δA is the parameter correction;

[0025] Based on the adjustment criteria, the parameter corrections are obtained.

[0026] Calculate the perpendicular distance from the scatter points to the fitted sphere:

[0027]

[0028] Where x0', y0', z0', and r0' are the initial values ​​of the relevant parameters.

[0029] Preferably, the three-dimensional coordinates are checked for correctness. The coordinates of the center of the common spherical target at different scanning points are used as the control points for registration. After coordinate rotation, registration is completed, including:

[0030] Calculate the variance and standard deviation of the perpendicular distance from the scatter points to the fitted sphere;

[0031]

[0032] Where, δ 2 δ represents the variance, and δ represents the standard deviation.

[0033] Use twice the standard deviation as the limit for testing;

[0034] If the value is greater than the limit, delete it; if the value is less than the limit, keep it.

[0035] Preferably, point cloud data acquired from different scanning positions are integrated into the same coordinate system, including: using a target overlap mode between scanner stations, and ensuring that at least four or more targets are set up on the construction control points, measuring and recording the target height, and obtaining the three-dimensional coordinates of the targets.

[0036] Preferably, based on the cross-sectional diagram of the tunnel section, a tunnel cross-sectional coordinate system is established on the calculated centerline, and the coordinates are spliced ​​to generate the tunnel design cross-section, including:

[0037] Based on the cross-sectional diagram of the tunnel section, with the center point of the tunnel cross-section as the origin of the coordinate system, the direction parallel to the track surface as the X-axis, and the direction perpendicular to the track surface as the Y-axis, a cross-sectional coordinate system for the tunnel is established. Four arcs with a central angle of 90 degrees are drawn and spliced ​​together to generate the tunnel design cross-section.

[0038] Preferably, a batch denoising system for point cloud models of shield tunnels includes:

[0039] The data acquisition and registration module is used to acquire point cloud data from different scanning locations in the tunnel, integrate the point cloud data acquired from different scanning locations into the same coordinate system, select control points, and perform registration.

[0040] The solution module is used to solve the centerline of the shield tunnel using the curve element method based on the same coordinate system, and obtain the solved centerline.

[0041] The splicing module is used to establish a tunnel cross-section coordinate system based on the cross-section diagram of the tunnel section and the calculated centerline, and to splice the tunnel design cross-section.

[0042] The batch denoising module is used to perform batch denoising on the point cloud model based on the calculated centerline, the established tunnel cross-section coordinate system, and the tunnel design cross-section.

[0043] The output module is used to perform point cloud format conversion after noise reduction and output standard data.

[0044] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method and system for batch denoising inside the point cloud model of a shield tunnel, which has the following beneficial effects: (1) Three-dimensional laser scanning technology is used to obtain tunnel point cloud data, because errors are unavoidable in each stage of actual measurement. For example, model error, human error, instrument error, etc. always exist, and there are also variables in the coefficient matrix K, which is not a constant coefficient matrix, so there are also errors. In order to improve the observation accuracy, the overall least squares method is used for fitting and positioning, and the influence of random errors on the coefficient matrix is ​​fully considered.

[0045] (2) The curve element method is used to solve the center line of the shield tunnel, providing a reference axis for batch denoising. Batch denoising is then carried out, which solves the problem that the denoising work is extremely cumbersome due to the large number of impurities in the tunnel point cloud model, and cannot accurately and efficiently grasp the tunnel forming quality. The impact of problems such as cross-sectional deformation and tunnel convergence in the tunnel is also analyzed.

[0046] (3) Based on the cross-sectional diagram of the tunnel section, establish the cross-sectional coordinate system of the tunnel, draw four arcs with a central angle of 90 degrees, and splice them together to form the design section. This not only vividly and intuitively expresses the design section, but is also concise, clear and easy to understand. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0048] Figure 1 This is a flowchart of a method provided in an embodiment of the present invention.

[0049] Figure 2 A flowchart of least squares principle fitting for denoising spherical targets provided for embodiments of the present invention.

[0050] Figure 3 A cross-sectional schematic diagram provided for an embodiment of the present invention (taking a circular shield tunnel model as an example).

[0051] Figure 4 This is a cross-sectional coordinate system diagram provided for an embodiment of the present invention.

[0052] Among them, 1—smoke exhaust duct; 2—tunnel inner diameter; 3—left line of the large shield tunnel section; 4—rail surface position; 5—U-shaped component; 6—right line of the large shield tunnel section; 7—center of the circular tunnel structure; 8—Y-axis of the two-dimensional coordinate system; 9—X-axis of the two-dimensional coordinate system; 10—center of the left line tunnel of the section; 11—center of the right line tunnel of the section; 12—outer diameter of the tunnel. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] This invention discloses a batch denoising method for the internal point cloud model of a shield tunnel, such as... Figure 1 As shown, it includes:

[0055] S100. Acquire point cloud data from different scanning locations in the tunnel, integrate the point cloud data acquired from different scanning locations into the same coordinate system, select control points and perform registration.

[0056] S200. Based on the same coordinate system, the centerline of the shield tunnel is calculated using the curve element method to obtain the calculated centerline.

[0057] S300. Based on the cross-sectional diagram of the tunnel section, establish the tunnel cross-sectional coordinate system on the basis of the calculated centerline, and splice it to generate the tunnel design cross-section.

[0058] S400. Based on the calculated centerline, the established tunnel cross-section coordinate system, and the tunnel design cross-section, perform batch noise reduction processing on the point cloud model.

[0059] The S500 performs noise reduction and point cloud format conversion, outputting standard data.

[0060] Specifically, the selection and registration of control points includes: using the coordinates of the center of the target ball as the control point;

[0061] A mathematical model is established based on the spatial spherical equation. The overall weighted least squares method is used to solve the fitted hemispherical point cloud data. A spherical fitting program is written to calculate the three-dimensional coordinates of the center of the spherical target.

[0062] The three-dimensional coordinates are verified for correctness. The coordinates of the center of the common spherical target at different scanning points are used as the control points for registration. After coordinate rotation, the registration is completed.

[0063] This invention employs three-dimensional laser scanning technology to acquire tunnel point cloud data, using the center coordinates of a spherical target as control points for registration. A mathematical model is established using the spatial spherical equation, and the hemispherical point cloud data is fitted based on the principle of global weighted least squares. A program for spherical fitting is written to calculate the three-dimensional coordinates of the spherical target center, and its correctness is verified. The center coordinates of a common spherical target set at different scanning points are used as control points for registration. After coordinate rotation, registration is achieved. To improve observation accuracy, global least squares noise reduction fitting is used, fully considering the influence of random errors on the coefficient matrix. Figure 2 As shown.

[0064] Specifically, based on the symmetrical geometric characteristics of the spherical target, the laser beam of a 3D laser scanner can be incident from different directions to obtain point cloud data of the hemispherical surface. By fitting the measured hemispherical point cloud data, the 3D coordinates of the target sphere's center can be calculated. That is, the point cloud data of the hemispherical surface is (x... i y i , z i ), where i = 1, 2, 3...n. Usually, the radius of the spherical target is a known value. When verifying the accuracy of this method, the radius is generally considered to be an unknown parameter value, and the coordinates of the center of the spherical target are obtained by fitting.

[0065] Specifically, a mathematical model is established based on the equations of a sphere in space, including:

[0066] The equation of the spatial sphere of the spherical target is defined as follows:

[0067] (x-x0) 2 +(y-y0) 2 +(z-z0) 2 =r0 2 ;

[0068] Where (x0, y0, z0) are the three-dimensional coordinates of the center of the spherical target, r0 is the radius of the sphere, and (x, y, z) are the hemispherical observation data obtained by the three-dimensional laser scanner.

[0069] Expanding the above equation, we get:

[0070] x 2 +y 2 +z 2 =2x*x0+2y*y0+2z*z0+r0 2 -x0 2 -y0 2 -z0 2 ;

[0071] In the formula, x 2 +y 2 +z 2Consider 2x, 2y, 2z as new observations of the equation, x0, y0, z0, r0 2 -x0 2 -y0 2 -z0 2 Treating it as a new parameter to be estimated, a multiple linear regression model is established to achieve parameter estimation.

[0072] Specifically, based on the spatial spherical equation of the spherical target, a multiple linear regression model is established:

[0073] Y = KB + K0;

[0074] Where K0 represents the random error during scanning observation, and Y and K are the new observation matrix of the equation, respectively. B represents the new parameter to be estimated. Assume that K0 has an effect on the observed value matrix Y, but no effect on the coefficient matrix K.

[0075] Specifically, disregarding the influence of the random error matrix K0 on the coefficient matrix K, the most probable value of the parameter to be estimated is obtained using the least squares method:

[0076]

[0077] The unit weighted variance is:

[0078]

[0079] In the above formula The variance of the parameter estimates is:

[0080] P=K T K,

[0081] The approximate estimates of the parameters obtained using the least squares method treat the observed value matrix as known matrices and the estimated parameter matrix as unknown matrices. From the above equation, we can see that the random error matrix K0 exists only in the vector Y and has no effect on the coefficient matrix K. However, this does not conform to actual measurement conditions because errors are unavoidable in every stage of actual measurement. For example, model errors, human errors, and instrument errors always exist. Furthermore, the coefficient matrix K also contains variables and is not a constant coefficient matrix, therefore it also contains errors.

[0082] Specifically, according to the least squares method for solving spherical targets, the spherical point cloud data contains errors, leading to errors in the coefficient matrix. To improve observation accuracy, a global least squares method is used for noise reduction and fitting. The approximate values ​​of the parameters in the expansion are set as x0', y0', z0', and r0', and the influence of random errors on the coefficient matrix is ​​considered. Therefore, it can be expressed as:

[0083]

[0084] In the formula, v x v y v z δx0, δy0, and δz0 are the corrections to the hemispherical observation data (x, y, z) obtained by the 3D laser scanner, respectively. δx0, δy0, and δz0 are the corrections to the center coordinates (x0, y0, z0) of the spherical target, respectively. δr0 is the correction to the radius r0 of the sphere.

[0085] Expanding the above expression and removing the quadratic term, we get:

[0086]

[0087] In the formula, s is:

[0088] s = x 2 +y 2 +z 2 -2x*x0'-2y*y0'-2z*z0'-r0' 2 +x0' 2 +y0' 2 +z0' 2 ;

[0089] Expand the expression and remove the quadratic term to write it in matrix form:

[0090] V = BδA - L.

[0091] Specifically, the overall weighted least squares method is used to solve the fitted hemispherical point cloud data, including:

[0092] Based on the influence of observation error on the coefficient matrix K, an error correction model is established:

[0093] V = BδA - L;

[0094] Where V is the correction vector, B is the coefficient matrix, L is the constant term vector, and δA is the parameter correction;

[0095]

[0096] Among them, v x ,v y ,v z δx0, δy0, δz0, δr0 are the observation corrections, δx0, δy0, δz0, δr0 are the parameter corrections, and x0', y0', z0', r0' are the parameter approximations;

[0097] Based on the adjustment criteria, the parameter corrections are obtained.

[0098] Specifically, assuming that all spherical point cloud data have the same precision, then V = 2(x - x0')v x +2(y-y0')v y +2(z-z0')v z According to the law of cofactor propagation, the following can be calculated:

[0099]

[0100] in:

[0101]

[0102] The adjustment criterion is the overall least squares criterion, i.e.: To minimize the sum of the squares of this positive number, we can calculate:

[0103] δA=(B T Q vv -1 B) -1 B T Q vv -1 L;

[0104] The unit weighted variance can be obtained as:

[0105]

[0106] The cofactor matrix of the parameter estimates is obtained as follows:

[0107] Q AA = (B T Q vv -1 B) -1 ;

[0108] Using the overall least squares method described above, we determine the initial values ​​of the relevant parameters x0', y0', z0', and r0'; using these initial parameter values, we calculate the perpendicular distance from the scatter points to the fitted sphere:

[0109]

[0110] Where x0', y0', z0', and r0' are the initial values ​​of the relevant parameters.

[0111] Specifically, the correctness of the three-dimensional coordinates is checked. The coordinates of the center of the common spherical target at different scanning points are used as the control points for registration. After coordinate rotation, registration is completed, including:

[0112] Calculate the variance and standard deviation of the perpendicular distance from the scatter points to the fitted sphere;

[0113]

[0114] Where, δ 2 δ represents the variance, and δ represents the standard deviation.

[0115] Use twice the standard deviation as the limit for testing;

[0116] If the value is greater than the limit, delete it; if the value is less than the limit, keep it.

[0117] Specifically, point cloud data acquired from different scanning locations are integrated into the same coordinate system. This includes using a target-overlapping mode between scanner stations, ensuring at least four targets are set up at construction control points, measuring and recording target heights, and obtaining the three-dimensional coordinates of the targets. This achieves high-precision unification of the free station coordinate system and the construction coordinate system, effectively processing and analyzing high-resolution massive point cloud data.

[0118] Specifically, in S200, the tunnel centerline is calculated using the curve element method to provide a reference axis for batch noise reduction. The special characteristics of some tunnel designs, including long and short chains, vibration-damped track transition sections, and the alignment of some utility tunnels, are fully considered to improve data reliability.

[0119] Specifically, in S300, based on the cross-sectional diagram of the tunnel section, a tunnel cross-sectional coordinate system is established on the calculated centerline, and the tunnel design cross-section is generated by splicing the coordinates, including:

[0120] Based on the cross-sectional diagram of the tunnel section, with the center point of the tunnel cross-section as the origin, the direction parallel to the rail surface as the X-axis, and the direction perpendicular to the rail surface as the Y-axis, a coordinate system for the tunnel cross-section is established. Four arcs with a central angle of 90 degrees are drawn and spliced ​​together to generate the tunnel design cross-section, as shown below. Figure 3 and Figure 4 As shown.

[0121] Figure 3 This is a schematic cross-sectional view of a single-bore, double-track shield tunnel provided in an embodiment of the present invention. The single-bore, double-track section uses a single-tube tunnel (with a designed inner diameter of 5.4 meters and an outer diameter of 5.9 meters) as the vehicular tunnel, dividing the tunnel into three levels: upper, middle, and lower. The upper level serves as a disaster prevention ventilation and smoke exhaust channel 1. The middle level is equipped with a partition wall to separate the left line 3 and right line 6 of the large shield tunnel section, and evacuation is facilitated by a safety door between the two lines. Simultaneously, lighting fixtures, fire hydrant pipes, drainage pipes, cables, and signal equipment are arranged on both sides of the track surface 4. The lower level of the tunnel is a U-shaped section 5, with a wastewater pumping station located at the lowest point to remove tunnel seepage water and fire-fighting wastewater.

[0122] Figure 4 Cross-sectional diagram of the tunnel section Figure 3Based on this, a cross-sectional coordinate system for the tunnel is established. For circular tunnels, the center 7 of the circular tunnel structure is used as the origin of the coordinate system. The direction parallel to the track surface is the X-axis 9 of the two-dimensional coordinate system, and the direction perpendicular to the track surface is the Y-axis 8 of the two-dimensional coordinate system. Four arcs with a central angle of 90 degrees are drawn and spliced ​​to generate the tunnel design cross-section. For the left and right lines of the large shield tunnel, the center 10 of the left line tunnel and the center 11 of the right line tunnel are used as the origins of their respective coordinate systems. This method vividly and intuitively represents the design cross-section while being concise, clear, and easy to understand.

[0123] Specifically, in S400, batch denoising within the point cloud model is performed: based on the tunnel design cross-section established by solving the tunnel centerline and cross-section coordinate system using the curve element method, batch denoising is carried out.

[0124] Specifically, in S500, the denoised point cloud format conversion is completed: the batch denoised point clouds are stored as .pts or .las data formats, providing basic data for subsequent cross-section analysis of tunnel cross-section deformation, tunnel convergence, etc.

[0125] The technical problem to be solved by this invention is that the equipment inside the tunnel was not completely removed in time during the acquisition of the point cloud data model, resulting in a lot of impurities inside the tunnel point cloud model. The noise reduction work is extremely cumbersome and cannot accurately and efficiently grasp the tunnel forming quality, analyze the cross-sectional deformation and tunnel convergence inside the tunnel. The invention uses three-dimensional laser scanning technology to acquire tunnel point cloud data, and uses spherical targets as registration control points between different stations. In order to improve the observation accuracy, the overall least squares method is used for fitting and positioning. The influence of random errors on the coefficient matrix is ​​fully considered. The invention designs batch noise reduction inside the tunnel point cloud model based on the tunnel centerline (calculated by the curve element method) and the tunnel cross-sectional coordinate system.

[0126] This invention addresses the issue that during point cloud scanning, elements such as ventilation ducts, walkway slabs, water inlet and outlet pipes, connecting passage construction equipment, and reinforcing steel segments within the tunnel were not promptly and completely removed, resulting in numerous impurities within the tunnel point cloud model. This leads to extremely cumbersome noise reduction work, making it difficult to accurately and efficiently assess the tunnel's forming quality and analyze cross-sectional deformation and tunnel convergence within the tunnel. The invention develops a batch noise reduction method for the internal structure of shield tunnel point cloud models, which is simple, clear, easy to understand, and readily applicable.

[0127] In one specific embodiment of the present invention, a batch denoising system for a point cloud model of a shield tunnel includes:

[0128] The data acquisition and registration module is used to acquire point cloud data from different scanning locations in the tunnel, integrate the point cloud data acquired from different scanning locations into the same coordinate system, select control points, and perform registration.

[0129] The solution module is used to solve the centerline of the shield tunnel using the curve element method based on the same coordinate system, and obtain the solved centerline.

[0130] The splicing module is used to establish a tunnel cross-section coordinate system based on the cross-section diagram of the tunnel section and the calculated centerline, and to splice the tunnel design cross-section.

[0131] The batch denoising module is used to perform batch denoising on the point cloud model based on the calculated centerline, the established tunnel cross-section coordinate system, and the tunnel design cross-section.

[0132] The output module is used to perform point cloud format conversion after noise reduction and output standard data.

[0133] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0134] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for batch denoising within a point cloud model of a shield tunnel, characterized in that, include: Obtain point cloud data from different scanning locations in the tunnel, integrate the point cloud data from different scanning locations into the same coordinate system, select control points and perform registration; Based on the same coordinate system, the centerline of the shield tunnel is calculated using the curve element method, and the calculated centerline is obtained. Based on the cross-sectional diagram of the tunnel section, a cross-sectional coordinate system for the tunnel is established on the basis of the calculated centerline, and the tunnel design cross-section is generated by splicing the coordinates. Based on the calculated centerline, the established tunnel cross-section coordinate system, and the tunnel design cross-section, batch denoising processing is performed on the point cloud model. After denoising is completed, the point cloud format is converted and standard data is output.

2. The method for batch denoising within a point cloud model of a shield tunnel as described in claim 1, characterized in that, The selection and registration of control points includes: using the center coordinates of the ball target as control points; A mathematical model is established based on the spatial spherical equation. The overall weighted least squares method is used to solve the fitted hemispherical point cloud data. A spherical fitting program is written to calculate the three-dimensional coordinates of the center of the spherical target. The three-dimensional coordinates are verified for correctness. The coordinates of the center of the common spherical target at different scanning points are used as the control points for registration. After coordinate rotation, the registration is completed.

3. The method for batch denoising within a point cloud model of a shield tunnel as described in claim 2, characterized in that, A mathematical model is established based on the equations of a sphere in space, including: The equation of the spatial sphere of the spherical target is defined as follows: (x-x0) 2 +(y-y0) 2 +(z-z0) 2 =r0 2 4 Where (x0, y0, z0) are the three-dimensional coordinates of the center of the spherical target, r0 is the radius of the sphere, and (x, y, z) are the hemispherical observation data obtained by the three-dimensional laser scanner. Based on the spatial spherical equation of the spherical target, a multiple linear regression model is established: Y = KB + K0; Where K0 represents the random error during scanning observation, and Y and K are the matrices of the new observation values, respectively. B represents the new parameter to be estimated.

4. The method for batch denoising within a point cloud model of a shield tunnel according to claim 3, characterized in that, The fitted hemispherical point cloud data is solved using the overall weighted least squares method, including: Based on the influence of observation error on the coefficient matrix K, an error correction model is established: V = BδA - L; Where V is the correction vector, B is the coefficient matrix, L is the constant term vector, and δA is the parameter correction; Based on the adjustment criteria, the parameter corrections are obtained. Calculate the perpendicular distance from the scatter points to the fitted sphere: Where x0', y0', z0', and r0' are the initial values ​​of the relevant parameters.

5. The method for batch denoising within a point cloud model of a shield tunnel according to claim 4, characterized in that, The correctness of the three-dimensional coordinates is verified. The coordinates of the center of the common spherical target at different scanning points are used as the control points for registration. After coordinate rotation, registration is completed, including: Calculate the variance and standard deviation of the perpendicular distance from the scatter points to the fitted sphere; Where, δ 2 δ represents the variance, and δ represents the standard deviation. Use twice the standard deviation as the limit for testing; If the value is greater than the limit, delete it; if the value is less than the limit, keep it.

6. The method for batch denoising within a point cloud model of a shield tunnel according to claim 1, characterized in that, Integrate point cloud data acquired from different scanning locations into the same coordinate system, including: using a target overlap mode between scanner stations, ensuring that at least 4 targets are set up on construction control points, measuring and recording the target height, and obtaining the three-dimensional coordinates of the targets.

7. The method for batch denoising within a point cloud model of a shield tunnel according to claim 1, characterized in that, Based on the cross-sectional diagram of the tunnel section, and on the calculated centerline, a tunnel cross-sectional coordinate system is established, and the designed tunnel cross-section is generated by splicing the coordinates, including: Based on the cross-sectional diagram of the tunnel section, with the center point of the tunnel cross-section as the origin of the coordinate system, the direction parallel to the track surface as the X-axis, and the direction perpendicular to the track surface as the Y-axis, a cross-sectional coordinate system for the tunnel is established. Four arcs with a central angle of 90 degrees are drawn and spliced ​​together to generate the tunnel design cross-section.

8. A batch denoising system for the internal point cloud model of a shield tunnel, employing the batch denoising method for the internal point cloud model of a shield tunnel as described in any one of claims 1-7, characterized in that, include: The data acquisition and registration module is used to acquire point cloud data from different scanning locations in the tunnel, integrate the point cloud data acquired from different scanning locations into the same coordinate system, select control points, and perform registration. The solution module is used to solve the centerline of the shield tunnel using the curve element method based on the same coordinate system, and obtain the solved centerline. The splicing module is used to establish a tunnel cross-section coordinate system based on the cross-section diagram of the tunnel section and the calculated centerline, and to splice the tunnel design cross-section. The batch denoising module is used to perform batch denoising on the point cloud model based on the calculated centerline, the established tunnel cross-section coordinate system, and the tunnel design cross-section. The output module is used to perform point cloud format conversion after noise reduction and output standard data.

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