Historic building repair construction real-time monitoring method and device based on laser radar

Abstract

The invention discloses an ancient building repair construction real-time monitoring method and device based on a laser radar, and relates to the technical field of engineering construction monitoring. The method comprises the following steps: actually measuring a to-be-repaired ancient building, and generating a digital twinborn model of the ancient building; when an ancient building is repaired, a laser radar unit is deployed on a construction site; collecting laser radar point cloud data of the laser radar unit; filtering the laser radar point cloud data to obtain ancient building point cloud data; performing gridding processing on the point cloud data of the historic building to obtain grid data of the historic building; and performing correction according to the difference value between the historic building grid data and the corresponding digital twinborn model. According to the method, the historic building is actually measured, the digital twin model of the historic building is constructed, the repair process of the historic building is monitored in real time by using the laser radar, and an alarm is given in time when the difference with the digital twin model is found, so that the difference between building repair and drawings is avoided, the building construction efficiency is improved, and the error rate is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of engineering construction monitoring, and particularly relates to a real-time monitoring method and device for ancient building restoration construction based on lidar. Background Art

[0002] Many ancient towns and most large cities still retain some ancient buildings. The principle of ancient building protection and restoration is that it must be protected in-situ, with as little intervention as possible, and regular daily maintenance should be carried out to protect the original state of the existing physical objects and historical information. When repairing, it should be carried out in accordance with the French characteristics, material texture, style and techniques of the building, as well as the records in documents, inscriptions, or epigraphs, to identify the age of the existing building and the historical remains at the time of its initial construction, reconstruction, or renovation, and to draw up a plan to repair it or take protective measures in accordance with the existing French characteristics and structural features.

[0003] During the existing ancient building restoration, in order to avoid large differences from the old ancient buildings being found after large-scale construction is completed, with high additional costs and long time for re-repair, this application document provides a real-time monitoring method and device for ancient building restoration construction based on lidar to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time monitoring method and device for ancient building restoration construction based on lidar. By measuring the ancient building in-situ, constructing a digital twin model of the ancient building, and using lidar to monitor the restoration process of the ancient building in real-time, and giving an alarm in time once a difference from the digital twin model of the ancient building is found, it solves the problems of large differences from the old ancient buildings after large-scale construction is completed, high additional costs and long time for re-repair.

[0005] To solve the above technical problems, the present invention is realized through the following technical solutions:

[0006] The present invention is a real-time monitoring method for ancient building restoration construction based on lidar, including the following steps:

[0007] Step S1: Measure the ancient building to be restored in-situ to generate a digital twin model of the ancient building;

[0008] Step S2: Deploy a lidar unit at the construction site during the ancient building restoration;

[0009] Step S3: The lidar point cloud data collected by the lidar unit;

[0010] Step S4: Filter the lidar point cloud data to obtain the ancient building point cloud data;

[0011] Step S5: Perform grid processing on the ancient building point cloud data to obtain the ancient building grid data;

[0012] Step S6: Make corrections according to the difference between the ancient building grid data and the corresponding digital twin model.

[0013] As a preferred technical solution, in the step S1, the generation process of the digital twin model of the ancient building is as follows:

[0014] Step S11: According to the actual measurement of the ancient building, use the BIM platform to complete the design of each part of the ancient building and establish a library of each part of the ancient building;

[0015] Step S12: Model each part of the ancient building based on the Dynamo underlying technology;

[0016] Step S13: According to the ancient building drawings, assemble each part to construct a three-dimensional digital twin model of the ancient building.

[0017] As a preferred technical solution, in the step S11, the process of constructing the three-dimensional digital twin model of the ancient building is as follows:

[0018] Step S111: Based on the actual measurement data of the ancient building, simulate the actual situation of the ancient building by means of multi-position, multi-angle, and multi-direction;

[0019] Step S112: Record the components required for the ancient building and draw the actual measurement drawings of the CAD two-dimensional of the ancient building;

[0020] Step S113: Import the CAD two-dimensional actual measurement drawings into the three-dimensional modeling tool of the BIM platform, generate a three-dimensional digital twin model of the ancient building, and input the three-dimensional models of the components required for the generated ancient building into the standard library.

[0021] As a preferred technical solution, in the step S3, the images collected by the lidar unit need to be preprocessed to obtain the lidar point cloud data, and the specific process is as follows:

[0022] Step S31: Set the size of the cropping area of the image collected by the lidar unit to an area of 782*694;

[0023] Step S31: The cropped image is a three-band color image, and set the threshold value of 150 to perform binary processing on the image;

[0024] Step S31: Use morphological reconstruction to extract the ancient building area in the binary image and calculate the centroid of the component.

[0025] As a preferred technical solution, in the step S4, the lidar point cloud data is filtered to obtain the ancient building point cloud data, and the specific process is as follows:

[0026] Step S41: Obtain the planar coordinates and elevations of each lidar point in the lidar point cloud data;

[0027] Step S42: Convert the planar coordinates of the lidar points into the ancient building coordinates of the lidar points according to the Gauss inverse calculation;

[0028] Step S43: Use bilinear interpolation to calculate the elevation value corresponding to the ancient building coordinates of the lidar points, and determine it as the reference elevation value;

[0029] Step S44: Calculate the absolute value of the difference between the elevation of the lidar points and the corresponding reference elevation value, and determine the lidar points with the absolute value of the difference less than or equal to the preset difference threshold as the ancient building point data, so as to obtain the ancient building point cloud data.

[0030] As a preferred technical solution, in the step S5, the process of performing grid processing on the ancient building point cloud data to obtain the ancient building grid data is as follows:

[0031] Step S51: Allocate each ancient building point data in the ancient building point cloud data to the grid through an affine matrix;

[0032] Step S52: Calculate the average number of ancient building point data of each grid in the grid;

[0033] Step S53: If the average number is greater than the preset first quantity value, reduce the size of each grid in the grid, and re-allocate each ancient building point data in the ancient building point cloud data to the grid after reducing the grid size through the affine matrix;

[0034] Step S54: If the average number is less than the preset second quantity value, increase the size of each grid in the grid, and re-allocate each ancient building point data in the ancient building point cloud data to the grid after reducing the grid size through the affine matrix; where the second quantity value is less than the first quantity value;

[0035] Step S55: If the average number is less than the first quantity value and greater than the second quantity value, determine the grid as the ancient building grid data.

[0036] As a preferred technical solution, in the step S6, according to the difference between the ancient building grid data and the corresponding digital twin model, the specific steps include: calculating the weights of each lidar point at the corresponding agreed point position:

[0037]

[0038] In the formula, w i is the weight of the i-th lidar point, d iis the distance between the i-th lidar point and the corresponding digital twin model point, and n is the number of lidar points within a preset range;

[0039] The difference value is calculated in the following way:

[0040]

[0041] In the formula, ΔZ is the difference between the ancient building grid data and the corresponding digital twin model, and dZ i is the difference between the lidar point and the corresponding digital twin model.

[0042] As a preferred technical solution, in step S6, the specific process of the difference between the ancient building grid data and the corresponding digital twin model is as follows:

[0043] Step S61: Generate a corresponding lidar model in the 3D digital twin model of the ancient building according to the actual deployment of the lidar;

[0044] Step S62: The BIM platform directly calculates the distance between the lidar model and the 3D digital twin model of the ancient building;

[0045] Step S63: Compare the ancient building grid data obtained by the lidar with the data of the digital twin model to determine whether the current ancient building is consistent with the digital twin model.

[0046] In step S63, the process of determining whether the current ancient building is consistent with the digital twin model is as follows:

[0047] Step 1: With the positions of the fixed points and the moving points determined, the least squares sum of the Laplacian change values of all points is minimized, that is:

[0048] min(||L(v)-Δ(v)||2;

[0049] Step 2: Convert the Laplace transform equation Ax = b0 into Ax = a*K + b;

[0050] Step 3: Solve the formula Arcmin||x - x0||, s.tAx = a*K + b;

[0051] Step 4: According to the root formula x = (A T A) -1 A T (ak + b), calculate the final k value;

[0052] Step 5: Scale to make the sizes of the grid and the point cloud consistent and perform iterative registration;

[0053] During the alignment, the ancient building is directly proportional to all the set parameters k. Divide the calculated parameter k by the digital twin model ratio to obtain the frame information at the corresponding position, judge the error of the frame information, and determine the parts with errors.

[0054] The present invention is a real-time monitoring device for the restoration construction of ancient buildings based on lidar, including a control computer, a portable radar unit and a sliding track; the portable radar unit includes a linear scanner, a radar sensor and a power module; the radar sensor and the linear scanner are slidably installed on the sliding track; both the radar sensor and the linear scanner are electrically connected to the control computer; the control computer is built-in with processing software; the processing software performs acquisition parameter setting and system diagnosis; the processing software performs data processing and output visualization, is used for real-time processing of radar data, and can provide a reference output of the ancient building in the form of a displacement and speed diagram, and performs alarm processing when the ancient building exceeds the digital twin model.

[0055] As a preferred technical solution, the output of the processing software can be exported to GIS, CAD or conventional software.

[0056] The present invention has the following beneficial effects:

[0057] The present invention measures the ancient building in actuality, constructs a digital twin model of the ancient building, uses lidar to monitor the restoration process of the ancient building in real time, and once it is found that it is different from the digital twin model of the ancient building, it will alarm in time, avoiding the difference between the building restoration and the drawing, improving the building construction efficiency and reducing the error rate.

[0058] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0060] Figure 1 It is a flowchart of a real-time monitoring method for the restoration construction of ancient buildings based on lidar of the present invention;

[0061] Figure 2 It is a working parameter table of the portable radar unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0063] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0064] To make the purpose, technical solution and advantages of the present application clearer, the following will be combined with the attached Figure 1 The embodiments of the present application will be further described in detail.

[0065] To make the purpose, technical solution and advantages of the present application clearer and more understandable, the following will be combined with the attached Figure 1-2 And embodiments, the present application will be further described in detail. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] Embodiment 1

[0067] Please refer to Figure 1 As shown, the present invention is a real-time monitoring method for the restoration construction of ancient buildings based on lidar, including the following steps:

[0068] Step S1: Conduct actual measurements on the ancient building to be restored to generate a digital twin model of the ancient building;

[0069] Among them, in step S1, the generation process of the digital twin model of the ancient building is as follows:

[0070] Step S11: According to the actual measurements of the ancient building, use the BIM platform to complete the design of each part of the ancient building and establish a library of each part of the ancient building;

[0071] Step S12: Model each part of the ancient building based on the Dynamo underlying technology;

[0072] Step S13: According to the ancient building drawings, assemble each part to construct a three-dimensional digital twin model of the ancient building.

[0073] In step S11, the process of constructing the three-dimensional digital twin model of the ancient building is as follows:

[0074] Step S111: Based on the actual measurement data of the ancient building, simulate the actual situation of the ancient building by means of multi-position, multi-angle and multi-direction;

[0075] Step S112: Record the components required for the ancient building and draw the actual measured 2D CAD drawings of the ancient building;

[0076] Step S113: Import the actual measured 2D CAD drawings into the 3D modeling tool of the BIM platform to generate a 3D digital twin model of the ancient building, and input the 3D models of the components required for the ancient building into the standard library.

[0077] Step S2: During the repair of the ancient building, deploy a lidar unit at the construction site. The lidar unit is arranged around the ancient building. To save costs, the lidar unit can be installed on a track. By using the sliding of the lidar unit on the track, data collection at multiple points of the lidar unit can be realized;

[0078] Step S3: Process the lidar point cloud data collected by the lidar unit;

[0079] In Step S3, the images collected by the lidar unit need to be preprocessed to obtain the lidar point cloud data. The specific process is as follows:

[0080] Step S31: Set the size of the cropping area of the image collected by the lidar unit to an area of 782 * 694;

[0081] Step S31: The cropped image is a three-band color image. Set the threshold to 150 to perform binary processing on the image;

[0082] Step S31: Use morphological reconstruction to extract the ancient building area in the binary image and calculate the centroid of the component.

[0083] Step S4: Filter the lidar point cloud data to obtain the ancient building point cloud data;

[0084] In Step S4, the specific process of filtering the lidar point cloud data to obtain the ancient building point cloud data is as follows:

[0085] Step S41: Obtain the planar coordinates and elevations of each lidar point in the lidar point cloud data;

[0086] Step S42: Convert the planar coordinates of the lidar points to the ancient building coordinates of the lidar points according to the Gauss inverse calculation;

[0087] Step S43: Use bilinear interpolation to calculate the elevation value corresponding to the ancient building coordinates of the lidar points and determine it as the reference elevation value;

[0088] Step S44: Calculate the absolute value of the difference between the elevation of the lidar points and the corresponding reference elevation value, and determine the lidar points with the absolute value of the difference less than or equal to the preset difference threshold as the ancient building point data to obtain the ancient building point cloud data.

[0089] Step S5: Perform grid processing on the ancient building point cloud data to obtain ancient building grid data;

[0090] In step S5, the process of performing grid processing on the ancient building point cloud data to obtain ancient building grid data is as follows:

[0091] Step S51: Allocate each ancient building point data in the ancient building point cloud data to the grid through an affine matrix;

[0092] Step S52: Calculate the average number of ancient building point data of each grid in the grid;

[0093] Step S53: If the average number is greater than a preset first quantity value, reduce the size of each grid in the grid, and re-allocate each ancient building point data in the ancient building point cloud data to the grid after reducing the grid size through the affine matrix;

[0094] Step S54: If the average number is less than a preset second quantity value, increase the size of each grid in the grid, and re-allocate each ancient building point data in the ancient building point cloud data to the grid after reducing the grid size through the affine matrix; where the second quantity value is less than the first quantity value;

[0095] Step S55: If the average number is less than the first quantity value and greater than the second quantity value, determine the grid as the ancient building grid data.

[0096] Step S6: Perform correction according to the difference between the ancient building grid data and the corresponding digital twin model. According to the difference between the ancient building grid data and the corresponding digital twin model, the specific steps include: calculating the weights of each lidar point at the corresponding agreed point position:

[0097]

[0098] In the formula, w i is the weight of the i-th lidar point, d i is the distance between the i-th lidar point and the corresponding digital twin model point, and n is the number of lidar points within a preset range;

[0099] Calculate the difference through the following method:

[0100]

[0101] In the formula, ΔZ is the difference between the ancient building grid data and the corresponding digital twin model, dZ i is the difference between the lidar point and the corresponding digital twin model

[0102] In step S6, the difference between the ancient building grid data and the corresponding digital twin model is as follows:

[0103] Step S61: According to the actual deployment of lidar, generate a corresponding lidar model in the 3D digital twin model of the ancient building;

[0104] Step S62: The BIM platform directly calculates the distance between the lidar model and the 3D digital twin model of the ancient building;

[0105] Step S63: Compare the ancient building grid data obtained by lidar with the data of the digital twin model to determine whether the current ancient building is consistent with the digital twin model.

[0106] In step S63, the process of determining whether the current ancient building is consistent with the digital twin model is as follows:

[0107] Step 1: When ensuring the positions of the fixed points and moving points are determined, the least-squares sum of the Laplacian change values of all points is minimized, that is:

[0108] min(||L(v)-Δ(v)||2;

[0109] Step 2: Convert the Laplace transform equation Ax = b0 into Ax = a*K + b;

[0110] Step 3: Solve the formula Arcmin||x - x0||, s.t Ax = a*K + b;

[0111] Step 4: According to the root formula x = (A T A) -1 A T (ak + b), calculate the final k value;

[0112] Step 5: Scale to make the sizes of the grid and the point cloud consistent and perform iterative registration;

[0113] During the registration, the ancient building and the set parameter k are all directly proportional. Divide the calculated parameter k by the scale of the digital twin model to obtain the frame information at the corresponding position, judge the error of the frame information, and determine the parts with errors.

[0114] In the Laplace transform, when calculating those solved coordinates, a least-squares problem needs to be optimized, that is, when ensuring the positions of the fixed points and moving points are determined, the least-squares sum of the Laplace coordinate change values of all points is minimized. That is:

[0115] min(||L(v)-Δ(v)||2;

[0116] This can be transformed into a problem of solving an overdetermined linear equation system Ax = b0. Here, x is a vector composed of the coordinates of the points to be solved. Each row of A represents the component of the Laplace coordinate of one component of a point, and b consists of the original Laplace coordinates and the coordinates and their weights of the known fixed points;

[0117] The above Ax = b0 is an equation for solving the Laplace transform. In our application, since what we ultimately want to solve are the ancient building parameters we set, so, b0 in Ax = b0 is an undetermined quantity because the coordinates of those moving points change with the change of the parameters. However, since when setting the parameters, our parameters are all linear with the coordinates, so b0 here must also satisfy a certain linear relationship with the parameters.

[0118] That is to say, when multiple parameters form a linear transformation matrix K, b0 can be expressed as a*K + b.

[0119] For example, when there is only one base k as a parameter, the coordinates of all the "moving points" controlled by the base all satisfy vi’ = a[i]*k + c[i]. Among the two parts that make up b0, the Laplace coordinates of v[i] do not change, so, b0 can also be linearly represented by k.

[0120] Then we obtain an equation Ax = a*K + b (1).

[0121] Next, we need to consider the meaning of this equation. Our ultimate goal is to find a parameter matrix K that is as close as possible so that the solved x is as close as possible to the point cloud we scanned.

[0122] That is, we need to solve:

[0123] Arcmin||x - x0||, s.t Ax = a*K + b;

[0124] Since the equation (1) is an overdetermined equation system and has no true solution, we can only obtain the generalized solution of x. Here, we use the root formula:

[0125] x = (A T A) -1 A T (ak + b).

[0127] So, the problem is transformed into solving:

[0128] Arcmin||x - x0||, x = (A T A) -1 A T (ak + b).

[0129] This naturally leads to another equation:

[0130] (A T A) -1 A T ·a·k = x 0 -(A T A) -1 A T b (2)

[0131] Solving Equation 2 can obtain the final value of k.

[0132] To obtain the corresponding x0 in the point cloud for each point x in the grid, through rough scaling, the sizes of the grid and the point cloud are made consistent to facilitate subsequent registration.

[0133] Then the method adopted is iteration, which is specifically divided into two steps:

[0134] ① For each point in the grid, find the nearest point in the point cloud, then regard these points as x0, and use the method described above to solve the current optimal corresponding parameter K;

[0135] ② Bring the parameter K into the grid for Laplace deformation, obtain a new grid to replace the initial grid, and then go back to step ①.

[0136] The above loop is terminated after 10 iterations to obtain a better registration effect.

[0137] Embodiment 2

[0138] Refer to Figure 2 As shown, the present invention is a real-time monitoring device for ancient building restoration construction based on lidar, which can be used to execute the method content of Embodiment 1 of the present invention, including a control computer, a portable radar unit and a sliding track; the portable radar unit includes a linear scanner, a radar sensor and a power module; the radar sensor and the linear scanner are slidably installed on the sliding track; both the radar sensor and the linear scanner are electrically connected to the control computer; the control computer is built with processing software; the processing software performs acquisition parameter setting and system diagnosis; the processing software performs data processing and output visualization, is used to process radar data in real time, and can provide a reference output for ancient buildings in the form of displacement and velocity maps, and performs alarm processing when the ancient building exceeds the digital twin model; the output of the processing software can be exported to GIS, CAD or conventional software.

[0139] It should be noted that in the above system embodiment, the included units are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for easy distinction from each other and do not limit the protection scope of the present invention.

[0140] In addition, those of ordinary skill in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium.

[0141] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A real-time monitoring method for ancient building restoration construction based on laser radar, characterized in that: The steps include: Step S1: Conduct actual measurement on the ancient building to be restored and generate a digital twin model of the ancient building; Step S2: deploying a LiDAR unit at the construction site during the restoration of the ancient building; Step S3: collecting laser radar point cloud data from the laser radar unit; Step S4: filtering the laser radar point cloud data to obtain ancient building point cloud data; Step S5: gridding the ancient building point cloud data to obtain ancient building grid data; Step S6: Make corrections based on the difference between the ancient building grid data and the corresponding digital twin model.

2. According to claim 1, a real-time monitoring method for ancient building restoration construction based on laser radar is characterized in that: In step S1, the process of generating the digital twin model of the ancient building is as follows: Step S11: Based on the actual measurement of the ancient building, the BIM platform is used to complete the design of various parts of the ancient building, and a library of various parts of the ancient building is established; Step S12: Modeling various parts of the ancient building based on Dynamo underlying technology; Step S13: According to the ancient building drawings, assemble the parts to construct a three-dimensional digital twin model of the ancient building.

3. The real-time monitoring method for ancient building restoration construction based on laser radar according to claim 2 is characterized in that: In step S11, the process of constructing the three-dimensional digital twin model of the ancient building is as follows: Step S111: Based on the measured data of the ancient building, a multi-position, multi-angle, and multi-directional method is used to simulate the actual situation of the ancient building; Step S112: record the components required for the ancient building and draw a two-dimensional CAD drawing of the ancient building; Step S113: Import the CAD two-dimensional measured drawings into the three-dimensional modeling tool of the BIM platform to generate a three-dimensional digital twin model of the ancient building, and enter the generated three-dimensional models of the components required for the ancient building into the standard library.

4. The real-time monitoring method for ancient building restoration construction based on laser radar according to claim 1 is characterized in that: In step S3, the image collected by the laser radar unit is preprocessed to obtain the laser radar point cloud data. The specific process is as follows: Step S31: setting the size of the cropping area of ​​the image collected by the laser radar unit to an area of ​​782*694; Step S31: the cropped image is a three-band color image, and a threshold of 150 is set to perform a binary processing on the image; Step S31: extracting the ancient building area in the binary image by morphological reconstruction and calculating the centroid of the components.

5. The real-time monitoring method for ancient building restoration construction based on laser radar according to claim 1 is characterized in that: In step S4, the specific process of filtering the laser radar point cloud data to obtain the ancient building point cloud data is as follows: Step S41: obtaining the plane coordinates and elevation of each laser radar point in the laser radar point cloud data; Step S42: converting the plane coordinates of the laser radar point into the ancient building coordinates of the laser radar point according to Gaussian back calculation; Step S43: using bilinear interpolation to calculate the elevation value corresponding to the coordinates of the ancient building at the laser radar point, and determining it as the reference elevation value; Step S44: Calculate the absolute value of the difference between the elevation of the laser radar point and the corresponding reference elevation value, and determine the laser radar point whose absolute value of the difference is less than or equal to the preset difference threshold as the ancient building point data to obtain the ancient building point cloud data.

6. The real-time monitoring method for ancient building restoration construction based on laser radar according to claim 1 is characterized in that: In step S5, the ancient building point cloud data is gridded to obtain the ancient building grid data in the following process: Step S51: allocating each ancient building point data in the ancient building point cloud data to a grid through an affine matrix; Step S52: Calculate the average number of ancient building point data in each grid; Step S53: if the average number is greater than a preset first number value, the size of each grid in the grid is reduced, and each ancient building point data in the ancient building point cloud data is redistributed to the grid with the reduced grid size through an affine matrix; Step S54: if the average number is less than a preset second number value, enlarging the size of each grid in the grid, and redistributing each ancient building point data in the ancient building point cloud data to the grid with a reduced grid size through an affine matrix; wherein the second number value is less than the first number value; Step S55: If the average quantity is less than the first quantity value and greater than the second quantity value, the grid is determined as ancient building grid data.

7. The real-time monitoring method for ancient building restoration construction based on laser radar according to claim 1 is characterized in that: In step S6, according to the difference between the ancient building grid data and the corresponding digital twin model, the specific steps include: calculating the weight of each laser radar point corresponding to the same point position: In the formula, w i is the weight of the i-th lidar point, d i is the distance between the i-th lidar point and the corresponding digital twin model point, and n is the number of lidar points within the preset range; The difference is calculated in the following way: Where ΔZ is the difference between the ancient building grid data and the corresponding digital twin model, dZ i is the difference between the lidar point and the corresponding digital twin model.

8. The real-time monitoring method for ancient building restoration construction based on laser radar according to claim 1 is characterized in that: In step S6, the difference between the ancient building grid data and the corresponding digital twin model is calculated as follows: Step S61: generating a corresponding laser radar model in the three-dimensional digital twin model of the ancient building according to the actual deployment of the laser radar; Step S62: The BIM platform directly calculates the distance between the laser radar model and the three-dimensional digital twin model of the ancient building; Step S63: Compare the ancient building grid data acquired by the laser radar with the data of the digital twin model to determine whether the current ancient building is consistent with the digital twin model.

9. The real-time monitoring method for ancient building restoration construction based on laser radar according to claim 8 is characterized in that: In step S63, the process of judging whether the current ancient building is consistent with the digital twin model is as follows: Step 1: While ensuring that the positions of the fixed points and the moving points are determined, the least squares sum of the Laplace changes of all points is as small as possible, that is: min(||L(v)-Δ(v)||2; Step 2: Convert the Laplace transform equation Ax=b0 into Ax=a*K+b; Step 3: Solve the formula Arcmin||x-x0||, s.tAx=a*K+b; Step 4: According to the root-finding formula x = (A T A) -1 A T (ak+b), calculate the final k value; Step 5: Scale the mesh to make it consistent with the size of the point cloud and perform iterative registration; During the alignment, the ancient buildings are all proportional to the set parameter k. The calculated parameter k is divided by the proportion of the digital twin model to obtain the structural information of the corresponding position, perform error judgment on the structural information, and determine the parts with errors.

10. A real-time monitoring device for ancient building restoration construction based on laser radar, characterized in that: It includes a control computer, a portable radar unit and a sliding track; the portable radar unit includes a linear scanner, a radar sensor and a power module; the radar sensor and the linear scanner are slidably installed on the sliding track; the radar sensor and the linear scanner are electrically connected to the control computer; the control computer has built-in processing software; the processing software performs acquisition parameter settings and system diagnosis; the processing software performs data processing and output visualization, is used to process radar data in real time, and can provide ancient building reference output in the form of displacement and velocity diagrams, and perform alarm processing when the ancient building exceeds the digital twin model.

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