Point cloud-based automatic detection method, device and storage medium for building exterior wall quality

Abstract

The present invention discloses a method, device, and storage medium for automatic inspection of building exterior wall quality based on point clouds. The method comprises: selecting calibration points and setting the installation position of a point cloud scanning device according to the calibration points; performing point cloud scanning on the building exterior wall using the point cloud scanning device to obtain point cloud data of the building exterior wall; performing corresponding processing on the point cloud data of the building exterior wall to detect the flatness and verticality of the building exterior wall, and generating flatness inspection results and verticality inspection results of the building exterior wall; finally, constructing a 3D model of the building exterior wall based on the point cloud data of the building exterior wall and calculating the dimensional data of the building exterior wall; then, combining the flatness inspection results and verticality inspection results of the building exterior wall to generate a quality inspection report of the building exterior wall, and displaying the quality inspection report and the 3D model of the building exterior wall through a visual display platform. The present invention can solve the problems of low efficiency and large errors in manual inspection of building exterior walls.

Description

Technical Field

[0001] The present invention relates to house quality inspection, and in particular to a point cloud-based automatic inspection method, device and storage medium for building exterior wall quality. Background Art

[0002] Currently, traditional automated inspection methods for building exterior wall quality typically rely on manual observation and measurement tools, such as levels, distance meters, goniometers, and visual inspection. During the inspection process, inspectors must individually check the exterior walls for flatness, verticality, and any cracks or other defects. They also use simple instruments like levels to assess the horizontal and vertical state of the exterior wall surface. However, this manual inspection process is inefficient, and the results are subject to subjectivity and easily influenced by human factors, limited by the operator's experience and judgment. Even for the same cast object, different inspectors may have different judgments on the exterior wall's flatness or verticality, making it impossible to ensure consistent and reliable inspection results. Furthermore, in some specific scenarios, such as the exterior walls of high-rise and super-high-rise buildings, manual climbing may be required to complete the measurement, posing a safety hazard. Summary of the Invention

[0003] In order to overcome the shortcomings of the existing technology, one of the purposes of the present invention is to provide a point cloud-based automatic detection method for building exterior wall quality, which can solve the problems of low efficiency and large errors in the existing traditional method of automatic detection of building exterior wall quality mainly based on manual operation.

[0004] The second purpose of the present invention is to provide a point cloud-based automatic detection device for building exterior wall quality, which can solve the problems of low efficiency and large errors in the existing traditional method of automatic detection of building exterior wall quality mainly based on manual operation.

[0005] The third object of the present invention is to provide a computer-readable storage medium that can solve the problems of low efficiency and large errors in the existing traditional method of automatic detection of building exterior wall quality based on manual operation.

[0006] One of the purposes of the present invention is achieved by the following technical solution:

[0007] A point cloud-based automatic detection method for building exterior wall quality, the automatic detection method for building exterior wall quality comprising:

[0008] Point cloud acquisition step: selecting calibration points and setting the installation position of the point cloud scanning device according to the calibration points, and performing point cloud scanning on the building exterior wall by the point cloud scanning device to obtain point cloud data of the building exterior wall;

[0009] Flatness detection step: dividing the point cloud data of the building exterior wall into multiple point cloud subsets, performing plane fitting on each point cloud subset to obtain a point cloud plane, and judging whether each point cloud plane meets the requirements according to the flatness detection standard of the building exterior wall, and then generating a flatness detection result of the building exterior wall based on the judgment results of the multiple point cloud planes;

[0010] A verticality detection step comprises: constructing a standard horizontal plane according to the installation position of the point cloud scanning device, and calculating the inclination angle between the points in the point cloud data of the building exterior wall and the standard horizontal plane, thereby obtaining the inclination angle between the building exterior wall and the standard horizontal plane and combining it with the verticality detection standard of the building exterior wall to obtain the verticality detection result of the building exterior wall;

[0011] Quality report display step: construct a 3D model of the building exterior wall based on the point cloud data of the building exterior wall and generate a quality inspection report of the building exterior wall in combination with the flatness inspection results and verticality inspection results of the building exterior wall, and then display the quality inspection report and 3D model of the building exterior wall through a visual display platform.

[0012] Furthermore, the flatness detection step specifically includes:

[0013] Subset division step: grouping the point cloud data of the building exterior wall into a plurality of point cloud subsets according to system preset rules;

[0014] Plane fitting step: constructing a standard vertical plane according to the installation position of the point cloud scanning device, and performing plane fitting on some or all points in each point cloud subset by the least squares method according to the standard vertical plane to obtain a point cloud plane corresponding to each point cloud subset;

[0015] Plane judgment step: calculating the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and then judging whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset and the flatness detection standard of the building exterior wall;

[0016] The step of obtaining the flatness detection result is as follows: obtaining the point cloud plane that does not meet the requirements to generate the flatness detection result of the building exterior wall.

[0017] Furthermore, the step of determining whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset specifically includes: firstly calculating the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and determining whether the corresponding deviation distance is greater than a preset threshold; if so, recording the point as a deviation point; if not, recording the point as a normal point;

[0018] Then, the number of deviation points in each point cloud subset is counted, and when the number of deviation points in the corresponding point cloud subset is greater than a preset number, the point cloud plane corresponding to the point cloud subset does not meet the requirements.

[0019] Furthermore, the point cloud plane judgment step also includes: when there are any two point cloud planes that meet the requirements and the two point cloud planes are adjacent, the point cloud subsets corresponding to the two point cloud planes are combined to form a new point cloud subset and plane fitting is performed based on the new point cloud subset to obtain a new point cloud plane, and then the point cloud plane step is executed.

[0020] Furthermore, the quality report display step also includes: obtaining the uneven areas of the building exterior wall based on the point cloud subset corresponding to each point cloud plane that does not meet the requirements and the point cloud data of the building exterior wall; and highlighting the uneven areas of the building exterior wall in the 3D model of the building exterior wall and then displaying the 3D model of the building exterior wall through a visual display platform.

[0021] Furthermore, the plane fitting step also includes: constructing a standard vertical plane according to the installation position of the scanning device, and calculating the horizontal distance between the points in each point cloud subset and the standard vertical plane, and selecting some or all of the points in the corresponding point cloud subset according to the horizontal distance, so as to perform plane fitting based on some or all of the points in the corresponding point cloud subset to obtain the corresponding point cloud plane.

[0022] Furthermore, the point cloud acquisition step also includes: preprocessing the point cloud data of the building exterior wall; wherein the preprocessing includes deleting invalid points in the point cloud data of the building exterior wall, and deleting irrelevant points in the point cloud data of the building exterior wall according to the characteristic identifiers on the building exterior wall.

[0023] Furthermore, the verticality detection step specifically includes: determining whether each point in the point cloud data of the building exterior wall is an abnormal point based on the inclination angle between the point and the standard horizontal plane and the verticality detection standard of the building exterior wall, and counting the position of each abnormal point on the building exterior wall, so as to obtain a verticality abnormal area of ​​the building exterior wall based on the distribution positions of multiple abnormal points on the building exterior wall, and obtaining a verticality detection result of the building exterior wall based on the verticality abnormal area of ​​the building exterior wall;

[0024] The quality report display step further includes: highlighting the abnormal verticality area of ​​the building exterior wall according to the abnormal verticality area of ​​the building exterior wall and the 3D model of the building exterior wall and displaying it through a visual display platform.

[0025] The second object of the present invention is achieved by adopting the following technical solution:

[0026] A point cloud-based automatic detection device for building exterior wall quality includes a memory and a processor, wherein the memory stores an automatic detection program for building exterior wall quality running on the processor, and the automatic detection program for building exterior wall quality is a computer program. When the processor executes the automatic detection program for building exterior wall quality, the steps of the point cloud-based automatic detection method for building exterior wall quality adopted as one of the purposes of the present invention are implemented.

[0027] The third object of the present invention is achieved by adopting the following technical solution:

[0028] A computer-readable storage medium stores a program for automatically detecting the quality of building exterior walls. The program is a computer program. When executed by a processor, the program implements the steps of a point cloud-based automatic detection method for the quality of building exterior walls as one of the purposes of the present invention.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] This invention utilizes point cloud scanning technology to generate point cloud data of building exterior walls. Based on this point cloud data, the system automatically monitors the flatness and verticality of the building exterior walls, thereby enabling quality inspection of the building exterior walls. This addresses the inefficiencies and errors inherent in existing manual inspections or those performed with specialized tools. This invention is particularly suitable for inspecting the exterior walls of taller and larger buildings, enabling digital, intelligent, and visual automated inspections of building exterior walls. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Flowchart of the automatic detection method for building exterior wall quality based on point cloud provided by the present invention;

[0032] Figure 2 for Figure 1 Flowchart of step S2 in FIG.

[0033] Figure 3 This is a schematic diagram of the flatness measurement results of a partial area of ​​a wall provided by the present invention;

[0034] Figure 4 A heat map of the detection results of a partial area of ​​a wall provided by the present invention;

[0035] Figure 5 A schematic diagram of suggested repairs for defects in a partial section of a wall provided by the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0037] Example 1

[0038] The present invention uses point cloud scanning technology to realize the quality inspection of building exterior walls, solving the problems of poor efficiency, low accuracy and personal safety hazards in existing manual inspection. Specifically, the present invention provides a preferred embodiment, a point cloud-based automatic inspection method for building exterior wall quality, such as Figure 1 Shown, including:

[0039] Step S1: select calibration points and set the installation position of the point cloud scanning device according to the calibration points, and perform point cloud scanning on the building exterior wall through the point cloud scanning device to obtain point cloud data of the building exterior wall.

[0040] Among them, the point cloud scanning equipment is a three-dimensional laser scanning equipment, which scans the exterior wall of the building to obtain the point cloud data of the exterior wall of the building, so as to construct a 3D model of the exterior wall of the building and realize the quality inspection of the exterior wall of the building.

[0041] When selecting the building exterior wall that needs to be inspected, first select the calibration points based on the reference object, determine the installation position of the 3D laser scanning equipment based on the calibration points, and then start the 3D laser scanning equipment to scan the building exterior wall to obtain the point cloud data of the building exterior wall.

[0042] More specifically, point cloud data of building exterior walls refers to point cloud data containing information about the exterior surface of the building exterior walls. For example, at a construction site, 3D laser scanning equipment can be installed to scan the building exterior walls and obtain point cloud data. This method does not require climbing or touching the building exterior walls, and can therefore be used on both completed and ongoing building exterior walls.

[0043] In addition, since point cloud scanning may be affected by noise, such as changes in external ambient light, dust and other obstacles, the scanned point cloud data may contain invalid points or noise points. Therefore, after obtaining the point cloud data of the building's exterior wall, the point cloud data must first be preprocessed to remove unnecessary noise points or invalid points to ensure the stability and continuity of the point cloud data. More specifically, preprocessing includes denoising and data alignment. Denoising refers to removing unnecessary noise points to ensure the stability and continuity of point cloud data; data alignment refers to aligning point cloud data from multiple scanning perspectives to form a complete point cloud data model of the building's exterior wall. Through the preprocessing steps, the errors in point cloud data caused by acquisition angles or equipment placement can be effectively reduced, laying an accurate data foundation for subsequent defect detection.

[0044] In addition, there may be feature markers such as doors and windows on the exterior walls of buildings. For the areas where feature markers such as doors and windows are located, it is generally not necessary to perform flatness and verticality detection on them. Therefore, when feature markers are present on the exterior walls of buildings, it is also necessary to delete the points in the area where the corresponding feature markers are located from the point cloud data, reducing the number of points in subsequent data processing and improving system processing efficiency. In addition, when there are feature markers on the exterior walls of buildings, the part of the feature markers is first removed from the point cloud data, and then the remaining point cloud data is divided according to the size of the preset point cloud subsets to form multiple point cloud subsets. Among them, feature markers include windows, balconies, etc. For the parts of the exterior walls of buildings where feature markers are present, flatness detection is not required. Therefore, before performing flatness detection, the feature markers of the exterior walls of buildings are first identified and their positions are determined, and then the points corresponding to the feature markers are removed from the point cloud data of the exterior walls of buildings to obtain the point cloud data of the exterior walls of buildings.

[0045] Step S2: Divide the point cloud data of the building exterior wall into multiple point cloud subsets, perform plane fitting on each point cloud subset to obtain a point cloud plane, and judge whether each point cloud plane meets the requirements according to the flatness detection standard of the building exterior wall, and then generate the flatness detection result of the building exterior wall based on the judgment results of the multiple point cloud planes.

[0046] More specifically, when performing plane fitting based on multiple points, the present invention uses the least squares plane fitting method to perform plane fitting on the points in each point cloud subset to obtain the corresponding point cloud plane. Specifically, when performing plane fitting, multiple points are selected, specifically, by constructing a coordinate system, such as by constructing a coordinate system with the installation position of a three-dimensional laser scanner as the center point. The points for plane fitting can then be selected based on the horizontal distance between each point in the point cloud plane and the center point of the coordinate system. Specifically, the horizontal distance between each point in the point cloud plane and the center point of the coordinate system is calculated, and it is determined whether the horizontal distance is less than a preset value. If so, the point is added to the plane fitting point set. Then, the least squares plane fitting method is used to perform plane fitting based on the points in the plane fitting point set to obtain the point cloud plane.

[0047] Similarly, the point cloud plane corresponding to each point cloud subset can be obtained according to the above methods.

[0048] More preferably, in the present invention, the point cloud data of the building exterior wall is divided into multiple point cloud subsets based on a preset point cloud subset size. The size of the point cloud subset is related to the flatness evaluation criteria for the building exterior wall. Since the balance requirements for building exterior walls of different specifications vary, the size of the point cloud subset can be set empirically, specifically based on experience and experimentation. For example, the point cloud data of the building exterior wall can be continuously divided into M×M pixel points.

[0049] Step S3: construct a standard horizontal plane according to the installation position of the point cloud scanning device, and calculate the inclination angle between the points in the point cloud data of the building exterior wall and the standard horizontal plane, thereby obtaining the inclination angle between the building exterior wall and the standard horizontal plane and combining it with the verticality detection standard of the building exterior wall to obtain the verticality detection result of the building exterior wall.

[0050] Specifically, building exterior wall quality inspections not only check balance but also verticality. For taller buildings, verticality deviations can gradually increase. Large verticality deviations can pose a potential threat to building safety. This invention determines whether the verticality of a building's exterior wall meets preset requirements by calculating the inclination angle of the deviated exterior wall relative to a standard surface, thereby providing more accurate verticality test results.

[0051] The standard horizontal plane is constructed based on the installation location of the 3D laser scanning device, using the geodetic coordinate system as its coordinate system. Specifically, the standard horizontal plane is constructed using the horizontal plane XOY in the coordinate system, centered at the installation location of the 3D laser scanning device. Once the standard horizontal plane is determined, the inclination angle between each point in the point cloud data of the building's exterior wall and the standard horizontal plane is calculated. The verticality of the building's exterior wall is then determined based on this inclination angle, and the verticality test result is then combined with the verticality determination results to determine the verticality of the building's exterior wall.

[0052] Step S4: construct a 3D model of the building exterior wall based on the point cloud data of the building exterior wall and generate a quality inspection report of the building exterior wall in combination with the flatness inspection results and verticality inspection results of the building exterior wall, and display the quality inspection report of the building exterior wall and the 3D model of the building exterior wall through a visual display platform.

[0053] By displaying the quality inspection report and 3D model of the building's exterior wall to construction workers through a visual display platform, construction workers can promptly discover abnormal areas and promptly repair the building's exterior wall during the construction process to ensure the safety of the building's exterior wall. At the same time, they can also promptly discover building exterior walls with hidden dangers to ensure the safety of construction workers.

[0054] At the same time, when inspecting the quality of building exterior walls, the present invention can not only detect the flatness and verticality, but also perform inspections based on the dimensional data of the 3D model to determine the construction progress of the building exterior walls.

[0055] More preferably, the present invention also provides a specific implementation process for detecting the flatness of the building exterior wall, that is, Figure 2 As shown, step S2 also includes:

[0056] Step S21: grouping the point cloud data of the building exterior wall into multiple point cloud subsets according to system preset rules.

[0057] Specifically, for example, the point cloud data of a building exterior wall is grouped into multiple point cloud subsets based on an M×M pixel size. By segmenting a larger area of ​​the building exterior wall into multiple smaller areas, the flatness of each small area is determined based on the point cloud subsets of the multiple small areas, thereby determining the flatness of the entire building exterior wall.

[0058] Step S22: construct a standard vertical plane according to the installation position of the point cloud scanning device, and perform plane fitting on some or all points in each point cloud subset by the least squares method based on the standard vertical plane as a reference to obtain the point cloud plane corresponding to each point cloud subset.

[0059] Specifically, a standard vertical plane is constructed based on the installation position of the scanning device. The horizontal distance between each point in the point cloud subset and the standard vertical plane is calculated. Based on this horizontal distance, some or all points in the corresponding point cloud subset are selected for plane fitting to obtain the corresponding point cloud plane. The standard vertical plane is determined by calibration points, so the horizontal distance between the building exterior wall and the standard vertical plane is fixed. Therefore, the corresponding points are selected based on this horizontal distance to construct the point cloud plane, ensuring that the fitted point cloud plane is uniform.

[0060] Step S23: Calculate the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and then determine whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset and the flatness detection standard of the building exterior wall.

[0061] Specifically, once a point cloud plane is constructed, its conformance is determined based on the deviation distance between the points in the point cloud subset and the corresponding point cloud plane. The deviation distance refers to the distance between a point in the point cloud subset and the corresponding point cloud plane, and this distance is used to determine whether the point has a deviation. For example, under normal circumstances, when the flatness of the wall area corresponding to the point cloud subset is acceptable, the distance between the corresponding point and the corresponding point cloud plane is zero or within the allowable error range.

[0062] More specifically, determining whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset in step S23 further includes: first calculating the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and determining whether the corresponding deviation distance is greater than a preset threshold; if so, record the point as a deviation point; if not, record the point as a normal point;

[0063] Then, the number of deviation points in each point cloud subset is counted, and when the number of deviation points in the corresponding point cloud subset is greater than a preset number, the point cloud plane corresponding to the point cloud subset does not meet the requirements.

[0064] That is, when the number of deviation points in a point cloud subset exceeds a preset value, it is considered that the point cloud plane does not meet the requirements, that is, it is not flat.

[0065] Step S24: Obtain the point cloud plane that does not meet the requirements to generate a flatness detection result of the building exterior wall.

[0066] Specifically, the flatness detection result of the building exterior wall is obtained based on the obtained point cloud plane that does not meet the requirements.

[0067] More preferably, after step S23, the method further includes: when there are any two point cloud planes that meet the requirements and the two point cloud planes are adjacent, the point cloud subsets corresponding to the two point cloud planes are combined to form a new point cloud subset, and plane fitting is performed based on the new point cloud subset to obtain a new point cloud plane, and then the point cloud plane step is executed. That is, when a small point cloud plane meets the requirements, the point cloud subsets of the two adjacent point cloud planes can be combined to form a large point cloud subset, and then the plane fitting is performed in the same manner and the point cloud plane of the corresponding point cloud subset is again determined to meet the requirements. This avoids the situation where the flatness of a small area on the building exterior wall meets the requirements while the flatness of a larger area does not meet the requirements, thereby making the flatness detection result of the building exterior wall more accurate.

[0068] Furthermore, step S4 also includes: obtaining uneven areas on the building exterior wall based on the point cloud subset corresponding to each point cloud plane that does not meet the requirements and the point cloud data of the building exterior wall; and highlighting the uneven areas on the building exterior wall in the 3D model of the building exterior wall, and then displaying the 3D model of the building exterior wall through a visualization display platform.

[0069] Specifically, based on the point cloud subset and point cloud data corresponding to the point cloud plane that does not meet the requirements, it can be concluded that there are uneven areas on the building's exterior wall. In this way, the abnormal areas can be highlighted on the 3D model and then displayed through a visual display platform, making the detection results more intuitive and convenient for subsequent maintenance by construction personnel.

[0070] Similarly, step S3 also includes: determining whether each point is an abnormal point based on the inclination angle between the point in the point cloud data of the building exterior wall and the standard horizontal plane and the verticality detection standard of the building exterior wall, and counting the position of each abnormal point on the building exterior wall, so as to obtain the verticality abnormal area of ​​the building exterior wall according to the distribution positions of multiple abnormal points on the building exterior wall, and obtain the verticality detection result of the building exterior wall according to the verticality abnormal area of ​​the building exterior wall.

[0071] Step S4 also includes: highlighting the abnormal verticality area of ​​the building exterior wall according to the abnormal verticality area of ​​the building exterior wall and the 3D model of the building exterior wall and displaying it through a visual display platform.

[0072] This invention proposes an automated method for inspecting the flatness and verticality of building exterior walls based on three-dimensional point clouds, enabling high-precision inspection of the flatness and verticality of building exterior walls. This method addresses the inefficiency and low accuracy of traditional manual inspection methods. Specifically, the invention employs a least-squares plane fitting method for flatness inspection of localized areas of the building exterior wall. Furthermore, verticality inspection is achieved by comparing the point cloud to a reference plane, overcoming the significant environmental impact of traditional inspection equipment. This method achieves higher monitoring accuracy and efficiency, providing a highly effective means of controlling exterior wall construction quality and ensuring building safety and stability. Flatness inspection is a key metric in automated inspection of building exterior wall quality. This method utilizes a least-squares plane fitting method on scanned point cloud data to perform plane fitting on localized point clouds, enabling flatness inspection and ensuring the ability to generate a flatness report for the building exterior wall surface. This method not only accurately identifies subtle flatness deviations but also generates a comprehensive flatness report, providing high-resolution data support for quality inspection during construction. At the same time, through the least squares plane fitting algorithm, the system can adapt to subtle variations in different areas and obtain flatness information for each local area. Compared with the existing technology, the present invention can achieve higher accuracy in local detection for rough evaluation of large-area data, effectively meeting the needs of high-quality exterior wall inspection. The present invention can achieve higher accuracy in local detection, effectively meeting the needs of high-quality building exterior wall inspection.

[0073] Furthermore, in verticality testing, the present invention uses precise point cloud comparisons with reference planes, enabling the system to provide accurate tilt angle data and generate deviation analysis reports. This reference plane-based comparison overcomes the interference of the external environment and the placement of acquisition equipment on the test results, making it particularly suitable for verticality testing of high-rise buildings. Furthermore, the present invention displays the test results in a visual report format, using a three-dimensional visualization interface. The test report not only includes numerical data on flatness and verticality, but also intuitively presents problem areas through color gradients and area identification.

[0074] At the same time, the quality inspection method for building exterior walls provided by the present invention can be applied to quality monitoring throughout the entire construction process. By using the exterior wall point cloud data acquired in real time through three-dimensional laser scanning, the system can dynamically monitor the flatness and verticality of the exterior wall during the construction process, and promptly detect problems during construction. Specifically, for example, after the construction of each wall is completed, the system can immediately analyze the flatness and verticality of the wall. Once unqualified areas are found, the construction team can immediately take repair measures to avoid subsequent rework costs. This real-time quality control is particularly important in large-scale construction projects, and can significantly improve construction quality and ensure that the building meets design standards.

[0075] Furthermore, in addition to detecting the flatness and verticality of building exterior walls, the present invention also generates a quality inspection report based on the inspection results. This report displays the distribution and severity of existing problems on the building exterior walls through a visual interface, helping engineers quickly locate problem areas for repair. The quality inspection report includes the wall inspection results and a schematic diagram of recommended defect repairs. Specifically, the inspection results for a portion of a wall surface provided in this embodiment are shown in Table 1:

[0076] Detection range Total number of detection points Explosive points Pass rate Total length Explosive feet Score Area weight 4-6 floors 297730 6977 97.66% 265 30 98 20% 7th-9th floors 296269 19876 93.29% 265 29 95 20% 10th-12th floors 289474 50773 82.46% 262 49 88 20% 13th-15th floors 252837 16344 93.54% 263 45 96 20% 16-18th floors 205831 19469 90.54% 263 42 94 20%

[0077] Table 1

[0078] Similarly, the wall detection results can also be displayed through a visual schematic diagram, such as Figure 3 The figure shows the wall flatness test results for the 35th to 37th floors. Different colors are used to mark the concave and convex results of each point on the wall. The horizontal axis represents the width of the wall in mm, and the vertical axis represents the number of floors. In addition, the point cloud data is simulated to achieve the results obtained by manual measurement using a ruler, as shown in the figure below. Figure 3 The red line in the chart indicates that the measurement result is abnormal, and the yellow background number such as 7.8 indicates that the result is abnormal to the extent that it exceeds the scale.

[0079] In addition, the present invention can also display the measured result of a wall in the form of a thermal map, and display the thermal map of the detection result of the wall in different colors, such as Figure 4 As shown in the figure, the red area indicates that the wall is in the convex area, and the blue area indicates that the wall is in the concave area. Different wall surfaces correspond to different scales, which can be set according to actual conditions.

[0080] Furthermore, the present invention also provides a schematic diagram of a wall surface flatness defect repair suggestion, such as Figure 5 As shown, the red area indicates the area that needs to be modified. According to the test results, the red area is a raised area (for example, a 16mm raised area), so the raised area of ​​the area needs to be reduced. By giving a defect repair suggestion diagram, data support is provided for subsequent wall repairs.

[0081] The automatic detection of building exterior wall quality provided by the present invention has the characteristics of automation and precision, which makes the quality detection of building exterior walls enter an era of high efficiency and intelligence, and provides reliable protection for the safety and quality of building exterior walls and buildings.

[0082] Example 2

[0083] The point cloud-based automatic detection device for building exterior wall quality includes a memory and a processor. The memory stores an automatic detection program for building exterior wall quality running on the processor. The automatic detection program for building exterior wall quality is a computer program. When the processor executes the automatic detection program for building exterior wall quality, the following steps are implemented:

[0084] Point cloud acquisition steps: selecting calibration points and setting the installation position of the point cloud scanning device according to the calibration points, and performing point cloud scanning on the building exterior wall by the point cloud scanning device to obtain point cloud data of the building exterior wall;

[0085] Flatness detection step: Divide the point cloud data of the building exterior wall into multiple point cloud subsets, perform plane fitting on each point cloud subset to obtain a point cloud plane, and judge whether each point cloud plane meets the requirements according to the flatness detection standard of the building exterior wall. Then, generate the flatness detection result of the building exterior wall based on the judgment results of the multiple point cloud planes;

[0086] Verticality detection steps: Construct a standard horizontal plane based on the installation position of the point cloud scanning device, and calculate the inclination angle between the points in the point cloud data of the building exterior wall and the standard horizontal plane. Then, the inclination angle between the building exterior wall and the standard horizontal plane is obtained and combined with the verticality detection standard of the building exterior wall to obtain the verticality detection result of the building exterior wall;

[0087] Quality report display steps: construct a 3D model of the building exterior wall based on the point cloud data of the building exterior wall and generate a quality inspection report of the building exterior wall in combination with the flatness inspection results and verticality inspection results of the building exterior wall, and display the quality inspection report and 3D model of the building exterior wall through a visual display platform.

[0088] Furthermore, the flatness detection step specifically includes:

[0089] Subset division step: grouping the point cloud data of the building exterior wall into multiple point cloud subsets according to the system preset rules;

[0090] Plane fitting step: construct a standard vertical plane according to the installation position of the point cloud scanning device, and perform plane fitting on some or all points in each point cloud subset using the least squares method based on the standard vertical plane to obtain the point cloud plane corresponding to each point cloud subset;

[0091] Plane judgment step: Calculate the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and then judge whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset and the flatness detection standard of the building exterior wall;

[0092] Steps for obtaining flatness detection results: Obtain the point cloud plane that does not meet the requirements to generate the flatness detection results of the building exterior wall.

[0093] Furthermore, judging whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset specifically includes: firstly calculating the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and judging whether the corresponding deviation distance is greater than a preset threshold, if so, recording the point as a deviation point; if not, recording the point as a normal point;

[0094] Then, the number of deviation points in each point cloud subset is counted, and when the number of deviation points in the corresponding point cloud subset is greater than a preset number, the point cloud plane corresponding to the point cloud subset does not meet the requirements.

[0095] Furthermore, the point cloud plane judgment step also includes: when there are any two point cloud planes that meet the requirements and the two point cloud planes are adjacent, the point cloud subsets corresponding to the two point cloud planes are combined to form a new point cloud subset and plane fitting is performed based on the new point cloud subset to obtain a new point cloud plane, and then the point cloud plane step is executed.

[0096] Furthermore, the quality report display step also includes: obtaining uneven areas on the building exterior wall based on the point cloud subset corresponding to each point cloud plane that does not meet the requirements and the point cloud data of the building exterior wall; and highlighting the uneven areas on the building exterior wall in the 3D model of the building exterior wall, and then displaying the 3D model of the building exterior wall through a visual display platform.

[0097] Furthermore, the plane fitting step also includes: constructing a standard vertical plane according to the installation position of the scanning device, and calculating the horizontal distance between the points in each point cloud subset and the standard vertical plane, and selecting some or all of the points in the corresponding point cloud subset according to the horizontal distance, so as to perform plane fitting based on some or all of the points in the corresponding point cloud subset to obtain the corresponding point cloud plane.

[0098] Furthermore, the point cloud acquisition step also includes: preprocessing the point cloud data of the building exterior wall; wherein the preprocessing includes deleting invalid points in the point cloud data of the building exterior wall, and deleting irrelevant points in the point cloud data of the building exterior wall according to the characteristic markers on the building exterior wall.

[0099] Furthermore, the verticality detection step specifically includes: determining whether each point in the point cloud data of the building exterior wall is an abnormal point based on an inclination angle between the point and the standard horizontal plane and the verticality detection standard of the building exterior wall, and counting the position of each abnormal point on the building exterior wall, so as to obtain a verticality abnormal area of ​​the building exterior wall based on the distribution positions of multiple abnormal points on the building exterior wall, and obtaining a verticality detection result of the building exterior wall based on the verticality abnormal area of ​​the building exterior wall;

[0100] The quality report display step also includes: highlighting the abnormal verticality area of ​​the building exterior wall according to the abnormal verticality area of ​​the building exterior wall and the 3D model of the building exterior wall and displaying it through a visual display platform.

[0101] Example 3

[0102] A computer-readable storage medium stores a program for automatically detecting the quality of exterior walls of buildings. The program is a computer program that, when executed by a processor, implements the following steps:

[0103] Point cloud acquisition steps: selecting calibration points and setting the installation position of the point cloud scanning device according to the calibration points, and performing point cloud scanning on the building exterior wall by the point cloud scanning device to obtain point cloud data of the building exterior wall;

[0104] Flatness detection step: Divide the point cloud data of the building exterior wall into multiple point cloud subsets, perform plane fitting on each point cloud subset to obtain a point cloud plane, and judge whether each point cloud plane meets the requirements according to the flatness detection standard of the building exterior wall. Then, generate the flatness detection result of the building exterior wall based on the judgment results of the multiple point cloud planes;

[0105] Verticality detection steps: Construct a standard horizontal plane based on the installation position of the point cloud scanning device, and calculate the inclination angle between the points in the point cloud data of the building exterior wall and the standard horizontal plane. Then, the inclination angle between the building exterior wall and the standard horizontal plane is obtained and combined with the verticality detection standard of the building exterior wall to obtain the verticality detection result of the building exterior wall;

[0106] Quality report display steps: construct a 3D model of the building exterior wall based on the point cloud data of the building exterior wall and generate a quality inspection report of the building exterior wall in combination with the flatness inspection results and verticality inspection results of the building exterior wall, and display the quality inspection report and 3D model of the building exterior wall through a visual display platform.

[0107] Furthermore, the flatness detection step specifically includes:

[0108] Subset division step: grouping the point cloud data of the building exterior wall into multiple point cloud subsets according to the system preset rules;

[0109] Plane fitting step: construct a standard vertical plane according to the installation position of the point cloud scanning device, and perform plane fitting on some or all points in each point cloud subset using the least squares method based on the standard vertical plane to obtain the point cloud plane corresponding to each point cloud subset;

[0110] Plane judgment step: Calculate the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and then judge whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset and the flatness detection standard of the building exterior wall;

[0111] Steps for obtaining flatness detection results: Obtain the point cloud plane that does not meet the requirements to generate the flatness detection results of the building exterior wall.

[0112] Furthermore, judging whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset specifically includes: firstly calculating the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and judging whether the corresponding deviation distance is greater than a preset threshold, if so, recording the point as a deviation point; if not, recording the point as a normal point;

[0113] Then, the number of deviation points in each point cloud subset is counted, and when the number of deviation points in the corresponding point cloud subset is greater than a preset number, the point cloud plane corresponding to the point cloud subset does not meet the requirements.

[0114] Furthermore, the point cloud plane judgment step also includes: when there are any two point cloud planes that meet the requirements and the two point cloud planes are adjacent, the point cloud subsets corresponding to the two point cloud planes are combined to form a new point cloud subset and plane fitting is performed based on the new point cloud subset to obtain a new point cloud plane, and then the point cloud plane step is executed.

[0115] Furthermore, the quality report display step also includes: obtaining uneven areas on the building exterior wall based on the point cloud subset corresponding to each point cloud plane that does not meet the requirements and the point cloud data of the building exterior wall; and highlighting the uneven areas on the building exterior wall in the 3D model of the building exterior wall, and then displaying the 3D model of the building exterior wall through a visual display platform.

[0116] Furthermore, the plane fitting step also includes: constructing a standard vertical plane according to the installation position of the scanning device, and calculating the horizontal distance between the points in each point cloud subset and the standard vertical plane, and selecting some or all of the points in the corresponding point cloud subset according to the horizontal distance, so as to perform plane fitting based on some or all of the points in the corresponding point cloud subset to obtain the corresponding point cloud plane.

[0117] Furthermore, the point cloud acquisition step also includes: preprocessing the point cloud data of the building exterior wall; wherein the preprocessing includes deleting invalid points in the point cloud data of the building exterior wall, and deleting irrelevant points in the point cloud data of the building exterior wall according to the characteristic markers on the building exterior wall.

[0118] Furthermore, the verticality detection step specifically includes: determining whether each point in the point cloud data of the building exterior wall is an abnormal point based on an inclination angle between the point and the standard horizontal plane and the verticality detection standard of the building exterior wall, and counting the position of each abnormal point on the building exterior wall, so as to obtain a verticality abnormal area of ​​the building exterior wall based on the distribution positions of multiple abnormal points on the building exterior wall, and obtaining a verticality detection result of the building exterior wall based on the verticality abnormal area of ​​the building exterior wall;

[0119] The quality report display step also includes: highlighting the abnormal verticality area of ​​the building exterior wall according to the abnormal verticality area of ​​the building exterior wall and the 3D model of the building exterior wall and displaying it through a visual display platform.

[0120] The above embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and replacements made by technicians in this field on the basis of the present invention fall within the scope of protection required by the present invention.

Claims

1. The automatic detection method of building exterior wall quality based on point cloud is characterized by: The automatic detection method for building exterior wall quality comprises: The point cloud acquisition step includes selecting calibration points and setting an installation position of a point cloud scanning device according to the calibration points, performing a point cloud scan of a building exterior wall using the point cloud scanning device to obtain point cloud data of the building exterior wall; and preprocessing the point cloud data of the building exterior wall; wherein the preprocessing includes deleting invalid points in the point cloud data of the building exterior wall, and deleting irrelevant points in the point cloud data of the building exterior wall according to characteristic markers on the building exterior wall. Flatness detection step: dividing the point cloud data of the building exterior wall into multiple point cloud subsets, performing plane fitting on each point cloud subset to obtain a point cloud plane, and judging whether each point cloud plane meets the requirements according to the flatness detection standard of the building exterior wall, and then generating a flatness detection result of the building exterior wall based on the judgment results of the multiple point cloud planes; the flatness detection step specifically includes: Subset division step: grouping the point cloud data of the building exterior wall into a plurality of point cloud subsets according to system preset rules; Plane fitting step: constructing a standard vertical plane according to the installation position of the point cloud scanning device, and performing plane fitting on some or all points in each point cloud subset by the least squares method according to the standard vertical plane to obtain a point cloud plane corresponding to each point cloud subset; Plane judgment step: calculating the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and then judging whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset and the flatness detection standard of the building exterior wall; A step of obtaining a flatness detection result: obtaining a point cloud plane that does not meet the requirements to generate a flatness detection result of the building exterior wall; After the plane determination step, the method further includes: when there are any two point cloud planes that meet the requirements and the two point cloud planes are adjacent, combining the point cloud subsets corresponding to the two point cloud planes to form a new point cloud subset, performing plane fitting based on the new point cloud subset to obtain a new point cloud plane, and then executing the plane determination step; The verticality detection step comprises: constructing a standard horizontal plane according to the installation position of the point cloud scanning device, and calculating the inclination angle between the point in the point cloud data of the building exterior wall and the standard horizontal plane, thereby obtaining the inclination angle between the building exterior wall and the standard horizontal plane and obtaining the verticality detection result of the building exterior wall in combination with the verticality detection standard of the building exterior wall; the verticality detection step comprises: determining whether each point is an abnormal point according to the inclination angle between the point in the point cloud data of the building exterior wall and the standard horizontal plane and the verticality detection standard of the building exterior wall, and counting the position of each abnormal point on the building exterior wall, so as to obtain the verticality abnormal area of ​​the building exterior wall according to the distribution position of multiple abnormal points on the building exterior wall, and obtaining the verticality detection result of the building exterior wall according to the verticality abnormal area of ​​the building exterior wall; Quality report display step: constructing a 3D model of the building exterior wall according to the point cloud data of the building exterior wall and generating a quality inspection report of the building exterior wall in combination with the flatness inspection results and the verticality inspection results of the building exterior wall, and then displaying the quality inspection report and the 3D model of the building exterior wall through a visual display platform; the quality report display step also includes: highlighting the abnormal verticality area of ​​the building exterior wall according to the abnormal verticality area of ​​the building exterior wall and the 3D model of the building exterior wall and displaying it through a visual display platform.

2. The automatic detection method for building exterior wall quality based on point cloud according to claim 1 is characterized in that: The method of determining whether each point cloud plane meets the requirements based on the deviation distance of each point in each point cloud subset specifically includes: firstly calculating the deviation distance between each point in each point cloud subset and the corresponding point cloud plane, and determining whether the corresponding deviation distance is greater than a preset threshold; if so, recording the point as a deviation point; if not, recording the point as a normal point; Then, the number of deviation points in each point cloud subset is counted, and when the number of deviation points in the corresponding point cloud subset is greater than a preset number, the point cloud plane corresponding to the point cloud subset does not meet the requirements.

3. The automatic detection method for building exterior wall quality based on point cloud according to claim 1 is characterized in that: The quality report display step also includes: determining the uneven areas of the building exterior wall based on the point cloud subset corresponding to each point cloud plane that does not meet the requirements and the point cloud data of the building exterior wall; and highlighting the uneven areas of the building exterior wall in the 3D model of the building exterior wall and then displaying the 3D model of the building exterior wall through a visualization display platform.

4. The automatic detection method for building exterior wall quality based on point cloud according to claim 1 is characterized in that: The plane fitting step also includes: constructing a standard vertical plane according to the installation position of the scanning device, calculating the horizontal distance between the points in each point cloud subset and the standard vertical plane, and selecting some or all of the points in the corresponding point cloud subset according to the horizontal distance, so as to perform plane fitting based on some or all of the points in the corresponding point cloud subset to obtain the corresponding point cloud plane.

5. A point cloud-based automatic detection device for building exterior wall quality, comprising a memory and a processor, wherein the memory stores an automatic detection program for building exterior wall quality running on the processor, the automatic detection program for building exterior wall quality being a computer program, characterized in that: When the processor executes the automatic building exterior wall quality detection program, the steps of the point cloud-based automatic building exterior wall quality detection method as described in any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium storing a program for automatically detecting the quality of building exterior walls, characterized in that: The automatic building exterior wall quality detection program is a computer program. When the automatic building exterior wall quality detection program is executed by a processor, the steps of the point cloud-based automatic building exterior wall quality detection method according to any one of claims 1 to 4 are implemented.

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