Airfield pavement project quality acceptance method and system based on three-dimensional laser scanning

Abstract

The invention relates to the technical field of airport pavement project quality acceptance, in particular to an airport pavement project quality acceptance method and system based on three-dimensional laser scanning. The method comprises the following steps: firstly, acquiring an original point cloud data model of an airport pavement to be measured by using a three-dimensional laser scanner, and preprocessing data to generate a standardized three-dimensional point cloud model; according to a preset acceptance requirement, processing data required by acceptance by adopting a TIN construction technology, and extracting an average point location elevation and a plane coordinate of an acceptance area to form an elevation point cloud model; and the elevation point cloud model and the three-dimensional point cloud model are intelligently spliced, and a preset evaluation standard is combined, so that the pavement quality is automatically judged. According to the method, the problem of low data processing efficiency in a traditional acceptance method is solved, accurate modeling of a complex pavement structure is realized through a TIN construction technology, and the reliability of an acceptance result is improved; and more efficient and reliable technical support is provided for quality control of airport pavement engineering.

Description

Technical Field

[0001] This application relates to the technical field of airport pavement engineering quality acceptance, and in particular to a method and system for airport pavement engineering quality acceptance based on three-dimensional laser scanning. Background Technology

[0002] Airport concrete pavement structures consist of a water-stabilized subbase, a superbase, and a concrete surface layer, forming a typical layered structure. During construction, strict control over the thickness and top elevation of each layer is crucial to ensuring compliance with quality acceptance standards. The relevant quality inspection and evaluation standards for civil airport runway engineering specify the inspection methods and evaluation criteria for various indicators of each structural layer. As a major construction project in the airport runway area, improving construction efficiency and ensuring project quality are key to effectively advancing airport construction projects.

[0003] Traditional airport pavement engineering quality acceptance testing often employs contact testing, requiring extensive manual operation. This results in significant problems such as a large workload, low testing frequency, low efficiency, high dependence on the operator's skill level, and the fact that testing can only be conducted during the day. The solutions adopted by traditional testing methods include multi-party witnessed inspections, standardized inspection methods, clearly defined values ​​and allowable deviations, and scientifically sound inspection frequencies, in order to ensure the reliability of measured data while maintaining construction efficiency.

[0004] Existing technologies employ three-dimensional laser scanning to acquire point cloud data of the pavement surface for quality assessment. This allows for the rapid and accurate collection of three-dimensional data of large pavement areas, which is then analyzed using data processing software to achieve an objective evaluation of pavement quality.

[0005] However, the lack of systematic methods for extracting and analyzing acceptance data in existing technologies makes the acceptance process cumbersome and time-consuming, and this situation needs further improvement. Summary of the Invention

[0006] To address the problem of the lack of systematic data extraction and analysis methods in existing airport pavement engineering quality acceptance processes, which makes the acceptance process cumbersome and time-consuming, this application provides a method and system for airport pavement engineering quality acceptance based on three-dimensional laser scanning, employing the following technical solution: In a first aspect, this application provides a method for quality acceptance of airport pavement engineering based on three-dimensional laser scanning, comprising the following steps: S1, using a 3D laser scanner to obtain the original point cloud data model of the airport pavement to be tested; S2, preprocess the original point cloud data model to generate a three-dimensional point cloud model; S3. Based on the preset acceptance requirements, construct a TIN for the data required for acceptance, and extract the average point elevation and plane coordinates of the acceptance area to form an elevation point cloud model. S4. The elevation point cloud model and the three-dimensional point cloud model are spliced ​​together, and the test runway surface is automatically determined to be qualified based on the preset evaluation criteria.

[0007] By adopting the above technical solution, this application first uses a 3D laser scanner to obtain the original point cloud data model of the airport pavement to be tested, and then preprocesses the data to generate a standardized 3D point cloud model. According to the preset acceptance requirements, TIN (Triangulated Irregular Network) construction technology is used to process the data required for acceptance, and the average elevation and plane coordinates of the acceptance area are extracted to form an elevation point cloud model. By intelligently stitching the elevation point cloud model and the 3D point cloud model, combined with the preset evaluation standards, the pavement quality is automatically judged. This not only solves the problem of low data processing efficiency in traditional acceptance methods, but also achieves accurate modeling of complex pavement structures through TIN construction technology, improving the reliability of acceptance results. It provides more efficient and reliable technical support for the quality control of airport pavement engineering.

[0008] Optionally, in S2, the original point cloud data model is preprocessed, specifically including the following steps: S201, Perform coordinate transformation on the original point cloud data model to obtain point cloud data in a general format; S202, perform splicing, optimization, fusion and noise reduction processing on the point cloud data in the general format.

[0009] By adopting the above technical solution, this application obtains point cloud data in a general format by performing coordinate transformation on the initial point cloud data model, and then performs splicing, optimization, fusion and noise reduction processing on the point cloud data to make the point cloud data more accurate and complete.

[0010] Optionally, S3 includes the following sub-steps: S301. Based on the pavement block diagram, extract the elevation data of the plane coordinate position of each pavement to form a pavement elevation point cloud model. S302, based on the pavement block diagram, select multiple points in the plane coordinates of each block in the upper and lower layers of the structural layer, extract the elevation data of the corresponding sampling points in the upper and lower layers, form the upper layer elevation point cloud model and the lower layer elevation point cloud model respectively, and fit the upper layer elevation point cloud model and the lower layer elevation point cloud model to form the pavement thickness point cloud model. S303. Based on the pavement block diagram, select the elevation data of the plane coordinate position of the midpoint of the edge line of each pavement block slab to form a cloud model of the height difference between adjacent pavement slabs.

[0011] By adopting the above technical solution, this application, based on the pavement block diagram, firstly extracts the planar coordinate position elevation data of each pavement block to form a complete pavement elevation point cloud model; secondly, selects multiple intermediate points on the upper and lower layers of the structural layer, extracts the elevation data of these points, and fits them to generate a point cloud model that reflects the pavement thickness distribution; finally, by selecting the elevation data of the midpoint of the edge line of each block, constructs a height difference point cloud model for evaluating the connection quality of adjacent blocks; through a systematic data extraction process, comprehensive monitoring of the three key indicators of pavement elevation, thickness, and height difference is achieved; this not only improves the completeness and accuracy of acceptance data, but also makes the acceptance process more standardized and regulated, effectively avoiding quality judgment deviations caused by incomplete data collection in traditional methods.

[0012] Optionally, in S4, when stitching the elevation point cloud model with the three-dimensional point cloud model, the same sampling points of the elevation point cloud model and the three-dimensional point cloud model are matched one-to-one for multi-dimensional data analysis of points, lines, and surfaces.

[0013] By adopting the above technical solution, the same sampling points of the elevation point cloud model and the three-dimensional point cloud model are matched one-to-one, realizing multi-dimensional data analysis of points, lines and surfaces.

[0014] Optionally, in S4, based on preset inspection and evaluation standards, the system automatically determines whether the test runway surface is qualified, specifically including the following steps: S401, Identify the pavement type and obtain the design pavement elevation, design pavement thickness and design adjacent slab height difference corresponding to the pavement type; S402, Analyze the elevation point cloud model and the three-dimensional point cloud model to obtain the elevation difference between the elevation of each sampling point and the design pavement elevation, the thickness deviation between the slab thickness of each sampling point and the design pavement thickness, and the adjacent slab height difference deviation between the adjacent slab height difference and the design adjacent slab height difference. S403, determine whether the elevation difference, thickness deviation value, and adjacent plate height difference deviation value meet the preset inspection and evaluation standards, and automatically display whether the test results are qualified and the pass rate, and draw an acceptance conclusion.

[0015] By adopting the above technical solution, this application first identifies the pavement type and automatically retrieves the corresponding design parameters, including the design pavement elevation, design pavement thickness, and design height difference between adjacent slabs. The system performs registration analysis between the elevation point cloud model and the three-dimensional point cloud model, and calculates the elevation difference, thickness deviation, and adjacent slab height difference deviation values ​​for each sampling point. By automatically comparing with the preset evaluation standards, the system can not only provide the judgment results of whether each indicator is qualified, but also automatically calculate the pass rate and generate the acceptance conclusion. This not only improves the acceptance efficiency, but also ensures the objectivity and accuracy of the judgment results, making the acceptance work more standardized and reliable.

[0016] Secondly, this application provides a system for quality acceptance of airport pavement engineering based on three-dimensional laser scanning, comprising: The data acquisition module is used to acquire the original point cloud data model of the airport pavement under test using a 3D laser scanner; The preprocessing module is used to preprocess the original point cloud data model to generate a three-dimensional point cloud model; The data construction module is used to construct a TIN based on the preset acceptance requirements, extract the average point elevation and plane coordinates of the acceptance area, and form an elevation point cloud model. The judgment module is used to stitch the elevation point cloud model with the three-dimensional point cloud model, and automatically determine whether the test runway surface is qualified by combining the preset evaluation criteria.

[0017] Optionally, the preprocessing module includes: The coordinate transformation unit is used to transform the original point cloud data model into point cloud data in a general format; The data processing unit is used to stitch, optimize, fuse, and denoise the point cloud data in the general format.

[0018] Optionally, the data construction module includes: The elevation data extraction unit is used to extract the elevation data of the plane coordinate position of each pavement according to the pavement block map, and form a pavement elevation point cloud model. The thickness data extraction unit is used to select multiple points in the plane coordinates of each block of the upper and lower layers of the structure according to the pavement block diagram, extract the elevation data of the corresponding sampling points of the upper and lower layers, form the upper layer elevation point cloud model and the lower layer elevation point cloud model respectively, and fit the upper layer elevation point cloud model and the lower layer elevation point cloud model to form the pavement thickness point cloud model. The elevation difference data extraction unit is used to select the elevation data of the midpoint plane coordinate position of the edge line of each pavement block according to the pavement block map, and form a point cloud model of the elevation difference between adjacent pavement blocks.

[0019] Optionally, the determination module includes a stitching unit for matching the same sampling points of the elevation point cloud model with those of the three-dimensional point cloud model.

[0020] Optionally, the determination module further includes: The identification unit is used to identify the type of pavement to be detected and to obtain the design pavement elevation, design pavement thickness and design height difference between adjacent slabs corresponding to the type of pavement to be detected. The analysis unit is used to analyze the elevation point cloud model and the three-dimensional point cloud model to obtain the elevation difference between the elevation of each sampling point and the design pavement elevation, the thickness deviation between the slab thickness of each sampling point and the design pavement thickness, and the adjacent slab height difference deviation between the adjacent slab height difference and the design adjacent slab height difference. The judgment unit determines whether the elevation difference, thickness deviation value, and adjacent plate height difference deviation value meet the preset inspection and evaluation standards, and automatically displays whether the test results are qualified and the pass rate, and draws an acceptance conclusion.

[0021] In summary, this application includes at least one of the following beneficial technical effects: This application first utilizes a 3D laser scanner to acquire the original point cloud data model of the airport pavement to be tested. Then, the data is preprocessed to generate a standardized 3D point cloud model. Based on pre-defined acceptance requirements, TIN (Triangulated Irregular Network) construction technology is used to process the data required for acceptance, and the average elevation and planar coordinates of the acceptance area are extracted to form an elevation point cloud model. By intelligently stitching the elevation point cloud model with the 3D point cloud model, combined with pre-defined evaluation standards, automated judgment of pavement quality is achieved. This not only solves the problem of low data processing efficiency in traditional acceptance methods but also achieves accurate modeling of complex pavement structures through TIN construction technology, improving the reliability of acceptance results. It provides more efficient and reliable technical support for airport pavement engineering quality control. Compared with traditional acceptance methods, this application saves a lot of time with the same amount of work, and can ensure daytime construction by scanning at night after the flight, thus improving the efficiency of project quality acceptance. Based on the pavement segmentation map, this application first extracts the planar coordinate position elevation data of each pavement segment to form a complete pavement elevation point cloud model. Second, multiple intermediate points are selected on the upper and lower layers of the structural layer, and the elevation data of these points are extracted and fitted to generate a point cloud model that reflects the pavement thickness distribution. Finally, by selecting the elevation data of the midpoint of the edge line of each segment slab, a height difference point cloud model is constructed to evaluate the connection quality of adjacent slabs. Through a systematic data extraction process, comprehensive monitoring of the three key indicators of pavement elevation, thickness, and height difference is achieved. This not only improves the completeness and accuracy of acceptance data but also makes the acceptance process more standardized and regulated, effectively avoiding quality judgment deviations caused by incomplete data collection in traditional methods. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the detection method flow according to an embodiment of this application; Figure 2 This is a preprocessed laser point cloud model of the airport pavement surface in the embodiments of this application; Figure 3This is a point cloud model of airport pavement thickness fitting in an embodiment of this application; Figure 4 This is the sampling distribution of the measured items in the embodiments of this application; Figure 5 This is a schematic diagram of the module of the airport pavement engineering quality acceptance method system based on three-dimensional laser scanning according to an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Firstly, please refer to Figure 1 This application provides a method for quality acceptance of airport pavement engineering based on three-dimensional laser scanning, which includes the following steps: Step S1, Data Acquisition: The airport pavement area that meets acceptance criteria is scanned layer by layer using a 3D laser scanner to obtain the original point cloud data model of the airport pavement. 3D laser scanning technology utilizes the principle of phase ranging to quickly reconstruct a 3D model of the airport pavement by recording the 3D coordinates, reflectivity, and texture information of a large number of dense points on the surface of each structural layer of the airport pavement. Specifically, this includes: Step S101: Conduct an on-site survey to investigate the surrounding environment of the airport pavement area that meets the acceptance criteria, and determine that it has the terrain conditions for three-dimensional laser scanning.

[0025] Step S102: Set up control points and target spheres. Based on the airport pavement environment and terrain conditions, determine and set up a sufficient number of high-level leveling control points. During the scanning process, set up corresponding targets to achieve the splicing of data measured by the two stations.

[0026] Step S103: Perform laser scanning. Based on the established scanning route and control points, perform three-dimensional laser scanning on the airport pavement area that needs to be inspected to obtain point cloud data of each structural layer of the airport pavement.

[0027] Step S104: Data export. Export the scan data to obtain the original point cloud model of the airport pavement.

[0028] Step S2, data processing: S201, perform coordinate transformation on the original point cloud data model to obtain point cloud data in a general format; S202, perform stitching, optimization, fusion, and noise reduction on the point cloud data to make it more complete and accurate, and form a preprocessed 3D point cloud model, such as... Figure 2 As shown; Furthermore, in step S2, after S201, adaptive optimization processing of the point cloud data is also included. First, based on the material characteristics of the pavement type, the corresponding surface roughness coefficient is called according to the preset material parameter library. Combined with the environmental noise level and scanner accuracy parameters, the optimal denoising threshold is automatically calculated using an adaptive weight calculation formula. For concrete pavement, a baseline threshold is set considering the surface texture depth and aggregate exposure degree. For asphalt pavement, the threshold range is dynamically adjusted considering the asphalt content and compaction index. An improved region growing method is used to identify the pavement block boundaries. The initial seed points of the boundaries are determined by setting grayscale gradient thresholds and growth direction constraints. A high-density sampling area with a width of 2-5 cm is set around the boundary. The sampling interval in the high-density sampling area is automatically adjusted to 1 / 3 to 1 / 5 of the sampling interval of the surrounding area according to the boundary sharpness index.

[0029] In some embodiments, the system identifies feature points of defects such as cracks, potholes, and ruts, and locally densifies the area around the feature points with a radius of 3 times the feature size. The density of the densified points is not less than 3 times the original density. Non-feature areas are simplified by gridding. The grid size is dynamically adjusted according to the local flatness index. For areas where the flatness index is higher than a preset threshold, the grid size can be enlarged to 2-3 times the original sampling interval to optimize and reduce data redundancy.

[0030] Step S3, model extraction: Based on the acceptance requirements for elevation-related measured items in the airport pavement engineering quality acceptance items, and using other software such as ArcGIS, a TIN (Triangular Network of Irregularities) is constructed from the data of the required engineering parts. The average elevation and plane coordinates of the acceptance area are then extracted to form an elevation point cloud model. Please refer to [link / reference]. Figure 4 The model extraction scheme involved in S3 specifically includes: Step S301: Extract pavement elevation data. Based on the pavement block diagram, extract the elevation data of the four block corners (A1, A2, A3, A4) of each pavement block in plane coordinates. This forms a point cloud model of pavement elevation; Step S302: Extract structural layer thickness data. Based on the pavement block diagram, select the plane coordinates of six points (A5, A6, A7, A8, A9, A10; B5, B6, B7, B8, B9, B10) in each block of the upper and lower structural layers, and extract the elevation data of the corresponding sampling points for the upper and lower layers. Elevation point cloud models of the upper and lower layers are formed respectively, and the two models are fitted into a pavement thickness point cloud model. Step S303: Extract the data of the height difference between adjacent slabs. According to the pavement block diagram, select the elevation data of the midpoint (A11, A12, A13, A14) of the edge line of each pavement block slab respectively to form a height difference point cloud model of adjacent pavement slabs. , and form a height difference point cloud model of adjacent pavement slabs; Step S4: Fitting analysis of the model. For the model generated in S3, splice it with the design reference model in the same coordinate system to ensure the one-to-one correspondence of the same sampling points of the two models, and realize the multi-dimensional data analysis of points, lines, and surfaces. By developing a model analysis plug-in on the model processing software, according to the type of pavement to be analyzed, combined with the corresponding inspection and evaluation standards of the actual measurement items in the relevant quality inspection and evaluation standards for the airfield pavement engineering in the civil airport flight area, obtain the designed pavement elevation, designed pavement thickness, and designed height difference between adjacent slabs of the corresponding detected pavement type, and automatically determine whether the actual measurement items of the sampling points are qualified. Among them, the data analysis scheme for the actual measurement items involved in S4 specifically includes: Step S401: Pavement elevation analysis. The model analysis plug-in first identifies the type of the detected pavement, and automatically analyzes the elevation point cloud model and the design reference model of each sampling point determined in S301 to judge whether the elevation difference ( ) of each sampling point meets the specified value or allowable deviation of the elevation of the corresponding pavement type in the current airport pavement engineering quality inspection and evaluation standard, and automatically displays whether the result of the inspection item is qualified and the qualification rate ( ), and draw an acceptance conclusion; In this embodiment, according to the provisions of the "Quality Inspection and Evaluation Standards for Airfield Pavement Engineering in Civil Airport Flight Areas" (MH5007-2017), the extreme value range of the elevation deviation value of the cement concrete runway surface layer is, which is a general item, and the acceptance qualification rate is 85%; Among them, is ; When -8 ≤ ≤ 8 (extreme value), the elevation detection of this point is judged to be qualified; When < -8 or > 8, the elevation detection of this point is judged to be unqualified; When ≥ 85%, the elevation detection of this pavement area is judged to be qualified and passes the acceptance; When < 85%, the elevation detection of this pavement area is judged to be unqualified and fails to pass the acceptance;​​​​ ), determine the deviation between the plate thickness at each sampling point and the design thickness ( Whether the thickness of the pavement conforms to the specified value or allowable deviation of the corresponding pavement type in the current airport pavement engineering quality inspection and evaluation standards, and automatically displays the test results and pass rate. ), and thus draw an acceptance conclusion; This embodiment, according to the "Standard for Quality Inspection and Evaluation of Airfield Engineering in Civil Airport Flight Areas" (MH5007-2017), specifies that the extreme range of the thickness deviation value for cement concrete runway surface layer is as follows: To ensure the project's success, the acceptance rate was 95%. in, The formula for the model fitting algorithm is: i is 5, 6, 7, 8, 9, 10; The thickness h of each segment of the plate is the average thickness of the 6 sampling points obtained, that is, the algorithm formula for plate thickness h is: i is 5, 6, 7, 8, 9, 10; When -6≤ When the value is ≤6 (extreme value), the thickness of the pavement segment plate is deemed acceptable. when <6 or When the thickness of the pavement slab is greater than 6, the thickness of the pavement slab is deemed unqualified. when When the thickness of the pavement slab in that area is ≥95%, the pavement thickness test is deemed qualified and passes the acceptance test. when When the thickness of the pavement slab in that area is less than 95%, the pavement slab thickness test is deemed unqualified and the acceptance test will not be passed. Thickness is measured for each surface section, with a plate thickness measurement frequency of 100%. Step S403, adjacent slab elevation difference analysis: The model analysis plugin first identifies the pavement type and automatically analyzes the elevation point cloud model of two sampling points on the same edge line of two adjacent pavement slabs determined in S303, automatically calculating the deviation value of the elevation difference between adjacent slabs. The system determines whether the height difference deviation between adjacent slabs meets the specified value or allowable deviation for the corresponding pavement type in the current airport pavement engineering quality inspection and evaluation standards, and automatically displays the test results and pass rate. ), and thus draw an acceptance conclusion; According to the "Standard for Quality Inspection and Evaluation of Airfield Engineering in Civil Airport Flight Areas" (MH5007-2017), the extreme range of the height difference deviation between adjacent slabs in this embodiment is as follows: For general projects, the pass rate was 85%. in, The formula of the model fitting algorithm is as follows: , is the elevation data of the first block of pavement sampling points; is the elevation data of the second block of pavement sampling points on the same sampling side line; i is 11, 12, 13, 14; When -4 ≤ ≤ 4, the detection and determination of the height difference between adjacent slabs are qualified; When < -4 or > 4, the detection and determination of the height difference between adjacent slabs are unqualified; When ≥ 85%, the detection and evaluation of the height difference between adjacent slabs in this pavement area are qualified and pass the acceptance; When < 85%, the detection and evaluation of the height difference between adjacent slabs in this pavement area are unqualified and do not pass the acceptance; The height difference between adjacent slabs is detected for every two blocks of pavement, and the detection frequency of the height difference between adjacent slabs reaches 100%; Step S5, complete the quality acceptance of the airport pavement, and draw a conclusion on the quality acceptance of the airport pavement project according to the analysis results of step S4.

[0031] In the second aspect, the present application provides an airport pavement project quality acceptance system based on three-dimensional laser scanning. The airport pavement project quality acceptance system based on three-dimensional laser scanning of the present application will be described below in combination with the above-mentioned airport pavement project quality acceptance method based on three-dimensional laser scanning.

[0032] Referring to Figure 5 , an airport pavement project quality acceptance method system based on three-dimensional laser scanning includes: A data acquisition module, which is used to obtain the original point cloud data model of the待测 airport pavement by using a three-dimensional laser scanner; A preprocessing module, which is used to preprocess the original point cloud data model to generate a three-dimensional point cloud model; A data construction module, which is used to construct a TIN for the data required for acceptance according to the preset acceptance requirements, and extract the average point elevation and plane coordinates of the acceptance area to form an elevation point cloud model; A determination module, which is used to splice the elevation point cloud model and the three-dimensional point cloud model, and automatically determine whether the measured airport pavement is qualified in combination with the preset acceptance and evaluation criteria.

[0033] In one embodiment, the preprocessing module includes: A coordinate conversion unit, which is used to perform coordinate conversion on the original point cloud data model to obtain point cloud data in a common format; A data processing unit, which is used to perform splicing, optimization, fusion and denoising processing on the point cloud data in the common format.

[0034] In one embodiment, the data building module includes: The elevation data extraction unit is used to extract the elevation data of the plane coordinate position of each pavement according to the pavement block map, and form a pavement elevation point cloud model. The thickness data extraction unit is used to select multiple points in the plane coordinates of each block of the upper and lower layers of the structure according to the pavement block diagram, extract the elevation data of the corresponding sampling points of the upper and lower layers, form the upper layer elevation point cloud model and the lower layer elevation point cloud model respectively, and fit the upper layer elevation point cloud model and the lower layer elevation point cloud model to form the pavement thickness point cloud model. The elevation difference data extraction unit is used to select the elevation data of the midpoint plane coordinate position of the edge line of each pavement block according to the pavement block map, and form a point cloud model of the elevation difference between adjacent pavement blocks.

[0035] In one embodiment, the determination module includes a stitching unit for matching the same sampling points of the elevation point cloud model with those of the 3D point cloud model.

[0036] In one embodiment, the determination module further includes: The identification unit is used to identify the type of pavement to be detected and to obtain the design pavement elevation, design pavement thickness and design height difference between adjacent slabs for the corresponding pavement type. The analysis unit is used to analyze the elevation point cloud model and the 3D point cloud model to obtain the elevation difference between each sampling point elevation and the design pavement elevation, the thickness deviation between each sampling point slab thickness and the design pavement thickness, and the adjacent slab height difference deviation between the adjacent slab height difference and the design adjacent slab height difference. The judgment unit determines the elevation difference, thickness deviation, and height difference deviation of adjacent plates, as well as whether they meet the preset inspection and evaluation standards. It automatically displays the test results and pass rate, and draws an acceptance conclusion.

[0037] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0038] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for quality acceptance of airport pavement engineering based on three-dimensional laser scanning, characterized in that, Includes the following steps: S1, using a 3D laser scanner to obtain the original point cloud data model of the airport pavement to be tested; S2, preprocess the original point cloud data model to generate a three-dimensional point cloud model; S3. Based on the preset acceptance requirements, construct a TIN for the data required for acceptance, and extract the average point elevation and plane coordinates of the acceptance area to form an elevation point cloud model. S4. The elevation point cloud model and the three-dimensional point cloud model are spliced ​​together, and the test runway surface is automatically determined to be qualified based on the preset evaluation criteria.

2. The airport pavement engineering quality acceptance method based on three-dimensional laser scanning according to claim 1, characterized in that, In S2, the raw point cloud data model is preprocessed, specifically including the following steps: S201, Perform coordinate transformation on the original point cloud data model to obtain point cloud data in a general format; S202, perform splicing, optimization, fusion and noise reduction processing on the point cloud data in the general format.

3. The airport pavement engineering quality acceptance method based on three-dimensional laser scanning according to claim 1, characterized in that, S3 specifically includes the following steps: S301. Based on the pavement block diagram, extract the elevation data of the plane coordinate position of each pavement to form a pavement elevation point cloud model. S302, based on the pavement block diagram, select multiple points in the plane coordinates of each block in the upper and lower layers of the structural layer, extract the elevation data of the corresponding sampling points in the upper and lower layers, form the upper layer elevation point cloud model and the lower layer elevation point cloud model respectively, and fit the upper layer elevation point cloud model and the lower layer elevation point cloud model to form the pavement thickness point cloud model. S303. Based on the pavement block diagram, select the elevation data of the plane coordinate position of the midpoint of the edge line of each pavement block slab to form a cloud model of the height difference between adjacent pavement slabs.

4. The airport pavement engineering quality acceptance method based on three-dimensional laser scanning according to claim 1, characterized in that, In S4, when the elevation point cloud model is stitched together with the three-dimensional point cloud model, the same sampling points of the elevation point cloud model and the three-dimensional point cloud model are matched one-to-one for multi-dimensional data analysis of points, lines and surfaces.

5. The airport pavement engineering quality acceptance method based on three-dimensional laser scanning according to claim 1, characterized in that, In S4, based on preset inspection and evaluation standards, the system automatically determines whether the test runway surface is up to standard. It includes the following steps: S401, Identify the pavement type and obtain the design pavement elevation, design pavement thickness and design adjacent slab height difference corresponding to the pavement type; S402, Analyze the elevation point cloud model and the three-dimensional point cloud model to obtain the elevation difference between the elevation of each sampling point and the design pavement elevation, the thickness deviation between the slab thickness of each sampling point and the design pavement thickness, and the adjacent slab height difference deviation between the adjacent slab height difference and the design adjacent slab height difference. S403, determine whether the elevation difference, thickness deviation value, and adjacent plate height difference deviation value meet the preset inspection and evaluation standards, and automatically display whether the test results are qualified and the pass rate, and draw an acceptance conclusion.

6. A system for quality acceptance of airport pavement engineering based on three-dimensional laser scanning, characterized in that, include: The data acquisition module is used to acquire the original point cloud data model of the airport pavement under test using a 3D laser scanner; The preprocessing module is used to preprocess the original point cloud data model to generate a three-dimensional point cloud model; The data construction module is used to construct a TIN based on the preset acceptance requirements, extract the average point elevation and plane coordinates of the acceptance area, and form an elevation point cloud model. The judgment module is used to stitch the elevation point cloud model with the three-dimensional point cloud model, and automatically determine whether the test runway surface is qualified by combining the preset evaluation criteria.

7. The airport pavement engineering quality acceptance method system based on three-dimensional laser scanning according to claim 6, characterized in that, The preprocessing module includes: The coordinate transformation unit is used to transform the original point cloud data model into point cloud data in a general format; The data processing unit is used to stitch, optimize, fuse, and denoise the point cloud data in the general format.

8. The airport pavement engineering quality acceptance method system based on three-dimensional laser scanning according to claim 6, characterized in that, The data construction module includes: The elevation data extraction unit is used to extract the elevation data of the plane coordinate position of each pavement according to the pavement block map, and form a pavement elevation point cloud model. The thickness data extraction unit is used to select multiple points in the plane coordinates of each block of the upper and lower layers of the structure according to the pavement block diagram, extract the elevation data of the corresponding sampling points of the upper and lower layers, form the upper layer elevation point cloud model and the lower layer elevation point cloud model respectively, and fit the upper layer elevation point cloud model and the lower layer elevation point cloud model to form the pavement thickness point cloud model. The elevation difference data extraction unit is used to select the elevation data of the midpoint plane coordinate position of the edge line of each pavement block according to the pavement block map, and form a point cloud model of the elevation difference between adjacent pavement blocks.

9. The airport pavement engineering quality acceptance method system based on three-dimensional laser scanning according to claim 6, characterized in that, The determination module includes a splicing unit, which is used to match the same sampling points of the elevation point cloud model with the three-dimensional point cloud model one by one.

10. The airport pavement engineering quality acceptance method system based on three-dimensional laser scanning according to claim 6, characterized in that, The determination module also includes: The identification unit is used to identify the type of pavement to be detected and to obtain the design pavement elevation, design pavement thickness and design height difference between adjacent slabs corresponding to the type of pavement to be detected. The analysis unit is used to analyze the elevation point cloud model and the three-dimensional point cloud model to obtain the elevation difference between the elevation of each sampling point and the design pavement elevation, the thickness deviation between the slab thickness of each sampling point and the design pavement thickness, and the adjacent slab height difference deviation between the adjacent slab height difference and the design adjacent slab height difference. The judgment unit determines whether the elevation difference, thickness deviation value, and adjacent plate height difference deviation value meet the preset inspection and evaluation standards, and automatically displays whether the test results are qualified and the pass rate, and draws an acceptance conclusion.

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