Method, device and equipment for calculating volume of internal disease of road and reconstructing geometry, and medium

Abstract

The application discloses a kind of volume calculation and geometric reconstruction methods, devices, equipment and medium inside road disease, which comprises: standardization processing to the three-dimensional simulation point cloud data of inside road disease, to obtain standardization simulation point cloud data;Pretreatment data and identify disease connected sub-region, determine the internal starting point set to construct starting point set, based on starting point set Construction space step direction and step search boundary point, generate boundary point set, fusion boundary point set obtains disease space profile, based on disease space profile completes disease volume calculation and three-dimensional geometric shape reconstruction;Since the application accurately locates the disease boundary by internal starting point constraint combined with multi-direction step search, restores complete disease geometry by fusing boundary points, accurately realizes the topological reconstruction and volume calculation of complex disease network, effectively solves the problem of incomplete disease point cloud boundary and complex topology of internal complex disease of road, and improves the integrity of disease reconstruction and the accuracy of volume calculation.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and in particular to a method, apparatus, equipment and medium for calculating the volume and geometric reconstruction of internal road defects. Background Technology

[0002] As a core infrastructure of the transportation system, the structural integrity of roads directly affects traffic safety and service life. During long-term service, roads are susceptible to complex internal defects such as cracks, voids, and loosening due to multiple factors including vehicle loads, environmental erosion, and material aging. These defects often intertwine to form complex networks, and failure to detect and assess them promptly and accurately can lead to serious safety hazards such as road collapse and structural damage. With the development of 3D detection technology, numerical simulation, ground-penetrating radar inversion, CT imaging, and multi-source fusion have been widely applied to the detection of internal road defects. These methods can acquire simulated point cloud data containing the 3D spatial coordinates and related attribute information of the defective areas, providing a data foundation for the quantitative analysis and 3D reconstruction of defects. Point cloud technology, due to its advantage of high-fidelity preservation of spatial geometric information, has become a core technical support for the refined analysis of road defects.

[0003] Currently, the topology reconstruction and volume calculation of simulated road defects are hampered by the unique characteristics of the complex defect network within roads, making existing technologies insufficient to meet practical needs and exhibiting significant shortcomings: First, simulated defect point clouds typically exist in discrete form, and existing methods lack effective internal constraint mechanisms, making it impossible to accurately determine defect boundaries; second, 3D defect reconstruction cannot fully cover the complex extension range of defects, resulting in the omission of boundary points in some defect areas and incomplete boundary extraction; third, the fusion processing of discrete boundary points does not consider topological constraints and spatial correlations, only performing simple deduplication or sorting, making it difficult to form a continuous and complete spatial outline of defects; fourth, volume calculation methods have poor adaptability to the complex topological structure of defects, failing to optimize the calculation logic in conjunction with defect geometric features, resulting in insufficient volume calculation accuracy. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, device, and medium for calculating the volume and geometric reconstruction of road internal defects. This invention aims to solve the technical problems of existing technologies that fail to accurately determine defect boundaries and do not consider topological constraints and spatial correlations in simulations or detections of complex road internal defect networks, resulting in incomplete reconstruction of road internal defects and inaccurate calculation of defect volume.

[0005] To achieve the above objectives, the present invention provides a method for volume calculation and geometric reconstruction of internal road defects, the method comprising the following steps:

[0006] Collect three-dimensional simulation point cloud data of road internal defects and perform standardization processing to obtain standardized simulation point cloud data. The standardized simulation point cloud data includes three-dimensional spatial coordinate information and attribute information of each point in the defect area. The attribute information includes point density, reflection intensity and / or damage index.

[0007] The standardized simulation point cloud data is preprocessed to identify the diseased connected sub-regions;

[0008] Determine the starting point inside the disease from each connected sub-region of the disease, and construct a set of disease starting points;

[0009] Based on the disease initiation point set, multiple spatial stepping directions are constructed with the initiation point inside the disease as the center, and stepping search is performed based on the spatial stepping directions to determine the disease boundary points of each disease connected sub-region and generate a disease boundary point set.

[0010] The disease boundary point set is fused to obtain the disease spatial contour, and the disease volume is calculated and the three-dimensional geometry is reconstructed based on the disease spatial contour.

[0011] Optionally, the preprocessing of the standardized simulation point cloud data to identify diseased connected sub-regions includes:

[0012] Based on spatial neighborhood relationships, isolated noise points are removed from the standardized simulated point cloud data to obtain noise-removed point cloud data.

[0013] Density smoothing is performed on the noise-removed point cloud data to obtain candidate point cloud data;

[0014] Set a distance threshold between points, and construct the spatial connectivity relationship of the candidate point cloud data based on the distance threshold between points;

[0015] Based on the spatial connectivity relationship, the diseased point cloud region of the candidate point cloud data is divided, and one or more diseased connected sub-regions are identified.

[0016] Optionally, the step of determining the starting point within each connected sub-region of the disease and constructing a set of disease starting points includes:

[0017] Extract the spatial distribution information of each diseased connected sub-region, the spatial distribution information including point cloud density distribution information, spatial boundary information and geometric center information;

[0018] Based on the spatial distribution information, at least one disease-internal starting point is determined from each disease-connected sub-region, and all disease-internal starting points are integrated to construct a disease-internal starting point set. The disease-internal starting point satisfies at least one of the following conditions:

[0019] Located at a position where the density of the disease point cloud is higher than the preset density threshold;

[0020] Located at a position where the distance to the nearest non-disease area or road structure boundary is greater than a preset distance threshold;

[0021] The location within the preset range of the geometric center of the disease-connected sub-region.

[0022] Optionally, the step of constructing multiple spatial stepping directions centered on the disease's internal starting point based on the disease initiation point set, and performing a stepping search based on the spatial stepping directions to determine the disease boundary points of each disease-connected sub-region and generate a disease boundary point set includes:

[0023] Centered on the internal starting points of each disease in the disease starting point set, multiple spatial stepping directions are constructed, and a set of spatial stepping directions is generated. The spatial stepping directions include multiple angular directions set along the horizontal direction, directions perpendicular to the road structure layer, and directions along the main extension direction of the disease and its orthogonal directions.

[0024] Based on a preset step size, the search is performed step by step along the spatial step direction in the set of spatial step directions, starting from the corresponding starting point.

[0025] Extract neighborhood point cloud information at each step position and determine whether the current step position is located inside the diseased area;

[0026] In response to the current step position meeting the disease boundary determination condition, the current step position is marked as a disease boundary point, and the step search in the current spatial step direction is stopped. The current spatial step direction is removed from the set of spatial step directions, and the process returns to the step of performing the step search step by step from the corresponding starting point along the spatial step direction in the set of spatial step directions based on the preset step length.

[0027] In response to the set of spatial stepping directions being empty, disease boundary points marked on all spatial stepping directions are integrated to generate a disease boundary point set.

[0028] Optionally, the criteria for determining the disease boundary include at least one of the following:

[0029] The number of disease points within the preset neighborhood of the current step position is lower than the preset disease point threshold;

[0030] The density of disease point clouds within the preset neighborhood of the current step position is lower than that of the previous step position, and the difference in the density of disease point clouds is lower than the preset difference threshold.

[0031] The current step position exceeds the disease-connected sub-region;

[0032] The current stepping position has entered a non-damaged material layer or an area outside the road structure boundary.

[0033] Optionally, the step of fusing the set of disease boundary points to obtain the spatial contour of the disease includes:

[0034] Spatial coordinates are compared on the set of disease boundary points, and duplicate boundary points are deduplicated to obtain the deduplicated set of boundary points.

[0035] Based on spatial topological relationships, the deduplicated boundary point set is spatially sorted to obtain an ordered boundary point set.

[0036] The spatial continuity of the ordered boundary point set is detected, and missing points are marked based on the spatial continuity check results;

[0037] The missing points are filled in using a spatial interpolation algorithm to obtain the completed boundary point set.

[0038] The completed boundary point set is locally smoothed to obtain a smoothed boundary point set, thereby eliminating local deviations in the point cloud.

[0039] A continuous spatial surface is constructed based on the smoothed boundary point set to obtain the spatial contour of the disease.

[0040] Optionally, the step of calculating the disease volume and reconstructing the three-dimensional geometry based on the disease spatial contour includes:

[0041] Within the spatial range of the disease spatial outline, voxels are divided to obtain regular voxel units;

[0042] The number of regular voxel units located within the diseased area is counted, and the voxelized volume of the disease is calculated.

[0043] Multiple parallel sections are constructed based on the preset cross-sectional spacing along the main extension direction of the disease;

[0044] Calculate the intersection area between each parallel section and the spatial outline of the disease, and obtain the accumulated volume of the disease section by integrating the accumulated area of ​​the sections;

[0045] Based on the voxelized volume of the disease and the accumulated volume of the disease section, the disease volume is calculated to obtain the final disease volume calculation result.

[0046] Based on the spatial contour of the lesion and the final volume calculation result of the lesion, a three-dimensional geometric shape reconstruction is performed to complete the three-dimensional geometric shape reconstruction of the lesion.

[0047] Furthermore, to achieve the above objectives, the present invention also proposes a device for volume calculation and geometric reconstruction of internal road defects. The device is configured to implement the steps of the method for volume calculation and geometric reconstruction of internal road defects as described above. The device for volume calculation and geometric reconstruction of internal road defects includes:

[0048] The point cloud data acquisition module is used to collect three-dimensional simulation point cloud data of road internal defects and perform standardization processing to obtain standardized simulation point cloud data. The standardized simulation point cloud data includes three-dimensional spatial coordinate information and attribute information of each point in the defect area. The attribute information includes point density, reflection intensity and / or damage index.

[0049] The connected region identification module is used to preprocess the standardized simulation point cloud data and identify the diseased connected sub-regions;

[0050] The starting point positioning module is used to determine the starting point inside the disease from each connected sub-region of the disease and construct a set of disease starting points;

[0051] The boundary point search module is used to construct multiple spatial stepping directions based on the disease starting point set, with the internal starting point of the disease as the center, and perform stepping search based on the spatial stepping directions to determine the disease boundary points of each disease connected sub-region and generate a disease boundary point set.

[0052] The disease contour construction module is used to fuse the disease boundary point set to obtain the disease spatial contour, and to perform disease volume calculation and three-dimensional geometric shape reconstruction based on the disease spatial contour.

[0053] Furthermore, to achieve the above objectives, this application also proposes a device for calculating the volume and geometric reconstruction of internal road defects. The device includes: a memory, a processor, and a program for calculating the volume and geometric reconstruction of internal road defects stored in the memory. The processor is used to run the program for calculating the volume and geometric reconstruction of internal road defects. The computer program is configured to implement the steps of the method for calculating the volume and geometric reconstruction of internal road defects as described above.

[0054] In addition, to achieve the above objectives, this application also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for calculating the volume and geometrically reconstructing internal road defects as described above.

[0055] This invention treats internal road defects as connected regions in three-dimensional space. Within these regions, a step-by-step search is performed, centered on the starting point of each defect. This effectively uncovers the spatial characteristics and quantitative indicators of internal road defects, enabling precise location of defect boundaries. By fusing boundary points to restore complete defect geometry, it accurately reconstructs the topology and calculates the volume of complex defect networks. This effectively solves the problems of incomplete defect point cloud boundaries and complex topology in complex internal road defects, improving the completeness of defect reconstruction and the accuracy of volume calculation. Thus, it achieves precise processing of defects throughout the entire process, from data capture and identification to quantification and visualization, improving the accuracy and reliability of defect detection and analysis. The standardized and automated processing reduces manual intervention, lowers the workload of manual detection and analysis, and improves the efficiency of defect treatment, while avoiding errors caused by manual operation. Volume calculation enables quantitative characterization of defects, and three-dimensional geometric reconstruction enables visual presentation of defects. This provides scientific, precise, and intuitive support for the diagnosis, assessment, and treatment plan development of road defects, facilitating precise treatment of road defects, reducing treatment costs, extending road service life, and ensuring road traffic safety. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the structure of the equipment for calculating the volume and reconstructing the geometry of internal road defects in the hardware operating environment involved in the embodiments of the present invention.

[0058] Figure 2 This is a flowchart illustrating the first embodiment of the method for calculating the volume and reconstructing the geometry of internal road defects according to the present invention.

[0059] Figure 3 This is a flowchart illustrating the second embodiment of the method for calculating the volume and reconstructing the geometry of internal road defects according to the present invention.

[0060] Figure 4 This is a flowchart illustrating the third embodiment of the method for calculating the volume and reconstructing the geometry of internal road defects according to the present invention.

[0061] Figure 5 This is a flowchart illustrating the fourth embodiment of the method for calculating the volume and geometric reconstruction of internal road defects according to the present invention.

[0062] Figure 6 This is a structural block diagram of the first embodiment of the device for calculating the volume and reconstructing the geometry of internal road defects according to the present invention.

[0063] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0064] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0065] Reference Figure 1 , Figure 1 This is a schematic diagram of the equipment structure for calculating the volume and geometric reconstruction of internal road defects in the hardware operating environment involved in the embodiments of the present invention.

[0066] like Figure 1 As shown, the device for calculating the volume and geometric reconstruction of internal road defects may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to establish communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; the user interface 1003 may also include standard wired and wireless interfaces. The network interface 1004 may optionally include standard wired and wireless interfaces (such as Wireless-Fidelity (Wi-Fi) interfaces). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0067] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the equipment for volume calculation and geometric reconstruction of internal road defects. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0068] like Figure 1 As shown, the memory 1005, which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a program for calculating the volume and geometric reconstruction of road internal defects.

[0069] exist Figure 1In the illustrated device for calculating and reconstructing the volume of internal road defects, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the device for calculating and reconstructing the volume of internal road defects can be set in the device, and the device for calculating and reconstructing the volume of internal road defects can call the program for calculating and reconstructing the volume of internal road defects stored in the memory 1005 through the processor 1001, and execute the method for calculating and reconstructing the volume of internal road defects provided in the embodiment of the present invention.

[0070] This invention provides a method for calculating the volume and geometric reconstruction of internal road defects, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the method for calculating the volume and geometric reconstruction of internal road defects according to the present invention.

[0071] In this embodiment, the method for calculating the volume and geometrically reconstructing internal road defects includes the following steps:

[0072] Step S10: Collect three-dimensional simulation point cloud data of road internal defects and perform standardization processing to obtain standardized simulation point cloud data.

[0073] It should be understood that the executing entity of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a terminal electronic device capable of performing the above functions. The following description uses a road internal defects volume calculation and geometric reconstruction device (hereinafter referred to as the terminal device) as an example to illustrate this embodiment and the following embodiments.

[0074] It should be noted that the 3D simulation point cloud data can be derived from numerical simulation, ground-penetrating radar inversion, CT imaging, or multi-source fusion results. The standardized simulation point cloud data includes the 3D spatial coordinates and attribute information of each point within the diseased area, including point density, reflection intensity, and / or damage indicators.

[0075] In specific implementation, the terminal device collects three-dimensional simulation point cloud data of road internal defects from numerical simulation, ground-penetrating radar inversion, CT imaging, or multi-source fusion results; extracts three-dimensional spatial coordinate information and optional point density, reflection intensity, and damage index attribute information from the three-dimensional simulation point cloud data; performs coordinate unification processing on the three-dimensional simulation point cloud data to achieve spatial coordinate normalization matching; performs scale normalization processing on the point cloud data with completed coordinate unification to eliminate the influence of scale differences; and integrates the point cloud data with completed coordinate and scale processing to obtain standardized simulation point cloud data.

[0076] In some embodiments, the terminal device may employ professional detection equipment such as ground-penetrating radar and terrestrial 3D laser scanners to comprehensively scan road sections with internal defects and collect original 3D point cloud data of the defect areas. During the scanning process, the device's movement speed and scanning density are controlled to ensure coverage of all potential defect areas and avoid data omissions. The original point cloud data is then normalized by converting the 3D spatial coordinates of all points into a unified local road coordinate system, eliminating coordinate deviations caused by different collection locations. Secondly, the data format is standardized by converting heterogeneous point cloud data collected by different devices into the same standard format for easier subsequent unified processing. Finally, the attribute information is standardized by normalizing parameters such as point density, reflection intensity, and damage indicators to the same order of magnitude, eliminating analytical biases caused by differences in parameter ranges.

[0077] In some embodiments, the terminal device can also perform data cleaning on the normalized point cloud data, such as removing isolated noise points based on statistical filtering methods and eliminating abnormal outliers based on the average neighborhood distance.

[0078] It is understandable that this embodiment achieves non-contact and comprehensive data capture of road internal defects by collecting three-dimensional simulation point cloud data, which can completely obtain the spatial distribution and characteristic information of defects, avoiding the omission or damage of defects by traditional detection methods; the standardized processing eliminates the deviation and heterogeneity in the original data, and unifies the data format and parameter range.

[0079] In practical implementation, the terminal device acquires three-dimensional simulation point cloud data of road internal defects and constructs a defect point cloud set:

[0080]

[0081] in, This represents a collection of disease point clouds; Indicates the i-th defect point; These represent the three-dimensional spatial coordinates of the point; This represents the total number of point clouds.

[0082] In some embodiments, each point may also include additional attributes:

[0083]

[0084] in, It indicates the disease response value, damage index, or intensity of abnormal signal.

[0085] Step S20: Preprocess the standardized simulation point cloud data to identify the diseased connected sub-regions.

[0086] It should be noted that in this embodiment, the disease can be regarded as a connected region in three-dimensional space, that is, as a disease connected sub-region. The disease connected sub-region can be a set of point clouds in standardized point cloud data that are spatially connected and all belong to the disease region, that is, the part of the same disease that is continuously distributed in space. Different disease connected sub-regions correspond to different independent diseases (or different independent parts of the same disease).

[0087] In some embodiments, the terminal device can effectively remove noise, interference points, and invalid points by preprocessing the standardized simulation point cloud data through filtering, noise reduction, and other means, thereby purifying the point cloud data, improving data quality, reducing the impact of interference information on subsequent identification, and ensuring the accuracy of disease area identification. Identifying disease-connected sub-regions can separate different independent diseases (or different independent parts of the same disease), avoid mutual interference between different diseases, and provide accurate processing objects for subsequent operations such as determining the starting point and boundary search for individual diseases.

[0088] In the specific implementation, the terminal device establishes adjacency relationships based on the spatial distance between point clouds. Let the neighborhood radius be... When satisfied Time is considered point With point Adjacent.

[0089] Based on this, a point cloud adjacency graph is constructed:

[0090]

[0091] in, ; Let it be the set of edges; edges Point With point Adjacency relationship.

[0092] Graph analysis using connected component analysis algorithm The decomposition yields several diseased connected sub-regions:

[0093]

[0094] in, Show the first Each diseased sub-region; This represents the number of diseased sub-regions.

[0095] Step S30: Determine the starting point inside the disease from each connected sub-region of the disease, and construct a set of disease starting points.

[0096] It should be noted that the starting point inside the disease can be a feature point within each connected sub-region of the disease that is far from the disease boundary and can represent the core area of ​​the disease. It serves as the starting reference point for subsequent boundary search and contour extraction. The disease starting point set is a unified set of points formed by summarizing the determined starting points inside all connected sub-regions of the disease.

[0097] It is understood that by determining the starting point inside the disease, this embodiment provides a clear and reliable starting benchmark for subsequent boundary search, avoiding boundary search deviations caused by unreasonable selection of the starting point (such as being close to the boundary or located in an interference area), and ensuring the accuracy of subsequent disease boundary identification.

[0098] In some embodiments, the terminal device can, for each disease-connected sub-region, first calculate the centroid of the point cloud (i.e., the average of the three-dimensional coordinates of all points) of that region, and use the centroid as the initial reference point; then, based on attribute information filtering, it can remove points near the centroid that have too low point density, abnormal reflection intensity, or damage indicators that do not meet the core disease characteristics, and finally determine one or more qualified points as the internal starting points of that disease-connected sub-region. All disease-connected sub-regions determined are then collected and organized uniformly, and the disease-connected sub-regions corresponding to each starting point are labeled to form a complete set of disease starting points.

[0099] In the specific implementation, for each disease sub-region Determine at least one internal starting point. .

[0100] One feasible approach is based on the principle of maximum internal distance:

[0101] First, calculate the centroid of the sub-region:

[0102]

[0103] in, The centroid of the subregion; This represents the number of points in the sub-region.

[0104] Then the distance from each point to the centroid is calculated:

[0105]

[0106] Select the point with the largest distance as the starting point:

[0107]

[0108] The starting point is usually located inside the diseased area.

[0109] Furthermore, in order to effectively avoid the problems of subsequent boundary search deviation and inaccurate contour extraction caused by unreasonable selection of the starting point, the above step S30 may include:

[0110] Step S301: Extract the spatial distribution information of each disease-connected sub-region.

[0111] It should be noted that the spatial distribution information can be a comprehensive information that characterizes the distribution status, boundary position and core area position of the point cloud in the three-dimensional space of each disease-connected sub-region. It is used as a basis for screening the starting point inside the disease. The spatial distribution information includes point cloud density distribution, spatial boundary information and geometric center information.

[0112] It should be noted that the point cloud density distribution information can be the point cloud density values ​​and distribution patterns at different spatial locations within a connected sub-region of the disease, reflecting the differences in the density of the point cloud within that sub-region, and can be used to determine the core area of ​​the disease (usually the core area has a higher point cloud density).

[0113] Spatial boundary information can be the boundary contour and location parameters of the connected sub-regions of the disease, including the boundary between the diseased area and the normal road area, and the boundary between the diseased area and the road structure boundary, which is used to determine whether a certain point is far away from the non-disease area and the road structure boundary.

[0114] The geometric center information can be the geometric center coordinates and related parameters of the disease-connected sub-region. The geometric center is the spatial point corresponding to the average value of the three-dimensional coordinates of all point clouds in the sub-region.

[0115] Step S302: Based on the spatial distribution information, determine at least one internal starting point of the disease from each disease-connected sub-region, and integrate all internal starting points of the disease to construct a disease starting point set.

[0116] It should be noted that the starting point inside the disease satisfies at least one of the following conditions:

[0117] Located at a position where the density of the disease point cloud is higher than the preset density threshold;

[0118] Located at a position where the distance to the nearest non-disease area or road structure boundary is greater than a preset distance threshold;

[0119] The location within the preset range of the geometric center of the disease-connected sub-region.

[0120] It should be noted that the preset density threshold can be a density critical value set based on the average point cloud density of each disease-connected sub-region and in combination with the disease type (such as voids or loose areas). It is used to determine whether a certain point is located in the core area of ​​a disease with dense point clouds. For example, it can be set to 1.2-1.5 times the average point cloud density of the sub-region.

[0121] The preset distance threshold can be a critical distance value set based on the common size of road defects and the needs of defect treatment. It is used to determine whether a point is far away from non-defect areas and road structure boundaries, ensuring that the point is located in the core area inside the defect and avoiding proximity to the boundary.

[0122] The preset range of the geometric center can be a three-dimensional spherical range (determined by setting the radius) based on the geometric center of the disease-connected sub-region. This range covers the core area around the geometric center and is used to define the location range of the starting point. For example, the radius can be set to 1 / 3 to 1 / 2 of the average distance from the geometric center to the boundary.

[0123] In a specific implementation, for each diseased connected sub-region, at least one starting point is determined from within it, and the starting point satisfies one or a combination of the following conditions:

[0124] Located in areas with high density of disease point clouds;

[0125] Located in the area with the greatest distance to the nearest non-disease area or the boundary of the road structure;

[0126] It is located near the geometric center of the diseased connected sub-region.

[0127] By using the above method, we can ensure that the starting point is located inside the diseased area, so as to avoid prematurely touching the disease boundary during the stepping process.

[0128] Step S40: Based on the disease starting point set, construct multiple spatial stepping directions with the internal starting point of the disease as the center, and perform stepping search based on the spatial stepping directions to determine the disease boundary points of each disease connected sub-region and generate a disease boundary point set.

[0129] It should be noted that the spatial stepping direction refers to the search path set out from the starting point inside the disease in all possible directions of disease extension in space. This is used to comprehensively cover the potential extension range of the disease and ensure that no boundary areas are missed. Stepping search can be a search method that starts from the starting point inside the disease and advances step by step along each set spatial stepping direction according to a preset fixed step size, and checks and judges the point cloud data along the path one by one.

[0130] It should be noted that the disease boundary point can be the dividing point between the diseased area and the normal area. That is, when searching along the spatial step direction, it is the critical point from the diseased area to the normal area, and its attribute information (such as reflection intensity and damage index) will undergo significant abrupt changes. The disease boundary point set refers to the unified set of points formed by summarizing the disease boundary points determined in all disease-connected sub-regions, which covers the boundary feature points of all diseases.

[0131] It is understood that this embodiment, by constructing multiple spatial stepping directions, can comprehensively cover all potential extension ranges of the disease, avoid boundary omissions caused by incomplete search directions, and ensure the integrity of disease boundary identification; the stepping search method can accurately detect points on each search path, accurately determine disease boundary points through attribute information mutations, and improve the accuracy of boundary identification; and generate a disease boundary point set to integrate the boundary information of all diseases.

[0132] In some embodiments, the construction of spatial stepping directions may include: for each internal starting point of a disease in the disease starting point set, taking that point as the center, based on a three-dimensional spatial coordinate system, setting multiple uniformly distributed spatial stepping directions, including the forward and reverse directions of the x-axis, the forward and reverse directions of the y-axis, the forward and reverse directions of the z-axis, and the diagonal directions between each coordinate axis, to ensure coverage of all spatial directions that the disease may extend to; depending on the complexity of the disease, the number of stepping directions may be appropriately increased to improve the comprehensiveness of the search.

[0133] In some embodiments, the terminal device can advance step by step along each spatial step direction according to a preset step size (combined with the point cloud density setting to ensure that the step size is reasonable, neither missing point clouds nor reducing search efficiency). At each step, the attribute information and spatial position of the current point are detected. By comparing the attribute information differences between the current point and adjacent points (such as sudden changes in reflection intensity or damage indicators dropping to the normal range), it is determined whether the point is a disease boundary point. For each disease-connected sub-region, all disease boundary points determined in all spatial step directions are collected, and redundant and duplicate boundary points are removed. The boundary points of all disease-connected sub-regions are summarized, and the disease-connected sub-regions corresponding to each boundary point are labeled to generate a complete disease boundary point set.

[0134] Step S50: Perform fusion processing on the disease boundary point set to obtain the disease spatial contour, and perform disease volume calculation and three-dimensional geometric shape reconstruction based on the disease spatial contour.

[0135] It should be noted that the spatial outline of the disease can be a three-dimensional outline structure that can completely and continuously reflect the spatial morphology, range and boundary characteristics of the disease after the boundary point set is fused.

[0136] It should be noted that the volume calculation of road defects refers to calculating the volume of the space enclosed by the spatial contour of the defect using relevant algorithms, thereby achieving a quantitative representation of road defects. Three-dimensional geometric reconstruction refers to the process of reconstructing the three-dimensional geometric shape of the interior and exterior of the defect based on the spatial contour and point cloud data, using three-dimensional modeling technology to form a visualized three-dimensional model of the defect.

[0137] Understandably, terminal devices can fuse redundant, discrete, and deviation points in the disease boundary point set, and smoothly connect adjacent boundary points to form continuous, complete, and consistent boundary data, thereby eliminating the discreteness and redundancy of the boundary point set.

[0138] In some embodiments, the terminal device can remove redundant points from the disease boundary point set, deleting duplicate and invalid boundary points; secondly, it can perform interpolation to complete the discrete boundary points, filling the gaps between the boundary points so that the boundary points form a continuous curve / surface; finally, it can perform smoothing processing on the boundary point set to eliminate local deviations of the boundary points, making the disease boundary more regular and continuous, and obtaining a complete disease spatial outline.

[0139] In some embodiments, the volume of the disease can be calculated based on the obtained spatial contour of the disease. The spatial integration method or the tetrahedral partitioning method is used to divide the space enclosed by the spatial contour of the disease into multiple simple geometric units. The volume of the entire disease is obtained by calculating the volume of each geometric unit and summing them. The three-dimensional spatial coordinates of the boundary points are combined in the calculation process to ensure the accuracy of the volume calculation.

[0140] In some embodiments, the three-dimensional geometric shape reconstruction can employ surface rendering or volume rendering techniques. Based on the spatial contour of the disease, and combined with the attribute information of standardized simulation point cloud data, a three-dimensional mesh model of the disease is constructed. By performing texture mapping and color rendering on the mesh model (the severity of the disease can be distinguished according to the damage index), the three-dimensional geometric shape of the disease is restored, forming a visualized three-dimensional model of the disease, clearly presenting the spatial distribution, morphological characteristics, and severity of the disease.

[0141] In the specific implementation, the terminal device assembles the boundary points obtained from all directions into a set:

[0142]

[0143] For sets The process includes: spatial clustering for deduplication; boundary point sorting; and surface interpolation reconstruction.

[0144] Obtain the spatial boundary of the disease:

[0145]

[0146] in, Indicates the affected area; Indicates the boundary of the disease.

[0147] At the boundary of disease Volume calculations are performed under constraints.

[0148] The voxel method is represented as:

[0149]

[0150] in, The volume of the diseased tissue; Voxel units; Let be the voxel side length.

[0151] Subsequently, a three-dimensional mesh model is generated based on the boundary points to complete the reconstruction of the three-dimensional geometry of the lesion.

[0152] This embodiment treats internal road defects as connected regions in three-dimensional space. Within these regions, a step-by-step search is performed centered on the starting point of each defect. This effectively uncovers the spatial characteristics and quantitative indicators of internal road defects, enabling precise location of defect boundaries. By fusing boundary points to restore complete defect geometry, it accurately reconstructs the topology and calculates the volume of complex defect networks. This effectively solves the problems of incomplete defect point cloud boundaries and complex topology in complex internal road defects, improving the completeness of defect reconstruction and the accuracy of volume calculation. Thus, it achieves precise processing of defects throughout the entire process from data capture and identification to quantification and visualization, improving the accuracy and reliability of defect detection and analysis. The standardized and automated processing reduces manual intervention, lowers the workload of manual detection and analysis, and improves the efficiency of defect processing, while avoiding errors caused by manual operation. Volume calculation enables quantitative characterization of defects, and three-dimensional geometric reconstruction enables visual presentation of defects. This provides scientific, precise, and intuitive support for the diagnosis, assessment, and treatment plan formulation of road defects, helping to achieve precise treatment of road defects, reduce treatment costs, extend road service life, and ensure road traffic safety.

[0153] refer to Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the method for calculating the volume and geometric reconstruction of internal road defects according to the present invention.

[0154] Based on the first embodiment described above, in this embodiment, step S20 further includes:

[0155] Step S201: Based on spatial neighborhood relationships, perform isolated noise point removal processing on the standardized simulation point cloud data to obtain noise-removed point cloud data.

[0156] It should be noted that spatial neighborhood relationships refer to the positional association between each point and its surrounding neighboring points in three-dimensional space within standardized simulation point cloud data. Typically, a spherical neighborhood with a fixed radius or a fixed number of neighboring points is defined around a specific point to define the neighborhood range of that point. The point cloud data after noise removal can be a dataset that has undergone isolated noise point removal processing, eliminating isolated interference points and retaining the valid point cloud (including normal road points and defect points).

[0157] In practical implementation, the terminal device can identify and delete isolated points (i.e., noise points) in the point cloud data that have no sufficient neighboring points and are disconnected from the surrounding point cloud distribution based on spatial neighborhood relationships, thereby distinguishing between effective disease points and interfering isolated points.

[0158] It is understood that this embodiment accurately identifies isolated noise points through spatial neighborhood relationships, which can effectively remove discrete interference points caused by equipment vibration and environmental interference during the acquisition process, and avoid noise points interfering with subsequent density smoothing and connectivity construction; at the same time, it retains effective point cloud data and does not destroy the spatial distribution characteristics of the disease points.

[0159] Step S202: Perform density smoothing processing on the noise-removed point cloud data to obtain candidate point cloud data.

[0160] It should be noted that density smoothing can be applied to point cloud data after noise removal. By using algorithms to adjust the local point cloud density, it can eliminate abnormal fluctuations in point cloud density caused by acquisition errors, such as uneven point cloud density, excessively dense or sparse local point clouds, and make the point cloud distribution more uniform.

[0161] Understandably, density smoothing effectively eliminates abnormal fluctuations in local point cloud density, making the point cloud distribution of candidate point cloud data more uniform and avoiding increased computational load due to excessively dense local point clouds and broken connectivity relationships due to excessively sparse local point clouds. At the same time, it preserves the spatial location and attribute characteristics of potential disease points, providing a uniform and stable point cloud foundation for subsequent construction of connectivity relationships based on the distance threshold between points, thereby improving the accuracy and efficiency of connectivity relationship construction.

[0162] In some embodiments, the terminal device may employ a moving window smoothing algorithm to perform density smoothing processing. A fixed-size three-dimensional moving window (the window size is set based on the average density of the point cloud) is set, and the moving window traverses every region of the point cloud data after noise removal. For regions where the point cloud is too dense within the window, a random sampling method is used to remove some redundant points, reducing the point cloud density of the region to a reasonable range. For regions where the point cloud is too sparse within the window, a linear interpolation method is used to supplement virtual points and fill the gaps in the point cloud. After all windows have been traversed, the processed point cloud data is integrated to obtain candidate point cloud data.

[0163] Step S203: Set the distance threshold between points, and construct the spatial connectivity relationship of the candidate point cloud data based on the distance threshold between points.

[0164] It should be noted that the distance threshold between points can be a critical value set based on the average point distance of the candidate point cloud data and the common dimensions of road defects. It is used to determine whether two adjacent points belong to the same connected region. If the three-dimensional spatial distance between two points is less than the threshold, the two points are determined to be connected.

[0165] Spatial connectivity can be a relationship between points in candidate point cloud data constructed based on the distance threshold between points. Points whose three-dimensional spatial distance to each other is less than the distance threshold between points are considered to be connected to form a network, which intuitively reflects the spatial aggregation state of the point cloud.

[0166] It is understandable that this embodiment can accurately distinguish between adjacent points in the same disease area and discrete points in different areas by reasonably setting the distance threshold between points. This avoids misjudging different disease points as connected due to an excessively large threshold, and misjudging the same disease point as discrete due to an excessively small threshold. The spatial connectivity relationship constructed based on this threshold can truly reflect the spatial aggregation state of the candidate point cloud data, clearly associate all points in the same disease area, and provide a clear association basis for the subsequent division of disease connected sub-regions, ensuring the rationality of connected sub-region identification.

[0167] Step S204: Divide the diseased point cloud region of the candidate point cloud data according to the spatial connectivity relationship, and identify one or more diseased connected sub-regions.

[0168] In some embodiments, the terminal device may use a connected component analysis algorithm for region division. First, a screening threshold is set based on attribute information (reflection intensity, damage index) to filter all diseased point cloud regions from the candidate point cloud data (excluding normal road point clouds). Then, combined with the constructed spatial connectivity matrix, each point in the diseased point cloud region is traversed, and all points with connectivity to that point are grouped into a cluster unit to form an initial connected region. The above operation is repeated for all unmarked points until all diseased points are marked. Each independent cluster unit obtained is a diseased connected sub-region. If there are multiple independent cluster units, multiple diseased connected sub-regions are identified, and each sub-region is numbered and labeled.

[0169] It is understood that this embodiment can accurately divide the disease point cloud region into multiple independent disease connected sub-regions through region division, accurately distinguish different independent diseases (or different independent parts of the same disease), and avoid confusion between different diseases; at the same time, it ensures that the points in each disease connected sub-region are interconnected and the boundaries are complete, providing accurate and independent processing units for determining the internal starting point of the disease and searching for the boundary points of the disease in subsequent steps.

[0170] refer to Figure 4 , Figure 4 This is a flowchart illustrating the third embodiment of the method for calculating the volume and geometric reconstruction of internal road defects according to the present invention.

[0171] Based on the above embodiments, in this embodiment, step S40 further includes:

[0172] Step S401: Using the internal starting points of each disease in the disease starting point set as the center, construct multiple spatial stepping directions and generate a set of spatial stepping directions.

[0173] It should be noted that the spatial stepping direction refers to the spatial orientation used to conduct stepping searches and locate disease boundary points, centered on the starting point inside the disease. It covers the horizontal, vertical, and main extension dimensions that the disease may extend, and is the core guide for stepping searches. The set of spatial stepping directions is a collection of all preset spatial stepping directions that cover the potential extension dimensions of the disease, used to ensure that the stepping search has no blind spots and fully covers the disease area.

[0174] It should be noted that the spatial stepping direction includes multiple angular directions set along the horizontal direction, directions perpendicular to the road structure layer, directions along the main extension direction of the disease and their orthogonal directions.

[0175] The main extension direction of the disease refers to the axial direction in which the disease extends the longest and has the widest distribution range in space. It is determined by fitting the spatial distribution information of the disease's connected sub-regions and reflects the main spread trend of the disease. The orthogonal direction refers to the spatial direction that is perpendicular to the main extension direction of the disease and is on the same horizontal plane. It is used to supplement and cover the disease extension areas on both sides of the main extension direction to avoid missing boundaries.

[0176] Understandably, by constructing multi-dimensional spatial stepping directions, a comprehensive coverage of the disease's extension range can be achieved, avoiding boundary omissions due to missing directions. Combining the main extension direction of the disease and its orthogonal directions, it conforms to the actual distribution characteristics of the disease, laying the foundation for accurate search of boundary points in the future, while reducing invalid search directions and improving subsequent search efficiency.

[0177] In the specific implementation, multiple spatial stepping directions are constructed centered on the starting point inside the disease. These spatial stepping directions are finite in number and are used to cover the possible expansion range of the disease. The spatial stepping directions include: multiple angular directions along the horizontal direction; directions perpendicular to the road structure layer; and directions along the main extension direction of the disease and its orthogonal directions. All spatial stepping directions together form a direction set for subsequent stepping searches.

[0178] In some embodiments, to improve search efficiency, a main direction analysis is performed on the disease point cloud.

[0179] Construct the covariance matrix:

[0180]

[0181] in, It is the covariance matrix; This indicates the matrix transpose.

[0182] Solving the eigenvalue problem:

[0183]

[0184] in, For eigenvalues; Eigenvectors; the eigenvector corresponding to the largest eigenvalue. Indicates the main direction of disease progression.

[0185] Construct a set of search directions based on the principal direction vector:

[0186]

[0187] in, Represents a set of directions; These are the main direction and two orthogonal directions.

[0188] In some embodiments, auxiliary directions can also be generated using angle interpolation:

[0189]

[0190] in, For the first One directional vector; For direction and angle.

[0191] Step S402: Based on the preset step length, perform a step search stepwise from the corresponding starting point along the spatial step direction in the set of spatial step directions.

[0192] It should be noted that the preset step size refers to the fixed or adaptive distance that is advanced along the spatial step direction each time during the step search process. It is a key parameter for balancing search accuracy and search efficiency and can be adjusted according to the actual situation of the disease point cloud.

[0193] In the specific implementation, along each of the aforementioned spatial stepping directions, starting from the initial point inside the lesion, a step-by-step search is performed according to a preset step size. At each step position, the neighboring point cloud information of that position is acquired, and it is determined whether that position is still within the lesion area.

[0194] The preset step size can be a fixed step size or it can be adaptively adjusted according to the local point cloud density to balance computational efficiency and boundary positioning accuracy.

[0195] It is understood that this embodiment achieves systematic traversal of the diseased area by using an ordered step search with a preset step size, avoiding confusion and repetition in the search process and ensuring that each extension direction can be evenly investigated; the adaptive step size setting can balance accuracy and efficiency, ensuring the accuracy of boundary recognition in dense point cloud areas and improving the search speed in sparse point cloud areas, while reducing overall computational redundancy.

[0196] In the specific implementation, for each direction From the starting point Depart for a step-by-step search:

[0197]

[0198] in, For the first The first direction Next step position; To make progress step by step; This represents the number of steps.

[0199] Step S403: Extract neighborhood point cloud information at each step position and determine whether the current step position is located inside the diseased area.

[0200] It should be noted that the neighborhood point cloud information refers to the set of three-dimensional coordinates and attribute information (point density, reflection intensity, damage index, etc.) of all point clouds within the preset neighborhood range, which is used to determine whether the current step position belongs to the interior of the disease.

[0201] In some embodiments, at each step position, the terminal device defines a fixed-range neighborhood (preset neighborhood range) and extracts relevant information (i.e., neighborhood point cloud information) of all point clouds within that neighborhood, including but not limited to attribute information such as the three-dimensional spatial coordinates, point density, reflection intensity, and damage index of points within the neighborhood. Based on the extracted neighborhood point cloud information, it is determined whether the damage attributes of the point clouds within the neighborhood match the basic characteristics of a diseased area, thereby determining whether the current step position is within a diseased area.

[0202] It is understood that this embodiment achieves accurate judgment of the current step position by extracting neighborhood point cloud information, avoiding misjudgment caused by relying solely on information from a single point; the preset neighborhood range setting can filter out interference from individual abnormal points, improving the stability and reliability of the judgment results.

[0203] In the specific implementation, the terminal device at each candidate position Constructing a neighborhood:

[0204]

[0205] in, is the neighborhood radius.

[0206] Calculate the number of neighboring points:

[0207]

[0208] Calculate local point density:

[0209]

[0210] If one of the following conditions is met:

[0211]

[0212]

[0213] This location is then determined to be the boundary point of the disease:

[0214]

[0215] in, For the first Boundary points in each direction; Density threshold; This is the threshold for density change.

[0216] Step S404: In response to the current step position satisfying the disease boundary determination condition, mark the current step position as the disease boundary point, stop the step search in the current spatial step direction, remove the current spatial step direction from the set of spatial step directions, and return to execute the step of step search based on the preset step length along the spatial step direction in the set of spatial step directions starting from the corresponding starting point.

[0217] It should be noted that the disease boundary determination criteria are preset standards used to determine whether the stepping position is a disease boundary point, and are used to distinguish between the inside and outside of the disease and to locate the boundary point. A disease boundary point refers to a stepping position that meets the disease boundary determination criteria and is located at the edge of the disease area; it is the basic point that constitutes the spatial outline of the disease.

[0218] In its implementation, the terminal device compares the information of the current stepping position with preset disease boundary determination conditions. If the current stepping position meets any one of the disease boundary determination conditions, it marks the stepping position as a disease boundary point, stops further searching in that spatial stepping direction, and removes that direction from the set of spatial stepping directions to avoid duplicate searches. Then, it returns to the search steps for the directions that were not removed and continues the stepping search along the remaining spatial stepping directions until all directions in the set of spatial stepping directions have been searched or removed.

[0219] It is understood that this embodiment can quickly locate the boundary points of the disease by using clear boundary determination conditions, avoid the continuous advancement of invalid searches, and greatly improve the efficiency of boundary identification. The search iteration mechanism (removing directions that have been searched) can avoid repeated calculations, reduce the system's computing power consumption, and at the same time ensure that the boundary points of all extension directions can be accurately captured, avoiding boundary omissions.

[0220] Furthermore, in order to accurately identify the disease boundary, in one embodiment, the disease boundary determination condition includes at least one of the following:

[0221] The number of disease points within the preset neighborhood of the current step position is lower than the preset disease point threshold;

[0222] The density of disease point clouds within the preset neighborhood of the current step position is lower than that of the previous step position, and the difference in the density of disease point clouds is lower than the preset difference threshold.

[0223] The current step position exceeds the disease-connected sub-region;

[0224] The current stepping position has entered a non-damaged material layer or an area outside the road structure boundary.

[0225] It should be noted that the preset neighborhood range refers to a fixed spatial range (such as a spherical area with a radius of 5-10cm centered on the step position) used to extract point cloud information at each step position, and is used to determine the current position's affiliation. The preset disease point threshold refers to a pre-set critical value used to determine whether the number of disease points in the neighborhood reaches the standard for disease area, and is a quantitative standard used to distinguish between the interior and boundary of disease.

[0226] Understandably, by determining the number of disease points in the neighborhood, the edge of the disease area can be quickly identified. The number of disease points in the neighborhood inside the disease is relatively large, while the number of disease points at the boundary is significantly reduced. This condition determination logic is simple and has a small computational load, which can greatly improve the efficiency of boundary determination and is suitable for disease scenarios with relatively uniform point cloud distribution.

[0227] It should be noted that the disease point cloud density refers to the number of disease points per unit volume within a preset neighborhood, reflecting the density of disease points in that area. The previous step position refers to the position preceding the current step position in the same spatial step direction, separated from the current position by a preset step distance. The preset difference threshold is a pre-set critical value used to determine whether the change in disease point cloud density tends to level off, capturing the density gradient characteristics from the interior to the boundary of the disease.

[0228] Understandably, the point cloud density of the disease can capture the density gradient characteristics from the inside of the disease to the boundary, avoiding boundary misjudgment caused by sudden density changes. The density inside the disease is stable, while the density at the boundary gradually decreases and changes gently. This condition can accurately identify such gradient boundaries, improve the accuracy of boundary judgment, and is suitable for disease scenarios with uneven point cloud density and gradient boundaries.

[0229] It should be understood that this embodiment can directly determine whether the current position exceeds the disease itself by comparing it with the spatial range of the disease-connected sub-regions, clarify the spatial limit boundary of the disease, avoid invalid boundary point markings caused by searching beyond the disease range, reduce the redundancy of subsequent data processing, and at the same time ensure that all boundary points are within the actual range of the disease.

[0230] It should be noted that non-damaged material layers refer to structural layers in the road structure that are undamaged and functioning normally. These layers differ significantly from damaged areas in terms of material properties and reflectivity, such as normal asphalt surface layers and cement base layers. The road structure boundary refers to the spatial boundary of the overall road structure, including the roadbed edge, the lateral boundaries on both sides of the road, and the longitudinal boundaries of the upper and lower surfaces of the road. It serves as the dividing line between the road structure and the external environment.

[0231] It is understandable that this embodiment combines the actual structural features of the road to determine the boundary, which can accurately distinguish between defects and normal road structures, avoid misjudging the edge of the normal road structure as the boundary of the defect, and improve the rationality and accuracy of the boundary determination. At the same time, it can effectively avoid searching beyond the road structure range, ensuring that the boundary points are all related to the defects inside the road, and providing accurate boundary references for subsequent defect volume calculation and reconstruction.

[0232] Step S405: In response to the set of spatial stepping directions being empty, integrate all disease boundary points marked in the spatial stepping directions to generate a disease boundary point set.

[0233] In the specific implementation, when the terminal device detects that the set of spatial stepping directions is empty, it determines that all spatial stepping directions have been searched, collects the marked boundary points in all stepping directions, and summarizes the relevant information to form an initial set; it uses an algorithm to remove redundant points and abnormal points to ensure that the boundary points are accurate and unique; and it classifies and organizes them according to the connected sub-regions of the disease to form a structured set of disease boundary points.

[0234] It is understandable that this embodiment ensures the accuracy and completeness of the disease boundary point set by deduplication, filtering and classification of boundary points, avoiding interference from duplicate points and abnormal points on subsequent contour fusion and volume calculation; it is classified according to connected sub-regions to adapt to scenarios with multiple disease regions, providing support for the independent contour construction and volume calculation of each disease in the future.

[0235] This embodiment significantly improves the efficiency and accuracy of disease boundary identification. It avoids boundary omissions by covering the multi-dimensional spatial step direction and avoids boundary misjudgment by combining multiple conditions. At the same time, it reduces invalid calculations and lowers computing power consumption through an iterative search mechanism. The generated complete and accurate disease boundary point set provides high-quality data support for subsequent disease spatial contour fusion, volume calculation and three-dimensional geometric reconstruction, effectively solving the problems of low accuracy, low efficiency and boundary omission or misjudgment in traditional boundary identification.

[0236] refer to Figure 5 , Figure 5 This is a flowchart illustrating the fourth embodiment of the method for calculating the volume and geometric reconstruction of internal road defects according to the present invention.

[0237] Based on the above embodiments, in this embodiment, step S50 further includes:

[0238] Step S501: Compare the spatial coordinates of the disease boundary point set and perform deduplication of the boundary points to obtain the deduplicated boundary point set.

[0239] It is understandable that this embodiment uses boundary point deduplication to remove duplicate boundary points, thereby avoiding duplicate data from consuming computing resources and interfering with subsequent sorting and contour construction, while ensuring the purity of the boundary point set.

[0240] In the specific implementation, the terminal device can compare the three-dimensional spatial coordinates of all boundary points in the disease boundary point set one by one, preset a small coordinate difference threshold (to avoid small coordinate deviations caused by measurement errors). If the three-dimensional coordinate difference of two or more boundary points is less than the threshold, it is determined to be a duplicate boundary point. Only one valid boundary point is retained, and the remaining duplicate points are removed to complete the boundary point deduplication process, and finally the deduplicated boundary point set is obtained.

[0241] Step S502: Based on the spatial topological relationship, the deduplicated boundary point set is spatially sorted to obtain an ordered boundary point set.

[0242] It is understandable that this embodiment transforms discrete boundary points into an ordered sequence through spatial sorting, clarifying the spatial relationships between boundary points and avoiding confusion in subsequent continuity detection and surface construction.

[0243] In the specific implementation, based on spatial topological relationships (including neighborhood relationships, distance relationships, and orientation relationships between boundary points), all boundary points within the deduplicated boundary point set are spatially sorted. The sorting method conforms to the spatial distribution characteristics of the disease, and can adopt sorting logic from one end of the disease to the other, from the edge to the center, or clockwise / counterclockwise according to orientation, so that the discrete boundary points are arranged in an orderly manner, and finally an ordered boundary point set is obtained.

[0244] Step S503: Detect the spatial continuity of the ordered boundary point set, and mark the missing points based on the spatial continuity check results.

[0245] It should be noted that spatial continuity refers to a cluster of ordered boundary points where the spatial distance and azimuth angle between adjacent boundary points conform to the normal extension pattern of disease boundaries, without obvious abrupt changes or breaks, reflecting the integrity of the boundary point sequence. Missing points refer to boundary points in a cluster of ordered boundary points where breaks occur between adjacent boundary points due to omissions in boundary identification, missing point clouds, or other reasons, requiring the addition of boundary point locations.

[0246] It is understood that this embodiment can accurately identify fracture areas and missing points in the orderly boundary point set through spatial continuity detection, so as to avoid gaps and breaks in the subsequently constructed disease spatial contour due to missing boundary points, and ensure the integrity of the contour.

[0247] In a specific implementation, the terminal device can detect the spatial continuity between two adjacent boundary points one by one based on an ordered set of boundary points. It can preset spatial distance thresholds and angle thresholds. If the spatial distance between two adjacent boundary points is greater than the preset distance threshold, or the azimuth angle of the line connecting the two points changes abruptly beyond the preset angle threshold, it is determined that there are missing boundary points in the area, and the approximate spatial location of the missing point is marked to form missing point marking information.

[0248] Step S504: Use a spatial interpolation algorithm to complete the missing points to obtain the completed boundary point set.

[0249] It is understandable that this embodiment uses spatial interpolation to fill in missing points and gaps in the boundary point set, ensuring the continuity and integrity of the boundary point set. This makes the subsequently constructed spatial outline of the disease more regular and closer to the actual shape of the disease, while avoiding outline deviations caused by missing points and improving the accuracy of outline construction.

[0250] In practical implementation, the terminal device can use the marked missing point information, combined with the three-dimensional coordinates and spatial distribution characteristics of the surrounding valid boundary points, to complete the missing points using a suitable spatial interpolation algorithm. The interpolation algorithm can be flexibly selected according to the distribution density of boundary points to ensure that the spatial continuity between the completed boundary points and the surrounding valid boundary points is consistent. After completing all missing points, the results are integrated to obtain the completed boundary point set.

[0251] Step S505: Perform local smoothing on the completed boundary point set to obtain a smoothed boundary point set, thereby eliminating local deviations in the point cloud.

[0252] In the specific implementation, the terminal device performs local smoothing processing on the completed boundary point set. It uses a preset smoothing algorithm to fine-tune the three-dimensional coordinates of each boundary point, and removes local deviation points (abnormally convex or concave boundary points) caused by point cloud measurement errors and interpolation errors, so that the local trend of the boundary point set is smoother and more continuous, eliminating the influence of local deviations in the point cloud, and finally obtaining the smoothed boundary point set.

[0253] Step S506: Construct a continuous spatial surface based on the smoothed boundary point set to obtain the spatial contour of the disease.

[0254] In practical implementation, the terminal device uses a 3D surface reconstruction algorithm based on the smoothed boundary point set to fit the ordered, continuous, and smooth boundary points, constructing a continuous spatial surface. The surface construction process conforms to the spatial distribution characteristics of the boundary points, ensuring that the surface can completely cover all boundary points and conform to the actual boundary shape of the disease. The final continuous spatial surface is the spatial outline of the disease.

[0255] Furthermore, in order to accurately transform abstract data into an intuitive three-dimensional model and clearly present the spatial morphology and size of road internal defects, step S50 above also includes:

[0256] Step S5011: Divide the space within the spatial range of the disease spatial outline to obtain regular voxel units.

[0257] It should be noted that a regular voxel unit refers to a three-dimensional spatial unit (commonly a cube unit) with a regular shape and uniform size. Each voxel unit has a fixed volume and is the basic unit for voxel filling.

[0258] In practical implementation, the terminal device divides the entire spatial area covered by the disease's spatial outline into multiple uniformly sized and regularly shaped voxel units using a preset voxel partitioning rule. The size of the voxel units can be flexibly adjusted according to the accuracy requirements of the disease volume. If the accuracy requirement is high, the voxel units can be appropriately reduced; if the accuracy requirement is moderate, the voxel units can be increased to improve computational efficiency. After partitioning, a set of all regular voxel units is obtained.

[0259] It is understandable that this embodiment transforms irregular disease spaces into regular voxel units through voxel partitioning, thus avoiding the problems of high difficulty and low efficiency in calculating the volume of irregular spaces and simplifying the volume calculation process.

[0260] Step S5012: Count the number of regular voxel units located within the disease area and calculate the voxelized volume of the disease.

[0261] In the specific implementation, each regular voxel unit after division is judged one by one to determine whether each voxel unit is completely or mainly located inside the disease area (the judgment basis is the positional relationship between the voxel unit and the spatial outline of the disease; if the center of the voxel unit or a volume exceeding a preset proportion is located inside the outline, it is judged as a voxel within the disease area). The number of all regular voxel units located within the disease area is counted, and combined with the fixed volume of a single voxel unit, the voxelized volume of the disease is calculated by multiplying the number by the single volume.

[0262] Step S5013: Based on the preset cross-sectional spacing, construct multiple parallel cross-sections by setting the preset cross-sectional spacing along the main extension direction of the disease.

[0263] In a practical implementation, the terminal device can set the cross-sectional spacing based on the main extension direction of the disease, and uniformly set multiple parallel cross-sections along the main extension direction of the disease at the specified spacing. All cross-sections are perpendicular to the main extension direction of the disease and cover the entire extension range of the disease spatial contour, ensuring that each cross-section can intersect with the disease spatial contour, and finally constructing a set of multiple parallel cross-sections.

[0264] Step S5014: Calculate the intersection area between each parallel section and the spatial outline of the disease, and obtain the accumulated volume of the disease section by accumulating the cross-sections.

[0265] It should be noted that the intersection area refers to the area of ​​the two-dimensional region formed by the intersection of a single parallel cross section and the spatial outline of the disease, reflecting the size and shape of the disease at that cross section. Cross section accumulation integral refers to the method of calculating the volume of an irregular space by accumulating the intersection areas of adjacent parallel cross sections and combining the cross section spacing; essentially, it fits the volume of a continuous space through discrete cross section areas.

[0266] In the specific implementation, the intersection area between each parallel section and the spatial outline of the disease is calculated one by one. Through graphic fitting and area calculation algorithms, the intersection area corresponding to each parallel section is obtained. Based on the intersection area of ​​all parallel sections, the average intersection area of ​​two adjacent sections is taken by the section spacing, and then the calculation results of all adjacent sections are summed to finally obtain the cumulative volume of the disease section.

[0267] Step S5015: Calculate the disease volume based on the voxelized volume of the disease and the accumulated volume of the disease cross section to obtain the final disease volume calculation result.

[0268] In the specific implementation, the calculation results of the voxelized volume of the disease and the accumulated volume of the disease section are combined, and a preset fusion algorithm is used to fuse the two. This allows for weighted fusion based on the calculation accuracy and reliability of the two volumes, or the average value of the two can be taken. At the same time, abnormal deviations in the two calculation results are eliminated to ensure that the fused volume result is more accurate and reliable, and finally the final disease volume calculation result is obtained.

[0269] Step S5016: Based on the spatial contour of the lesion and the final lesion volume calculation result, perform three-dimensional geometric shape reconstruction to complete the three-dimensional geometric shape reconstruction of the lesion.

[0270] In practical implementation, based on the spatial outline of the lesion and combined with the final lesion volume calculation results, a three-dimensional geometric reconstruction algorithm is used to completely reconstruct the three-dimensional geometry of the lesion. During the reconstruction process, it is ensured that the reconstructed three-dimensional model is consistent with the shape of the lesion's spatial outline and that the volume matches the final calculation results. At the same time, spatial details of the lesion are supplemented, so that the reconstructed three-dimensional model can intuitively and accurately present the spatial shape, size, and extension trend of the lesion, ultimately completing the reconstruction of the lesion's three-dimensional geometry.

[0271] This embodiment effectively eliminates redundancy, missing values, and deviations in the boundary point set through a series of processes such as deduplication, sorting, completion, and smoothing, thereby improving the accuracy, continuity, and completeness of the boundary point set. The continuous spatial surface constructed based on the processed boundary point set can accurately and completely reflect the spatial morphology and boundary range of the disease, solving the problems of contour breakage, local deviation, and morphological distortion existing in traditional boundary point fusion. It achieves efficient transformation from discrete boundary points to complete disease contours, balancing processing efficiency and contour accuracy. The fusion method combining voxelization calculation and cross-section accumulation calculation effectively compensates for the shortcomings of single volume calculation methods, significantly improving the accuracy and reliability of disease volume calculation, and solving the problems of low accuracy and large errors in traditional disease volume calculation. Based on the three-dimensional geometric reconstruction of the disease spatial contour and final volume data, the transformation from abstract data to an intuitive three-dimensional model is realized, clearly presenting the spatial morphology and size of the disease inside the road.

[0272] Furthermore, this embodiment of the invention also proposes a computer-readable storage medium storing a program for calculating the volume and reconstructing the geometry of internal road defects. When the program is executed by a processor, it implements the steps of the method for calculating the volume and reconstructing the geometry of internal road defects as described above.

[0273] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0274] The aforementioned computer-readable storage medium may be included in the equipment for calculating and reconstructing the volume of internal road defects; or it may exist independently and not be assembled into the equipment for calculating and reconstructing the volume of internal road defects.

[0275] Furthermore, this invention also proposes a computer program product, including a program for calculating the volume and reconstructing the geometry of internal road defects. When the program is executed by a processor, it implements the steps of the method for calculating the volume and reconstructing the geometry of internal road defects as described above.

[0276] The specific implementation of the computer program product of the present invention is basically the same as the embodiments of the above-mentioned method for calculating the volume and geometric reconstruction of internal road defects, and will not be repeated here.

[0277] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the device for calculating the volume and reconstructing the geometry of internal road defects according to the present invention.

[0278] like Figure 6 As shown, the device for calculating the volume and reconstructing the geometry of internal road defects proposed in this embodiment of the invention includes:

[0279] The point cloud data acquisition module 10 is used to acquire three-dimensional simulation point cloud data of road internal defects and perform standardization processing to obtain standardized simulation point cloud data. The standardized simulation point cloud data includes three-dimensional spatial coordinate information and attribute information of each point in the defect area. The attribute information includes point density, reflection intensity and / or damage index.

[0280] The connected region identification module 20 is used to preprocess the standardized simulation point cloud data and identify the diseased connected sub-regions;

[0281] The starting point positioning module 30 is used to determine the internal starting point of the disease from each connected sub-region of the disease and to construct a set of disease starting points.

[0282] The boundary point search module 40 is used to construct multiple spatial stepping directions based on the disease starting point set, with the internal starting point of the disease as the center, and perform stepping search based on the spatial stepping directions to determine the disease boundary points of each disease connected sub-region and generate a disease boundary point set.

[0283] The lesion contour construction module 50 is used to perform fusion processing on the lesion boundary point set to obtain the lesion spatial contour, and to perform lesion volume calculation and three-dimensional geometric shape reconstruction based on the lesion spatial contour.

[0284] This embodiment treats internal road defects as connected regions in three-dimensional space. Within these regions, a step-by-step search is performed centered on the starting point of each defect. This effectively uncovers the spatial characteristics and quantitative indicators of internal road defects, enabling precise location of defect boundaries. By fusing boundary points to restore complete defect geometry, it accurately reconstructs the topology and calculates the volume of complex defect networks. This effectively solves the problems of incomplete defect point cloud boundaries and complex topology in complex internal road defects, improving the completeness of defect reconstruction and the accuracy of volume calculation. Thus, it achieves precise processing of defects throughout the entire process from data capture and identification to quantification and visualization, improving the accuracy and reliability of defect detection and analysis. The standardized and automated processing reduces manual intervention, lowers the workload of manual detection and analysis, and improves the efficiency of defect processing, while avoiding errors caused by manual operation. Volume calculation enables quantitative characterization of defects, and three-dimensional geometric reconstruction enables visual presentation of defects. This provides scientific, precise, and intuitive support for the diagnosis, assessment, and treatment plan formulation of road defects, helping to achieve precise treatment of road defects, reduce treatment costs, extend road service life, and ensure road traffic safety.

[0285] The road internal defects volume calculation and geometric reconstruction device provided in this application adopts the road internal defects volume calculation and geometric reconstruction method in the above embodiments, which can solve the technical problem of road internal defects volume calculation and geometric reconstruction. Compared with the prior art, the beneficial effects of the road internal defects volume calculation and geometric reconstruction device provided in this application are the same as the beneficial effects of the road internal defects volume calculation and geometric reconstruction method provided in the above embodiments, and other technical features in the road internal defects volume calculation and geometric reconstruction device are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0286] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0287] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0288] In addition, for technical details not described in detail in this embodiment, please refer to the method for volume calculation and geometric reconstruction of internal road defects provided in any embodiment of the present invention, which will not be repeated here.

[0289] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0290] It should be noted that the user information (including but not limited to user device information, user personal information, user location information, user behavior information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0291] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0292] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0293] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for calculating the volume and geometrically reconstructing internal road defects, characterized in that, The methods for calculating the volume and reconstructing the geometry of internal road defects include: Collect three-dimensional simulation point cloud data of road internal defects and perform standardization processing to obtain standardized simulation point cloud data. The standardized simulation point cloud data includes three-dimensional spatial coordinate information and attribute information of each point in the defect area. The attribute information includes point density, reflection intensity and / or damage index. The standardized simulation point cloud data is preprocessed to identify the diseased connected sub-regions; Determine the starting point inside the disease from each connected sub-region of the disease, and construct a set of disease starting points; Based on the disease initiation point set, multiple spatial stepping directions are constructed with the initiation point inside the disease as the center, and stepping search is performed based on the spatial stepping directions to determine the disease boundary points of each disease connected sub-region and generate a disease boundary point set. The disease boundary point set is fused to obtain the disease spatial contour, and the disease volume is calculated and the three-dimensional geometry is reconstructed based on the disease spatial contour. The process involves constructing multiple spatial stepping directions centered on the disease's internal starting points, based on the disease's initiation point set, and performing a stepping search based on these spatial stepping directions to determine the disease boundary points of each connected sub-region, thereby generating a disease boundary point set. This includes: Centered on the internal starting points of each disease in the disease starting point set, multiple spatial stepping directions are constructed, and a set of spatial stepping directions is generated. The spatial stepping directions include multiple angular directions set along the horizontal direction, directions perpendicular to the road structure layer, and directions along the main extension direction of the disease and its orthogonal directions. Based on a preset step size, the search is performed step by step along the spatial step direction in the set of spatial step directions, starting from the corresponding starting point. Extract neighborhood point cloud information at each step position and determine whether the current step position is located inside the diseased area; In response to the current step position meeting the disease boundary determination condition, the current step position is marked as a disease boundary point, and the step search in the current spatial step direction is stopped. The current spatial step direction is removed from the set of spatial step directions, and the process returns to the step of performing the step search step by step from the corresponding starting point along the spatial step direction in the set of spatial step directions based on the preset step length. In response to the fact that the set of spatial stepping directions is empty, the disease boundary points marked on all spatial stepping directions are integrated to generate a disease boundary point set; The calculation of disease volume and reconstruction of three-dimensional geometry based on the disease spatial contour includes: Within the spatial range of the disease spatial outline, voxels are divided to obtain regular voxel units; The number of regular voxel units located within the diseased area is counted, and the voxelized volume of the disease is calculated. Multiple parallel sections are constructed based on the preset cross-sectional spacing along the main extension direction of the disease; Calculate the intersection area between each parallel section and the spatial outline of the disease, and obtain the accumulated volume of the disease section by integrating the accumulated area of ​​the sections; Based on the voxelized volume of the disease and the accumulated volume of the disease section, the disease volume is calculated to obtain the final disease volume calculation result. Based on the spatial contour of the lesion and the final volume calculation result of the lesion, a three-dimensional geometric shape reconstruction is performed to complete the three-dimensional geometric shape reconstruction of the lesion.

2. The method for volume calculation and geometric reconstruction of internal road defects as described in claim 1, characterized in that, The preprocessing of the standardized simulation point cloud data to identify diseased connected sub-regions includes: Based on spatial neighborhood relationships, isolated noise points are removed from the standardized simulated point cloud data to obtain noise-removed point cloud data. Density smoothing is performed on the noise-removed point cloud data to obtain candidate point cloud data; Set a distance threshold between points, and construct the spatial connectivity relationship of the candidate point cloud data based on the distance threshold between points; Based on the spatial connectivity relationship, the diseased point cloud region of the candidate point cloud data is divided, and one or more diseased connected sub-regions are identified.

3. The method for volume calculation and geometric reconstruction of internal road defects as described in claim 1, characterized in that, The step of determining the starting point within each connected sub-region of the disease and constructing a set of disease starting points includes: Extract the spatial distribution information of each diseased connected sub-region, the spatial distribution information including point cloud density distribution information, spatial boundary information and geometric center information; Based on the spatial distribution information, at least one internal starting point of the disease is determined from each connected sub-region of the disease, and all internal starting points of the disease are integrated to construct a set of disease starting points. The internal starting points of the disease satisfy at least one of the following conditions: Located at a position where the density of the disease point cloud is higher than the preset density threshold; Located at a position where the distance to the nearest non-disease area or road structure boundary is greater than a preset distance threshold; The location within the preset range of the geometric center of the disease-connected sub-region.

4. The method for volume calculation and geometric reconstruction of internal road defects as described in claim 1, characterized in that, The criteria for determining the boundary of the disease include at least one of the following: The number of disease points within the preset neighborhood of the current step position is lower than the preset disease point threshold; The density of disease point clouds within the preset neighborhood of the current step position is lower than that of the previous step position, and the difference in the density of disease point clouds is lower than the preset difference threshold. The current step position exceeds the disease-connected sub-region; The current stepping position has entered a non-damaged material layer or an area outside the road structure boundary.

5. The method for volume calculation and geometric reconstruction of internal road defects as described in claim 1, characterized in that, The process of fusing the set of boundary points of the disease to obtain the spatial contour of the disease includes: Spatial coordinates are compared on the set of disease boundary points, and duplicate boundary points are deduplicated to obtain the deduplicated set of boundary points. Based on spatial topological relationships, the deduplicated boundary point set is spatially sorted to obtain an ordered boundary point set. The spatial continuity of the ordered boundary point set is detected, and missing points are marked based on the spatial continuity check results; The missing points are filled in using a spatial interpolation algorithm to obtain the completed boundary point set. The completed boundary point set is locally smoothed to obtain a smoothed boundary point set, thereby eliminating local deviations in the point cloud. A continuous spatial surface is constructed based on the smoothed boundary point set to obtain the spatial contour of the disease.

6. A device for calculating the volume and reconstructing the geometry of internal road defects, characterized in that, The device is configured to implement the method for volume calculation and geometric reconstruction of road internal defects as described in any one of claims 1 to 5, and the device includes: The point cloud data acquisition module is used to collect three-dimensional simulation point cloud data of road internal defects and perform standardization processing to obtain standardized simulation point cloud data. The standardized simulation point cloud data includes three-dimensional spatial coordinate information and attribute information of each point in the defect area. The attribute information includes point density, reflection intensity and / or damage index. The connected region identification module is used to preprocess the standardized simulation point cloud data and identify the diseased connected sub-regions; The starting point positioning module is used to determine the starting point inside the disease from each connected sub-region of the disease and construct a set of disease starting points; The boundary point search module is used to construct multiple spatial stepping directions based on the disease starting point set, with the internal starting point of the disease as the center, and perform stepping search based on the spatial stepping directions to determine the disease boundary points of each disease connected sub-region and generate a disease boundary point set. The disease contour construction module is used to fuse the disease boundary point set to obtain the disease spatial contour, and to perform disease volume calculation and three-dimensional geometric shape reconstruction based on the disease spatial contour.

7. A device for calculating the volume and reconstructing the geometry of internal road defects, characterized in that, The device for calculating and reconstructing the volume of internal road defects includes: a memory, a processor, and a program for calculating and reconstructing the volume of internal road defects stored in the memory. The processor is used to run the program for calculating and reconstructing the volume of internal road defects, and the program for calculating and reconstructing the volume of internal road defects is configured to implement the method for calculating and reconstructing the volume of internal road defects as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for calculating the volume and reconstructing the geometry of internal road defects. When the program is executed by a processor, it implements the method for calculating the volume and reconstructing the geometry of internal road defects as described in any one of claims 1 to 5.

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