Ultrahigh-speed three-dimensional laser scanning pavement skid resistance evaluation method and system

Abstract

The invention discloses an ultra-high-speed three-dimensional laser scanning pavement skid resistance evaluation method and system, and the method comprises the steps: scanning a target pavement through a three-dimensional laser scanner, obtaining the three-dimensional point cloud original data of the pavement, carrying out the preprocessing of the three-dimensional point cloud original data of the pavement, and carrying out the calculation of the three-dimensional point cloud original data of the pavement; constructing a road surface three-dimensional texture model according to the preprocessed road surface three-dimensional point cloud data, constructing a reference surface of a target road surface, projecting the reference surface into the road surface three-dimensional texture model, extracting a plurality of feature parameters related to the anti-skid performance of the road surface based on the projected road surface three-dimensional texture model, and calculating the anti-skid performance of the road surface according to the extracted feature parameters. According to the method, the weights of the extracted multiple characteristic parameters related to the anti-skid performance of the road surface are determined, the multiple characteristic parameters are subjected to weighting processing, and the environment penalty factor is introduced to correct the weighting processing result to obtain the anti-skid performance index, so that the anti-skid performance of the target road surface is evaluated by using the anti-skid performance index, and the evaluation accuracy of the anti-skid performance of the target road surface is improved. Therefore, the evaluation accuracy and evaluation efficiency of the anti-skid performance of the road surface are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pavement engineering, and in particular to a super-high-speed three-dimensional laser scanning pavement anti-skid performance evaluation method and system. BACKGROUND

[0002] Pavement anti-skid performance is one of the core indicators for ensuring road traffic safety, directly affecting the braking distance, steering stability and handling safety during vehicle driving, especially in bad weather conditions such as rain, snow and ice, insufficient pavement anti-skid performance can easily cause vehicle skidding, rollover and other traffic accidents. With the continuous growth of highway and urban road mileage in China and the rapid increase of motor vehicle ownership, accurate detection and scientific evaluation of pavement anti-skid performance have become a key requirement for road maintenance management and traffic risk prevention and control, and are also an important technical support for extending pavement service life and reducing maintenance costs.

[0003] At present, the main pavement anti-skid performance evaluation methods at home and abroad are mainly divided into two categories: traditional contact detection method and modern non-contact detection method. The two methods have significant differences in detection principle, efficiency and accuracy, and generally have technical limitations, which are difficult to meet the current demand for high precision and high efficiency in road engineering. SUMMARY

[0004] Therefore, the present application provides a super-high-speed three-dimensional laser scanning pavement anti-skid performance evaluation method and system, which solves the technical problem that the current pavement anti-skid performance evaluation method has technical limitations and is difficult to meet the current demand for high precision and high efficiency in road engineering.

[0005] The first aspect of the present application provides a super-high-speed three-dimensional laser scanning pavement anti-skid performance evaluation method, comprising: scanning the target pavement using a three-dimensional laser scanner to obtain pavement three-dimensional point cloud raw data; preprocessing the pavement three-dimensional point cloud raw data to obtain preprocessed pavement three-dimensional point cloud data; constructing a pavement three-dimensional texture model according to the preprocessed pavement three-dimensional point cloud data; wherein the pavement three-dimensional texture model comprises a micro-texture sub-model and a macro-texture sub-model; constructing a reference surface of the target pavement and projecting the reference surface into the pavement three-dimensional texture model to obtain a projected pavement three-dimensional texture model; extracting a plurality of feature parameters related to pavement anti-skid performance based on the projected pavement three-dimensional texture model; determining the weights of the plurality of feature parameters related to pavement anti-skid performance, weighting the plurality of feature parameters according to the weights, and introducing an environmental penalty factor to correct the weighting result to obtain an anti-skid performance index; According to the anti-skid performance index, the anti-skid performance of the target road is evaluated, and an evaluation result of the anti-skid performance of the target road is obtained.

[0006] Preferably, the preprocessing includes noise removal and outlier removal.

[0007] Preferably, constructing the road surface three-dimensional texture model according to the preprocessed road surface three-dimensional point cloud data comprises: The preprocessed road surface three-dimensional point cloud data is gridded according to a predetermined grid size, and a plurality of grid units are obtained; For each grid unit, the mean value of the coordinates of all point clouds in the grid unit is calculated, and the mean value of the coordinates is taken as the height value of the grid unit, and a road surface height matrix is obtained; The road surface height matrix is decomposed into a plurality of sub-matrices of different scales by wavelet transform, and the texture change gradient and the texture wavelength of each sub-matrix are determined; The texture change gradient of each sub-matrix is compared with a preset gradient threshold value, and a first comparison result is obtained, and the texture wavelength of each sub-matrix is compared with a preset texture wavelength threshold value, and a second comparison result is obtained; According to the first comparison result and the second comparison result, all sub-matrices are divided into high-frequency sub-matrices and low-frequency sub-matrices; The high-frequency sub-matrices and the low-frequency sub-matrices are respectively reconstructed by inverse wavelet transform, and the micro-texture sub-model and the macro-texture sub-model are obtained; wherein the micro-texture sub-model reflects the small concave-convex features of the road surface, and the macro-texture sub-model reflects the large-scale undulation features of the road surface as a whole.

[0008] The micro-texture sub-model is superimposed on the corresponding coordinate position in the macro-texture sub-model, and the road surface three-dimensional texture model is obtained.

[0009] Preferably, according to the first comparison result and the second comparison result, all sub-matrices are divided into high-frequency sub-matrices and low-frequency sub-matrices, comprising: For each sub-matrix, the sub-matrix whose texture change gradient is greater than the preset gradient threshold value is divided into a first high-frequency sub-matrix, and the sub-matrix whose texture change gradient is not greater than the preset gradient threshold value is divided into a first low-frequency sub-matrix; It is judged whether the texture wavelength of the sub-matrix in the first high-frequency sub-matrix is less than the preset texture wavelength threshold value, if the texture wavelength of the sub-matrix in the first high-frequency sub-matrix is not less than the preset texture wavelength threshold value, the sub-matrix in the first high-frequency sub-matrix is divided into the first low-frequency sub-matrix, and a second high-frequency sub-matrix and a second low-frequency sub-matrix are obtained. determining whether the texture wavelength of the sub-matrix in the second low-frequency sub-matrix is less than a preset texture wavelength threshold, if the texture wavelength of the sub-matrix in the second low-frequency sub-matrix is not less than the preset texture wavelength threshold, reducing the preset gradient threshold, and based on the reduced preset gradient threshold, proceeding to the comparison of the texture change gradient of each sub-matrix with the preset gradient threshold to obtain a first comparison result, and the comparison of the texture wavelength of each sub-matrix with the preset texture wavelength threshold to obtain a second comparison result, until all sub-matrices are divided into high-frequency sub-matrices or low-frequency sub-matrices as required.

[0010] Preferably, the method comprises the following steps: performing plane fitting on the plurality of preprocessed road surface three-dimensional point cloud data by using a least square method to construct the reference surface of the target road surface; projecting the reference surface of the target road surface into the road surface three-dimensional texture model in a perpendicular projection manner to obtain an initial projected road surface three-dimensional texture model; performing coordinate correction on the initial projected road surface three-dimensional texture model to obtain the projected road surface three-dimensional texture model.

[0011] Preferably, the characteristic parameters include a friction area characteristic parameter. The method comprises the following steps: dividing the projected road surface three-dimensional texture model into a plurality of grids; for each grid, extracting a point cloud corresponding to the average value of the Z-axis coordinates of all point clouds in the grid as a feature point cloud of the grid; for each feature point cloud, determining a texture height of the feature point cloud relative to the texture height of the reference surface of the target road surface; comparing the texture height of each feature point cloud with a preset height threshold, screening out a feature point cloud whose texture height is greater than the preset height threshold, and determining the grid corresponding to the screened feature point cloud as an effective friction contact area grid; determining an effective friction contact area according to the number of the effective friction contact area grids and the unit grid area; determining an effective friction contact area ratio as the friction area characteristic parameter according to the proportion of the effective friction contact area relative to the total area corresponding to all grids.

[0012] Preferably, the characteristic parameters further include a texture morphology parameter; the texture morphology parameter includes an average texture depth, a texture sharpness, and a friction area distribution entropy; The method further includes extracting a plurality of characteristic parameters related to road surface anti-skid performance based on the projected road surface three-dimensional texture model, including: Determining an average texture depth of all the feature point clouds according to the texture height of each feature point cloud; wherein the average texture depth is an average value of the texture height of all the feature point clouds; Determining a texture height standard deviation according to the texture height of all the feature point clouds, and taking a ratio of the texture height standard deviation to the average texture depth as a texture sharpness; Dividing the projected road surface three-dimensional texture model into a plurality of sub-regions according to a preset size, and calculating an effective friction contact area proportion of each sub-region; Determining a friction area distribution entropy according to the effective friction contact area proportion of each sub-region; wherein the friction area distribution entropy is used to represent the uniformity of the distribution of road surface texture in space.

[0013] Preferably, the method further includes determining weights of the extracted plurality of characteristic parameters related to road surface anti-skid performance, performing weighted processing on the plurality of characteristic parameters according to the weights, and introducing an environmental penalty factor to correct the weighted processing result to obtain an anti-skid performance index, including: Performing standardization processing on each characteristic parameter to obtain each standardized characteristic parameter; Determining a grey correlation degree and a Pearson correlation coefficient of each standardized characteristic parameter and a reference characteristic parameter; Respectively performing normalization processing on the grey correlation degree and the Pearson correlation coefficient, and performing linear fusion on the normalized grey correlation degree and the normalized Pearson correlation coefficient to obtain the weight of each characteristic parameter; Performing weighted processing on each standardized characteristic parameter according to the weight of each characteristic parameter to obtain an initial anti-skid performance index; Obtaining a road surface water content and a road surface service life of the target road, and respectively determining a road surface humidity correction coefficient and a service life correction coefficient according to the road surface water content and the road surface service life; Determining the environmental penalty factor according to the road surface humidity correction coefficient and the service life correction coefficient; Correcting the initial anti-skid performance index by the environmental penalty factor to obtain the anti-skid performance index.

[0014] Preferably, the method further includes evaluating the anti-skid performance of the target road according to the anti-skid performance index to obtain an evaluation result of the anti-skid performance of the target road, including: Multiple anti-skid performance thresholds of different anti-skid performance levels are obtained, and the anti-skid performance index is compared with the multiple anti-skid performance thresholds. The anti-skid performance level of the target road surface is determined according to the comparison results. The higher the anti-skid performance level, the higher the anti-skid performance of the target road surface.

[0015] Secondly, the present invention also provides an ultra-high-speed three-dimensional laser scanning pavement anti-skid performance evaluation system, comprising: The point cloud acquisition module is used to scan the target road surface using a 3D laser scanner to acquire the original 3D point cloud data of the road surface. The preprocessing module is used to preprocess the original three-dimensional point cloud data of the road surface to obtain preprocessed three-dimensional point cloud data of the road surface. The texture model construction module is used to construct a three-dimensional texture model of the road surface based on the preprocessed three-dimensional point cloud data of the road surface; wherein, the three-dimensional texture model of the road surface includes a micro-texture sub-model and a macro-texture sub-model; The texture model projection module is used to construct the reference surface of the target road surface and project the reference surface onto the three-dimensional texture model of the road surface to obtain the projected three-dimensional texture model of the road surface. The feature parameter extraction module is used to extract multiple feature parameters related to the anti-skid performance of the road surface based on the projected three-dimensional texture model of the road surface; The skid resistance index determination module is used to determine the weights of multiple feature parameters related to the extracted road surface skid resistance performance, perform weighted processing on the multiple feature parameters according to the weights, and introduce an environmental penalty factor to correct the weighted processing result to obtain the skid resistance performance index. The skid resistance performance evaluation module is used to evaluate the skid resistance performance of the target road surface based on the skid resistance performance index, and obtain the evaluation result of the skid resistance performance of the target road surface.

[0016] From the above technical scheme can be seen, the present application uses three-dimensional laser scanner to scan the target road surface, obtains the road surface three-dimensional point cloud original data, pre-processes the road surface three-dimensional point cloud original data, constructs the road surface three-dimensional texture model according to the pre-processed road surface three-dimensional point cloud data, and uses the model to integrate the micro-texture sub-model and the macro-texture sub-model, constructs the datum plane of the target road surface, and projects the datum plane into the road surface three-dimensional texture model, extracts a plurality of characteristic parameters related to the road surface skid resistance performance based on the projected road surface three-dimensional texture model, determines the weight of the plurality of characteristic parameters related to the road surface skid resistance performance, performs weighted processing on the plurality of characteristic parameters, and introduces an environmental penalty factor to correct the weighted processing result to obtain a skid resistance performance index, so as to evaluate the skid resistance performance of the target road surface by using the skid resistance performance index, thereby increasing the technical adaptability of the road surface skid resistance performance evaluation method and improving the accuracy and evaluation efficiency of the road surface skid resistance performance evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0018] Figure 1 A flow chart of a super-high-speed three-dimensional laser scanning road surface skid resistance performance evaluation method provided by the embodiment of the present application; Figure 2 A structural schematic diagram of a super-high-speed three-dimensional laser scanning road surface skid resistance performance evaluation system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] As Figure 1 shown, the present application provides a super-high-speed three-dimensional laser scanning road surface skid resistance performance evaluation method, which includes the following steps S1 to S7. Wherein: Step S1, using a three-dimensional laser scanner to scan the target road surface, and obtaining road surface three-dimensional point cloud original data.

[0021] The three-dimensional laser scanner can set the scanning resolution to be 0.1-1.0 mm, the scanning frequency to be 50-200 kHz, and adopt a "serpentine reciprocating scanning" mode based on the accuracy requirement, and the overlap rate of adjacent scanning lines is not less than 15% to scan the area for evaluating the road surface skid resistance performance of the target road surface.

[0022] In the scanning process, a local three-dimensional coordinate system is established with the intersection of the road edge line and the center line at the starting end of the scanning area as the origin (the X axis points to the driving direction along the road center line, the Y axis is perpendicular to the X axis and points to the outside of the road, and the Z axis is perpendicular to the road surface and points to the sky), and the local coordinates are converted into geodetic coordinates through the GPS positioning module to ensure the spatial uniqueness of the data.

[0023] Step S2, the original road surface three-dimensional point cloud data is preprocessed to obtain preprocessed road surface three-dimensional point cloud data.

[0024] The preprocessing includes noise removal and abnormal point elimination.

[0025] The noise removal is to use a Gaussian filtering algorithm to smooth the original road surface three-dimensional point cloud data to eliminate random noise generated in the scanning process due to device vibration, environmental interference and other factors. Specifically, by setting appropriate Gaussian kernel size and standard deviation, a weighted average of each point and its neighborhood points is performed to obtain smoothed point cloud data.

[0026] The abnormal point elimination is based on the statistical characteristics of the point cloud data and is performed by using a statistical filtering method. First, the average distance of each point in the point cloud data to its neighborhood points is calculated, and the abnormal points with a distance greater than the average distance are eliminated, and further isolated noise is removed to ensure the accuracy and reliability of the point cloud data.

[0027] Step S3, a road surface three-dimensional texture model is constructed according to the preprocessed road surface three-dimensional point cloud data; wherein the road surface three-dimensional texture model includes a micro-texture sub-model and a macro-texture sub-model.

[0028] The road surface three-dimensional texture model is constructed by organically integrating micro-texture features and macro-texture features. The micro-texture sub-model reflects the small concave-convex features of the road surface, and the macro-texture sub-model reflects the large-scale undulation features of the road surface as a whole, so that the road surface three-dimensional texture model can accurately reflect the real skid resistance performance of the road surface.

[0029] Step S4, a reference surface of the target road surface is constructed, and the reference surface is projected into the road surface three-dimensional texture model to obtain a projected road surface three-dimensional texture model.

[0030] The reference surface of the target road surface is obtained by plane fitting of the pretreated multiple road surface three-dimensional point cloud data by the least square method. The reference surface serves as a reference for subsequent projection and feature extraction, which can ensure the accuracy of the projection and the reliability of the feature parameters. The reference surface can measure the relative height of the road surface texture, that is, the degree of protrusion / depression of the texture relative to the reference surface.

[0031] By projecting the reference surface into the road surface three-dimensional texture model, the reference surface and the road surface three-dimensional texture model are integrated, which facilitates the extraction of related feature parameters in the subsequent steps.

[0032] Step S5, based on the projected road surface three-dimensional texture model, multiple feature parameters related to the road surface skid resistance performance are extracted.

[0033] Wherein, after obtaining the projected road surface three-dimensional texture model, the reference surface and the point cloud data can be used to accurately extract multiple feature parameters related to the road surface skid resistance performance.

[0034] Step S6, determine the weight of the extracted multiple feature parameters related to the road surface skid resistance performance, weight the multiple feature parameters according to the weight, and introduce an environmental penalty factor to correct the weighted processing result, to obtain a skid resistance performance index.

[0035] Wherein, through the correction of the environmental penalty factor, the factor comprehensively considers the influence of the water content and the service life of the road surface on the skid resistance performance, so as to ensure that the evaluation result can truly reflect the skid resistance ability of the road surface under actual use conditions.

[0036] Step S7, according to the skid resistance performance index, the skid resistance performance of the target road surface is evaluated, and the evaluation result of the skid resistance performance of the target road surface is obtained.

[0037] Wherein, by comparing the skid resistance performance index with the preset multiple skid resistance performance level thresholds, the skid resistance performance level of the target road surface can be clearly divided. For example, four levels of excellent, good, medium and poor can be set, which correspond to different ranges of skid resistance performance index, so as to intuitively display the skid resistance performance of the road surface.

[0038] It should be noted that the embodiment of the present application utilizes a three-dimensional laser scanner to scan the target road surface, obtains road surface three-dimensional point cloud original data, pre-processes the road surface three-dimensional point cloud original data, constructs a road surface three-dimensional texture model according to the pre-processed road surface three-dimensional point cloud data, and utilizes the model to integrate a micro-texture sub-model and a macro-texture sub-model, constructs a reference surface of the target road surface, projects the reference surface into the road surface three-dimensional texture model, extracts a plurality of characteristic parameters related to road surface anti-skid performance based on the projected road surface three-dimensional texture model, determines the weights of the plurality of extracted characteristic parameters related to road surface anti-skid performance, performs weighted processing on the plurality of characteristic parameters, introduces an environmental penalty factor to correct the weighted processing result, obtains an anti-skid performance index, and thus evaluates the anti-skid performance of the target road surface by using the anti-skid performance index, thereby increasing the technical adaptability of the road surface anti-skid performance evaluation method and improving the accuracy and evaluation efficiency of the road surface anti-skid performance evaluation.

[0039] In some embodiments, constructing a road surface three-dimensional texture model according to the pre-processed road surface three-dimensional point cloud data comprises: Step S301, performing grid processing on the pre-processed road surface three-dimensional point cloud data according to a predetermined grid size to obtain a plurality of grid units.

[0040] The grid size can be in a range of 1mm*1mm to 5mm*5mm. By reasonably setting the grid size, the model accuracy and the calculation efficiency can be balanced, that is, the micro-texture features can be fully captured, and the processing burden caused by excessive data amount can be avoided. Specifically, the grid size can be dynamically adjusted according to the actual scanning resolution and the roughness of the road surface, for example, a larger grid (such as 5mm*5mm) is used for rough road surfaces, and a smaller grid (such as 1mm*1mm) is used for smooth road surfaces.

[0041] Step S302, for each grid unit, calculating the mean value of the coordinates of all point clouds in the grid unit, and taking the mean value of the coordinates as the height value of the grid unit to obtain a road surface height matrix.

[0042] The road surface height matrix is denoted as H, the dimension of H is M*N, M is the number of rows of grid units, N is the number of columns of grid units, and the elements in the road surface height matrix are the height values of the grid units.

[0043] Step S303, performing multi-scale decomposition on the road surface height matrix by using wavelet transform to obtain a plurality of sub-matrices of different scales, and determining the texture change gradient and the texture wavelength of each sub-matrix.

[0044] Wherein, the decomposition scale number of wavelet transform is determined according to the road surface type, wherein the decomposition scale number of wavelet transform of asphalt pavement is 5, the decomposition scale number of wavelet transform of cement pavement is 3, the decomposition scale number of wavelet transform of composite pavement of asphalt and cement is 4, and a biorthogonal wavelet is selected as a wavelet base function to perform multi-layer biorthogonal wavelet transform on the road surface height matrix to obtain a plurality of sets of sub-matrices of different scales, denoted as Wherein, The horizontal, vertical and diagonal direction detail sub-matrices of the kth layer are respectively, and k is the decomposition layer to which the sub-matrix belongs.

[0045] The texture change gradient of the sub-matrix is the degree of fluctuation of the texture in the sub-matrix, the more intense the fluctuation, the greater the texture change gradient, corresponding to high-frequency texture (micro); the more gentle the fluctuation, the smaller the texture change gradient, corresponding to low-frequency texture (macro).

[0046] The calculation process of the texture change gradient of the sub-matrix is as follows: The two-dimensional gradient module G i,j of each element w i,j (i=1,2,...,p;j=1,2,...,q, ) of each sub-matrix is calculated, reflecting the elevation change rate of the element and the adjacent element:

[0047] In the formula, is the actual space sampling interval corresponding to the kth layer sub-matrix, , and s is the length of the grid unit; w i,j is the element in the i-th row and the j-th column of the sub-matrix, w i+1,j is the element in the i+1th row and the jth column of the sub-matrix, and w i,j+1 is the element in the i-th row and the j+1th column of the sub-matrix.

[0048] The weighted mean of the gradient modules of all elements of each sub-matrix is calculated as the texture change gradient of the sub-matrix, and the weight is the square of the gradient module, that is, the texture change gradient of the sub-matrix is:

[0049] In the formula, is the texture change gradient of the sub-matrix.

[0050] The texture wavelength is:

[0051] In the formula, is the texture wavelength, is the main peak value corresponding to the spatial frequency of the power spectral density function obtained by performing two-dimensional Fourier transform on the sub-matrix, reflecting the frequency of the most prominent texture in the sub-matrix.

[0052] Wherein, the texture wavelength can reflect the spatial scale of the most significant texture in the sub-matrix, the longer the texture wavelength, the larger the scale of the texture, corresponding to the macro-texture characteristics; the shorter the texture wavelength, the smaller the scale of the texture, corresponding to the micro-texture characteristics. By calculating the texture gradient and the texture wavelength of each sub-matrix, the feature information of the micro-texture and the macro-texture of the road surface can be extracted respectively.

[0053] Step S304, compare the texture gradient of each sub-matrix with the preset gradient threshold to obtain a first comparison result, and compare the texture wavelength of each sub-matrix with the preset texture wavelength threshold to obtain a second comparison result.

[0054] Step S305, according to the first comparison result and the second comparison result, all sub-matrices are divided into high-frequency sub-matrices and low-frequency sub-matrices.

[0055] Wherein, the gradient threshold can be selected as the mean value of the texture gradient of multiple sub-matrices at the initial time, and the texture wavelength threshold is the micro-macro texture demarcation wavelength, which is 5mm by default, or based on the actual situation.

[0056] Specifically, according to the first comparison result and the second comparison result, all sub-matrices are divided into high-frequency sub-matrices and low-frequency sub-matrices, including: Step S3041, for each sub-matrix, the sub-matrix whose texture gradient is greater than the preset gradient threshold is divided into a first high-frequency sub-matrix, and the sub-matrix whose texture gradient is not greater than the preset gradient threshold is divided into a first low-frequency sub-matrix.

[0057] Wherein, on the level of texture gradient, the sub-matrix is initially divided, the sub-matrix with rapid texture change is classified into high-frequency category, reflecting the micro-uneven characteristics of the road surface; and the sub-matrix with gentle texture change is classified into low-frequency category, reflecting the macro-undulation characteristics of the road surface.

[0058] Step S3042, judge whether the texture wavelength of the sub-matrix in the first high-frequency sub-matrix is less than the preset texture wavelength threshold, if the texture wavelength of the sub-matrix in the first high-frequency sub-matrix is not less than the preset texture wavelength threshold, the sub-matrix in the first high-frequency sub-matrix is divided into the first low-frequency sub-matrix, to obtain the second high-frequency sub-matrix and the second low-frequency sub-matrix.

[0059] The first high-frequency sub-matrix and the first low-frequency sub-matrix are subjected to secondary screening and adjustment at the level of the texture wavelength. By setting a texture wavelength threshold, the sub-matrix originally classified as high frequency but having a longer texture wavelength is reclassified to the low frequency category, so that the high-frequency sub-matrix more accurately reflects the microscopic texture characteristics, and the low-frequency sub-matrix more accurately reflects the macroscopic texture characteristics. This double screening mechanism effectively improves the accuracy of the pavement texture model construction, providing a more reliable feature parameter basis for subsequent anti-skid performance evaluation.

[0060] In step S3043, it is determined whether the texture wavelength of the sub-matrix in the second low-frequency sub-matrix is less than the preset texture wavelength threshold. If the texture wavelength of the sub-matrix in the second low-frequency sub-matrix is not less than the preset texture wavelength threshold, the preset gradient threshold is reduced, and the texture change gradient of each sub-matrix is compared with the preset gradient threshold to obtain a first comparison result, and the texture wavelength of each sub-matrix is compared with the preset texture wavelength threshold to obtain a second comparison result, until all sub-matrices are classified into high-frequency sub-matrices or low-frequency sub-matrices according to the requirements.

[0061] In the second low-frequency sub-matrix, for the sub-matrix whose texture wavelength does not satisfy the preset threshold, the gradient threshold is dynamically adjusted and the comparison process is re-executed to realize the iterative optimization of the sub-matrix category.

[0062] Specifically, the gradient threshold is reduced by 10%-15% (for example, from the initial mean value of 0.8 mm to 0.7 mm) each time, and the comparison relationship between the texture change gradient and the wavelength of all sub-matrices is recalculated until the texture wavelength of all sub-matrices in the second low-frequency sub-matrix is less than the preset threshold, or the maximum number of iterations (such as 20 times) is reached. This mechanism ensures that the high-frequency sub-matrix is strictly focused on the microscopic texture (wavelength < 5 mm and gradient > threshold), and the low-frequency sub-matrix accurately represents the macroscopic texture (wavelength ≥ 5 mm and gradient ≤ threshold), and finally forms a clear micro-macro texture double-layer classification structure, providing an accurate feature classification basis for subsequent three-dimensional texture model construction.

[0063] In step S306, the high-frequency sub-matrix and the low-frequency sub-matrix are subjected to wavelet inverse transform reconstruction, respectively, to obtain a microscopic texture sub-model and a macroscopic texture sub-model. The microscopic texture sub-model reflects the fine concave-convex features of the pavement surface, and the macroscopic texture sub-model reflects the large-scale undulation features of the pavement as a whole.

[0064] Wherein, when respectively carrying out wavelet inverse transformation to high-frequency sub-matrix and low-frequency sub-matrix, the corresponding inverse transformation scale needs to be selected according to the decomposition layer number to which the sub-matrix belongs. In the specific operation, the high-frequency sub-matrix adopts the biorthogonal wavelet inverse transformation of the same layer number as the decomposition, and the micro-texture sub-model is reconstructed, which restores the millimeter-level concave-convex features of the road surface through the high-frequency detail coefficient, such as reflecting the texture details with a wavelength of <5mm, such as stone edges and corners, and small depressions; and the low-frequency sub-matrix reconstructs the macro-texture sub-model through the low-frequency approximation coefficient, reflecting the meter-level undulation features of the road surface, such as the texture trend with a wavelength of ≥5mm, such as rut and road surface undulation.

[0065] Step S307, superimpose the micro-texture sub-model to the corresponding coordinate position in the macro-texture sub-model to obtain the road surface three-dimensional texture model.

[0066] Wherein, in the superimposition process, it is necessary to ensure that the coordinate system of the micro-texture sub-model is aligned with the macro-texture sub-model to avoid model distortion due to position offset. Specifically, by recording the spatial coordinates of each grid element in the original point cloud data, the height values of each grid in the micro-texture sub-model are accurately superimposed on the corresponding coordinates of the macro-texture sub-model, forming a road surface three-dimensional texture model with both micro details and macro features. Through the fusion of the double-layer structure (micro layer + macro layer), the model not only retains the high-frequency features such as stone edges and corners, and small cracks, but also embodies the low-frequency features such as rut and wave, providing a complete multi-scale texture description for subsequent anti-skid performance parameter extraction.

[0067] In some embodiments, a reference surface of the target road surface is constructed, and the reference surface is projected into the road surface three-dimensional texture model to obtain a projected road surface three-dimensional texture model, comprising: Step S401, using the least square method to fit the plane of the plurality of pre-processed road surface three-dimensional point cloud data, and constructing the reference surface of the target road surface.

[0068] Wherein, the least square method is used to fit the road surface reference surface, specifically:

[0069] In the formula, is the number of point clouds participating in fitting (taking 80% of the total number of pre-processed point clouds), 、 、 is the x, y, z axis coordinates of the mth point cloud, and a, b, c are the coefficients of the reference surface equation.

[0070] By solving the above least square problem, the reference surface coefficients a, b, and c are obtained, and the subsequent texture parameter calculation is all referenced to this reference surface.

[0071] Step S402, project the reference surface of the target road surface into the road surface three-dimensional texture model in a vertical projection manner to obtain an initial projected road surface three-dimensional texture model.

[0072] In the vertical projection process, each point P (x, y, ax+by+c) of the reference surface is replaced by the height value of the corresponding position in the road surface three-dimensional texture model by keeping the x and y coordinates unchanged, so as to realize the spatial alignment of the reference surface and the texture model. Specifically, for any point P on the reference surface, find the grid cell with the same x and y coordinates in the road surface three-dimensional texture model, and take the height value H of the grid as the z coordinate after projection to generate the projection point P' (x, y, H (x, y)). The overall projection is completed by traversing all points on the reference surface to form the initial projected road surface three-dimensional texture model. The model takes the reference surface as the reference plane, retains the micro-macro double-layer characteristics of the texture model, and eliminates the global tilt error of the original point cloud, thereby providing a standardized geometric reference framework for subsequent anti-skid performance parameter extraction.

[0073] Step S403, coordinate correction is performed on the initial projected road surface three-dimensional texture model to obtain a projected road surface three-dimensional texture model.

[0074] In the vertical projection process, each point P (x, y, ax+by+c) of the reference surface is replaced by the height value of the corresponding position in the road surface three-dimensional texture model by keeping the x and y coordinates unchanged, so as to realize the spatial alignment of the reference surface and the texture model. Specifically, for any point P on the reference surface, find the grid cell with the same x and y coordinates in the road surface three-dimensional texture model, and take the height value H of the grid as the z coordinate after projection to generate the projection point P' (x, y, H (x, y)). The overall projection is completed by traversing all points on the reference surface to form the initial projected road surface three-dimensional texture model. The model takes the reference surface as the reference plane, retains the micro-macro double-layer characteristics of the texture model, and eliminates the global tilt error of the original point cloud, thereby providing a standardized geometric reference framework for subsequent anti-skid performance parameter extraction.

[0075] In some embodiments, the feature parameters include friction area feature parameters; in this case, based on the projected road surface three-dimensional texture model, a plurality of feature parameters related to the anti-skid performance of the road surface are extracted, including: Step S501, divide the projected road surface three-dimensional texture model into a plurality of grids.

[0076] The grid size is determined by a preset scanning resolution. Specifically, the scanning resolution can be set according to actual needs, such as one grid per millimeter or one grid per two millimeters. The entire projected 3D road surface texture model is divided into regular grids, which are evenly distributed in the horizontal and vertical directions, providing the basic units for subsequent calculation of friction area feature parameters. Each grid has a clear boundary and a corresponding spatial coordinate range, which can accurately cover the local area information in the 3D road surface texture model. Therefore, by analyzing each grid, friction area-related features reflecting the road surface's anti-skid performance can be obtained.

[0077] Step S502: For each grid cell, extract the point cloud corresponding to the average Z-axis coordinate of all point clouds within the grid cell as the feature point cloud of the grid cell.

[0078] The average Z-axis coordinate of all point clouds within a grid is obtained by calculating the average height of all point clouds in the vertical direction (Z-axis). The point cloud corresponding to this average value represents the road surface height characteristics of the grid area. The specific calculation process is as follows: First, all point clouds within the grid are traversed, and the Z-axis coordinate value of each point cloud is extracted. Then, all Z-axis coordinate values ​​are summed and divided by the number of point clouds to obtain the average Z-axis coordinate. Finally, a point cloud with this average Z-axis coordinate is selected or generated as a feature point cloud (if no such feature point cloud exists, a virtual point with this coordinate can be generated to represent it). This point cloud is located at the center height of the grid in space and can reflect the average road surface elevation within the grid area. These feature point clouds not only preserve the microscopic undulation information of the road surface 3D texture model but also achieve data regularization and simplification through rasterization processing, facilitating efficient friction area analysis.

[0079] Step S503: For each feature point cloud, determine the texture height of the Z-axis height of the feature point cloud relative to the reference surface of the target road surface.

[0080] The texture height is the difference between the Z-axis height of the feature point cloud and the Z-axis height of the corresponding point cloud on the reference surface of the target road surface. This height reflects the vertical undulation of the grid area containing the feature point cloud relative to the reference surface. The specific calculation is as follows:

[0081] In the formula, For the first The Z-axis height of a feature point cloud relative to the texture height of the reference surface of the target road surface. For the first The Z-axis height of the feature point cloud.

[0082] Among them, if >0 means that the point is a convex part of the road surface texture (participates in friction and support), otherwise it is a flat or depressed part (does not participate in support, may be a pore, a groove).

[0083] Step S504, compare the texture height of each feature point cloud with the preset height threshold, filter out the feature point cloud whose texture height is greater than the preset height threshold, and determine the grid corresponding to the filtered feature point cloud as the effective friction contact area grid.

[0084] The preset height threshold is 0.2-0.5mm. By filtering out the feature point cloud whose texture height is greater than the preset height threshold, the feature point cloud and the grid participating in friction and support can be determined, and the area that can produce effective friction with the tire tread (excluding small protrusions that are too sharp, easy to wear or unable to fit with the tread) can be determined.

[0085] Step S505, determine the effective friction contact area according to the number of effective friction contact area grids and the unit grid area.

[0086] The product of the number of effective friction contact area grids and the unit grid area is the effective friction contact area.

[0087] Step S506, determine the effective friction contact area ratio as the friction area feature parameter according to the proportion of the effective friction contact area relative to the total area corresponding to all grids.

[0088] The ratio of the effective friction contact area to the total area of all grids can quantify the proportion of the effective area of the road surface actually participating in friction. This ratio can intuitively reflect the support ability and friction contribution of the road surface texture to the tire. For example, when the effective friction contact area ratio exceeds 60%, it indicates that the road surface has good anti-skid potential; if the ratio is less than 40%, there may be a risk of insufficient anti-skid performance.

[0089] In some embodiments, the feature parameters further include a texture morphology parameter; the texture morphology parameter includes an average texture depth, a texture sharpness, and a friction area distribution entropy. Based on the projected road surface three-dimensional texture model, a plurality of feature parameters related to road surface anti-skid performance are extracted, including: Step S511, determine the average texture depth of all feature point clouds according to the texture height of each feature point cloud; wherein the average texture depth is the average value of the texture height of all feature point clouds.

[0090] The calculation process of the average texture depth is as follows: first, the texture height data of all feature point clouds are summarized, and the height data reflects the vertical fluctuation degree of each local area of the road surface relative to the reference surface; then, the texture height values of all feature point clouds are added and divided by the total number of feature point clouds, and the result is the average texture depth. The parameter can comprehensively represent the depth of the overall texture of the road surface. The greater the average texture depth, the more significant the concave-convex features of the road surface, which usually means that the tire can produce a stronger mechanical interlocking effect when in contact with the road surface, thereby providing a more reliable geometric basis for the anti-skid performance.

[0091] In step S512, the texture height standard deviation is determined according to the texture height of all feature point clouds, and the ratio of the texture height standard deviation to the average texture depth is taken as the texture sharpness.

[0092] The calculation process of the texture sharpness is as follows: first, the standard deviation of the texture height data of all feature point clouds is calculated, which reflects the dispersion degree of the texture height of the road surface, that is, the fluctuation range of the texture; then, the calculated texture height standard deviation is divided by the average texture depth, and the obtained ratio is the texture sharpness. The texture sharpness can quantify the steepness of the road surface texture. The greater the ratio, the more sharp protrusions or depressions exist on the road surface, which will cause more intense stress changes during tire rolling, thereby having a significant impact on the anti-skid performance of the road surface.

[0093] In step S513, the projected three-dimensional texture model of the road surface is divided into a plurality of sub-regions according to a preset size, and for each sub-region, the proportion of the effective friction contact area of each sub-region is calculated.

[0094] The projected three-dimensional texture model of the road surface can be divided into UxL sub-regions according to a region size of 0.5m x 0.5m, and the feature point clouds of each sub-region can be selected and the proportion of the effective friction contact area of each sub-region can be calculated in a similar manner as in steps S502-S506. The specific process is as follows: performing grid division on each sub-region, extracting the feature point cloud, determining the texture height, selecting the effective friction contact area grid, and then calculating the effective friction contact area and the proportion of the effective friction contact area in the total area of the sub-region.

[0095] In step S514, the friction area distribution entropy is determined according to the proportion of the effective friction contact area of each sub-region. The friction area distribution entropy is used to represent the uniformity of the spatial distribution of the road surface texture.

[0096] The calculation of the friction area distribution entropy is based on the information entropy theory, which quantifies the dispersion degree of the proportion of the effective friction contact area of each sub-region to reflect the spatial distribution characteristics of the road surface texture. The specific calculation is as follows:

[0097] wherein, is the friction area distribution entropy, the effective friction contact area ratio of the u-th row and the v-th column sub-region, U is the number of sub-regions in the row direction, and L is the number of sub-regions in the column direction. l

[0098] In some embodiments, the weight of the extracted plurality of feature parameters related to the road anti-skid performance is determined, the plurality of feature parameters are weighted according to the weight, and an environmental penalty factor is introduced to correct the weighted processing result to obtain an anti-skid performance index, comprising: Step S601, standardizing each feature parameter to obtain each standardized feature parameter.

[0099] Wherein, since the dimensions of each feature parameter are different, the standardization processing is required to obtain the dimensionless feature parameter, and the dimensionless feature parameter is between 0 and 1.

[0100] Step S602, determining the grey correlation degree and the Pearson correlation coefficient of each standardized feature parameter and the reference feature parameter.

[0101] Wherein, the reference feature parameter is selected by the feature parameter corresponding to the historical road anti-skid performance index, the similarity between each standardized feature parameter and the reference feature parameter can be determined by the grey correlation analysis, and the higher the correlation degree, the more significant the influence of the feature on the anti-skid performance; at the same time, the Pearson correlation coefficient is used to measure the linear correlation between each feature parameter and the reference parameter, and the closer the absolute value of the correlation coefficient to 1, the stronger the positive / negative correlation between the two. In specific calculation, firstly, the standardized feature parameter matrix and the reference parameter vector are constructed, and the grey correlation degree value and the Pearson correlation coefficient value of each feature parameter and the reference parameter are calculated, which will be used as an important basis for subsequent weight allocation. For example, when the grey correlation degree of a certain feature parameter reaches 0.8 or more and the absolute value of the Pearson correlation coefficient exceeds 0.6, it can be determined that the feature has a strong correlation with the anti-skid performance, and higher weight should be given in weight allocation. The specific grey correlation degree and Pearson correlation coefficient are conventional techniques, which will not be described here.

[0102] Step S603, respectively normalizing the grey correlation degree and the Pearson correlation coefficient, and linearly fusing the normalized grey correlation degree and the normalized Pearson correlation coefficient to obtain the weight of each feature parameter.

[0103] Wherein, the linear fusion process is:

[0104] wherein, is the weight,​ is a weight coefficient in linear fusion, , is a normalized grey correlation degree and is a normalized Pearson correlation coefficient, respectively.

[0105] Step S604, the weight of each feature parameter is weighted and processed according to the standard, and the initial anti-skid performance index is obtained.

[0106] Step S605, the road surface water content and the road service life of the target road are obtained, and the road surface humidity correction coefficient and the service life correction coefficient are determined according to the road surface water content and the road service life, respectively.

[0107] Among them, the road surface water content is obtained by real-time measurement of the humidity sensor at the preset monitoring point position of the target road, and the average road surface water content is obtained by averaging the measurement values of each monitoring point. According to experimental data and engineering experience, a mapping relationship table of road surface water content and humidity correction coefficient is established, and different humidity correction coefficient values are obtained when the road surface average water content is in different intervals. For example, when the water content is less than 5%, the humidity correction coefficient is 1.0; when the water content is between 5%-10%, the humidity correction coefficient is 0.9; when the water content is higher than 10%, the humidity correction coefficient is 0.8. The road service life is obtained by consulting the road construction archives or the records of the relevant management department, and the service life correction coefficient is set by using the exponential decay model according to the service life and the road performance decay law. Generally speaking, the service life correction coefficient is:

[0108] In the formula, is the service life correction coefficient, is the decay coefficient, which is 0.3-0.5, is the service life.

[0109] Step S606, the environmental penalty factor is determined according to the road surface humidity correction coefficient and the service life correction coefficient.

[0110] Among them, the calculation method of the environmental penalty factor is: multiplying the road surface humidity correction coefficient and the service life correction coefficient, and the product is the environmental penalty factor.

[0111] Step S607, the initial anti-skid performance index is corrected by the environmental penalty factor, and the anti-skid performance index is obtained.

[0112] Among them, the environmental penalty factor and the initial anti-skid performance index are multiplied, that is, the initial anti-skid performance index is corrected, and the anti-skid performance index is obtained.

[0113] In some embodiments, the anti-skid performance of the target road surface is evaluated according to the anti-skid performance index, and an evaluation result of the anti-skid performance of the target road surface is obtained, including: obtaining a plurality of anti-skid performance thresholds of different anti-skid performance levels, comparing the anti-skid performance index with the plurality of anti-skid performance thresholds, and determining the anti-skid performance level to which the target road surface belongs according to the comparison result; wherein the higher the anti-skid performance level, the higher the anti-skid performance of the target road surface.

[0114] Wherein the anti-skid performance level can include excellent, good, medium and poor, and different anti-skid performance thresholds are set for each anti-skid performance level. For example, when the anti-skid performance level is excellent, the corresponding lower limit of the anti-skid performance threshold can be set to 0.85; when the level is good, the threshold range is set to 0.7-0.85; when the level is medium, the threshold range is set to 0.55-0.7; and when the level is poor, the upper limit of the threshold is set to 0.55. By normalizing the anti-skid performance index and comparing the calculated normalized anti-skid performance index with the threshold of each level, the anti-skid performance level to which the target road surface belongs can be determined. For example, when the anti-skid performance index is 0.88, it is determined to be excellent; when the index is 0.65, it is determined to be medium. This grading evaluation method can intuitively reflect the advantages and disadvantages of the anti-skid performance of the road surface, and provide a quantitative basis for road maintenance decision-making.

[0115] Based on the same inventive concept, the embodiments of the present application also provide a super-high-speed three-dimensional laser scanning road surface anti-skid performance evaluation system for implementing the above-mentioned super-high-speed three-dimensional laser scanning road surface anti-skid performance evaluation method.

[0116] The implementation scheme for solving the problem provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more super-high-speed three-dimensional laser scanning road surface anti-skid performance evaluation system embodiments provided below can refer to the limitations of the super-high-speed three-dimensional laser scanning road surface anti-skid performance evaluation method described above, and will not be repeated here.

[0117] As shown in Figure 2 The embodiments of the present application provide a super-high-speed three-dimensional laser scanning road surface anti-skid performance evaluation system, which includes: A point cloud acquisition module 100 is configured to scan a target road surface by using a three-dimensional laser scanner, and acquire road surface three-dimensional point cloud original data. A preprocessing module 200 is configured to preprocess the road surface three-dimensional point cloud original data, and obtain preprocessed road surface three-dimensional point cloud data. A texture model construction module 300 is configured to construct a road surface three-dimensional texture model according to the preprocessed road surface three-dimensional point cloud data; wherein the road surface three-dimensional texture model includes a micro-texture sub-model and a macro-texture sub-model. The texture model projection module 400 is configured to construct a reference surface of the target road surface, and project the reference surface into the three-dimensional texture model of the road surface to obtain a projected three-dimensional texture model of the road surface. The characteristic parameter extraction module 500 is configured to extract a plurality of characteristic parameters related to the anti-skid performance of the road surface based on the projected three-dimensional texture model of the road surface. The anti-skid index determination module 600 is configured to determine the weights of the plurality of characteristic parameters related to the anti-skid performance of the road surface, perform weighted processing on the plurality of characteristic parameters according to the weights, and correct the weighted processing result by introducing an environmental penalty factor to obtain an anti-skid performance index. The anti-skid performance evaluation module 700 is configured to evaluate the anti-skid performance of the target road surface according to the anti-skid performance index to obtain an evaluation result of the anti-skid performance of the target road surface.

[0118] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0119] It should be understood that although each step in the flowchart involved in each embodiment described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise stated herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0120] In several embodiments provided by the present application, it should be understood that the disclosed system and method can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner in actual implementation; for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0121] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the anti-skid performance of a superhigh-speed three-dimensional laser scanned pavement, characterized in that, The method comprises the following steps: scanning a target road surface by using a three-dimensional laser scanner to obtain original three-dimensional point cloud data of the road surface; preprocessing the original three-dimensional point cloud data of the road surface to obtain preprocessed three-dimensional point cloud data of the road surface; constructing a three-dimensional texture model of the road surface according to the preprocessed three-dimensional point cloud data of the road surface, wherein the three-dimensional texture model of the road surface comprises a micro-texture sub-model and a macro-texture sub-model; constructing a reference surface of the target road surface and projecting the reference surface into the three-dimensional texture model of the road surface to obtain a projected three-dimensional texture model of the road surface; extracting a plurality of characteristic parameters related to the anti-skid performance of the road surface based on the projected three-dimensional texture model of the road surface; determining the weights of the plurality of characteristic parameters related to the anti-skid performance of the road surface, performing weighted processing on the plurality of characteristic parameters according to the weights, and introducing an environmental penalty factor to correct the weighted processing result to obtain an anti-skid performance index; evaluating the anti-skid performance of the target road surface according to the anti-skid performance index to obtain an evaluation result of the anti-skid performance of the target road surface.

2. The super-speed three-dimensional laser scanning pavement anti-skid performance evaluation method according to claim 1, characterized in that, The preprocessing comprises noise removal and abnormal point elimination.

3. The super-speed three-dimensional laser scanning pavement anti-skid performance evaluation method according to claim 1, characterized in that, The construction of the three-dimensional texture model of the road surface according to the preprocessed three-dimensional point cloud data of the road surface comprises the following steps: grid processing the preprocessed three-dimensional point cloud data of the road surface according to a predetermined grid size to obtain a plurality of grid units; for each grid unit, calculating the mean value of the coordinates of all point clouds in the grid unit, and taking the mean value of the coordinates as the height value of the grid unit to obtain a road surface height matrix; performing multi-scale decomposition on the road surface height matrix by using wavelet transform to obtain a plurality of sub-matrices of different scales, and determining the texture change gradient and texture wavelength of each sub-matrix; comparing the texture change gradient of each sub-matrix with a preset gradient threshold to obtain a first comparison result, and comparing the texture wavelength of each sub-matrix with a preset texture wavelength threshold to obtain a second comparison result; dividing all the sub-matrices into high-frequency sub-matrices and low-frequency sub-matrices according to the first comparison result and the second comparison result; respectively performing inverse wavelet transform reconstruction on the high-frequency sub-matrices and the low-frequency sub-matrices to obtain the micro-texture sub-model and the macro-texture sub-model; wherein the micro-texture sub-model reflects the fine concave-convex features of the road surface, and the macro-texture sub-model reflects the large-scale undulation features of the road surface as a whole; superimposing the micro-texture sub-model to the corresponding coordinate position in the macro-texture sub-model to obtain the three-dimensional texture model of the road surface.

4. The super-speed three-dimensional laser scanning pavement anti-skid performance evaluation method according to claim 3, characterized in that, The division of all the sub-matrices into high-frequency sub-matrices and low-frequency sub-matrices according to the first comparison result and the second comparison result comprises the following steps: for each sub-matrix, dividing the sub-matrix whose texture change gradient is greater than the preset gradient threshold into a first high-frequency sub-matrix, and dividing the sub-matrix whose texture change gradient is not greater than the preset gradient threshold into a first low-frequency sub-matrix; determining whether the texture wavelength of the sub-matrix in the first high-frequency sub-matrix is less than a preset texture wavelength threshold, and if the texture wavelength of the sub-matrix in the first high-frequency sub-matrix is not less than the preset texture wavelength threshold, dividing the sub-matrix in the first high-frequency sub-matrix into the first low-frequency sub-matrix to obtain a second high-frequency sub-matrix and a second low-frequency sub-matrix; determining whether the texture wavelength of the sub-matrix in the second low-frequency sub-matrix is less than a preset texture wavelength threshold, and if the texture wavelength of the sub-matrix in the second low-frequency sub-matrix is not less than the preset texture wavelength threshold, reducing the preset gradient threshold, and based on the reduced preset gradient threshold, returning to the step of comparing the texture change gradient of each sub-matrix with the preset gradient threshold to obtain a first comparison result, and comparing the texture wavelength of each sub-matrix with the preset texture wavelength threshold to obtain a second comparison result, until all sub-matrices are divided into high-frequency sub-matrices or low-frequency sub-matrices according to requirements.

5. The super-speed three-dimensional laser scanning pavement anti-skid performance evaluation method according to claim 1, characterized in that, The method further comprises the steps of: performing plane fitting on the plurality of preprocessed road three-dimensional point cloud data by using a least square method to construct the reference surface of the target road; projecting the reference surface of the target road into the road three-dimensional texture model in a perpendicular projection manner to obtain an initial projected road three-dimensional texture model; performing coordinate correction on the initial projected road three-dimensional texture model to obtain the projected road three-dimensional texture model.

6. The super-speed three-dimensional laser scanning pavement anti-skid performance evaluation method according to claim 1 or 5, characterized in that, The characteristic parameters include a friction area characteristic parameter; The method further comprises the steps of: dividing the projected road three-dimensional texture model into a plurality of grids; for each grid, extracting a point cloud corresponding to a Z-axis coordinate average value of all point clouds in the grid as a feature point cloud of the grid; for each feature point cloud, determining a texture height of the feature point cloud relative to the reference surface of the target road; comparing the texture height of each feature point cloud with a preset height threshold, screening out a feature point cloud whose texture height is greater than the preset height threshold, and determining a grid corresponding to the screened feature point cloud as an effective friction contact area grid; determining an effective friction contact area according to the number of effective friction contact area grids and a unit grid area; determining an effective friction contact area ratio as the friction area characteristic parameter according to a proportion of the effective friction contact area relative to a total area corresponding to all grids.

7. The super-speed three-dimensional laser scanning pavement anti-skid performance evaluation method according to claim 6, characterized in that, The characteristic parameters further include a texture morphology parameter; the texture morphology parameter includes an average texture depth, a texture sharpness, and a friction area distribution entropy. The method further comprises the steps of: determine an average texture depth of all the feature point clouds according to the texture heights of each of the feature point clouds, wherein the average texture depth is an average value of the texture heights of all the feature point clouds; determine a texture height standard deviation according to the texture heights of all the feature point clouds, and take a ratio of the texture height standard deviation to the average texture depth as a texture sharpness; divide the projected road surface three-dimensional texture model into a plurality of sub-regions according to a preset size, and calculate an effective friction contact area proportion of each of the sub-regions; determine a friction area distribution entropy according to the effective friction contact area proportions of the sub-regions, wherein the friction area distribution entropy is used to represent the uniformity of the distribution of road surface texture in space.

8. The super-speed three-dimensional laser scanning pavement anti-skid performance evaluation method according to claim 7, characterized in that, The method for determining the weights of the extracted multiple feature parameters related to road surface anti-skid performance, performing weighted processing on the multiple feature parameters according to the weights, and introducing an environmental penalty factor to correct the weighted processing result to obtain an anti-skid performance index, comprises: performing standardization processing on each of the feature parameters to obtain each of the standardized feature parameters; determining a grey correlation degree and a Pearson correlation coefficient between each of the standardized feature parameters and a reference feature parameter; performing normalization processing on the grey correlation degree and the Pearson correlation coefficient respectively, and performing linear fusion on the normalized grey correlation degree and the normalized Pearson correlation coefficient to obtain the weight of each of the feature parameters; performing weighted processing on each of the standardized feature parameters according to the weight of each of the feature parameters to obtain an initial anti-skid performance index; obtaining a road surface water content and a road surface service life of the target road surface, and determining a road surface humidity correction coefficient and a service life correction coefficient according to the road surface water content and the road surface service life respectively; determining the environmental penalty factor according to the road surface humidity correction coefficient and the service life correction coefficient; correcting the initial anti-skid performance index by the environmental penalty factor to obtain the anti-skid performance index.

9. The super-speed three-dimensional laser scanning pavement anti-skid performance evaluation method according to claim 1, characterized in that, The method for evaluating the anti-skid performance of the target road surface according to the anti-skid performance index to obtain an evaluation result of the anti-skid performance of the target road surface, comprises: obtaining multiple anti-skid performance thresholds of different anti-skid performance levels, comparing the anti-skid performance index with the multiple anti-skid performance thresholds, and determining an anti-skid performance level to which the target road surface belongs according to a comparison result, wherein the higher the anti-skid performance level is, the higher the anti-skid performance of the target road surface is.

10. A super-high-speed three-dimensional laser scanning pavement anti-skid performance evaluation system, characterized in that, The method comprises: a point cloud acquisition module configured to scan a target road surface by using a three-dimensional laser scanner to obtain road surface three-dimensional point cloud raw data; a preprocessing module configured to perform preprocessing on the road surface three-dimensional point cloud raw data to obtain preprocessed road surface three-dimensional point cloud data; a texture model construction module configured to construct a road surface three-dimensional texture model according to the preprocessed road surface three-dimensional point cloud data, wherein the road surface three-dimensional texture model comprises a micro-texture sub-model and a macro-texture sub-model; a texture model projection module, configured to construct a reference surface of the target road surface, and project the reference surface into the road surface three-dimensional texture model to obtain a projected road surface three-dimensional texture model; a characteristic parameter extraction module, configured to extract a plurality of characteristic parameters related to road surface anti-skid performance based on the projected road surface three-dimensional texture model; an anti-skid index determination module, configured to determine weights of the plurality of characteristic parameters related to road surface anti-skid performance, perform weighted processing on the plurality of characteristic parameters according to the weights, and introduce an environmental penalty factor to correct the weighted processing result to obtain an anti-skid performance index; an anti-skid performance evaluation module, configured to evaluate the anti-skid performance of the target road surface according to the anti-skid performance index to obtain an evaluation result of the anti-skid performance of the target road surface.

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