Intelligent measurement method and system for paving thickness of asphalt pavement

Abstract

The application relates to the technical field of asphalt thickness measurement, in particular to an intelligent paving thickness measurement method and system for an asphalt pavement, which comprises the following steps: respectively determining thickness characteristic values of each asphalt characteristic point and height characteristic values of each roadbed characteristic point; determining regular characteristic values based on the height characteristic values and the thickness characteristic values; performing surface fitting on each asphalt characteristic point and all adjacent points thereof, determining continuity characteristic values based on fitting errors in the fitting process, combining the regular characteristic values to determine uniform characteristic values of the asphalt characteristic points, and screening out to-be-de-noised characteristic points from all the asphalt characteristic points; and smoothing the asphalt thickness at the to-be-de-noised characteristic points to obtain asphalt thickness measurement results of a construction road section. The application improves the accuracy of asphalt thickness measurement by solving the problem of incorrect judgment of asphalt thickness caused by random noise.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of asphalt thickness measurement, in particular to an intelligent measurement method and system for paving thickness of asphalt pavement. BACKGROUND

[0002] In the paving process of pavement asphalt, inaccurate adjustment of the paving machine parameters usually causes the newly paved asphalt pavement to have uneven thickness, thereby affecting the durability of the asphalt pavement. The existing method usually uses three-dimensional ground penetrating radar detection technology to quickly measure the asphalt thickness of the pavement after asphalt paving, and draws an asphalt thickness distribution map according to the thickness data of the asphalt surface structure layer obtained by measurement, so as to intuitively show the uniformity of the pavement asphalt thickness distribution of the construction pavement after asphalt paving.

[0003] When three-dimensional ground penetrating radar is used to measure the thickness of the asphalt pavement, a large amount of random noise exists in the measurement data due to the instrument itself and environmental interference. These noises will form local thick or thin false images in the thickness distribution map, thereby reducing the accuracy of the asphalt paving thickness measurement. SUMMARY

[0004] In order to solve the above technical problems, the purpose of the present application is to provide an intelligent measurement method and system for paving thickness of asphalt pavement, and the technical solution adopted is as follows:

[0005] In a first aspect, the present application provides an intelligent measurement method for paving thickness of asphalt pavement, which comprises the following steps:

[0006] Obtaining the pavement elevation data before paving asphalt at each measurement point on the surface of the construction section and the asphalt thickness after paving asphalt;

[0007] Analyzing the distribution of the asphalt thickness and the distribution of the pavement elevation data at all measurement points respectively, to screen out asphalt feature points and roadbed feature points from all measurement points respectively, determining the thickness characteristic value of each asphalt feature point and the height characteristic value of each roadbed feature point based on the difference between the asphalt thickness of each asphalt feature point and the average distribution of the asphalt thickness of all measurement points except the asphalt feature points, and the difference between the pavement elevation data of each roadbed feature point and the average distribution of the pavement elevation data of all measurement points except the roadbed feature points, and determining the regular characteristic value of each asphalt feature point based on the height characteristic value of the roadbed feature point with the same coordinate as each asphalt feature point and the thickness characteristic value of each asphalt feature point;

[0008] Determine the neighboring points of each asphalt feature point based on the distance between each asphalt feature point and all other measurement points, perform surface fitting on each asphalt feature point and all its neighboring points, determine the continuity feature value of each asphalt feature point based on the fitting error in the fitting process, and determine the uniformity feature value of each asphalt feature point in combination with the regularity feature value, to screen out the feature points to be denoised from all asphalt feature points.

[0009] Smooth the asphalt thickness at the feature points to be denoised to obtain the asphalt thickness measurement result of the surface of the construction section.

[0010] Preferably, the screening out of the asphalt feature points and the subgrade feature points from all measurement points respectively comprises:

[0011] Respectively input the asphalt thickness and the pavement elevation data of all measurement points as the input of the threshold segmentation algorithm, and respectively output the segmentation threshold of the asphalt thickness and the segmentation threshold of the pavement elevation data.

[0012] Take the measurement points with asphalt thickness greater than the corresponding segmentation threshold as the asphalt feature points, and take the measurement points with pavement elevation data greater than the corresponding segmentation threshold as the subgrade feature points.

[0013] Preferably, the determination of the thickness feature value of each asphalt feature point and the height feature value of each subgrade feature point respectively comprises:

[0014] Take the difference between the asphalt thickness of each asphalt feature point and the average asphalt thickness of all measurement points except the asphalt feature points as the thickness feature value of each asphalt feature point.

[0015] Take the difference between the pavement elevation data of each subgrade feature point and the average pavement elevation data of all measurement points except the subgrade feature points as the height feature value of each asphalt feature point.

[0016] Preferably, the regularity feature value of each asphalt feature point is the reciprocal of the absolute value of the sum of the height feature value of the subgrade feature point with the same coordinates as the asphalt feature point and the thickness feature value of the corresponding asphalt feature point.

[0017] Preferably, the neighboring points of each asphalt feature point are the measurement points corresponding to the first preset number of distances in the ascending order arrangement result of the distances between each asphalt feature point and all other measurement points.

[0018] Preferably, the continuity feature value of each asphalt feature point is the reciprocal of the fitting error obtained by performing surface fitting on each asphalt feature point and all its neighboring points.

[0019] Preferably, the uniformity feature value of each asphalt feature point is the positive fusion result of the normalized value of the regularity feature value and the normalized value of the continuity feature value.

[0020] Preferably, the filtering out of the to-be-de-noised feature points from all the asphalt feature points comprises:

[0021] The uniform feature value of all the asphalt feature points is taken as an input of a threshold segmentation algorithm, the output segmentation threshold is recorded as a uniform threshold, and the asphalt feature point with a uniform feature value less than the uniform threshold is taken as the to-be-de-noised feature point.

[0022] Preferably, the obtaining of the asphalt thickness measurement result of the construction road section surface comprises:

[0023] The asphalt thickness at all the to-be-de-noised feature points is smoothed by using a smoothing algorithm, the result of the smoothing is taken as the corrected asphalt thickness at the corresponding to-be-de-noised feature point, and the asphalt thickness at all the measurement points on the construction road section surface is obtained.

[0024] In a second aspect, the embodiments of the present application further provide an asphalt pavement paving thickness intelligent measurement system, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the asphalt pavement paving thickness intelligent measurement method according to any one of the above aspects when executing the computer program.

[0025] The present application has at least the following beneficial effects:

[0026] The present application screens feature points and calculates thickness and height feature values of the feature points by analyzing the distribution of asphalt thickness and pavement elevation data, further constructs regular feature values to evaluate whether the roadbed height and asphalt thickness before and after paving conform to the compensation rule, thereby effectively identifying noise interference points, correcting the asphalt thickness of the noise points, and improving the accuracy of asphalt thickness measurement; further, the embodiments of the present application construct a uniform feature value by combining the regular feature value and the continuity feature value of the asphalt feature points, filter out to-be-de-noised feature points based on threshold segmentation, effectively identify noise points that do not conform to the paving rule and are not continuous in space, further smooth the asphalt thickness at the noise points, and thereby improve the accuracy of asphalt thickness measurement; further, the present application performs smoothing filtering processing on the measured asphalt thickness at the to-be-de-noised feature points, obtains corrected asphalt thickness after de-noising, and improves the accuracy of asphalt thickness measurement. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0028] Figure 1A step flow chart of an asphalt pavement paving thickness intelligent measurement method provided by an embodiment of the present application is shown in FIG. 1.

[0029] Figure 2 A uniform feature value extraction process schematic diagram provided by an embodiment of the present application is shown in FIG. 4. DETAILED DESCRIPTION

[0030] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purposes, the specific embodiments, structures, features and effects of the asphalt pavement paving thickness intelligent measurement method and system according to the present application are described in detail as follows in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0032] The specific scheme of the asphalt pavement paving thickness intelligent measurement method and system provided by the present application is described in detail below in combination with the drawings.

[0033] Please refer to Figure 1 which shows a step flow chart of an asphalt pavement paving thickness intelligent measurement method provided by an embodiment of the present application. The method includes the following steps:

[0034] Step S1: Obtain the pavement elevation data before paving asphalt at each measurement point on the surface of the construction section and the asphalt thickness after paving asphalt.

[0035] The pavement elevation data before paving asphalt at each measurement point on the surface of the construction section is measured using a vehicle-mounted laser flatness meter. The plane coordinates (X, Y) of each measurement point on the surface of the construction section are recorded synchronously using a GPS positioning system. The vehicle-mounted laser flatness meter travels along the construction section at a fixed speed v. The distance between measurement points is set to L, i.e. the pavement elevation data is measured every L distance. Further, in order to visualize and compare and analyze later, the pavement elevation data is normalized. The processed data is converted into a two-dimensional matrix form. The rows of the matrix represent the Y axis, representing the pavement width direction. The columns represent the X axis, representing the pavement length direction. The elements in the matrix represent the pavement elevation data of the corresponding measurement point. The two-dimensional matrix is input into the GIS software, and the pavement elevation distribution map of the construction pavement is output.

[0036] The fixed speed v and the distance L between the measuring points are both artificially set, in the embodiment, the value of v is 60 km / h, and the value of L is 10 cm. In actual application, the implementer can set them according to the specific situation, which is not limited in the embodiment.

[0037] Similarly, the asphalt thickness distribution map is drawn by using the vehicle-mounted three-dimensional ground penetrating radar to synchronously measure the asphalt thickness of each measuring point on the surface of the construction road section, and by using the above method of obtaining the road elevation distribution map. The specific process is not described again. Thus, the road elevation data and the asphalt thickness of the construction road are visualized, and the road elevation data and the asphalt thickness of each measuring point are obtained.

[0038] It should be noted that there are many commonly used normalization methods. In the embodiment, the maximum and minimum value normalization method is used to normalize the collected road elevation data and asphalt thickness. In actual application, as other implementation manners, the implementer can use other normalization methods such as z-score standardization method according to the specific situation. The selection of the normalization method is not limited in the embodiment.

[0039] The maximum and minimum value normalization method is a known technology, and the specific process of normalizing the road elevation data and the asphalt thickness is not described again.

[0040] Step S2: analyze the distribution of the asphalt thickness and the distribution of the road elevation data of all measuring points respectively, to screen out the asphalt feature points and the roadbed feature points from all measuring points respectively, determine the thickness characteristic value of each asphalt feature point and the height characteristic value of each roadbed feature point based on the difference between the asphalt thickness of each asphalt feature point and the average distribution of the asphalt thickness of all measuring points except the asphalt feature points, and the difference between the road elevation data of each roadbed feature point and the average distribution of the road elevation data of all measuring points except the roadbed feature points, and determine the regular characteristic value of each asphalt feature point based on the height characteristic value of the roadbed feature point with the same coordinate as the asphalt feature point and the thickness characteristic value of each asphalt feature point.

[0041] When paving asphalt on a road surface using a paver, since the screed of the paver is flat and the compaction coefficient of the asphalt mixture used is also certain, when paving asphalt on a high roadbed surface, the asphalt thickness at the corresponding position of the paved road surface will be thin, and the degrees of thinness and thickness are relatively consistent, and when paving asphalt on a low roadbed surface, the asphalt thickness at the corresponding position of the paved road surface will be thick, and the degrees of thinness and thickness are also relatively consistent. For the convenience of subsequent processing, this feature before and after paving asphalt on the road is recorded as the regular feature between the roadbed height and the asphalt thickness before and after paving asphalt on the road surface, and the random noise data in all asphalt thickness data measured by the vehicle-mounted laser flatness meter usually does not have this regular feature.

[0042] Based on the above analysis, the embodiment first analyzes the distribution of the asphalt thickness at all measurement points and the distribution of the road surface elevation data, respectively, to screen out asphalt feature points and roadbed feature points from all measurement points, to screen out areas where the surface asphalt thickness of the construction section is thin or thick and positions where the roadbed surface is relatively high or low before paving asphalt, further, the embodiment respectively determines the thickness feature value of each asphalt feature point and the height feature value of each roadbed feature point based on the difference between the asphalt thickness of each asphalt feature point and the average distribution of the asphalt thickness of all measurement points except the asphalt feature points, and the difference between the road surface elevation data of each roadbed feature point and the average distribution of the road surface elevation data of all measurement points except the roadbed feature points; based on the height feature value of the roadbed feature point with the same coordinate as each asphalt feature point and the thickness feature value of each asphalt feature point, the regular feature value of each asphalt feature point is determined to determine whether the measured asphalt thickness at the asphalt feature point is disturbed by noise, and the specific process is as follows:

[0043] In the embodiment, first, the distribution of the asphalt thickness at all measurement points and the distribution of the road surface elevation data are analyzed respectively to screen out asphalt feature points and roadbed feature points from all measurement points, specifically:

[0044] In the embodiment, the asphalt thickness and the road surface elevation data of all measurement points are respectively taken as the input of the threshold segmentation algorithm, and the segmentation threshold of the asphalt thickness and the segmentation threshold of the road surface elevation data are respectively output;

[0045] Further, the measurement point with asphalt thickness greater than the corresponding segmentation threshold is taken as an asphalt feature point, and the measurement point with road surface elevation data greater than the corresponding segmentation threshold is taken as a roadbed feature point.

[0046] Among them, the asphalt feature point is used to represent the position where the asphalt thickness on the surface of the construction section after paving asphalt is thick or thin, and the roadbed feature point is used to represent the position where the roadbed surface of the construction section is relatively high or low before paving asphalt.

[0047] It should be noted that there are many commonly used threshold segmentation algorithms, and in the present embodiment, the maximum inter-class variance algorithm is used to screen the asphalt feature points and the roadbed feature points. In actual application, as an alternative, the implementer can also select other threshold segmentation algorithms according to the specific circumstances, and the selection of the threshold segmentation algorithm is not particularly limited in the present embodiment.

[0048] Further, the present embodiment determines the thickness characteristic value of each asphalt feature point and the height characteristic value of each roadbed feature point based on the difference between the asphalt thickness of each asphalt feature point and the average distribution of the asphalt thickness of all the measurement points other than all the asphalt feature points, and the difference between the road surface elevation data of each roadbed feature point and the average distribution of the road surface elevation data of all the measurement points other than all the roadbed feature points, respectively. Specifically:

[0049] In the present embodiment, the difference between the asphalt thickness of each asphalt feature point and the average value of the asphalt thickness of all the measurement points other than all the asphalt feature points is taken as the thickness characteristic value of each asphalt feature point, which is used to evaluate the degree of thick or thin of the asphalt thickness at the measurement point.

[0050] The difference between the road surface elevation data of each roadbed feature point and the average value of the road surface elevation data of all the measurement points other than all the roadbed feature points is taken as the height characteristic value of each asphalt feature point, which is used to evaluate the degree of high or low of the road surface elevation data at the measurement point before asphalt paving.

[0051] Further, the present embodiment determines the regularity characteristic value of each asphalt feature point based on the height characteristic value of the roadbed feature point with the same coordinate as each asphalt feature point and the thickness characteristic value of each asphalt feature point, specifically:

[0052] In the present embodiment, the reciprocal of the absolute value of the sum of the height characteristic value of the roadbed feature point with the same coordinate as each asphalt feature point and the thickness characteristic value of the corresponding asphalt feature point is taken as the regularity characteristic value of each asphalt feature point.

[0053] According to the regularity characteristic value of each asphalt feature point, if the thickness characteristic value at the current asphalt feature point is greater than 0, the height characteristic value is less than 0, and the absolute value of the sum of the thickness characteristic value and the height characteristic value tends to be 0, it means that the asphalt is thicker for the area with low road surface, which conforms to the paving rule. In addition, if the thickness characteristic value at the current asphalt feature point is less than 0, the height characteristic value is greater than 0, and the absolute value of the sum of the thickness characteristic value and the height characteristic value tends to be 0, it means that the asphalt should be thinner for the area with high road surface, which conforms to the paving rule, indicating that the possibility of noise interference of the measured asphalt thickness at the current asphalt feature point is smaller.

[0054] On the contrary, when paving the road surface with asphalt using a paver, since the screed of the paver is flat and the compaction coefficient of the asphalt mixture used is also certain, the farther the absolute value of the sum of the thickness characteristic value and the height characteristic value of the current asphalt feature point deviates from 0, the more likely the asphalt thickness measured at the current asphalt feature point is disturbed by noise, which does not conform to the paving rule.

[0055] So far, by analyzing the distribution of the asphalt thickness and the road surface elevation data, the embodiment screens out the feature points and calculates the thickness and height characteristic values thereof, and further constructs the rule characteristic value, so as to evaluate whether the roadbed height and the asphalt thickness before and after paving conform to the compensation rule, thereby effectively identifying the noise interference points, correcting the asphalt thickness of the noise points, and improving the accuracy of the asphalt thickness measurement.

[0056] Step S3: Based on the distance between each asphalt feature point and all the other measurement points, the neighboring points of each asphalt feature point are determined, the surface fitting is performed on each asphalt feature point and all the neighboring points thereof, the continuity characteristic value of each asphalt feature point is determined based on the fitting error in the fitting process, and the uniformity characteristic value of each asphalt feature point is determined in combination with the rule characteristic value, so as to screen out the to-be-de-noised feature points from all the asphalt feature points.

[0057] In the process of paving the road surface with asphalt using a paver, the paver usually needs to be paved slowly, uniformly and continuously without interruption, so that the change of the asphalt thickness at each position of the road surface after paving is usually continuous in space. For the convenience of subsequent processing, such continuous change is recorded as the continuity characteristic of the asphalt thickness after the road surface is paved with asphalt, and the random noise data in all the asphalt thickness data measured by the vehicle-mounted laser flatness instrument usually does not have such continuity characteristic due to its randomness.

[0058] Based on the above analysis, the embodiment first determines the neighboring points of each asphalt feature point based on the distance between each asphalt feature point and all the other measurement points, and further performs surface fitting on each asphalt feature point and all the neighboring points thereof, determines the continuity characteristic value of each asphalt feature point based on the fitting error in the fitting process, and determines the uniformity characteristic value of each asphalt feature point in combination with the rule characteristic value, so as to screen out the to-be-de-noised feature points from all the asphalt feature points. The specific process is as follows:

[0059] In the embodiment, first, the measurement points corresponding to the first pre-set number of distances in the ascending order arrangement result of the distances between each asphalt feature point and all the other measurement points are taken as the neighboring points of each asphalt feature point.

[0060] It should be noted that the preset number is a value set by human, and in the embodiment, the preset number is 25. In actual application, as another implementation manner, the implementer can set it by himself according to the specific situation, and the embodiment does not have special limitation.

[0061] Further, the embodiment performs surface fitting on each asphalt feature point and all adjacent points thereof, determines the continuity characteristic value of each asphalt feature point based on the fitting error in the fitting process, and specifically:

[0062] In the embodiment, the plane coordinates and asphalt thickness of each asphalt feature point and all adjacent points thereof are taken as the input of the surface fitting algorithm, and the fitted surface is output. The mean square error of all asphalt thicknesses relative to the fitted value on the fitted surface in the fitting process is taken as the fitting error, and the reciprocal of the fitting error is taken as the continuity characteristic value of each asphalt feature point.

[0063] It should be noted that there are many commonly used surface fitting algorithms, and in the embodiment, the polynomial surface fitting algorithm based on least squares is used for surface fitting. In actual application, as another implementation manner, the implementer can use other surface fitting algorithms according to the specific situation, and the embodiment does not have special limitation on the selection of the surface fitting algorithm.

[0064] The polynomial surface fitting algorithm based on least squares is a known technology, and the specific process of using it to perform surface fitting on each asphalt feature point and all adjacent points thereof will not be described here.

[0065] According to the continuity characteristic value of each asphalt feature point, it can be understood that the continuity characteristic value reflects the continuity of the asphalt thickness in space, and is used to determine whether the asphalt feature point belongs to a part of continuous change in space or is an isolated noise point. If the fitting error of the current asphalt feature point is smaller, it means that the current asphalt feature point is highly smooth and the asphalt thickness of the surrounding asphalt feature points is consistent, indicating that the paving is uniform, and the possibility of noise interference on the asphalt thickness at the current asphalt feature point is smaller.

[0066] On the contrary, if the fitting error of the current asphalt feature point is larger, it means that the current asphalt feature point is highly mutated and the difference of the asphalt thickness of the surrounding asphalt feature points is significant, indicating that the paving is not uniform, and the possibility of noise interference on the asphalt thickness at the current asphalt feature point is larger.

[0067] Further, the embodiment combines the regularity characteristic value and the continuity characteristic value of each asphalt feature point to determine the uniformity characteristic value of each asphalt feature point, so as to screen out the to-be-de-noised feature points from all asphalt feature points, and specifically:

[0068] In the embodiment, the positive fusion result of the regularity characteristic value and the continuity characteristic value of each asphalt feature point is taken as the uniformity characteristic value of each asphalt feature point.

[0069] Preferably, the uniform feature value extraction process provided by the embodiment is shown in the schematic diagram as Figure 2

[0070] It should be understood that positive fusion refers to combining two or more indicators together through addition or multiplication or other methods, so as to obtain a comprehensive indicator, so as to more comprehensively and accurately evaluate a phenomenon or a problem. The fusion method is not limited to simple arithmetic operation, but can also include more complex statistical models and analysis methods, and the implementer can select them according to specific circumstances, and the embodiment does not make special limitations.

[0071] Preferably, as a specific implementation, in the embodiment, the mean value of the regularity feature value normalized value and the continuity feature value normalized value of each asphalt feature point is taken as the uniform feature value of each asphalt feature point. In actual application, as other implementation, the implementer can also use other positive fusion methods such as multiplication or addition according to specific circumstances, and the embodiment does not make special limitations.

[0072] According to the uniform feature value of each asphalt feature point, it can be understood that the uniform feature value reflects the degree of both regularity and spatial continuity of the asphalt feature point, and is used to screen out noise points that need to be corrected. If the regularity feature value of the current asphalt feature point is larger, it means that the asphalt paving of the current asphalt feature point conforms to the paving regularity, and the possibility of noise interference of the current asphalt feature point is smaller, so the corresponding uniform feature value is larger. At the same time, if the continuity feature value of the current asphalt feature point is larger, it means that the asphalt thickness of the current asphalt feature point is more continuous in space, that is, the asphalt paving at the current asphalt feature point is more uniform, and the possibility of noise interference of the measured asphalt height of the current asphalt feature point is smaller, so the corresponding uniform feature value is larger.

[0073] On the contrary, if the regularity feature value of the current asphalt feature point is smaller, it means that the asphalt paving of the current asphalt feature point does not conform to the paving regularity, and the possibility of noise interference of the current asphalt feature point is larger, so the corresponding uniform feature value is smaller. At the same time, if the continuity feature value of the current asphalt feature point is smaller, it means that the asphalt thickness of the current asphalt feature point is discontinuous in space, that is, the asphalt paving at the current asphalt feature point is not uniform, and the possibility of noise interference of the measured asphalt height of the current asphalt feature point is larger, so the corresponding uniform feature value is smaller.

[0074] Further, based on the uniform feature value, the to-be-de-noised feature points are selected from all the asphalt feature points, specifically:

[0075] ​In the embodiment, the uniform feature value of all asphalt feature points is taken as the input of the threshold segmentation algorithm, the output segmentation threshold is recorded as the uniform threshold, and the asphalt feature point with a uniform feature value less than the uniform threshold is taken as the feature point to be denoised.

[0076] It should be noted that there are many commonly used threshold segmentation algorithms. In the embodiment, the maximum between-cluster variance algorithm is used to screen the feature points to be denoised. In actual application, as other implementation manners, the implementer can also use other threshold segmentation algorithms according to the specific circumstances. The selection of the threshold segmentation algorithm is not specially limited in the embodiment.

[0077] The process of screening the modified feature points by using the maximum between-cluster variance algorithm is a known technology, and will not be described in detail.

[0078] So far, the embodiment constructs the uniform feature value by combining the regular feature value and the continuity feature value of the asphalt feature points, screens the feature points to be denoised based on the threshold segmentation, effectively identifies the noise points that do not conform to the paving rules and are not continuous in space, and further improves the accuracy of the asphalt thickness measurement by smoothing the asphalt thickness at the noise points.

[0079] Step S4: Smoothing the asphalt thickness at the feature points to be denoised to obtain the asphalt thickness measurement result of the surface of the construction road section.

[0080] Based on the screened feature points to be denoised, a smoothing algorithm is used to smooth the asphalt thickness at all the feature points to be denoised. The result of the smoothing processing is taken as the modified asphalt thickness at the corresponding feature points to be denoised, and the asphalt thickness at all the measurement points on the surface of the construction road section is obtained. Specifically,

[0081] The asphalt thickness distribution graph and the feature points to be denoised thereon are taken as the input of the smoothing algorithm. The smoothing window is set to The modified asphalt thickness of the smoothing processing of the feature points to be denoised is output. The original asphalt thickness at the feature points to be denoised in the asphalt thickness distribution graph is replaced by the modified asphalt thickness, and the smoothing processed asphalt thickness distribution graph is obtained, that is, the asphalt thickness measurement result of each measurement point on the asphalt pavement is obtained.

[0082] It should be noted that the value of N is artificially set. In the embodiment, the value of N is 50 cm. In actual application, as other implementation manners, the implementer can also set it by himself according to the specific circumstances. The embodiment does not make special limitation.

[0083] It should be understood that there are many commonly used smoothing algorithms, and the mean smoothing filter algorithm in the embodiment is used to correct the asphalt thickness at the feature points to be denoised. In actual application, as other implementation manners, implementers can also use median filter algorithm or Gaussian filter algorithm or other methods according to specific conditions, and the embodiment does not make special limitation.

[0084] The mean smoothing filter algorithm is a known technology, and the specific process of using the mean smoothing filter algorithm to perform smoothing filter processing on the asphalt feature value at the feature point to be denoised will not be described in detail.

[0085] So far, the embodiment constructs the regularity feature value and the continuity feature value by analyzing the distribution of the asphalt thickness and the pavement elevation data, and then fuses the two to form the uniform feature value, which is used to identify the noise points in the measurement data, and the asphalt thickness measured at the feature point to be denoised is subjected to smoothing filter processing, so that the corrected asphalt thickness after denoising is obtained, and the accuracy of the asphalt thickness measurement is improved.

[0086] Based on the same inventive concept as the above method, the embodiment of the present application also provides an intelligent measurement system for paving thickness of asphalt pavement, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the methods in the above intelligent measurement method for paving thickness of asphalt pavement when executing the computer program.

[0087] It should be noted that the above sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0088] Each embodiment in the present specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

[0089] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent measurement method for paving thickness of asphalt pavement, characterized in that, The method comprises the following steps: Obtaining the road surface elevation data of each measuring point on the surface of the construction section before paving asphalt and the asphalt thickness after paving asphalt; Analyzing the distribution of the asphalt thickness and the distribution of the road surface elevation data at all measuring points respectively to screen out asphalt feature points and roadbed feature points from all measuring points respectively, determining the thickness characteristic value of each asphalt feature point and the height characteristic value of each roadbed feature point based on the difference between the asphalt thickness of each asphalt feature point and the average distribution of the asphalt thickness of all measuring points except the asphalt feature points and the difference between the road surface elevation data of each roadbed feature point and the average distribution of the road surface elevation data of all measuring points except the roadbed feature points respectively, determining the regular characteristic value of each asphalt feature point based on the height characteristic value of the roadbed feature point with the same coordinate as each asphalt feature point and the thickness characteristic value of each asphalt feature point; Determining the neighboring points of each asphalt feature point based on the distance between each asphalt feature point and all other measuring points, performing surface fitting on each asphalt feature point and all its neighboring points, determining the continuity characteristic value of each asphalt feature point based on the fitting error in the fitting process, and combining the regular characteristic value to determine the uniformity characteristic value of each asphalt feature point to screen out the feature points to be denoised from all asphalt feature points; Smoothing the asphalt thickness at the feature points to be denoised to obtain the asphalt thickness measurement result of the surface of the construction section; The regular characteristic value of each asphalt feature point is the reciprocal of the absolute value of the sum of the height characteristic value of the roadbed feature point with the same coordinate as each asphalt feature point and the thickness characteristic value of the corresponding asphalt feature point; The continuity characteristic value of each asphalt feature point is the reciprocal of the fitting error obtained by performing surface fitting on each asphalt feature point and all its neighboring points; The uniformity characteristic value of each asphalt feature point is the positive fusion result of the normalized value of the regular characteristic value and the normalized value of the continuity characteristic value.

2. The intelligent measurement method for paving thickness of asphalt pavement according to claim 1, characterized in that, The screening of the asphalt feature points and the roadbed feature points from all measuring points respectively comprises: Taking the asphalt thickness and the road surface elevation data of all measuring points as the input of the threshold segmentation algorithm respectively to output the segmentation threshold of the asphalt thickness and the segmentation threshold of the road surface elevation data respectively; Taking the measuring point with the asphalt thickness greater than the corresponding segmentation threshold as the asphalt feature point and taking the measuring point with the road surface elevation data greater than the corresponding segmentation threshold as the roadbed feature point.

3. The intelligent measurement method for paving thickness of asphalt pavement according to claim 1, characterized in that, The determination of the thickness characteristic value of each asphalt feature point and the height characteristic value of each roadbed feature point respectively comprises: Taking the difference between the asphalt thickness of each asphalt feature point and the average value of the asphalt thickness of all measuring points except the asphalt feature points as the thickness characteristic value of each asphalt feature point; Taking the difference between the road surface elevation data of each roadbed feature point and the average value of the road surface elevation data of all measuring points except the roadbed feature points as the height characteristic value of each asphalt feature point.

4. The intelligent measurement method for paving thickness of asphalt pavement according to claim 1, characterized in that, The neighboring points of each asphalt feature point are the measuring points corresponding to the first preset number of distances in the ascending order arrangement result of the distances between each asphalt feature point and all other measuring points.

5. The intelligent measurement method for paving thickness of asphalt pavement according to claim 1, characterized in that, The screening of the feature points to be denoised from all asphalt feature points comprises: The uniform feature values of all asphalt feature points are taken as input of the threshold segmentation algorithm, the output segmentation threshold is recorded as a uniform threshold, and the asphalt feature points with uniform feature values less than the uniform threshold are taken as feature points to be denoised.

6. The intelligent measurement method for paving thickness of asphalt pavement according to claim 1, characterized in that, The asphalt thickness measurement result of the construction road section surface is obtained, and the method comprises the following steps: The smooth algorithm is used to perform smooth processing on the asphalt thickness at all feature points to be denoised, and the result of the smooth processing is taken as the corrected asphalt thickness at the corresponding feature points to be denoised, so as to obtain the asphalt thickness at all measurement points on the construction road section surface.

7. An intelligent paving thickness measurement system for asphalt pavement, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the steps of the asphalt pavement paving thickness intelligent measurement method according to any one of claims 1-6.

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