A method for detecting error of road wear detection equipment

Abstract

The present application relates to the field of road surface detection, and is about an error detection method for road surface wear detection equipment, the error detection method comprising: obtaining road surface morphology information; fitting the road surface morphology information with a preset data fitting processing algorithm to obtain at least one structural depth change curve; determining a first road surface wear value based on each structural depth change curve and preset configuration information; generating a test plate corresponding to each structural depth change curve based on each structural depth change curve; detecting the test plate with a road surface wear detection equipment to determine a second road surface wear value; determining the detection error of the road surface wear detection equipment based on the first road surface wear value and the second road surface wear value. A large amount of raw data can be fitted by a preset data fitting processing algorithm, so that the structural depth change curve can meet the needs of different scenarios. By generating a test plate, the authenticity of the detection error is guaranteed, and the applicability and accuracy of the error detection method are improved.

Description

Technical Field

[0001] The present application relates to the field of road surface detection, and in particular to an error detection method for road wear detection equipment. Background Art

[0002] In recent years, with the rapid development of detection technology and the continuous iteration of detection equipment, detection equipment with multiple road surface detection functions has been widely used in the field of road condition assessment. The detection equipment is based on multi-point laser technology, image processing technology or positioning technology, and can obtain detection results that characterize the degree of road wear by driving on the road surface with assessment requirements.

[0003] However, the use of relevant technologies for road wear detection will lead to detection errors in the road wear detection equipment due to complex and changeable road conditions, aging of detection equipment, and interference from the external environment. Since the detection error cannot be accurately quantified based solely on the detection results of the detection equipment, it is difficult to calibrate, analyze and correct the detection error of the road wear detection equipment. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present application provides an error detection method for a road wear detection device.

[0005] According to some embodiments of the present application, a method for detecting an error in a road wear detection device is provided, and the method for detecting an error in a road wear detection device comprises:

[0006] Acquiring road surface morphology information, where the road surface morphology information is used to characterize elevation changes of multiple original road surfaces;

[0007] Fitting the road surface morphology information using a preset data fitting processing algorithm to obtain at least one structural depth change curve;

[0008] Determining a first road surface wear value based on each of the structural depth variation curves and preset configuration information, wherein the preset configuration information is used to characterize a corresponding relationship between the structural depth and the road surface wear value;

[0009] Based on each of the structural depth variation curves, a test plate corresponding to each of the structural depth variation curves is generated, and the extension path of the edge of the test plate is consistent with the corresponding structural depth variation curve;

[0010] Using the road wear detection device to detect the test plate, so that the road wear detection device determines a second road wear value based on the test plate and the preset configuration information;

[0011] Based on the first road wear value and the second road wear value, a detection error of the road wear detection device is determined.

[0012] In some embodiments of the present application, the road surface morphology information includes a plurality of elevation values ​​corresponding to each of the original road surfaces;

[0013] The method of fitting the road surface morphology information with a preset data fitting processing algorithm to obtain at least one structural depth variation curve includes:

[0014] Based on the regional location information of each of the original road surfaces, the plurality of elevation values ​​are divided into a plurality of first initial elevation value sets, wherein the plurality of first initial elevation value sets correspond one-to-one to a plurality of preset geographical areas;

[0015] Determining the first initial elevation value set corresponding to the target geographical area as a first target elevation value set, and performing fitting processing on the elevation values ​​in the first target elevation value set to obtain at least one structural depth variation curve; or,

[0016] Based on the preset elevation value range in which each elevation value is located, the plurality of elevation values ​​are divided into a plurality of second initial elevation value sets, wherein the plurality of second initial elevation value sets correspond one-to-one to a plurality of preset structural depth ranges;

[0017] The second initial elevation value set having the largest number of elevation values ​​is used as a second target elevation value set, and the elevation values ​​in the second target elevation value set are fitted to obtain a fitting curve, wherein the fitting curve matches the preset construction depth range corresponding to the second target elevation value set;

[0018] Based on the fitting curve, determining at least one mapping curve so that each of the mapping curves matches the preset construction depth range corresponding to any one of the second initial elevation value sets except the second target elevation value set;

[0019] The fitting curve and each of the mapping curves are used as a plurality of structural depth variation curves.

[0020] In some embodiments of the present application, the fitting process includes any one or any combination of linear regression fitting process, polynomial fitting process, spline interpolation fitting process, surface fitting process, and least squares fitting process.

[0021] In some embodiments of the present application, the construction depth includes a left wheel track belt construction depth, a lane centerline construction depth, and a right wheel track belt construction depth;

[0022] The number of the structural depth variation curves is greater than or equal to 3.

[0023] In some embodiments of the present application, determining the first road surface wear value based on each of the structural depth change curves and the preset configuration information includes:

[0024] Determining any three of the structural depth change curves as three target structural depth change curves;

[0025] Determine the first left wheel track zone structural depth, the first lane centerline structural depth and the first right wheel track zone structural depth at the first preset position based on the three target structural depth change curves respectively;

[0026] Determining the first road surface wear value based on the first left wheel track structure depth, the first lane centerline structure depth, the first right wheel track structure depth and the preset configuration information;

[0027] The testing the test plate with the road wear detection device so that the road wear detection device determines a second road wear value based on the test plate and the preset configuration information, comprises:

[0028] The pavement wear detection device is used to detect the three test plates generated respectively based on the three target structural depth change curves, so that the pavement wear detection device determines the second left wheel track belt structural depth, the second lane centerline structural depth and the second right wheel track belt structural depth at the second preset position respectively based on the three test plates, and determines the second pavement wear value based on the second left wheel track belt structural depth, the second lane centerline structural depth, the second right wheel track belt structural depth and the preset configuration information, and the first preset position of the target structural depth change curve matches the second preset position of the test plate.

[0029] In some embodiments of the present application, different target structure depth variation curves match different preset structure depth ranges;

[0030] Among the maximum values ​​of three preset structural depth ranges respectively matched by the three target structural depth variation curves, the maximum value of the preset structural depth range matched with the target structural depth variation curve used to determine the structural depth of the centerline of the first lane is the smallest.

[0031] In some embodiments of the present application, after generating test plates corresponding to each of the structure depth change curves based on each of the structure depth change curves, the error detection method of the road wear detection device further includes:

[0032] Performing a process error detection on each of the generated test boards to determine the dimensional error between the extension path of the edge of the test board and the structural depth variation curve caused by the processing technology of the test board;

[0033] The testing of the test plate by the road wear detection device includes:

[0034] The road wear detection device is used to detect the test plate whose size error is less than a preset error.

[0035] In some embodiments of the present application, the test board is generated by any one of three-dimensional printing, precision machining, and three-axis computer numerical control machining, or any combination of multiple thereof.

[0036] In some embodiments of the present application, the material of the test plate is aluminum, the thickness of the test plate is 0.5-1.5 cm, and the surface of the test plate is matte treated.

[0037] In some embodiments of the present application, the error detection method of the road wear detection device further includes:

[0038] Based on the detection error, the road wear detection device is subjected to any one or any combination of the following processes:

[0039] Detection error calibration processing;

[0040] Analyze and handle the causes of detection errors;

[0041] Detection parameter correction processing.

[0042] The technical solution provided by the embodiment of the present application may include the following beneficial effects: by generating a structural depth variation curve and a test plate corresponding to the structural depth variation curve, the first pavement wear value under the standard theoretical calculation condition can be determined according to the structural depth variation curve, and the second pavement wear value under the actual detection condition can be obtained according to the test plate and the pavement wear detection device, thereby determining the detection error of the pavement wear detection device according to the difference between the first pavement wear value and the second pavement wear value, realizing the accurate quantification of the detection error of the pavement wear detection device, and providing a basis for the calibration, analysis and correction of the detection error. A large amount of raw data can be fitted by a preset data fitting processing algorithm, so that the structural depth variation curve can meet the needs of different scenarios to characterize complex and diverse road conditions. By generating a test plate, the road conditions represented by the structural depth variation curve are truly restored, so that the pavement wear detection device can obtain the actual detection result corresponding to the standard theoretical calculation result by detecting the test plate, thereby ensuring the authenticity of the detection error and improving the applicability and accuracy of the error detection method.

[0043] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0045] Figure 1 The figure is a flow chart of an error detection method of a road wear detection device according to an exemplary embodiment.

[0046] Figure 2 is a schematic diagram of a test board according to an exemplary embodiment.

[0047] Figure 3 The present invention is a flowchart of performing fitting processing on road morphology information using a preset data fitting processing algorithm according to an exemplary embodiment to obtain at least one construction depth variation curve.

[0048] Figure 4 It is a flowchart showing a method of fitting road morphology information with a preset data fitting processing algorithm to obtain at least one construction depth variation curve according to another exemplary embodiment.

[0049] Figure 5 The present invention is a flowchart of determining a first road surface wear value based on each structural depth variation curve and preset configuration information according to an exemplary embodiment. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0051] In recent years, with the rapid development of road condition detection technology and the continuous iteration of detection equipment, detection equipment with multiple road surface detection functions, such as multi-functional detection vehicles, has been widely used in the field of road condition assessment. The detection equipment is based on detection technologies such as multi-point laser technology, image processing technology or positioning technology. By driving on the road surface with evaluation requirements, the detection results representing the degree of road wear can be obtained to determine the wear condition of the road surface structure.

[0052] However, due to the complex and changeable road conditions of the target road, the aging of the detection equipment, and interference from the external environment, the road wear detection equipment has detection errors when conducting road wear detection. Since it is impossible to accurately quantify the detection error based solely on the detection results of the detection equipment, it is difficult to calibrate, analyze and correct the detection error of the road wear detection equipment, which seriously affects the use effect and experience of the road wear detection equipment.

[0053] Based on this, the exemplary embodiment of the present application provides an error detection method for a road wear detection device. By generating a structural depth change curve and a test plate corresponding to the structural depth change curve, the first road wear value under the standard theoretical calculation condition can be determined according to the structural depth change curve, and the second road wear value under the actual detection condition can be obtained according to the test plate and the road wear detection device, so as to determine the detection error of the road wear detection device according to the difference between the first road wear value and the second road wear value, and realize the accurate quantification of the detection error of the road wear detection device, and provide a basis for the calibration, analysis and correction of the detection error. A large amount of raw data can be fitted by a preset data fitting processing algorithm, so that the structural depth change curve can meet the needs of different scenarios to characterize complex and diverse road conditions. By generating a test plate, the road condition represented by the structural depth change curve is truly restored, so that the road wear detection device can obtain the actual detection result corresponding to the standard theoretical calculation result by detecting the test plate, which ensures the authenticity of the detection error and improves the applicability and accuracy of the error detection method.

[0054] In an exemplary embodiment, a method for detecting an error in a road wear detection device is provided, referring to Figure 1 As shown, the error detection method of the road wear detection device includes:

[0055] S100: Obtain road surface morphology information, where the road surface morphology information is used to characterize elevation changes of multiple original road surfaces.

[0056] In step S100, road surface morphology information is obtained, and the road surface morphology information can characterize the elevation changes of multiple original road surfaces, that is, the changes in the height of the original road surfaces relative to the reference surface. The road surface morphology information is data obtained by measuring the original road surface, and the road surface morphology information can be, for example, elevation data or point cloud data, etc., which can reflect or extract the road surface elevation.

[0057] The location, type and quantity of the original road surfaces can be selected according to the road condition simulation requirements or error detection requirements. When enough original road surfaces are selected, the road surface morphology information can include a large amount of data, which can correspond to a variety of different locations and road conditions, making it easy to screen information according to actual needs, so that the error detection method can be applied to different scenarios.

[0058] S200: Performing fitting processing on road surface morphology information using a preset data fitting processing algorithm to obtain at least one structural depth variation curve.

[0059] In step S200, fitting processing is a data processing method for fitting a large amount of irregular data into a curve that meets certain characteristics. The preset data fitting processing algorithm is a method for performing the fitting processing. The preset data fitting processing algorithm can be selected according to the fitting requirements or the characteristics of the original data. For example, the preset data fitting processing algorithm may include classification fitting processing, average fitting processing, threshold fitting processing, etc.

[0060] Since the pavement morphology information can characterize the elevation change of the original pavement, it can reflect the change in the structural depth of the original pavement. The structural depth refers to the depth of the open pores of the uneven pavement, which is an important indicator for measuring the roughness of the pavement and determining the pavement wear. The pavement morphology information is fitted with a preset data fitting processing algorithm to obtain one or more structural depth change curves, so that each structural depth change curve can characterize the change in the pavement structural depth, so as to simulate the real road conditions through the numerical curve. The shape and number of the structural depth change curves can be selected according to the error detection requirements. For example, if the pavement wear detection equipment uses a three-line detection method to determine the pavement wear value, the number of structural depth change curves can be set to 3, and the shapes of the three structural depth change curves are different. Fitting the pavement morphology information with a preset data fitting processing algorithm can fit a large amount of original data, so that the structural depth change curve can meet the needs of different scenarios and characterize complex and diverse road conditions.

[0061] S300: Determine a first road surface wear value based on each structural depth variation curve and preset configuration information, where the preset configuration information is used to characterize a corresponding relationship between the structural depth and the road surface wear value.

[0062] In step S300, the preset configuration information can characterize the corresponding relationship between the structural depth and the road wear value. The preset configuration information can be, for example, a calculation formula with the structural depth as a variable and the road wear value as a calculation result. The preset configuration information can also be other algorithms or models that can determine the road wear value according to the structural depth. The road wear value can be, for example, the road wear index PWI or the wear rate WR proposed in the "Highway Technical Condition Assessment Standard" (JTG5210-2018). In this case, the determination of the road wear value requires obtaining three structural depths at the same time, and at least three structural depth change curves are required.

[0063] The first pavement wear value can be determined according to the structural depth variation curve and preset configuration information, so that the first pavement wear value is the pavement wear value under the standard theoretical calculation condition corresponding to the structural depth variation curve, i.e., the numerical curve, which represents the detection result that should be obtained if there is no detection error in the pavement wear detection equipment, and provides a basis for comparison with subsequent actual measurement results.

[0064] S400, based on each structural depth variation curve, generating a test plate corresponding to each structural depth variation curve, wherein the extension path of the edge of the test plate is consistent with the corresponding structural depth variation curve.

[0065] In step S400, for each structural depth variation curve, a corresponding structure depth variation curve can be generated according to the structural depth variation curve through the plate manufacturing process. Figure 2 The test plate shown in the figure is ensured to ensure that the extension path of the edge of the test plate is consistent with the corresponding structural depth variation curve, so that the test plate can truly restore the road conditions represented by the structural depth variation curve, and has the same structural depth as the structural depth variation curve at the same horizontal position. The size and material of the test plate can be selected according to the type of road wear detection equipment and the road conditions to be restored. For example, the length and width of the test plate need to meet the minimum detection range of the road wear detection equipment, and the structural depth variation formed by the edge extension path of the test plate needs to reach the minimum detection accuracy of the road wear detection equipment.

[0066] S500: Detect the test plate with a road wear detection device, so that the road wear detection device determines a second road wear value based on the test plate and preset configuration information.

[0067] In step S500, the test plate is tested by the road wear detection device. For example, the test plate can be placed on a road surface with a relatively high flatness so as to restore the road condition represented by the structural depth change curve through the test plate, and the road wear detection device is passed through the area where the test plate is located to detect the test plate. After the road wear detection device detects the test plate, the second road wear value can be determined according to the structural depth change of the test plate and the same preset configuration information as when determining the first road wear value, so that the second road wear value is the road wear value when the road wear detection device uses the same calculation theory to actually detect the test plate, reflecting the detection effect of the road wear detection device on the real road surface.

[0068] S600: Determine a detection error of a road wear detection device based on a first road wear value and a second road wear value.

[0069] In step S600, since the first road wear value and the second road wear value are both determined based on the same preset configuration information, i.e., the calculation theory, and the first road wear value is the road wear value under the standard theoretical calculation corresponding to the structural depth variation curve, and the second road wear value is the road wear value under the actual detection corresponding to the real road surface restored by the test plate, the difference between the first road wear value and the second road wear value represents the difference between the standard theoretical calculation result and the actual detection result of the road wear detection device, and therefore the detection error of the road wear detection device can be determined according to the first road wear value and the second road wear value. Exemplarily, for example, the difference between the first road wear value and the second road wear value can be used as the detection error of the road wear detection device.

[0070] In this embodiment, by generating a structural depth variation curve and a test plate corresponding to the structural depth variation curve, the first pavement wear value under the standard theoretical calculation condition can be determined according to the structural depth variation curve, and the second pavement wear value under the actual detection condition can be obtained according to the test plate and the pavement wear detection device, thereby determining the detection error of the pavement wear detection device according to the difference between the first pavement wear value and the second pavement wear value, achieving accurate quantification of the detection error of the pavement wear detection device, and providing a basis for the calibration, analysis and correction of the detection error. A large amount of raw data can be fitted by a preset data fitting processing algorithm, so that the structural depth variation curve can meet the needs of different scenarios to characterize complex and diverse road conditions. By generating a test plate, the road conditions represented by the structural depth variation curve are truly restored, so that the pavement wear detection device can obtain the actual detection result corresponding to the standard theoretical calculation result by testing the test plate, ensuring the authenticity of the detection error and improving the applicability and accuracy of the error detection method.

[0071] In some embodiments, the road surface morphology information includes a plurality of elevation values ​​corresponding to each original road surface.

[0072] The road surface morphology information may include elevation values ​​of a plurality of different positions corresponding to each original road surface, so as to directly represent the elevation change of the original road surface through the elevation values ​​of different positions.

[0073] refer to Figure 3 As shown, the road surface morphology information is fitted using a preset data fitting processing algorithm to obtain at least one structural depth change curve, including:

[0074] S210. Based on the regional location information of each original road surface, divide the multiple elevation values ​​into multiple first initial elevation value sets, and the multiple first initial elevation value sets correspond to multiple preset geographical areas one by one.

[0075] In step S210, the regional location information corresponding to the original road surface is used to characterize the location of the geographical area where the original road surface is located. According to the regional location information of each original road surface, each elevation value in the road surface morphology information can be divided into multiple first initial elevation value sets, so that the multiple first initial elevation value sets correspond to multiple preset geographical areas one by one, so as to classify the elevation values ​​in the road surface morphology information according to the different areas where the original road surface is located. Multiple preset geographical areas can be selected according to the distribution of the original road surface, and the road conditions of the original road surfaces in the same preset geographical area have certain similarities.

[0076] S220: Determine a first initial elevation value set corresponding to the target geographical area as a first target elevation value set, and perform fitting processing on the elevation values ​​in the first target elevation value set to obtain at least one structural depth variation curve.

[0077] In step S220, a target geographical area may be selected from a plurality of preset geographical areas, and the target geographical area may be selected according to the actual use area of ​​the road wear detection device. After the target geographical area is determined, the first initial elevation value set corresponding to the target geographical area is determined as the first target elevation value set, so as to screen out a first initial elevation value set from the plurality of first initial elevation value sets, and perform fitting processing on the plurality of discrete elevation values ​​in the first target elevation value set, so as to obtain at least one structural depth variation curve corresponding to the elevation values.

[0078] The above method is used to generate a structural depth change curve, so that the structural depth change curve can match the elevation value corresponding to the target geographical area, so as to characterize the road conditions with certain regularities in the target geographical area through the structural depth change curve. When determining the detection error later, the detection error of the road wear detection equipment under the road conditions and detection environment with certain geographical area regularities can be more accurately reflected.

[0079] In this embodiment, according to the regional location information of each original road surface, multiple elevation values ​​are divided into multiple first initial elevation value sets, the first initial elevation value set corresponding to the target geographical area is determined as the first target elevation value set, and the elevation values ​​in the first target elevation value set are fitted to obtain at least one structural depth change curve, thereby achieving the acquisition of the structural depth change curve, and providing a basis for the generation of the test plate and the determination of the detection error. The regional location information of the original road surface is used as the basis for dividing the elevation values, and the elevation values ​​in the first initial elevation value set corresponding to the target geographical area are fitted, so that the generated structural depth change curve can match the elevation value corresponding to the target geographical area, ensuring that the final determined detection error can adapt to the road conditions and detection environment with certain geographical regional laws, further improving the authenticity of the detection error and the applicability and accuracy of the error detection method.

[0080] In other embodiments, the road surface morphology information includes a plurality of elevation values ​​corresponding to each original road surface.

[0081] The road surface morphology information may include elevation values ​​of a plurality of different positions corresponding to each original road surface, so as to directly represent the elevation change of the original road surface through the elevation values ​​of different positions.

[0082] refer to Figure 4 As shown, the road surface morphology information is fitted using a preset data fitting processing algorithm to obtain at least one structural depth change curve, including:

[0083] S230. Based on a preset elevation value range in which each elevation value is located, divide the plurality of elevation values ​​into a plurality of second initial elevation value sets, wherein the plurality of second initial elevation value sets correspond one-to-one to the plurality of preset structural depth ranges.

[0084] In step S230, multiple preset elevation value ranges can be determined according to the size of the elevation value, and each elevation value in the road surface morphology information can be divided into multiple second initial elevation value sets according to the preset elevation value range, so that the multiple second initial elevation value sets correspond one-to-one to the multiple preset construction depth ranges, so as to classify the elevation values ​​in the road surface morphology information according to the different construction depth ranges, and the number and interval size of the preset elevation value ranges can be selected according to the overall distribution of the elevation values.

[0085] S240. Taking the second initial elevation value set with the largest number of elevation values ​​as the second target elevation value set, and performing fitting processing on the elevation values ​​in the second target elevation value set to obtain a fitting curve, wherein the fitting curve matches a preset construction depth range corresponding to the second target elevation value set.

[0086] In step S240, after the elevation values ​​are divided into multiple second initial elevation value sets, the number of elevation values ​​in each second initial elevation value set is different. The second initial elevation value set with the largest number of elevation values ​​can be used as the second target elevation value set to screen out a second initial elevation value set from the multiple second initial elevation value sets, and fit the multiple discrete elevation values ​​in the second target elevation value set to obtain a fitting curve corresponding to this part of the elevation values, so that the fitting curve can match the preset construction depth range corresponding to the second target elevation value set.

[0087] S250: Determine at least one mapping curve based on the fitting curve, so that each mapping curve matches a preset construction depth range corresponding to any second initial elevation value set except the second target elevation value set.

[0088] In step S250, one or more mapping curves can be generated in a mapping manner based on the obtained fitting curve, so that each mapping curve matches the preset construction depth range corresponding to any other second initial elevation value set, thereby mapping the curve corresponding to the preset construction depth range with the largest amount of elevation value data to other preset construction depth ranges. The mapping curves corresponding to other preset construction depth ranges can be obtained without processing the second initial elevation value set other than the second target elevation value set.

[0089] S260: Use the fitting curve and each mapping curve as a plurality of structural depth variation curves.

[0090] In step S260, the fitting curve and the mapping curves are used as multiple structural depth change curves, so that the multiple structural depth change curves can match multiple preset structural depth ranges, thereby ensuring the diversity of the structural depth change curves, making the multiple structural depth change curves adaptable to a variety of different road conditions, and improving the applicability and accuracy of determining detection errors.

[0091] In this embodiment, according to the preset elevation value range in which each elevation value is located, multiple elevation values ​​are divided into multiple second initial elevation value sets, the second initial elevation value set with the largest number of elevation values ​​is used as the second target elevation value set, and the elevation values ​​in the second target elevation value set are fitted to obtain a fitting curve, and at least one mapping curve is determined based on the fitting curve, and the fitting curve and each mapping curve can be used as multiple structural depth change curves, thereby realizing the determination of multiple structural depth change curves, and providing a basis for the generation of the test plate and the determination of the detection error. The preset elevation value range in which the elevation value is located is used as the basis for dividing the elevation value, and only the elevation values ​​in the second initial elevation value set with the largest number of elevation values ​​are fitted, and the mapping curve is determined based on the generated fitting curve, which reduces the amount of calculation while ensuring the authenticity of the mapping curve, and further improves the authenticity of the detection error and the applicability and accuracy of the error detection method.

[0092] In some embodiments, the fitting process includes any one or a combination of any multiple of a linear regression fitting process, a polynomial fitting process, a spline interpolation fitting process, a surface fitting process, and a least squares fitting process.

[0093] Any one or any combination of linear regression fitting, polynomial fitting, spline interpolation fitting, surface fitting, and least square fitting can be used as a fitting processing method for multiple elevation values. When any combination of the above processing is used as a fitting processing method, a preliminary fitting can be performed by one processing method, and another processing method can be selected according to the fitting effect to perform fitting again. It can be understood that the above fitting processing methods can be used when fitting the elevation values ​​in the first target elevation value set or the second target elevation value set.

[0094] Among them, the linear regression fitting process finds a straight line so that the sum of the vertical distances between the straight line and each elevation value in the elevation value set is minimized. It is suitable for elevation values ​​with linear relationships. The calculation is simple and easy to understand and implement. The polynomial fitting process uses a polynomial function to fit the elevation value, which can minimize the sum of the distances between all elevation values ​​and the polynomial function, and can capture elevation values ​​with complex nonlinear relationships. The spline interpolation fitting process fits the elevation value data through piecewise polynomials. Each segment of the polynomial is smoothly connected and can be used to process more complex data. The surface fitting process can be extended to multi-dimensional space. By fitting a multivariate function to describe the distribution of elevation value data, it is suitable for multivariate data fitting processing. The least squares fitting process can determine the function form with the smallest sum of squared errors. It can be applied to scenarios where the fitting effect needs to be optimized and has strong versatility.

[0095] In this embodiment, by using any one or any combination of linear regression fitting processing, polynomial fitting processing, spline interpolation fitting processing, surface fitting processing, and least squares fitting processing as a method of fitting multiple elevation values, the advantages of each fitting processing method can be utilized to fit a large amount of original data, so that the constructed depth change curve can have a strong fitting effect and meet the needs of different scenarios to characterize complex and diverse road conditions.

[0096] In some embodiments, the construction depth includes a left wheel track construction depth, a lane centerline construction depth, and a right wheel track construction depth, and the number of construction depth variation curves is greater than or equal to 3.

[0097] In the correspondence between the construction depth represented by the preset configuration information and the pavement wear value, the construction depth may include the left wheel track construction depth, the lane centerline construction depth, and the right wheel track construction depth, so that the correspondence represented by the preset configuration information can determine the corresponding pavement wear value according to the left wheel track construction depth, the lane centerline construction depth, and the right wheel track construction depth. In this case, the standard theoretical calculation principle of the pavement wear value defined by the preset configuration information conforms to the calculation principle of the pavement wear index PWI or the wear rate WR proposed in the Highway Technical Condition Assessment Standard (JTG5210-2018), ensuring the accuracy of determining the first pavement wear value and the second pavement wear value based on the preset configuration information.

[0098] For example, when the construction depth includes the left wheel track belt construction depth MPD L , Lane centerline construction depth MPD C and right wheel track depth MPD R When the wear rate WR and the pavement wear index PWI are determined by the following formulas (1) and (2), respectively:

[0099] (1)

[0100] (2)

[0101] in, and are model parameters, exemplarily, You can use 1.696, 0.785 can be used.

[0102] In this case, when determining the first pavement wear value and the second pavement wear value, in order to simultaneously provide the left wheel track belt structural depth, the lane centerline structural depth and the right wheel track belt structural depth, it is necessary to ensure that the number of structural depth variation curves is greater than or equal to 3, so that the number of test plates is also greater than or equal to 3, so as to provide the above three structural depths when determining the first pavement wear value through at least three structural depth variation curves, and provide the above three structural depths when determining the second pavement wear value through at least three test plates. When the number of structural depth variation curves is greater than three, the first pavement wear value and the second pavement wear value corresponding to multiple different situations can be determined based on the arrangement and combination of any three structural depth variation curves and their corresponding test plates.

[0103] In this embodiment, the left wheel track belt structural depth, the lane center line structural depth and the right wheel track belt structural depth are used as structural depths at the same time, which provides a theoretical basis and calculation method that meets the standard for determining the pavement wear value, and ensures the accuracy of determining the first pavement wear value and the second pavement wear value based on the preset configuration information. The number of structural depth variation curves is set to be greater than or equal to 3, so that when determining the first pavement wear value and the second pavement wear value, the left wheel track belt structural depth, the lane center line structural depth and the right wheel track belt structural depth can be provided at the same time, and when the number of structural depth variation curves is greater than 3, the corresponding first pavement wear value and the second pavement wear value under multiple different conditions can be obtained through the arrangement and combination of any 3 structural depth variation curves and their corresponding test plates, which increases the data volume of the first pavement wear value and the corresponding second pavement wear value, and further improves the accuracy of the detection error.

[0104] In some embodiments, reference Figure 5 As shown, based on each structural depth variation curve and preset configuration information, determining the first road surface wear value includes:

[0105] S310. Determine any three structural depth variation curves as three target structural depth variation curves.

[0106] In step S310, when the structural depth includes the structural depth of the left wheel track belt, the structural depth of the lane centerline, and the structural depth of the right wheel track belt, when determining the first pavement wear value according to each structural depth variation curve and the preset configuration information, it is necessary to simultaneously provide three structural depths as the structural depth of the left wheel track belt, the structural depth of the lane centerline, and the structural depth of the right wheel track belt through the structural depth variation curve, so as to provide a basis for determining the first pavement wear value. At this time, any three structural depth variation curves can be selected from at least three structural depth variation curves as three target structural depth variation curves.

[0107] S320, determining a first left wheel track belt structural depth, a first lane centerline structural depth, and a first right wheel track belt structural depth at a first preset position based on the three target structural depth variation curves respectively.

[0108] In step S320, after determining the three target structural depth variation curves, the structural depths corresponding to the three target structural depth variation curves at the first preset position are determined respectively according to the three target structural depth variation curves, so as to serve as the first left wheel track belt structural depth, the first lane centerline structural depth and the first right wheel track belt structural depth respectively. The first preset position can be selected according to the length of the three target structural depth variation curves and the detection accuracy of the road wear detection device, so as to ensure that the three target structural depth variation curves have structural depths capable of determining the road wear value at the first preset position, and to ensure that the road wear detection device can realize the actual detection of the road wear value at the corresponding position of the test plate.

[0109] S330. Determine a first road surface wear value based on a first left wheel track structure depth, a first lane centerline structure depth, a first right wheel track structure depth, and preset configuration information.

[0110] In step S330, since the preset configuration information can characterize the correspondence between the left wheel track belt construction depth, the lane centerline construction depth and the right wheel track belt construction depth and the pavement wear value when the construction depth includes the left wheel track belt construction depth, the lane centerline construction depth and the right wheel track belt construction depth, the first pavement wear value can be determined according to the first left wheel track belt construction depth, the first lane centerline construction depth, the first right wheel track belt construction depth and the preset configuration information, so that the first pavement wear value is the pavement wear value under the standard theoretical calculation condition corresponding to the target construction depth variation curve, i.e., the numerical curve, which represents the detection result that should be obtained if the pavement wear detection equipment has no detection error, and provides a basis for comparison with subsequent actual measurement results.

[0111] A pavement wear detection device is used to detect a test plate so that the pavement wear detection device determines a second pavement wear value based on the test plate and preset configuration information, including: using the pavement wear detection device to detect three test plates respectively generated based on three target structural depth change curves, so that the pavement wear detection device determines a second left wheel track belt structural depth, a second lane centerline structural depth, and a second right wheel track belt structural depth at a second preset position based on the three test plates respectively, and determines a second pavement wear value based on the second left wheel track belt structural depth, the second lane centerline structural depth, the second right wheel track belt structural depth and the preset configuration information, and the first preset position of the target structural depth change curve matches the second preset position of the test plate.

[0112] In the case where the construction depth includes the left wheel track construction depth, the lane centerline construction depth and the right wheel track construction depth, when the test plate is inspected with a pavement wear detection device so that the pavement wear detection device determines the second pavement wear value based on the test plate and preset configuration information, it is necessary to simultaneously provide three construction depths through the test plate as the left wheel track construction depth, the lane centerline construction depth and the right wheel track construction depth, to provide a basis for determining the second pavement wear value.

[0113] At this time, the road wear detection equipment can be used to detect the three test plates generated based on the three target structural depth change curves. The road wear detection equipment can determine the structural depths corresponding to the three test plates at the second preset positions according to the three test plates, respectively, as the second left wheel track belt structural depth, the second lane centerline structural depth and the second right wheel track belt structural depth.

[0114] Since the preset configuration information can characterize the correspondence between the left wheel track construction depth, the lane centerline construction depth and the right wheel track construction depth and the pavement wear value when the construction depth includes the left wheel track construction depth, the lane centerline construction depth and the right wheel track construction depth, the second pavement wear value can be determined according to the second left wheel track construction depth, the second lane centerline construction depth, the second right wheel track construction depth and the preset configuration information, so that the second pavement wear value is the pavement wear value when the test plate is actually tested by the pavement wear detection equipment using the same calculation theory, reflecting the detection effect of the pavement wear detection equipment on the real road surface.

[0115] The first preset position of the target structure depth change curve matches the second preset position of the test plate, so that the first pavement wear value and the second pavement wear value are both based on the same preset configuration information, that is, the pavement wear value at the same position determined by the calculation theory, thereby ensuring that the difference between the first pavement wear value and the second pavement wear value can represent the difference between the standard theoretical calculation result and the actual detection result of the pavement wear detection equipment, so as to realize the determination of the detection error through the first pavement wear value and the second pavement wear value.

[0116] In this embodiment, any three structural depth change curves are determined as three target structural depth change curves, and the first left wheel track belt structural depth, the first lane centerline structural depth and the first right wheel track belt structural depth of the first preset position are determined respectively according to the three target structural depth change curves. According to the first left wheel track belt structural depth, the first lane centerline structural depth, the first right wheel track belt structural depth and preset configuration information, the first pavement wear value can be determined, which provides a basis for comparison with the subsequent second pavement wear value. The pavement wear detection equipment is used to detect three test plates generated based on three target structural depth change curves, so that the pavement wear detection equipment can determine the second left wheel track structural depth, the second lane centerline structural depth and the second right wheel track structural depth at the second preset position based on the three test plates respectively, and can determine the second pavement wear value according to the second left wheel track structural depth, the second lane centerline structural depth, the second right wheel track structural depth and preset configuration information, ensuring that the difference between the first pavement wear value and the second pavement wear value can represent the difference between the standard theoretical calculation result and the actual detection result of the pavement wear detection equipment at the same position, providing a basis for determining the detection error.

[0117] In some embodiments, different target structural depth variation curves are matched with different preset structural depth ranges. Among the maximum values ​​of the three preset structural depth ranges matched by the three target structural depth variation curves, the maximum value of the preset structural depth range matched with the target structural depth variation curve used to determine the structural depth of the centerline of the first lane is the smallest.

[0118] Different target structural depth variation curves are configured to match different preset structural depth ranges, and the maximum value of the preset structural depth range matched with the target structural depth variation curve for determining the structural depth of the center line of the first lane is the smallest among the maximum values ​​of the three preset structural depth ranges matched by the three target structural depth variation curves. The first left wheel track belt structural depth, the first lane center line structural depth and the first right wheel track belt structural depth determined according to the three target structural depth variation curves can be in different preset structural depth ranges, so as to restore the real and complex road conditions through the difference and diversity of the numerical ranges between the first left wheel track belt structural depth, the first lane center line structural depth and the first right wheel track belt structural depth, and ensure the accuracy and reference value of the calculation result of the first road surface wear value. And the first lane center line structural depth is made smaller than the first left wheel track belt structural depth and the first right wheel track belt structural depth, so as to restore the normal wear of the road surface under driving conditions.

[0119] As mentioned above, when generating a structural depth variation curve, a fitting curve can be obtained by fitting the elevation values ​​in the second target elevation value set, and at least one mapping curve can be obtained by mapping according to the fitting curve, so that the fitting curve and each mapping curve match different preset structural depth ranges. In this case, when three of the multiple structural depth variation curves are selected as target structural depth variation curves, the three target depth structural depth variation curves can be matched with three different preset structural depth ranges, and the target structural depth variation curve that matches the preset structural depth range with the smallest maximum value is selected as the target structural depth variation curve for determining the structural depth of the centerline of the first lane.

[0120] In this embodiment, different target construction depth change curves are configured to match different preset construction depth ranges, and among the three preset construction depth ranges respectively matched by the three target construction depth change curves, the maximum value of the preset construction depth range matched with the target construction depth change curve used to determine the construction depth of the centerline of the first lane is the smallest, which can restore the real and complex road conditions and the normal wear of the road surface under driving conditions, and further improve the accuracy and reference value of determining the first road surface wear value based on the first left wheel track belt construction depth, the first lane centerline construction depth and the first right wheel track belt construction depth.

[0121] After generating test plates corresponding to each structural depth change curve based on each structural depth change curve, the error detection method of the pavement wear detection equipment also includes: performing process error detection on each generated test plate to determine the dimensional error between the extension path of the edge of the test plate and the structural depth change curve caused by the processing technology of the test plate.

[0122] It is understandable that, due to the limitation of the processing technology when the test board is generated, there may be a certain dimensional error between the extension path of the edge of the test board generated according to each structural depth change curve and the corresponding structural depth change curve. After the test board is generated, the generated test boards can be subjected to process error detection to determine the dimensional error of each test board through process error detection. Exemplarily, for example, the test board can be subjected to process error detection by a probe of a high-precision three-dimensional coordinate measuring machine to determine the dimensional error between each test board and the corresponding structural depth change curve.

[0123] The test plate is tested by using the road wear testing device, including: the test plate whose size error is less than the preset error is tested by using the road wear testing device.

[0124] When the size error is too large, the extension path representing the edge of the test plate is significantly different from the corresponding structural depth change curve, and the test plate does not restore the road conditions represented by the structural depth change curve to an insufficient degree, so that the difference between the first pavement wear value and the second pavement wear value also includes the difference between the test plate and the structural depth change curve, and the detection error of the pavement wear detection device cannot be accurately determined by the first pavement wear value and the second pavement wear value. Therefore, when the pavement wear detection device detects the test plate, the pavement wear detection device detects the test plate whose size error is less than the preset error, so as to control the size error of the test plate actually used for detection within the preset error, so that the second pavement wear value can correspond to the actual detection result under the same road conditions as the first pavement wear value, and ensure the reference value provided by the second pavement wear value for determining the detection error.

[0125] In this embodiment, by performing process error detection on each generated test plate to determine the dimensional error between the extension path of the edge of the test plate and the structural depth variation curve caused by the processing technology of the test plate, the pavement wear detection equipment can be used to detect the test plate whose dimensional error is less than the preset error, thereby achieving control of the dimensional error of the test plate caused by the manufacturing process, and ensuring that the second pavement wear value obtained by the pavement wear detection equipment when detecting the test plate can correspond to the first pavement wear value determined according to the structural depth variation curve, thereby further improving the accuracy of determining the detection error based on the first pavement wear value and the second pavement wear value.

[0126] In some embodiments, the test board is generated by any one of three-dimensional printing, precision machining, and three-axis computer numerical control machining, or any combination of multiple thereof.

[0127] Any one of three-dimensional printing, precision machining, and three-axis computer numerical control machining, or any combination of any two or more of them, can be used as a method for generating a test plate, so as to generate a test plate corresponding to each structural depth change curve according to each structural depth change curve, and ensure that the extension path of the edge of the test plate is consistent with the corresponding structural depth change curve.

[0128] Among them, 3D printing creates 3D objects by stacking materials layer by layer. It can realize the rapid and batch generation of test boards based on 3D data, and is suitable for making complex plate structures. Precision machining uses high-precision mechanical or electronic means to cut, grind and other operations on materials to obtain high-precision and high-quality test boards, which is suitable for making structures with strict dimensional requirements. Three-axis computer numerical control machining, or three-axis CNC machining, uses tools that move along three axes to process fixed workpieces, and has the characteristics of low cost and simple operation.

[0129] In this embodiment, any one or any combination of three-dimensional printing, precision machining, and three-axis computer numerical control machining is used as a method for generating the test plate, which can achieve the generation of a test plate that corresponds one-to-one to each structural depth change curve, provides a basis for determining the second pavement wear value, and can select a suitable generation method according to demand to ensure the production accuracy of the test plate and control the production cost of the test plate.

[0130] In some embodiments, the test plate is made of aluminum, has a thickness of 0.5-1.5 cm, and has a matte surface treatment.

[0131] The material of the test plate is set to aluminum, so that the test plate can have the characteristics of light weight, high strength, corrosion resistance, easy processing, recyclability and high fatigue strength, so that the test plate is easy to produce and arrange, reusable and can carry road wear detection equipment. The thickness of the test plate is set to 0.5~1.5cm, and the surface of the test plate is matte treated, so that the test plate can have the same structural depth variation as the real road surface, adapt to the detection accuracy of the road wear detection equipment, and improve the degree of restoration of the test plate to the real road surface.

[0132] In this embodiment, the material of the test plate is set to aluminum, the thickness is set to 0.5~1.5cm, and the surface of the test plate is matte treated, so that the test plate is easy to produce and arrange, reusable and has a certain bearing capacity, which improves the degree of restoration of the test plate to the real road surface, and is conducive to using the test plate as a detection object of the road wear detection equipment to realize the determination of the second road wear value.

[0133] In some embodiments, the error detection method of the road wear detection equipment also includes: based on the detection error, performing any one or any combination of the following processing on the road wear detection equipment: detection error calibration processing, detection error cause analysis processing, and detection parameter correction processing.

[0134] After determining the detection error of the pavement wear detection equipment based on the first pavement wear value and the second pavement wear value, the pavement wear detection equipment can be subjected to at least one of detection error calibration processing, detection error cause analysis processing and detection parameter correction processing based on the detection error, so as to realize calibration, analysis or correction of the detection error of the pavement wear detection equipment.

[0135] Among them, the detection error calibration process is used to calibrate the current detection error of the road wear detection equipment, and present the detection error that will exist if the road wear detection equipment continues to be put into use in a numerical form, so that the user can choose whether to continue to use the road wear detection equipment based on the calibrated detection error, or can adjust the detection result based on the calibrated detection error, so that the adjusted detection result is closer to the actual result.

[0136] The detection error cause analysis process is used to analyze the cause of the detection error of the road wear detection device based on the detection error. For example, multiple detection errors can be determined multiple times through the above method. If the difference between the multiple detection errors is small, it can be considered that the road wear detection device has a detection error due to the device's own reasons such as equipment aging, component failure, etc., so that the road wear detection device can be repaired or maintained. If the difference between the multiple detection errors is large, it can be considered that the road wear detection device has a detection error due to complex and changeable road conditions or external environmental factors, and a road wear detection device that is more suitable for the current road conditions or environment can be selected for detection.

[0137] The detection parameter correction process is used to debug the equipment parameters of the road wear detection equipment, so as to perform appropriate correction on the detection parameters through the feedback of the detection error, so as to eliminate or reduce the detection error of the road wear detection equipment after the parameter correction. For example, the model parameters in the preset configuration information adopted by the road wear detection equipment can be adjusted according to the detection error to change the corresponding relationship between the construction depth and the road wear value, so that the road wear value finally outputted is more accurate.

[0138] In this embodiment, based on the detection error, the road wear detection equipment is calibrated for detection error, analyzed for cause of detection error, and corrected for detection parameters. This enables calibration, analysis or correction of the detection error of the road wear detection equipment, is beneficial for selecting suitable road wear detection equipment for different scenarios, and can ensure the stability and accuracy of the detection results of the road wear detection equipment, thereby improving the user experience of the road wear detection equipment.

[0139] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this application. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0140] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for detecting an error in a road wear detection device, characterized in that: The error detection method of the road wear detection device comprises: Acquiring road surface morphology information, where the road surface morphology information is used to characterize elevation changes of multiple original road surfaces; Fitting the road surface morphology information using a preset data fitting processing algorithm to obtain at least one structural depth change curve; Determining a first road surface wear value based on each of the structural depth variation curves and preset configuration information, wherein the preset configuration information is used to characterize a corresponding relationship between the structural depth and the road surface wear value; Based on each of the structural depth variation curves, a test plate corresponding to each of the structural depth variation curves is generated, and the extension path of the edge of the test plate is consistent with the corresponding structural depth variation curve; Using the road wear detection device to detect the test plate, so that the road wear detection device determines a second road wear value based on the test plate and the preset configuration information; determining a detection error of the road wear detection device based on the first road wear value and the second road wear value; Wherein, after generating test plates corresponding to each of the structure depth change curves based on each of the structure depth change curves, the error detection method of the road wear detection device further includes: Performing a process error detection on each of the generated test boards to determine the dimensional error between the extension path of the edge of the test board and the structural depth variation curve caused by the processing technology of the test board; The testing of the test plate by the road wear detection device includes: The road wear detection device is used to detect the test plate whose size error is less than a preset error.

2. The error detection method of the road wear detection device according to claim 1, characterized in that: The road surface morphology information includes a plurality of elevation values ​​corresponding to each of the original road surfaces; The method of fitting the road surface morphology information with a preset data fitting processing algorithm to obtain at least one structural depth variation curve includes: Based on the regional location information of each of the original road surfaces, the plurality of elevation values ​​are divided into a plurality of first initial elevation value sets, wherein the plurality of first initial elevation value sets correspond one-to-one to a plurality of preset geographical areas; Determining the first initial elevation value set corresponding to the target geographical area as a first target elevation value set, and performing fitting processing on the elevation values ​​in the first target elevation value set to obtain at least one structural depth variation curve; or, Based on the preset elevation value range in which each elevation value is located, the plurality of elevation values ​​are divided into a plurality of second initial elevation value sets, wherein the plurality of second initial elevation value sets correspond one-to-one to a plurality of preset structural depth ranges; The second initial elevation value set having the largest number of elevation values ​​is used as a second target elevation value set, and the elevation values ​​in the second target elevation value set are fitted to obtain a fitting curve, wherein the fitting curve matches the preset construction depth range corresponding to the second target elevation value set; Based on the fitting curve, determining at least one mapping curve so that each of the mapping curves matches the preset construction depth range corresponding to any one of the second initial elevation value sets except the second target elevation value set; The fitting curve and each of the mapping curves are used as a plurality of structural depth variation curves.

3. The error detection method of the road wear detection device according to claim 2, characterized in that: The fitting process includes any one or any combination of linear regression fitting process, polynomial fitting process, spline interpolation fitting process, surface fitting process, and least square fitting process.

4. The error detection method of the road wear detection device according to claim 1, characterized in that: The construction depth includes the left wheel track construction depth, the lane centerline construction depth and the right wheel track construction depth; The number of the structural depth variation curves is greater than or equal to 3.

5. The error detection method of the road wear detection device according to claim 4, characterized in that: The determining of the first road surface wear value based on each of the structural depth change curves and the preset configuration information includes: Determining any three of the structural depth change curves as three target structural depth change curves; Determine the first left wheel track zone structural depth, the first lane centerline structural depth and the first right wheel track zone structural depth at the first preset position based on the three target structural depth change curves respectively; Determining the first road surface wear value based on the first left wheel track structure depth, the first lane centerline structure depth, the first right wheel track structure depth and the preset configuration information; The testing the test plate with the road wear detection device so that the road wear detection device determines a second road wear value based on the test plate and the preset configuration information, comprises: The pavement wear detection device is used to detect the three test plates generated respectively based on the three target structural depth change curves, so that the pavement wear detection device determines the second left wheel track belt structural depth, the second lane centerline structural depth and the second right wheel track belt structural depth at the second preset position respectively based on the three test plates, and determines the second pavement wear value based on the second left wheel track belt structural depth, the second lane centerline structural depth, the second right wheel track belt structural depth and the preset configuration information, and the first preset position of the target structural depth change curve matches the second preset position of the test plate.

6. The error detection method of the road wear detection device according to claim 5, characterized in that: Different target structure depth variation curves match different preset structure depth ranges; Among the maximum values ​​of three preset structural depth ranges respectively matched by the three target structural depth variation curves, the maximum value of the preset structural depth range matched with the target structural depth variation curve used to determine the structural depth of the centerline of the first lane is the smallest.

7. The error detection method of the road wear detection device according to any one of claims 1 to 6, characterized in that: The test board is generated by any one of three-dimensional printing, precision machining, and three-axis computer numerical control machining, or any combination of multiple thereof.

8. The error detection method of the road wear detection device according to any one of claims 1 to 6, characterized in that: The material of the test plate is aluminum, the thickness of the test plate is 0.5-1.5 cm, and the surface of the test plate is matte treated.

9. The error detection method of the road wear detection device according to any one of claims 1 to 6, characterized in that: The error detection method of the road wear detection device also includes: Based on the detection error, the road wear detection device is subjected to any one or any combination of the following processes: Detection error calibration processing; Analyze and handle the causes of detection errors; Detection parameter correction processing.

Images