Method for identifying abnormal wear of tire through color difference of tire tread of automobile tire

Abstract

The invention relates to the technical field of tire production, in particular to a method for identifying abnormal wear of a tire through color difference of an automobile tire tread, which comprises the following steps: determining scanning parameters; correcting scanning parameters; obtaining a point cloud picture; quantitative parameters are selected, and a wear state model is trained based on historical detection data; establishing a mapping relation; according to the detection data of the to-be-detected automobile tire and the wear state model, obtaining a quantization parameter value of the automobile tire; generating a quantization parameter point cloud picture; verifying whether the quantization parameter value is accurate or not according to the spectrogram of the reflected light and the quantization parameter point cloud atlas; and generating a visual quantization parameter point cloud picture. According to the method, scanning parameters are dynamically corrected according to the actual perimeter to ensure the accuracy of point cloud data, automatic calculation of quantization parameters is realized in combination with a machine learning model, and finally, abstract wear data are converted into a visual image through color mapping, so that the wear condition is directly obtained by observing colors, and the accuracy of the wear condition is improved. And the detection efficiency of abnormal wear of the tire is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tire production, and in particular to a method for identifying abnormal tire wear through color difference of automobile tire treads. Background Art

[0002] As the only part of a vehicle that comes into contact with the road, the wear of its tires directly impacts driving safety, handling, and fuel economy. Statistics show that over 30% of traffic accidents are related to abnormal tire wear, making tire wear detection a core component of vehicle maintenance.

[0003] Existing tire wear detection methods fall into three main categories: Manual inspection methods rely on maintenance personnel visually observing tread depth and wear uniformity, or using simple calipers to measure local parameters. This method is heavily influenced by subjective experience, has low accuracy, and struggles to meet the needs of large-scale testing. Contact instrumentation methods utilize tools such as tread depth gauges and wear gauges to contact the tread for measurement. While these methods can obtain local quantitative data, they require point-by-point operation, resulting in a limited detection range and the potential for mechanical contact to scratch the tread rubber. These methods are particularly unsuitable for high-performance or run-flat tires. Non-contact technologies include laser scanning and machine vision. Laser scanning can generate a three-dimensional point cloud of the tread, but existing technologies often focus solely on geometric parameters and fail to correlate color variations caused by wear. While machine vision can capture color variations, it is significantly affected by lighting conditions and lacks integration with three-dimensional spatial coordinates, making it difficult to accurately quantify the degree of wear.

[0004] Therefore, the existing technology has defects in achieving high-precision quantitative detection and intuitive visual presentation of tire wear, and establishing a correlation between the degree of wear and tread color difference, and urgently needs improvement. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for identifying abnormal tire wear through automobile tire tread color difference, so as to solve the problems that the existing technology has defects in achieving high-precision quantitative detection and intuitive visual presentation of tire wear, and establishing the correlation between the degree of wear and tread color difference.

[0006] The present invention provides a method for identifying abnormal tire wear by using color difference of automobile tire tread, comprising: Determine the point cloud density based on standard tire parameters and actual accuracy requirements, and determine the scanning parameters based on the point cloud density; Placing the tire to be tested on a fixing device, collecting the actual circumference of the tire to be tested, and correcting the scanning parameters based on the actual circumference; The fixing device is rotated to scan the tire tread to obtain a point cloud image including spatial coordinates; Select quantitative parameters of automobile tire wear, obtain historical detection data based on the quantitative parameters, and train a wear status model based on the data set; Establishing a mapping relationship between the numerical range of the quantization parameter and a specific color in a predefined color space; Scanning and testing the tire to be tested to obtain test data, and obtaining a quantitative parameter value of the tire to be tested based on the test data and the wear state model; Matching the point cloud image with the quantization parameter value to generate a quantization parameter point cloud image; Matching the spectrum of the reflected light with the quantization parameter point cloud map, and verifying whether the quantization parameter value is accurate based on the matching result; In response to the quantization parameter value being accurate, the quantization parameter point cloud map is rendered according to the mapping relationship to generate a visualized quantization parameter point cloud map.

[0007] As a preferred technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, the scanning parameters include: laser scanning line frequency and scanning speed.

[0008] As an optimal technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, the scanning parameters are corrected based on the actual circumference of the tire to be tested, the maximum outer diameter of the tire cross section is measured by a laser rangefinder and the actual circumference of the tire to be tested is calculated, the point cloud coordinates are radially scaled and corrected based on the ratio of the measured circumference to the standard circumference, and the scanning parameters are corrected according to the corrected point cloud coordinates.

[0009] As a preferred technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, the radial scaling correction specifically includes: Establish a cylindrical coordinate system with the tire rotation axis as the origin and convert the Cartesian coordinates of the point cloud into polar coordinates; According to the ratio coefficient between the measured circumference and the standard circumference, the radial coordinate value is scaled proportionally; The corrected polar coordinates are converted back into Cartesian coordinate point cloud.

[0010] As an optimal technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, the scanning parameters are corrected according to the corrected point cloud coordinates, including: correcting the scanning speed in proportion to the ratio between the actual circumference and the standard circumference, and correcting the laser scanning line emission frequency according to the corrected scanning speed and the preset point cloud density requirements.

[0011] As a preferred technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, the quantitative parameter is any one of wear depth, shoulder height difference, surface roughness, groove residual rate or abnormal wear area.

[0012] As a preferred technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, the wear state model is built using the relationship between the propagation parameter of the reflected light and the quantization parameter value as input data. If the propagation parameter is input, the quantization parameter value is output; The propagation parameters include: the reflected light intensity and the propagation time of the laser from emission to return; The detection data is data formed by detecting propagation parameters of reflected light.

[0013] As a preferred technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, matching the spectrum of reflected light with the quantitative parameter point cloud map and verifying whether the quantitative parameter value is accurate based on the matching result includes: determining tire wear conditions according to the quantitative parameter values; Obtain the spectrum of reflected light and the spectral absorption characteristics of tire rubber to determine the relative wear status of each point cloud image of the tire; comparing the relative wear state with the quantization parameter value, and in response to a mismatch between the relative wear state and the quantization parameter value, determining that the detection result is abnormal, replacing the quantization parameter and performing the test again; In response to the relative wear state matching the quantitative parameter value, the detection result is determined to be normal.

[0014] As an optimal technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, after obtaining the point cloud image, it also includes a point cloud preprocessing step, removing scanning noise points through a bilateral filtering algorithm, calculating the point cloud normal vector using the principal component analysis method, and correcting the point cloud orientation deviation based on the normal vector direction.

[0015] As an optimal technical solution for the method of identifying abnormal tire wear through automobile tire tread color difference, the predefined color space is the Lab color space, and the establishment of the mapping relationship includes: mapping the minimum value of the quantization parameter to the dark blue of L=20, a=0, and b=0 in the Lab space, and mapping the maximum value to the orange-red of L=90, a=50, and b=50, and the intermediate values ​​correspond to the transition colors in the Lab space through linear interpolation.

[0016] Compared with existing technologies, the present invention offers the following advantages: by establishing a mapping relationship between quantitative parameters and Lab color space, it transforms abstract wear data into an intuitive color distribution, with a gradient from dark blue to orange-red corresponding one-to-one with the degree of wear from light to dark. This allows operators to quickly locate areas of abnormal wear and determine the severity of wear simply by observing the color differences in the visualized point cloud without requiring specialized knowledge. This direct "color difference-wear" mapping allows operators to directly determine the wear status by observing the color, improving the efficiency of detecting abnormal tire wear.

[0017] Furthermore, the present invention ensures the high precision and integrity of point cloud data by dynamically adjusting scanning parameters, correcting point cloud coordinates, and optimizing preprocessing, laying a reliable foundation for the extraction of wear quantification parameters. The wear state model trained based on historical data achieves a precise correlation between reflected light parameters and wear degree, so that the detection error of quantitative parameters (such as wear depth, surface roughness, etc.) is controlled within ±0.05mm, which is more than 10 times the accuracy of traditional manual detection, effectively avoiding the missed detection of hidden wear. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flowchart of a method for identifying abnormal tire wear by using color difference of automobile tire treads according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0020] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0021] See also Figure 1 As shown, it is a flowchart of the steps of a method for identifying abnormal tire wear by color difference of automobile tire tread according to an embodiment of the present invention, comprising: Step S1, determining the point cloud density based on standard tire parameters and actual accuracy requirements, and determining scanning parameters based on the point cloud density; Step S2, placing the tire to be tested on a fixing device, collecting the actual circumference of the tire to be tested, and correcting the scanning parameters based on the actual circumference; Step S3, rotating the fixing device and scanning the tire tread to obtain a point cloud image including spatial coordinates; Step S4, selecting quantitative parameters of automobile tire wear, obtaining historical detection data based on the quantitative parameters, and training a wear state model based on the data set; Step S5, establishing a mapping relationship between the numerical range of the quantization parameter and a specific color in a predefined color space; Step S6, scanning and testing the tire to be tested to obtain test data, and obtaining quantitative parameter values ​​of the tire to be tested based on the test data and the wear state model; Step S7, matching the point cloud image with the quantization parameter value to generate a quantization parameter point cloud image; Step S8, matching the spectrum of the reflected light with the quantization parameter point cloud map, and verifying whether the quantization parameter value is accurate based on the matching result; Step S9 : in response to the quantization parameter value being accurate, rendering the quantization parameter point cloud map according to the mapping relationship to generate a visualized quantization parameter point cloud map.

[0022] During implementation, the point cloud density is determined based on the standard tire parameters and the actual accuracy requirements. The scanning parameters are determined based on the point cloud density. Referring to the diameter (such as 19 inches) and width (such as 235mm) of the standard tire, combined with the actual detection accuracy requirements (such as error ≤ 0.1mm), the point cloud density is set to 60 points per square centimeter. Based on this, the scanning parameters are preliminarily set to a laser power of 5mW and a scanning line frequency of 800Hz. This process is all existing technology and will not be repeated here.

[0023] In detail, the present invention collects the circumference of a standard tire and dynamically corrects the scanning parameters according to the actual circumference to ensure the accuracy of the point cloud data. It combines the machine learning model to realize the automatic calculation of quantitative parameters, and finally converts the abstract wear data into an intuitive visual image through color mapping. By mapping the relationship between color difference and wear, the operator can directly obtain the wear condition by observing the color, thereby improving the detection efficiency of abnormal tire wear.

[0024] Furthermore, the scanning parameters include: The laser scan line frequency, that is, the number of scan lines emitted by the laser transmitter per unit time (e.g. 500 Hz means 500 scan lines are emitted per second), determines the lateral density of the point cloud; The scanning speed, that is, the speed at which the tire tread moves relative to the scanner when the tire rotates (e.g., 10 mm / s), affects the longitudinal density of the point cloud.

[0025] In detail, the present invention ensures uniform point cloud data density for tires of different sizes by matching the laser scanning line frequency and scanning speed with the tire. On the one hand, this avoids the loss of details due to data sparsity or the increased computational load caused by data redundancy. On the other hand, it eliminates the blurred wear boundaries caused by point cloud sparsity, providing a stable data basis for subsequent quantitative analysis, ensuring that the red-blue transition zone in the color difference image is sharp and clear. Inspectors can determine the boundary without image magnification, making it convenient for subsequent inspectors to obtain the wear situation directly by observing the color, thereby further improving the detection efficiency of abnormal tire wear.

[0026] Furthermore, the scanning parameters are corrected based on the actual circumference of the tire to be tested. The maximum outer diameter of the tire cross section is measured by a laser rangefinder and the actual circumference of the tire to be tested is calculated. Based on the ratio of the measured circumference to the standard circumference, the point cloud coordinates are radially scaled and corrected, and the scanning parameters are corrected according to the corrected point cloud coordinates.

[0027] During implementation, a laser rangefinder was used to measure the outer diameter of the worn tire's cross section (e.g., measured 620mm, standard 650mm). The tire curve was collected and integrated to calculate the actual circumference, which was approximately 1948mm. The standard circumference was π × 650, which was approximately 2042mm. The ratio was 1948 / 2042, which was approximately 0.954 (the circumference decreased due to wear). Based on this ratio, a radial scaling correction was performed on the original point cloud: the radial coordinates (distance from the rotation axis) of the point cloud were uniformly multiplied by 0.954 (e.g., if the original radial distance was 325mm, the corrected value would be 325 × 0.954, approximately 310mm). This ensured that the point cloud was consistent with the dimensions of the worn tire. The scanning parameters were also adjusted: the original scanning speed of 12mm / s was adjusted to 12 × 0.954, approximately 11.45mm / s, to ensure that the tread length scanned per unit time matched the required point cloud density.

[0028] Furthermore, the present invention addresses the reduction in circumference caused by wear by proportionally correcting the point cloud coordinates and scanning parameters, thereby eliminating the impact of dimensional deviation on detection, allowing the point cloud data to accurately reflect the actual contour of the tire after wear, providing a reliable benchmark for quantitative analysis, and facilitating subsequent inspection personnel to intuitively obtain the wear situation through the relationship mapping between color difference and wear, thereby further improving the detection efficiency of abnormal tire wear.

[0029] Specifically, radial scaling corrections include: Establish a cylindrical coordinate system with the tire rotation axis as the origin and convert the Cartesian coordinates of the point cloud into polar coordinates; According to the ratio coefficient between the measured circumference and the standard circumference, the radial coordinate value is scaled proportionally; The corrected polar coordinates are converted back into Cartesian coordinate point cloud.

[0030] Furthermore, the present invention uses radial scaling correction to make the point cloud accurately correspond to the size of the tire after wear, avoiding spatial coordinate deviation caused by circumference reduction, ensuring accurate calculation of the wear position and degree, and providing a basis for subsequent accurate mapping of the relationship between color difference and wear. It is convenient for subsequent inspection personnel to intuitively obtain the wear situation through the mapping of the relationship between color difference and wear, thereby further improving the detection efficiency of abnormal tire wear.

[0031] Furthermore, the scanning parameters are corrected according to the corrected point cloud coordinates, including: correcting the scanning speed in proportion to the ratio between the actual circumference and the standard circumference, so that the scanning speed and the adjusted rotational stepping angular velocity keep changing synchronously, ensuring that the ratio of the tread arc length scanned per unit time remains unchanged, and dynamically increasing or decreasing the laser scanning line emission frequency according to the corrected scanning speed and the preset point cloud density requirements to ensure that the number of point clouds obtained per unit area of ​​the tread is constant.

[0032] In practice, if the measured circumference is 2000mm, the standard circumference is 2100mm, and the original scanning speed is 800mm / s, then the ratio k=0.95, which will be corrected to 800×0.95=760mm / s.

[0033] Laser frequency correction: If the preset point cloud density is 2 points / mm, the corrected frequency = scanning speed × point cloud density = 760 × 2 = 1520 Hz (original frequency 1600 Hz). The frequency parameters are updated in real time through the laser controller.

[0034] Furthermore, the present invention maintains a constant point cloud density by reasonably adjusting the scanning speed and frequency, thereby avoiding striped artifacts in the color difference image caused by speed changes, ensuring that high-quality point cloud data can be obtained under various tire sizes and wear conditions. This provides a basis for subsequent accurate mapping of the relationship between color difference and wear, and facilitates subsequent inspection personnel to intuitively obtain the wear situation through the mapping of the relationship between color difference and wear, thereby further improving the detection efficiency of abnormal tire wear.

[0035] Furthermore, the quantization parameters include: Wear depth, i.e. the vertical distance from the bottom of the tread groove to the worn surface, is measured using a laser displacement sensor to measure the height difference between a certain point on the tread and the standard tread, with an accuracy of ±0.02mm and a range of 0-5mm; Shoulder height difference, that is, the average height deviation between the shoulder area and the center area of ​​the tire. Extract the point cloud of the tire shoulder (50mm from the sidewall) and calculate the Z coordinate difference between the highest and lowest points in the range of 0-3mm; Surface roughness, i.e., the microscopic unevenness calculated by the variance of the point cloud normal vector, is calculated by the distribution of reflected light intensity and is expressed as Ra value (0.5-5μm). The greater the roughness, the more scattered the reflected light. Groove residual rate, that is, the percentage of the actual groove depth to the designed depth; Abnormal wear area refers to the continuous area that exceeds the preset wear threshold.

[0036] Furthermore, the embodiment of the present invention selects wear depth as a quantitative parameter.

[0037] Specifically, this invention uses color mapping of multi-dimensional quantitative parameters to visually distinguish different wear types. For example, uneven wear appears as a single orange-red color, while groove wear appears as dark stripes. This allows operators to quickly identify wear types based on color type and distribution, improving the efficiency of detecting abnormal tire wear.

[0038] Furthermore, the wear state model is constructed using the relationship between the propagation parameter of the reflected light and the quantization parameter value as input data, and if the propagation parameter is input, the quantization parameter value is output; The propagation parameters include: the intensity of the reflected light and the propagation time of the laser from emission to return; The detection data is data formed by detecting the propagation parameters of the reflected light.

[0039] In implementation, a ToF camera is used to synchronously collect the reflected light intensity (0-255) and propagation time (0-100ns) from the tire surface. Each set of data corresponds to a point cloud coordinate. A dataset is constructed with the inputs being the reflected light intensity and propagation time, and the output being the wear depth.

[0040] Furthermore, the present invention realizes non-contact quantitative detection through the wear state model of reflected light parameters, avoiding damage to the tire caused by mechanical measurement. By mapping the relationship between color difference and wear, the operator can directly obtain the wear condition by observing the color, thereby improving the detection efficiency of abnormal tire wear.

[0041] Furthermore, the accuracy of the quantization parameter value is verified based on the spectrum of the reflected light and the matching result of the quantization parameter value, including: determining tire wear conditions according to the quantitative parameter values; Obtain the spectrum of reflected light and the spectral absorption characteristics of tire rubber to determine the relative wear status of each point cloud image of the tire; comparing the relative wear state with the quantization parameter value, and in response to a mismatch between the relative wear state and the quantization parameter value, determining that the detection result is abnormal, replacing the quantization parameter and re-performing; In response to the relative wear state matching the quantitative parameter value, the detection result is determined to be normal.

[0042] Furthermore, assuming that there are four point clouds A, B, C, and D, the spectral absorption characteristics of tire rubber are: with the increase of the degree of oxidation, the degree of red shift of absorption in the ultraviolet-visible region gradually increases; according to the comparison of the measured quantitative parameter values, it is known that the wear degree values ​​corresponding to the four point clouds A, B, C, and D are A, C, D, and B from low to high respectively.

[0043] Now each of the four point clouds A, B, C, and D corresponds to a spectrum graph, which are recorded as a, b, c, and d respectively. The degree of absorption red shift in the ultraviolet-visible region in a, b, c, and d is compared.

[0044] The first case: If the four point clouds A, B, C, and D are sorted in order from low to high according to the degree of red shift in the ultraviolet-visible absorption in a, b, c, and d, they are A, C, D, and B respectively. Combined with the spectral absorption characteristics of tire rubber, it can be seen that the degree of oxidation from low to high is A, C, D, and B, that is, the degree of wear from low to high is A, C, D, and B. In other words, the relative wear state matches the quantitative parameter value, and the test result is normal.

[0045] The second case: If the four point clouds A, B, C, and D are sorted in order from low to high according to the degree of red shift in the ultraviolet-visible absorption in a, b, c, and d, they are A, B, C, and D respectively. Combined with the spectral absorption characteristics of tire rubber, it can be seen that the degree of oxidation from low to high is A, B, C, and D, that is, the degree of wear from low to high is A, B, C, and D. In other words, the relative wear state does not match the quantitative parameter value, and the test result is abnormal.

[0046] Due to different exposure times to air, spectral differences between the fresh and aged surfaces of the exposed rubber in the worn area occur. For low oxidation levels, the characteristic peaks of the rubber's carbon chain and double bonds dominate, while the peaks of oxygen-containing functional groups are faint. For high oxidation levels, the peaks of oxygen-containing functional groups (especially carbonyl groups) are significantly enhanced, while the original carbon chain / double bond peaks are weakened or deformed. This change in the conjugated system may cause a red-shift in UV-visible absorption. Since new rubber surfaces are created after wear, their exposure time to air will differ from that of the original surface, resulting in different spectral effects. This difference is combined with the coordinates of each point cloud to generate a relative wear state for each point cloud coordinate relative to the other point cloud coordinates. Relative comparison of the values ​​of quantitative parameters also yields a wear state for each cloud point relative to the others. If these two wear states do not match, the test results are inaccurate. Spectral verification of the monitoring results is then used to increase the accuracy of abnormal tire wear detection.

[0047] Furthermore, after obtaining the point cloud image, a point cloud preprocessing step is also included. The scanning noise points are removed by the bilateral filtering algorithm, the point cloud normal vector is calculated by the principal component analysis method, and the point cloud orientation deviation is corrected based on the normal vector direction.

[0048] In implementation, the principal component analysis method is used to calculate the normal vector of each point, and points with an angle greater than 10° with the tire radial direction are rotated and corrected to ensure that the normal vector consistently points to the outside of the tread.

[0049] Specifically, the present invention avoids "false color" interference caused by noise points and color misalignment caused by directional deviation by pre-processing the point cloud image, making the worn area easier to identify. By mapping the relationship between color difference and wear, the operator can directly obtain the wear condition by observing the color, thereby improving the detection efficiency of abnormal tire wear.

[0050] Furthermore, the predefined color space is the Lab color space, and the establishment of the mapping relationship includes: mapping the minimum value of the quantization parameter to the dark blue of L=20, a=0, and b=0 in the Lab space, and mapping the maximum value to the orange-red of L=90, a=50, and b=50, and the intermediate value corresponds to the transition color in the Lab space through linear interpolation.

[0051] Specifically, this invention establishes a linear mapping in the Lab color space, creating a positive correlation between wear severity and color (gradually shifting from dark blue to orange-red), which aligns with the human eye's color perception. Operators can now determine wear severity simply by observing color depth without specialized training. By mapping the relationship between color difference and wear, operators can directly assess wear status by observing color, improving the efficiency of detecting abnormal tire wear.

[0052] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. A person skilled in the art would be able to make other variations or modifications based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying abnormal tire wear by color difference of automobile tire tread, characterized in that: include: Determine the point cloud density based on standard tire parameters and actual accuracy requirements, and determine the scanning parameters based on the point cloud density; Placing the tire to be tested on a fixing device, collecting the actual circumference of the tire to be tested, and correcting the scanning parameters based on the actual circumference; The fixing device is rotated to scan the tire tread to obtain a point cloud image including spatial coordinates; Select quantitative parameters of automobile tire wear, obtain historical detection data based on the quantitative parameters, and train a wear status model based on the data set; Establishing a mapping relationship between the numerical range of the quantization parameter and a specific color in a predefined color space; Scanning and testing the tire to be tested to obtain test data, and obtaining a quantitative parameter value of the tire to be tested based on the test data and the wear state model; Matching the point cloud image with the quantization parameter value to generate a quantization parameter point cloud image; Matching the spectrum of the reflected light with the quantization parameter point cloud map, and verifying whether the quantization parameter value is accurate based on the matching result; In response to the quantization parameter value being accurate, the quantization parameter point cloud map is rendered according to the mapping relationship to generate a visualized quantization parameter point cloud map.

2. The method for identifying abnormal tire wear by using color difference of automobile tire tread according to claim 1, characterized in that: The scanning parameters include: laser scanning line frequency and scanning speed.

3. The method for identifying abnormal tire wear by using color difference of automobile tire tread according to claim 2, characterized in that: The scanning parameters are corrected based on the actual circumference, the maximum outer diameter of the tire cross section is measured by a laser rangefinder and the actual circumference of the tire to be measured is calculated, the point cloud coordinates are radially scaled and corrected based on the ratio of the measured circumference to the standard circumference, and the scanning parameters are corrected according to the corrected point cloud coordinates.

4. The method for identifying abnormal tire wear by using color difference of automobile tire tread according to claim 3, characterized in that: The radial scaling correction specifically includes: Establish a cylindrical coordinate system with the tire rotation axis as the origin and convert the Cartesian coordinates of the point cloud into polar coordinates; According to the ratio coefficient between the measured circumference and the standard circumference, the radial coordinate value is scaled proportionally; The corrected polar coordinates are converted back into Cartesian coordinate point cloud.

5. The method for identifying abnormal tire wear by using color difference of automobile tire tread according to claim 4, characterized in that: The step of correcting the scanning parameters according to the corrected point cloud coordinates includes: According to the proportional relationship between the actual circumference and the standard circumference, the scanning speed is corrected in the same proportion, and according to the corrected scanning speed and the preset point cloud density requirement, the laser scanning line emission frequency is corrected.

6. The method for identifying abnormal tire wear by using color difference of automobile tire tread according to claim 1, characterized in that: The quantitative parameter is any one of wear depth, shoulder height difference, surface roughness, groove residual rate or abnormal wear area.

7. The method for identifying abnormal tire wear by using color difference of automobile tire tread according to claim 1, characterized in that: The wear state model is built using the relationship between the propagation parameter of the reflected light and the quantization parameter value as input data. If the propagation parameter is input, the quantization parameter value is output; The propagation parameters include: the reflected light intensity and the propagation time of the laser from emission to return; The detection data is data formed by detecting propagation parameters of reflected light.

8. The method for identifying abnormal tire wear by using color difference of automobile tire tread according to claim 7, characterized in that: The matching of the spectrum of the reflected light with the quantization parameter point cloud map and verifying whether the quantization parameter value is accurate according to the matching result includes: determining tire wear conditions according to the quantitative parameter values; Obtain the spectrum of reflected light and the spectral absorption characteristics of tire rubber to determine the relative wear status of each point cloud image of the tire; comparing the relative wear state with the quantization parameter value, and in response to a mismatch between the relative wear state and the quantization parameter value, determining that the detection result is abnormal, replacing the quantization parameter, and re-performing the test; In response to the relative wear state matching the quantitative parameter value, the detection result is determined to be normal.

9. The method for identifying abnormal tire wear by using color difference of automobile tire tread according to claim 1, characterized in that: After obtaining the point cloud image, a point cloud preprocessing step is also included, in which scanning noise points are removed by a bilateral filtering algorithm, a point cloud normal vector is calculated by a principal component analysis method, and the point cloud orientation deviation is corrected based on the normal vector direction.

10. The method for identifying abnormal tire wear through automobile tire tread color difference according to claim 1, characterized in that: The predefined color space is the Lab color space, and the establishment of the mapping relationship includes: mapping the minimum value of the quantization parameter to the dark blue of L=20, a=0, and b=0 in the Lab space, mapping the maximum value to the orange-red of L=90, a=50, and b=50, and the intermediate value corresponds to the transition color in the Lab space through linear interpolation.

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