A low-noise tire pattern pitch optimization method and device based on image processing and a medium

Abstract

The application discloses a low-noise tire pattern pitch optimization method and device based on image processing and a medium, and the method comprises the following steps: S1, processing initial pattern information and calculating pitch length; S2, extracting groove boundaries and tire mark boundaries of a pattern pitch graph; S3, inputting basic parameters such as pitch and mark; S4, introducing an A-weighting filter for acoustic weighting; S5, calculating an initial noise spectrum; S6, optimizing a spectrum peak value in a harmonic range; S7, iteratively adjusting a pitch ratio and quantity until noise energy meets a threshold value, and outputting an optimal pitch sequence. Through the fusion of image processing technology and acoustic analysis, the pattern pitch design is systematically optimized, tire noise is significantly reduced, design efficiency is improved, and a research and development cycle is shortened.

Description

Technical Field

[0001] This invention relates to the field of tire noise control technology, specifically to a method, device, and medium for optimizing low-noise tire tread pitch based on image processing. Background Technology

[0002] With the rapid development of the vehicle industry, people's requirements for vehicle comfort are increasing day by day. Tire noise, as one of the main noise sources during vehicle operation, has received widespread attention.

[0003] In particular, with the EU introducing a series of labeling laws for tire performance, major domestic tire companies are paying more and more attention to tire noise performance and gradually strengthening their research on low-noise tire technology.

[0004] The noise generated by tires during vehicle operation mainly consists of aerodynamic noise and vibration noise. Research shows that the factors affecting tire noise, from most significant to least significant, are tread pattern, structure, compound, and profile parameters, with profile parameters having a relatively small impact on noise. In tire design, tread pattern designers are more concerned with noise related to the tire tread pattern. Studies have shown a positive correlation between tread pitch design and tire noise. Traditional tread pitch design methods often rely on empirical formulas and extensive experimental testing, which is time-consuming, costly, and difficult to accurately control complex noise characteristics.

[0005] Furthermore, existing technologies lack in-depth analysis of tire groove design parameters, tire imprint patterns, and the relationship between noise during tread pitch optimization, failing to fully utilize the advantages of image processing technology, thus leaving significant room for improvement in tire noise reduction. Therefore, an innovative and efficient tread pitch optimization method is urgently needed to address these issues. Summary of the Invention

[0006] The technical problem to be solved by the present invention is that the existing technology lacks in-depth exploration of the relationship between tire groove design parameters, tire imprint and noise in the process of tread pitch optimization, and fails to make full use of the advantages of image processing technology, resulting in a large room for improvement in tire noise reduction effect. In order to solve the above problems, a low-noise tire tread pitch optimization method, equipment and medium based on image processing is provided.

[0007] The object of this invention is achieved in the following manner: A low-noise tire tread pitch optimization method based on image processing includes the following steps: S1. Initial pattern information processing steps: Obtain the initial tread pattern information of the tire, which includes the tire outer diameter, total number of pitches, and pitch design type, including equal pitch design and variable pitch design. If the initial tread pattern is an equal pitch design, the equal pitch length is calculated based on the total number of pitches and the tire outer diameter; if the initial tread pattern is a variable pitch design, the length of each type of pitch is calculated based on the set number of pitch types, the proportional relationship between each type of pitch, the number of each type of pitch, and the tire outer diameter. S2. Tire image feature extraction steps: S2.1 Extract the pitch boundaries and groove boundaries of each pattern pitch map, and mark them with different colors according to the groove depth; S2.2. Obtain a static tire imprint image, extract the imprint boundary, and use no less than 10 planar coordinate points to define the imprint boundary; S3. Parameter Input Steps: Input basic optimization parameters, which include at least the pitch length, total number of pitches, pattern depth, imprint boundary control point coordinates, and initial pitch sequence obtained from step S1; S4. Acoustic weighting processing steps: An A-weighted filter is used to frequency-weight the noise spectrum to simulate the characteristics of human hearing. S5. Noise spectrum calculation steps: Based on the basic optimization parameters input in step S3, the noise spectrum corresponding to the initial pitch design is calculated. S6. Spectrum peak optimization steps: While keeping the pitch ratio constant, the peak value of the noise spectrum obtained in step S5 is optimized within the preset harmonic frequency range. S7. Iterative optimization and output steps: Keeping the total number of pitches constant, adjust the pitch ratio and / or the number of different pitch types to generate new pitch length combinations, and repeat steps S1 to S6 for iterative calculation until the energy amplitude of the noise spectrum meets the preset optimization threshold. Then, output the corresponding optimal pitch sequence and the final noise spectrum diagram.

[0008] In step S1, when the initial pattern is an equal pitch design and the noise spectrum does not meet the requirements after optimization, the equal pitch design is converted into a variable pitch design for re-optimization.

[0009] In step S1, the variable pitch design is a three-variable pitch design, including long pitch L, medium pitch M and short pitch S, and satisfies the proportional relationship L / S>L / M>1. The number of each type of pitch is preferably a prime number that is different from each other.

[0010] In step S2.1, image processing software is used to extract the pitch boundary and groove boundary; in step S2.2, the Canny operator is used to perform edge detection on the tire imprint image to extract the imprint boundary.

[0011] In step S6, the preset harmonic frequency range is determined based on the fundamental frequency of the tire noise, and the formula for calculating the fundamental frequency f0 is: where, , where n is the number of effective contact patch blocks of the tire, v is the vehicle speed, and p is the tire tread pitch length.

[0012] In step S7, the preset optimization threshold is that the noise spectrum energy amplitude is ≤1.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the low-noise tire tread pitch optimization method.

[0014] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the aforementioned low-noise tire tread pitch optimization method.

[0015] The beneficial effects of this invention are as follows: This invention deeply integrates the geometric image features (pattern, imprint) of the tire with an acoustic physical model, making the pitch optimization process more data-driven and the results more scientific and accurate. Through digital image processing and automated iterative optimization, the reliance on physical prototype tires and the number of repeated tests are significantly reduced, significantly shortening the R&D cycle and lowering production costs. Through targeted optimization of the noise spectrum, noise energy can be effectively dispersed and reduced, especially unpleasant single-peak noise, thereby improving the overall quietness, comfort, and environmental performance of the tire. Attached Figure Description

[0016] Figure 1 This is a general technical roadmap for the low-noise tire tread pitch optimization method provided in the embodiments of the present invention.

[0017] Figure 2 This is a schematic diagram of groove boundary extraction and depth labeling in the pitch diagram.

[0018] Figure 3 A schematic diagram of tire imprint images and their boundary control points extraction.

[0019] Figure 4 This is a schematic diagram of the noise spectrum corresponding to the initial pitch design before optimization.

[0020] Figure 5 This is a schematic diagram of the noise spectrum corresponding to the pitch design optimized by the method of this invention.

[0021] Figure 6Example diagram of noise test results designed for the initial pitch.

[0022] Figure 7 Example diagram of noise test results for optimizing the pitch design. Detailed Implementation

[0023] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0024] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same technical meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0025] like Figures 1-7 As shown, this invention discloses a low-noise tire tread pitch optimization method based on image processing, comprising the following steps: S1. Initial pattern information processing steps: Obtain the initial tread pattern information of the tire, which includes the tire outer diameter, total number of pitches, and pitch design type, including equal pitch design and variable pitch design. If the initial tread pattern is an equal pitch design, the equal pitch length is calculated based on the total number of pitches and the tire outer diameter; if the initial tread pattern is a variable pitch design, the length of each type of pitch is calculated based on the set number of pitch types, the proportional relationship between each type of pitch, the number of each type of pitch, and the tire outer diameter. S2. Tire image feature extraction steps: S2.1 Extract the pitch boundaries and groove boundaries of each pattern pitch map, and mark them with different colors according to the groove depth; S2.2. Obtain a static tire imprint image, extract the imprint boundary, and use no less than 10 planar coordinate points to define the imprint boundary; S3. Parameter Input Steps: Input basic optimization parameters, which include at least the pitch length, total number of pitches, pattern depth, imprint boundary control point coordinates, and initial pitch sequence obtained from step S1; S4. Acoustic weighting processing steps: An A-weighted filter is used to frequency-weight the noise spectrum to simulate the characteristics of human hearing. S5. Noise spectrum calculation steps: Based on the basic optimization parameters input in step S3, the noise spectrum corresponding to the initial pitch design is calculated. S6. Spectrum peak optimization steps: While keeping the pitch ratio constant, the peak value of the noise spectrum obtained in step S5 is optimized within the preset harmonic frequency range. S7. Iterative optimization and output steps: Keeping the total number of pitches constant, adjust the pitch ratio and / or the number of different pitch types to generate new pitch length combinations, and repeat steps S1 to S6 for iterative calculation until the energy amplitude of the noise spectrum meets the preset optimization threshold. Then, output the corresponding optimal pitch sequence and the final noise spectrum diagram.

[0026] In step S1, when the initial pattern is an equal pitch design and the noise spectrum does not meet the requirements after optimization, the equal pitch design is converted into a variable pitch design for re-optimization.

[0027] In step S1, the variable pitch design is a three-variable pitch design, including long pitch L, medium pitch M and short pitch S, and satisfies the proportional relationship L / S>L / M>1. The number of each type of pitch is preferably a prime number that is different from each other.

[0028] In step S2.1, image processing software (such as MATLAB or Adobe Photoshop) is used to extract the pitch boundary and groove boundary; in step S2.2, the Canny operator is used to perform edge detection on the tire imprint image to extract the imprint boundary.

[0029] In step S6, the preset harmonic frequency range is determined based on the fundamental frequency of the tire noise, and the formula for calculating the fundamental frequency f0 is: where, , where n is the number of effective contact patch blocks of the tire, v is the vehicle speed, and p is the tire tread pitch length.

[0030] In step S7, the preset optimization threshold is that the noise spectrum energy amplitude is ≤1.

[0031] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the low-noise tire tread pitch optimization method.

[0032] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the aforementioned low-noise tire tread pitch optimization method.

[0033] Example: This invention provides a low-noise tire tread pitch optimization method based on image processing. By accurately analyzing tire surface image features, taking into account tire tracks, and combining acoustic principles, it achieves precise optimization of tread pitch, effectively reducing tire noise generated during driving, improving vehicle comfort, and simultaneously shortening tire development cycles and reducing production costs. The method includes the following steps: Step 1: Process the initial pattern information to obtain the initial pattern information; (1) When the initial tread pattern is of equal pitch, if the total number of pitches is n (constant), the pitch length is C, and the tire outer diameter is D (constant), the pitch length C can be obtained according to n*C=π*D. If the noise spectrum cannot meet the requirements of low-noise tires after traversing steps two to five, then the equal pitch needs to be optimized into a variable pitch design. Directly let C=M, traverse step one (2), and by controlling the pitch ratio and the number of pitches of each pitch, different pitch lengths can be obtained. Traverse steps two to five.

[0034] (2) When the initial tread pattern is variable pitch, define the tread pitch type, pitch ratio, and total number of pitches (constant) to obtain a single tread pitch diagram; assuming the tire outer diameter is D, then the tire tread pitch type is three variable pitches (L, M, S). If the pitch ratio of the long pitch to the short pitch of the tire tread pattern is L / S=1.12, then the pitch ratio of the long pitch to the middle pitch of the tread pattern is L / M=1.06; the number of long pitches, middle pitches, and short pitches of the tire tread pattern are X, Y, and Z respectively, where the selection of the number of pitches is Z>Y>X, preferably a prime number, then X+Y+Z=n (constant), then S*X+M*Y+L*Z=π*D, S*X+1.06S*Y+1.12S*Z=π*D. By changing the pitch ratio and setting the corresponding X, Y, Z, the pitch lengths of S, M, and L can be calculated respectively. Traverse steps two through five.

[0035] Step 2: 1) Based on image processing software, extract the pitch boundary and tread groove boundary of each tread pitch image, and use different RGB colors to define and distinguish the different groove depths of the tire tread. 2) By simulating the static state of the tire, the tire imprint image is obtained. Based on image processing software, the Canny operator is used to process the tire imprint image, extract the imprint boundary, and define the imprint boundary using planar coordinate points. The imprint boundary is controlled by ≥10 coordinate points. Step 3: Input basic parameters including: pattern pitch length, number of pitches, pattern depth, imprint boundary control points, and initial pitch sequence.

[0036] Step 4: Input the A-weighted filter. The frequency response of the A-weighted filter can be obtained using acoustic analysis software. For example, in Python, the `freqz` function in the `scipy.signal` module can be used. A-weighting is a frequency weighting function that simulates the human ear's sensitivity to different frequencies of sound. The A-weighted sound level is the sound pressure level measured after processing through a specific filter. This filter is designed to attenuate low and high frequencies while amplifying mid-frequency frequencies, making the measurement result closer to the actual sound intensity perceived by the human ear.

[0037] Step 5: Calculate the noise spectrum of the initial pitch.

[0038] Step Six: Keep the pitch ratio constant; pass through the harmonic range (assuming it is m). 0~ m1) Optimize the peak spectrum. If the basic formula for calculating the fundamental frequency of tire noise (applicable to tread noise) is: Where, f0: fundamental frequency of tire noise (in Hz); n: Number of effective contact tread blocks of the tire, i.e., the number of tread blocks that actually participate in contact within the tire contact imprint area; v: Vehicle speed (unit: km / h); p: Tire tread pitch (unit: m) but: , , Among them, f 1~ f2 is the peak range of the spectrum.

[0039] Step 7: Keep the total number of pitches unchanged, adjust the pitch ratio and the number of pitches of different pitch types to obtain a new set of pitch lengths different from the initial pitches. Repeat from Step 1 to Step 6. Currently, optimization is only stopped when the energy amplitude is ≤1. Output the corresponding pitch sequence and spectrum.

[0040] First, execute step S1. Take a tire with a target outer diameter of D as an example. If the initial design is equal pitch, with a total of n pitches, then the length of each pitch is C = πD / n. If subsequent evaluation shows the noise level is unsatisfactory, a variable pitch design is adopted. Assume three pitch types are used: long (L), medium (M), and short (S), with L / S = 1.12, L / M = 1.06, and the quantities X, Y, and Z satisfy Z > Y > X and are prime numbers, X + Y + Z = n. Using the formula S*X + 1.06S*Y + 1.12S*Z = πD, the specific lengths of S, M, and L can be solved.

[0041] Next, proceed to step S2. Open the tire tread CAD drawing using image processing software, automatically identify and extract the boundary lines of each groove, and mark areas of different depths with different colors (e.g., ...). Figure 2 (As shown). Simultaneously, static tire imprint images under standard load are acquired via a pressure sensor plate. The Canny edge detection algorithm is used to extract the imprint contour, and 11 key points are selected to record their planar coordinates (e.g., ...). Figure 3 (As shown).

[0042] Then, the parameters are integrated (step S3), and the A-weighted filter parameters are loaded (step S4). Based on the tire noise fundamental frequency formula f0 = (n * v) / (60 * p) and the relevant acoustic model, the initial noise spectrum is calculated (step S5), and its spectrum may show obvious peaks (e.g. Figure 4 (As shown).

[0043] Then, the optimization phase begins (step S6). A harmonic range is set (e.g., m0 to m1), and while keeping the current pitch ratio unchanged, the algorithm parameters are adjusted to reduce the spectral peaks within this range.

[0044] Finally, the process is iterated (step S7). The system automatically fine-tunes the pitch ratio (e.g., adjusting L / S from 1.12 to 1.15) or reallocates the combinations of X, Y, and Z values ​​to form a new scheme, and repeats the above calculation and evaluation process. This process continues until the energy amplitude of the output spectrum (e.g., ...) is reached. Figure 5 (As shown) compared to Figure 4 The value is significantly reduced and meets the threshold requirement of ≤1. The corresponding interval sequence at this point is the optimal solution.

[0045] To verify the effect, actual noise tests were conducted on the solutions before and after optimization. The test results show that the optimized solution (e.g., Figure 7 As shown, 70dB) is better than the initial scheme (e.g. Figure 6 As shown, the noise was reduced by 3dB (73dB), proving the effectiveness of this method.

[0046] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several changes and improvements without departing from the overall concept of the present invention, and these should also be considered within the scope of protection of the present invention.

Claims

1. A low-noise tire pattern pitch optimization method based on image processing, characterized in that: Includes the following steps: S1. Initial pattern information processing steps: Obtain the initial tread pattern information of the tire, which includes the tire outer diameter, total number of pitches, and pitch design type, including equal pitch design and variable pitch design. If the initial tread pattern is an equal pitch design, the equal pitch length is calculated based on the total number of pitches and the tire outer diameter; if the initial tread pattern is a variable pitch design, the length of each type of pitch is calculated based on the set number of pitch types, the proportional relationship between each type of pitch, the number of each type of pitch, and the tire outer diameter. S2. Tire image feature extraction steps: S2.1 Extract the pitch boundaries and groove boundaries of each pattern pitch map, and mark them with different colors according to the groove depth; S2.

2. Obtain a static tire imprint image, extract the imprint boundary, and use no less than 10 planar coordinate points to define the imprint boundary; S3. Parameter Input Steps: Input basic optimization parameters, which include at least the pitch length, total number of pitches, pattern depth, imprint boundary control point coordinates, and initial pitch sequence obtained from step S1; S4. Acoustic weighting processing steps: An A-weighted filter is used to frequency-weight the noise spectrum to simulate the characteristics of human hearing. S5. Noise spectrum calculation steps: Based on the basic optimization parameters input in step S3, the noise spectrum corresponding to the initial pitch design is calculated. S6. Spectrum peak optimization steps: While keeping the pitch ratio constant, the peak value of the noise spectrum obtained in step S5 is optimized within the preset harmonic frequency range. S7. Iterative optimization and output steps: Keeping the total number of pitches constant, adjust the pitch ratio and / or the number of different pitch types to generate new pitch length combinations, and repeat steps S1 to S6 for iterative calculation until the energy amplitude of the noise spectrum meets the preset optimization threshold. Then, output the corresponding optimal pitch sequence and the final noise spectrum diagram.

2. The image processing based low noise tire pattern pitch optimization method according to claim 1, characterized in that: In step S1, when the initial pattern is an equal pitch design and the noise spectrum does not meet the requirements after optimization, the equal pitch design is converted into a variable pitch design for re-optimization.

3. The image processing based low noise tire pattern pitch optimization method as claimed in claim 1, wherein: In step S1, the variable pitch design is a three-variable pitch design, including long pitch L, medium pitch M and short pitch S, and satisfies the proportional relationship L / S > L / M > 1. The number of each type of pitch is preferably a prime number that is different from each other.

4. The low-noise tire tread pitch optimization method based on image processing according to claim 1, characterized in that: In step S2.1, image processing software is used to extract the pitch boundary and groove boundary; in step S2.2, the Canny operator is used to perform edge detection on the tire imprint image to extract the imprint boundary.

5. The low-noise tire tread pitch optimization method based on image processing according to claim 1, characterized in that: In step S6, the preset harmonic frequency range is determined based on the fundamental frequency of the tire noise, and the formula for calculating the fundamental frequency f0 is: ， Where n is the number of effective contact patch blocks of the tire, v is the vehicle speed, and p is the tire tread pitch length.

6. The low-noise tire tread pitch optimization method based on image processing according to claim 1, characterized in that: In step S7, the preset optimization threshold is that the noise spectrum energy amplitude is ≤1.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the low-noise tire tread pitch optimization method as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the low-noise tire tread pitch optimization method as described in any one of claims 1 to 6.

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