A tire polishing curve fitting method

Abstract

The present application discloses a tire polishing curve fitting method, including the following specific steps: scanning data is subjected to mean linear filtering, and coordinate system conversion and processing, the scanning data is converted into polar coordinate representation, the pattern groove data is extracted, and further converted back to rectangular coordinates for maximum filtering to obtain approximate data of the tire surface without the pattern groove; on the basis of data preprocessing, the present application uses logical analysis method to summarize the law of traditional genetic algorithm, proposes an optimal state genetic algorithm, and applies it to tire laser scanning data processing, the optimal state genetic algorithm has the advantages of fast algorithm convergence speed, small calculation amount and the like in processing tire scanning data and fitting tire pattern groove curve, effectively improves the algorithm search speed and accuracy, and makes up for the deficiency of tire scanning algorithm theoretical research and practical application.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of tire polishing technology, in particular to a tire polishing curve fitting method. BACKGROUND

[0002] The problem of waste tire treatment is extremely important to the environment and human health. A large number of waste rubber is accumulated together, which not only occupies land, pollutes the environment, but also has the risk of inducing fire. For a long time, waste tire management has been a global problem. The main recycling methods of waste tires include retreading, recycling, heat energy utilization, and pyrolysis. Tire retreading is the preferred way of resource recycling of waste tires recognized internationally.

[0003] The main process of tire retreading is to polish, repair, tread fit, vulcanize and test the old tires that meet the retreading requirements. Tread forming is one of the tire retreading processes, which forms the retreaded tire after polishing. The key step is to process the data of laser scanning of old tires of various sizes. There have been a lot of research on tire retreading process and equipment improvement in the tire industry at home and abroad. There are also more research on tire structure, road friction, tire parameters, etc. However, there is little research on curve fitting algorithm after tire tread scanning. In the tire retreading process, it is found that the depth of the remaining groove in the tire laser scanning groove curve directly affects the quality and performance of the retreaded tire. An excellent tire groove curve fitting algorithm can improve the quality of the groove during tire retreading. Therefore, it is necessary to study the tire groove curve fitting algorithm.

[0004] Genetic algorithm is a search algorithm used to solve optimization problems in computational mathematics, which is a kind of evolutionary algorithm. Evolutionary algorithm is originally applied to data calculation and search field from some phenomena in evolutionary biology, including genetics, mutation, natural selection and hybridization, etc. The basic idea is to transform an optimization problem into a population of a certain number of candidate solutions, and make the population evolve to better solutions. Evolution starts from a population of completely random individuals, and then one generation after another. In each iteration, the fitness of the entire population is evaluated, a part of individuals are randomly selected from the current population based on their evaluation with a certain probability, and a new population with better evaluation is generated through natural selection and mutation, etc. The population in the next iteration of the algorithm becomes the current population, so that the individuals in the population tend to the global optimal solution.

[0005] At present, the manual adjustment method is used to fit the curve in the factory, which is low in efficiency and difficult to fit high-quality polishing curves. Therefore, a tire polishing curve fitting method is invented. SUMMARY

[0006] In view of the above and / or the problems existing in the prior art tire polishing curve fitting method, the present application is proposed.

[0007] Therefore, the purpose of the present application is to provide a tire polishing curve fitting method that can solve the above-mentioned problems existing in the prior art.

[0008] To solve the above technical problems, according to one aspect of the present application, the present application provides the following technical solutions:

[0009] A tire polishing curve fitting method, which comprises the following specific steps:

[0010] Step one, preprocessing of scanning data: the scanned data is subjected to mean linear filtering, and the coordinate system of the data is converted and processed, the scanning data is converted into polar coordinates, the groove data is extracted, and the approximate data of the tire surface without grooves is obtained by further converting back to rectangular coordinates and performing maximum filtering;

[0011] Step two, on the basis of data preprocessing, using genetic algorithm for curve fitting: fitting the first curve to approximate the basic form of the tire surface, taking the first curve as the reference, adding groove depth information to fit the second curve.

[0012] As a preferred scheme of the tire polishing curve fitting method according to the present application, wherein: the preprocessing of the scanning data comprises: mean linear filtering, groove data judgment and processing;

[0013] The mean linear filtering is specifically: using fixed-width mean linear filtering to filter the original data after scanning, using the mean value to replace each coordinate value in the original image, i.e. for the current coordinate point (x, y) to be processed, selecting a suitable linear filtering function, the coordinate value is composed of the linear combination of a plurality of coordinate values in the vicinity, assigning the mean value to the current coordinate point (x, y) as the coordinate value f(x) of the processed image at the point, i.e. Where num0 is the total number of pixels in the fixed-width range including the current pixel, and a(x) is a linear filtering function;

[0014] The groove data judgment and processing is specifically: in order to facilitate the judgment of groove information in the subsequent steps, converting the rectangular coordinates into polar coordinates, setting the rectangular coordinates of any point M on the plane as (x, y) and the polar coordinates as (p, θ), and the conversion formula is p 2 =x 2 +y 2, tanθ=y / x(x≠0), secondly, set pa as the average of the modulus of all data points contained in the data curve in the polar coordinate system, because different types of tires will cause differences in tire shape, which leads to that the groove cannot be accurately segmented and extracted based on a single pa parameter, therefore, according to the tire type, a corresponding deviation value e is introduced, set r as the sum of pa and e, and finally the groove position is determined according to the value of r, in order to obtain the fitting curve, the groove needs to be filled next to make the curve continuous and close to the tire surface shape, secondly, based on the conversion formula x=p*cosθ, y=p*sinθ, the polar coordinates are converted back to rectangular coordinates, and the maximum value filtering algorithm is used to further filter the data, that is, the maximum data value in the set axial tire width is used to replace the data value of all coordinates in this width, so as to further remove noise and obtain the envelope line data graph.

[0015] As a preferred scheme of the tire polishing curve fitting method, the groove position judgment method is: set a point q in the polar coordinate system, q x is the horizontal coordinate value of the rectangular coordinate system corresponding to the point q, based on the experience value of tire data, the groove width is estimated as w, if the polar coordinate modulus value of all coordinate points existing in the range with w / 2 as the radius near the point q is less than r, it can be determined that these coordinate points belong to the groove coordinate points, and it can be determined that the area in the range of {q x -w / 2, q x +w / 2} is the groove.

[0016] As a preferred scheme of the tire polishing curve fitting method, the step two specifically includes: parameter setting and evaluation function design, curve fitting based on optimal state genetic algorithm and curve symmetry processing.

[0017] As a preferred scheme of the tire polishing curve fitting method, the parameter setting and evaluation function design includes: parameter optimization, constraint of tangency between circular arcs, and curve fitting effect evaluation.

[0018] The parameter optimization specifically includes: based on genetic algorithm, by retaining the optimal individual and expanding the influence of the dominant individual, a more efficient optimal state genetic algorithm is proposed, the optimal state genetic algorithm needs to optimize the preset parameters according to experience, and the preset parameters to be optimized include:

[0019] The maximum population m, that is, the maximum number of individuals participating in the genetic algorithm optimization;

[0020] The number of curve segmentation n, that is, the number of circular arc segments of the fitting curve;

[0021] Random range b, namely the fluctuation range of gene position when random population individuals are generated;

[0022] Deletion probability v, namely the individual elimination rate;

[0023] Hybridization ratio c, namely the efficiency of hybridization to generate new individuals;

[0024] Optimal value mutation rate z, namely the mutation frequency of optimal individuals;

[0025] Optimal value reproduction number s, namely the speed of reproduction of optimal individuals;

[0026] Empirical setting coefficient k, namely the mutation and hybridization probabilities of genetic algorithm, and a too small k value will lead to a decrease in algorithm efficiency, and a higher k value will result in a higher strength of random mutation and hybridization;

[0027] Secondly, to improve the efficiency of the algorithm, the internal logical relationship of the data is constrained as follows:

[0028] b n ∝m;

[0029] z n ∝s;

[0030] S∝m-cv*k;

[0031] c*v∝m;

[0032] The constraint that the circular arcs are tangent to each other is that, according to the requirement that each two of the multiple circular arcs are tangent to each other, the radii of the adjacent two circular arcs are made collinear, and the process of fitting the circular arc of the entire curve is as follows:

[0033] Process one: selecting an initial center point, the initial center point is selected based on the positions A corresponding to the starting point and B corresponding to the ending point of the X-axis direction data, and the position C corresponding to the highest point in the Y-axis direction, and the specific principle is: selecting the image center line, that is, the mean value of the X coordinate values of the points A and B, and selecting a point I on the image center line such that the distance difference between I and the three points A, B and C is minimized, and the point I is the initial center point;

[0034] Process two: starting from the initial center point, a circular arc with a certain angle is drawn in a direction with a certain length of line segment as the radius;

[0035] Process three: returning to the center point of the new circular arc drawn in the previous step along the radius line corresponding to the new circular arc;

[0036] Process four: in the process of returning to the center point, the center point of the next circular arc is selected based on the genetic algorithm within the range specified by the algorithm, and the distance between the two center points is g i , and then the next circular arc is drawn with the ending point of the previous circular arc as the starting point;

[0037] Flow five: return to flow three, i plus 1, until the total arc angle is greater than the difference between the start and end angles of the original scan data graph, and the curve composed of the tangent arc and the corresponding array g of the center distance of the adjacent arc of one dimension corresponding to the arc ends drawing the arc i , i = 0 ~ n-1, g i The value range of g

[0038] The curve fitting effect evaluation is specifically: genetic algorithm is a powerful evolutionary algorithm, and its evolution direction depends on the selection of the environment, which is abstracted into the effect evaluation of its results. In order to make the optimization direction of genetic algorithm consistent with the expected effect, it is necessary to evaluate the degree of each fitting curve approaching the expected effect and give a score as the standard of natural selection, wherein the fitting curve ① and the curve ② are needed;

[0039] The effect evaluation function of curve ① is shown in formula 1:

[0040]

[0041] In the formula, i represents the ith arc, and the function is composed of two parts. One part is Its meaning is the average value of the distance h1(i) between the same side endpoint of each arc and the nearest point on the envelope line, and the envelope line is the maximum value filtered curve. h1(i) is the distance between the side endpoint of the ith arc and the nearest point on the envelope line. The role of this part is to fit the tire surface shape. The other part is Its meaning is the average value of the distance h2(i) between the center point corresponding to each arc and the initial center point. h2(i) is the distance between the center point corresponding to the ith arc and the initial center point. The role of this part is to keep the shape of the whole curve ① approximately as an arc. k2 is a weight coefficient, which can be set according to experience, and its role is to ensure that the scoring proportion is reasonable;

[0042] The effect evaluation function of curve ② is shown in formula 2:

[0043]

[0044] In the formula, i represents the ith arc, and the function is composed of three parts. The first part is The meaning is the average of the absolute distance h3(i) of the nearest coordinates on the new curve obtained by translating curve 1 downward by a certain distance, h3(i) being the absolute distance of the nearest coordinates on the new curve obtained by translating curve 1 downward by a certain distance, wherein the downward translation distance is calculated according to the average groove depth and the preset reserved groove depth, the average groove depth being the average of the coordinate values of the deepest points of all grooves in the rectangular coordinate system, and the purpose of this part is to make curve 2, i.e., the grinding curve, consistent with the original shape of the tire tread, and the second part is The meaning is the average of the distance h4(i) from the center point of each segment of curve 2 to the initial center point, h4(i) being the distance from the center point of the i-th segment of curve 2 to the initial center point, and the purpose of this part is to make the shape of the entire curve 2 approximate to an arc, and the third part is Wherein, d(j) is the distance between a preset depth coordinate point of a certain groove and a point j on curve 2 within a certain range, the preset depth coordinate point of the groove being a coordinate point obtained by translating the groove bottom position determined based on the algorithm upward by a preset groove depth, and a groove may correspond to multiple points on curve 2, if a preset depth coordinate point of a certain groove does not contain a point on curve 2 within a certain range, the nearest point on curve 2 is taken, The meaning is the average of the distance d(j) between all preset depth coordinate points of grooves and all coordinate points on curve 2 within a certain distance range, there being a total of num points, and the purpose of this part is to constrain the tire after grinding to be able to retain a preset groove depth, and k3 and k4 are weight coefficients set according to experience, and their role is to ensure the proportionality of the evaluation.

[0045] As a preferred scheme of the tire grinding curve fitting method, the curve fitting based on the optimal state genetic algorithm is specifically: the curve fitting based on the genetic algorithm is to correspond each arc shape to a determined one-dimensional array, regard each array as a separate individual, and perform gene coding, based on the genetic algorithm, if n is the curve segmentation number, b is the range of gene change, m is the maximum population number, and v is the coefficient for determining the elimination strength, a random number sequence is generated within the range b according to the curve segmentation number n until the maximum number m, the random number sequence is taken as the input to fit the curve, and the score is calculated according to the evaluation function, the sorting is performed according to the score, and the elimination is performed according to the probability calculated based on v, after the set remaining population number, the cross, mutation, and optimal mutation are sequentially performed, the above process is cycled until the maximum cycle number is reached, and the result is output;

[0046] The curve fitting based on the optimal state genetic algorithm includes gene coding, population reproduction, natural selection, cross reproduction and half grafting, self-replication and mutation of the optimal state.

[0047] The gene coding is specifically: first, the chromosome coding is carried out, and the coding mode is G = {g i , g2,..., g n}, wherein the value of the gene site g i ,i=0~n-1 is the distance between the center of the i-th circular arc and the center of the i+1-th circular arc, so that the chromosome has n gene sites, n is the number of the final fitting curve circular arc segmentation, with the increase of the segmentation number of the curve setting, the number of the chromosome gene sites increases, b is the random change range of the chromosome gene site in the genetic process, so that the value range of each gene site g i is b.

[0048] The population breeding is specifically: at the beginning of each round of breeding, a plurality of new chromosomes are added to the population as newborn individuals, which is a one-dimensional random array with a data range of b and a length of n, and the number of chromosomes in the population is m.

[0049] The natural selection is specifically: the optimal state genetic algorithm is adopted, specifically, the elimination rate v is set according to the individual score, the elimination probability is used for elimination, the higher the score, the smaller the elimination probability, wherein the elimination rate of the optimal individual is 0, that is, it is unconditionally reserved;

[0050] The cross breeding and half grafting are specifically: the dominant gene fragment can improve the overall evaluation value, the combination of the dominant genes of the parents can produce better individuals through cross breeding, a hybridization ratio c is set as the selection probability to extract a certain number of individuals from the population to exchange gene fragments, in addition, because the two halves of the gene correspond to the curves fitted from the center to both sides, the total score can be regarded as the sum of the two part scores, so one half of the gene can be kept unchanged, and the other half of the gene of different chromosomes can be combined, finally the more dominant overall gene is obtained, the half grafting is realized, and the optimal individual composed of the two halves of the optimal gene combination is obtained;

[0051] The optimal state self-replication and mutation are specifically: the reservation, self-replication and mutation of the optimal individual are improvements made to the traditional genetic algorithm, the optimal individual is reserved and put back into the population at the end of each iteration, which can ensure that the optimal value of each iteration is monotonically non-decreasing, and the new individual generated by random mutation and replication centered on the optimal value is put into the population.

[0052] As a preferred scheme of the tire polishing curve fitting method, wherein: the curve symmetry processing is specifically: the symmetry is realized by further processing the curve obtained after all rounds of operation based on the genetic algorithm, that is, the left half or the right half of the finally obtained fitting curve is taken, and the whole fitting curve is obtained by mirror symmetry along the radial line of the axial center.

[0053] As a preferred scheme of the tire polishing curve fitting method, the symmetry processing is that the circular arc curve is constrained to be left-right symmetric with the center position in the tire axial direction during the operation of the genetic algorithm, that is, the gene value sequence in G is even symmetric, and a symmetric tire polishing curve is obtained.

[0054] Compared with the prior art, the tire polishing curve fitting method based on the genetic algorithm is proposed, which can automatically adjust and adapt to various tire specifications, and has obvious advantages in accuracy and adaptability.

[0055] The tire polishing curve fitting method based on the genetic algorithm is proposed, which can automatically adjust and adapt to various tire specifications, and has obvious advantages in accuracy and adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The laser scanning original data curve of the application is shown in the figure;

[0057] Figure 2 The target fitting curve of the application is shown in the figure;

[0058] Figure 3 The mean linear filtering output curve of the application is shown in the figure;

[0059] Figure 4 The data curve converted from rectangular coordinates to polar coordinates of the application is shown in the figure;

[0060] Figure 5 The curve of the application for removing the groove is shown in the figure;

[0061] Figure 6 The maximum value filtering curve of the application is shown in the figure

[0062] Figure 7 The two-by-two tangent graph of the multi-segment circular arc curve of the application is shown in the figure;

[0063] Figure 8 The tire surface fitting curve of the application is shown in the figure;

[0064] Figure 9 The tire groove bottom fitting curve of the application is shown in the figure;

[0065] Figure 10 The algorithm error comparison graph of the application is shown in the figure;

[0066] Figure 11 For the algorithm power consumption comparison chart of the present application. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solutions and advantages of the present application clearer, the embodiments of the present application will be described in further detail below with reference to the drawings.

[0068] In the tire retreading process, the tire needs to be placed into a scanning instrument for laser scanning of the tire surface to obtain scanning data of the tire surface. The tire tread scanning data is saved in a CSV file, the first column is X-axis coordinate data, the second column is Y-axis coordinate data, the X-axis is the axial width of the tire, the Y-axis is the radial height of the tire, and the data amount is about 7500 rows, and the number of rows will be different according to different tire widths. Figure 1 The original data of the surface pattern shown in the actual application contains the pattern groove and the burr introduced due to noise and other errors.

[0069] For the original scanning data as shown in Figure 1 , a certain data processing algorithm needs to be used for processing and fitting to form a grinding curve, and the grinding machine is controlled based on the grinding curve to grind the tire surface. In actual application, there are specific requirements for grinding depth and surface roughness, etc., so there are strict requirements for the fitting of the grinding curve. Specifically:

[0070] (1) Two curves need to be fitted. The first curve is a tire surface curve fitted by removing the pattern groove data and possible abnormal points based on the original curve of the scanning data, as shown in curve ① in Figure 2 ; the second curve is a curve fitted according to the retained pattern groove depth, i.e. the length measured from the bottom of the pattern groove upwards, and the information of all pattern groove bottoms, as shown in curve ② in Figure 2 , this curve can make the grinding according to the set pattern groove depth, and the curve obtained after grinding can keep all the pattern groove depths consistent.

[0071] (2) The grinding curve requires multiple circular arc curves, and the adjacent circular arcs require tangency, so the algorithm needs to give the starting point, ending point, center coordinates and arc radius information of each circular arc.

[0072] Example 1:

[0073] The present application provides a tire grinding curve fitting method, please refer to Figures 1-11 , including the following specific steps:

[0074] Step one, pre-processing of scanning data: the scanning data is linearly filtered by mean value, and the coordinate system of data is converted and processed, the scanning data is converted into polar coordinate representation, the pattern groove data is extracted, and the approximate data of the tire surface without pattern groove is obtained by further converting back to rectangular coordinate and maximum filtering;

[0075] Step two, on the basis of data pre-processing, using genetic algorithm for curve fitting: the first curve is fitted to approximate the basic form of tire surface, and the second curve is fitted by adding the pattern groove depth information based on the first curve.

[0076] The pre-processing of scanning data includes: mean linear filtering, pattern groove data judgment and processing;

[0077] The mean linear filtering is specifically: the original data after scanning is filtered by fixed width mean linear filtering, and each coordinate value in the original image is replaced by mean value, that is, for the current coordinate point (x, y) to be processed, a suitable linear filtering function is selected, the coordinate value is composed of linear combination of a number of neighboring coordinate values, the mean value is assigned to the current coordinate point (x, y) as the coordinate value f(x) of the processed image at the point, that is Where num0 is the total number of pixels in the fixed width range including the current pixel, a(x) is the linear filtering function, and the filtering effect is as shown in Figure 3

[0078] The pattern groove data judgment and processing is specifically: in order to facilitate the judgment of pattern groove information in the subsequent steps, the rectangular coordinate is converted into polar coordinate form, assuming that the rectangular coordinate of any point M on the plane is (x, y), and the polar coordinate is (p, θ), the conversion formula is p 2 = x 2 +y 2 , tanθy / x(x≠0), Figure 3 The curve shown in Figure 4 ​As shown in the curve, secondly, let pa be the mean of the modulus of all data points contained in the data curve in the polar coordinate system, and different types of tires will cause differences in tire shape, which leads to that the groove and the tread cannot be accurately segmented based on a single pa parameter, so the corresponding deviation value e is introduced according to the tire type, let r be the sum of pa and e, and finally the groove position is determined according to the value of r, in order to obtain the fitting curve, the groove needs to be filled next so that the curve is continuous and approximates the tire surface shape, the way adopted in the present application is to simply use the coordinate mean of several data points at the edge of the groove to replace the coordinate values of the data in the groove, experiments show that the specific number of data points taken at the edge of the groove, and whether it is taken from the left edge or the right edge has little effect on the result, secondly, based on the conversion formula x=p*cos theta, y=p*sin theta, the polar coordinates are converted back to rectangular coordinates, as shown in Figure 5 , and the maximum value filtering algorithm is further used to filter the data in Figure 5 , that is, the maximum data value within the set axial tire width is used to replace the data values of all coordinates in this width, so as to further remove noise and obtain the envelope line data graph, in actual implementation, the width obtained is 6 data points, experiments show that the change in width in a small range has little effect on the result, and the data obtained after maximum value filtering is as shown in Figure 6 .

[0079] The judgment method of the groove position is: let q be any point in the polar coordinate system, q x is the horizontal coordinate value of the rectangular coordinate system corresponding to the q point, based on the experience value of the tire data, the groove width is estimated to be w, if the polar coordinate modulus values of all coordinate points existing in the range with w / 2 as the radius near the q point are all less than r, it can be determined that these coordinate points belong to the coordinate points of the groove, and it can also be determined that the area within the range of {q x -w / 2, q x +w / 2} is the groove.

[0080] Step two specifically includes: parameter setting and evaluation function design, curve fitting based on optimal state genetic algorithm and curve symmetry processing. Among them, the genetic algorithm is a search algorithm used to solve optimization problems in computational mathematics, which is a kind of evolutionary algorithm. Evolutionary algorithm is originally applied to data calculation and search field from some phenomena in evolutionary biology, including genetic, mutation, natural selection and hybridization. Curve symmetry processing: experiments show that the curve obtained based on the genetic algorithm of the present application is almost perfectly symmetrical to the left and right of the center position of the tire axis, but due to the error of the processing technology of the scanned tire and the influence of the noise of the laser scanning data, it is difficult to achieve perfect symmetry based on the data curve fitting theory. In view of the symmetry requirement of tire manufacturing, the present application proposes two symmetry methods.

[0081] Parameter setting and evaluation function design includes: parameter optimization, tangent constraint between circular arcs, curve fitting effect evaluation;

[0082] Parameter optimization is specifically: based on genetic algorithm, by retaining the optimal individual and expanding the influence of dominant individuals, a more efficient optimal state genetic algorithm is proposed, the optimal state genetic algorithm needs to optimize the preset parameters according to experience, and the preset parameters to be optimized include:

[0083] The maximum population m, that is, the maximum number of individuals participating in the genetic algorithm optimization, the number set by the application is 150;

[0084] The number of curve segmentation n, that is, the number of circular arc segments of the fitting curve, the number set by the application is 16;

[0085] Random range b, that is, the gene bit fluctuation range when the random population individual is generated, the experience value of the application is 200;

[0086] The deletion probability v, that is, the individual elimination rate, the application is set to 0.5;

[0087] The hybridization ratio c, that is, the efficiency of hybridization to generate new individuals, the number set by the application is 0.2;

[0088] The optimal value mutation rate z, that is, the optimal individual mutation frequency, the number set by the application is 0.9;

[0089] The optimal value breeding number s, that is, the speed of optimal individual breeding, the number set by the application is 30;

[0090] The experience setting coefficient k, that is, the mutation and hybridization probability of genetic algorithm, too small k value will lead to reduced algorithm efficiency, the higher the k value, the higher the random mutation and hybridization strength, the number set by the application is 0.8;

[0091] Secondly, in order to improve the algorithm efficiency, the internal logical relationship of the data is constrained as follows:

[0092] b n ∝m;

[0093] z n ∝s;

[0094] S∝m-cv*k;

[0095] c*v∝m;

[0096] The tangent constraint between circular arcs is specifically: according to the requirement that each two of the multiple circular arcs are tangent to each other, the adjacent two circular arc radii are made to be collinear to realize the process of fitting the whole curve circular arc problem:

[0097] Process one: select the initial center point, the initial center point is selected based on the position A corresponding to the starting point of the X-axis direction data, the position B corresponding to the ending point, and the position C corresponding to the highest point in the Y-axis direction. The specific principle is: select the image center line, that is, the mean value of the X coordinate values of points A and B. Select a point I on the image center line, so that the distance difference between I and A, B, and C is minimized. The point I is the initial center point;

[0098] Process two: start with the initial center point, draw a circular arc with a certain length of line segment as the radius in a certain direction;

[0099] Process three: return to the center point of the new circular arc in the direction of the radius line corresponding to the circular arc;

[0100] Process four: select the center point of the next circular arc based on the genetic algorithm within the range specified by the algorithm during the return to the center point. The distance between the two center points is g i Then draw the next circular arc with the endpoint of the previous circular arc as the starting point;

[0101] Process five: return to process three, i + 1, until the total angle of the circular arcs is greater than the difference between the start and end angles of the original scan data graph. The resulting multiple tangent circular arcs are shown in Figure 7 The curve composed of tangent circular arcs and the corresponding one-dimensional center distance array g i , i = 0 ~ n-1, g i The value range of g

[0102] The curve fitting effect evaluation is as follows: genetic algorithm is a powerful evolutionary algorithm, and its evolution direction depends on the selection of the environment. Abstracted to the algorithm, it is the effect evaluation of its results. To make the genetic algorithm optimization direction consistent with the expected effect, the degree of approximation of each fitting curve to the expected effect needs to be evaluated and scored as a standard for natural selection. Among them, the curves ① and ② need to be fitted.

[0103] The effect evaluation function of curve ① is shown in formula 1:

[0104]

[0105] In the formula, i represents the i-th circular arc, and the function is composed of two parts. One part is The meaning is the mean value of the distance h1(i) between the same side endpoints of each circular arc and the coordinates of the nearest point on the envelope line. The envelope line is Figure 6The maximum value shown after filtering, h1(i) is the distance between the side end point of the i-th circular arc and the nearest point on the envelope curve, this part is used to fit the tire surface shape, and the other part is The meaning is the average of the distance h2(i) between the center point of each circular arc and the initial center point, h2(i) is the distance between the center point of the i-th circular arc and the initial center point, and the purpose of this part is to keep the shape of the whole curve ① as a circular arc, k2 is a weight coefficient, which can be set according to experience, and the purpose is to ensure the proportion of scoring is reasonable, and in the present application, the value is 0.4;

[0106] The effect evaluation function of curve ② is shown in formula 2:

[0107]

[0108] In the formula, i represents the i-th circular arc, and the function is added by three parts, the first part is The meaning is the average of the absolute distance h3(i) between the same side end point of each circular arc and the nearest coordinate on the new curve obtained by translating curve ① downward by a certain distance, h3(i) is the absolute distance between the same side end point of the i-th circular arc and the nearest coordinate on the new curve obtained by translating curve ① downward by a certain distance, wherein the distance of downward translation is calculated according to the average depth of the pattern groove and the preset reserved pattern groove depth, the average depth of the pattern groove refers to the average of the coordinate values of the deepest part of all pattern grooves in the rectangular coordinate system, and the purpose of this part is to make the curve ②, i.e. the grinding curve, consistent with the original shape of the tire tread, the second part is The meaning is the average of the distance h4(i) between the center point of each circular arc of curve ② and the initial center point, h4(i) is the distance between the center point of the i-th circular arc and the initial center point, and the purpose of this part is to make the shape of the whole curve ② as a circular arc, and the third part is Wherein, d(j) is the distance between a preset depth coordinate point of a certain pattern groove and a point j on a certain curve ② within a certain range, and in the present algorithm, the range is set as a circle with a radius of 10 mm, the preset depth coordinate point of the pattern groove is obtained by translating the position of the bottom of the pattern groove as judged based on the algorithm upward by a preset pattern groove depth, one pattern groove may correspond to multiple points on the curve ②, if the preset depth coordinate point of a certain pattern groove does not contain the point on the curve ② within the set range, the nearest point on the curve ② is taken, The meaning is the average of the distance d(j) between all preset depth coordinate points of the pattern grooves and all coordinate points on the curve ② within a certain distance range of each, and there are a total of num points, and the purpose of this part is to constrain the tire after grinding to be able to reserve the preset pattern groove depth, and k3 and k4 are weight coefficients set according to experience, and the purpose is to ensure the proportionality of the judgment, and in the present application, the values are 0.4 respectively.

[0109] The curve fitting based on the optimal state genetic algorithm is specifically: the curve fitting based on the genetic algorithm is that each arc shape is corresponded to a determined one-dimensional array, each array is regarded as a single individual, and gene coding is carried out, based on the idea of genetic algorithm, if n is the curve segmentation number, b is the range of gene change, m is the maximum population number, v is the coefficient for determining the elimination intensity, according to the curve segmentation number n, a random number sequence is generated in the range b until the maximum number m, the random number sequence is used as input to fit the curve, and scoring is carried out according to the evaluation function, the scoring is sorted, and elimination is carried out according to the probability calculated based on v, after the set remaining population number, cross, mutation, optimal mutation are carried out in turn, the above process is cycled until the maximum cycle number is reached, and the result is output;

[0110] In the application, the optimal state genetic algorithm is proposed, and a selection algorithm for sorting individuals according to individual evaluation in a population is used to replace the roulette method of the traditional genetic algorithm. The roulette method may lead to insufficient population competitiveness due to small differences in individual fitness, and the advantage of the application is that the optimal value is retained each time iteration, and mutation and self-reproduction are carried out in the optimal value state.

[0111] The curve fitting based on the optimal state genetic algorithm includes: gene coding, population reproduction, natural selection, cross breeding and half grafting, self-replication and mutation of the optimal state;

[0112] The gene coding is specifically: first, chromosome coding is carried out, the coding mode is G={g i , g2,..., g n}, wherein the value of the gene site g i =0~n-1 is the distance between the center of the ith arc and the center of the i+1th arc, so the chromosome has n gene sites, n is the number of arc segmentation of the final fitting curve, as the number of segmentation of the curve set increases, the number of chromosome gene sites increases, b is the random change range of the chromosome gene site in the genetic process, so the value range of each gene site g i is

[0113] The population reproduction is specifically: at the beginning of each round of reproduction, a number of new chromosomes are added to the population as newborn individuals, which is a one-dimensional random array with a gene site data range of b and a length of n, the number of chromosomes in the population is m;

[0114] The natural selection is specifically: the optimal state genetic algorithm is adopted, specifically, the elimination rate v is set according to the individual score, the elimination probability is used for elimination, the higher the score, the smaller the elimination probability, wherein the elimination rate of the optimal individual is 0, that is, the optimal individual is unconditionally retained;

[0115] The analysis and basis of the optimal state genetic algorithm lie in that if random individuals are regarded as points on a hyperplane, each individual has a corresponding evaluation value, and since the evaluation functions S1 and S2 are continuous, it can be inferred that the evaluation distribution function is also continuous, and therefore there must be values close to the optimal distribution in the neighborhood of the optimal value distribution, and the local optimal value in the iteration process is probably close to the global optimal value, so that reserving the optimal value and performing mutation and self-reproduction based on the optimal value state can effectively improve the search efficiency and accuracy of the algorithm.

[0116] The cross breeding and half grafting specifically refer to that the dominant gene fragments can improve the overall evaluation value, the parent dominant gene combination can produce more excellent individuals through cross breeding, a certain number of individuals are extracted from the population as selection probability to exchange gene fragments, and in addition, since the two halves of the gene correspond to the curves fitted from the center to the two sides, the total score can be regarded as the sum of two partial scores, therefore, one side gene can be kept unchanged to combine different half genes of the chromosomes, finally, more dominant overall genes are obtained, the half grafting is realized, and the optimal individual composed of the two half optimal gene combinations is obtained.

[0117] The self-reproduction and mutation of the optimal state specifically refer to that the reservation, self-reproduction and mutation of the optimal individual are improvements made to the traditional genetic algorithm, the optimal individual is reserved and self-reproduced and mutated at the center of the optimal value at the end of each iteration, which can ensure that the optimal value of each iteration is monotonically non-decreasing, and the new individual generated by random mutation and reproduction at the center of the optimal value is put into the population, experiments show that the search based on the local optimal value has a higher global optimal value coverage probability than the search based on the general random value, and forms an effect similar to the random gradient ascent on the hyperplane function, which can greatly improve the efficiency and accuracy of searching for the global optimal individual.

[0118] The curve symmetry processing specifically refers to that the symmetry is realized by further processing the curve obtained after all rounds of running based on the genetic algorithm, that is, the left half or right half of the obtained fitting curve is taken, and the whole fitting curve is obtained by mirror symmetry along the radial line of the axial center, for example, if the left half of the circular arc is taken, the right half of the circular arc can be obtained by radial symmetry of the center.

[0119] Example 2

[0120] The present application provides a tire polishing curve fitting method, and the curve symmetry processing specifically refers to that the circular arc curve is constrained to be left-right symmetric with the center position of the tire axis during the running of the genetic algorithm, that is, the digital sequence composed of the gene values in G is constrained to be even symmetric, and the symmetric tire polishing curve is obtained.

[0121] Experiments show that the effects of the two symmetry processing methods are almost the same.

[0122] In summary, the Figure 8 The curve ① shown in the figure is a tire surface fitting curve composed of multiple circular arcs after the genetic algorithm of the present application runs for 1200 rounds. Compared with the curve after mean filtering, it is found that the curve ① can perfectly fit the surface of the tire scanning data.

[0123] As described in the algorithm, the curve ① can provide reference data for the curve ②. Figure 9 The curve ② shown in the figure is a special case of the curve ② after the genetic algorithm of the present application runs for 1200 rounds, with the preset depth of the groove being zero. As shown in the figure, the curve ② composed of multiple circular arcs with radius lines as boundaries can perfectly fit the bottom of the tire groove under the condition of keeping similar to the shape of the tire surface.

[0124] The optimal state genetic algorithm used in the present application can fit the curve with high quality, meet the requirements of industrial control, and has good adaptability to various types and specifications of tires. For example, Figure 10 As shown in the figure, the optimal state genetic algorithm used in the present application is compared with the traditional genetic algorithm and the general curve fitting algorithm. The adjustment based on artificial is a method commonly used in current factories. Limited by the resolution ability of human beings, it is difficult to obtain a high-quality fitting curve based on artificial adjustment. The experiment shows that the limit of artificial adjustment is equivalent to the effect of the traditional genetic algorithm running for about 400 rounds. The traditional genetic algorithm and the optimal state genetic algorithm have similar effects within the initial dozens of rounds of operation. The experiment shows that both the optimal state genetic algorithm and the traditional genetic algorithm can approach the global optimal solution. The subsequent operation shows that the traditional genetic algorithm has a low speed of approaching the global optimal solution in the iteration process due to its roulette characteristic, and it is easy to lose the local optimal value, resulting in curve fluctuation. In comparison, the optimal state genetic algorithm of the present application has a significantly improved speed of approaching the global optimal solution in the iteration process. The operation error, i.e., the score based on the evaluation function, shows a downward trend, indicating that it has a faster convergence speed and higher operation efficiency.

[0125] To further verify the superiority of the optimal state genetic algorithm, the power consumption of the traditional genetic algorithm and the optimal state genetic algorithm is compared under the same evaluation function score. The power consumption measurement standard used is the international power consumption measurement standard Hashrate. As shown in the figure, Figure 11 As shown in the figure, under the same evaluation score, with the increase of the gene position, the power required by the traditional genetic algorithm increases by several times, while the power required by the optimal state genetic algorithm is nearly linear with the number of gene positions. The experiment shows that the optimal state genetic algorithm has been greatly improved in terms of power requirement, and has industrial application value.

[0126] Although the present application has been described with reference to the embodiments above, various changes and modifications can be suggested to one skilled in the art, and it is intended that the present application encompass such changes and modifications as fall within the scope of the appended claims. Particularly, each feature disclosed in the description and / or the claims can be used in the combination with each of the features disclosed in the description and / or the claims, unless specifically stated otherwise. Therefore, the present application is not intended to be limited to the particular embodiments disclosed in the description and / or the claims.

Claims

1. A tire sipe curve fitting method, characterized by, The specific steps include: Step one, pre-processing of scanning data: the scanned data is linearly filtered by mean value, and the coordinate system of the data is converted and processed, the scanning data is converted into polar coordinates, the pattern groove data is extracted, and the approximate data of the tire surface without pattern groove is obtained by further converting back to rectangular coordinates and maximum value filtering; Step two, on the basis of data preprocessing, using genetic algorithm for curve fitting: fitting the first curve, approximating the basic form of the tire surface, taking the first curve as the reference, adding the pattern groove depth information to obtain the second curve; Parameter setting and evaluation function design, curve fitting based on optimal state genetic algorithm and curve symmetry processing; The curve fitting based on optimal state genetic algorithm is specifically: in the iteration process of genetic algorithm, the optimal individual is retained in each round, and is put back into the population at the end of each iteration, so that the optimal value of each iteration is monotonically non-decreasing, and the new individual generated by random mutation and replication with the optimal value as the center is put into the population; The curve symmetry processing is specifically: through further processing of the curve obtained after all rounds of genetic algorithm running, the symmetry is realized, that is, the left half or right half of the finally obtained fitting curve is taken, and the whole fitting curve is obtained by mirror symmetry along the radial line of the axial center; The curve symmetry processing is specifically: by constraining the circular arc curve in the process of genetic algorithm running to be left-right symmetric with the center position of the tire axial, that is, the digital sequence composed of the gene values in G is even symmetric, and the symmetric tire polishing curve is obtained.

2. A tire sipe curve fitting method according to claim 1, wherein, The pre-processing of scanning data includes: mean value linear filtering, pattern groove data judgment and processing.

3. A tire sipe curve fitting method according to claim 2, wherein The specific steps of the pattern groove data judgment and processing are as follows: Step one, on the basis of mean value linear filtering of the curve, the rectangular coordinates are converted into polar coordinates, the mean value of the modulus of all data points contained in the data curve in the polar coordinate system is calculated, and the mean value of the modulus is corrected according to the tire model, and the pattern groove position is judged according to the corrected mean value of the modulus; Step two, fill the pattern groove, so that the curve is continuous and approximates the tire surface form, convert the polar coordinates back to rectangular coordinates, and further filter the data using the maximum value filtering algorithm to further remove noise and obtain the envelope line data graph.

4. A tire sipe curve fitting method according to claim 3, wherein The pattern groove position judgment method in step one is specifically: based on the empirical value of tire data, the pattern groove width in rectangular coordinate system is estimated, if the modulus values of all points within the width in rectangular coordinate system are less than the corrected mean value of the modulus, it can be determined that these coordinate points belong to the coordinate points of the pattern groove.

5. A tire sipe curve fitting method according to claim 1 wherein, The parameter setting and evaluation function design includes: parameter optimization, constraint of tangency between circular arcs, and curve fitting effect evaluation.

6. A tire sipe curve fitting method according to claim 5 wherein, The constraint of tangency between circular arcs is specifically: the tangency between adjacent circular arcs is realized by making the radii of adjacent two circular arcs collinear, and the whole curve circular arc drawing process of a round is specifically: Process one: select the initial center point; Process two: draw a circular arc with a certain angle in a certain direction with the starting center point as the starting point and a certain length of line segment as the radius; Process three: return to the end point of the new arc drawn, along the radius of the arc corresponding to the center point of the direction of the arc; Flow four: in the process of returning to the center point of the circle, according to the selection range of the center of the circle specified by the algorithm, the center point of the next arc is selected based on the genetic algorithm, and the distance between the two center points is the gene position g of the chromosome in the genetic algorithm i Then draw the next arc with the endpoint of the previous arc as the starting point; Process five: return to process three until the total arc angle is greater than the difference between the start and end angles of the original scan data graph, and then end a round of arc drawing.

7. A tire sipe curve fitting method according to claim 6 wherein, The initial center point selection method in the process one is: taking the position A corresponding to the start point of the X-axis direction data, the position B corresponding to the end point, and the position C corresponding to the highest point on the Y-axis direction as the reference, and the specific principle is: selecting the image center line, that is, the mean value of the X coordinate values of the A point and the B point, selecting a point I on the image center line, and making the distance difference between I and A, B, and C three points minimum, and the I point is the initial center point.

8. A tire sipe curve fitting method according to claim 5 wherein, The curve fitting effect evaluation is specifically: fitting two curves: curve ① and curve ②; The effect evaluation function of curve ① is shown in formula 1: In the formula, i represents the i-th circular arc, n is the number of arc segments, the function is added by two parts, one part is is the distance between the side end point of the i-th circular arc and the nearest point on the envelope data line, and the other part is is the distance from the center point corresponding to the i-th circular arc to the initial center point, and k2 is a weight coefficient which can be set according to experience; The effect evaluation function of curve ② is shown in formula 2: In the formula, i represents the i-th circular arc, n is the number of arc segmentation, the function is added by three parts, the first part is , the second part is , and the third part is , wherein d(j) is the distance between a preset depth coordinate point of a certain pattern groove and a point j on a certain curve ② within a certain range, the total number of the point j on the curve ② is recorded as num, and k3 and k4 are weight coefficients set according to experience.

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