Method and device for optimizing tire pitch, non-transitory storage medium

Abstract

The application discloses a method and device for optimizing tire pitch, and a nonvolatile storage medium. The method comprises the following steps: obtaining tire specification information and an optimization task type, wherein the tire specification information comprises related information of the pitch; generating a plurality of initial pitch sequences according to the tire specification information, and generating a first round of iteration population according to the plurality of initial pitch sequences; performing an optimization strategy corresponding to the optimization task type on the current round of iteration population to obtain an optimization result, wherein the optimization strategy indicates that, in the process of each round of iteration optimization, the current round of iteration population is comprehensively evaluated from a plurality of noise evaluation dimensions, and the next round of iteration population is determined according to the comprehensive evaluation result until a preset iteration termination condition is reached; and determining a target pitch sequence according to the optimization result. The application solves the technical problems that, in the related art, a single dimension is taken as an evaluation index when the pitch sequence is optimized, full-band noise suppression cannot be achieved, and the technology cannot be applied to actual production and life.

Description

Technical Field

[0001] This application relates to the field of tire design technology, and more specifically, to a method and apparatus for optimizing tire pitch, and a non-volatile storage medium. Background Technology

[0002] With the rapid development of the automotive industry and the continuous improvement of people's living standards, vehicle comfort, safety, and environmental friendliness have become the focus of vehicle manufacturing. As the only component of a car in contact with the ground, tires directly affect the vehicle's driving quality. Tire noise, as a significant source of vehicle noise, has a substantial impact on the driving and passenger experience. In related technologies, single-objective methods (such as minimizing the maximum order amplitude) are used to evaluate tire noise in order to reduce it. While this may reduce the peak noise level, it can exacerbate the "noise spike" phenomenon in the mid-to-high frequency range, failing to achieve comprehensive suppression of noise across the entire frequency band and thus limiting the effectiveness of tire noise optimization.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method and apparatus for optimizing tire pitch, as well as a non-volatile storage medium, to at least solve the technical problem that the use of a single dimension as an evaluation index when optimizing pitch sequences in related technologies makes it impossible to achieve full-band noise suppression and thus impossible to apply to actual production and life.

[0005] According to one aspect of the embodiments of this application, a method for optimizing tire pitch is provided, comprising: acquiring tire specification information and optimization task type, wherein the tire specification information includes: pitch-related information, where pitch represents the distance between any two tread patterns on the tire; generating multiple initial pitch sequences based on the tire specification information, and generating a first-round iteration population based on the multiple initial pitch sequences; executing an optimization strategy corresponding to the optimization task type on the current iteration population to obtain an optimization result, wherein the optimization strategy instructs that during each round of iteration optimization, the current iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next-round iteration population is determined based on the comprehensive evaluation result, until a preset iteration termination condition is reached; when the optimization strategy is executed for the first time, the current iteration population is the first-round iteration population, and when the optimization strategy is not executed for the first time, the current iteration population is the next-round iteration population determined by the previous round of iteration optimization; determining a target pitch sequence based on the optimization result, wherein the noise of the tire generated based on the target pitch sequence is less than the noise of the tire corresponding to the tire specification information.

[0006] Optionally, the tire specification information also includes: tire circumference, and the functional relationship between tire circumference and pitch; pitch-related information includes: the number of pitch types, the total number of pitches, and the pitch ratio, where the pitch ratio represents the ratio of two adjacent pitches in a tire; generating multiple initial pitch sequences based on the tire specification information, including: generating multiple pitch sets based on tire circumference, functional relationship, number of pitch types, and pitch ratio, where each pitch set corresponds to one pitch type, and each pitch set contains multiple optional pitch values; combining the optional pitch values ​​contained in the multiple pitch sets to obtain multiple initial pitch sequences, where the number of parameters contained in each initial pitch sequence is equal to the total number of pitches, and the number of parameter types contained in each initial pitch sequence is equal to the number of pitch types, and the parameters are used to represent optional pitch values.

[0007] Optionally, multiple first-round iteration populations are generated based on multiple initial pitch sequences, including: for each initial pitch sequence, determining the noise energy corresponding to the initial pitch sequence, wherein the noise energy is used to indicate the energy of noise generated by the tire manufactured according to the initial pitch sequence; arranging the multiple initial pitch sequences according to the noise energy corresponding to each initial pitch sequence to obtain an arrangement result; and selecting a preset number of initial pitch sequences from the arrangement result according to the order of noise energy from smallest to largest to form the first-round iteration population.

[0008] Optionally, an optimization strategy corresponding to the optimization task type is executed on the current iteration population to obtain optimization results, including: performing genetic operations on the current iteration population to obtain multiple sequences to be optimized, wherein the genetic operations indicate adjustments to the interval values ​​contained in multiple interval sequences in the current iteration population; when the optimization task type allows the total number of intervals, a correction operation is performed on the multiple sequences to be optimized to obtain correction results, and the multiple correction results are comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation results, until a preset iteration termination condition is reached, and the next iteration population selected last based on the comprehensive evaluation results is output as the optimization result; when the optimization task type does not allow changes in the total number of intervals, the multiple sequences to be optimized are comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation results, until a preset iteration termination condition is reached, and the next iteration population selected last based on the comprehensive evaluation results is output as the optimization result.

[0009] Optionally, a genetic operation is performed on the current iteration population to obtain multiple sequences to be optimized, including: combining any two different first-generation individuals in the current iteration population into a combination to be optimized, resulting in multiple combinations to be optimized; for each combination to be optimized, generating a random number, and generating at least one first position index if the random number is less than the preset parameter exchange probability; exchanging the two parameters of the two first-generation individuals in the combination to be optimized that are located at the same first position index to obtain two offspring individuals; generating a sequence to be optimized based on the offspring individuals; and determining any one of the first-generation individuals in the combination to be optimized as the sequence to be optimized if the random number is greater than or equal to the preset parameter exchange probability value.

[0010] Optionally, generating a sequence to be optimized based on offspring individuals includes: for each offspring individual, if the random number is less than the preset parameter modification probability, generating at least one second position number, replacing the parameter to be modified at the position indicated by the second position number in the offspring individual with other parameters to obtain the sequence to be optimized, wherein the other parameters are parameters in the current iteration population that are different from the parameter to be modified; if the random number is greater than or equal to the preset parameter modification probability, determining the offspring individual as the sequence to be optimized.

[0011] Optionally, a correction operation is performed on multiple sequences to be optimized to obtain a correction result, including: determining multiple new pitch values ​​based on the tire specification information containing the tire circumference, the functional relationship between the tire circumference and the pitch ratio, wherein the pitch ratio represents the ratio of two adjacent pitches in a tire; and determining the sequence generated by arranging the multiple new pitch values ​​according to preset constraints as the correction result, wherein the constraints include: the maximum consecutive number of rows is less than or equal to the preset consecutive number of rows, and the maximum new pitch value is not adjacent to the minimum new pitch value, wherein the maximum consecutive number of rows is used to indicate the number of times new pitch values ​​with the same value appear consecutively.

[0012] Optionally, multiple sequences to be optimized are comprehensively evaluated from multiple noise assessment dimensions, including: for each sequence to be optimized, determining the peak evaluation index and the fluctuation amplitude evaluation index of the sequence to be optimized, wherein the peak evaluation index is used to quantify the evaluation of the iterative optimization object from the noise assessment dimension of noise peak concentration, and the fluctuation amplitude evaluation index is used to quantify the evaluation of the iterative optimization object from the noise assessment dimension of noise distribution uniformity; and determining the weighted sum of the peak evaluation index and the fluctuation amplitude evaluation index as the comprehensive evaluation result of the sequence to be optimized.

[0013] According to another aspect of the embodiments of this application, an apparatus for optimizing tire pitch is also provided, comprising: an acquisition module for acquiring tire specification information and an optimization task type, wherein the tire specification information includes: relevant information about pitch, where pitch represents the distance between any two tread patterns on the tire; a generation module for generating multiple initial pitch sequences based on the tire specification information, and generating a first-round iteration population based on the multiple initial pitch sequences; an optimization module for executing an optimization strategy corresponding to the optimization task type on the current iteration population to obtain an optimization result, wherein the optimization strategy instructs that during each round of iteration optimization, the current iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next round iteration population is determined based on the comprehensive evaluation result, until a preset iteration termination condition is reached; when the optimization strategy is executed for the first time, the current iteration population is the first-round iteration population, and when the optimization strategy is not executed for the first time, the current iteration population is the next round iteration population determined by the previous round of iteration optimization; and a determination module for determining a target pitch sequence based on the optimization result, wherein the noise of the tire generated based on the target pitch sequence is less than the noise of the tire corresponding to the tire specification information.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, which stores a computer program, wherein the above-described method for optimizing tire pitch is executed by running the computer program in the device where the non-volatile storage medium is located.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to execute the above-described method for optimizing tire pitch through the computer program.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including computer instructions that, when executed by a processor, implement the steps of the above-described method for optimizing tire pitch.

[0017] In this embodiment, the method involves acquiring tire specification information and optimizing task type. The tire specification information includes information related to pitch, where pitch represents the distance between any two tread patterns on the tire. Multiple initial pitch sequences are generated based on the tire specification information, and a first-round iteration population is generated based on these initial pitch sequences. An optimization strategy corresponding to the optimization task type is executed on the current iteration population to obtain the optimization result. The optimization strategy instructs that during each iteration, the current iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation result, until a preset iteration termination condition is reached. When the optimization strategy is executed for the first time, the current iteration population is the first-round iteration population; when the optimization strategy is not executed for the first time, ... The previous iteration population is the next iteration population determined by the previous iteration optimization. The target pitch sequence is determined based on the optimization results. The noise of the tire generated based on the target pitch sequence is less than that of the tire corresponding to the tire specification information. By using peak evaluation index and fluctuation amplitude evaluation index to jointly evaluate the noise performance of the tire corresponding to the pitch sequence during the pitch sequence optimization process, the purpose of evaluating tire noise from multiple dimensions is achieved. This achieves the technical effect of avoiding getting trapped in local optima during the pitch sequence optimization process and outputting the globally optimal pitch sequence. In turn, it solves the technical problem that the use of a single dimension as the evaluation index when optimizing the pitch sequence in related technologies cannot achieve full-band noise suppression and cannot be applied to actual production and life. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a hardware structure block diagram of a computer terminal for implementing a method for optimizing tire pitch according to an embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating the steps of a method for optimizing tire pitch according to an embodiment of this application;

[0021] Figure 3 This is a pitch parameter recording table according to an embodiment of this application;

[0022] Figure 4 This is a flowchart of an execution optimization strategy according to an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of an exchange operation according to an embodiment of this application;

[0024] Figure 6This is a schematic diagram of a variation operation according to an embodiment of this application;

[0025] Figure 7 This is a schematic diagram of a pitch time-domain sequence according to an embodiment of this application;

[0026] Figure 8 This is a schematic diagram of a noise order spectrum according to an embodiment of this application;

[0027] Figure 9 This is a structural diagram of a device for optimizing tire pitch according to an embodiment of this application;

[0028] Figure 10 This is a schematic diagram comparing the order information of a target pitch sequence 1 and the pitch before optimization according to an embodiment of this application;

[0029] Figure 11 This is a schematic diagram comparing the order information of a target pitch sequence 2 and the pitch before optimization according to an embodiment of this application. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Tire noise mainly consists of three parts: aerodynamic noise, mechanical vibration noise, and tread noise. Among these, tread noise, generated by the periodic impact of tire tread blocks on the ground, dominates the overall tire noise. This noise is not only related to parameters such as the shape, size, and depth of the tread blocks, but is also directly affected by the tread pitch (i.e., the distance between adjacent tread blocks) and its arrangement. In related technologies, schemes for pitch sequence optimization are mainly limited to changing the pitch arrangement order, and the noise performance of the tire generated based on the pitch sequence is measured from a single dimension during the optimization process, falling into the category of local optimization; furthermore, the optimization algorithms used also have the problem of only finding local optima. To solve this problem, this application provides relevant solutions, which are described in detail below.

[0033] According to an embodiment of this application, a method embodiment for optimizing tire pitch is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0034] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of a computer terminal for implementing a method for optimizing tire pitch is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0035] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0036] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for optimizing tire pitch in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned method for optimizing tire pitch. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0037] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0038] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0039] This application provides a method for optimizing tire pitch that can operate under the above-described operating environment. Figure 2 This is a flowchart illustrating the steps of the method for optimizing tire pitch provided in an embodiment of this application, as follows: Figure 2 As shown, the method includes the following steps:

[0040] Step S202: Obtain tire specification information and optimize task type. The tire specification information includes: information related to pitch, where pitch represents the distance between any two tread patterns on the tire.

[0041] The method provided in this application embodiment is based on the principle of minimizing tire pitch noise energy, and designs the pitch of low-noise tires. It effectively solves the problem of complex permutations and combinations, poor initial genetic information, and difficulty in achieving a globally optimal solution when the number of pitches is large. In step S202, the received input information includes tire specification information. Since the purpose of the solution provided in this application embodiment is pitch sequence optimization, the tire specification information should contain relevant pitch information. The distance between any two tread patterns on the tire is called the pitch. Furthermore, to clarify the type of pitch sequence optimization, the input information received in step S202 also includes the optimization task type. The optimization task type indicates the method for optimizing the pitch sequence. In this application embodiment, the optimization task type includes two types: pitch design and pitch arrangement. When performing a task of pitch design, it is allowed to change the total number of pitches during the pitch sequence optimization process. However, when performing a task of pitch arrangement, it is not allowed to change the total number of pitches during the pitch sequence optimization process.

[0042] Step S204: Generate multiple initial pitch sequences based on tire specification information, and generate the first iteration population based on the multiple initial pitch sequences.

[0043] In step S204, multiple initial pitch sequences are generated based on the tire specification information obtained in step S202. Each initial pitch sequence is a sequence of multiple pitch values, which are determined according to the tire specification information. These values ​​can be the actual tread spacing of the tire or the pitch values ​​input by the user. The numerical range of the pitch values ​​is limited by the tire specification information. The multiple initial pitch sequences generated above will be used to generate the objects for the first execution of the optimization strategy (i.e., the first iteration population).

[0044] According to some optional embodiments of this application, the tire specification information further includes: tire circumference, and the functional relationship between tire circumference and pitch; the relevant information about pitch includes: the number of pitch types, the total number of pitches, and the pitch ratio, wherein the pitch ratio represents the ratio of two adjacent pitches in a tire; generating multiple initial pitch sequences based on the tire specification information includes: generating multiple pitch sets based on tire circumference, functional relationship, number of pitch types, and pitch ratio, wherein each pitch set corresponds to one pitch type, and each pitch set contains multiple optional pitch values; combining the optional pitch values ​​contained in the multiple pitch sets to obtain multiple initial pitch sequences, wherein the number of parameters contained in each initial pitch sequence is equal to the total number of pitches, and the number of parameter types contained in each initial pitch sequence is equal to the number of pitch types, and the parameters are used to represent optional pitch values.

[0045] The tire specification information received in step S202 also includes: tire circumference, and the functional relationship between tire circumference L and pitch. The relevant information regarding tire pitch includes: tread pitch type (i), total number of tread pitches (or the range of values ​​for the total number), and pitch ratio. In this embodiment, the pitch ratio is set to a fixed value. Therefore, the pitch ratio can refer to the ratio of the maximum pitch length to the minimum pitch length, or the ratio of two adjacent pitches. The above-mentioned functional relationship between tire circumference and pitch ( In this context, N represents the number of pitch types. Represents the length of the i-th type (class) pitch. This represents the number of pitches of type i.

[0046] In this embodiment, when generating multiple initial pitch sequences based on tire specification information, energy calculation is performed within the numerical range of the pitch values ​​given in the tire specification information, based on the principle of minimizing tire tread noise energy. A preset number of pitch information values ​​with the minimum energy are output. That is, the pitch information output based on the tire specification information and the principle of minimizing tire tread noise energy should include the types of pitches and the number of each type. In this embodiment, multiple (optional) pitch values ​​belonging to the same type form a pitch set. Randomly combining these multiple (optional) pitch values ​​can generate multiple initial pitch sequences. Each initial pitch sequence contains multiple pitch values ​​of all pitch types, and the number of pitch values ​​in each initial pitch sequence is the total number of pitches specified in the tire specification information. For example, if the tire specification information records N=3 pitch types, then the initial pitch sequence contains pitch values ​​of 3 different pitch types. Figure 3 It is a pitch parameter record table. Figure 3 The given pitch parameter record table records three types of pitches (1-pitch, 2-pitch, and 3-pitch). When generating pitch sequences, codes (i.e., parameters) can be used to replace pitch values. For example, multiple pitch values ​​contained in the first type (1-pitch) can be uniformly encoded as 1, multiple pitch values ​​contained in the second type (2-pitch) can be uniformly encoded as 2, and multiple pitch values ​​contained in the third type (3-pitch) can be uniformly encoded as 3. Then, according to... Figure 3 The pitch sequence generated by the pitch values ​​recorded in the code can be represented as "11223211123223133211123221123". In this coding system, each number represents a pitch of a specific length, and the entire number sequence constitutes a complete initial pitch sequence containing 30 pitch values.

[0047] When generating multiple pitch sets based on tire circumference, functional relationship, number of pitch types, and pitch ratio, the following method can be used: For any tire specification, the tire circumference satisfies the following condition (i.e., functional relationship): The length ratio of adjacent pitches (i.e., pitch ratio): It is a constant value, that is Combining the two formulas above, since the tire specification information provides the pitch ratio ( Given the tire circumference L and the two formulas above, we can determine the preset number (n) of optional pitch values ​​included in each pitch type (i). These optional pitch values ​​( ) are used to form a pitch set.

[0048] The method provided in this embodiment, based on the principle of minimizing tire pitch noise energy, is used to design the pitch of low-noise tires, effectively solving the problem of difficulty in achieving the global optimal solution due to poor initial genetic information.

[0049] According to some alternative embodiments of this application, multiple first-round iteration populations are generated based on multiple initial pitch sequences, including: for each initial pitch sequence, determining the noise energy corresponding to the initial pitch sequence, wherein the noise energy is used to indicate the energy of noise generated by the tire manufactured according to the initial pitch sequence; arranging the multiple initial pitch sequences according to the noise energy corresponding to each initial pitch sequence to obtain an arrangement result; and selecting a preset number of initial pitch sequences from the arrangement result in ascending order of noise energy to form the first-round iteration population.

[0050] Tire pitch noise can be measured from an energy perspective. The mechanism of sound generation is that tire vibration causes the surrounding air to vibrate. Air molecules vibrate back and forth around their equilibrium positions, resulting in compression and expansion. The former gives the air vibrational kinetic energy, and the latter gives it deformation potential energy. The sum of these two parts is the sound energy that the air gains during tire vibration. When evaluating tire noise energy, the tire can be considered as a sufficiently small volume element in the sound field, with its original volume being... The pressure is P0, and the density is Due to the acoustic disturbance, the volume element gains kinetic energy ( )for: ,in, This represents the vibration velocity. Due to acoustic disturbance, the pressure of this volume element increases from P0 to (P0+p), thus the volume element acquires potential energy ( ), In the formula, the negative sign indicates that the changes in pressure and volume within a volume element are in opposite directions. The changes in volume and pressure of a medium are interrelated and can be derived from the equation of state. In the formula, Represents the speed of sound in air. This represents the pressure change caused by the acoustic disturbance. This represents the change in density; considering that the mass of a volume element remains constant during compression and expansion, there is a relationship between the changes in volume and density of the volume element. For small-amplitude sound waves, it can be simplified to Substituting into the above formula, we have Substituting into the potential energy formula, we get The total sound energy within a volume element is the sum of kinetic energy and potential energy. .

[0051] In the solution provided in this application embodiment, the premise is that "the amplitude of vibration is directly proportional to the area of ​​the tread block." Therefore, for a non-variable pitch tread design, the width of the tread pitch is usually consistent with the cross-sectional width of the tire, while the length corresponds to the pitch length. Under this structure, the area of ​​the tread block can be approximated as the product of the width (i.e., the tire cross-sectional width) and the length (i.e., the pitch length). Since the width is constant, the change in amplitude mainly depends on the change in the pitch length. In other words, the amplitude is also directly proportional to the length of the tread pitch. .in, The amplitude of the i-th pitch is given by α, where α is the proportionality coefficient. Based on this, the pitch noise energy can be simplified to... In the solution provided in this application embodiment, when evaluating the noise energy (E) of each interval sequence (including the initial interval sequence, the interval sequence to be optimized, the first generation individual, the offspring individual, etc.), the formula is used. An evaluation is conducted, in which, This is a proportionality constant, the value of which is given in advance. It represents the original volume of the volume element of the tire. It is the pitch length (pitch value) of the i-th pitch. It is the static density of air. Represents the speed of sound in air. It is a noise frequency. It is the spatial rate of change of the sound wave, and t is the duration of the noise generation. It is a way of expressing plural numbers. It is the imaginary unit; there exists a relationship between e, j, and x. =cosx +jsinx.

[0052] Using the above noise energy calculation formula ( After determining the noise energy corresponding to each initial pitch sequence, a minimum energy search strategy is executed based on the noise energy. Within the specified pitch type and total number of pitches, the pitch sequence with the minimum energy is searched and output. That is, a preset number (e.g., 100) of pitch sequences are output as the initial pitch sequences in order of increasing noise energy.

[0053] The method provided in this embodiment selects some interval sequences with relatively low noise energy to form the first round of iterative population, so that the optimization process starts with high-quality individuals with low noise characteristics, effectively avoiding the interference of high-noise inferior sequences on the evolutionary process and improving the execution efficiency of the optimization strategy.

[0054] Step S206: Execute the optimization strategy corresponding to the optimization task type on the current iteration population to obtain the optimization result. The optimization strategy indicates that during each iteration optimization process, the current iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation result, until the preset iteration termination condition is reached. When the optimization strategy is executed for the first time, the current iteration population is the first iteration population. When the optimization strategy is not executed for the first time, the current iteration population is the next iteration population determined by the previous iteration optimization.

[0055] In step S206, the optimization strategy is executed iteratively. The first time the optimization strategy is executed, the first-round iteration population generated in step S204 is used as the current iteration population, and the optimization strategy is executed on the first-round iteration population. In subsequent executions, the output result of the previous round of optimization strategy execution (i.e., the next-round iteration population) is used as the object of the current optimization strategy execution (i.e., the current iteration population). In the scheme provided in this embodiment, during each round of iterative optimization, a multi-objective fitness evaluation is used to comprehensively evaluate each individual in the current iteration population from multiple noise evaluation dimensions. Based on the comprehensive evaluation result (i.e., the comprehensive evaluation result), the next iteration population is selected from the current iteration population. The aforementioned multi-objective fitness evaluation refers to considering multiple noise-related performance characteristics simultaneously when evaluating each individual, in order to achieve a comprehensive judgment of the overall noise performance of each individual.

[0056] When selecting the next iteration population based on the comprehensive evaluation results, individuals are selected from the current iteration population in descending order of noise performance (i.e., noise values ​​from largest to smallest). During the execution of the optimization strategy, when the preset iteration termination condition is detected, the iteration optimization stops, and the output result of the last iteration optimization before stopping (i.e., the next iteration population output at the last time) is determined as the optimization result.

[0057] In the solution provided in this application embodiment, the preset iteration termination conditions include: the number of iterations reaches the preset number of optimizations, and the fluctuation amplitude evaluation index reaches the preset fluctuation amplitude. The iteration optimization can be stopped when either iteration termination condition is met. The above-mentioned fluctuation amplitude evaluation index is the evaluation result of amplitude evaluation dimension among multiple noise evaluation dimensions.

[0058] According to some optional embodiments of this application, an optimization strategy corresponding to the optimization task type is executed on the current iteration population to obtain optimization results, including: performing genetic operations on the current iteration population to obtain multiple sequences to be optimized, wherein the genetic operations indicate adjustments to the interval values ​​contained in the multiple interval sequences in the current iteration population; when the optimization task type is a task type that allows the total number of intervals, a correction operation is performed on the multiple sequences to be optimized to obtain correction results, and the multiple correction results are comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation results until a preset iteration termination condition is reached, and the next iteration population selected last based on the comprehensive evaluation results is output as the optimization result; when the optimization task type does not allow changes in the total number of intervals, the multiple sequences to be optimized are comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation results until a preset iteration termination condition is reached, and the next iteration population selected last based on the comprehensive evaluation results is output as the optimization result.

[0059] The method provided in this application incorporates an elite strategy based on a genetic algorithm into the optimization strategy. The genetic algorithm ensures that the genes of superior individuals are not lost during the crossover and mutation processes, making the optimization process more efficient. Specifically, the optimization strategy is executed iteratively. In each iteration, the optimization strategy is only applied to the current iteration population. When the optimization strategy is first executed, the current iteration population is based on the first iteration population generated in step S204. The output of each optimization strategy execution (i.e., the next iteration population) will be used as the object of optimization in the next iteration (i.e., the current iteration population). The output of the last iteration before reaching the preset iteration termination condition (i.e., the next iteration population) is the optimization result output by the optimization strategy. Before stopping the iteration, each round of optimization strategy execution requires operations such as crossover, mutation, correction, and fitness evaluation.

[0060] Figure 4 It is a flowchart of the optimization strategy execution, such as Figure 4As shown, after the first iteration population is formed according to the input pitch parameters in steps S202 and S204 (corresponding to the case where the tire specification information input in step S202 includes a pitch sequence), or the first iteration population is determined according to the principle of minimum energy (corresponding to the case where the input tire specification information does not include a pitch sequence but includes information such as tire circumference, pitch type, and pitch ratio), the execution process of the optimization strategy is as follows: Genetic operation is performed on the current iteration population to obtain multiple sequences to be optimized (each pitch sequence contained in the current iteration population after the genetic operation is completed). The genetic operation is implemented based on a genetic algorithm, and performing the genetic operation can adjust the pitch value in the multiple pitch sequences contained in the current iteration population. Next, after the genetic operation is completed, it is determined whether to perform a correction operation based on the type of optimization task. Performing a correction operation on the sequence to be optimized generated by the genetic operation ensures that the sequence meets the requirements of the adjacent and consecutive arrangement criteria. If the individual (sequence to be optimized) after the genetic operation still has the original pitch length, the total circumference of the pitch sequence it represents may no longer meet the tire specification circumference requirements. Therefore, it may be necessary to correct the pitch length information of the individual after the genetic operation. As mentioned above, whether to perform a correction operation depends on the type of optimization task. Specifically, when the optimization task type is a pitch design that allows changing the total number of pitches during the pitch sequence optimization process, a correction operation can be performed; otherwise, when the optimization task type is a pitch arrangement that does not allow changing the total number of pitches during the pitch sequence optimization process, no correction operation is required.

[0061] Still Figure 4 As shown, regardless of whether a correction operation is performed, a fitness evaluation operation is required when executing an optimization strategy. In this embodiment, the fitness evaluation operation refers to jointly evaluating the segment sequence from multiple noise dimensions. If a correction operation is required, the fitness evaluation operation is performed on the sequence to be optimized after the correction operation (i.e., the correction result); if no correction operation is required, the fitness evaluation operation is performed on the segment sequence after the genetic operation (i.e., the sequence to be optimized). Finally, based on the fitness evaluation result (i.e., the comprehensive evaluation result), segment sequences to form the next iteration population are selected from the current iteration population. For example, the fitness evaluation result (i.e., the comprehensive evaluation result) is used as the selection probability, and a predetermined number of individuals are selected from the current iteration population in descending order of fitness evaluation result (i.e., the comprehensive evaluation result) to form the next iteration population. Individuals with higher fitness evaluation results have a higher probability of being selected, thereby ensuring that excellent genes are preserved and passed on.

[0062] The method provided in this embodiment incorporates multi-dimensional comprehensive evaluation during the optimization process of the interval arrangement using a genetic algorithm. It not only considers the optimization of a single-order value (noise peak) but also integrates the optimization of multi-order fluctuations (fluctuation amplitude), thus avoiding the optimization result from getting trapped in a local optimum.

[0063] Optionally, a genetic operation is performed on the current iteration population to obtain multiple sequences to be optimized, including: combining any two different first-generation individuals in the current iteration population into a combination to be optimized, resulting in multiple combinations to be optimized; for each combination to be optimized, generating a random number, and generating at least one first position index if the random number is less than the preset parameter exchange probability; exchanging the two parameters of the two first-generation individuals in the combination to be optimized that are located at the same first position index to obtain two offspring individuals; generating a sequence to be optimized based on the offspring individuals; and determining any one of the first-generation individuals in the combination to be optimized as the sequence to be optimized if the random number is greater than or equal to the preset parameter exchange probability value.

[0064] like Figure 4 As shown, the genetic operations mentioned in the previous embodiment include a crossover operation (i.e., crisscross inheritance). This crossover operation is performed on both the parent and parent individuals. The parent and parent individuals are any two distinct first-generation individuals from the multiple individuals (i.e., first-generation individuals) included in the current iteration population. Therefore, in this embodiment, when performing the crossover operation, any two distinct first-generation individuals are first combined into an optimized combination. One of the first-generation individuals in this optimized combination serves as the parent individual, and the other serves as the parent-parent combination. Next, based on the generated random number and the preset crossover probability, it is determined whether to perform the crossover operation to generate offspring individuals or directly use either the parent or parent individual as the offspring individual. Figure 4 As shown, if the random number is greater than or equal to the preset swap probability, either the parent or the mother individual will be used as the child individual; if the random number is less than the preset swap probability, the swap operation will be performed.

[0065] Figure 5 This is a diagram illustrating a swap operation, such as... Figure 5 As shown, when performing a swap operation, the pitch values ​​at the same position number in the parent and mother individuals are exchanged. Therefore, when a swap operation is required, at least one position number (i.e., the first position number, or a random number) is generated to indicate the position where the swap operation is performed, to support the implementation of the swap operation. For example... Figure 5As shown, the parent individual is "13332131111323311231332233333211211133132111222122", and the mother individual is "22133321213321231133331112333322311233221111331333". When performing the swap operation, the pitch values ​​at positions 5, 11, 19, 32, 35, 36, 41, and 48 are swapped to obtain the offspring individual "13333131113323311231332233333212211233131111222322".

[0066] The method provided in this embodiment performs an exchange operation on the first generation individuals in the parent population, which enriches the types of individuals. When the random number (used to determine whether to perform the exchange operation) is greater than or equal to the preset exchange probability, one first generation individual is retained as a child individual, thus avoiding invalid iterations caused by excessive randomness in genetic operations.

[0067] Optionally, generating a sequence to be optimized based on offspring individuals includes: for each offspring individual, if the random number is less than the preset parameter modification probability, generating at least one second position number, replacing the parameter to be modified at the position indicated by the second position number in the offspring individual with other parameters to obtain the sequence to be optimized, wherein the other parameters are parameters in the current iteration population that are different from the parameter to be modified; if the random number is greater than or equal to the preset parameter modification probability, determining the offspring individual as the sequence to be optimized.

[0068] Still Figure 4 As shown, during the genetic operation, after obtaining offspring individuals by performing genetic operations on the first-generation individuals, it is determined whether to perform a mutation operation on the offspring individuals based on the generated random number and the preset mutation probability (i.e., the preset parameter modification probability). In this embodiment, if the random number is greater than or equal to the preset mutation probability, there is no need to perform a mutation operation on the offspring individuals; instead, the offspring individuals are directly identified as the sequence to be optimized. If the random number is less than the preset mutation probability, a mutation operation needs to be performed on the offspring individuals, and the offspring individuals after the mutation operation are identified as the sequence to be optimized. When performing the mutation operation, a position number (i.e., the second position number) needs to be generated to indicate the location of the mutation operation. The internode value or the code representing the internode value in the offspring individuals located at the second position number is used to determine the parameter to be modified. Next, the parameter to be modified is replaced with other parameters contained in the current iteration population to complete the mutation operation. The second position number can be generated by using a random number as the position number.

[0069] Figure 6 This is a diagram illustrating the mutation operation, such as... Figure 6 As shown, after Figure 5 After the exchange operation shown, the resulting offspring is "13333131113323311231332233333212211233131111222322". The mutation operations are performed at positions 7, 8, 14, 19, 33, 41, and 42. The parameters (interval values) at these positions in the offspring are replaced with other parameters, resulting in... Figure 6 The mutation result shown (a sequence to be optimized) is "13333122113321311221332233333212111233132311222322".

[0070] The method provided in this embodiment performs a mutation operation on the offspring individuals obtained by performing a swap operation, thereby enhancing the ability to capture non-local optimal solutions.

[0071] According to some optional embodiments of this application, a correction operation is performed on multiple sequences to be optimized to obtain a correction result, including: determining multiple new pitch values ​​based on tire specification information containing tire circumference, the functional relationship between tire circumference and pitch, and pitch ratio, wherein the pitch ratio represents the ratio of two adjacent pitches in a tire; and determining the sequence generated by arranging the multiple new pitch values ​​according to preset constraints as the correction result, wherein the constraints include: the maximum consecutive number of rows is less than or equal to the preset consecutive number of rows, and the maximum new pitch value is not adjacent to the minimum new pitch value, wherein the maximum consecutive number of rows is used to indicate the number of times new pitch values ​​with the same value appear consecutively.

[0072] This application also introduces a correction mechanism for consecutive rows of the same pitch and adjacent maximum and minimum pitches. This mechanism optimizes pitch noise while ensuring that other performance parameters of the tire tread, such as grip and stiffness uniformity, are not affected, thus achieving the optimization of overall performance.

[0073] In this embodiment, a correction function is added to the optimization strategy to address the tire circumference variation that may result from changes in the number of different pitch types during optimization, thus ensuring the practicality and accuracy of the optimization results. Specifically, this is achieved through the functional relationship between tire circumference and pitch value: Compared to a fixed pitch ( Representing a fixed value (in this embodiment, the pitch ratio can be fixed at 2 or 1.6), a new pitch value can be calculated by combining the values. Arranging the new pitch values ​​according to the constraints yields the corrected sequence to be optimized (i.e., the corrected result). As mentioned in the above embodiments, the correction operation is used to correct offspring individuals to ensure they meet the requirements of the adjacency criterion and the consecutive arrangement criterion. Both the adjacency criterion and the consecutive arrangement criterion are recorded in the constraints in a quantified form. The quantified form of the adjacency criterion is that the largest new pitch value (i.e., the maximum new pitch value) and the largest new pitch value (i.e., the minimum new pitch value) are not adjacent. The quantified form of the consecutive arrangement criterion is that the number of consecutive occurrences of new pitch values ​​with the same value (i.e., the maximum consecutive number) or the number of consecutive occurrences of the same parameter (i.e., the maximum consecutive number) is less than the preset consecutive number.

[0074] According to some optional embodiments of this application, a comprehensive evaluation of multiple sequences to be optimized is performed from multiple noise evaluation dimensions, including: for each sequence to be optimized, determining the peak evaluation index and the fluctuation amplitude evaluation index of the sequence to be optimized, wherein the peak evaluation index is used to quantify the evaluation of the iterative optimization object from the noise evaluation dimension of noise peak concentration, and the fluctuation amplitude evaluation index is used to quantify the evaluation of the iterative optimization object from the noise evaluation dimension of noise distribution uniformity; and determining the weighted sum of the peak evaluation index and the fluctuation amplitude evaluation index as the comprehensive evaluation result of the sequence to be optimized.

[0075] The optimization strategy provided in this application not only considers the optimization of a single-order value when optimizing the pitch arrangement, but also integrates a multi-objective function optimization strategy with multi-order fluctuations, thereby efficiently achieving the optimization of the pitch arrangement. The comprehensive evaluation of the sequence to be optimized from multiple noise evaluation dimensions in this application mainly focuses on two dimensions: the concentration of noise peaks and the uniformity of noise distribution.

[0076] Figure 7 This is a schematic diagram of the pitch time-domain sequence. Figure 8 This is a schematic diagram of the noise order spectrum. For example, the sequence to be optimized is: "11223211123223133211123221123", and its time-domain sequence is as follows: Figure 7 As shown, the time-domain function of the pitch sequence is obtained by fitting the pitch with the Dirac function in the time domain: ,in, Let represent the instant when the i-th pitch contacts the ground, t represent the total contact time between the tire and the ground, and n represent the total number of pitches contained in the tires of the pitch sequence to be optimized; for the above time-domain function Perform a Fourier series transform to obtain Figure 8 The order spectrum is shown. For example... Figure 8As shown, the spectral amplitudes for orders 35 to 65 are given. A good interval arrangement aims to minimize the maximum order value (i.e., minimize the noise peak). For example, in a population of N individuals, the maximum value for each individual is... The minimum value is All The maximum value in is minimum value Then the peak evaluation index ( ) can be represented as The smaller this indicator is, the closer the individual's order peak is to the minimum value in the population, and the better the performance.

[0077] In addition to the peak value, the method provided in this application also introduces a fluctuation amplitude evaluation index ( ), to more comprehensively assess the strengths and weaknesses of individuals; among them, the fluctuation range evaluation index ( This is used to evaluate the uniformity of noise distribution, and its formula can be expressed as follows: In the formula, yes Figure 8 The magnitude of each order is included in the order spectrum diagram shown. yes Figure 8 The average amplitude of all orders included, where n is the number of order points (e.g., there are 31 order points in the amplitude spectrum of orders 35-65). From the above formula, it can be seen that the fluctuation amplitude evaluation index ( The standard deviation can be calculated based on the standard deviation of the order amplitude spectrum. It is used to quantify the fluctuation of an individual within the range of the order of interest (such as the 35th to 65th order in this embodiment, which can be set manually). The smaller the standard deviation, the smaller the fluctuation amplitude, the better the fluctuation amplitude evaluation index, and the better the noise performance of the produced tires (i.e., the lower the tire noise).

[0078] In this embodiment, when comprehensively evaluating each sequence to be optimized, the peak evaluation index of each sequence to be optimized is determined using the method described above. ) and volatility evaluation indicators ( After that, in order to obtain a comprehensive and complete fitness evaluation result (i.e., a comprehensive evaluation result), in this embodiment, the two evaluation indicators mentioned above are weighted and averaged to construct the final fitness evaluation function: In the formula, m is the peak evaluation index ( The weighting coefficients of the volatility evaluation index (n) are used, where n is the value of the volatility evaluation index. The result obtained by weighting according to the above formula As a comprehensive evaluation result, and using the comprehensive evaluation result to screen the pitch sequence, the evaluation process takes into account both the control of the order peak value (reflected by the peak value evaluation index) and the stability of the order fluctuation amplitude (reflected by the fluctuation amplitude evaluation index).

[0079] Step S208: Determine the target pitch sequence based on the optimization results, wherein the noise of the tire generated based on the target pitch sequence is less than the noise of the tire corresponding to the tire specification information.

[0080] In step S208, the pitch sequence (i.e., the target result sequence) that achieves optimal tire noise performance (i.e., minimum noise value) is determined based on the optimization results of the iterative optimization. Specifically, if the optimization results contain multiple pitch sequences, the multi-objective fitness evaluation mentioned in step S206 is used to evaluate each pitch sequence in the optimization results. After obtaining the multi-objective fitness evaluation results for each pitch sequence, the pitch sequence with optimal noise performance (minimum noise value) indicated by the multi-objective fitness evaluation results is determined as the target pitch sequence.

[0081] Through the above steps, a synergistic trade-off and global optimization of multi-order tire noise contribution factors can be achieved, breaking through the limitations of relying solely on a single noise index for local optimization in related technologies. By using a multi-dimensional comprehensive evaluation mechanism, it effectively avoids getting trapped in local optima, enabling the optimized pitch sequence to effectively reduce the overall tire noise level, significantly outperforming the noise performance of the original tire specifications. This effectively solves the problem in related technologies where tire tread pitch arrangement optimization is difficult to achieve multi-order comprehensive noise evaluation and global optimization.

[0082] Figure 9 This is a structural diagram of the device for optimizing tire pitch according to an embodiment of this application, as shown below. Figure 9As shown, the device for optimizing tire pitch includes: an acquisition module 90, used to acquire tire specification information and optimization task type, wherein the tire specification information includes relevant information about pitch, where pitch represents the distance between any two tread patterns on the tire; a generation module 92, used to generate multiple initial pitch sequences based on the tire specification information, and generate a first-round iteration population based on the multiple initial pitch sequences, wherein the initial pitch sequence consists of multiple pitch values; an optimization module 94, used to execute an optimization strategy corresponding to the optimization task type on the current iteration population to obtain optimization results, wherein the optimization strategy indicates that during each round of iteration optimization, the current round iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next round iteration population is determined based on the comprehensive evaluation results, until a preset iteration termination condition is reached. When the optimization strategy is executed for the first time, the current round iteration population is the first-round iteration population; when the optimization strategy is not executed for the first time, the current round iteration population is the next round iteration population determined by the previous round of iteration optimization; and a determination module 96, used to determine a target pitch sequence based on the optimization results, wherein the noise of the tire generated based on the target pitch sequence is less than the noise of the tire corresponding to the tire specification information.

[0083] use Figure 9 When the device for optimizing tire pitch shown executes the solution provided in this application embodiment, the acquisition module 90 receives tire specification information input by the user or transmitted from other terminals. This tire specification information includes pitch-related information, such as: total number of pitches N=50, pitch type 3, pitch ratio 1.5 (the ratio of the maximum pitch to the minimum pitch length, also the ratio of adjacent pitches). The tire specification information may also include: tire circumference 2340.43 mm. Simultaneously, along with the tire specifications, the input information is: "Optimize a certain tire scheme for a vehicle speed of 80 km / h," indicating that the input information does not contain a pitch sequence, and the optimization task type is pitch design. Next, the generation module 92 performs pitch type design based on the principle of minimum energy and the parameters received by the acquisition module 90, obtaining... Figure 3 The pitch parameters are shown. Generation module 92 will... Figure 3The internode parameters are arranged to generate multiple initial internode sequences, and an initial population (i.e., the first iteration population) is generated based on these initial internode sequences. Next, the optimization module 94 executes the first optimization strategy on the first iteration population. After the first optimization strategy is completed, the optimization strategy is executed again on the result of the optimization strategy (i.e., the next iteration population), and the above iterative optimization continues until the preset iteration stopping condition is reached. The result of the last iteration before the preset iteration stopping condition is reached (i.e., the next iteration population) is determined as the optimization result. In each execution of the optimization strategy, the individuals in the current iteration population are first crossbred according to the selected crossover logic to generate new offspring individuals. Then, the offspring individuals generated after the crossover operation are mutated to increase the diversity of the population. After the crossover and mutation operations, if the optimization task type is internode design, the offspring individuals are modified to ensure that they meet the requirements of the adjacency criterion and the consecutive arrangement criterion. After the modification is completed, the fitness evaluation function is used to calculate the fitness value of each offspring individual (i.e., the comprehensive evaluation result) to quantify the quality of each offspring individual based on the comprehensive evaluation result. Specifically, the fitness value (i.e., the comprehensive evaluation result) is used as the selection probability to select offspring individuals, generating a new population. Individuals with higher fitness have a greater probability of being selected, thus ensuring that excellent genes are preserved and passed on. The new population generated in the previous step is used as the next generation population (i.e., the next round of offspring population), and crossover, mutation, correction, and fitness evaluation operations are performed until the termination condition (i.e., the preset stopping condition) is met, and the result is output. After the optimization strategy is executed, the determination module 96 determines the pitch sequence (i.e., the target pitch sequence) that can reduce tire noise from the output of the optimization strategy. The optimization results may include multiple pitch sequences (i.e., target pitch sequences) that can reduce tire noise. For example, after performing the above operations, the following two target pitch sequences can be obtained in this embodiment: Target pitch sequence 1 "31113231311123332113313313311121233111233111311233" and Target pitch sequence 2 "12312323312232332111332121133221131122221131232232". The length of each of the three types of pitches in target pitch sequence 1 changes to 38.0105 (mm), 46.5532 (mm), and 57.0158 (mm) compared to the three types of pitches in the original pitch sequence. The pitch length of the target pitch sequence remains unchanged compared to the original pitch sequence. Figure 10 This is a schematic diagram comparing the order information of the target pitch sequence 1 with the pitch before optimization; Figure 11 This is a diagram comparing the order information of the target pitch sequence 2 and the pitch before optimization, as shown in the figure. Figure 10 and Figure 11 As shown, the noise performance of the target pitch sequence is better than that of the unoptimized pitch sequence. Figure 11 Although the pitch length and the number of pitch types remain unchanged, the order of the pitches has changed, and the optimized order spectrum is smaller than that before optimization, thus achieving the goal of reducing order noise in the frequency band of interest. Therefore, pitch optimization has also been achieved.

[0084] It should be noted that, Figure 9 Preferred embodiments of the shown examples can be found in [reference needed]. Figure 2 The relevant descriptions of the embodiments shown will not be repeated here.

[0085] This application also provides a non-volatile storage medium storing a computer program, wherein the above-mentioned method for optimizing tire pitch is executed by running the computer program on the device where the non-volatile storage medium is located.

[0086] The aforementioned non-volatile storage medium is used to store a program that performs the following functions: acquiring tire specification information and optimization task type, wherein the tire specification information includes: pitch-related information, where pitch represents the distance between any two tread patterns on the tire; generating multiple initial pitch sequences based on the tire specification information, and generating a first-round iteration population based on the multiple initial pitch sequences; executing an optimization strategy corresponding to the optimization task type on the current iteration population to obtain optimization results, wherein the optimization strategy instructs that during each iteration optimization process, the current iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation results, until a preset iteration termination condition is reached. When the optimization strategy is executed for the first time, the current iteration population is the first-round iteration population; when the optimization strategy is not executed for the first time, the current iteration population is the next iteration population determined by the previous iteration optimization; determining a target pitch sequence based on the optimization results, wherein the noise of the tire generated based on the target pitch sequence is less than the noise of the tire corresponding to the tire specification information.

[0087] This application also provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor is configured to execute the above-described method for optimizing tire pitch through the computer program.

[0088] The processor in the aforementioned electronic device is used to run a program that performs the following functions: acquiring tire specification information and optimization task type, wherein the tire specification information includes: information related to pitch, where pitch represents the distance between any two tread patterns on the tire; generating multiple initial pitch sequences based on the tire specification information, and generating a first-round iteration population based on the multiple initial pitch sequences; executing an optimization strategy corresponding to the optimization task type on the current iteration population to obtain optimization results, wherein the optimization strategy instructs that during each round of iteration optimization, the current iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next round iteration population is determined based on the comprehensive evaluation results, until a preset iteration termination condition is reached. When the optimization strategy is executed for the first time, the current iteration population is the first-round iteration population; when the optimization strategy is not executed for the first time, the current iteration population is the next round iteration population determined by the previous round of iteration optimization; determining a target pitch sequence based on the optimization results, wherein the noise of the tire generated based on the target pitch sequence is less than the noise of the tire corresponding to the tire specification information.

[0089] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the above-described method for optimizing tire pitch.

[0090] It should be noted that the modules in the above-mentioned device for optimizing tire pitch can be program modules (e.g., a set of program instructions to implement a specific function) or hardware modules. For the latter, they can be in the following forms, but are not limited to these: each of the above modules is in the form of a processor, or the functions of each of the above modules are implemented by a processor.

[0091] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0092] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0097] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for optimizing tire pitch, characterized in that, include: Obtain tire specification information and optimize task type, wherein the tire specification information includes: relevant information about pitch, wherein the pitch represents the distance between any two tread patterns on the tire; Multiple initial pitch sequences are generated based on tire specification information, and a first-round iteration population is generated based on the multiple initial pitch sequences; An optimization strategy corresponding to the optimization task type is executed on the current iteration population to obtain the optimization result. The optimization strategy indicates that during each iteration optimization process, the current iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation result, until a preset iteration termination condition is reached. When the optimization strategy is executed for the first time, the current iteration population is the first iteration population. When the optimization strategy is not executed for the first time, the current iteration population is the next iteration population determined by the previous iteration optimization. The target pitch sequence is determined based on the optimization results, wherein the noise of the tire generated based on the target pitch sequence is less than the noise of the tire corresponding to the tire specification information.

2. The method according to claim 1, characterized in that, The tire specification information also includes: tire circumference, and the functional relationship between the tire circumference and the pitch; the relevant information about the pitch includes: the number of pitch types, the total number of pitches, and the pitch ratio, wherein the pitch ratio represents the ratio of two adjacent pitches in a tire; Multiple initial pitch sequences are generated based on tire specification information, including: Multiple pitch sets are generated based on the tire circumference, the functional relationship, the number of pitch types, and the pitch ratio, wherein each pitch set corresponds to one pitch type, and each pitch set contains multiple selectable pitch values; The optional pitch values ​​contained in the multiple pitch sets are combined to obtain multiple initial pitch sequences, wherein the number of parameters contained in each initial pitch sequence is equal to the total number of pitches, and the number of types of parameters contained in each initial pitch sequence is equal to the number of pitch types, and the parameters are used to represent the optional pitch values.

3. The method according to claim 1, characterized in that, Multiple first-round iteration populations are generated based on multiple initial interval sequences, including: For each initial pitch sequence, the noise energy corresponding to the initial pitch sequence is determined, wherein the noise energy is used to indicate the energy of noise generated by a tire manufactured according to the initial pitch sequence; The initial pitch sequences are arranged according to the noise energy corresponding to each initial pitch sequence to obtain the arrangement result; Based on the order of noise energy from smallest to largest, a preset number of initial interval sequences are selected from the arrangement results to form the first round of iterative population.

4. The method according to claim 1, characterized in that, Execute the optimization strategy corresponding to the optimization task type on the current iteration population to obtain the optimization results, including: Genetic operations are performed on the current iteration population to obtain multiple sequences to be optimized, wherein the genetic operations indicate that the interval values ​​contained in the multiple interval sequences in the current iteration population are adjusted; When the optimization task type is a task type that allows the total number of intervals, a correction operation is performed on multiple sequences to be optimized to obtain correction results. The multiple correction results are comprehensively evaluated from multiple noise evaluation dimensions. The next iteration population is determined based on the comprehensive evaluation results until the preset iteration termination condition is reached. The next iteration population selected last time based on the comprehensive evaluation results is output as the optimization result. When the optimization task type does not allow changes in the total number of segments, multiple sequences to be optimized are comprehensively evaluated from multiple noise evaluation dimensions. The next iteration population is determined based on the comprehensive evaluation results until the preset iteration termination condition is reached. The next iteration population selected based on the last comprehensive evaluation results is then output as the optimization result.

5. The method according to claim 4, characterized in that, Genetic operations are performed on the current iteration population to obtain multiple sequences to be optimized, including: Combine any two different first-generation individuals in the current iteration population into a combination to be optimized, resulting in multiple combinations to be optimized. For each of the combinations to be optimized, a random number is generated. If the random number is less than the preset parameter exchange probability, at least one first position number is generated. The two parameters of the two first-generation individuals contained in the combination to be optimized that are located at the same first position number are exchanged to obtain two offspring individuals. The sequence to be optimized is generated based on the offspring individuals. If the random number is greater than or equal to the preset parameter exchange probability value, any one of the initial individuals in the combination to be optimized is determined as the sequence to be optimized.

6. The method according to claim 5, characterized in that, The sequence to be optimized is generated based on the offspring individuals, including: For each offspring individual, if the random number is less than the preset parameter modification probability, at least one second position number is generated, and the parameter to be modified at the position indicated by the second position number in the offspring individual is replaced with other parameters to obtain the sequence to be optimized. The other parameters are parameters that are different from the parameter to be modified and are included in the current round of iteration population. If the random number is greater than or equal to the preset parameter modification probability, the offspring individual is determined as the sequence to be optimized.

7. The method according to claim 4, characterized in that, The correction operation is performed on multiple sequences to be optimized to obtain correction results, including: Multiple new pitch values ​​are determined based on the tire specifications, including the tire circumference, the functional relationship between the tire circumference and the pitch ratio, where the pitch ratio represents the ratio of two adjacent pitches in a tire. The sequence generated by arranging multiple new pitch values ​​according to preset constraints is determined as the correction result. The constraints include: the maximum number of consecutive rows is less than or equal to the preset number of consecutive rows, and the maximum new pitch value and the minimum new pitch value are not adjacent. The maximum number of consecutive rows is used to indicate the number of times the new pitch values ​​with the same value appear consecutively.

8. The method according to claim 4, characterized in that, A comprehensive evaluation of the multiple sequences to be optimized is performed from multiple noise evaluation dimensions, including: For each of the sequences to be optimized, a peak evaluation index and a fluctuation amplitude evaluation index are determined for the sequence to be optimized. The peak evaluation index is used to quantitatively evaluate the iterative optimization object from the noise assessment dimension of noise peak concentration, and the fluctuation amplitude evaluation index is used to quantitatively evaluate the iterative optimization object from the noise assessment dimension of noise distribution uniformity. The weighted sum of the peak value evaluation index and the fluctuation range evaluation index is determined as the comprehensive evaluation result of the sequence to be optimized.

9. A device for optimizing tire pitch, characterized in that, include: The acquisition module is used to acquire tire specification information and optimize task type. The tire specification information includes: relevant information about pitch, where pitch represents the distance between any two tread patterns on the tire. A generation module is used to generate multiple initial pitch sequences based on tire specification information, and to generate a first-round iteration population based on the multiple initial pitch sequences; An optimization module is used to execute an optimization strategy corresponding to the optimization task type on the current iteration population to obtain optimization results. The optimization strategy indicates that during each iteration optimization process, the current iteration population is comprehensively evaluated from multiple noise evaluation dimensions, and the next iteration population is determined based on the comprehensive evaluation results, until a preset iteration termination condition is reached. When the optimization strategy is executed for the first time, the current iteration population is the first iteration population. When the optimization strategy is not executed for the first time, the current iteration population is the next iteration population determined by the previous iteration optimization. A determination module is used to determine a target pitch sequence based on the optimization results, wherein the noise of the tire generated based on the target pitch sequence is less than the noise of the tire corresponding to the tire specification information.

10. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a computer program, wherein the device containing the non-volatile storage medium executes the method for optimizing tire pitch according to any one of claims 1 to 8 by running the computer program.

11. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method for optimizing tire pitch according to any one of claims 1 to 8 through the computer program.

12. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method for optimizing tire pitch as described in any one of claims 1 to 8.

Images