Tire design factor optimization method and apparatus

Abstract

The application discloses a tire design factor optimization method and device, and relates to the technical field of tire design. The method comprises the following steps: performing rolling simulation on a benchmark tire model of a tire under a target working condition to obtain a first simulation result; determining a plurality of optimization schemes corresponding to a to-be-optimized design factor of the tire, adjusting the benchmark tire model by using each optimization scheme, and performing rolling simulation on a plurality of target tire models after the adjustment under the target working condition to obtain corresponding second simulation results; determining a multi-dimensional evaluation index of each optimization scheme based on the first simulation result and the second simulation results of the plurality of optimization schemes, and determining a target optimization direction of the tire according to the multi-dimensional evaluation index of each optimization scheme and a preset optimization target, so as to optimize the to-be-optimized design factor. The application solves the technical problems that related technologies lack quantitative evaluation means, it is difficult to accurately judge the influence of different optimization schemes on steering wheel shaking, and the optimization effect is poor and the efficiency is low.

Description

Technical Field

[0001] This application relates to the field of tire structure design technology, and more specifically, to a tire design factor optimization method and apparatus. Background Technology

[0002] Steering wheel vibration is a common NVH (Noise, Vibration, and Harshness) problem during high-speed vehicle operation, seriously affecting driving comfort and safety. As the only component of a vehicle in contact with the ground, tires exhibit uneven circumferential mass distribution, geometric dimension distribution, and material stiffness distribution. These unevennesses generate periodic mechanical excitations during tire rotation, which are transmitted through the wheel center to the suspension and steering systems. When the excitation frequency approaches the vehicle system's natural frequency, resonance is easily triggered, leading to steering wheel vibration, increased body vibration, and decreased ride comfort.

[0003] Currently, existing technologies mainly address this issue by improving production uniformity control or adjusting tire structure design, but these methods have significant drawbacks: First, traditional structural optimization methods (such as adjusting design parameters like tread thickness, belt layer angle, belt layer width, crown belt material, cord material, triangular rubber material, sidewall thickness, and wrap height) heavily rely on engineers' experience and limited test tire results, lacking quantitative evaluation methods. This makes it difficult to accurately determine the specific impact of different structural factors on steering wheel vibration, leading to blind optimization and low efficiency. Second, existing joint distribution design methods (such as controlling tread joints, cord joints, belt layer joints, rubber distribution, and dynamic balance residue) are mostly based on process experience (such as uniformly staggering joints), failing to consider the differentiated impact of different joints on steering wheel vibration, resulting in limited optimization effects.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a tire design factor optimization method and apparatus to at least solve the technical problem that the lack of quantitative evaluation means in related technologies makes it difficult to accurately determine the impact of different optimization schemes on steering wheel vibration, resulting in poor optimization effect and low efficiency.

[0006] According to one aspect of the embodiments of this application, a tire design factor optimization method is provided, comprising: performing rolling simulation on a reference tire model under target operating conditions to obtain a first simulation result; determining multiple optimization schemes corresponding to the tire design factors to be optimized, adjusting the reference tire model using each optimization scheme, and performing rolling simulation on the adjusted multiple target tire models under target operating conditions to obtain a second simulation result corresponding to each optimization scheme; determining multi-dimensional evaluation indicators for each optimization scheme based on the first simulation result and the second simulation results corresponding to the multiple optimization schemes, and determining the target optimization direction of the tire based on the multi-dimensional evaluation indicators of the multiple optimization schemes and a preset optimization target, wherein the preset optimization target is used to characterize the expected adjustment direction of each evaluation indicator; and optimizing the tire design factors to be optimized according to the target optimization direction.

[0007] According to another aspect of the embodiments of this application, a tire design factor optimization device is also provided, comprising: a first simulation module, configured to perform rolling simulation on a reference tire model under a target working condition to obtain a first simulation result; a second simulation module, configured to determine multiple optimization schemes corresponding to the tire design factors to be optimized, adjust the reference tire model using each optimization scheme, and perform rolling simulation on the adjusted multiple target tire models under the target working condition to obtain a second simulation result corresponding to each optimization scheme; a determination module, configured to determine a multi-dimensional evaluation index for each optimization scheme based on the first simulation result and the second simulation result corresponding to the multiple optimization schemes, and determine the target optimization direction of the tire based on the multi-dimensional evaluation index of the multiple optimization schemes and a preset optimization target, wherein the preset optimization target is used to characterize the expected adjustment direction of each evaluation index; and an optimization module, configured to optimize the tire design factors to be optimized according to the target optimization direction.

[0008] In an exemplary embodiment, the above-described apparatus is further configured to perform rolling simulation on a reference tire model of the tire under a target working condition by means of the following method to obtain a first simulation result: constructing a reference tire model based on the tire parameters of the tire; parsing the working condition setting instructions input by the target object to obtain the working condition parameters of the target working condition, and configuring the simulation environment of the reference tire model according to the working condition parameters, wherein the working condition parameters include at least: rated air pressure, vertical load, camber angle and rolling boundary conditions, and the rolling boundary conditions include at least: rolling speed; controlling the reference tire model to perform rolling simulation in the simulation environment, and obtaining the first simulation result of the reference tire model after satisfying the rolling boundary conditions.

[0009] In an exemplary embodiment, the multidimensional evaluation index includes at least: a time-domain influence index and a frequency-domain influence index, and the tire parameters include at least: the tire rolling radius; the above-mentioned device is further used to determine the multidimensional evaluation index of each optimization scheme based on the first simulation result and the second simulation result corresponding to multiple optimization schemes by the following method: for each optimization scheme, based on the first simulation result and the second simulation result of the optimization scheme, determine the corresponding wheel center radial force difference sequence and wheel center longitudinal force difference sequence; determine the time-domain influence index based on the wheel center radial force difference sequence and the wheel center longitudinal force difference sequence; perform Fourier transform on the wheel center radial force difference sequence and the wheel center longitudinal force difference sequence respectively, and determine the frequency-domain influence index by combining the rolling speed and tire rolling radius in the target working condition.

[0010] In an exemplary embodiment, the time-domain influence index includes at least: wheel center radial force fluctuation and wheel center longitudinal force fluctuation; the above-mentioned device is further configured to determine the time-domain influence index based on the wheel center radial force difference sequence and the wheel center longitudinal force difference sequence by the following method: determining a first maximum difference and a first minimum difference within the wheel center radial force difference sequence, and taking the difference between the first maximum difference and the first minimum difference as the wheel center radial force fluctuation; determining a second maximum difference and a second minimum difference within the wheel center longitudinal force difference sequence, and taking the difference between the second maximum difference and the second minimum difference as the wheel center longitudinal force fluctuation.

[0011] In an exemplary embodiment, the frequency domain influence index includes at least: the radial force harmonic component and the longitudinal force harmonic component at the wheel center; the device is further configured to perform Fourier transform on the radial force difference sequence and the longitudinal force difference sequence at the wheel center respectively by the following method, and determine the frequency domain influence index in combination with the rolling speed and tire rolling radius in the target working condition, including: determining the rotation frequency corresponding to each order based on the rolling speed, tire rolling radius and multiple orders; performing Fourier transform on the radial force difference sequence and the longitudinal force difference sequence at the wheel center respectively to obtain the corresponding radial force frequency domain response spectrum and longitudinal force frequency domain response spectrum, and determining the amplitude at the rotation frequency corresponding to each order in the radial force frequency domain response spectrum and the longitudinal force frequency domain response spectrum respectively, to obtain the radial force harmonic component and the longitudinal force harmonic component at the wheel center.

[0012] In an exemplary embodiment, the above-described apparatus is further configured to determine the target optimization direction of the tire based on the multidimensional evaluation indicators of multiple optimization schemes and a preset optimization objective, by means of the following method: determining the average value of each dimension evaluation indicator corresponding to the design factor to be optimized at each value level based on the multidimensional evaluation indicators corresponding to the multiple optimization schemes respectively; for each evaluation indicator, calculating the difference between the maximum and minimum values ​​of the average values ​​of the design factor to be optimized at each value level under the evaluation indicator, using the obtained difference as the response range of the design factor to be optimized to the evaluation indicator, and determining the response trend of the design factor to be optimized to the evaluation indicator based on the changing pattern of the average value of the design factor to be optimized at each value level; constructing an influence matrix with the design factor to be optimized as the row, the multidimensional evaluation indicators as the column, and the response range and response trend of the design factor to be optimized at each value level as matrix elements; and determining the target optimization direction of the tire based on the influence matrix and the preset optimization objective.

[0013] In an exemplary embodiment, the above-described apparatus is further configured to determine the target optimization direction of the tire based on the influence matrix and a preset optimization objective by the following method: determining key design factors to be optimized based on the extreme values ​​of the responses of each design factor to be optimized in the influence matrix under each evaluation index; for each key design factor to be optimized, determining the target value level of the key design factor to be optimized based on the matching result between the response trend of the key design factor to be optimized in the influence matrix under each evaluation index and the preset optimization objective; and summarizing the target value levels of all key design factors to be optimized to obtain the target optimization direction of the tire.

[0014] In an exemplary embodiment, the design factors to be optimized include: structural design factors and / or joint distribution factors, wherein the structural design factors include at least one of the following: tread thickness, tread material, tread rubber material, tread rubber height, belt layer material, and belt layer thickness, and the joint distribution factors include at least one of the following: tread joint position, inner liner joint position, belt layer joint position, and crown belt structure position.

[0015] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, which stores a computer program, wherein the computer-readable storage medium, when executed by a processor, performs the steps in any of the above method embodiments.

[0016] According to another aspect of the embodiments of this application, a computer program product or computer program is also provided, which includes a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, causing the computer device to perform the steps in any of the method embodiments described above.

[0017] 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 steps of any of the above method embodiments through the computer program.

[0018] In this embodiment, the first simulation result of the benchmark tire model under target working conditions is obtained. Then, the benchmark tire model is adjusted according to multiple optimization schemes corresponding to the tire's design factors to be optimized, and the second simulation result of the adjusted target tire models under target working conditions is obtained. Based on the first simulation result, each of the second simulation results, and the preset optimization target, the target optimization direction that can reduce the risk of tire steering wheel vibration is determined. Finally, the design factors to be optimized are optimized according to the target optimization direction. This enables quantitative evaluation of the influence of different structural parameters and joint distribution on the wheel center force response, overcomes the problem of traditional optimization relying on experience and high trial and error costs, and achieves the purpose of reducing the risk of tire steering wheel vibration and improving the optimization efficiency and accuracy of tire structure and joint distribution. This solves the technical problem that related technologies lack quantitative evaluation methods, making it difficult to accurately judge the influence of different optimization schemes on steering wheel vibration, resulting in poor optimization effect and low efficiency. Attached Figure Description

[0019] 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:

[0020] Figure 1 This is a flowchart illustrating an optional tire design factor optimization method according to an embodiment of this application.

[0021] Figure 2 This is a schematic diagram of an optional process for determining a first simulation result according to an embodiment of this application;

[0022] Figure 3 This is a flowchart illustrating an optional method for determining multidimensional evaluation indicators for optimization, according to an embodiment of this application.

[0023] Figure 4 This is a schematic diagram of an optional process for determining the target optimization direction of a tire according to an embodiment of this application;

[0024] Figure 5 This is a structural block diagram of an optional tire design factor optimization device according to an embodiment of this application;

[0025] Figure 6This is a structural block diagram of an optional computer terminal for implementing a tire design factor optimization method according to an embodiment of this application. Detailed Implementation

[0026] 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.

[0027] It should be noted that the terms "first," "second," etc., used in the specification, claims, and 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.

[0028] According to an embodiment of this application, a tire design factor optimization method 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.

[0029] Figure 1 This is a flowchart illustrating a tire design factor optimization method according to an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps:

[0030] Step S102: Perform a rolling simulation on the reference tire model of the tire under the target working condition to obtain the first simulation result.

[0031] In the embodiments of this application, the above-mentioned target working condition refers to the specific boundary conditions and load states that the tire bears when the target tire model is subjected to rolling simulation. These conditions include, but are not limited to: rated air pressure (i.e., the tire is inflated to the specified standard pressure), vertical load (i.e., the load on the tire in the vertical direction of the vehicle), camber angle (i.e., the angle between the tire center plane and the vertical direction), and rolling boundary conditions (such as rolling speed).

[0032] In the embodiments of this application, the aforementioned benchmark tire model is a finite element simulation model that represents the initial design state or standard design state of the tire without any specific structural parameter adjustments or joint distribution optimization.

[0033] In the embodiments of this application, the above-mentioned hybrid simulation is to simulate the dynamic process of a tire rotating around the axle and contacting the road surface under the target working conditions by using finite element analysis or rigid-flexible coupling dynamics. This process involves relative sliding or pure rolling.

[0034] In the embodiments of this application, the first simulation result is the reaction force time-domain signal sequence at the wheel center reference point collected when the tire with uniform parameters is in a stable rolling state under the target working condition. It typically includes: the wheel center radial force sequence (i.e., the sequence of the force on the tire center in the radial direction changing with time) and the wheel center longitudinal force sequence (i.e., the sequence of the force on the tire center in the vertical direction changing with time) within a single rolling cycle.

[0035] Step S104: Determine multiple optimization schemes corresponding to the design factors to be optimized for the tire, adjust the benchmark tire model using each optimization scheme, and perform rolling simulations on the adjusted multiple target tire models under the target working conditions to obtain the second simulation results corresponding to each optimization scheme.

[0036] In this embodiment of the application, the aforementioned design factors to be optimized are a set of adjustable variables that affect the risk of tire steering wheel vibration, including but not limited to: structural design factors (such as tread thickness, tread material, sidewall thickness, triangular rubber material, triangular rubber height, ply material, crown belt material, belt layer material, belt layer width, belt layer angle and reverse wrap height), and joint distribution factors (such as tread joint position, inner liner joint position, first ply joint position, second ply joint position, belt layer joint position, crown belt joint position, and circumferential phase angle between multiple joints).

[0037] In the embodiments of this application, the above-mentioned optimization schemes are specific discrete value combinations set for the above-mentioned design factors to be optimized. Specifically, when the design factor to be optimized is a structural design factor, orthogonal experiments, Taguchi experiments, etc., can be used to establish a structural scheme matrix, and each optimization scheme corresponds to a specific set of structural design parameters, such as optimization scheme A, which reduces the tread thickness by 5mm and the belt layer angle by 2 degrees; when the design factor to be optimized is a joint distribution factor, orthogonal experiments, Taguchi experiments, etc., can also be used to establish a joint distribution scheme matrix, and each optimization scheme corresponds to a specific joint phase arrangement, such as optimization scheme B, where cord joint 1 and cord joint 2 are distributed at 90 degrees; when the design factors to be optimized are structural design factors and joint distribution factors, a comprehensive candidate scheme matrix can be established, and each optimization scheme corresponds to a specific set of structural design parameters and a specific joint phase arrangement.

[0038] In this embodiment, the target tire model is a finite element simulation model in a test state generated by replacing the design factors to be optimized (such as structural distribution factors) in the baseline tire model with specific values ​​or positions set in the corresponding optimization scheme. Therefore, each optimization scheme corresponds to an independent target tire model.

[0039] In this embodiment, the second simulation result is a sequence of reaction forces at the wheel center reference point collected after each target tire model performs rolling simulation under the same target working conditions. This typically includes: a wheel center radial force sequence (i.e., a sequence showing the change in force on the tire center radially over time) and a wheel center longitudinal force sequence (i.e., a sequence showing the change in force on the tire center vertically over time) within a single rolling cycle. This simulation result reflects the tire's mechanical response under a specific optimization scheme.

[0040] Step S106: Based on the first simulation results and the second simulation results corresponding to multiple optimization schemes, determine the multi-dimensional evaluation index of each optimization scheme, and determine the target optimization direction of the tire based on the multi-dimensional evaluation index of multiple optimization schemes and the preset optimization target.

[0041] In this embodiment of the application, the above-mentioned multidimensional evaluation index is a set of values ​​that reflect the degree of influence of each optimization scheme on the wheel center force fluctuation and periodic excitation, based on the first simulation results and the second simulation results of each optimization scheme through time domain statistical analysis and frequency domain analysis.

[0042] In the embodiments of this application, the above-mentioned preset optimization objective is the expected adjustment direction or threshold requirement of each dimension of evaluation index set according to the steering wheel vibration risk control requirements. It is usually characterized as these evaluation indicators should be "minimized" or "below a certain limit" as much as possible to suppress vehicle vibration caused by uneven input.

[0043] In this embodiment, the aforementioned target optimization direction is determined by comparing and analyzing the multi-dimensional evaluation indicators of the preset optimization target and each optimization scheme to determine the adjustment trend of the design factor to be optimized. Therefore, the target optimization direction does not refer to a specific final value, but rather a parameter adjustment trend, used to guide engineers or automatic optimization algorithms in which direction to modify design variables, thereby minimizing the wheel center force fluctuation of the tire under the same uneven input, and ultimately achieving the goal of reducing the risk of steering wheel vibration.

[0044] Step S108: Optimize the design factors of the tire according to the target optimization direction.

[0045] In the embodiments of this application, the above-mentioned optimization refers to correcting the design factors to be optimized in the tire according to the target optimization direction (i.e. parameter adjustment trend) obtained from the previous step, so as to reduce the risk of tire steering wheel vibration.

[0046] Based on the scheme defined in steps S102 to S108 above, it can be understood that in this embodiment, the first simulation result of the benchmark tire model under target working conditions is first obtained, and then the benchmark tire model is adjusted according to multiple optimization schemes corresponding to the tire's design factors to be optimized, and the second simulation result of the adjusted multiple target tire models under target working conditions is obtained. Based on the first simulation result, each of the second simulation results and the preset optimization target, the target optimization direction that can reduce the risk of tire steering wheel vibration is determined. Finally, the design factors to be optimized are optimized according to the target optimization direction, which realizes the ability to quantitatively evaluate the influence of different structural parameters and joint distribution on the wheel center force response, overcomes the problem of traditional optimization relying on experience and high trial and error costs, and achieves the purpose of reducing the risk of tire steering wheel vibration and improving the optimization efficiency and accuracy of tire structure and joint distribution.

[0047] The following describes each step of the tire design factor optimization method provided in the embodiments of this application, with reference to specific implementation processes.

[0048] As an optional approach, the first simulation results can be obtained through the following... Figure 2 The process steps shown yield the following results:

[0049] Step S11: Construct a baseline tire model based on the tire parameters.

[0050] In this embodiment, the tire parameters include geometrical parameters, material property parameters, and structural definition parameters required to construct a reference tire model. Geometrical parameters include, but are not limited to, tire rolling radius, tire specification parameters (such as section width, aspect ratio, rim diameter, etc.), and geometrical dimensions of each component (such as tread thickness, belt layer thickness, carcass thickness, bead size, etc.). Material property parameters include, but are not limited to, density parameters, elastic modulus parameters, Poisson's ratio, rubber hardness parameters, cord parameters, and carcass material parameters. Structural definition parameters include, but are not limited to, the number of layers in each tire component, ply angles (such as belt layer angles and carcass cord angles), tread type, and depth.

[0051] For example, step S11 above can be understood as follows: First, based on the geometric dimension parameters, a reference geometric model of the tire is constructed, wherein the three-dimensional geometric models of each component in the reference geometric model have a uniform structural distribution in the circumferential direction; based on the material property parameters, corresponding material properties are assigned to each three-dimensional geometric model in the reference geometric model, and the ply angle and thickness of each three-dimensional geometric model in the reference geometric model are defined based on the structural definition parameters, thereby obtaining a reference tire model with a hyperelastic constitutive relationship.

[0052] Step S12: parse the working condition setting instructions input by the target object, obtain the working condition parameters of the target working condition, and configure the simulation environment of the benchmark tire model according to the working condition parameters.

[0053] In the embodiments of this application, the above-mentioned operating condition setting instructions are a set of data structures, signals or text information input by the target object to define the specific operating environment of tire simulation or testing. The form of these instructions may be a file, an API call request or a discrete control signal, etc.

[0054] In this embodiment of the application, the target operating condition is a set of specific operating states determined after analysis and used in this simulation or experiment. The operating condition parameters are specific quantitative values ​​or state variables that constitute the target operating condition, including but not limited to: rated air pressure (e.g., 2.5 bar), vertical load (e.g., 3000 N), outward tilt angle (e.g., -1.0 degrees), and rolling boundary conditions, etc., and the rolling boundary conditions include at least: rolling speed (e.g., 100 km / h).

[0055] For example, step S12 above can be understood as follows: The system receives the working condition setting instruction input by the target object (such as a user terminal or host computer), reads the instruction content, identifies and extracts key fields (such as air pressure value, load value, angle value, speed value, etc.), and converts them into numerical or string-type working condition parameters that can be processed internally by the system; then, in the simulation software or finite element preprocessing interface, a mapping relationship table between the working condition parameters and the simulation environment variables of the benchmark tire model is established, and the model input node or boundary condition attribute corresponding to each working condition parameter is clarified (for example, mapping "rated air pressure" to the air pressure boundary condition attribute of the model, mapping "vertical load" to the boundary condition attribute of the model, mapping "rated air pressure" to the ... (The system maps the load to the force constraints at the wheel center). Then, based on the mapping relationship, it reads the analytical working condition parameter values ​​and automatically calls the simulation software's API or script interface to dynamically configure the simulation environment of the benchmark tire model. This involves assigning the extracted values ​​to the corresponding boundary condition variables, thereby completing the initialization settings of simulation environment parameters such as inflation pressure, external load, motion constraints, and rolling speed. Finally, after configuration, the system automatically reads the current environmental variable values ​​of the model and compares them with the input working condition parameters. If they match, the simulation environment configuration is confirmed to be successful, and the system is ready to enter the simulation stage. If they do not match, an error is reported and a parameter configuration error is indicated.

[0056] Step S13: Control the reference tire model to perform rolling simulation in the simulation environment, and obtain the first simulation result of the reference tire model after satisfying the rolling boundary conditions.

[0057] In this embodiment, the state of the benchmark tire model after satisfying the rolling boundary conditions is called the "stable simulation state." This state is when the fluctuation amplitude of the internal stress field, strain field, contact pressure distribution, and wheel center force (such as radial force, longitudinal force, lateral force, etc.) of the benchmark tire model over time (or rolling angle) converges to a preset threshold range under the action of inflation, loading, and rolling boundary conditions. At this time, the tire vibration response no longer increases significantly with the rolling cycle, but enters a periodic and stable dynamic equilibrium stage.

[0058] For example, step S13 above can be understood as follows: After the simulation environment is configured, the solver engine is called to initialize the solution process; the solver first performs tire inflation and static contact search until the model reaches mechanical equilibrium; after static equilibrium is satisfied, the set rolling boundary conditions (such as wheel center displacement, rotation speed or road surface movement speed) are applied to drive the reference tire model into dynamic rolling state; the solver iteratively calculates the contact mechanical behavior and internal stress and strain distribution between the tire and the road surface through a time stepping algorithm; during the rolling process, the system continuously monitors the tire state, and when it detects that the tire has entered the stable rolling stage (i.e., the wheel center force fluctuation tends to be periodically stable and there is no longer a significant transient impact), it starts to collect reaction force data and kinematic data at the wheel center reference point (or equivalent wheel center position) at preset time intervals or rotation angle intervals; the original signal sequence of the reaction force data and kinematic data at the wheel center reference point (or equivalent wheel center position) collected in the stable rolling stage as a function of time or rotation angle, and the corresponding working condition parameter information, are packaged into the first simulation result file.

[0059] Based on the scheme defined in steps S11 to S13 above, benchmark response data under steady-state conditions can be obtained, providing an accurate comparable reference for subsequent quantitative evaluation of the impact of local non-uniform parameters on tire dynamic performance.

[0060] Furthermore, the influencing factors affecting tire steering wheel vibration are identified and used as design factors to be optimized for the tire. These factors include at least: structural design factors (i.e., structural parameters sensitive to wheel center force response) and / or structural distribution factors (i.e., the positional parameters of the forming joint). Multiple optimization schemes corresponding to the design factors to be optimized are then determined. Different value levels must be set for the multiple optimization schemes of the same design factor to ensure clear evaluation indicators. For example, if the design factor to be optimized is a structural design factor, different candidate schemes must reflect changes in structural design parameters. For instance, two or more levels can be set for optimization schemes of different structural design factors. For example, tread thickness can be set to three levels: thinning, baseline, and thickening; belt layer angle can be set to three levels: small angle, medium angle, and large angle; and belt layer material, cord material, and crown material can be set to different stiffnesses or material specifications according to design requirements. If the design factor to be optimized is a joint distribution factor, different candidate schemes must reflect changes in the circumferential phase of the joint. For example, multiple joints can be set to the same circumferential position, spaced 90° apart, 120° apart, 180° apart, or other specified phase relationships. If the design factors to be optimized are structural design factors and joint distribution factors, then the combination relationship between structural parameters and joint distribution should be considered simultaneously.

[0061] Furthermore, the baseline tire finite element model, after mesh generation, material definition, and boundary condition setting, is first copied to generate multiple model copies, matching the number of optimization schemes, as the tire models to be processed. Then, based on the design factors to be optimized, the tire models to be processed are differentiated and adjusted accordingly.

[0062] If the design factor to be optimized is a structural design factor, the geometric dimension parameters of the corresponding components (such as tread, belt layer, ply layer, triangular rubber, sidewall, etc.) can be modified according to the values ​​in each optimization scheme. In particular, if the design material of the optimization scheme is changed, the material property data of the corresponding layer is replaced to obtain the corresponding target tire model.

[0063] If the design factor to be optimized is a structural design factor, then according to the preset circumferential segmentation, the set of joint area elements for simulating the joint can be identified in the baseline tire model (e.g., if the tire circumference is divided into 360 degrees, all elements in the range of 0°-4° can be selected as the set of joint area elements). Then, the set of joint area elements is isolated from the original continuous skeleton layer or cover layer, and the geometry and mesh nodes of the set of joint area elements are moved to the specified overlap position. By defining contact relationships, binding constraints, or shared nodes, the physical connection state and overlap length at the joint are simulated. Finally, according to the circumferential phase angle set in the corresponding optimization scheme, the constructed joint structure is rotated to a specific angular position in the tire circumference to form a specific joint distribution pattern, thereby obtaining the corresponding target tire model.

[0064] As an optional approach, for each adjusted target tire model, rolling simulation can be performed on the target tire model under the target working condition in the following manner to obtain the second simulation result corresponding to the optimization scheme: parsing the working condition setting command input by the target object to obtain the working condition parameters of the target working condition, and configuring the simulation environment of the benchmark tire model according to the working condition parameters. The working condition parameters include at least: rated air pressure, vertical load, camber angle, simulation time, rim conditions, rolling boundary conditions, etc., and the rolling boundary conditions include at least: rolling speed; controlling the target tire model to perform rolling simulation in the simulation environment, and obtaining the second simulation result of the target tire model after satisfying the rolling boundary conditions.

[0065] Since the simulation process has been described in detail in the determination of the first simulation result, it will not be repeated here. It should also be noted that, to ensure the comparability of the simulation results, the simulation conditions corresponding to the first simulation result and the second simulation results for each optimization scheme are the same.

[0066] As an optional approach, for the optimized scheme, based on the first simulation result and the corresponding second simulation result, it can be determined according to... Figure 3 The process shown is used to determine the multidimensional evaluation metrics for the optimization scheme, including:

[0067] Step S21: Based on the first simulation results and the second simulation results of the target tire model, determine the radial force difference sequence and the longitudinal force difference sequence at the wheel center.

[0068] In this embodiment, the aforementioned wheel center radial force difference sequence is the wheel center radial force sequence within the first simulation result. The wheel center radial force sequence in the second simulation results of the target tire model The sequence of differences at the same time point (or the same rolling angle) can be denoted as: This sequence reflects the additional radial force fluctuation component generated when a specific non-uniformity exists in a local area of ​​the tire.

[0069] In this embodiment, the aforementioned wheel center longitudinal force difference sequence is the wheel center longitudinal force sequence within the first simulation result. The wheel center longitudinal force sequence in the second simulation results of the target tire model The sequence of differences at the same time point (or the same rolling angle) can be denoted as: This sequence reflects the additional longitudinal force fluctuation component generated when there is a specific non-uniformity in a local area of ​​the tire.

[0070] Step S22: Determine the time-domain influence index based on the wheel center radial force difference sequence and the wheel center longitudinal force difference sequence. The aforementioned time-domain influence index includes at least: wheel center radial force fluctuation and wheel center longitudinal force fluctuation.

[0071] Specifically, the aforementioned time-domain influence index can be determined by the following method: determining the first maximum difference and the first minimum difference within the wheel center radial force difference sequence, and taking the difference between the first maximum difference and the first minimum difference as the wheel center radial force fluctuation; determining the second maximum difference and the second minimum difference within the wheel center longitudinal force difference sequence, and taking the difference between the second maximum difference and the second minimum difference as the wheel center longitudinal force fluctuation.

[0072] Therefore, the expression for the radial force fluctuation at the wheel center can be written as: The expression for the longitudinal force fluctuation at the wheel center mentioned above can be written as: In the formula, Represents the radial force difference sequence at the wheel center The first maximum difference within, Represents the radial force difference sequence at the wheel center The first minimum difference within, Represents the longitudinal force difference sequence at the wheel center The second largest difference within, Represents the longitudinal force difference sequence at the wheel center The second smallest difference within.

[0073] Step S23: Perform Fourier transform on the radial force difference sequence and the longitudinal force difference sequence of the wheel center respectively, and determine the frequency domain influence index by combining the rolling speed and tire rolling radius in the target working condition.

[0074] Specifically, the aforementioned frequency domain influence indicators can be determined by the following method: based on the rolling speed, tire rolling radius, and multiple orders, determine the rotation frequency corresponding to each order; perform Fourier transform on the radial force difference sequence and the longitudinal force difference sequence of the wheel center respectively to obtain the corresponding radial force frequency domain response spectrum and longitudinal force frequency domain response spectrum, and determine the amplitude at the rotation frequency corresponding to each order in the radial force frequency domain response spectrum and the longitudinal force frequency domain response spectrum respectively to obtain the wheel center radial force harmonic component and the wheel center longitudinal force harmonic component.

[0075] The expressions for the rotation frequencies corresponding to each of the above orders can be written as: In the formula The value represents the fundamental frequency generated when the tire rotates one revolution, v represents the rolling speed within the target working condition (in m / s), R represents the tire rolling radius (in m), and N represents the order.

[0076] The radial force frequency response spectrum mentioned above is a spectrum obtained by performing a Fourier transform on the radial force difference sequence at the wheel center. Its horizontal axis represents frequency (or order), and its vertical axis represents amplitude (i.e., the magnitude of the force). It shows the magnitude of the force of each frequency component excited by the local non-uniform line in the radial (vertical) direction.

[0077] Among them, the above longitudinal force frequency domain response spectrum is a spectrum obtained by performing a Fourier transform on the longitudinal force difference sequence of the wheel center. Its horizontal axis is frequency (or order) and its vertical axis is amplitude (i.e. the magnitude of the force). It shows the magnitude of the force of each frequency component excited by the local non-uniform line in the longitudinal direction (forward direction).

[0078] Among them, the aforementioned radial force harmonic components of the wheel center are the amplitudes at the rotational frequencies corresponding to each order in the radial force frequency domain response spectrum. For example, "first-order radial force harmonic component" represents the amplitude of radial force fluctuations excited by one time the rotational frequency.

[0079] Among them, the above-mentioned longitudinal force harmonic components of the wheel center are the amplitudes at the rotation frequencies corresponding to each order in the longitudinal force frequency domain response spectrum. For example, "second-order longitudinal force harmonic component" means the amplitude of longitudinal force fluctuation excited by twice the rotation frequency.

[0080] By constructing a multi-dimensional evaluation index system that includes time-domain fluctuation amplitude (reflecting the extreme characteristics of force change) and frequency-domain harmonic components (reflecting the degree of energy concentration at a specific frequency), the influence of tire structure and joint distribution on wheel center force response can be comprehensively quantified from two dimensions: macroscopic fluctuation amplitude and microscopic periodic excitation. This effectively overcomes the one-sidedness of evaluation by a single time-domain or frequency-domain index.

[0081] As an optional approach, based on the multidimensional evaluation indicators of multiple optimization schemes and the preset optimization objectives, through methods such as... Figure 4 The process steps shown determine the target optimization direction for the tire, including:

[0082] Step S31: Based on the extreme values ​​of the responses of each design factor to be optimized in the influence matrix under each evaluation index, determine the key design factors to be optimized.

[0083] In this embodiment, the aforementioned key design factors to be optimized are ranked from largest to smallest based on the magnitude of the response extreme values ​​under each evaluation index. The top N design factors in the ranking are then selected as key email objects. This is because the response range reflects the magnitude of the change in the evaluation index caused by the change in the value level of the design factor to be optimized; therefore, the larger the response range, the more significant and sensitive the impact of the design factor to be optimized on the evaluation index. Thus, the top-ranked design factors are the primary causes of fluctuations in the evaluation index, and adjusting them can bring the most significant performance improvement. Therefore, these design factors are prioritized for optimization.

[0084] Step S32: For each key design factor to be optimized, determine the target value level of the key design factor to be optimized based on the matching results of the response trend of the key design factor to be optimized under each evaluation index in the influence matrix and the preset optimization target.

[0085] In this embodiment, the above-mentioned matching refers to matching the response trend of the key design factor to be optimized under each evaluation index with the expected improvement direction of each evaluation index in the preset optimization target. If the response trend of the design factor to be optimized under the target evaluation index is consistent with the expected improvement direction of the target evaluation index in the preset optimization target, then the design factor to be optimized is determined to be positively optimized under the target evaluation index. Conversely, if the response trend of the design factor to be optimized under the target evaluation index is opposite to the expected improvement direction of the target evaluation index in the preset optimization target, then the design factor to be optimized is determined to be negatively optimized under the target evaluation index. Furthermore, if the key structural design factor is determined to be positively optimized under all or most of the multidimensional evaluation indicators, its corresponding optimal level is selected as the target optimization level. The aforementioned optimal level is the level at which the arithmetic mean of the evaluation indicators corresponding to the key structural design factor reaches the optimal value, under the premise of satisfying the expected improvement direction of the preset optimization objective. When there is a conflict between positive and negative optimization of the key structural design factor under different evaluation indicators, the candidate value levels of the key structural design factor under each evaluation indicator are weighted according to the priority weight of each evaluation indicator in the preset optimization objective (i.e., the importance of each evaluation indicator in the overall optimization, and the sum of the priority weights of all evaluation indicators equals 1). The comprehensive scores corresponding to various combinations of candidate value levels are compared, and the candidate value level combination with the best comprehensive score (the smallest score for evaluation indicators that are better the smaller the better, and the largest score for evaluation indicators that are better the larger the better) is selected as the target value level of the key structural design factor. It should be noted that if multiple candidate value level combinations have the same or similar comprehensive scores, then the candidate value level combination with lower process implementation difficulty, lower cost, or better compatibility with other unoptimized design factors shall be selected as the target value level of the key structural design factor.

[0086] Step S34: Summarize the target value levels of all key design factors to be optimized to obtain the target optimization direction of the tire.

[0087] The above technical solution can accurately identify the key design factors that have the most significant impact on the risk and uniformity of tire steering wheel vibration, and automatically match the optimal value level according to the preset optimization target. This effectively addresses the problems of selection difficulties, high trial and error costs, and unclear optimization direction caused by multi-factor coupling in traditional tire design.

[0088] In summary, the tire design factor optimization method provided in this application evaluates the influence of structural design factors and / or structural distribution factors on wheel center force by conducting finite element simulations or experiments on the optimization schemes corresponding to structural design factors and / or structural distribution factors under a unified target working condition. This coupled evaluation method can reveal the interaction between structural parameters and joint distribution, avoiding the negative effects of neglecting one aspect while optimizing another. Secondly, by analyzing the response range (degree of influence) and response trend (direction of influence) of each design factor to be optimized at different levels to the evaluation index, a visualized influence matrix is ​​constructed to clearly identify which factors are key influencing factors and in which direction the key influencing factors should be adjusted. This not only overcomes the interference caused by differences in tire manufacturing and achieves decoupling and quantification of the influence of design factors, but also provides engineers with intuitive and quantitative optimization basis. Combining time-domain and frequency-domain indicators directly relates the tire structure / joint to the vibration problem (such as steering wheel vibration) in actual vehicle driving, making the optimization results have clear physical meaning and direct vehicle performance improvement effect, solving the problem of the disconnect between evaluation indicators and actual driving comfort in traditional methods.

[0089] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0090] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / random access memory (RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0091] According to another aspect of the embodiments of this application, a tire design factor optimization apparatus for implementing the tire design factor optimization method provided in the above embodiments is also provided, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0092] Figure 5 This is a structural block diagram of an optional tire design factor optimization device according to an embodiment of this application, such as... Figure 5 As shown, the tire design factor optimization device includes:

[0093] The first simulation module 52 is used to perform rolling simulation on the reference tire model of the tire under the target working condition and obtain the first simulation result;

[0094] The second simulation module 54 is used to determine multiple optimization schemes corresponding to the design factors to be optimized for the tire optimization object, adjust the benchmark tire model using each optimization scheme, and perform rolling simulations on the adjusted multiple target tire models under the target working conditions to obtain the second simulation results corresponding to each optimization scheme.

[0095] The determination module 56 is used to determine the multi-dimensional evaluation index of each optimization scheme based on the first simulation result and the second simulation result corresponding to multiple optimization schemes, and to determine the target optimization direction of the tire based on the multi-dimensional evaluation index of multiple optimization schemes and the preset optimization target. The preset optimization target is used to characterize the expected adjustment direction of each evaluation index.

[0096] The optimization module 58 is used to optimize the design factors of the tire optimization object according to the target optimization direction.

[0097] In an exemplary embodiment, the above-described apparatus is further configured to perform rolling simulation on a reference tire model of the tire under a target working condition by means of the following method to obtain a first simulation result: constructing a reference tire model based on the tire parameters of the tire; parsing the working condition setting instructions input by the target object to obtain the working condition parameters of the target working condition, and configuring the simulation environment of the reference tire model according to the working condition parameters, wherein the working condition parameters include at least: rated air pressure, vertical load, camber angle and rolling boundary conditions, and the rolling boundary conditions include at least: rolling speed; controlling the reference tire model to perform rolling simulation in the simulation environment, and obtaining the first simulation result of the reference tire model after satisfying the rolling boundary conditions.

[0098] In an exemplary embodiment, the multidimensional evaluation index includes at least: a time-domain influence index and a frequency-domain influence index, and the tire parameters include at least: the tire rolling radius; the above-mentioned device is further used to determine the multidimensional evaluation index of each optimization scheme based on the first simulation result and the second simulation result corresponding to multiple optimization schemes by the following method: for each optimization scheme, based on the first simulation result and the second simulation result of the optimization scheme, determine the corresponding wheel center radial force difference sequence and wheel center longitudinal force difference sequence; determine the time-domain influence index based on the wheel center radial force difference sequence and the wheel center longitudinal force difference sequence; perform Fourier transform on the wheel center radial force difference sequence and the wheel center longitudinal force difference sequence respectively, and determine the frequency-domain influence index by combining the rolling speed and tire rolling radius in the target working condition.

[0099] In an exemplary embodiment, the time-domain influence index includes at least: wheel center radial force fluctuation and wheel center longitudinal force fluctuation; the above-mentioned device is further configured to determine the time-domain influence index based on the wheel center radial force difference sequence and the wheel center longitudinal force difference sequence by the following method: determining a first maximum difference and a first minimum difference within the wheel center radial force difference sequence, and taking the difference between the first maximum difference and the first minimum difference as the wheel center radial force fluctuation; determining a second maximum difference and a second minimum difference within the wheel center longitudinal force difference sequence, and taking the difference between the second maximum difference and the second minimum difference as the wheel center longitudinal force fluctuation.

[0100] In an exemplary embodiment, the frequency domain influence index includes at least: the radial force harmonic component and the longitudinal force harmonic component at the wheel center; the device is further configured to perform Fourier transform on the radial force difference sequence and the longitudinal force difference sequence at the wheel center respectively by the following method, and determine the frequency domain influence index in combination with the rolling speed and tire rolling radius in the target working condition, including: determining the rotation frequency corresponding to each order based on the rolling speed, tire rolling radius and multiple orders; performing Fourier transform on the radial force difference sequence and the longitudinal force difference sequence at the wheel center respectively to obtain the corresponding radial force frequency domain response spectrum and longitudinal force frequency domain response spectrum, and determining the amplitude at the rotation frequency corresponding to each order in the radial force frequency domain response spectrum and the longitudinal force frequency domain response spectrum respectively, to obtain the radial force harmonic component and the longitudinal force harmonic component at the wheel center.

[0101] In an exemplary embodiment, the above-described apparatus is further configured to determine the target optimization direction of the tire based on the multidimensional evaluation indicators of multiple optimization schemes and a preset optimization objective, by means of the following method: determining the average value of each dimension evaluation indicator corresponding to the design factor to be optimized at each value level based on the multidimensional evaluation indicators corresponding to the multiple optimization schemes respectively; for each evaluation indicator, calculating the difference between the maximum and minimum values ​​of the average values ​​of the design factor to be optimized at each value level under the evaluation indicator, using the obtained difference as the response range of the design factor to be optimized to the evaluation indicator, and determining the response trend of the design factor to be optimized to the evaluation indicator based on the changing pattern of the average value of the design factor to be optimized at each value level; constructing an influence matrix with the design factor to be optimized as the row, the multidimensional evaluation indicators as the column, and the response range and response trend of the design factor to be optimized at each value level as matrix elements; and determining the target optimization direction of the tire based on the influence matrix and the preset optimization objective.

[0102] In an exemplary embodiment, the above-described apparatus is further configured to determine the target optimization direction of the tire based on the influence matrix and a preset optimization objective by means of the following method:

[0103] Based on the extreme values ​​of the responses of each design factor to be optimized in the influence matrix under each evaluation index, the key design factors to be optimized are determined. For each key design factor to be optimized, the target value level of the key design factor to be optimized is determined based on the matching results of the response trends of the key design factors to be optimized in the influence matrix under each evaluation index and the preset optimization objectives. The target value levels of all key design factors to be optimized are summarized to obtain the target optimization direction of the tire.

[0104] In an exemplary embodiment, the design factors to be optimized include: structural design factors and / or joint distribution factors, wherein the structural design factors include at least one of the following: tread thickness, tread material, tread rubber material, tread rubber height, belt layer material, and belt layer thickness, and the joint distribution factors include at least one of the following: tread joint position, inner liner joint position, belt layer joint position, and crown belt structure position.

[0105] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0106] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0107] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0108] Optionally, in this embodiment, the computer program in the above-described computer program product can be configured to perform the following steps when executed by a processor:

[0109] Step S1: Perform a rolling simulation on the reference tire model under the target working condition to obtain the first simulation result;

[0110] Step S2: Determine multiple optimization schemes corresponding to the design factors to be optimized for the tire, adjust the benchmark tire model using each optimization scheme, and perform rolling simulations on the adjusted multiple target tire models under the target working conditions to obtain the second simulation results corresponding to each optimization scheme.

[0111] Step S3: Based on the first simulation results and the second simulation results corresponding to multiple optimization schemes, determine the multi-dimensional evaluation index of each optimization scheme, and determine the target optimization direction of the tire according to the multi-dimensional evaluation index of multiple optimization schemes and the preset optimization target. The preset optimization target is used to characterize the expected adjustment direction of each evaluation index.

[0112] Step S4: Optimize the design factors of the tire according to the target optimization direction.

[0113] According to another aspect of the embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is configured to perform the steps of any of the method embodiments described above via the computer program. In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0114] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0115] According to another aspect of the embodiments of this application, a computer program product is also provided, the computer program product including a computer program / instructions containing program code for performing the method shown in the flowchart.

[0116] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0117] Step S1: Perform a rolling simulation on the reference tire model under the target working condition to obtain the first simulation result;

[0118] Step S2: Determine multiple optimization schemes corresponding to the design factors to be optimized for the tire, adjust the benchmark tire model using each optimization scheme, and perform rolling simulations on the adjusted multiple target tire models under the target working conditions to obtain the second simulation results corresponding to each optimization scheme.

[0119] Step S3: Based on the first simulation results and the second simulation results corresponding to multiple optimization schemes, determine the multi-dimensional evaluation index of each optimization scheme, and determine the target optimization direction of the tire according to the multi-dimensional evaluation index of multiple optimization schemes and the preset optimization target. The preset optimization target is used to characterize the expected adjustment direction of each evaluation index.

[0120] Step S4: Optimize the design factors of the tire according to the target optimization direction.

[0121] As an alternative implementation, the above-mentioned electronic device may exist in the form of a mobile terminal, a computer terminal, or a similar computing device. Figure 6 A hardware block diagram of a computer terminal for implementing a tire design factor optimization method is shown. Figure 6 As shown, the computer terminal 60 may include a processor 602, a memory 604 for storing data, and a transmission device 606 for communication functions. The processor 602 may include one or more processors. For ease of explanation, Figure 6 The diagram illustrates multiple processors, including a first processor 602a, a second processor 602b, ..., an nth processor 602n. Processor 602 may be, but is not limited to, a processing device such as a microprocessor (MCU) or a field-programmable gate array (FPGA).

[0122] In addition, the computer terminal 60 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 6 The structure shown is for illustrative purposes only and does not limit the structure of the computer terminal described above. For example, computer terminal 60 may also include... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0123] Processor 602 may include one or more processing units. For example, processor 602 may include processing devices such as central processing unit (CPU), graphics processing unit (GPU), digital signal processing (DSP) chip, microcontroller unit (MCU), field-programmable gate array (FPGA), neural network processing unit (NPU), tensor processing unit (TPU), artificial intelligence (AI) type processor, etc. Different processing units may be independent components or integrated into one or more processors.

[0124] It should be noted that the aforementioned one or more processors 602 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 60. As described 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).

[0125] The memory 604 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the tire design factor optimization method in this embodiment. The processor 602 executes various functional applications and data processing by running the software programs and modules stored in the memory 604, thereby realizing the aforementioned tire design factor optimization method. The memory 604 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 604 may further include memory remotely located relative to the processor 602, and these remote memories can be connected to the computer terminal 60 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.

[0126] The transmission device 606 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 60. In one example, the transmission device 606 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 606 may be a Radio Frequency (RF) module, used for data interaction with external networks or devices via wired and / or wireless network connections.

[0127] The display can be, for example, a liquid crystal display (LCD) or a touchscreen display (also known as a "touchscreen" or "touch display screen"). This LCD allows the user to interact with the user interface of the mobile terminal. Furthermore, the computer terminal 60 can be connected to a cursor control device and a keyboard via an input / output interface. The cursor control device (e.g., a mouse, trackball, or touchpad) is primarily used to transmit directional information and command selections to the terminal and control the movement of the cursor on the display; the keyboard is primarily used to receive letters, numbers, and other control commands input by the user, thereby enabling interaction between the user and the computer terminal 60.

[0128] It should be noted here that, in some optional embodiments, the above... Figure 6 The computer terminal shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 6 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computer terminal.

[0129] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0130] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for optimizing tire design factors, characterized in that, include: A rolling simulation was performed on the reference tire model under the target working condition to obtain the first simulation result; Multiple optimization schemes corresponding to the design factors to be optimized of the tire are determined. The benchmark tire model is adjusted using each optimization scheme. Rolling simulations are performed on the adjusted multiple target tire models under the target working condition to obtain the second simulation results corresponding to each optimization scheme. Based on the first simulation results and the second simulation results corresponding to the multiple optimization schemes, a multidimensional evaluation index for each optimization scheme is determined, and the target optimization direction of the tire is determined according to the multidimensional evaluation index of the multiple optimization schemes and the preset optimization target, wherein the preset optimization target is used to characterize the expected adjustment direction of each evaluation index. The design factors of the tire to be optimized are optimized according to the target optimization direction.

2. The method according to claim 1, characterized in that, A rolling simulation was performed on the baseline tire model under the target working condition, and the first simulation results were obtained, including: The baseline tire model is constructed based on the tire parameters of the tire. The working condition setting command input by the target object is parsed to obtain the working condition parameters of the target working condition, and the simulation environment of the reference tire model is configured according to the working condition parameters. The working condition parameters include at least: rated air pressure, vertical load, camber angle and rolling boundary conditions, and the rolling boundary conditions include at least: rolling speed. The reference tire model is controlled to perform rolling simulation in the simulation environment, and the first simulation result of the reference tire model after satisfying the rolling boundary conditions is obtained.

3. The method according to claim 1, characterized in that, The multidimensional evaluation indicators include at least: time-domain influence indicators and frequency-domain influence indicators, and the tire parameters include at least: tire rolling radius; Based on the first simulation results and the second simulation results corresponding to the multiple optimization schemes, a multi-dimensional evaluation index is determined for each optimization scheme, including: For each optimization scheme, based on the first simulation result and the second simulation result of the optimization scheme, the corresponding wheel center radial force difference sequence and wheel center longitudinal force difference sequence are determined; The time-domain influence index is determined based on the radial force difference sequence and the longitudinal force difference sequence at the wheel center; Fourier transforms are performed on the radial force difference sequence and the longitudinal force difference sequence of the wheel center, respectively, and the frequency domain influence index is determined by combining the rolling speed and the tire rolling radius in the target working condition.

4. The method according to claim 3, characterized in that, The time-domain influence indicators include at least: radial force fluctuation at the wheel center and longitudinal force fluctuation at the wheel center; The time-domain influence index is determined based on the radial force difference sequence and the longitudinal force difference sequence at the wheel center, including: Determine the first maximum difference and the first minimum difference within the wheel center radial force difference sequence, and use the difference between the first maximum difference and the first minimum difference as the wheel center radial force fluctuation; Determine the second maximum difference and the second minimum difference within the wheel center longitudinal force difference sequence, and use the difference between the second maximum difference and the second minimum difference as the wheel center longitudinal force fluctuation.

5. The method according to claim 3, characterized in that, The frequency domain influence index includes at least: the radial force harmonic component of the wheel center and the longitudinal force harmonic component of the wheel center; Fourier transforms are performed on the radial force difference sequence and the longitudinal force difference sequence at the wheel center, respectively. Combined with the rolling speed and tire rolling radius within the target operating condition, frequency domain influence indices are determined, including: Based on the rolling speed, the tire rolling radius, and multiple orders, determine the rotation frequency corresponding to each order; Fourier transforms are performed on the radial force difference sequence and the longitudinal force difference sequence of the wheel center respectively to obtain the corresponding radial force frequency domain response spectrum and longitudinal force frequency domain response spectrum. The amplitude at the rotation frequency corresponding to each order is determined in the radial force frequency domain response spectrum and the longitudinal force frequency domain response spectrum respectively to obtain the radial force harmonic component and the longitudinal force harmonic component of the wheel center.

6. The method according to claim 1, characterized in that, The target optimization direction of the tire is determined based on the multidimensional evaluation indicators and preset optimization objectives of multiple optimization schemes, including: Based on the multidimensional evaluation indicators corresponding to the various optimization schemes, the average value of each dimension evaluation indicator corresponding to the design factor to be optimized at each value level is determined; For each evaluation index, calculate the difference between the maximum and minimum values ​​of the average values ​​of the design factor to be optimized at each value level under the evaluation index, and use the obtained difference as the response range of the design factor to be optimized to the evaluation index. Based on the changing pattern of the average value of the design factor to be optimized at each value level, determine the response trend of the design factor to be optimized to the evaluation index. An influence matrix is ​​constructed with the design factor to be optimized as the rows, the multidimensional evaluation index as the columns, and the response range and response trend of the design factor to be optimized under each evaluation index as matrix elements. The target optimization direction of the tire is determined based on the influence matrix and the preset optimization objective.

7. The method according to claim 6, characterized in that, Determining the target optimization direction of the tire based on the influence matrix and the preset optimization objective includes: Based on the extreme values ​​of the responses of each design factor to be optimized in the influence matrix under each evaluation index, the key design factors to be optimized are determined. For each of the key design factors to be optimized, the target value level of the key design factors to be optimized is determined based on the matching result between the response trend of the key design factors to be optimized in the influence matrix under each evaluation index and the preset optimization target. By summarizing the target value levels of all key design factors to be optimized, the target optimization direction of the tire can be obtained.

8. The method according to claim 1, characterized in that, The design factors to be optimized include: structural design factors and / or joint distribution factors, wherein the structural design factors include at least one of the following: tread thickness, tread material, tread rubber material, tread rubber height, belt layer material, and belt layer thickness, and the joint distribution factors include at least one of the following: tread joint position, inner liner joint position, belt layer joint position, and crown belt structure position.

9. A tire design factor optimization device, characterized in that, include: The first simulation module is used to perform rolling simulation on the reference tire model of the tire under the target working condition and obtain the first simulation result. The second simulation module is used to determine multiple optimization schemes corresponding to the design factors to be optimized of the tire, adjust the benchmark tire model using each optimization scheme, and perform rolling simulations on the adjusted multiple target tire models under the target working condition to obtain the second simulation results corresponding to each optimization scheme. The determination module is used to determine the multi-dimensional evaluation index of each optimization scheme based on the first simulation result and the second simulation result corresponding to the multiple optimization schemes, and to determine the target optimization direction of the tire based on the multi-dimensional evaluation index of the multiple optimization schemes and the preset optimization target, wherein the preset optimization target is used to characterize the expected adjustment direction of each evaluation index. The optimization module is used to optimize the design factors of the tire according to the target optimization direction.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the device containing the computer-readable storage medium executes the tire design factor optimization method according to any one of claims 1 to 8 by running the computer program.

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