Tire design parameter optimization method and device

Abstract

The invention discloses a tire design parameter optimization method and device. The method comprises the following steps: acquiring a dynamic test result of a target tire; extracting tire structure parameters, inputting the tire structure parameters into CD-tire software, carrying out dynamic simulation in combination with multiple groups of preset attribute parameters, comparing the difference between a simulation result and a test result, and determining target attribute parameters; adjusting the structure parameters to generate candidate structure parameters, performing dynamic simulation again in combination with the target attribute parameters, and screening out a second simulation result meeting a preset performance requirement; and determining an optimal candidate structure parameter from the candidate structure parameters corresponding to the second simulation results meeting the preset performance requirements as a target structure parameter of the target tire. The technical problem that time and labor are consumed due to the fact that finite element simulation tests need to be carried out for many times in a traditional tire design parameter optimization scheme is solved.

Description

Technical Field

[0001] The present application relates to the technical field of tire parameter design, and in particular to a method and device for optimizing tire design parameters. Background Art

[0002] In the current tire manufacturing industry, tire manufacturers not only have to ensure product quality, but also need to quickly adapt to the OEM's customized requirements for tire characteristics. In this process, the simulation and optimization of tire performance become crucial. Traditional tire performance evaluation relies on finite element simulation, but each round of simulation often takes weeks or even months, which makes it difficult for structural design engineers to efficiently compare multiple solutions and quickly iterate in the early stages of product development, seriously restricting the speed and efficiency of product development. In addition, tire finite element simulation may have non-convergence problems in some cases, making it impossible to obtain effective simulation results, increasing the risk and uncertainty of product development. Therefore, existing tire performance evaluation methods, especially six-component finite element simulation, have problems such as long simulation cycle, low efficiency and insufficient reliability, and cannot meet the rapid iteration needs of the new energy vehicle market.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present application provide a method and device for optimizing tire design parameters to at least solve the technical problem that traditional tire design parameter optimization schemes require multiple finite element simulation tests, which are time-consuming and labor-intensive.

[0005] According to one aspect of an embodiment of the present application, a method for optimizing tire design parameters is provided, including: obtaining test results obtained by performing a dynamic test on a target tire; obtaining structural parameters of the target tire, and combining multiple sets of preset attribute parameters with the structural parameters and inputting them into CD-tire identification software for dynamic simulation to obtain multiple first simulation results; determining target attribute parameters corresponding to the target tire from the multiple sets of attribute parameters based on the differences between each first simulation result and the test result; adjusting the structural parameters multiple times to obtain multiple sets of candidate structural parameters, and combining the multiple sets of candidate structural parameters with the target attribute parameters and inputting them into CD-tire identification software for dynamic simulation to obtain multiple second simulation results; determining the target structural parameters of the target tire from the candidate structural parameters corresponding to each second simulation result whose corresponding tire performance meets the preset performance requirements.

[0006] Optionally, obtaining test results obtained from a dynamic test on a target tire includes: obtaining multiple test results obtained from performing multiple dynamic tests on the target tire, wherein the type of dynamic test includes at least one of the following: a contact print test, a static stiffness test, a steady-state six-component force test, a static cleat test, and a dynamic cleat test, and each test result includes at least one tire performance indicator corresponding to the dynamic test.

[0007] Optionally, obtaining the structural parameters of the target tire includes: obtaining a material distribution map of the target tire; reading distribution curve data in multiple layers corresponding to different structural areas of the target tire in the material distribution map; for each layer, interpolating the distribution curve data corresponding to the layer to obtain a discrete point set corresponding to the layer, and sorting each discrete point in the discrete point set according to geometric characteristics of the structural area corresponding to the layer to obtain an ordered point sequence corresponding to the layer; using the ordered point sequence corresponding to each layer as the structural parameters of the target tire, and storing the structural parameters as a file in a format recognizable by CD-tire recognition software.

[0008] Optionally, multiple sets of preset attribute parameters are combined with structural parameters and input into CD-tire identification software for dynamic simulation to obtain multiple first simulation results, including: inputting structural parameters into CD-tire identification software to construct a first simulation model of the target tire; for each dynamic test, determining a first type of tire attribute parameter associated with the dynamic test, and determining multiple sub-attribute parameters of the first type; substituting each sub-attribute parameter into the first simulation model for dynamic simulation to obtain multiple first sub-simulation results corresponding to the test results of the dynamic test, wherein each first sub-simulation result includes at least one tire performance indicator corresponding to the dynamic simulation, the dynamic simulation is of the same type as the dynamic test, and the simulation condition parameters of the dynamic simulation are the same as the test condition parameters of the dynamic test.

[0009] Optionally, target attribute parameters corresponding to the target tire are determined from multiple groups of attribute parameters based on the differences between each first simulation result and the test results, including: for each first-type sub-attribute parameter, determining the index difference between the tire performance index in the first sub-simulation result corresponding to each sub-attribute parameter and the tire performance index in the test result corresponding to the sub-attribute parameter, and determining the sub-attribute parameter corresponding to the first sub-simulation result with the smallest corresponding index difference as the target sub-attribute parameter; and combining multiple first-type target sub-attribute parameters as the target attribute parameter corresponding to the target tire.

[0010] Optionally, the structural parameters are adjusted multiple times to obtain multiple groups of candidate structural parameters, including: determining a second type of tire performance indicators to be optimized for the target tire, and determining target sub-structural parameters in the structural parameters associated with the second type of tire performance indicators; and adjusting the target sub-structural parameters in the structural parameters multiple times to obtain multiple groups of candidate structural parameters, wherein the multiple groups of candidate structural parameters include unadjusted structural parameters.

[0011] Optionally, multiple groups of candidate structural parameters are combined with target attribute parameters and input into CD-tire identification software for dynamic simulation to obtain multiple second simulation results, including: inputting other sub-structure parameters and target attribute parameters in the structural parameters except the target sub-structure parameters into the CD-tire identification software to construct a second simulation model of the target tire; for each group of candidate structural parameters, substituting the target sub-structure parameters in the candidate structural parameters into the second simulation model, performing dynamic simulation corresponding to the second type of tire performance indicators, and obtaining multiple second simulation results, wherein each second simulation result includes the second type of tire performance indicators, and the simulation condition parameters corresponding to each group of candidate structural parameters are the same.

[0012] Optionally, the target structural parameters of the target tire are determined from the candidate structural parameters corresponding to each second simulation result whose corresponding tire performance meets the preset performance requirements, including: determining the second simulation result corresponding to the unadjusted structural parameter as the standard simulation result; for other second simulation results, if the tire performance index in the second simulation result is better than the tire performance index in the standard simulation result, determining that the second simulation result meets the preset performance requirements, and using the second simulation result as the candidate simulation result; determining the target structural parameters of the target tire from the candidate structural parameters corresponding to each candidate simulation result.

[0013] Optionally, the target structural parameters of the target tire are determined from the candidate structural parameters corresponding to each candidate simulation result, including: for each candidate structural parameter corresponding to the candidate simulation result, performing finite element analysis on the target tire based on the candidate structural parameters to obtain tire performance indicators corresponding to the candidate structural parameters; and determining the candidate structural parameters with the best corresponding tire performance indicators as the target structural parameters of the target tire.

[0014] According to another aspect of an embodiment of the present application, a device for optimizing tire design parameters is also provided, including: an acquisition module for acquiring test results obtained from a dynamic test on a target tire; a first simulation module for acquiring structural parameters of the target tire, and combining multiple preset sets of attribute parameters with the structural parameters and inputting them into CD-tire identification software for dynamic simulation to obtain multiple first simulation results; a first determination module for determining target attribute parameters corresponding to the target tire from multiple sets of attribute parameters based on the differences between each first simulation result and the test result; a second simulation module for adjusting the structural parameters multiple times to obtain multiple sets of candidate structural parameters, and combining the multiple sets of candidate structural parameters with the target attribute parameters and inputting them into CD-tire identification software for dynamic simulation to obtain multiple second simulation results; a second determination module for determining the target structural parameters of the target tire from the candidate structural parameters corresponding to each second simulation result whose corresponding tire performance meets the preset performance requirements.

[0015] According to another aspect of an embodiment of the present application, a computer program product is further provided. The computer program product includes: a computer program, wherein when the computer program is executed by a processor, the above-mentioned tire design parameter optimization method is implemented.

[0016] According to another aspect of an embodiment of the present application, an electronic device is provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above-mentioned tire design parameter optimization method through the computer program.

[0017] In an embodiment of the present application, the tire structural parameters are extracted and input into the CD-tire software, and dynamic simulation is performed in combination with multiple sets of preset attribute parameters. The simulation results are compared with the test results to determine the target attribute parameters; the structural parameters are adjusted to generate candidate structural parameters, and dynamic simulation is performed again in combination with the target attribute parameters to screen out a second simulation result that meets the preset performance requirements; through comparison, the optimal candidate structural parameters are determined as the target structural parameters of the target tire. Among them, by introducing CD-tire identification software to perform rapid dynamic simulation of multiple sets of attribute parameters and structural parameters, the time for tire performance evaluation can be greatly shortened; by comparing the differences between the first simulation results and the actual dynamic test results, a set of target attribute parameters can be effectively screened out to ensure that the simulation model is highly consistent with the actual tire performance; after determining the target attribute parameters, the structural parameters are adjusted multiple times and dynamic simulation is performed again, which can systematically explore the impact of different structural parameters on tire performance. This method allows designers to try multiple structural changes in a short period of time and identify those key parameters that can optimize tire performance; finally, pre-screening and verification of multiple combinations of structural parameters and attribute parameters can greatly reduce the number of prototype tires and the number of physical tests that need to be trial-produced before actual production, thereby solving the technical problem of multiple finite element simulation tests required in traditional tire design parameter optimization schemes, which is time-consuming and labor-intensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a flow chart of an optional tire design parameter optimization method according to an embodiment of the present application;

[0020] Figure 2 is a schematic structural diagram of an optional tire design parameter optimization device according to an embodiment of the present application;

[0021] Figure 3 It is a schematic structural diagram of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the specification, claims, and drawings of the present application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0024] In order to better understand the embodiments of the present application, some nouns or terms that appear in the description of the embodiments of the present application are first translated and explained as follows:

[0025] Static cleat testing: A static cleat test is conducted with the tire stationary. The test simulates contact between the tire and a bump on the road by placing the tire on a test bench with a fixed cleat. A vertical load is typically applied, and the deformation, force magnitude, and distribution generated by the tire contacting the cleat are recorded. This test analyzes the tire's response to small obstacles on the road under vertical load, focusing specifically on vertical stiffness, tread material properties, and the distribution of the tire's internal structure.

[0026] Dynamic Cleaving Test: This test is conducted on a rolling tire, simulating its behavior when a vehicle encounters a series of obstacles on the road. The tire rolls on a test bench equipped with movable bumps while being tested at varying speeds and loads. The dynamic response of the rolling tire to the bumps is recorded, including transient force fluctuations, deformation recovery characteristics, and internal stress changes. The dynamic cleating test better reflects the comfort, handling, and durability of the tire during actual driving, as it accounts for the dynamic effects of the tire's continuous interaction with the road.

[0027] Dynamic testing: Dynamic testing involves testing tires under dynamic conditions during the tire design and evaluation process to measure their performance under load, deformation, and motion. These tests typically include, but are not limited to, tire contact patch testing, stiffness testing under static and dynamic conditions, and mechanical property testing under varying loads and speeds. Dynamic testing aims to fully understand tire performance in actual use and provide measured data for optimized tire design.

[0028] CD-tire Identification Software: CD-tire Identification Software is a tool specifically designed for tire performance simulation and analysis. It primarily establishes a dynamic tire model to predict and analyze tire behavior under various operating conditions, such as cornering forces during cornering, grip on wet or dry roads, and tire response to vehicle vibrations. The software's core function is to identify (i.e., calibrate) the model parameters by modeling the tire's physical structure and material properties and integrating them with a series of test data to ensure that the model's predictions closely match actual tire performance. The identification process typically involves the following steps: 1) Input tire structural parameters: including detailed information such as tire geometry, laminate structure, and material distribution; 2) Set initial property parameters: such as the material's elastic modulus, Poisson's ratio, and friction coefficient, which reflect the physical properties of the tire material. 3) Import test data: Tire performance data obtained from dynamic tests, such as contact patch, static stiffness, and tire six-component force, serves as the target baseline for model identification. 3) Adjust model parameters: Through repeated simulation runs, the software automatically or manually adjusts the model's attribute parameters so that the simulation results gradually approach the test data. 4) Verify model accuracy: When the difference between the simulation results and the test data falls within an acceptable range, the model is considered successfully identified, and the parameter set at this point becomes the target attribute parameters. Once an accurate tire model is established, designers can use CD-tire software to quickly evaluate the impact of various hypothetical structural parameter changes on tire performance without the need for time-consuming finite element simulations or actual tire sample testing, greatly accelerating the product development and optimization process.

[0029] Example 1

[0030] According to an embodiment of the present application, a method for optimizing tire design parameters is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] Figure 1 FIG. 1 is a flow chart of a tire design parameter optimization method according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0032] Step S102, obtaining a test result obtained by performing a dynamic test on the target tire;

[0033] Step S104, obtaining structural parameters of the target tire, and combining the preset multiple sets of attribute parameters with the structural parameters and inputting them into CD-tire identification software for dynamic simulation to obtain a plurality of first simulation results;

[0034] Step S106, determining target attribute parameters corresponding to the target tire from the multiple groups of attribute parameters based on the differences between the first simulation results and the test results;

[0035] Step S108, adjusting the structural parameters multiple times to obtain multiple sets of candidate structural parameters, and combining the multiple sets of candidate structural parameters with the target attribute parameters and inputting them into CD-tire identification software for dynamic simulation to obtain multiple second simulation results;

[0036] Step S110 , determining target structural parameters of the target tire from candidate structural parameters corresponding to respective second simulation results whose corresponding tire performance meets preset performance requirements.

[0037] The following describes the various steps of the tire design parameter optimization method in conjunction with a specific implementation process.

[0038] As an optional embodiment, obtaining the test results obtained from a dynamic test on a target tire can be achieved in the following manner: obtaining multiple test results obtained from performing multiple dynamic tests on the target tire, wherein the type of dynamic test includes at least one of the following: a contact print test, a static stiffness test, a steady-state six-component force test, a static cleat test, and a dynamic cleat test, and each test result includes at least one tire performance indicator corresponding to the dynamic test.

[0039] Obtaining multiple test results from various dynamic tests on a target tire involves a series of sophisticated testing activities designed to comprehensively evaluate the tire's dynamic performance under various operating conditions. Dynamic test types include at least one or all of the following: contact patch test, static stiffness test, steady-state six-component force test, static cleat test, and dynamic cleat test. Each test meticulously measures and records specific tire performance indicators.

[0040] In the contact patch test, by observing and measuring the tire's contact pattern under different load conditions at 0° and 6° camber angles, the tire's contact area and pressure distribution can be accurately depicted. This is an important basis for evaluating tire grip and wear uniformity.

[0041] The static stiffness test tests the radial stiffness, lateral stiffness, longitudinal stiffness and torsional stiffness of the tire when it is stationary, at 0° and 6° camber angles, different air pressure levels (such as 2 bar, 2.5 bar, 3 bar) and load conditions. This series of indicators is directly related to the tire's load-bearing capacity and deformation characteristics, and has a significant impact on the vehicle's driving comfort and handling stability.

[0042] The steady-state six-component force test measures the torque and force during lateral deviation and longitudinal sliding by subjecting the tire to camber angles of 0° and 6° and varying loads at standard air pressure. The test measures the lateral force, longitudinal force, and torsional moment, in particular. These data are crucial for understanding the mechanical response of the tire in curves and straight driving.

[0043] Static cleat tests and dynamic cleat tests are used to examine the mechanical performance of tires when encountering stationary and moving bumps, respectively. In the static cleat test, the tire needs to withstand bumps fixed in different positions to test the impact of the bumps on the tire under different loads, focusing on the response of vertical force and radial force. The dynamic cleat test, on the other hand, records the dynamic force and vibration characteristics generated when the tire collides with the bump at different speeds (such as 20 km / h, 40 km / h, 60 km / h, 80 km / h) and load conditions (0.4 times, 0.8 times, 1.2 times the standard load) when the tire is rolling. This is extremely critical for understanding the comfort and safety of the tire when driving on uneven roads.

[0044] By executing these five dynamic tests, a comprehensive series of tire performance indicators can be obtained, including but not limited to the tire's grip characteristics, rigidity, force transmission efficiency, responsiveness to obstacles, and stability under dynamic loads. These test results provide indispensable real-world data support for subsequent model attribute parameter identification and performance optimization using CD-tire software, ensuring that every adjustment to the tire design is based on real dynamic feedback, effectively improving the tire's overall performance and market competitiveness.

[0045] As an optional implementation, obtaining the structural parameters of the target tire can be achieved in the following manner: obtaining a material distribution map of the target tire; reading distribution curve data in multiple layers corresponding to different structural areas of the target tire in the material distribution map; for each layer, interpolating the distribution curve data corresponding to the layer to obtain a discrete point set corresponding to the layer, and sorting each discrete point in the discrete point set according to the geometric characteristics of the structural area corresponding to the layer to obtain an ordered point sequence corresponding to the layer; using the ordered point sequence corresponding to each layer as the structural parameters of the target tire, and storing the structural parameters as a file in a format recognizable by CD-tire recognition software.

[0046] During specific implementation, the material distribution map of the target tire is obtained. This step involves collecting detailed geometric and physical layout information of each component of the tire. The material distribution map is the basis of tire design and includes the position and size of key components such as the tread, cord layer, and belt layer. It is crucial for the subsequent establishment of an accurate tire model.

[0047] First, a custom-written modeling tool program retrieves the distribution curve data from multiple layers of the material distribution map corresponding to different structural areas of the target tire. This process extracts the curve data within each layer from the CAD file (material distribution map DXF format), including the curve type (straight line, circular arc, or spline) and the curve's specific coordinates in space. Layer definition and naming are pre-defined during tire design, ensuring data consistency from the material distribution map to the software model.

[0048] Next, for each layer, the corresponding distribution curve data is interpolated to obtain the corresponding discrete point set. This interpolation ensures that the tire's digital model accurately reflects its physical structure, allowing each curve to be represented in the software with a sufficiently dense number of points to accurately reconstruct the tire's three-dimensional structure. For straight-line curves, the required number of points is determined by calculating the distance between two points and the interpolation step size, and then the coordinates are calculated point by point. For arc-shaped curves, the center and radius information is combined with the starting and ending angles to interpolate in small angular increments to generate coordinate points distributed along the arc.

[0049] Afterwards, the discrete points in the set are sorted according to the geometric characteristics of the structural area corresponding to the layer, resulting in an ordered sequence of points corresponding to the layer. This ensures the correct construction order when building the tire model. The sorting process is divided into two steps. First, the starting point of the centerline of the material distribution diagram is located. By analyzing the relationship between the curve and the outer diameter of the tire, the closest point to the farthest point of the extended outer diameter of the tire is found as the centerline starting point. Next, starting from the centerline starting point, all points are traversed in order, ensuring that the addition of each point follows the natural arrangement of the internal structure of the tire, thus forming a closed-loop ordered sequence of points, which is the starting point required to form the boundary of the tire model.

[0050] Finally, the ordered point sequences corresponding to each layer are used as the structural parameters of the target tire and stored in a file format recognizable by the CD-tire identification software, typically an ASC file. The ASC file contains detailed parameters of the tire structure, such as material distribution, layer structure, and geometric dimensions, arranged in a software-readable format. By converting the ordered point sequence into an ASC file, the digital tire model can be efficiently imported into the CD-tire identification software, eliminating the need for tedious manual operations. This greatly simplifies the tire designer's workflow and improves the speed and accuracy of model building.

[0051] The above process rapidly converts the material distribution map into a file recognizable by the CD-tire software. This process not only involves precise interpolation of curves within different layers, ensuring the precision of the digital tire model, but also includes an algorithm for logically sorting discrete points, guaranteeing the correctness and completeness of the tire model. Ultimately, the resulting ASC file becomes a bridge between the physical tire and the digital simulation world, enabling subsequent dynamic simulation and performance optimization to be carried out efficiently in a virtual environment.

[0052] As an optional implementation, multiple sets of preset attribute parameters are combined with structural parameters and input into CD-tire identification software for dynamic simulation to obtain multiple first simulation results. This can be achieved in the following way: input the structural parameters into CD-tire identification software to construct a first simulation model of the target tire; for each dynamic test, determine the first type of tire attribute parameters associated with the dynamic test, and determine multiple sub-attribute parameters of the first type; substitute each sub-attribute parameter into the first simulation model for dynamic simulation to obtain multiple first sub-simulation results corresponding to the test results of the dynamic test, wherein each first sub-simulation result includes at least one tire performance indicator corresponding to the dynamic simulation, the dynamic simulation is of the same type as the dynamic test, and the simulation condition parameters of the dynamic simulation are the same as the test condition parameters of the dynamic test.

[0053] In specific implementation, the technical solution of this application focuses on achieving highly reproducible and optimized target tire performance indicators through systematic parameter adjustment and dynamic simulation. First, multiple sets of attribute parameters are selected from a preset parameter library. These attribute parameters include, but are not limited to, material stiffness, damping coefficient, friction coefficient, and component mass distribution. Each set of attribute parameters represents a potential tire configuration. These attribute parameters are combined with the previously processed structural parameters and input into the CD-tire identification software for dynamic simulation, generating multiple first simulation results.

[0054] Building a first simulation model of the target tire is the cornerstone of the entire process. By importing structural parameters into the software, it quickly establishes the basic tire framework, upon which subsequent dynamic analysis is based. For each dynamic test, such as the contact patch test or static stiffness test, the first type of tire property parameters directly associated with it is determined. For example, during the contact patch test, the focus may be on identifying the tire's compression characteristics and the friction of the surface material; while in the static stiffness test, the focus is more on the material's elastic modulus and thickness distribution.

[0055] Subsequently, the sub-attribute parameters in the first type are refined and adjusted. The determination of these sub-attribute parameters depends on the specific requirements of the test and the material composition of the tire. For example, the lateral and longitudinal stiffness of the tread rubber play a key role in different tests. Each sub-attribute parameter is applied one by one to the first simulation model for dynamic simulation. Each simulation produces a first sub-simulation result corresponding to the dynamic test. These first sub-simulation results contain a rich set of performance indicators, covering the tire's response characteristics under different operating conditions, such as force changes during cornering and the degree of deformation under load conditions.

[0056] It's important to note that dynamics simulations fully correspond to the types of dynamics tests being conducted, ensuring high consistency between simulation and test conditions. For example, in a static cleat test, the simulation will replicate the static interaction between the tire and a fixed obstacle, while in a dynamic cleat test, the simulation will replicate the contact between the tire and the dynamic obstacle during rolling motion. This allows for precise verification of the validity and accuracy of the simulation model, ensuring that the parameters identified by the software can be validated in the real world.

[0057] After each dynamics simulation, the system automatically generates simulation curves that intuitively demonstrate the tire's mechanical performance under specific operating conditions. The simulation curves are then compared and analyzed with the test data to assess the accuracy of the identification model. If significant deviations between the simulation results and the test data are detected, further fine-tuning of the tire's property parameters, including the material's mechanical properties, friction coefficient, and mass distribution, is necessary until the simulation accuracy meets the strict pre-set requirements.

[0058] Parameter configurations that meet accuracy requirements are considered optimal. These parameters not only accurately reflect the tire's physical characteristics but also demonstrate high consistency and reliability in dynamic testing and simulation. Ultimately, these repeatedly verified and adjusted parameters are fixed and used to build a high-performance model for the target tire, ensuring a solid data foundation and support for subsequent model-based virtual testing and performance optimization.

[0059] In summary, by combining multiple sets of preset property parameters with structural parameters for targeted dynamic simulation, this method can not only quickly screen out the property parameter combination that best meets the tire performance requirements, but also ensure that the simulation model is highly consistent with the actual tire in terms of dynamic characteristics, providing a powerful digital tool for tire design.

[0060] As an optional implementation, determining the target attribute parameters corresponding to the target tire from multiple groups of attribute parameters based on the differences between each first simulation result and the test results can be achieved in the following manner: for each first-type sub-attribute parameter, determining the index difference between the tire performance index in the first sub-simulation result corresponding to each sub-attribute parameter and the tire performance index in the test result corresponding to the sub-attribute parameter, and determining the sub-attribute parameter corresponding to the first sub-simulation result with the smallest corresponding index difference as the target sub-attribute parameter; and combining multiple first-type target sub-attribute parameters as the target attribute parameter corresponding to the target tire.

[0061] First, the technical solution imports structural parameters and preset property parameters into CD-tire identification software for dynamic simulation, generating multiple first-stage simulation results. Each result corresponds to a specific set of property parameters. These simulation results include various tire performance indicators under different dynamic test conditions. For example, in the contact patch test, the first-stage simulation results display the tire contact area and pressure distribution; in the static stiffness test, stiffness values ​​in different directions are provided; and in the steady-state six-component force test, data such as cornering force, longitudinal force, and moment are provided.

[0062] Next, for each first-type sub-attribute parameter, such as the lateral stiffness of the tread compound or the damping coefficient of the tire ply, we extract the tire performance indicators from the first sub-simulation results dominated by that sub-attribute parameter. This series of simulated indicators is then compared with the actual test data, and the difference between each pair of indicators is calculated (either as an absolute value difference, a percentage difference, or other mathematical measure). This difference in indicator reflects the degree of deviation between the tire model's prediction and reality when the sub-attribute parameter is set.

[0063] Adopting the principle of minimizing the difference in indicators means that within each sub-attribute parameter type, we search for the first sub-simulation result that most closely matches the experimental results. This process may require multiple iterations, adjusting parameters until the optimal match is found. For example, for the longitudinal stiffness of a tire, we might try various stiffness values ​​until we find the setting that best matches the experimental data in the first sub-simulation result.

[0064] Once the optimal sub-attribute parameters for each first-type are determined, known as the target sub-attribute parameters, the next step is to rationally combine these parameters. It's important to note that combining multiple first-type target sub-attribute parameters forms a complete set of target attribute parameters, which becomes the final configuration of the target tire model. During this combination process, the interactions and constraints between the various attribute parameters must be considered to ensure that all aspects of the tire model are optimized, not just the optimization of a single metric.

[0065] In this way, the initial simulation results were systematically compared with the test results to identify the configuration with the smallest indicator difference. Ultimately, the target attribute parameters were integrated, achieving efficient identification of the tire model and high reproducibility of performance indicators. This set of target attribute parameters not only ensured the model's predictive accuracy under dynamic test conditions but also laid a solid theoretical foundation for subsequent tire optimization design, virtual sampling, and sample testing. The entire process demonstrated a deep understanding of tire physical properties and fully utilized the powerful features of CD-tire identification software, bringing innovative solutions to the tire industry, effectively shortening product development cycles, reducing R&D costs, and improving the accuracy and efficiency of tire design.

[0066] As an optional implementation, multiple adjustments are made to the structural parameters to obtain multiple groups of candidate structural parameters. This can be achieved in the following manner: determining a second type of tire performance indicator to be optimized for the target tire, and determining target sub-structural parameters in the structural parameters that are associated with the second type of tire performance indicator; and multiple adjustments are made to the target sub-structural parameters in the structural parameters to obtain multiple groups of candidate structural parameters, wherein the multiple groups of candidate structural parameters include unadjusted structural parameters.

[0067] During implementation, the process of repeatedly adjusting structural parameters to obtain multiple candidate sets of structural parameters is a key step in the overall tire performance improvement strategy. The core of this strategy lies in accurately identifying specific performance indicators that require optimization within the tire design. Through targeted structural adjustments, multiple potential solutions are explored to achieve the desired performance improvement.

[0068] First, determine the second type of tire performance indicators to be optimized for the target tire. These may include, but are not limited to, handling stability, rolling resistance, noise level, or wet road grip. These performance indicators are selected based on market research, user needs, or to meet specific OEM specifications. They directly reflect the performance of the tire in actual use scenarios.

[0069] Secondly, the technical solution focuses on the target sub-structural parameters in the structural parameters that are closely related to the second type of tire performance indicators. For example, in order to enhance the handling stability of the tire, it may be necessary to adjust the tire's belt width, thickness, or the number and angle of the cord layer. Changes in these structural details directly affect the tire's lateral stiffness and stability. The adjustment of the target sub-structural parameters is not a one-time adjustment, but is carried out through multiple iterations. Each adjustment is based on the feedback of the previous result, gradually approaching the optimal solution. The adjustment process may involve increasing or decreasing material thickness, changing material distribution, adjusting component size and position, etc. After each round of adjustment, a new set of candidate structural parameters will be obtained. These parameters constitute potential solutions for tire design.

[0070] It's important to note that the multiple candidate structural parameter sets include not only the adjusted parameter sets but also the original, unadjusted structural parameters as a control. This approach allows for a direct comparison between the original design and the improved solution, allowing for evaluation of the effectiveness of each structural adjustment and ensuring that the optimization process remains grounded in a reliable foundation.

[0071] As an optional implementation, multiple groups of candidate structural parameters are combined with target attribute parameters and input into the CD-tire identification software for dynamic simulation to obtain multiple second simulation results. This can be achieved in the following way: other sub-structure parameters and target attribute parameters in the structural parameters except the target sub-structure parameters are input into the CD-tire identification software to construct a second simulation model of the target tire; for each group of candidate structural parameters, the target sub-structure parameters in the candidate structural parameters are substituted into the second simulation model, and dynamic simulation corresponding to the second type of tire performance indicators is performed to obtain multiple second simulation results, wherein each second simulation result includes the second type of tire performance indicators, and the simulation condition parameters corresponding to each group of candidate structural parameters are the same.

[0072] First, building a second simulation model of the target tire is a prerequisite for the entire process. This requires that, in addition to the target substructure parameters to be adjusted, the model also includes all other substructure parameters and the previously determined target property parameters. The target property parameters are a set of optimal parameters selected based on a precise comparison of the first simulation results with the test data, using the principle of minimizing the difference in indicators. These parameters cover multiple dimensions, including material mechanical properties, friction coefficient, and mass distribution, representing the physical property configuration of the tire.

[0073] Based on the second simulation model, the technical solution performs dynamic simulations on the target sub-structural parameters within each set of candidate structural parameters. These target sub-structural parameters encompass specific structural elements in tire design that are directly associated with the second type of performance indicators, such as the number of carcass plies, tread width, or belt angles. Designers can adjust and modify these structural parameters by adjusting the initial material distribution map. By replacing and adjusting these parameters one by one in the simulation model, the impact of structural changes on tire performance indicators can be observed and evaluated.

[0074] Dynamic simulations covered all test types relevant to Type II tire performance indicators, such as cornering stiffness, rolling resistance, and noise levels. Throughout the simulations, identical simulation conditions and parameters, including load, air pressure, and contact patch properties, were strictly maintained. This ensured that the parameters of different candidate structures were compared against the same benchmark, making the simulation results comparable and reliable.

[0075] Each set of candidate structural parameters generates a second simulation result, which contains a series of second-type tire performance indicators. For example, in a cornering stiffness simulation, the result may be a displacement curve of the tire under lateral force; in a rolling resistance simulation, the result may show the energy dissipation of the tire during rolling; and in a noise level simulation, the sound pressure level data of the tire under specific driving conditions may be reported.

[0076] These secondary simulation results provide designers with intuitive and specific performance feedback, helping them quantify the positive or negative impact of structural adjustments on tire performance. Decision-makers can quickly identify structural parameter combinations that lead to improved performance and clearly identify areas for correction or optimization. More importantly, this process often inspires new structural adjustment ideas, propelling designers to the next stage of innovative iteration.

[0077] Overall, by combining multiple sets of candidate structural parameters with target property parameters to conduct systematic dynamic simulation, the technical solution not only deepens the understanding of the relationship between tire structure and performance, but also greatly accelerates the product development process, injecting scientific decision-making basis and efficient optimization process into tire design, ensuring the excellent performance and market competitiveness of the final product.

[0078] As an optional implementation, determining the target structural parameters of the target tire from the candidate structural parameters corresponding to each second simulation result whose corresponding tire performance meets the preset performance requirements can be achieved in the following manner: determining the second simulation result corresponding to the unadjusted structural parameter as the standard simulation result; for other second simulation results, if the tire performance index in the second simulation result is better than the tire performance index in the standard simulation result, determining that the second simulation result meets the preset performance requirements, and using the second simulation result as the candidate simulation result; determining the target structural parameters of the target tire from the candidate structural parameters corresponding to each candidate simulation result.

[0079] In specific implementation, first determine the second simulation result corresponding to the unadjusted structural parameters as the standard simulation result. This standard simulation result can be stored in the base folder. The standard simulation result is the dynamic simulation result performed in the initial state, that is, when the structural parameters have not been modified. It provides a stable reference point for subsequent performance evaluation, ensuring that the performance improvement or decline brought about by each structural change can be based on a solid basis.

[0080] The technical solution then turns its attention to other secondary simulation results, corresponding to candidate structural parameters that have undergone structural adjustments. Simulation results other than the standard simulation results can be stored in the workdir folder. For each secondary simulation result, its tire performance indicators are carefully analyzed and compared with the standard simulation results. If the tire performance indicators demonstrated in the secondary simulation results are superior to the standard simulation results—that is, if improvements in handling stability, rolling resistance, noise level, and other aspects are achieved, and the improvement meets the preset performance requirements—then this set of results is deemed to meet the preset performance requirements and becomes one of the candidate simulation results.

[0081] Selecting target structural parameters from candidate simulation results is a more complex and detailed task. The optimal combination of structural parameters can be selected by comprehensively considering the tire performance indicators of the candidate simulation results, combined with factors such as market analysis, cost control, and production process feasibility.

[0082] This selection process typically involves analyzing and comparing multiple rounds of simulation results, closely comparing them to the target product's objective test results. Using a custom post-processing program, the second simulation results are carefully interpreted. During data processing, the program locates key performance indicator data stored in text files. This data is then compared with the target product's actual objective test performance to verify the accuracy and reliability of the simulation results.

[0083] Furthermore, considering the impact of different operating conditions on tire performance, the technical proposal also places particular emphasis on the changing trends of tire performance indicators under varying air pressure and load conditions. This means that even a subtle parameter in a candidate simulation result may become a decisive factor due to its impact on performance under specific operating conditions.

[0084] Determining the target structural parameters from the second simulation results that meet preset performance requirements requires not only a deep understanding of the tire's mechanical behavior but also the flexible use of digital tools for efficient and accurate simulation, comparative analysis, and performance evaluation. This process embodies the close integration of scientific decision-making and innovative thinking in tire design, ensuring that the ultimately selected target structural parameters significantly improve tire performance, meeting market and user needs, while also introducing advanced digital design and optimization methods to the tire manufacturing industry.

[0085] As an optional implementation, determining the target structural parameters of the target tire from the candidate structural parameters corresponding to each candidate simulation result can be achieved in the following manner: for each candidate structural parameter corresponding to the candidate simulation result, performing finite element analysis on the target tire based on the candidate structural parameters to obtain tire performance indicators corresponding to the candidate structural parameters; and determining the candidate structural parameters with the best corresponding tire performance indicators as the target structural parameters of the target tire.

[0086] By comparing simulation results under different structural parameters, candidate structural parameters that surpass standard simulation results in performance can be identified. This approach, based on the optimization of performance indicators, aims to identify design solutions that enhance tire performance while maintaining or improving other key performance indicators. This approach ensures that tire designs not only meet basic performance requirements but also enhance key performance attributes such as grip, wear resistance, and comfort. By determining target structural parameters, the final tire design can be guided, ensuring excellent performance in practical applications and meeting user needs. Determining target structural parameters can be a multi-objective optimization process, considering multiple performance indicators to find the optimal overall solution. This approach is applicable not only to tire design but also to other product design areas requiring high performance and precision, such as automotive components and aerospace materials. This approach can significantly improve specific performance indicators while maintaining or improving overall product performance.

[0087] When verifying the performance of tires corresponding to different candidate structural parameters, it can be achieved not only through finite element simulation methods, but also by combining different candidate structural parameters and target attribute parameters to form different design parameters. Sample tires can be trial-produced under the guidance of different design parameters, and the performance of sample tires under different design parameters can be verified through experimental methods. By comparing the performance of sample tires under different design parameters, the candidate structural parameters under the design parameters with the best performance can be further obtained as the target structural parameters.

[0088] In the embodiments of the present application, the method first utilizes CD-tire identification software to perform dynamic simulations. By comparing the simulation results with actual test results, the tire's property parameters can be precisely adjusted to ensure that the tire performs as expected under various dynamic conditions. Secondly, through multiple adjustments and simulations of structural parameters, candidate structural design solutions can be identified. Finally, finite element simulations are performed on a small number of candidate design parameter solutions to screen for the optimal structural design solution, thereby resolving the time-consuming and labor-intensive technical issue of traditional tire design parameter optimization methods, which require multiple finite element simulation tests.

[0089] Example 2

[0090] According to an embodiment of the present application, a tire design parameter optimization device for implementing the tire design parameter optimization method in embodiment 1 is also provided, such as Figure 2 As shown, the tire design parameter optimization device at least includes: an acquisition module 21, a first simulation module 22, a first determination module 23, a second simulation module 24 and a second determination module 25, wherein:

[0091] An acquisition module 21 can acquire test results obtained by performing a dynamic test on a target tire;

[0092] The first simulation module 22 can obtain the structural parameters of the target tire and combine the preset multiple sets of attribute parameters with the structural parameters and input them into the CD-tire identification software to perform dynamic simulation to obtain multiple first simulation results;

[0093] A first determining module 23 may determine target attribute parameters corresponding to the target tire from multiple groups of attribute parameters based on differences between each first simulation result and the test result;

[0094] The second simulation module 24 can adjust the structural parameters multiple times to obtain multiple sets of candidate structural parameters, and combine the multiple sets of candidate structural parameters with the target attribute parameters and input them into the CD-tire identification software to perform dynamic simulation to obtain multiple second simulation results;

[0095] The second determining module 25 may determine target structural parameters of the target tire from candidate structural parameters corresponding to respective second simulation results whose corresponding tire performance meets preset performance requirements.

[0096] The functions of each module of the tire design parameter optimization device are described below in conjunction with a specific implementation process.

[0097] As an optional implementation, the acquisition module obtains the test results obtained by performing a dynamic test on the target tire, which can be achieved by: obtaining multiple test results obtained by performing multiple dynamic tests on the target tire, wherein the type of dynamic test includes at least one of the following: a contact print test, a static stiffness test, a steady-state six-component force test, a static cleat test, and a dynamic cleat test, and each test result includes at least one tire performance indicator corresponding to the dynamic test.

[0098] As an optional implementation, the first simulation module obtains the structural parameters of the target tire, which can be achieved in the following way: obtaining a material distribution map of the target tire; reading distribution curve data in multiple layers corresponding to different structural areas of the target tire in the material distribution map; for each layer, interpolating the distribution curve data corresponding to the layer to obtain a discrete point set corresponding to the layer, and sorting each discrete point in the discrete point set according to the geometric characteristics of the structural area corresponding to the layer to obtain an ordered point sequence corresponding to the layer; using the ordered point sequence corresponding to each layer as the structural parameters of the target tire, and storing the structural parameters as a file in a format recognizable by CD-tire recognition software.

[0099] As an optional implementation, the first simulation module combines the preset multiple groups of attribute parameters with the structural parameters and inputs them into the CD-tire identification software for dynamic simulation to obtain multiple first simulation results. This can be achieved in the following way: input the structural parameters into the CD-tire identification software to construct a first simulation model of the target tire; for each dynamic test, determine the first type of tire attribute parameters associated with the dynamic test, and determine multiple sub-attribute parameters of the first type; substitute each sub-attribute parameter into the first simulation model for dynamic simulation to obtain multiple first sub-simulation results corresponding to the test results of the dynamic test, wherein each first sub-simulation result includes at least one tire performance indicator corresponding to the dynamic simulation, the dynamic simulation is of the same type as the dynamic test, and the simulation condition parameters of the dynamic simulation are the same as the test condition parameters of the dynamic test.

[0100] As an optional implementation, the first determination module determines the target attribute parameters corresponding to the target tire from multiple groups of attribute parameters based on the differences between each first simulation result and the test results. This can be achieved in the following way: for each first-type sub-attribute parameter, determine the index difference between the tire performance index in the first sub-simulation result corresponding to each sub-attribute parameter and the tire performance index in the test result corresponding to the sub-attribute parameter, and determine the sub-attribute parameter corresponding to the first sub-simulation result with the smallest corresponding index difference as the target sub-attribute parameter; and combine multiple first-type target sub-attribute parameters as the target attribute parameter corresponding to the target tire.

[0101] As an optional implementation, the second simulation module adjusts the structural parameters multiple times to obtain multiple groups of candidate structural parameters, which can be achieved in the following way: determining the second type of tire performance indicators to be optimized for the target tire, and determining the target sub-structural parameters associated with the second type of tire performance indicators in the structural parameters; adjusting the target sub-structural parameters in the structural parameters multiple times to obtain multiple groups of candidate structural parameters, wherein the multiple groups of candidate structural parameters include unadjusted structural parameters.

[0102] As an optional implementation, the second simulation module combines multiple groups of candidate structural parameters with target attribute parameters and inputs them into the CD-tire identification software for dynamic simulation to obtain multiple second simulation results. This can be achieved in the following way: input other sub-structure parameters and target attribute parameters in the structural parameters except the target sub-structure parameters into the CD-tire identification software to construct a second simulation model of the target tire; for each group of candidate structural parameters, substitute the target sub-structure parameters in the candidate structural parameters into the second simulation model, perform dynamic simulation corresponding to the second type of tire performance indicators, and obtain multiple second simulation results, wherein each second simulation result includes the second type of tire performance indicators, and the simulation condition parameters corresponding to each group of candidate structural parameters are the same.

[0103] As an optional implementation, the second determination module determines the target structural parameters of the target tire from the candidate structural parameters corresponding to each second simulation result whose corresponding tire performance meets the preset performance requirements. This can be achieved in the following manner: determine the second simulation result corresponding to the unadjusted structural parameter as the standard simulation result; for other second simulation results, if the tire performance index in the second simulation result is better than the tire performance index in the standard simulation result, determine that the second simulation result meets the preset performance requirements, and use the second simulation result as the candidate simulation result; determine the target structural parameters of the target tire from the candidate structural parameters corresponding to each candidate simulation result.

[0104] As an optional implementation, the second determination module determines the target structural parameters of the target tire from the candidate structural parameters corresponding to each candidate simulation result, which can be achieved in the following manner: for each candidate structural parameter corresponding to the candidate simulation result, finite element analysis is performed on the target tire based on the candidate structural parameters to obtain the tire performance indicators corresponding to the candidate structural parameters; and the candidate structural parameters with the best corresponding tire performance indicators are determined as the target structural parameters of the target tire.

[0105] It should be noted that the modules in the tire design parameter optimization device in the embodiment of the present application correspond one-to-one to the implementation steps of the tire design parameter optimization method in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be elaborated on here.

[0106] Example 3

[0107] According to an embodiment of the present application, a computer program product is further provided. The computer program product includes a computer program, wherein when the computer program is executed by a processor, the method for optimizing tire design parameters in embodiment 1 is implemented.

[0108] According to an embodiment of the present application, a non-volatile storage medium is further provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the tire design parameter optimization method in Example 1 by running the computer program.

[0109] According to an embodiment of the present application, a processor is further provided, which is used to run a computer program, wherein the computer program executes the tire design parameter optimization method in Example 1 when running.

[0110] According to an embodiment of the present application, an electronic device is further provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the tire design parameter optimization method in Example 1 through the computer program.

[0111] Specifically, the computer program executes the following steps when it is running: obtaining the test results obtained by performing a dynamic test on the target tire; obtaining the structural parameters of the target tire, and combining the preset multiple sets of attribute parameters with the structural parameters and inputting them into the CD-tire identification software for dynamic simulation to obtain multiple first simulation results; determining the target attribute parameters corresponding to the target tire from the multiple sets of attribute parameters based on the differences between each first simulation result and the test result; adjusting the structural parameters multiple times to obtain multiple sets of candidate structural parameters, and combining the multiple sets of candidate structural parameters with the target attribute parameters and inputting them into the CD-tire identification software for dynamic simulation to obtain multiple second simulation results; determining the target structural parameters of the target tire from the candidate structural parameters corresponding to each second simulation result whose corresponding tire performance meets the preset performance requirements.

[0112] As an optional implementation, the electronic device may be in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 3 FIG1 shows a hardware structure block diagram of an electronic device for implementing a tire design parameter optimization method. Figure 3 As shown, the electronic device 30 may include one or more (illustrated as 302a, 302b, ..., 302n in the figure) processors 302 (the processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 304 for storing data, and a transmission device 306 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 the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components than shown, or with Figure 3 Different configurations shown.

[0113] It should be noted that the one or more processors 302 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the electronic device 30. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0114] Memory 304 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the tire design parameter optimization method in the embodiments of the present application. Processor 302 executes the software programs and modules stored in memory 304 to execute various functional applications and data processing, thereby implementing the aforementioned application vulnerability detection method. Memory 304 can include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 304 may further include memory remotely located from processor 302, which can be connected to electronic device 30 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.

[0115] The transmission device 306 is used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by the communications provider of the electronic device 30. In one embodiment, the transmission device 306 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one embodiment, the transmission device 306 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0116] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 30 .

[0117] The serial numbers of the above embodiments are for description only and do not represent the advantages or disadvantages of the embodiments.

[0118] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0120] Units described as separate components may or may not be physically separate, and 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 may be selected to achieve the purpose of the present embodiment according to actual needs.

[0121] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0122] If the integrated unit is implemented in the form of 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 the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code.

[0123] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for optimizing tire design parameters, characterized in that: include: Obtaining test results obtained from a dynamic test of a target tire; Obtaining structural parameters of the target tire, and combining a plurality of preset sets of attribute parameters with the structural parameters and inputting them into CD-tire identification software for dynamic simulation to obtain a plurality of first simulation results; determining target attribute parameters corresponding to the target tire from the multiple groups of attribute parameters according to differences between each of the first simulation results and the test results; Adjusting the structural parameters multiple times to obtain multiple sets of candidate structural parameters, and combining the multiple sets of candidate structural parameters with the target property parameters and inputting them into CD-tire identification software for dynamic simulation to obtain multiple second simulation results; The target structural parameters of the target tire are determined from the candidate structural parameters corresponding to the respective second simulation results whose corresponding tire performance meets the preset performance requirements.

2. The method according to claim 1, characterized in that Obtain test results from dynamic tests on target tires, including: Acquire multiple test results obtained by performing multiple dynamic tests on the target tire, wherein the types of the dynamic tests include at least one of the following: a contact footprint test, a static stiffness test, a steady-state six-component force test, a static cleat test, and a dynamic cleat test, and each test result includes at least one tire performance indicator corresponding to the dynamic test.

3. The method according to claim 1, characterized in that Obtaining structural parameters of the target tire includes: Obtaining a material distribution map of the target tire; Reading distribution curve data in a plurality of layers corresponding to different structural areas of the target tire in the material distribution map; For each layer, interpolation processing is performed on the distribution curve data corresponding to the layer to obtain a discrete point set corresponding to the layer, and each discrete point in the discrete point set is sorted according to the geometric characteristics of the structural area corresponding to the layer to obtain an ordered point sequence corresponding to the layer; The ordered point sequences corresponding to the respective layers are used as structural parameters of the target tire, and the structural parameters are stored as files in a format recognizable by CD-tire recognition software.

4. The method according to claim 2, characterized in that The preset multiple sets of property parameters are combined with the structural parameters and input into the CD-tire identification software for dynamic simulation to obtain multiple first simulation results, including: Inputting the structural parameters into CD-tire identification software to construct a first simulation model of the target tire; For each dynamic test, determining a first type of tire property parameter associated with the dynamic test, and determining a plurality of sub-property parameters of the first type; Each of the sub-attribute parameters is substituted into the first simulation model for dynamic simulation to obtain multiple first sub-simulation results corresponding to the test results of the dynamic test, wherein each first sub-simulation result includes at least one tire performance indicator corresponding to the dynamic simulation, the dynamic simulation is of the same type as the dynamic test, and the simulation condition parameters of the dynamic simulation are the same as the test condition parameters of the dynamic test.

5. The method according to claim 4, characterized in that Determining target attribute parameters corresponding to the target tire from the multiple groups of attribute parameters based on differences between each of the first simulation results and the test results includes: For each sub-attribute parameter of the first type, determining an index difference between a tire performance index in a first sub-simulation result corresponding to each sub-attribute parameter and a tire performance index in a test result corresponding to the sub-attribute parameter, and determining the sub-attribute parameter corresponding to the first sub-simulation result with the smallest corresponding index difference as a target sub-attribute parameter; A plurality of first-type target sub-attribute parameters are combined as target attribute parameters corresponding to the target tire.

6. The method according to claim 1, characterized in that The structural parameters are adjusted multiple times to obtain multiple sets of candidate structural parameters, including: determining a second type of tire performance indicator to be optimized for the target tire, and determining a target sub-structural parameter associated with the second type of tire performance indicator in the structural parameters; The target sub-structure parameters in the structure parameters are adjusted multiple times to obtain multiple groups of candidate structure parameters, wherein the multiple groups of candidate structure parameters include unadjusted structure parameters.

7. The method according to claim 6, characterized in that The plurality of sets of candidate structural parameters are respectively combined with the target property parameters and input into the CD-tire identification software for dynamic simulation to obtain a plurality of second simulation results, including: Inputting other substructure parameters of the structural parameters except the target substructure parameters and the target attribute parameters into CD-tire identification software to construct a second simulation model of the target tire; For each group of candidate structural parameters, the target sub-structural parameters in the candidate structural parameters are substituted into the second simulation model, and a dynamic simulation corresponding to the second type of tire performance index is performed to obtain multiple second simulation results, wherein each second simulation result includes the second type of tire performance index, and the simulation condition parameters corresponding to each group of candidate structural parameters are the same.

8. The method according to claim 7, characterized in that Determining target structural parameters of the target tire from candidate structural parameters corresponding to respective second simulation results corresponding to tire performance that meets preset performance requirements includes: Determining a second simulation result corresponding to the unadjusted structural parameter as a standard simulation result; For other second simulation results, if the tire performance index in the second simulation result is better than the tire performance index in the standard simulation result, it is determined that the second simulation result meets the preset performance requirement, and the second simulation result is used as a candidate simulation result; The target structural parameters of the target tire are determined from the candidate structural parameters corresponding to the candidate simulation results.

9. The method according to claim 8, characterized in that Determining target structural parameters of the target tire from candidate structural parameters corresponding to each candidate simulation result includes: For each candidate structural parameter corresponding to the candidate simulation result, performing finite element analysis on the target tire according to the candidate structural parameter to obtain a tire performance index corresponding to the candidate structural parameter; The candidate structural parameters that are optimal for the tire performance index are determined as the target structural parameters of the target tire.

10. A device for optimizing tire design parameters, characterized in that: include: An acquisition module, used to obtain test results obtained by performing a dynamic test on a target tire; A first simulation module is used to obtain structural parameters of the target tire, and combine a plurality of preset sets of attribute parameters with the structural parameters and input them into CD-tire identification software to perform dynamic simulation, thereby obtaining a plurality of first simulation results; a first determining module, configured to determine target attribute parameters corresponding to the target tire from the multiple groups of attribute parameters according to differences between the first simulation results and the test results; A second simulation module is used to adjust the structural parameters multiple times to obtain multiple sets of candidate structural parameters, and respectively combine the multiple sets of candidate structural parameters with the target property parameters and input them into CD-tire identification software to perform dynamic simulation to obtain multiple second simulation results; The second determining module is configured to determine target structural parameters of the target tire from candidate structural parameters corresponding to respective second simulation results whose corresponding tire performance meets preset performance requirements.

11. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the method for optimizing tire design parameters according to any one of claims 1 to 9 is implemented.

12. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the tire design parameter optimization method according to any one of claims 1 to 9 through the computer program.

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