Tire inflation performance prediction method and system

Abstract

The invention relates to the technical field of tires, in particular to a tire inflation performance prediction method and system. Comprising the following steps: generating a plurality of tire categories according to a preset tire evaluation index, and generating a preprocessing strategy of each tire category; establishing an original geometric model according to the scheme parameters of the to-be-predicted tire, and generating a target preprocessing strategy of the to-be-predicted tire; generating a simulation result of the to-be-predicted tire according to the target preprocessing strategy and the original geometric model, establishing a plurality of tire types according to the tire evaluation indexes, setting corresponding preprocessing strategies, generating optimal gradual change prestress parameters and grid division schemes of different types of tires, and predicting the optimal gradual change prestress parameters. Therefore, the original geometric model of the to-be-predicted tire is precisely processed, and the data processing efficiency and the precision of a simulation result are improved. Accurate prediction of the to-be-predicted tire performance is achieved, optimization iteration of the design scheme is rapidly completed, and the research and development period is shortened.

Description

Technical Field

[0001] The present application relates to the field of tire technology, and in particular to a method and system for predicting tire inflation performance. Background Art

[0002] As an important component of tire frame materials, the cap strip plays a vital role in restraining tire circumferential deformation and maintaining tire dimensional stability. Nylon-aramid blended cap strips have been increasingly widely used due to their superiority in tire high-speed performance and handling stability.

[0003] Due to the high modulus characteristics of the blended crown band, there is a big difference between the contour of the finished product after the tire is vulcanized and demolded and the contour of the mold. If the inflation performance analysis is performed directly based on the mold contour, it will seriously affect the simulation accuracy and affect the tire performance prediction. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides a tire inflation performance prediction method and system, aiming to improve the optimization and iteration efficiency of the design scheme by realizing the inflation performance prediction of the design scheme.

[0005] In some embodiments of the present application, multiple tire categories are established based on tire evaluation indicators, and corresponding preprocessing strategies are set to generate optimal gradient prestress parameters and mesh division schemes for tires of different categories, thereby accurately processing the original geometric model of the tire to be predicted, thereby improving data processing efficiency and the accuracy of simulation results.

[0006] In some embodiments of the present application, the parameters of the tire to be predicted are obtained through a design scheme, so as to construct a corresponding original geometric model, and the original geometric model is processed according to a corresponding preprocessing strategy. Based on the technology of gradual prestress, an accurate prediction of the performance of the tire to be predicted is achieved, the optimization iteration of the design scheme is quickly completed, and the research and development cycle is shortened.

[0007] In some embodiments of the present application, a method for predicting tire inflation performance is provided, comprising:

[0008] Generate multiple tire categories according to preset tire evaluation indicators, and generate preprocessing strategies for each tire category;

[0009] Establishing an original geometric model according to the solution parameters of the tire to be predicted, and generating a target preprocessing strategy for the tire to be predicted;

[0010] Generate simulation results of the tire to be predicted based on the target preprocessing strategy and the original geometric model;

[0011] Among them, when generating multiple tire categories, including:

[0012] A tire category sequence A is established, A = (a1, a2...ai...an), where ai is the i-th tire category and n is the number of tire categories.

[0013] In some embodiments of the present application, when generating a pre-processing strategy for each tire category, it includes:

[0014] According to the tire category sequence A, set ai as the target tire category in sequence;

[0015] Generate a sample data packet of the target tire category;

[0016] Generate an initial processing strategy for the target tire category, and generate a simulation deviation of the initial processing strategy based on the sample data packet;

[0017] Determine whether to iteratively optimize the initial processing strategy according to the simulation deviation;

[0018] Generate a preprocessing strategy for the target tire category based on the optimization results;

[0019] Generate preprocessing strategies for each tire category in turn;

[0020] A preprocessing strategy sequence B is established, B = (b1, b2...bi...bn), where bi is the preprocessing strategy for the i-th tire category.

[0021] In some embodiments of the present application, when generating a target preprocessing strategy for a tire to be predicted, the process includes:

[0022] Generate similarity evaluation values ​​of the tire to be predicted and each tire category in sequence according to the preset evaluation model;

[0023] Generate a similarity evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the similarity evaluation value between the tire to be predicted and the i-th tire category;

[0024]

[0025] Where k is the number of tire evaluation indicators; μ r is the influencing factor of the i-th tire evaluation index; c r is the reference value of the rth tire evaluation index in the solution parameters of the tire to be predicted; c' ir is the reference value of the rth tire evaluation index in the i-th tire category; Q is a fixed coefficient;

[0026] The target preprocessing strategy is set according to the similarity evaluation value sequence F.

[0027] In some embodiments of the present application, when setting a target preprocessing strategy according to the similarity evaluation value series F, it includes:

[0028] Preset a first similarity evaluation value threshold F1;

[0029] Get the maximum value fmax in the similarity evaluation value sequence F;

[0030] If fmax>F1, the preprocessing strategy of the tire category corresponding to fmax is the target preprocessing strategy;

[0031] If fmax<F1, the preprocessing strategies of the tire categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value sequence F are selected, and the target preprocessing strategy of the tire to be predicted is set according to the fusion correction results of the two selected preprocessing strategies.

[0032] In some embodiments of the present application, when generating the simulation result of the tire to be predicted, it includes:

[0033] Meshing the original geometric model according to the target preprocessing strategy;

[0034] Create an outer contour node set;

[0035] Set the mechanical properties of rubber and skeleton materials;

[0036] Generate a two-dimensional finite element model of the tire to be predicted;

[0037] Import the wheel rim rigid body parameters into the 2D finite element model;

[0038] Generate a gradual prestress parameter of the tire to be predicted according to the preprocessing strategy, and generate a working condition parameter according to the preprocessing strategy;

[0039] The simulation results of the tire to be predicted are generated according to the two-dimensional finite element model, the gradual prestress parameters and the working condition parameters.

[0040] In some embodiments of the present application, further comprising:

[0041] Preset update cycle;

[0042] Acquire all tire data to be predicted within the current update cycle according to the preset update time node;

[0043] Establish a maximum similarity evaluation value sequence H of the tire to be predicted, H = (h1, h2...hi...hm), where hi is the maximum similarity evaluation value of the i-th tire to be predicted in the current update cycle; m is the number of tires to be predicted in the current update cycle;

[0044] If hi<F1, generate a new tire category according to the tire evaluation index parameters of the i-th tire to be predicted, and generate a preprocessing strategy for the new tire category;

[0045] If hi>F1, no update instruction is generated.

[0046] In some embodiments of the present application, when generating a pre-processing strategy for a newly added tire category, the method includes:

[0047] Obtaining actual tire parameters of the simulation results of the tire to be predicted corresponding to the newly added tire category;

[0048] Generate a prediction deviation value g;

[0049] g = e1 * H1 + e2 * H2;

[0050] Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; H1 is a first reference value generated based on the inflation contour in the simulation result and the inflation contour in the actual parameter; H2 is a second reference value generated based on the outer edge size in the simulation result and the outer edge size in the actual parameter;

[0051] Preset a first prediction deviation value threshold G1;

[0052] If g<G1, generate a first-level processing instruction, and generate a pre-processing strategy for the newly added tire category according to the first-level processing instruction;

[0053] If g>G1, a secondary processing instruction is generated, and a preprocessing strategy for the newly added tire category is generated according to the secondary processing instruction.

[0054] In some embodiments of the present application, a tire inflation performance prediction system is provided, comprising:

[0055] A first processing module, used to generate multiple tire categories according to preset tire evaluation indicators, and generate a preprocessing strategy for each tire category;

[0056] A second processing module is used to establish an original geometric model according to the solution parameters of the tire to be predicted;

[0057] A third processing module is used to generate a target preprocessing strategy for the tire to be predicted;

[0058] A simulation module, used for generating simulation results of a tire to be predicted according to a target preprocessing strategy and an original geometric model;

[0059] The first processing module is also used for:

[0060] According to the tire category sequence A, set ai as the target tire category in sequence;

[0061] Generate a sample data packet of the target tire category;

[0062] Generate an initial processing strategy for the target tire category, and generate a simulation deviation of the initial processing strategy based on the sample data packet;

[0063] Determine whether to iteratively optimize the initial processing strategy according to the simulation deviation;

[0064] Generate a preprocessing strategy for the target tire category based on the optimization results;

[0065] Generate preprocessing strategies for each tire category in turn;

[0066] A preprocessing strategy sequence B is established, B = (b1, b2...bi...bn), where bi is the preprocessing strategy for the i-th tire category.

[0067] In some embodiments of the present application, the third processing module is further used to:

[0068] Generate similarity evaluation values ​​of the tire to be predicted and each tire category in sequence according to the preset evaluation model;

[0069] Generate a similarity evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the similarity evaluation value between the tire to be predicted and the i-th tire category;

[0070]

[0071] Where k is the number of tire evaluation indicators; μ r is the influencing factor of the i-th tire evaluation index; c r is the reference value of the rth tire evaluation index in the solution parameters of the tire to be predicted; c' ir is the reference value of the rth tire evaluation index in the i-th tire category; Q is a fixed coefficient;

[0072] Preset a first similarity evaluation value threshold F1;

[0073] Get the maximum value fmax in the similarity evaluation value sequence F;

[0074] If fmax>F1, the preprocessing strategy of the tire category corresponding to fmax is the target preprocessing strategy;

[0075] If fmax<F1, the preprocessing strategies of the tire categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value sequence F are selected, and the target preprocessing strategy of the tire to be predicted is set according to the fusion correction results of the two selected preprocessing strategies.

[0076] In some embodiments of the present application, the simulation module is also used for:

[0077] Meshing the original geometric model according to the target preprocessing strategy;

[0078] Create an outer contour node set;

[0079] Set the mechanical properties of rubber and skeleton materials;

[0080] Generate a two-dimensional finite element model of the tire to be predicted;

[0081] Import the wheel rim rigid body parameters into the 2D finite element model;

[0082] Generate a gradual prestress parameter of the tire to be predicted according to the preprocessing strategy, and generate a working condition parameter according to the preprocessing strategy;

[0083] The simulation results of the tire to be predicted are generated according to the two-dimensional finite element model, the gradual prestress parameters and the working condition parameters.

[0084] Compared with the prior art, the tire inflation performance prediction method and system of the present application embodiment has the following beneficial effects:

[0085] Multiple tire categories are established according to tire evaluation indicators, and corresponding preprocessing strategies are set to generate the optimal gradient prestress parameters and mesh division schemes for different categories of tires, so as to accurately process the original geometric model of the tire to be predicted and improve data processing efficiency and the accuracy of simulation results.

[0086] The parameters of the tire to be predicted are obtained through the design plan, so as to construct the corresponding original geometric model, and the original geometric model is processed according to the corresponding preprocessing strategy. Based on the technology of gradual prestressing, accurate prediction of the performance of the tire to be predicted is achieved, and the optimization and iteration of the design plan is quickly completed, shortening the R&D cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a flow chart of a tire inflation performance prediction method in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0088] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0089] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0090] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0091] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0092] like Figure 1 As shown, a tire inflation performance prediction method according to a preferred embodiment of the present application includes:

[0093] S101: generating multiple tire categories according to preset tire evaluation indicators, and generating a preprocessing strategy for each tire category;

[0094] S102: establishing an original geometric model according to the solution parameters of the tire to be predicted, and generating a target preprocessing strategy for the tire to be predicted;

[0095] S103: generating a simulation result of the tire to be predicted according to the target preprocessing strategy and the original geometric model;

[0096] Among them, when generating multiple tire categories, including:

[0097] A tire category sequence A is established, A = (a1, a2...ai...an), where ai is the i-th tire category and n is the number of tire categories.

[0098] Specifically, tire evaluation indicators include but are not limited to multiple parameters that affect tire inflation performance, such as rubber quantity, tire wall thickness, and tire size. Multiple tire categories are generated according to different value ranges of each tire evaluation indicator.

[0099] Specifically, the preprocessing strategy for each tire category is generated including:

[0100] According to the tire category sequence A, set ai as the target tire category in sequence;

[0101] Generate a sample data packet of the target tire category;

[0102] Generate an initial processing strategy for the target tire category, and generate a simulation deviation of the initial processing strategy based on the sample data packet;

[0103] Determine whether to iteratively optimize the initial processing strategy according to the simulation deviation;

[0104] Generate a preprocessing strategy for the target tire category based on the optimization results;

[0105] Generate preprocessing strategies for each tire category in turn;

[0106] A preprocessing strategy sequence B is established, B = (b1, b2...bi...bn), where bi is the preprocessing strategy for the i-th tire category.

[0107] Specifically, its initial processing strategy includes the grid division strategy for the original set model and how to define the gradual prestress parameters of the tire.

[0108] Specifically, the simulation deviation can be set according to the difference parameter between the simulation result corresponding to the initial processing strategy and the actual inflation performance of the tire in the sample data packet. The larger the simulation deviation, the lower the credibility of the current simulation result. The initial processing strategy should be optimized in time and the simulation should be performed again. Through continuous iterative optimization, the best initial processing strategy corresponding to the target tire category is generated and set as the preprocessing strategy for the target tire category.

[0109] In a preferred embodiment of the present application, when generating a target preprocessing strategy for a tire to be predicted, the strategy includes:

[0110] Generate similarity evaluation values ​​of the tire to be predicted and each tire category in sequence according to the preset evaluation model;

[0111] Generate a similarity evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the similarity evaluation value between the tire to be predicted and the i-th tire category;

[0112]

[0113] Where k is the number of tire evaluation indicators; μ r is the influencing factor of the i-th tire evaluation index; c r is the reference value of the rth tire evaluation index in the solution parameters of the tire to be predicted; c' ir is the reference value of the rth tire evaluation index in the i-th tire category; Q is a fixed coefficient;

[0114] Preset a first similarity evaluation value threshold F1;

[0115] Get the maximum value fmax in the similarity evaluation value sequence F;

[0116] If fmax>F1, the preprocessing strategy of the tire category corresponding to fmax is the target preprocessing strategy;

[0117] If fmax<F1, the preprocessing strategies of the tire categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value sequence F are selected, and the target preprocessing strategy of the tire to be predicted is set according to the fusion correction results of the two selected preprocessing strategies.

[0118] Specifically, the corresponding similarity evaluation value is set according to the difference between the parameters of the tire scheme to be tested and the tire parameters corresponding to each tire category. The larger the similarity evaluation value, the closer the performance of the tire corresponding to the current design scheme is to the tire corresponding to the tire category. The original geometric model of the tire to be predicted is processed by calling the corresponding preprocessing strategy, thereby improving the data processing efficiency.

[0119] It can be understood that in the above embodiment, multiple tire categories are established according to tire evaluation indicators, and corresponding preprocessing strategies are set to generate optimal gradient prestress parameters and mesh division schemes for different categories of tires, so as to accurately process the original geometric model of the tire to be predicted, thereby improving data processing efficiency and the accuracy of simulation results.

[0120] In a preferred embodiment of the present application, when generating the simulation result of the tire to be predicted, it includes:

[0121] Meshing the original geometric model according to the target preprocessing strategy;

[0122] Create an outer contour node set;

[0123] Set the mechanical properties of rubber and skeleton materials;

[0124] Generate a two-dimensional finite element model of the tire to be predicted;

[0125] Import the wheel rim rigid body parameters into the 2D finite element model;

[0126] Generate a gradual prestress parameter of the tire to be predicted according to the preprocessing strategy, and generate a working condition parameter according to the preprocessing strategy;

[0127] The simulation results of the tire to be predicted are generated according to the two-dimensional finite element model, the gradual prestress parameters and the working condition parameters.

[0128] Specifically, the corresponding mechanical property parameters of the rubber and frame materials and the rigid body parameters of the rim are extracted according to the design scheme of the tire to be predicted, and the simulation of the tire to be predicted is completed according to the method of gradual prestress.

[0129] It can be understood that in the above embodiment, the parameters of the tire to be predicted are obtained according to the design scheme, so as to construct the corresponding original geometric model, and the original geometric model is processed according to the corresponding preprocessing strategy. Based on the technology of gradual prestress, accurate prediction of the performance of the tire to be predicted is achieved, the optimization iteration of the design scheme is quickly completed, and the research and development cycle is shortened.

[0130] In a preferred embodiment of the present application, it also includes:

[0131] Preset update cycle;

[0132] Acquire all tire data to be predicted within the current update cycle according to the preset update time node;

[0133] Establish a maximum similarity evaluation value sequence H of the tire to be predicted, H = (h1, h2...hi...hm), where hi is the maximum similarity evaluation value of the i-th tire to be predicted in the current update cycle; m is the number of tires to be predicted in the current update cycle;

[0134] If hi<F1, generate a new tire category according to the tire evaluation index parameters of the i-th tire to be predicted, and generate a preprocessing strategy for the new tire category;

[0135] If hi>F1, no update instruction is generated.

[0136] Specifically, when generating the preprocessing strategy for the newly added tire category, it includes:

[0137] Obtaining actual tire parameters of the simulation results of the tire to be predicted corresponding to the newly added tire category;

[0138] Generate a prediction deviation value g;

[0139] g = e1 * H1 + e2 * H2;

[0140] Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; H1 is a first reference value generated based on the inflation contour in the simulation result and the inflation contour in the actual parameter; H2 is a second reference value generated based on the outer edge size in the simulation result and the outer edge size in the actual parameter;

[0141] Preset a first prediction deviation value threshold G1;

[0142] If g<G1, generate a first-level processing instruction, and generate a pre-processing strategy for the newly added tire category according to the first-level processing instruction;

[0143] If g>G1, a secondary processing instruction is generated, and a preprocessing strategy for the newly added tire category is generated according to the secondary processing instruction.

[0144] Specifically, the first-level processing instruction refers to setting the preprocessing strategy of the newly added tire category corresponding to the predicted tire in the initial simulation as the preprocessing strategy of the newly added tire category. The second-level processing instruction refers to generating a sample data packet of the newly added tire category and generating the corresponding preprocessing strategy according to the iterative optimization result.

[0145] It can be understood that in the above embodiments, the efficiency and accuracy of the simulation processing for tires are improved by continuously updating and refining tire categories.

[0146] Based on another preferred embodiment of a tire inflation performance prediction method in any of the above preferred embodiments, this preferred embodiment provides a tire inflation performance prediction system, including:

[0147] A first processing module, used to generate multiple tire categories according to preset tire evaluation indicators, and generate a preprocessing strategy for each tire category;

[0148] A second processing module is used to establish an original geometric model according to the solution parameters of the tire to be predicted;

[0149] A third processing module is used to generate a target preprocessing strategy for the tire to be predicted;

[0150] A simulation module, used for generating simulation results of a tire to be predicted according to a target preprocessing strategy and an original geometric model;

[0151] The first processing module is also used for:

[0152] According to the tire category sequence A, set ai as the target tire category in sequence;

[0153] Generate a sample data packet of the target tire category;

[0154] Generate an initial processing strategy for the target tire category, and generate a simulation deviation of the initial processing strategy based on the sample data packet;

[0155] Determine whether to iteratively optimize the initial processing strategy according to the simulation deviation;

[0156] Generate a preprocessing strategy for the target tire category based on the optimization results;

[0157] Generate preprocessing strategies for each tire category in turn;

[0158] A preprocessing strategy sequence B is established, B = (b1, b2...bi...bn), where bi is the preprocessing strategy for the i-th tire category.

[0159] In a preferred embodiment of the present application, the third processing module is also used for:

[0160] Generate similarity evaluation values ​​of the tire to be predicted and each tire category in sequence according to the preset evaluation model;

[0161] Generate a similarity evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the similarity evaluation value between the tire to be predicted and the i-th tire category;

[0162]

[0163] Where k is the number of tire evaluation indicators; μ r is the influencing factor of the i-th tire evaluation index; c r is the reference value of the rth tire evaluation index in the solution parameters of the tire to be predicted; c' ir is the reference value of the rth tire evaluation index in the i-th tire category; Q is a fixed coefficient;

[0164] Preset a first similarity evaluation value threshold F1;

[0165] Get the maximum value fmax in the similarity evaluation value sequence F;

[0166] If fmax>F1, the preprocessing strategy of the tire category corresponding to fmax is the target preprocessing strategy;

[0167] If fmax<F1, the preprocessing strategies of the tire categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value sequence F are selected, and the target preprocessing strategy of the tire to be predicted is set according to the fusion correction results of the two selected preprocessing strategies.

[0168] In a preferred embodiment of the present application, the simulation module is also used for:

[0169] Meshing the original geometric model according to the target preprocessing strategy;

[0170] Create an outer contour node set;

[0171] Set the mechanical properties of rubber and skeleton materials;

[0172] Generate a two-dimensional finite element model of the tire to be predicted;

[0173] Import the wheel rim rigid body parameters into the 2D finite element model;

[0174] Generate a gradual prestress parameter of the tire to be predicted according to the preprocessing strategy, and generate a working condition parameter according to the preprocessing strategy;

[0175] The simulation results of the tire to be predicted are generated according to the two-dimensional finite element model, the gradual prestress parameters and the working condition parameters.

[0176] According to the first concept of the present application, multiple tire categories are established according to tire evaluation indicators, and corresponding preprocessing strategies are set to generate optimal gradient prestress parameters and mesh division schemes for different categories of tires, so as to accurately process the original geometric model of the tire to be predicted, thereby improving data processing efficiency and the accuracy of simulation results.

[0177] According to the second concept of the present application, the parameters of the tire to be predicted are obtained through a design scheme, so as to construct a corresponding original geometric model, and the original geometric model is processed according to the corresponding preprocessing strategy. Based on the technology of gradual prestress, an accurate prediction of the performance of the tire to be predicted is achieved, the optimization and iteration of the design scheme is completed quickly, and the research and development cycle is shortened.

[0178] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A tire inflation performance prediction method, characterized in that: include: Generate multiple tire categories according to preset tire evaluation indicators, and generate preprocessing strategies for each tire category; Establishing an original geometric model according to the solution parameters of the tire to be predicted, and generating a target preprocessing strategy for the tire to be predicted; Generate simulation results of the tire to be predicted based on the target preprocessing strategy and the original geometric model; Among them, when generating multiple tire categories, including: A tire category sequence A is established, A = (a1, a2...ai...an), where ai is the i-th tire category and n is the number of tire categories.

2. The tire inflation performance prediction method according to claim 1, characterized in that: When generating a pre-processing strategy for each tire category, include: According to the tire category sequence A, set ai as the target tire category in sequence; Generate a sample data packet of the target tire category; Generate an initial processing strategy for the target tire category, and generate a simulation deviation of the initial processing strategy based on the sample data packet; Determine whether to iteratively optimize the initial processing strategy according to the simulation deviation; Generate a preprocessing strategy for the target tire category based on the optimization results; Generate preprocessing strategies for each tire category in turn; A preprocessing strategy sequence B is established, B = (b1, b2...bi...bn), where bi is the preprocessing strategy for the i-th tire category.

3. The tire inflation performance prediction method according to claim 2, characterized in that: When generating the target preprocessing strategy for the tire to be predicted, it includes: Generate similarity evaluation values ​​of the tire to be predicted and each tire category in sequence according to the preset evaluation model; Generate a similarity evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the similarity evaluation value between the tire to be predicted and the i-th tire category; Where k is the number of tire evaluation indicators; μ r is the influencing factor of the i-th tire evaluation index; c r is the reference value of the rth tire evaluation index in the solution parameters of the tire to be predicted; c' ir is the reference value of the rth tire evaluation index in the i-th tire category; Q is a fixed coefficient; The target preprocessing strategy is set according to the similarity evaluation value sequence F.

4. The tire inflation performance prediction method according to claim 3, characterized in that: When setting the target preprocessing strategy according to the similarity evaluation value series F, it includes: Preset a first similarity evaluation value threshold F1; Get the maximum value fmax in the similarity evaluation value sequence F; If fmax>F1, the preprocessing strategy of the tire category corresponding to fmax is the target preprocessing strategy; If fmax<F1, the preprocessing strategies of the tire categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value sequence F are selected, and the target preprocessing strategy of the tire to be predicted is set according to the fusion correction results of the two selected preprocessing strategies.

5. The tire inflation performance prediction method according to claim 4, characterized in that: When generating simulation results for the tire to be predicted, include: Meshing the original geometric model according to the target preprocessing strategy; Create an outer contour node set; Set the mechanical properties of rubber and skeleton materials; Generate a two-dimensional finite element model of the tire to be predicted; Import the wheel rim rigid body parameters into the 2D finite element model; Generate a gradual prestress parameter of the tire to be predicted according to the preprocessing strategy, and generate a working condition parameter according to the preprocessing strategy; The simulation results of the tire to be predicted are generated according to the two-dimensional finite element model, the gradual prestress parameters and the working condition parameters.

6. The tire inflation performance prediction method according to claim 5, characterized in that: Also includes: Preset update cycle; Acquire all tire data to be predicted within the current update cycle according to the preset update time node; Establish a maximum similarity evaluation value sequence H of the tire to be predicted, H = (h1, h2...hi...hm), where hi is the maximum similarity evaluation value of the i-th tire to be predicted in the current update cycle; m is the number of tires to be predicted in the current update cycle; If hi<F1, generate a new tire category according to the tire evaluation index parameters of the i-th tire to be predicted, and generate a preprocessing strategy for the new tire category; If hi>F1, no update instruction is generated.

7. The tire inflation performance prediction method according to claim 6, characterized in that: When generating a preprocessing strategy for a new tire category, include: Obtaining actual tire parameters of the simulation results of the tire to be predicted corresponding to the newly added tire category; Generate a prediction deviation value g; g = e1 * H1 + e2 * H2; Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; H1 is a first reference value generated based on the inflation contour in the simulation result and the inflation contour in the actual parameter; H2 is a second reference value generated based on the outer edge size in the simulation result and the outer edge size in the actual parameter; Preset a first prediction deviation value threshold G1; If g<G1, generate a first-level processing instruction, and generate a pre-processing strategy for the newly added tire category according to the first-level processing instruction; If g>G1, a secondary processing instruction is generated, and a preprocessing strategy for the newly added tire category is generated according to the secondary processing instruction.

8. A tire inflation performance prediction system, using the tire inflation performance prediction method according to any one of claims 1 to 7, characterized in that: include: A first processing module, used to generate multiple tire categories according to preset tire evaluation indicators, and generate a preprocessing strategy for each tire category; A second processing module is used to establish an original geometric model according to the solution parameters of the tire to be predicted; A third processing module is used to generate a target preprocessing strategy for the tire to be predicted; A simulation module, used for generating simulation results of a tire to be predicted according to a target preprocessing strategy and an original geometric model; The first processing module is also used for: According to the tire category sequence A, set ai as the target tire category in sequence; Generate a sample data packet of the target tire category; Generate an initial processing strategy for the target tire category, and generate a simulation deviation of the initial processing strategy based on the sample data packet; Determine whether to iteratively optimize the initial processing strategy according to the simulation deviation; Generate a preprocessing strategy for the target tire category based on the optimization results; Generate preprocessing strategies for each tire category in turn; A preprocessing strategy sequence B is established, B = (b1, b2...bi...bn), where bi is the preprocessing strategy for the i-th tire category.

9. The tire inflation performance prediction system according to claim 8, characterized in that: The third processing module is also used for: Generate similarity evaluation values ​​of the tire to be predicted and each tire category in sequence according to the preset evaluation model; Generate a similarity evaluation value sequence F, F = (f1, f2...fi...fn), where fi is the similarity evaluation value between the tire to be predicted and the i-th tire category; Where k is the number of tire evaluation indicators; μ r is the influencing factor of the i-th tire evaluation index; c r is the reference value of the rth tire evaluation index in the solution parameters of the tire to be predicted; c' ir is the reference value of the rth tire evaluation index in the i-th tire category; Q is a fixed coefficient; Preset a first similarity evaluation value threshold F1; Get the maximum value fmax in the similarity evaluation value sequence F; If fmax>F1, the preprocessing strategy of the tire category corresponding to fmax is the target preprocessing strategy; If fmax<F1, the preprocessing strategies of the tire categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value sequence F are selected, and the target preprocessing strategy of the tire to be predicted is set according to the fusion correction results of the two selected preprocessing strategies.

10. The tire inflation performance prediction system according to claim 9, characterized in that: The simulation module is also used for: Meshing the original geometric model according to the target preprocessing strategy; Create an outer contour node set; Set the mechanical properties of rubber and skeleton materials; Generate a two-dimensional finite element model of the tire to be predicted; Import the wheel rim rigid body parameters into the 2D finite element model; Generate a gradual prestress parameter of the tire to be predicted according to the preprocessing strategy, and generate a working condition parameter according to the preprocessing strategy; The simulation results of the tire to be predicted are generated according to the two-dimensional finite element model, the gradual prestress parameters and the working condition parameters.

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