Tire rubber testing method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of testing, in particular to a tire rubber testing method and device, electronic equipment and a storage medium, and the method comprises the following steps: acquiring real-time time-stress data in a tire vulcanization process; the stress data at least comprise vulcanization time, temperature data and angle data; and inputting the real-time time-stress data into a pre-constructed stress compensation model, and outputting a compensation stress value. According to the tire rubber testing method provided by the invention, the compensation stress value corresponding to the collected real-time time-stress data is calculated through the pre-constructed stress compensation model; and after data correction is carried out based on the compensation stress value, test parameters in the tire rubber test working condition are adjusted based on the corrected real-time time-target stress data, so that the test accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of testing technology, and in particular to a tire rubber testing method, device, electronic equipment and storage medium. Background Art

[0002] The pressure distribution during tire vulcanization has a crucial impact on the final performance of the tire. During the tire vulcanization process, accurate pressure control is of great significance for ensuring tire quality, improving production efficiency and extending equipment life. However, traditional pressure acquisition methods often face problems such as insufficient measurement accuracy, interference from environmental factors and nonlinear errors, which seriously affect the reliability and accuracy of pressure control. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a tire rubber testing method, device, electronic equipment, and storage medium.

[0004] In a first aspect, an embodiment of the present invention provides a tire rubber testing method, the method comprising: Acquire real-time time-stress data during tire vulcanization; the stress data at least includes: vulcanization time, temperature data, and angle data; Input the real-time time-stress data into the pre-built stress compensation model and output the compensated stress value; The real-time time-stress data is corrected according to the compensation stress value to obtain the real-time time-target stress data; Adjust test parameters based on real-time time-to-target stress data.

[0005] In combination with the first aspect, before the step of obtaining real-time time and stress data during tire vulcanization, the method includes: Obtaining measured time-measured stress data at multiple specific points in the historical tire vulcanization process to obtain a time-stress data set; Performing data cleaning on the time-stress data set to obtain a cleaned time-stress data set; Based on the time-stress data set after cleaning, a compensation model was constructed; The effectiveness of the compensation model is verified. If the effectiveness does not meet the preset requirements, the compensation model is optimized until a stress compensation model that meets the preset requirements is obtained.

[0006] In combination with the first aspect, the step of performing data cleaning on the time-stress data set to obtain a cleaned time-stress data set includes: Performing noise reduction processing on the time-stress data in the time-stress data set to obtain noise-reduced time-stress data; Perform density clustering on the denoised time-stress data to obtain the distribution characteristics of the data; Based on the distribution characteristics of the data, outlier detection and elimination are performed on the denoised time-stress data to obtain the cleaned time-stress data; All the time-stress data after cleaning are integrated to obtain the time-stress data set after cleaning.

[0007] In combination with the first aspect, the step of constructing a compensation model based on the time-stress data set after cleaning includes: Based on the test time development sequence, the cleaned time-stress data set is segmented to obtain multiple sub-interval data sets; For each sub-interval data set, the least square method is used to perform local polynomial fitting on the sub-interval data set to obtain the local fitting function corresponding to the sub-interval; According to the local fitting function and difference processing method, the characteristic curve corresponding to the sub-interval is drawn; Decompose the characteristic curve into orthogonal polynomials to obtain a set of polynomial coefficients; According to the polynomial coefficient set, a parameterized construction is performed to obtain a local model; Combine all local models to build a compensation model.

[0008] In combination with the first aspect, the step of performing parameterized construction according to the polynomial coefficient set to obtain the local model includes: The following formula is used for parameterization construction: S(A,D)=f(A)+t(D)+v(A,D); Where S is the compensation coefficient, A is the measurement angle, D is the measured stress data, f(A) is the angle-related term, t(D) is the measurement-related term, and v(A, D) is the mechanics-related term.

[0009] In combination with the first aspect, the effectiveness of the compensation model is verified. If the effectiveness does not meet the preset requirements, the compensation model is optimized until a stress compensation model that meets the preset requirements is obtained, including: Input the test time-stress data into the compensation model and output the initial stress compensation value; Correcting the test time-stress data based on the initial stress compensation value to obtain compensated test time-stress data; Calculate the residual sequence between the compensated test time-stress data and the test time-stress data; According to the residual sequence, the error contribution of the angle-related terms, stress-related terms and mechanical-related terms in the compensation model is analyzed respectively, and the angle error contribution ratio, stress error contribution ratio and mechanical error contribution ratio are obtained; The angle error contribution ratio, stress error contribution ratio and mechanical error contribution ratio are used as weights to calculate the root mean square error of the residual sequence; Determine whether the root mean square error is less than a preset value; If so, it is determined that the compensation model meets the preset requirements, and the compensation model is used as the target stress compensation model.

[0010] In combination with the first aspect, after the step of determining whether the root mean square error is less than a preset value, the method further includes: If not, based on the angle error contribution ratio, the stress error contribution ratio and the mechanical error contribution ratio, the parameters of the compensation model are corrected until a target stress compensation model that meets the preset requirements is obtained.

[0011] In a second aspect, an embodiment of the present application provides a tire rubber testing device, the device comprising: An acquisition module, used to acquire real-time time-stress data during tire vulcanization; An input module for inputting real-time time-stress data into a pre-built stress compensation model and outputting a compensated stress value; A correction module, used for correcting the real-time time-stress data according to the compensation stress value to obtain the real-time time-target stress data; Adjust test parameters based on real-time time-to-target stress data.

[0012] In a third aspect, the present application provides an electronic device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above method.

[0013] In a fourth aspect, the present application provides a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and executed by a processor, the above method is executed.

[0014] The embodiments of the present invention bring the following beneficial effects: the present application provides a tire rubber testing method, device, electronic device, and storage medium, the method obtains real-time time and stress data during the tire vulcanization process; the stress data at least includes: vulcanization time, temperature data, and angle data; the real-time time-stress data is input into a pre-built stress compensation model, and a compensated stress value is output; the real-time time-stress data is corrected according to the compensated stress value to obtain real-time time-target stress data; and the test parameters are adjusted based on the real-time time-target stress data.

[0015] In the tire rubber testing method provided in the present application, a pre-constructed stress compensation model is used to calculate the compensation stress value corresponding to the collected real-time time-stress data, and data correction is performed based on the compensation stress value, and then the test parameters in the test tire rubber working condition are adjusted based on the corrected real-time time-target stress data to improve the accuracy of the test.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A schematic diagram of a tire rubber testing method according to an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a tire rubber testing device provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0020] Reference numerals: 10-acquisition module, 20-input module, 30-correction module, 40-adjustment module; 130 - processor, 131 - memory, 132 - bus, 133 - communication interface. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] In order to facilitate the understanding of this embodiment, the application scenarios and design concepts of the embodiments of the present application are briefly introduced below.

[0023] Existing pressure acquisition methods often face problems such as insufficient measurement accuracy, interference from environmental factors, and nonlinear errors, which seriously affect the reliability and accuracy of pressure control.

[0024] Based on this, an embodiment of the present application provides a tire rubber testing method, device, electronic device, and storage medium. The method is applied to a control unit in a tire rubber vulcanization system. The tire rubber vulcanization system also includes a pressure testing device, which is connected to the control unit.

[0025] Example 1 The present application provides a tire rubber testing method, combined with Figure 1 As shown, the method includes: S110, acquiring real-time time-stress data during the tire vulcanization process; the stress data at least includes: vulcanization time, temperature data, and angle data.

[0026] S120, inputting the real-time time-stress data into a pre-built stress compensation model, and outputting a compensated stress value.

[0027] S130, correcting the real-time time-stress data according to the compensation stress value to obtain real-time time-target stress data.

[0028] S140, adjusting test parameters based on real-time time-target stress data.

[0029] In the tire rubber testing method provided in the present application, a pre-constructed stress compensation model is used to calculate the compensation stress value corresponding to the collected real-time time-stress data, and data correction is performed based on the compensation stress value, and then the test parameters in the test tire rubber working condition are adjusted based on the corrected real-time time-target stress data to improve the accuracy of the test.

[0030] In step S110, real-time time refers to each time point in the actual test process, such as the test from 10:00am-12:45am, according to the preset data collection time interval Δt=15min, the collected times are 10:15am, 10:30am, 10:45am, 11:00am, 11:15am, 11:30am, 11:45am, 12:00am, 12:15am, 12:30am, 12:45am. It can be understood that as Δt decreases, the amount of collected data increases, the amount of data processing increases accordingly, but it is conducive to improving the detection accuracy; conversely, when Δt increases, the amount of data processing decreases, but the detection accuracy is low. In actual situations, the data collection time interval Δt can be adjusted according to actual test requirements. The above is only an example and is not limited here.

[0031] In addition, each real time corresponds to stress data in the vulcanization process, so based on the determined real time and the stress data corresponding to the time, real time-stress data can be obtained.

[0032] In combination with the first aspect, before step S110, the method includes: S010, obtaining time-stress data of multiple specific points in a historical tire vulcanization process to obtain a time-stress data set.

[0033] S020, performing data cleaning processing on the time-stress data set to obtain a cleaned time-stress data set.

[0034] S030, constructing a compensation model based on the time-stress data set after cleaning.

[0035] S040, verifying the effectiveness of the compensation model. If the effectiveness does not meet the preset requirements, optimizing the compensation model until a stress compensation model that meets the preset requirements is obtained.

[0036] It can be understood that after obtaining the real-time time and stress data in step S110, the pre-built stress compensation model is input. Therefore, before step S110, the pre-built stress compensation model should be completed to meet the requirements of stress compensation in real-time testing.

[0037] Specifically, step S010 first constructs a measurement time-stress data set based on the measurement time-measurement stress data of multiple specific points collected during the historical tire vulcanization process. Specifically: Based on the pressure test equipment, a pressure test is performed on a specific point in the tire vulcanization process. The pressure test equipment is a vulcanization device for collecting pressure at a fixed point in the tire vulcanization process. The device includes a vulcanization mold, a through hole in the vulcanization mold, and a pressure sensor is arranged in the through hole. The pressure sensitive element of the pressure sensor is arranged corresponding to each part of the tire, and the measurement time-stress data set of the above-mentioned specific point is collected, wherein the stress data includes: based on the vulcanizer control console, the internal temperature, internal pressure, mold temperature, plate temperature, and vulcanization time are set to obtain stress data. Combined with Table 1, the collected test time-stress data is shown in a schematic table.

[0038] Table 1 is a schematic table of test time-stress data.

[0039]

[0040] In combination with the first aspect, step S020 includes: S021, performing noise reduction processing on the time-stress data in the time-stress data set to obtain noise-reduced time-stress data.

[0041] S022, performing density clustering processing on the denoised time-stress data to obtain distribution characteristics of the data.

[0042] S023, based on the distribution characteristics of the data, perform outlier detection and elimination processing on the denoised time-stress data to obtain cleaned time-stress data.

[0043] S024, integrating all the time-stress data after cleaning to obtain a time-stress data set after cleaning.

[0044] In step S021, the time-stress data is filtered to obtain the de-noised time-stress data. The purpose of the filtering process is to reduce the noise in the time-stress data set and improve the signal-to-noise ratio of the data so as to more accurately capture the pressure changes during the tire vulcanization process. In this embodiment, a low-pass filter is used to remove high-frequency noise to eliminate outliers and nonlinear trends.

[0045] Then, step S022 performs density clustering on the denoised time-stress data to obtain data distribution characteristics. The purpose of density clustering is to group similar data points in the denoised time-stress data set to identify the intrinsic distribution characteristics of the data.

[0046] Afterwards, step S023 performs outlier detection and elimination processing on the denoised time-stress data based on the data distribution characteristics to obtain the cleaned time-stress data, and then combines all the cleaned time-stress data in the order of time development to obtain the cleaned time-stress data set in the order of time development.

[0047] In combination with the first aspect, step S030 includes: S031, based on the test time development sequence, segment the cleaned time-stress data set to obtain multiple sub-interval data sets.

[0048] S032, for each sub-interval data set, use the least squares method to perform local polynomial fitting on the sub-interval data set to obtain a local fitting function corresponding to the sub-interval.

[0049] S033, drawing a characteristic curve according to the local fitting function and the difference processing method.

[0050] S034, performing orthogonal polynomial decomposition on the characteristic curve to obtain a polynomial coefficient set.

[0051] S035, performing parameterized construction according to the polynomial coefficient set to obtain a local model.

[0052] S036, combining all local models to construct a compensation model.

[0053] Step S031 , segmenting the cleaned time-stress data set obtained in step S020 to obtain a plurality of sub-interval data sets.

[0054] During the tire vulcanization process, the pressure response at different time periods may show different characteristics, such as rising, stable or falling trends. Therefore, the data set is divided into multiple sub-intervals according to the time interval, one interval every 10 minutes. This ensures that the data quality of each sub-interval is high enough for effective analysis. Through segmentation processing, a more accurate local model can be built for each sub-interval, improving the prediction accuracy of the overall model.

[0055] Afterwards, in step S032, a least square method is used to perform local polynomial fitting on each sub-interval data set to obtain a local fitting function.

[0056] Subsequently, in step S033, the sub-interval is interpolated according to the local fitting function to obtain a smooth transition curve; and the smooth transition curve is then interpolated by spline to obtain a characteristic curve corresponding to the sub-interval.

[0057] Then, step S304 performs orthogonal polynomial decomposition on the characteristic curve to obtain a polynomial coefficient set; step S305 performs parameterization construction based on the polynomial coefficient set to obtain a local model corresponding to the sub-interval. Finally, all local models are combined to construct a compensation model.

[0058] In combination with the first aspect, step S304 includes: The following formula is used for parameterization construction: S(A,D)=f(A)+t(D)+v(A,D); Where S is the compensation coefficient, A is the measurement angle, D is the measured stress data, f(A) is the angle-related term, t(D) is the measurement-related term, and v(A, D) is the mechanics-related term.

[0059] In this embodiment, f(A)=a0+a1A+a2A 2 +…+a n A n ; The temperature of the rubber compound inside the tire drops slowly after the mold is opened, and there is a considerable post-vulcanization effect, which shows that the angle is an important factor in the vulcanization process. Therefore, f(A) uses a polynomial function to describe the effect of angle A on the compensation coefficient S. Among them, a 0…… a n etc. are coefficients obtained by fitting the experimental data.

[0060] In this embodiment, the coefficients of the experimental data are estimated by the "least square method". The goal of the least square method is to minimize the sum of the squared differences between the model prediction value and the actual data.

[0061]

[0062] in, is the actual stress value of the ith data point, A i is the corresponding measurement angle and m is the number of data points.

[0063] Secondly, the vulcanization pressure has a significant effect on the mechanical properties of rubber. The vulcanization process is the process of forming cross-links between rubber molecular chains. The change in this cross-linking structure directly affects the mechanical properties of rubber, such as strength, elasticity and wear resistance. The vulcanization pressure also affects the microstructure of rubber, such as porosity and phase distribution. These structural characteristics have a direct impact on the macroscopic mechanical properties of rubber. The relationship between vulcanization pressure and rubber mechanical properties is often nonlinear, because the cross-linking reaction and the arrangement of molecular chains do not increase or decrease linearly with the change of pressure. Therefore, t(D) uses a nonlinear function to describe the influence of the measured data D on the compensation coefficient S, specifically: t(D)=b0+b1D+b2D 2 +…+b m D m ; Among them, t(D) is the measurement related term, b0…b n etc. are coefficients, which are obtained by fitting the experimental data using the least square method.

[0064] In addition, vulcanization pressure and vulcanization time have complex effects on the mechanical properties of rubber. Therefore, v(A,D) uses a polynomial function of interaction terms to describe this interaction and collects data on the mechanical properties of rubber under different vulcanization pressures D and vulcanization times A during the vulcanization process. Specifically: v(A,D)=C0+C1AD+C2A 2 D 2 +…+C p A P D P Among them, C1…C p are coefficients obtained by processing the experimental data based on the least squares method. C0 is a constant term, which represents the baseline value when A and D are both zero. The value range of C0 is [-3.524, 11.316]. p is the coefficient of the p-order term, which represents the direct interaction effect between the curing time and the curing pressure, C p The value range of is [0.09, 1.16].

[0065] In combination with the first aspect, step S040 includes: S041, input the test time-stress data into the compensation model, and output the initial stress compensation value.

[0066] The test time-stress data may be the time-stress data acquired again, which is input into the compensation model constructed in step S030 to obtain the initial stress compensation value output by the compensation model, so as to verify the effectiveness of the compensation model.

[0067] S042, correcting the test time-stress data based on the initial stress compensation value to obtain compensated test time-stress data.

[0068] The compensated test time-stress data is calculated using the following formula: Tc(t)=T(t)+S(t); Among them, S(t) is the initial stress compensation value, T(t) is the test time-stress data before correction; Tc(t) is the test time-stress data after correction.

[0069] S043, calculating a residual sequence between the compensated test time-stress data and the test time-stress data.

[0070] The residual calculation is performed on the compensated test time-stress data and the test time-stress data before compensation to obtain a residual sequence. The residual e(x) at time x can be expressed as: e(x)=T(x)-Tc(x); Among them, T(x) represents the stress measurement value at a certain test point x; Tc(x) represents the stress value after compensation at test x. It can be understood that it is the value of T(x) corrected by the compensation model, which is used to provide more accurate vulcanization parameter control; e(x) represents the residual at the test point x. It can be understood that it is the difference between T(x) and Tc(x) after compensation, which is used to quantify the accuracy and effectiveness of the compensation model.

[0071] S044, according to the residual sequence, the angle-related terms, stress-related terms and mechanical-related terms in the compensation model are respectively analyzed for error contribution, and the angle error contribution ratio, stress error contribution ratio and mechanical error contribution ratio are obtained.

[0072] In this embodiment, according to the residual sequence, the error contribution analysis is performed on the angle-related terms, stress-related terms and mechanical-related terms in the initial stress compensation model respectively to obtain the error contribution ratio of each term, specifically: f(A)R= ; t(D)R= ; v(A,D)R= ; Among them, ei represents the residual at time point i, represents the angle at time point i, is the stress data at time point i, N is the total number of data, f(A)R is the angle error contribution ratio, t(D)R is the stress error contribution ratio, and v(A, D)R is the mechanical error contribution ratio.

[0073] S045, taking the angle error contribution ratio, the stress error contribution ratio and the mechanical error contribution ratio as weights, performing root mean square error calculation on the residual sequence.

[0074] After step S044, the contribution ratio of each error is used as a weight, and the root mean square error (RMSE) of the residual sequence is calculated using the following formula: RMSE= ; Where N is the number of data points and ei is the residual at time i.

[0075] The overall model error is quantified by calculating RMSE.

[0076] During the calculation process, the following error contribution ratios were obtained: angle error contribution ratio was 0.27, measurement error contribution ratio was 0.38, and mechanical error contribution ratio was 0.35. These ratios were combined with the overall error index to conduct a comprehensive evaluation of the compensation effect.

[0077] S046, determining whether the root mean square error is less than a preset value.

[0078] If so, execute step S047; if not, execute step S048.

[0079] S047, determining that the compensation model meets preset requirements, and using the compensation model as a stress compensation model.

[0080] When the RMSE calculated in step S045 is less than the preset value, it means that the error of the compensation model is small and meets the use requirements. At this time, there is no need to further modify and optimize the compensation model, and it can be used as a stress compensation model. On the contrary, when the RMSE calculated is greater than the preset value, it means that the error of the compensation model is large and the model should be further optimized. For details, see step S048.

[0081] S048, based on the angle error contribution ratio, the stress error contribution ratio and the mechanical error contribution ratio, modify the parameters of the compensation model until a target stress compensation model that meets preset requirements is obtained.

[0082] Combined with the above example, the angle error contribution ratio is 0.27, which shows that the angle is a relatively minor factor affecting the accuracy of the compensation model. When optimizing the model, the polynomial form or coefficients of the angle-related terms should be adjusted to reduce their contribution to the total error.

[0083] The contribution ratio of measurement error is 0.38, which indicates that measurement data is an important factor affecting the accuracy of the compensation model. When optimizing the model, the nonlinear function form or coefficient of the measurement-related items should be adjusted to reduce their contribution to the total error.

[0084] The mechanical error contribution ratio is 0.35, which indicates that the interaction between vulcanization pressure and vulcanization time has a significant impact on the accuracy of the compensation model. When optimizing the model, it should be considered to adjust the polynomial form or coefficients of the mechanical related terms to more accurately capture the interaction between vulcanization pressure and vulcanization time.

[0085] After optimizing the model in the above manner, the time-stress data is input again and steps S041-S045 are repeated. The RMSE is compared with the preset value to determine whether the optimized compensation model meets the validity requirements. If not, continue to optimize, input, and verify until a stress compensation model that meets the validity requirements is obtained.

[0086] In this way, a satisfactory stress compensation model is constructed before actual application, and the vulcanization parameters are adjusted in time during actual testing to improve the vulcanization quality and efficiency.

[0087] In a second aspect, the present application provides a tire rubber testing method and device, combined with Figure 2 As shown, the device includes: an acquisition module 10, an input module 20, a correction module 30, and an adjustment module 40.

[0088] The acquisition module 10 is used to acquire real-time time-stress data during the tire vulcanization process.

[0089] The input module 20 is used to input the real-time time-stress data into the pre-built stress compensation model and output the compensated stress value.

[0090] The correction module 30 is used to correct the real-time time-stress data according to the compensation stress value to obtain the real-time time-target stress data; The adjustment module 40 is used to adjust the test parameters based on the real-time time-target stress data.

[0091] In a third aspect, the present application provides an electronic device, Figure 3 As shown, the electronic device includes a memory 131 and a processor 130. The memory 131 is used to store computer programs, and the processor 130 runs the computer programs to enable the electronic device to perform the above method.

[0092] Furthermore, combined with Figure 3 The electronic device shown further includes a bus 132 and a communication interface 133 , and the processor 130 , the communication interface 133 and the memory 131 are connected via the bus 132 .

[0093] The memory 131 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 133 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 132 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0094] The processor 130 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 130. The above processor 130 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 131, and the processor 130 reads the information in the memory 131 and completes the steps of the method of the above embodiment in combination with its hardware.

[0095] In a fourth aspect, an embodiment of the present application provides a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and executed by a processor, the above method is executed.

[0096] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0097] In addition, in the description of the embodiments of the present invention, 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 an indirect connection through an intermediate medium, and it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0098] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0099] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0100] Finally, it should be noted that the above embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A tire rubber testing method, characterized in that: The method comprises: Acquire real-time time-stress data during tire vulcanization; the stress data at least includes: vulcanization time, temperature data, and angle data; Inputting the real-time time-stress data into a pre-built stress compensation model and outputting a compensated stress value; Correcting the real-time time-stress data according to the compensation stress value to obtain real-time time-target stress data; Test parameters are adjusted based on the real-time time-target stress data.

2. The method according to claim 1, characterized in that: The steps for obtaining real-time time and stress data during tire vulcanization include: Obtaining measured time-measured stress data at multiple specific points in the historical tire vulcanization process to obtain a time-stress data set; Performing data cleaning on the time-stress data set to obtain a cleaned time-stress data set; constructing a compensation model based on the post-cleaning time-stress data set; The effectiveness of the compensation model is verified. If the effectiveness does not meet the preset requirements, the compensation model is optimized until the stress compensation model that meets the preset requirements is obtained.

3. The method according to claim 2, characterized in that The step of performing data cleaning on the time-stress data set to obtain a cleaned time-stress data set includes: Performing noise reduction processing on the time-stress data in the time-stress data set to obtain noise-reduced time-stress data; Performing density clustering processing on the denoised time-stress data to obtain distribution characteristics of the data; Based on the distribution characteristics of the data, outlier detection and elimination processing are performed on the time-stress data after noise reduction to obtain the cleaned time-stress data; All the time-stress data after cleaning are integrated to obtain the time-stress data set after cleaning.

4. The method according to claim 2, characterized in that The step of constructing a compensation model based on the post-cleaning time-stress data set comprises: Based on the test time development sequence, segmenting the cleaned time-stress data set to obtain multiple sub-interval data sets; For each sub-interval data set, a least square method is used to perform local polynomial fitting on the sub-interval data set to obtain a local fitting function corresponding to the sub-interval; According to the local fitting function and the difference processing method, drawing a characteristic curve corresponding to the sub-interval; Performing orthogonal polynomial decomposition on the characteristic curve to obtain a polynomial coefficient set; Perform parameterization construction according to the polynomial coefficient set to obtain a local model; The compensation model is constructed by combining all the local models.

5. The method according to claim 4, characterized in that The step of performing parameterized construction according to the polynomial coefficient set to obtain a local model comprises: The following formula is used for parameterization construction: S(A,D)=f(A)+t(D)+v(A,D); Where S is the compensation coefficient, A is the measurement angle, D is the measured stress data, f(A) is the angle-related term, t(D) is the measurement-related term, and v(A, D) is the mechanics-related term.

6. The method according to claim 1, characterized in that The step of verifying the effectiveness of the compensation model and optimizing the compensation model if the effectiveness does not meet the preset requirements until the stress compensation model that meets the preset requirements is obtained comprises: Inputting the test time-stress data into the compensation model and outputting the initial stress compensation value; Correcting the test time-stress data based on the initial stress compensation value to obtain compensated test time-stress data; calculating a residual sequence between the compensated test time-stress data and the test time-stress data; According to the residual sequence, error contribution analysis is performed on the angle-related terms, stress-related terms and mechanical-related terms in the compensation model respectively to obtain angle error contribution ratio, stress error contribution ratio and mechanical error contribution ratio; Using the angle error contribution ratio, the stress error contribution ratio and the mechanical error contribution ratio as weights, performing root mean square error calculation on the residual sequence; Determine whether the root mean square error is less than a preset value; If so, it is determined that the compensation model meets the preset requirements, and the compensation model is used as the target stress compensation model.

7. The method according to claim 6, characterized in that After the step of determining whether the root mean square error is less than a preset value, the method further includes: If not, based on the angle error contribution ratio, the stress error contribution ratio and the mechanical error contribution ratio, the parameters of the compensation model are corrected until a target stress compensation model that meets the preset requirements is obtained.

8. A tire rubber testing device, characterized in that: The device comprises: An acquisition module, used to acquire real-time time-stress data during tire vulcanization; An input module, used for inputting the real-time time-stress data into a pre-built stress compensation model and outputting a compensated stress value; A correction module, used for correcting the real-time time-stress data according to the compensation stress value to obtain real-time time-target stress data; Test parameters are adjusted based on the real-time time-target stress data.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the method according to any one of claims 1 to 7 is executed.

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