Battery capacity prediction method, device, computer equipment and storage medium

Abstract

The present application relates to a battery capacity prediction method, apparatus, computer equipment, and storage medium. The method comprises: for each preset fitting model, fitting the capacity decay curve of the battery under test using the capacity data of the battery under test under multiple charge and discharge cycles to generate a capacity decay curve for the battery under test under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting based on the capacity data and a prediction curve obtained by predicting the capacity data; determining a candidate capacity decay curve for the battery under test based on characteristic information of the prediction curve in each capacity decay curve; and determining a target capacity decay curve for the battery under test based on the coefficient of determination of the fitting model corresponding to the candidate capacity decay curve. This method improves the matching degree between the fitting model and the battery under test and the accuracy of the target capacity decay curve.

Description

Technical Field

[0001] The present application relates to the technical field of lithium-ion batteries, and in particular to a battery capacity prediction method, apparatus, computer equipment, and storage medium. Background Art

[0002] Lithium-ion batteries, with their high energy density, long cycle life, and environmental friendliness, are widely used in portable electronic devices, electric vehicles, and energy storage systems. However, as lithium-ion batteries age, their capacity gradually decays, leading to their end of life. Therefore, predicting the capacity decay curve of lithium-ion batteries is crucial for evaluating battery performance, guiding battery design, optimizing battery management, identifying potential problems, and reducing development costs and time.

[0003] Traditionally, the same fitting method has been used to predict the capacity decay curve of lithium-ion batteries of all different types. However, this method suffers from low accuracy in predicting the capacity decay curve. Summary of the Invention

[0004] Based on this, it is necessary to provide a battery capacity prediction method, device, computer equipment and storage medium that can improve the accuracy of battery capacity decay curve prediction in order to address the above technical problems.

[0005] In a first aspect, the present application provides a battery capacity prediction method, comprising:

[0006] For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate a capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting the capacity data and a predicted curve obtained by predicting the capacity data;

[0007] Determining a candidate capacity decay curve for the battery to be tested based on characteristic information of a predicted curve in each of the capacity decay curves;

[0008] The target capacity decay curve of the battery to be tested is determined according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve.

[0009] In one embodiment, the characteristic information includes an endpoint value of a prediction curve, and determining a candidate capacity decay curve of the battery to be tested based on the characteristic information of the prediction curve in each of the capacity decay curves includes:

[0010] Determine a capacity decay curve whose endpoint value of the prediction curve is greater than a preset threshold as an alternative capacity decay curve;

[0011] The candidate capacity fade curve is determined from the alternative capacity fade curves.

[0012] In one embodiment, the characteristic information further includes a slope of a prediction curve; and determining the candidate capacity decay curve from the alternative capacity decay curves includes:

[0013] An alternative capacity decay curve whose slope of the predicted curve is less than a preset slope threshold is determined as the candidate capacity decay curve.

[0014] In one embodiment, determining the target capacity decay curve of the battery to be tested according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve includes:

[0015] sorting the determination coefficients of the fitting models corresponding to the candidate capacity decay curves;

[0016] The candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient is determined as the target capacity decay curve.

[0017] In one embodiment, the method further comprises:

[0018] For each preset fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles, a total sum of squares and a residual sum of squares of each fitting model are obtained; wherein the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and the predicted value corresponding to each charge and discharge cycle in the fitting curve;

[0019] The coefficient of determination of each fitting model is determined according to the total sum of squares and the residual sum of squares of each fitting model.

[0020] In one embodiment, for each preset fitting model, the capacity data of the battery under test under multiple charge and discharge cycles are used to fit the capacity decay curve of the battery under test, and the capacity decay curve of the battery under test under each fitting model is generated, including:

[0021] For each fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data, an iterative optimization algorithm is used to optimize the parameters of the fitting model to obtain an optimized fitting model;

[0022] The capacity decay curves are generated by using the number of charge and discharge cycles of the battery to be tested and the optimized fitting models.

[0023] In one embodiment, the method further comprises:

[0024] Analyze the performance data of the battery to be tested according to the target capacity decay curve of the battery to be tested.

[0025] In a second aspect, the present application further provides a battery capacity prediction device, comprising:

[0026] a generation module, configured to fit a capacity decay curve of the battery under test using capacity data of the battery under test under multiple charge and discharge cycles for each preset fitting model, thereby generating a capacity decay curve of the battery under test under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting the capacity data and a predicted curve obtained by predicting the capacity data;

[0027] A first determining module is configured to determine a candidate capacity decay curve for the battery to be tested based on characteristic information of a predicted curve in each of the capacity decay curves;

[0028] The second determination module is configured to determine a target capacity decay curve of the battery to be tested according to a determination coefficient of a fitting model corresponding to the candidate capacity decay curve.

[0029] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0030] For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate a capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting the capacity data and a predicted curve obtained by predicting the capacity data;

[0031] Determining a candidate capacity decay curve for the battery to be tested based on characteristic information of a predicted curve in each of the capacity decay curves;

[0032] The target capacity decay curve of the battery to be tested is determined according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve.

[0033] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0034] For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate a capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting the capacity data and a predicted curve obtained by predicting the capacity data;

[0035] Determining a candidate capacity decay curve for the battery to be tested based on characteristic information of a predicted curve in each of the capacity decay curves;

[0036] The target capacity decay curve of the battery to be tested is determined according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve.

[0037] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0038] For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate a capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting the capacity data and a predicted curve obtained by predicting the capacity data;

[0039] Determining a candidate capacity decay curve for the battery to be tested based on characteristic information of a predicted curve in each of the capacity decay curves;

[0040] The target capacity decay curve of the battery to be tested is determined according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve.

[0041] The battery capacity prediction method, apparatus, computer equipment, and storage medium described above use the capacity data of the battery under test under multiple charge and discharge cycles to fit the capacity decay curve of the battery under test for each preset fitting model, generating a capacity decay curve for the battery under each fitting model. Each capacity decay curve includes a fitting curve obtained by fitting the capacity data and a prediction curve obtained by predicting the capacity data. Based on the characteristic information of the prediction curve in each capacity decay curve, a candidate capacity decay curve for the battery under test is determined. Based on the determination coefficient of the fitting model corresponding to the candidate capacity decay curve, a target capacity decay curve for the battery under test is determined. Based on the characteristic information of the prediction curve in the capacity decay curve and the determination coefficient of each fitting model, the capacity decay curve corresponding to each fitting model is screened twice to determine the fitting model with the highest accuracy for capacity prediction of the battery under test, thereby improving the matching degree between the fitting model and the battery under test and the accuracy of the target capacity decay curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A diagram showing an application environment of a battery capacity prediction method in one embodiment;

[0044] Figure 2 1 is a flow chart of a battery capacity prediction method according to an embodiment;

[0045] Figure 3 FIG1 is a flow chart of a battery capacity prediction method according to another embodiment;

[0046] Figure 4 This is a second flow chart of a battery capacity prediction method according to another embodiment;

[0047] Figure 5 This is a third flow chart of a battery capacity prediction method according to another embodiment;

[0048] Figure 6 This is a fourth flow chart of a battery capacity prediction method according to another embodiment;

[0049] Figure 7 Schematic diagram of all capacity decay curves corresponding to battery 1 in one embodiment;

[0050] Figure 8 Schematic diagram of a target capacity decay curve of a battery 1 in one embodiment;

[0051] Figure 9 Schematic diagram of all capacity decay curves corresponding to battery 2 in one embodiment;

[0052] Figure 10 Schematic diagram of a target capacity decay curve of battery 2 in one embodiment;

[0053] Figure 11 Schematic diagram of all capacity decay curves corresponding to battery 3 in one embodiment;

[0054] Figure 12 Schematic diagram of a target capacity decay curve of battery 3 in one embodiment;

[0055] Figure 13 FIG5 is a fifth flow chart of a battery capacity prediction method according to another embodiment;

[0056] Figure 14 6 is a flowchart of a battery capacity prediction method according to another embodiment;

[0057] Figure 15 is a structural block diagram of a battery capacity prediction device in one embodiment;

[0058] Figure 16 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0060] The battery capacity prediction method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The data acquisition device 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 can obtain the capacity data of the battery to be tested under multiple charge and discharge cycles from the data acquisition device 102, and then use the capacity data of the battery to be tested under multiple charge and discharge cycles and the fitting model to determine the capacity attenuation curve of the battery to be tested. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0061] In one embodiment, Figure 2 As shown, a battery capacity prediction method is provided, which is applied to Figure 1 The following is an example of a server in the example, including:

[0062] S201, for each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate the capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting based on the capacity data and a predicted curve obtained by predicting based on the capacity data.

[0063] The fitting model in this embodiment may be at least one of a bi-exponential fitting model, a linear-exponential mixed model, a linear fitting model, a single exponential fitting model, and a polynomial fitting model. The capacity data may be a data pair consisting of the number of cycles and the battery capacity corresponding to the number of cycles. For example, the number of cycles may be represented as x and the battery capacity as y, and the capacity data may be (x, y) at each number of cycles.

[0064] The polynomial fitting model can be expressed as , where a0, a1, ..., a n To determine the model parameters, the polynomial fitting model is used to capture the more complex nonlinear trends and multi-inflection point characteristics in the data. By setting the initial coefficients and parameter constraints for the polynomial fitting, the optimization process can be completed efficiently within the control search range, thereby obtaining fitting results with higher flexibility and accuracy.

[0065] Optionally, a data acquisition request can be sent to a data acquisition device to obtain capacity data of the battery to be tested under multiple charge and discharge cycles based on the data returned by the data acquisition device. For example, the data returned by the data acquisition device can be subjected to data cleaning processing, standardization processing and other operations, and the processed data can be determined as the capacity data of the battery to be tested under multiple charge and discharge cycles.

[0066] In an embodiment of the present application, for each preset fitting model, the model parameters of the fitting model corresponding to the battery to be tested are first fitted based on the capacity data of the battery to be tested under multiple charge and discharge cycles, as well as the fitting curve of the battery capacity of the battery to be tested under multiple charge and discharge cycles. The model parameters are then substituted into the initial fitting model to obtain the fitting model corresponding to the battery to be tested, and the capacity decay curve of the battery is predicted using the fitting model to obtain a prediction curve corresponding to the number of cycles after the multiple charge and discharge cycles. Further, a capacity decay curve including the fitting curve and the prediction curve is obtained. Optionally, the end cycle number of the prediction curve can be set, so that the prediction curve is generated based on the fitting model and the end cycle number.

[0067] For example, the double exponential fitting model can be expressed as , where A, B, and C are the model parameters to be determined. The double exponential fitting model is suitable for data scenarios that exhibit double exponential dynamics, such as complex multi-stage decay or growth processes. By presetting the initial parameters and parameter boundaries for the model, the effectiveness and accuracy of the fitting algorithm can be significantly improved. The linear-exponential mixed model can be expressed as , where A, B, C, and D are the model parameters to be determined. The linear-exponential hybrid model can simultaneously reflect the linear and exponential change characteristics. It is suitable for situations where the data has both linear trends and rapid exponential changes. By reasonably setting the initial parameters and boundary conditions, the fitting can converge quickly and obtain a stable parameter solution. The linear fitting model can be expressed as , where A and B are the model parameters to be determined. The linear fitting model is suitable for scenarios where the data presents an approximate linear relationship as a whole. Reasonable initial values ​​and strict boundary ranges can be specified for the parameters to ensure that the optimization process is completed efficiently in a reasonable parameter domain and to avoid unreasonable local extreme values. The single exponential fitting model can be expressed as , where A, B, and C are the model parameters to be determined. The single exponential fitting model has high applicability for data showing a single exponential trend. By setting appropriate initial parameters and upper and lower bounds of parameters, the model can be ensured to converge quickly and obtain high-precision fitting results.

[0068] S202 : Determine a candidate capacity decay curve for the battery to be tested based on characteristic information of the prediction curve in each capacity decay curve.

[0069] The characteristic information of the prediction curve in the capacity attenuation curve may include a change trend of the prediction curve, a predicted capacity range corresponding to the prediction curve, and the like.

[0070] In the embodiment of the present application, the credibility of each capacity decay curve can be determined based on the characteristic information of the predicted curve in each capacity decay curve, so that each capacity decay curve after removing the unreliable capacity decay curve can be determined as the candidate capacity decay curve of the battery to be tested.

[0071] Optionally, a capacity range threshold may be pre-set, and a capacity decay curve whose predicted capacity range does not completely fall within the capacity range threshold may be determined as an unreliable capacity decay curve; alternatively, a capacity decay curve whose changing trend in the predicted curve is an increasing capacity decay curve may be determined as an unreliable capacity decay curve.

[0072] S203 , determining a target capacity decay curve for the battery to be tested according to a determination coefficient of a fitting model corresponding to the candidate capacity decay curve.

[0073] In an embodiment of the present application, the determination coefficient of the fitting model corresponding to the candidate capacity decay curve is determined based on the candidate capacity decay curve and the capacity data, and then the target capacity decay curve of the battery to be tested is determined from the candidate capacity decay curve based on each determination coefficient.

[0074] Optionally, a determination coefficient range threshold may be preset, so that a candidate capacity decay curve having a determination coefficient within the determination coefficient range threshold is determined as the target capacity decay curve.

[0075] In the above-mentioned battery capacity prediction method, for each preset fitting model, the capacity data of the battery under test under multiple charge and discharge cycles is used to fit the capacity decay curve of the battery under test, generating a capacity decay curve for the battery under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting based on the capacity data and a prediction curve obtained by predicting the capacity data; based on the characteristic information of the prediction curve in each capacity decay curve, a candidate capacity decay curve for the battery under test is determined; based on the determination coefficient of the fitting model corresponding to the candidate capacity decay curve, a target capacity decay curve for the battery under test is determined. Based on the characteristic information of the prediction curve in the capacity decay curve and the determination coefficient of each fitting model, the capacity decay curve corresponding to each fitting model is screened twice to determine the fitting model with the highest accuracy for capacity prediction of the battery under test, thereby improving the matching degree between the fitting model and the battery under test and the accuracy of the target capacity decay curve.

[0076] In one embodiment, an implementation of the above S202 is provided, wherein the feature information includes the end point value of the prediction curve, such as Figure 3 As shown, the above “determining the candidate capacity decay curve of the battery to be tested based on the characteristic information of the prediction curve in each capacity decay curve” includes:

[0077] S301 : Determine a capacity decay curve whose endpoint value of the prediction curve is greater than a preset threshold as a candidate capacity decay curve.

[0078] In an embodiment of the present application, based on the predicted number of endpoint cycles for the battery to be tested, the endpoint value of the predicted curve is read from each capacity decay curve, and further, it is determined whether the endpoint value of each predicted curve is greater than a preset threshold value. If the endpoint value of the predicted curve is greater than the preset threshold value, the capacity decay curve corresponding to the predicted curve is determined as an alternative capacity decay curve; if the endpoint value of the predicted curve is not greater than the preset threshold value, the capacity decay curve corresponding to the predicted curve is unreliable.

[0079] Optionally, since the battery capacity must not be negative, the preset threshold may be 0. In this embodiment, a capacity decay curve with an end point value of the prediction curve greater than 0 may be determined as an alternative capacity decay curve.

[0080] S302: Determine a candidate capacity decay curve from the candidate capacity decay curves.

[0081] In an embodiment of the present application, a candidate capacity decay curve may be determined from the candidate capacity decay curves based on characteristic information of the prediction curve. For example, a candidate capacity decay curve may be determined from the candidate capacity decay curves based on a change trend of the prediction curve.

[0082] Optionally, the characteristic information also includes the slope of the prediction curve. The above-mentioned "determining a candidate capacity decay curve from the alternative capacity decay curves" may include: determining the alternative capacity decay curve whose slope of the prediction curve is less than a preset slope threshold as the candidate capacity decay curve.

[0083] It should be noted that, since the capacity of the battery decays with increasing usage time, the capacity decay curve shows a downward trend, that is, the slopes of all points in the decay curve should be less than 0. In this embodiment, the slopes of multiple points in the prediction curve are determined, and the alternative capacity decay curves whose slopes are all less than a preset slope threshold are determined as candidate capacity decay curves. For example, the alternative capacity decay curves whose slopes are all less than 0 are determined as candidate capacity decay curves. Optionally, the multiple points can be points corresponding to each cycle number in the decay curve, or the multiple points can be points at preset positions.

[0084] In the above-mentioned application embodiment, the capacity decay curve is first screened according to the characteristic information of the prediction curve, and the unreliable capacity decay curves in the capacity decay curve are removed to obtain the candidate capacity decay curve, thereby improving the credibility of the candidate capacity decay curve and improving the efficiency of subsequently determining the target capacity decay curve from the candidate capacity decay curve.

[0085] In one embodiment, an implementation of the above S203 is provided, such as Figure 4 As shown, the above “determining the target capacity decay curve of the battery to be tested according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve” includes:

[0086] S401 , sorting the determination coefficients of the fitting models corresponding to the candidate capacity decay curves.

[0087] In the embodiment of the present application, the coefficients of determination of the fitting models corresponding to the candidate capacity decay curves are sorted to obtain a sorted queue. For example, if the coefficient of determination of fitting model 1 is 0.2, the coefficient of determination of fitting model 2 is 0.9, and the coefficient of determination of fitting model 3 is 0.3, the sorted queue can be fitting model 2, fitting model 3, and fitting model 1.

[0088] S402 , determining the candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient as the target capacity decay curve.

[0089] It should be noted that the coefficient of determination is an indicator of the goodness of fit of the model, which is used to evaluate the degree to which the model explains the variation of the actual value. The value of R² is between [0,1]. If R²=1, it means that the model can fully explain all the variations of the target variable; if R²=0, it means that the model has no explanatory power.

[0090] In an embodiment of the present application, the candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient is determined as the target capacity decay curve. For example, if the sorting queue of the candidate capacity decay curves is fitting model 2, fitting model 3, and fitting model 1, the capacity decay curve corresponding to fitting model 2 is determined as the target capacity decay curve.

[0091] In the above application embodiment, the target capacity decay curve is determined from the candidate capacity decay curves according to the size of the determination coefficient, thereby improving the accuracy and reliability of the target capacity decay curve.

[0092] In one embodiment, Figure 5 As shown, the above battery capacity prediction method further includes:

[0093] S204, for each preset fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles, obtain the total sum of squares and the residual sum of squares of each fitting model; wherein the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and the predicted value corresponding to each charge and discharge cycle in the fitting curve.

[0094] In the embodiment of the present application, for each number of cycles, the process of determining the total sum of squares can be shown as Formula 1, and the process of determining the residual sum of squares can be shown as Formula 2:

[0095] (Formula 1)

[0096] (Equation 2)

[0097] Among them, SST is the total sum of squares, which is used to characterize the data fluctuation of capacity data, and SSR is the residual sum of squares. is the average value of the battery capacity of all sample batteries at this cycle number, is the fitting value corresponding to the number of cycles in the fitting curve, is the capacity value corresponding to the i-th battery sample in the capacity data.

[0098] S205 , determining the coefficient of determination of each fitting model according to the total sum of squares and the residual sum of squares of each fitting model.

[0099] In the embodiment of the present application, the coefficient of determination R of each intermediate feature is determined based on the ratio of the total sum of squares of the fitting model to the sum of squares of the residuals. 2 , for example, the determination process of the coefficient of determination is shown in Formula 3:

[0100] (Formula 3)

[0101] In the above-mentioned application embodiment, the determination coefficient of each fitting model is determined based on the capacity data and the fitting curve, and the fitting result of the fitting model is compared with the average value of the known data, that is, the fitting model is evaluated by the known average value, thereby improving the reliability of judging the accuracy of the fitting model.

[0102] In one embodiment, an implementation of the above S201 is provided, such as Figure 6 As shown, the above “for each preset fitting model, using the capacity data of the battery to be tested under multiple charge and discharge cycles to fit the capacity decay curve of the battery to be tested, and generating the capacity decay curve of the battery to be tested under each fitting model” includes:

[0103] S501 , for each fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data, an iterative optimization algorithm is used to optimize the parameters of the fitting model to obtain an optimized fitting model.

[0104] In an embodiment of the present application, for each fitting model, the nonlinear least squares method can be used to optimize the parameters of the fitting model to obtain a fitting curve. Specifically, during the parameter optimization process, an iterative optimization algorithm, such as the Gauss-Newton method or the Levenberg-Marquardt algorithm, can be used to gradually correct the initial parameters to obtain the optimal parameter combination based on minimizing the error function. The gradual correction can include: guiding the parameters to continuously update in the direction of reducing the error based on the gradient information of the error function to the parameters, and when the parameter adjustment meets the set convergence condition, the iteration is completed. For example, the convergence condition can be that the parameter change or the error function change is lower than a preset threshold, or the number of iterations reaches the maximum iteration.

[0105] S502 , generating capacity decay curves using the number of charge and discharge cycles of the battery to be tested and the optimized fitting models.

[0106] In the embodiment of the present application, the capacity decay curve corresponding to each optimized fitting model can be generated based on the optimized fitting model and the preset drawing tool. For example, Figure 7 All capacity decay curves corresponding to battery 1, among which the R² of the double exponential fitting model is 0.9981, the R² of the single exponential fitting model is 0.9949, the R² of the linear fitting model is 0.7736, and the R² of the polynomial fitting model is 0.9535; Figure 8 The optimal model of battery 1 determined by S202 and S203 above, i.e., the target capacity decay curve; Figure 9All capacity decay curves corresponding to battery 2, among which R²=0.9993 for the double exponential fitting model, R²=0.9993 for the linear-exponential mixed model, R²=0.9993 for the single exponential fitting model, R²=0.9935 for the linear fitting model, R²=0.8636 for the polynomial fitting model, and R²=0.9729 for the polynomial fitting model; Figure 10 The optimal model of battery 2 determined by S202 and S203 above, i.e., the target capacity decay curve; Figure 11 All capacity decay curves corresponding to battery 3, among which the R² of the double exponential fitting model is 0.9983, the R² of the single exponential fitting model is 0.9938, the R² of the linear fitting model is 0.7367, and the R² of the polynomial fitting model is 0.9362; Figure 12 The target capacity decay curve of battery 3 is determined by S202 and S203 above. It can be seen that the target capacity decay curves of battery 1, battery 2 and battery 3 are different.

[0107] Optionally, in one embodiment, Figure 13 As shown, the above battery capacity prediction method further includes:

[0108] S206 , analyzing the performance data of the battery to be tested according to the target capacity decay curve of the battery to be tested.

[0109] Optionally, in an embodiment of the present application, the predicted data in the target capacity decay curve can be extracted, and statistical analysis and other operations can be performed on the predicted data to evaluate the service life of each battery based on the predicted data; alternatively, the target capacity decay curve can be used as a basis for battery improvement or an evaluation indicator.

[0110] In the above application embodiment, the initial fitting model is iteratively optimized according to a preset algorithm to obtain an optimized fitting model, thereby improving the matching degree between the fitting model and the battery to be tested, as well as the accuracy of the capacity decay curve.

[0111] In one embodiment, a complete battery capacity prediction method is provided, such as Figure 14 As shown, the method includes:

[0112] S1, for each fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data, an iterative optimization algorithm is used to optimize the parameters of the fitting model to obtain an optimized fitting model.

[0113] S2, generating each capacity decay curve using the number of charge and discharge cycles of the battery to be tested and each optimized fitting model.

[0114] S3: Determine the capacity decay curve whose endpoint value of the prediction curve is greater than a preset threshold as a candidate capacity decay curve.

[0115] S4: Determine the alternative capacity decay curve whose slope of the predicted curve is less than a preset slope threshold as a candidate capacity decay curve.

[0116] S5. For each preset fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles, obtain the total sum of squares and the residual sum of squares of each fitting model; wherein the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and the predicted value corresponding to each charge and discharge cycle in the fitting curve.

[0117] S6. Determine the coefficient of determination of each fitting model based on the total sum of squares and the residual sum of squares of each fitting model.

[0118] S7, sorting the determination coefficients of the fitting models corresponding to the candidate capacity decay curves.

[0119] S8, determining the candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient as the target capacity decay curve.

[0120] S9, analyzing the performance data of the battery to be tested according to the target capacity decay curve of the battery to be tested.

[0121] In the above-mentioned battery capacity prediction method, for each preset fitting model, the capacity data of the battery under test under multiple charge and discharge cycles is used to fit the capacity decay curve of the battery under test, generating a capacity decay curve for the battery under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting based on the capacity data and a prediction curve obtained by predicting the capacity data; based on the characteristic information of the prediction curve in each capacity decay curve, a candidate capacity decay curve for the battery under test is determined; based on the determination coefficient of the fitting model corresponding to the candidate capacity decay curve, a target capacity decay curve for the battery under test is determined. Based on the characteristic information of the prediction curve in the capacity decay curve and the determination coefficient of each fitting model, the capacity decay curve corresponding to each fitting model is screened twice to determine the fitting model with the highest accuracy for capacity prediction of the battery under test, thereby improving the matching degree between the fitting model and the battery under test and the accuracy of the target capacity decay curve.

[0122] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0123] Based on the same inventive concept, the present application also provides a battery capacity prediction device for implementing the aforementioned battery capacity prediction method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more battery capacity prediction device embodiments provided below can be found in the limitations of the battery capacity prediction method above and will not be repeated here.

[0124] In one embodiment, Figure 15 As shown, a battery capacity prediction device is provided, comprising: a generation module 10, a first determination module 11 and a second determination module 12, wherein:

[0125] The generation module 10 is used to fit the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles for each preset fitting model, and generate the capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting based on the capacity data and a predicted curve obtained by predicting based on the capacity data.

[0126] The first determining module 11 is configured to determine a candidate capacity decay curve of the battery to be tested based on characteristic information of a prediction curve in each capacity decay curve.

[0127] The second determining module 12 is configured to determine a target attenuation curve from a plurality of candidate attenuation curves according to the capacity data and the extension curve in the attenuation curve.

[0128] In one embodiment, the first determining module 11 includes: a first determining unit and a second determining unit, wherein:

[0129] The first determining unit is configured to determine a capacity decay curve whose endpoint value of the prediction curve is greater than a preset threshold as a candidate capacity decay curve.

[0130] The second determining unit is configured to determine a candidate capacity decay curve from the candidate capacity decay curves.

[0131] In one embodiment, the second determining unit is specifically configured to determine an alternative capacity decay curve having a slope of the predicted curve less than a preset slope threshold as a candidate capacity decay curve.

[0132] In one embodiment, the second determination module includes: a sorting unit and a third determination unit, wherein:

[0133] The ranking unit is used to rank the determination coefficients of the fitting models corresponding to the candidate capacity decay curves.

[0134] The third determining unit is configured to determine the candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient as the target capacity decay curve.

[0135] In one embodiment, the battery capacity prediction device further includes: an acquisition module and a third determination module, wherein:

[0136] An acquisition module is used to obtain, for each preset fitting model, a total sum of squares and a residual sum of squares of each fitting model in the process of fitting the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles; wherein the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and the predicted value corresponding to each charge and discharge cycle in the fitting curve.

[0137] The third determination module is used to determine the coefficient of determination of each fitting model according to the total sum of squares and the residual sum of squares of each fitting model.

[0138] In one embodiment, the generation module includes an optimization unit and a generation unit, wherein:

[0139] The optimization unit is used to optimize the parameters of the fitting model using an iterative optimization algorithm in the process of fitting the capacity decay curve of the battery to be tested using the capacity data for each fitting model to obtain an optimized fitting model.

[0140] The generating unit is used to generate each capacity decay curve by using the number of charge and discharge cycles of the battery to be tested and each optimized fitting model.

[0141] In one embodiment, the battery capacity prediction device further includes: an analysis module configured to analyze performance data of the battery to be tested according to a target capacity decay curve of the battery to be tested.

[0142] Each module in the battery capacity prediction device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0143] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 16 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store battery capacity prediction data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a battery capacity prediction method is implemented.

[0144] Those skilled in the art will understand that Figure 16 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0145] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0146] For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate a capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting based on the capacity data and a predicted curve obtained by predicting based on the capacity data;

[0147] Determine a candidate capacity decay curve for the battery to be tested based on characteristic information of the prediction curve in each capacity decay curve;

[0148] The target capacity decay curve of the battery to be tested is determined according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve.

[0149] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0150] Determine a capacity decay curve whose endpoint value of the prediction curve is greater than a preset threshold as an alternative capacity decay curve;

[0151] A candidate capacity fade curve is determined from the candidate capacity fade curves.

[0152] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0153] An alternative capacity decay curve whose slope of the predicted curve is less than a preset slope threshold is determined as a candidate capacity decay curve.

[0154] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0155] Rank the determination coefficients of the fitting models corresponding to the candidate capacity decay curves;

[0156] The candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient is determined as the target capacity decay curve.

[0157] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0158] For each preset fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles, the total sum of squares and the residual sum of squares of each fitting model are obtained; wherein the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and the predicted value corresponding to each charge and discharge cycle in the fitting curve;

[0159] The coefficient of determination of each fitting model was determined based on the total sum of squares and the residual sum of squares of each fitting model.

[0160] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0161] For each fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data, an iterative optimization algorithm is used to optimize the parameters of the fitting model to obtain the optimized fitting model;

[0162] The capacity decay curves are generated using the number of charge and discharge cycles of the battery to be tested and the optimized fitting models.

[0163] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0164] Analyze the performance data of the battery to be tested based on the target capacity decay curve of the battery to be tested.

[0165] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0166] For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate a capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting based on the capacity data and a predicted curve obtained by predicting based on the capacity data;

[0167] Determine a candidate capacity decay curve for the battery to be tested based on characteristic information of the prediction curve in each capacity decay curve;

[0168] The target capacity decay curve of the battery to be tested is determined according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve.

[0169] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0170] Determine a capacity decay curve whose endpoint value of the prediction curve is greater than a preset threshold as an alternative capacity decay curve;

[0171] A candidate capacity fade curve is determined from the candidate capacity fade curves.

[0172] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0173] An alternative capacity decay curve whose slope of the predicted curve is less than a preset slope threshold is determined as a candidate capacity decay curve.

[0174] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0175] Rank the determination coefficients of the fitting models corresponding to the candidate capacity decay curves;

[0176] The candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient is determined as the target capacity decay curve.

[0177] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0178] For each preset fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles, the total sum of squares and the residual sum of squares of each fitting model are obtained; wherein the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and the predicted value corresponding to each charge and discharge cycle in the fitting curve;

[0179] The coefficient of determination of each fitting model was determined based on the total sum of squares and the residual sum of squares of each fitting model.

[0180] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0181] For each fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data, an iterative optimization algorithm is used to optimize the parameters of the fitting model to obtain the optimized fitting model;

[0182] The capacity decay curves are generated using the number of charge and discharge cycles of the battery to be tested and the optimized fitting models.

[0183] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0184] Analyze the performance data of the battery to be tested based on the target capacity decay curve of the battery to be tested.

[0185] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0186] For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate a capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting based on the capacity data and a predicted curve obtained by predicting based on the capacity data;

[0187] Determine a candidate capacity decay curve for the battery to be tested based on characteristic information of the prediction curve in each capacity decay curve;

[0188] The target capacity decay curve of the battery to be tested is determined according to the determination coefficient of the fitting model corresponding to the candidate capacity decay curve.

[0189] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0190] Determine a capacity decay curve whose endpoint value of the prediction curve is greater than a preset threshold as an alternative capacity decay curve;

[0191] A candidate capacity fade curve is determined from the candidate capacity fade curves.

[0192] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0193] An alternative capacity decay curve whose slope of the predicted curve is less than a preset slope threshold is determined as a candidate capacity decay curve.

[0194] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0195] Rank the determination coefficients of the fitting models corresponding to the candidate capacity decay curves;

[0196] The candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient is determined as the target capacity decay curve.

[0197] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0198] For each preset fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles, the total sum of squares and the residual sum of squares of each fitting model are obtained; wherein the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and the predicted value corresponding to each charge and discharge cycle in the fitting curve;

[0199] The coefficient of determination of each fitting model was determined based on the total sum of squares and the residual sum of squares of each fitting model.

[0200] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0201] For each fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data, an iterative optimization algorithm is used to optimize the parameters of the fitting model to obtain the optimized fitting model;

[0202] The capacity decay curves are generated using the number of charge and discharge cycles of the battery to be tested and the optimized fitting models.

[0203] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0204] Analyze the performance data of the battery to be tested based on the target capacity decay curve of the battery to be tested.

[0205] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0206] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0207] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A battery capacity prediction method, characterized in that: The method comprises: For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate a capacity decay curve of the battery to be tested under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting the capacity data and a predicted curve obtained by predicting the capacity data; Determining a candidate capacity decay curve for the battery to be tested based on characteristic information of a predicted curve in each of the capacity decay curves; Determining a target capacity decay curve for the battery to be tested according to a determination coefficient of a fitting model corresponding to the candidate capacity decay curve; The process of determining the coefficient of determination of each fitting model includes: for each preset fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data of the battery to be tested under multiple charge and discharge cycles, obtaining the total sum of squares and the residual sum of squares of each fitting model; determining the coefficient of determination of each fitting model based on the total sum of squares and the residual sum of squares of each fitting model; the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and the predicted value corresponding to each charge and discharge cycle in the fitting curve.

2. The method according to claim 1, characterized in that The characteristic information includes an endpoint value of a prediction curve, and determining a candidate capacity decay curve of the battery to be tested based on the characteristic information of the prediction curve in each of the capacity decay curves includes: Determine a capacity decay curve whose endpoint value of the prediction curve is greater than a preset threshold as an alternative capacity decay curve; The candidate capacity fade curve is determined from the alternative capacity fade curves.

3. The method according to claim 2, characterized in that The characteristic information further includes a slope of a prediction curve; and determining the candidate capacity decay curve from the alternative capacity decay curves includes: An alternative capacity decay curve whose slope of the predicted curve is less than a preset slope threshold is determined as the candidate capacity decay curve.

4. The method according to any one of claims 1 to 3, characterized in that The step of determining a target capacity decay curve of the battery to be tested according to a determination coefficient of a fitting model corresponding to the candidate capacity decay curve includes: sorting the determination coefficients of the fitting models corresponding to the candidate capacity decay curves; The candidate capacity decay curve corresponding to the fitting model with the largest determination coefficient is determined as the target capacity decay curve.

5. The method according to claim 1, characterized in that Determining the coefficient of determination of each fitting model according to the total sum of squares and the residual sum of squares of each fitting model includes: The coefficient of determination of each fitting model is determined according to the ratio of the total sum of squares of each fitting model to the residual sum of squares.

6. The method according to claim 1, characterized in that For each preset fitting model, the capacity decay curve of the battery to be tested is fitted using the capacity data of the battery to be tested under multiple charge and discharge cycles to generate the capacity decay curve of the battery to be tested under each fitting model, including: For each fitting model, in the process of fitting the capacity decay curve of the battery to be tested using the capacity data, an iterative optimization algorithm is used to optimize the parameters of the fitting model to obtain an optimized fitting model; The capacity decay curves are generated by using the number of charge and discharge cycles of the battery to be tested and the optimized fitting models.

7. The method according to claim 1, characterized in that The method further comprises: Analyze the performance data of the battery to be tested according to the target capacity decay curve of the battery to be tested.

8. A battery capacity prediction device, characterized in that: The device comprises: a generation module, configured to fit a capacity decay curve of the battery under test using capacity data of the battery under test under multiple charge and discharge cycles for each preset fitting model, thereby generating a capacity decay curve of the battery under test under each fitting model; each capacity decay curve includes a fitting curve obtained by fitting the capacity data and a predicted curve obtained by predicting the capacity data; A first determining module is configured to determine a candidate capacity decay curve for the battery to be tested based on characteristic information of a predicted curve in each of the capacity decay curves; A second determination module is configured to determine a target capacity decay curve of the battery to be tested based on a determination coefficient of a fitting model corresponding to the candidate capacity decay curve; an acquisition module, configured to obtain, for each preset fitting model, a total sum of squares and a residual sum of squares of each fitting model in the process of fitting the capacity decay curve of the battery under test using the capacity data of the battery under test under multiple charge and discharge cycles; the total sum of squares is determined based on the average value of the capacity data and the capacity data under each charge and discharge cycle, and the residual sum of squares is determined based on the average value of the capacity data and a predicted value corresponding to each charge and discharge cycle in the fitting curve; The third determination module is used to determine the determination coefficient of each fitting model according to the total sum of squares and the residual sum of squares of each fitting model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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