Battery core internal resistance abnormity diagnosis method and device

Abstract

The invention discloses a cell internal resistance abnormity diagnosis method and device, and belongs to the technical field of cell diagnosis, and the diagnosis method comprises the steps: obtaining a difference growth rate calculation parameter; wherein the difference growth rate calculation parameters comprise the ratio of the voltage difference to the current of each battery cell, the factory battery internal resistance of the battery cell in the current duration time and the service life state of the current battery cell internal resistance; calculating the difference growth rate of the internal resistance of the battery cell according to the difference growth rate calculation parameters; judging the state of the battery according to the difference growth rate of the internal resistance of the battery core; wherein the difference growth rate of the battery core internal resistance is obtained according to the ratio of the voltage difference to the current of each battery core and the ratio of the product of the factory battery internal resistance of the battery core in the current duration time and the service life state of the current battery core internal resistance. According to the invention, the internal resistance state of the cell can be estimated and predicted in real time, and the development and after-sales maintenance cost is reduced; through the four-order battery model, the application scene of the constant-current charging and discharging working condition can be met, the estimation precision is improved, and the loss caused by operation stopping is prevented.

Description

Technical Field

[0001] This invention belongs to the field of battery cell diagnostic technology, and particularly relates to a method and device for diagnosing abnormal internal resistance of battery cells. Background Technology

[0002] During the cycle of use, a series of chemical reactions occur inside the battery, which causes the electrical performance of the power battery to degrade, thus affecting the battery performance.

[0003] For example, patent CN119414256A discloses a real-time battery parameter monitoring system based on single-cell sampling. This system includes a main control chip and several single-cell sampling chips. Each single-cell sampling chip is mounted on the negative terminal of a battery for real-time monitoring of battery parameters. Each single-cell sampling chip includes an internal resistance monitoring unit with a built-in internal resistance prediction model and a temperature monitoring unit with an internal temperature prediction model. It achieves real-time online monitoring of individual battery cells through the single-cell sampling chips and provides early warnings for abnormal values ​​in battery temperature and internal resistance to monitor for battery thermal runaway or aging. Furthermore, it employs a built-in software algorithm on the single-cell sampling chip to compensate for and predict abnormal values ​​of the battery's internal temperature and internal resistance based on the monitored external temperature, eliminating measurement errors caused by complex influencing factors, improving measurement accuracy, and reducing hardware costs. Finally, it improves the accuracy of measurement data transmission through a two-wire daisy-chain or Bluetooth communication connection.

[0004] However, current early warning and monitoring of abnormal battery internal resistance often require additional hardware circuits such as AC signal generators to assist in implementation. The actual operating conditions are more complex, with greater interference, and additional costs are also required.

[0005] Therefore, there is an urgent need to develop methods and devices for diagnosing abnormal internal resistance of battery cells to solve the problems in the existing technology. Summary of the Invention

[0006] The purpose of this invention is to provide a method and device for diagnosing abnormal internal resistance of battery cells. By using the ratio of voltage difference to current of each battery cell, the factory internal resistance of the battery cell during the current duration, and the lifespan status of the current internal resistance of the battery cell, the difference growth rate of the internal resistance of the battery cell can be calculated to determine the battery status. This solves the problem mentioned in the background art that the early warning and monitoring of abnormal values ​​of battery internal resistance requires additional hardware circuits such as AC signal generators.

[0007] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0008] A method for diagnosing abnormal internal resistance of a battery cell includes the following steps:

[0009] Obtain the differential growth rate calculation parameters; wherein, the differential growth rate calculation parameters include the ratio of voltage difference to current of each cell, the factory internal resistance of the cell during the current duration, and the life status of the current cell internal resistance;

[0010] Calculate the difference growth rate of the cell's internal resistance based on the difference growth rate calculation parameters;

[0011] The battery status is determined by the rate of increase in the difference in the internal resistance of the cells;

[0012] The rate of increase in the difference in the internal resistance of the battery cell is obtained by the ratio of the voltage difference to the current of each battery cell and the ratio of the product of the battery cell's factory internal resistance during the current duration and the current life state of the battery cell's internal resistance.

[0013] Furthermore, the formula for calculating the growth rate of the difference in the internal resistance of the battery cell is as follows:

[0014] ;

[0015] in, The growth rate of the difference in cell internal resistance; This is the ratio of the voltage difference to the current in each battery cell. This represents the battery's internal resistance at the factory during the current duration. This refers to the current lifespan status of the cell's internal resistance.

[0016] Furthermore, the factory internal resistance of the battery cell during the current duration is calculated using the following formula:

[0017] ;

[0018] in, This represents the battery's internal resistance at the factory during the current duration. The ohmic resistance of the battery model;

[0019] Calculated using the following formula:

[0020] ;

[0021] in, The sampling interval time. Polarization resistor, It is a capacitor.

[0022] Furthermore, the polarization resistor and capacitor are obtained through the following steps:

[0023] Obtain the equivalent formulas for the polarization voltage, polarization resistance, and capacitance of the battery model;

[0024] Acquire pulse test data for the battery model; the pulse test data includes the cell capacity and OCV meter, wherein the OCV meter shows the voltage values ​​at different SOC and temperatures. ;

[0025] Based on the equivalent formula and pulse test data, the polarization resistance and capacitance are fitted.

[0026] Furthermore, the equivalent formula is expressed as follows:

[0027] ;

[0028] in, Let be the polarization voltage at time t-1, i∈[1,k], and k be the corresponding order of the battery model; The sampling interval time. Polarization resistor, It is a capacitor.

[0029] Furthermore, it also includes:

[0030] Based on several growth rate data, the fitting parameters are calculated using a nonlinear fitting formula.

[0031] Based on the fitted parameters and the anomaly threshold, calculate the number of days the differential growth rate reaches the anomaly threshold;

[0032] Calculate the remaining available time based on the number of days the differential growth rate reaches the abnormal threshold.

[0033] Furthermore, the nonlinear fitting formula is expressed as follows:

[0034] Rate = a * exp(b) * power(t, c);

[0035] Where Rate is the daily difference growth rate; a, b, and c are all fitting parameters; and t is the number of days.

[0036] Furthermore, the method of determining the battery status based on the rate of increase in the difference in the internal resistance of the cells includes:

[0037] Calculate the average growth rate of the difference in the internal resistance of several cells over a period of time.

[0038] When the average rate of difference growth exceeds the abnormal threshold, the battery is judged to be in an abnormal state.

[0039] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0040] A computer program product includes a computer program that, when executed by a processor, implements the steps of the method.

[0041] The present invention has the following advantages:

[0042] (1) The parameters calculated by the differential growth rate include the ratio of voltage difference to current of each cell, the internal resistance of the cell at the factory during the current duration, and the life state of the current internal resistance of the cell, as well as the parameters of the newly manufactured battery model. This is used to estimate the deviation of the voltage of each cell in the battery pack from the average voltage, thereby estimating the deviation rate of the internal resistance of the cell. The deviation rate is then compared with the preset abnormal threshold to evaluate the abnormal diagnosis of the battery. This method does not rely on additional hardware circuits, specific pulse conditions, or the accuracy of the battery model after battery aging. It has good accuracy and low cost.

[0043] (2) The battery internal resistance consistency is estimated based on the fourth-order battery model and polarization effect in the constant current charge and discharge conditions of big data. It does not depend on the pulse condition and solves the problem of battery internal resistance estimation under constant current conditions.

[0044] (3) The remaining usable time can be predicted in real time based on the historical data of the internal resistance difference ratio of each existing cell, which is beneficial to the after-sales maintenance plan and prevents the system from stopping operation.

[0045] Other features and advantages of the present invention will be disclosed in detail in the following detailed description and accompanying drawings. Attached Figure Description

[0046] Figure 1 This is a flowchart of the application process;

[0047] Figure 2 This is a schematic diagram of a fourth-order RC battery model;

[0048] Figure 3a HPPC test data Figure 1 ;

[0049] Figure 3b HPPC test data Figure 2 ;

[0050] Figure 3c Figure 3 shows the HPPC test data;

[0051] Figure 3d Figure 4 shows the HPPC test data;

[0052] Figure 4a A schematic diagram of the process of current and voltage changing over time in the test data. Figure 1 ;

[0053] Figure 4b A schematic diagram of the process of current and voltage changing over time in the test data. Figure 2 ;

[0054] Figure 5 A simulation diagram illustrating the growth rate of the difference in internal resistance during abnormal cell discharge;

[0055] Figure 6 A simulation diagram illustrating the growth rate of the internal resistance difference during charging of abnormal battery cells. Detailed Implementation

[0056] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.

[0057] A method for diagnosing abnormal internal resistance of battery cells, such as Figure 1 As shown, it includes the following steps:

[0058] Obtain the differential growth rate calculation parameters; wherein, the differential growth rate calculation parameters include the ratio of voltage difference to current of each cell, the factory internal resistance of the cell during the current duration, and the life status of the current cell internal resistance;

[0059] Calculate the difference growth rate of the cell's internal resistance based on the difference growth rate calculation parameters;

[0060] The battery status is determined by the rate of increase in the difference in the internal resistance of the cells;

[0061] The rate of increase in the difference in the internal resistance of the battery cell is obtained by the ratio of the voltage difference to the current of each battery cell and the ratio of the product of the battery cell's factory internal resistance during the current duration and the current life state of the battery cell's internal resistance.

[0062] This application is primarily applied to energy storage systems and is also suitable for new energy vehicle projects where battery data is uploaded to a cloud platform. Battery data is stored through the cloud platform, with a typical data acquisition interval of 10-30 seconds per instance. Based on the large battery data on the cloud platform, this application achieves abnormal battery internal resistance diagnosis and early warning by estimating the difference rate of cell internal resistance. The difference in internal resistance is defined as the ratio of the cell's internal resistance to the average internal resistance in the same power system. When this ratio reaches an abnormal threshold, a fault is identified. The abnormal threshold can be defined based on the characteristics of the cell itself. In this embodiment, the battery is a lithium iron phosphate (LFP) battery, which has a slower internal resistance growth rate; therefore, the abnormal threshold is defined as 1.5 times in this embodiment.

[0063] In this embodiment, obtaining the parameters for calculating the difference growth rate includes the following steps:

[0064] S11: Construct a battery model and obtain the equivalent formulas for polarization voltage, polarization resistance, and capacitance.

[0065] In this embodiment, since the RC circuit has the same charging and discharging characteristics as the battery, the battery model is established based on Thevenin theory. However, the operating conditions in the energy storage system are mainly continuous charging and discharging, so the equivalent RC circuit needs to be at least fourth order to satisfy the polarization effect with a large time constant.

[0066] like Figure 2 This is the fourth-order equivalent circuit in this embodiment. The equivalent formula is expressed as follows:

[0067] ;

[0068] in, Let be the polarization voltage at time t. Let be the polarization voltage at time t-1, i∈[1,k], and k be the corresponding order of the battery model; The sampling interval time. Polarization resistor, Let V be a capacitor. The polarization voltages Vp1, Vp2, Vp3, and Vp4 can be calculated using the first-order differential equations of an RC circuit.

[0069] This application uses a fourth-order battery model to meet the application scenarios of constant current charge and discharge conditions, improve estimation accuracy, and prevent losses caused by shutdown.

[0070] Cell terminal voltage The calculation formula is as follows:

[0071] ;

[0072] in, R0 is the sampled cell voltage, and R0 is the ohmic resistance of the battery model. For the battery current, This refers to the battery's OCV voltage.

[0073] S12: Obtain pulse test data for the battery model.

[0074] The pulse test data includes the cell capacity and OCV meter readings, where the OCV meter shows the voltage values ​​at different SOC and temperatures. .

[0075] The pulse test data is obtained through existing technology and will not be described in detail here. In this embodiment, the pulse test data can be obtained through capacity testing, OCV testing and HPPC testing. The test current and resting time can be defined according to the battery characteristics.

[0076] S13: Fit the polarization resistance and capacitance based on the equivalent formula and pulse test data.

[0077] After the cell testing is completed, the cell capacity Q and OCV (Optical Capacity Value) can be obtained. The OCV value refers to the voltage value at different SOC (State of Charge) and temperatures. Then, based on the aforementioned equivalent and HPPC pulse test data, fitting... and The fitting function can be the curvefit function in MATLAB, and R0 is calculated by the ratio of transient voltage drop to current during pulse charging and discharging.

[0078] The pulse test data is obtained through capacity testing, OCV testing, and HPPC testing. Specifically, capacity testing, OCV testing, and HPPC testing are existing technologies and will not be described in detail in this application.

[0079] S14: Obtain the cell voltage and average voltage, and calculate the voltage difference between the cell voltage and average voltage based on the cell voltage and average voltage.

[0080] Among them, the cell voltage and average voltage data can be obtained by reading and filtering the battery data of the current month on the cloud platform.

[0081] During operation, the BMS needs to connect to the network to upload data to the backend cloud server. Before computation, the data on the server needs to be downloaded to a personal computer, or the data can be accessed directly via a data link address.

[0082] When filtering data, taking a 37Ah lithium iron phosphate cell from a specific project as an example, the specific definition can be based on the actual application scenario and battery characteristics. For instance, the prerequisite for system diagnosis is that the absolute value of the current exceeds 0.25C, and abnormal cells will exhibit voltage differences during charging and discharging. The temperature difference must be below 5℃, as the cell's internal resistance is significantly affected by temperature, ensuring consistency in calculation conditions. The State of Charge (SOC) must be between 30% and 80%, with minimal changes in OCV within this SOC range, preventing estimation errors due to SOC differences. Continuous charging or discharging time must be at least 60 seconds to filter out synchronization errors caused by a 30-second sampling frequency.

[0083] The formula for calculating the voltage difference between the cell voltage and the average voltage is as follows:

[0084] ;

[0085] in, This is the voltage difference between the cell voltage and the average voltage. For the voltage of each cell sampled by the BMS, This is the average voltage of all battery cells.

[0086] S15: Calculate the ratio of voltage difference to current for each cell.

[0087] The calculation formula is as follows:

[0088] ;

[0089] in, This is the ratio of the voltage difference to the current in each battery cell. This is the voltage difference between the cell voltage and the average voltage. This represents the cell current.

[0090] S16: Calculate the battery's internal resistance at the factory for the current duration.

[0091] The battery cell's factory internal resistance during the current duration is calculated using the following formula:

[0092] ;

[0093] in, This represents the battery's internal resistance at the factory during the current duration. The ohmic resistance of the battery model;

[0094] Calculated using the following formula:

[0095] ;

[0096] in, The sampling interval time. Polarization resistor, It is a capacitor.

[0097] S17: Calculate the current lifespan status of the cell's internal resistance.

[0098] In this embodiment, the current lifespan state of the cell's internal resistance is calculated using the SOH model, which is existing technology. The formula is as follows:

[0099] ;

[0100] in, This represents the current lifespan status of the cell's internal resistance. This represents the current lifespan status of the battery cell's calendar capacity. The proportional parameter can be obtained through experimental testing, etc., and is existing technology, so it will not be described in detail in this application.

[0101] The current lifespan of the battery cell's calendar capacity can be calculated using the following formula:

[0102] ;

[0103] in, The parameters for the calendar capacity decay model are: T is the average temperature over the calendar time, t is the calendar time, and SOC is the average SOC over the calendar time. These parameters are obtained by fitting the life test data.

[0104] The step of calculating the difference growth rate of the cell internal resistance based on the difference growth rate calculation parameters includes the following steps:

[0105] S21: Calculate the growth rate of the difference in cell internal resistance. The calculation formula is as follows:

[0106] ;

[0107] in, The growth rate of the difference in cell internal resistance; This is the ratio of the voltage difference to the current in each battery cell. This represents the battery's internal resistance at the factory during the current duration. This refers to the current lifespan status of the cell's internal resistance, ranging from 1 to 1.5 times.

[0108] The method of determining battery status based on the rate of increase in the difference in cell internal resistance includes:

[0109] S31: Calculate the average value of the difference growth rate based on the difference growth rate of the internal resistance of several cells over a period of time.

[0110] In this embodiment, it represents the average growth rate of the difference in cell internal resistance for the current month.

[0111] The formula is as follows:

[0112] ;

[0113] Where m represents the number of times the internal resistance of the battery cell is estimated in the current month.

[0114] After the calculation is complete, the result can be... It is stored on the server monthly, with one value corresponding to each month.

[0115] S32: When the average rate of difference growth is greater than the abnormal threshold, the battery is judged to be in an abnormal state.

[0116] In this embodiment, if If the value is greater than 1.5, an abnormal battery status will be reported. The abnormal threshold can be adjusted according to actual needs.

[0117] This embodiment also includes: predicting the remaining available time. The specific steps are as follows:

[0118] S41: Calculate the fitting parameters based on several growth rate data and a nonlinear fitting formula.

[0119] The nonlinear fitting formula is expressed as follows:

[0120] Rate = a * exp(b) * power(t, c);

[0121] Where Rate is the daily difference growth rate; a, b, and c are all fitting parameters; and t is the number of days.

[0122] S42: Calculate the number of days when the differential growth rate reaches the abnormal threshold based on the fitting parameters and the abnormal threshold.

[0123] In this embodiment, the fitting function can be the curve_fit function in Python.

[0124] S43: Calculate the remaining available time based on the number of days the differential growth rate reaches the abnormal threshold.

[0125] In this embodiment, t is calculated when Rate > 1.5, and the remaining available time can be predicted by subtracting the number of days currently used from t.

[0126] To verify the feasibility of this application, HPPC test data of a 37Ah lithium iron phosphate cell from a certain project is used for simulation.

[0127] Among them, HPPC test data are as follows Figures 3a-3d The figure shows the process of current, voltage, temperature, and state of charge (SOC) changing over time in this test data used for simulation. Figure 3a Its vertical axis represents the current value, in amperes (A); for example... Figure 3b Its vertical axis represents the cell voltage value, in mV; for example Figure 3c Its vertical axis represents temperature, with the unit being °C; for example... Figure 3d Its vertical axis is SOC, and the unit is % Figures 3a-3d In the graph, the horizontal axis represents time, with the unit being seconds, and all times correspond to each other.

[0128] Assuming the system has 14 battery cells, the cell voltage of a specific cell when SOHRes = 1.5 is first simulated using the cell terminal voltage calculation formula. The modified voltage and the actual voltage change during a certain discharge pulse are as follows: Figure 4a and Figure 4b As shown.

[0129] Figure 4a The vertical axis represents the current value, in amperes (A). Figure 4b The vertical axis represents the cell voltage value in mV, and the horizontal axis represents time in seconds. Figure 4b The curve with a low voltage represents the simulated abnormal cell voltage, while the curve with a high voltage represents the voltage of other normal cells. Figure 4a and Figure 4b This illustrates the different voltage performance of abnormal and normal battery cells after being discharged with the same current. It is a schematic diagram showing the process of current and voltage changing over time in the test data used for simulation.

[0130] The aforementioned steps were used to simulate and calculate the abnormal growth rate of the internal resistance of these 14 battery cells. For example... Figure 5 The diagram shows a simulation of the growth rate of the internal resistance difference in abnormal battery cells. The vertical axis represents the abnormal growth rate of internal resistance, and the horizontal axis represents the number of times the internal resistance of the abnormal battery cell is estimated. Figure 6 The figure shows a simulation diagram of the growth rate of the difference in internal resistance during charging of abnormal cells. The vertical axis represents the abnormal growth rate of internal resistance, and the horizontal axis represents the number of times the internal resistance of abnormal cells is estimated. The average discharge growth rate is 1.5047, and the average charging growth rate is 1.4904, indicating that the design is consistent with the actual preset abnormal value of 1.5 times, and the current abnormal cell internal resistance can be detected, thus meeting the design requirements.

[0131] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0132] A computer program product includes a computer program that, when executed by a processor, implements the steps of the method.

[0133] The main innovations and advantages of this application are as follows:

[0134] 1) The deviation rate of the cell internal resistance is estimated by estimating the deviation of the voltage of each cell in the battery pack from the average voltage. This value is compared with a preset abnormal threshold to evaluate the abnormal diagnosis of the battery, without relying on the accuracy of the battery model after battery aging.

[0135] 2) The battery internal resistance consistency is estimated based on the fourth-order battery model and polarization effect under constant current charge and discharge conditions in big data, without relying on pulse conditions, thus solving the battery internal resistance estimation under constant current conditions.

[0136] 3) Real-time prediction of remaining usable time based on historical data of the internal resistance difference ratio of each existing cell is beneficial for after-sales maintenance planning and prevents the system from stopping operation.

[0137] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

[0138] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for diagnosing abnormal internal resistance of a battery cell, characterized in that, Includes the following steps: Obtain the differential growth rate calculation parameters; wherein, the differential growth rate calculation parameters include the ratio of voltage difference to current of each cell, the factory internal resistance of the cell during the current duration, and the life status of the current cell internal resistance; Calculate the difference growth rate of the cell's internal resistance based on the difference growth rate calculation parameters; The battery status is determined by the rate of increase in the difference in the internal resistance of the cells; The rate of increase in the difference in the internal resistance of the battery cell is obtained by the ratio of the voltage difference to the current of each battery cell and the ratio of the product of the battery cell's factory internal resistance during the current duration and the current life state of the battery cell's internal resistance.

2. The method for diagnosing abnormal internal resistance of a battery cell according to claim 1, characterized in that, The formula for calculating the growth rate of the difference in the internal resistance of the battery cell is as follows: ； in, The growth rate of the difference in cell internal resistance; This is the ratio of the voltage difference to the current in each battery cell. This represents the battery's internal resistance at the factory during the current duration. This refers to the current lifespan status of the cell's internal resistance.

3. The method for diagnosing abnormal internal resistance of a battery cell according to claim 2, characterized in that, The battery cell's factory internal resistance during the current duration is calculated using the following formula: ； in, This represents the battery's internal resistance at the factory during the current duration. The ohmic resistance of the battery model; Calculated using the following formula: ； in, The sampling interval time. Polarization resistor, It is a capacitor.

4. The method for diagnosing abnormal internal resistance of a battery cell according to claim 3, characterized in that, The polarization resistor and capacitor are obtained through the following steps: Obtain the equivalent formulas for the polarization voltage, polarization resistance, and capacitance of the battery model; Acquire pulse test data for the battery model; the pulse test data includes the cell capacity and OCV meter, wherein the OCV meter shows the voltage values ​​at different SOC and temperatures. ; Based on the equivalent formula and pulse test data, the polarization resistance and capacitance are fitted.

5. The method for diagnosing abnormal internal resistance of a battery cell according to claim 4, characterized in that, The equivalent formula is expressed as follows: ； in, Let be the polarization voltage at time t-1, i∈[1,k], and k be the corresponding order of the battery model; The sampling interval time. Polarization resistor, It is a capacitor.

6. The method for diagnosing abnormal internal resistance of a battery cell according to any one of claims 1-5, characterized in that, Also includes: Based on several growth rate data, the fitting parameters are calculated using a nonlinear fitting formula. Based on the fitted parameters and the anomaly threshold, calculate the number of days the differential growth rate reaches the anomaly threshold; Calculate the remaining available time based on the number of days the differential growth rate reaches the abnormal threshold.

7. The method for diagnosing abnormal internal resistance of a battery cell according to claim 6, characterized in that, The nonlinear fitting formula is expressed as follows: Rate = a * exp(b) * power(t, c); Where Rate is the daily difference growth rate; a, b, and c are all fitting parameters; and t is the number of days.

8. The method for diagnosing abnormal internal resistance of a battery cell according to any one of claims 1-5 or 7, characterized in that, The method of determining battery status based on the rate of increase in the difference in cell internal resistance includes: Calculate the average growth rate of the difference in the internal resistance of several cells over a period of time. When the average rate of difference growth exceeds the abnormal threshold, the battery is judged to be in an abnormal state.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-8.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-8.

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