Vehicle-mounted lead-acid battery remaining capacity estimation method, storage medium and equipment

Abstract

The invention discloses a vehicle-mounted lead-acid battery remaining capacity estimation method, a storage medium and equipment, and relates to the technical field of vehicle-mounted lead-acid batteries, and the method comprises the steps: judging the state of a battery, and if the battery is in a use state, carrying out the real-time estimation of the SOC through employing an ampere-hour integral method; if the system is in the standing state, estimating the SOC by using an open-circuit voltage method, and recording corresponding voltage, current and temperature; constructing a neural network, taking voltage, current and temperature as input, and taking the estimated SOC as expected output; the input is added with the bias through a neuron and passes through an activation function to obtain output; reversely inputting the output error into an input layer, and calculating the error of the neurons and the gradient of the weight value and the bias of the loss function on the neurons; updating the weight and bias of the neuron based on the gradient, and calculating a new output; and estimating the remaining capacity of the battery by using the trained neural network. The method can reduce the estimation error of the residual electric quantity of the battery and improve the estimation precision.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-mounted lead-acid batteries, and in particular to a method, storage medium and device for estimating the remaining power of a vehicle-mounted lead-acid battery. Background Art

[0002] Lead-acid batteries are auxiliary power sources for fuel vehicles, and are generally used to provide power for engine starting and auxiliary power supply. SOC (State of Charge) is used to describe the state of charge of a lead-acid battery, indicating the ratio of the remaining capacity of the battery to its fully charged capacity, usually expressed as a percentage. Its value range is 0 to 1. When SOC = 0, it means that the battery is fully discharged, and when SOC = 1, it means that the battery is fully charged. SOC is an important input parameter for the power management strategy of the fuel vehicle energy management system, and the estimation accuracy has an important impact on the effectiveness of the vehicle's energy management. By regularly monitoring SOC, it can be ensured that the battery remains within a reasonable power range during use, avoiding the impact of overcharging or discharging on battery performance and life.

[0003] The commonly used methods for estimating SOC at present include: the open circuit voltage method, which is to determine the SOC value based on the open circuit voltage OCV (Opencircuit voltage) of the battery. Measure the open circuit voltage of the battery at different SOC values, and then obtain the function of SOC-OCV through data fitting. The ampere-hour integration method integrates the current charged into or out of the battery with time. According to the SOC value of the battery at the initial moment, the remaining power in the battery at a certain moment can be obtained. Model-based methods, such as the Kalman filter method. And so on. However, various methods for estimating SOC are difficult to achieve accurate SOC estimation, and there are large errors, because in this process it will be interfered by factors such as temperature. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that it is difficult to accurately estimate the SOC in the prior art and there is a large error, and to propose a method for estimating the remaining power of a vehicle-mounted lead-acid battery, comprising the following steps:

[0005] S1. Conduct SOC-OCV experiment on the battery, draw the curve of battery SOC and OCV, and fit the relationship between battery SOC and OCV;

[0006] S2, measure the charge and discharge current of the battery, and judge whether the battery is in a static state or in use by the current;

[0007] S3: If the battery is in use, the open circuit voltage-ampere-hour integral estimation method is used to estimate the remaining battery capacity in real time; if the battery is in a static state, the relationship between the battery SOC and OCV fitted by S1 is used to estimate the remaining battery capacity, and the corresponding voltage, current, and temperature are recorded;

[0008] S4, construct a neural network, using the voltage, current, and temperature recorded in S3 as neural network inputs, and the estimated remaining battery capacity as the true output label;

[0009] S5, associating the input signal with each neuron and summing it with the bias, and then using the activation function to obtain the output of the neural network;

[0010] S6, reversely input the difference between the output of the neural network obtained in S5 and the true output label to the input layer of the neural network, and calculate the gradient of the loss function of the neural network for the output layer and the hidden layer;

[0011] S7, update the weights and biases of the hidden layer and the output layer based on the gradient, and calculate the new output using the updated weights and biases;

[0012] S8. Repeat S4-S7 until the output of the neural network reaches a preset value or the number of neural network training iterations reaches a preset value, and input the voltage, current, and temperature measured under various states of the battery into the trained neural network to obtain the final estimated remaining battery power.

[0013] Furthermore, the battery is subjected to a SOC-OCV experiment, specifically:

[0014] Fully charge the battery, let it stand for 1 hour, and measure the open circuit voltage when SOC = 100%;

[0015] The electronic load discharges the battery at a constant current of 10A for 1 hour, then leaves it to rest for 1.5 hours, and measures the open circuit voltage when SOC = 90%;

[0016] The open circuit voltage of each stage of SOC=80%, SOC=70%, SOC=10%, SOC=0% is measured in sequence;

[0017] Record the open circuit voltage, constant current discharge value and standing time at each stage.

[0018] Furthermore, the relationship between the fitted battery state of charge and the battery open circuit voltage is expressed as:

[0019] OCV=a+b×SOC+c×SOC2+d×SOC3

[0020] Among them, OCV represents open circuit voltage, SOC represents state of charge, and a, b, c, and d are parameters that need to be identified.

[0021] Furthermore, the open circuit voltage-ampere-hour integral estimation method is used to estimate the remaining battery capacity in real time as follows:

[0022]

[0023] Among them, SOC(t) represents the state of charge at time t, SOC(t0) represents the initial state of charge, and I(t) represents the current at time t.

[0024] Furthermore, the loss function of the neural network is expressed as:

[0025]

[0026]

[0027] z o =W T x+b

[0028] Among them, L represents the loss function, x represents the input of the neural network, σ(z o ) represents z o The output after the Sigmoid activation function, W represents the weight matrix of the input neuron, and b represents the bias of the hidden layer.

[0029] Furthermore, the gradient of the loss function of the neural network for the output layer and the hidden layer is expressed as:

[0030]

[0031] Among them, δ o represents the gradient of the loss function to the output layer, L represents the loss function, z o represents the input of the Sigmoid activation function, y represents the output of the neural network, and y true represents the true output label, σ′(z o ) = y(1-y) is the derivative of the output layer Sigmoid activation function;

[0032]

[0033] Among them, δ j represents the gradient of the loss function for the neurons in the jth hidden layer, z j represents the weighted sum of the jth hidden layer neurons, v j represents the weight from the jth neuron in the hidden layer to the output layer, σ′(z j )=a j (1-a j ) represents the derivative of the hidden layer Sigmoid activation function, a jRepresents the output value of the jth hidden layer neuron after passing through the activation function.

[0034] Furthermore, the weights and biases of the hidden layer and output layer are updated based on the gradient as follows:

[0035] Update the weights and bias of the output layer based on the gradient of the loss function to the output layer:

[0036] v j =v j -α·δ o ·a j

[0037] b o =b o -α·δ o

[0038] Among them, v j represents the weight from the jth neuron in the hidden layer to the output layer, α represents the learning rate, and δ o represents the gradient of the loss function to the output layer, a j represents the output value of the jth hidden layer neuron after passing through the activation function, b o represents the bias of the output layer;

[0039] Update the weights and biases of the hidden layer based on the gradient of the loss function to the hidden layer:

[0040] w ji =w ji -α·δ j ·x i

[0041] b j =b j -α·δ j

[0042] Among them, w ji represents the weight connecting the i-th input and the j-th neuron in the hidden layer, δ j represents the gradient of the loss function for the neurons in the jth hidden layer, x i represents the i-th input of the neural network, b j represents the bias of the jth hidden layer.

[0043] The present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for estimating the remaining power of a vehicle-mounted lead-acid battery is implemented.

[0044] The present invention also proposes an electronic device, comprising a processor and a memory, wherein the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises computer-readable instructions, and the processor is configured to call the computer-readable instructions to execute the above-mentioned method for estimating the remaining power of a vehicle-mounted lead-acid battery.

[0045] The beneficial effects brought by the technical solution provided by the present invention are:

[0046] The present invention proposes a method for estimating the remaining capacity of a vehicle-mounted lead-acid battery. The remaining capacity of the battery is estimated when the battery is in a static state and in use by an open circuit voltage method and an open circuit voltage-ampere-hour integral estimation method, and the corresponding voltage, current, and temperature are recorded. The voltage, current, and temperature are used as inputs of a neural network, the estimated remaining capacity of the battery is used as the expected output of the neural network, the neurons of the weighted input of the neural network are summed after passing through an activation function as the output of the neural network, and the weights of the neurons and the bias of the neural network are updated using gradients to iteratively train the neural network. The trained neural network is used for estimating the remaining capacity of the battery. The method of the present invention can reduce the error of estimating the remaining capacity of the battery and improve the estimation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a vehicle-mounted lead-acid battery management system according to an embodiment of the present invention;

[0048] Figure 2 is a schematic diagram of the structure of a battery data acquisition board in an embodiment of the present invention;

[0049] Figure 3 It is a flow chart of a reference model for estimating the remaining battery power of a battery built into an MCU in an embodiment of the present invention;

[0050] Figure 4 is a schematic diagram of a real-time model for estimating the remaining battery power based on a data-driven technology in an embodiment of the present invention;

[0051] Figure 5 is a flow chart of a method for estimating the remaining power of a vehicle-mounted lead-acid battery according to an embodiment of the present invention;

[0052] Figure 6 It is a block diagram of an electronic device in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0054] The vehicle-mounted lead-acid battery management system of the embodiment of the present invention is as follows Figure 1As shown, it includes: a main control communication board, a battery status measurement board, a cloud platform, and a user terminal.

[0055] The main control communication board includes: a 4G module for communicating with the cloud platform; a storage module for saving battery status data when power is off; a power module for powering the main control communication board; a BMS (Battery Management System) chip; a Bluetooth chip for communicating with the user end; an MCU main control chip for the battery management system to control Bluetooth and 4G communications, and a battery data acquisition board for real-time acquisition of vehicle battery operating status data.

[0056] The battery status measurement board includes: a power module for powering the battery status measurement board; a BMS chip, which is connected to the BMS chip of the main control communication board through two TTL (transistor-transistor logic integrated circuit) protocol signal lines to transmit the data measured by the battery status measurement board to the MCU main control chip.

[0057] The cloud platform is used to store the battery SOC estimation program, communicate with the main control communication board to obtain real-time battery operating status data, and perform battery SOC estimation.

[0058] refer to Figure 2 , Figure 2 It is a schematic diagram of the structure of the battery data acquisition board in the present invention, which specifically includes a voltage divider resistor, a dual-channel operational amplifier, a filter capacitor, and a BMS chip.

[0059] The voltage divider resistor can reduce the input voltage to a voltage range that the BMS chip can withstand; the dual-channel operational amplifier is designed as two voltage followers, the output voltage of the voltage follower closely follows the input voltage, and has the characteristics of high input impedance and low output impedance, isolating the signal source and the load, separating the power ground of the BMS chip and the battery signal acquisition ground, and providing greater driving capability to ensure the normal use of the BMS chip during the acquisition process; the filter capacitor removes high-frequency noise and interference signals in the circuit, and improves the quality of the power supply and acquisition voltage signals; the BMS chip can measure the battery voltage and temperature, and communicate with other BMS chips.

[0060] Please refer to Figure 3 , Figure 3 It is a flow chart of a reference model for estimating the remaining battery power built into the MCU in the present invention.

[0061] The open circuit voltage method estimates the SOC value by obtaining the battery pack voltage fitting curve, but due to the existence of the battery polarization effect, the SOC value cannot be calculated and updated in real time, and can only be performed when the battery pack is stationary. The ampere-hour integration method calculates the SOC value in real time by collecting the battery pack charge and discharge circuit, but there is a cumulative error in the calculation process, and the SOC initial value needs to be calibrated. In addition, it only calculates the external power and cannot estimate the internal influencing factors of the battery. Through the research and analysis of the above two methods, it is found that the advantages and disadvantages of the two methods are complementary. Therefore, the battery management device of the present invention uses two algorithms to calculate in parallel to obtain the SOC value. The open circuit voltage method obtains the SOC initial value, and the current is obtained through the Hall sensor. When the battery is in use, the main controller integrates the collected current and adds it to the SOC initial value to obtain the actual SOC value.

[0062] Please refer to Figure 4 , Figure 4 The real-time model for estimating the remaining battery capacity based on data-driven technology is a schematic diagram. The real-time model for estimating the remaining battery capacity based on data-driven technology uses the reference model for estimating the remaining battery capacity to obtain a three-input single-output data set training data model of voltage, current, and temperature-SOC, where voltage, current, and temperature are used as input signals, and weights W1, W2, W3…W are randomly preset. n The input signal is associated with each neuron and summed with the bias b after passing through the activation function f(x). Then the output is the SOC predicted by the data model. The error is calculated based on the output. The gradient of the loss function for the weight and bias of each neuron is reversely calculated based on the error and the weight value and bias are updated. The updated weight value and bias are used to recalculate the output SOC based on the voltage, current, and temperature until the error is lower than the preset threshold or the number of model iterations reaches the threshold.

[0063] The method of the present invention combines a battery remaining capacity estimation reference model and a real-time battery remaining capacity estimation model based on data-driven technology. The flowchart of the vehicle-mounted lead-acid battery remaining capacity estimation method of the present invention is as follows: Figure 5 , specifically including the following steps:

[0064] S1. Perform a SOC-OCV experiment on the battery, draw a curve of the battery state of charge and the battery open circuit voltage, and fit the relationship between the battery state of charge (SOC) and the battery open circuit voltage (OCV).

[0065] Specifically:

[0066] 1. Fully charge the battery, let it stand for 1 hour, and measure its open circuit voltage, which is the open circuit voltage corresponding to SOC = 100%;

[0067] 2. The electronic load discharges the battery at a constant current of 10A for 1 hour, and then leaves it to stand for 1.5 hours. The open circuit voltage measured at this time is the open circuit voltage when SOC = 90%;

[0068] 3. Repeat step 2 to obtain the open circuit voltage when SOC=80%, SOC=70%, SOC=10%, and SOC=0%. When SOC is approximately equal to 0, the voltage measured directly should be approximately equal to the discharge cut-off voltage. Similarly, the last time, the open circuit voltage of the battery should be measured after standing for 1.5 hours, corresponding to SOC=0%. Record the open circuit voltage, constant current discharge value, and standing time of each test stage.

[0069] The relationship between the fitted battery state of charge and the battery open circuit voltage is expressed as:

[0070] OCV=a+b×SOC+c×SOC 2 +d×SOC 3

[0071] Parameters a, b, c, and d are obtained through multiple sets of data.

[0072] S2. Measure the charge and discharge current of the battery, and judge whether the battery is in a static state or in use by the current.

[0073] When the charge and discharge current of the battery is 0, the battery is in a static state; when the charge and discharge current of the battery is not 0, the battery is in use.

[0074] S3. If the battery is in use, the open circuit voltage-ampere-hour integral estimation method is used to estimate the remaining battery capacity in real time.

[0075] The ampere-hour integration method is a basic method for battery power measurement. It uses the AH accumulation method to perform real-time SOC estimation on dynamic batteries. The calculation formula of the ampere-hour integration method is as follows:

[0076]

[0077] Among them, SOC(t) represents the state of charge at time t, SOC(t0) represents the initial state of charge, and I(t) represents the current at time t.

[0078] If the battery is in a stationary state, the relationship between the battery state of charge and the battery open circuit voltage fitted by S1 is used to estimate the remaining battery capacity.

[0079] According to the relationship between the battery state of charge and the battery open circuit voltage obtained by S1 fitting, the relevant parameters of the relationship are calculated through multiple sets of measured data, the battery open circuit voltage of the battery in a static state is measured, and the battery state of charge is calculated through the relationship.

[0080] And record the corresponding voltage, current and temperature.

[0081] S4: Build a neural network, use the voltage, current, and temperature recorded in S3 as the neural network input, and the estimated remaining battery power as the true output label. Preset the weights and biases of the neurons in the neural network.

[0082] S5. Associate the input signal with each neuron and sum it with the bias, then pass it through the activation function f(x), and then get the output, which is the SOC predicted by the data model.

[0083] S6. The difference between the output of the neural network obtained in S5 and the true output label is input back into the input layer of the neural network, and the gradient of the loss function of the neural network for the output layer and the hidden layer is calculated.

[0084] The loss function of the neural network is expressed as:

[0085]

[0086]

[0087] z o =W T x+b

[0088] Among them, L represents the loss function, x represents the input of the neural network, σ(z o ) represents z o The output after the Sigmoid activation function, W represents the weight matrix of the input neuron, and b represents the bias of the hidden layer.

[0089] The gradient is expressed as:

[0090]

[0091] Among them, δ o represents the gradient of the loss function to the output layer, L represents the loss function, z o represents the input of the Sigmoid activation function, y represents the output of the neural network, and y true represents the true output label, σ′(z o )=y(1-y) is the derivative of the output layer Sigmoid activation function.

[0092]

[0093] Among them, δ j represents the gradient of the loss function for the neurons in the jth hidden layer, z j represents the weighted sum of the jth hidden layer neurons, v jrepresents the weight from the jth neuron in the hidden layer to the output layer, σ′(z j )=a j (1-a j ) represents the derivative of the hidden layer Sigmoid activation function, a j Represents the output value of the jth hidden layer neuron after passing through the activation function.

[0094] S7. Update the weights and biases of the hidden layer and output layer based on the gradient, and calculate the new output using the updated weights and biases.

[0095] Update the weights and bias of the output layer based on the gradient of the loss function to the output layer:

[0096] v j =v j -α·δ o ·a j

[0097] b o =b o -α·δ o

[0098] Among them, v j represents the weight from the jth neuron in the hidden layer to the output layer, α represents the learning rate, and δ o represents the gradient of the loss function to the output layer, a j represents the output value of the jth hidden layer neuron after passing through the activation function, b o Represents the bias of the output layer.

[0099] Update the weights and biases of the hidden layer based on the gradient of the loss function to the hidden layer:

[0100] w ji =w ji -α·δ j ·x i

[0101] b j =b j -α·δ j

[0102] Among them, w ji represents the weight connecting the i-th input and the j-th neuron in the hidden layer, δ j represents the gradient of the loss function to the neurons in the jth hidden layer, x i represents the i-th input of the neural network, b j represents the bias of the jth hidden layer.

[0103] S8. Repeat S4-S7 until the output of the neural network reaches a preset value or the number of neural network training iterations reaches a preset value, and input the voltage, current, and temperature measured under various states of the battery into the trained neural network to obtain the final estimated remaining battery power.

[0104] In an exemplary embodiment, a computer-readable storage medium is included, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for estimating the remaining power of a vehicle-mounted lead-acid battery is implemented.

[0105] See also Figure 6 In an exemplary embodiment, an electronic device is also included, including at least one processor, at least one memory, and at least one communication bus.

[0106] Among them, a computer program is stored in the memory, and the computer program includes computer-readable instructions. The processor calls the computer-readable instructions stored in the memory through the communication bus to execute the above-mentioned method for estimating the remaining power of the on-board lead-acid battery.

[0107] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for estimating the remaining power of a vehicle-mounted lead-acid battery, characterized in that: The following steps are involved: S1. Conduct SOC-OCV experiment on the battery, draw the curve of battery SOC and OCV, and fit the relationship between battery SOC and OCV; S2, measure the charge and discharge current of the battery, and judge whether the battery is in a static state or in use by the current; S3: If the battery is in use, the open circuit voltage-ampere-hour integral estimation method is used to estimate the remaining battery capacity in real time; if the battery is in a static state, the relationship between the battery SOC and OCV fitted by S1 is used to estimate the remaining battery capacity, and the corresponding voltage, current, and temperature are recorded; S4, construct a neural network, using the voltage, current, and temperature recorded in S3 as neural network inputs, and the estimated remaining battery capacity as the true output label; S5, associating the input signal with each neuron and summing it with the bias, and then using the activation function to obtain the output of the neural network; S6, reversely input the difference between the output of the neural network obtained in S5 and the true output label to the input layer of the neural network, and calculate the gradient of the loss function of the neural network for the output layer and the hidden layer; S7, update the weights and biases of the hidden layer and the output layer based on the gradient, and calculate the new output using the updated weights and biases; S8. Repeat S4-S7 until the output of the neural network reaches a preset value or the number of neural network training iterations reaches a preset value, and input the voltage, current, and temperature measured under various states of the battery into the trained neural network to obtain the final estimated remaining battery power.

2. A method for estimating the remaining power of a vehicle-mounted lead-acid battery according to claim 1, characterized in that: The SOC-OCV experiment is carried out on the battery, specifically: Fully charge the battery, let it stand for 1 hour, and measure the open circuit voltage when SOC = 100%; The electronic load discharges the battery at a constant current of 10A for 1 hour, then leaves it to rest for 1.5 hours, and measures the open circuit voltage when SOC = 90%; The open circuit voltage of each stage of SOC=80%, SOC=70%, SOC=10%, SOC=0% is measured in sequence; Record the open circuit voltage, constant current discharge value and standing time at each stage.

3. The method for estimating the remaining power of a vehicle-mounted lead-acid battery according to claim 1, characterized in that: The relationship between the fitted battery state of charge and the battery open circuit voltage is expressed as: OCV=a+b×SOC+c×SOC 2 +d×SOC 3 Among them, OCV represents open circuit voltage, SOC represents state of charge, and a, b, c, and d are parameters that need to be identified.

4. The method for estimating the remaining power of a vehicle-mounted lead-acid battery according to claim 1, characterized in that: The open circuit voltage-ampere-hour integral estimation method is used to estimate the remaining battery capacity in real time: Among them, SOC(t) represents the state of charge at time t, SOC(t0) represents the initial state of charge, and I(t) represents the current at time t.

5. The method for estimating the remaining power of a vehicle-mounted lead-acid battery according to claim 1, characterized in that: The loss function of the neural network is expressed as: z o =W T Tx+b Among them, L represents the loss function, x represents the input of the neural network, σ(z o ) represents z o The output after the Sigmoid activation function, W represents the weight matrix of the input neuron, and b represents the bias of the hidden layer.

6. The method for estimating the remaining power of a vehicle-mounted lead-acid battery according to claim 1, characterized in that: The gradient of the loss function of the neural network for the output layer and the hidden layer is expressed as: Among them, δ o represents the gradient of the loss function to the output layer, L represents the loss function, z o represents the input of the Sigmoid activation function, y represents the output of the neural network, and y true represents the true output label, σ′(z o ) = y(1-y) is the derivative of the output layer Sigmoid activation function; Among them, δ j represents the gradient of the loss function for the neurons in the jth hidden layer, z j represents the weighted sum of the jth hidden layer neurons, v j represents the weight from the jth neuron in the hidden layer to the output layer, σ′(z j )=a j (1-a j ) represents the derivative of the hidden layer Sigmoid activation function, a j Represents the output value of the jth hidden layer neuron after passing through the activation function.

7. A method for estimating the remaining power of a vehicle-mounted lead-acid battery according to claim 6, characterized in that: The weights and biases of the hidden and output layers are updated based on the gradient as follows: Update the weights and bias of the output layer based on the gradient of the loss function to the output layer: v j =v j -a·d o ·a j b o =b o -a·d o Among them, v j represents the weight from the jth neuron in the hidden layer to the output layer, α represents the learning rate, and δ o represents the gradient of the loss function to the output layer, a j represents the output value of the jth hidden layer neuron after passing through the activation function, b o represents the bias of the output layer; Update the weights and biases of the hidden layer based on the gradient of the loss function to the hidden layer: w ji =w ji -a·d j ·x i b j =b j -a·d j Among them, w ji represents the weight connecting the i-th input and the j-th neuron in the hidden layer, δ j represents the gradient of the loss function for the neurons in the jth hidden layer, x i represents the i-th input of the neural network, b j represents the bias of the jth hidden layer.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises computer-readable instructions, and the processor is configured to call the computer-readable instructions to execute the method according to any one of claims 1 to 7.

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