Direct-current charging pile metrological verification method, system and equipment and storage medium

Abstract

The invention relates to a direct current charging pile metrological verification method, system and device and a storage medium, and the method comprises the following steps: collecting the charging data of different types of electric vehicles on the same charging pile, and carrying out the preprocessing of the collected charging data; collecting charging data of metering ammeters of all electric vehicles, and calculating a basic charging error of the charging pile based on the charging data of the metering ammeters of each electric vehicle and the charging data of the charging pile; classifying the charging data of the charging piles in different charging modes through an improved Gaussian mixture model; the error contribution degree of each charging mode is calculated through the charging data of the charging pile in each charging mode, and then the final charging error of the charging pile in each charging mode is calculated based on the error contribution degree of each charging mode and the basic charging error of the charging pile.

Description

Technical Field

[0001] This invention relates to a method, system, equipment, and storage medium for the metering and verification of DC charging piles, belonging to the field of electrical energy metering technology. Background Technology

[0002] Since the accuracy of charging pile metering directly affects the settlement of charging fees, even a small metering deviation can accumulate into huge economic losses in the context of large-scale transactions. This could harm consumers' legitimate rights and interests, and also impose an operational burden on power grid companies. Therefore, a common practice both within and outside the industry is to regularly conduct on-site verification of the metering performance of each DC charging pile to ensure that the metering accuracy of each charging pile meets standards. This verification work is tedious and meticulous, requiring professional personnel to travel to each charging pile location with standard verification equipment. It also needs to consider the wide distribution of charging piles and the varying environments, making it undoubtedly a time-consuming, labor-intensive, and costly task.

[0003] Patent announcement number "CN112505386B" discloses a method and system for verifying the current value of a DC charging pile. The method includes the following steps: determining the interpolated and expanded current / voltage data, and the influence characteristics of different environmental conditions; determining the voltage / current influencing factor matrix and measurement error matrix based on the influencing characteristics; establishing a correction model for the influence of factors on the measured voltage / current based on the voltage / current influencing factor matrix and measurement error matrix; correcting the current / voltage based on the correction model; determining the ripple influencing factor matrix and error matrix of the corrected current / voltage based on the corrected current / voltage; establishing an error model based on the corrected current / voltage ripple influencing factor matrix and error matrix; and determining the DC charging pile current value based on the error model. This invention achieves anti-interference verification of the voltage and current of the verification device under on-site environmental conditions.

[0004] However, the above scheme does not comprehensively consider the factors affecting error, resulting in insufficient accuracy of error calculation results. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a method, system, equipment, and storage medium for the metering and verification of DC charging piles.

[0006] The technical solution of the present invention is as follows: On the one hand, the present invention provides a method for metering and verifying DC charging piles, comprising the following steps: Collect charging data of different models of electric vehicles at the same charging station, and preprocess the collected charging data; Collect charging data from all electric vehicle meters and calculate the basic charging error of the charging pile based on the charging data from each electric vehicle's meter and the charging data from the charging pile. The charging data of charging piles under different charging modes are classified by an improved Gaussian mixture model; The error contribution of each charging mode is calculated by using charging data from charging piles under each charging mode. Then, based on the error contribution of each charging mode and the basic charging error of the charging pile, the final charging error of the charging pile under each charging mode is calculated.

[0007] In a preferred embodiment of the present invention, the preprocessing step of the collected charging data is as follows: The collected charging data is used to construct a sample set and then normalized, as shown in the following formula:

[0008] in: Represents any sample in the sample set; Represents a sample set; This represents the sample mean; Construct an autoencoder model, define the input layer, hidden layer and output layer of the autoencoder model, select the sigmoid activation function, use the root mean square error of the prediction residual as the loss function, and use the Adam optimizer to update the weights of the network. The normalized sample set is input into the autoencoder model for outlier cleaning, as shown in the following formula:

[0009] in: Represents the first in the sample set The actual value of each sample; The autoencoder model predicts the first... Predicted values ​​for each sample; This represents the standard deviation of the sample set; it replaces the sample mean with outliers to complete outlier cleaning.

[0010] In a preferred embodiment of the present invention, the specific calculation steps for the charging pile foundation charging error are as follows: Calculate the error between the charging data of the same charging pile and the charging data of each electric vehicle's meter, and construct a normal distribution model. The specific formula is as follows:

[0011] in: Represents the expected value of the error distribution; The variance represents the error distribution; Error data with a probability greater than 50% were selected to construct an error sample set, which was then mean-valued. Based on the Central Limit Theorem, the mean was used as the basic charging error of the charging pile. The specific formula is as follows:

[0012] in: Indicates the number of samples in the error sample set; Represents the first error sample in the error sample set. One sample.

[0013] In a preferred embodiment of the present invention, the charging data of the charging pile includes voltage data, current data, and battery capacity data, and the charging mode classification step is as follows: A variable sliding window is set up to perform sliding average processing on the voltage data, current data, and battery capacity data in the charging pile charging data, as shown in the following formula:

[0014] in: This represents charging data for any type of charging station after processing with a moving average. Indicates the length of the charging data for the charging station; Indicates the length of the sliding window; This indicates the first [item] in the charging data of this type of charging pile. One sample; Indicates the number of sliding windows; Based on the voltage, current, and battery capacity data after moving average processing, the weighting coefficients for the voltage, current, and battery capacity data under different charging modes are calculated, as shown in the following formula:

[0015]

[0016]

[0017] in: , , These represent the weighting coefficients of voltage data, current data, and battery capacity data in the current charging mode, respectively. This indicates the number of samples for each type of data after the moving average processing. , , These represent the voltage data, current data, and battery capacity data after moving average processing, respectively. One sample; , , These represent the voltage data, current data, and battery capacity data after moving average processing, respectively. One sample; Based on the voltage, current, and battery capacity data processed by the above moving average and their corresponding weighting coefficients, the Gaussian mixture model is constructed as shown in the following equation:

[0018] in: This represents the output of the Gaussian mixture model; The output of the Gaussian mixture model is then input into the multilayer perceptron, as shown in the following equation.

[0019] in: Represents a multilayer perceptron; This represents the output of the multilayer perceptron; These represent the parameters of a multilayer perceptron. Then the output of the multilayer perceptron is passed through The function calculates the probability output of the charging pile belonging to the current charging mode, as shown in the following formula:

[0020] in: This output indicates the probability that the charging station belongs to the current charging mode. Based on the probability output of all charging modes of the charging pile, the probability value of the charging pile belonging to each charging mode is obtained, as shown in the following formula:

[0021] in: These represent the probabilities of the charging station operating in fast, medium, and slow charging modes, respectively. This indicates the charging mode of the charging station.

[0022] In a preferred embodiment of the present invention, the formula for calculating the error contribution of each charging mode is as follows:

[0023]

[0024]

[0025] in: , , These represent the error contribution rates for fast, medium, and slow charging modes, respectively. This represents the average charging data of the charging piles after the moving average processing. , , These represent the average voltage data after moving average processing for fast, medium, and slow charging modes, respectively. , , These represent the average current data after moving average processing for fast, medium, and slow charging modes, respectively. , , These represent the average battery capacity data after moving average processing for fast, medium, and slow charging modes, respectively.

[0026] In a preferred embodiment of the present invention, the final charging error calculation formula for the charging pile is as follows:

[0027]

[0028]

[0029] in: , , These represent the final charging error of the charging station in fast, medium, and slow charging modes, respectively.

[0030] On the other hand, the present invention also provides a DC charging pile metering verification system, characterized in that it includes a data acquisition module, a basic error calculation module, a charging mode classification module and a final error calculation module; The data acquisition module is used to collect charging data of different models of electric vehicles on the same charging pile, and to preprocess the collected charging data. The basic error calculation module is used to collect charging data from all electric vehicle meters and calculate the basic charging error of the charging pile based on the charging data from each electric vehicle meter and the charging pile charging data. The charging mode classification module is used to classify charging pile charging data under different charging modes using an improved Gaussian mixture model. The final error calculation module is used to calculate the error contribution of each charging mode using the charging data of the charging pile under each charging mode, and then calculate the final charging error of the charging pile under each charging mode based on the error contribution of each charging mode and the basic charging error of the charging pile.

[0031] In another aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in any embodiment of the present invention.

[0032] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in any embodiment of the present invention.

[0033] The present invention has the following beneficial effects: 1. This invention analyzes and improves the accuracy level of electric vehicle energy meters based on the central limit theorem, and obtains the basic error of charging piles; it processes the data of each variable by moving average, and then uses the processed variable data to adaptively set the weight coefficients of the sensitivity of charging mode, constructs a feature matrix and inputs it into a Gaussian mixture model to identify the charging mode; it calculates the mode coefficients of the corresponding charging mode, reduces the influence of different charging modes on the error, and improves the accuracy of error measurement. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0037] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0038] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0039] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0040] Example 1: See Figure 1 A method for metering and verifying DC charging piles includes the following steps: Collect charging data of different models of electric vehicles at the same charging station, and preprocess the collected charging data; Collect charging data from all electric vehicle meters and calculate the basic charging error of the charging pile based on the charging data from each electric vehicle's meter and the charging data from the charging pile. The charging data of charging piles under different charging modes are classified by an improved Gaussian mixture model; The error contribution of each charging mode is calculated by using charging data from charging piles under each charging mode. Then, based on the error contribution of each charging mode and the basic charging error of the charging pile, the final charging error of the charging pile under each charging mode is calculated.

[0041] In a preferred embodiment of this invention, the preprocessing step for the collected charging data is as follows: The collected data undergoes preliminary data cleaning to ensure accuracy and prevent outliers from affecting the prediction accuracy of subsequent models, while also improving model robustness and adaptability. Since charging power is closely related to the capacity and remaining energy of electric vehicle batteries, it gradually decreases with increasing charging time. Therefore, deep learning algorithms are used to predict trends in charging station and electric vehicle data, enabling the identification of outliers.

[0042] The collected charging data is used to construct a sample set and then normalized, as shown in the following formula:

[0043] in: Represents any sample in the sample set; Represents a sample set; This represents the sample mean; Construct an autoencoder model, define the input layer, hidden layer and output layer of the autoencoder model, select the sigmoid activation function, use the root mean square error of the prediction residual as the loss function, and use the Adam optimizer to update the weights of the network. The normalized sample set is input into the autoencoder model for outlier cleaning, as shown in the following formula:

[0044] in: Represents the first in the sample set The actual value of each sample; The autoencoder model predicts the first... Predicted values ​​for each sample; This represents the standard deviation of the sample set; it replaces the sample mean with outliers to complete outlier cleaning.

[0045] As a preferred embodiment of this invention, the specific calculation steps for the charging pile foundation charging error are as follows: Because the accuracy class of a single ordinary electric vehicle meter is low, typically lower than that of the verification device, its verification error data only reflects its own error. In the physical method, a physical standard meter with an accuracy class higher than that of the verification device is used for verification, thus enabling the verification of the verification device. According to the central limit theorem, The average of the calibration data of ordinary smart energy meters produced in the same batch can be regarded as the calibration data of a virtual standard energy meter, and the accuracy class of this virtual standard energy meter is higher than that of a single ordinary energy meter. Times. If If the value is large enough, the accuracy level of the virtual standard energy meter can reach the accuracy level of the standard energy meter in the physical method, and it can be used for the verification of energy meters. The error between charging data from the same charging pile and charging data from the meter of each electric vehicle is calculated, assuming that the distribution follows a normal distribution, and a normal distribution model is constructed. The specific formula is as follows:

[0046] in: Represents the expected value of the error distribution; The variance represents the error distribution; Error data with a probability greater than 50% were selected to construct an error sample set, which was then mean-valued. Based on the Central Limit Theorem, the mean was used as the basic charging error of the charging pile. The specific formula is as follows:

[0047] in: Indicates the number of samples in the error sample set; Represents the first error sample in the error sample set. One sample; The charging pile foundation has a charging error. This is a virtual standard electricity meter.

[0048] In a preferred embodiment of this invention, the charging data of the charging pile includes voltage data, current data, and battery capacity data, and the charging mode classification step is as follows: The current-voltage trajectory during charging serves as a visual feature, clearly demonstrating the changing trends of charging modes. However, power differences between adjacent charging stations can interfere with the identification of charging modes for a single charging station. This study extracts data such as voltage, current, battery capacity, and temperature during the charging process, sets a variable sliding window, performs moving average processing on each type of data, and then calculates the correlation between each type of data and the charging mode after processing with windows of different lengths. The length of the window with the strongest correlation is selected to enhance the variable distinguishability among different charging modes. A variable sliding window is set up to perform sliding average processing on the voltage data, current data, and battery capacity data in the charging pile charging data, as shown in the following formula:

[0049] in: This represents charging data for any type of charging station after processing with a moving average. Indicates the length of the charging data for the charging station; Indicates the length of the sliding window; This indicates the first [item] in the charging data of this type of charging pile. One sample; Indicates the number of sliding windows; Based on the voltage, current, and battery capacity data after moving average processing, the weighting coefficients for the voltage, current, and battery capacity data under different charging modes are calculated, as shown in the following formula:

[0050]

[0051]

[0052] in: , , These represent the weighting coefficients of voltage data, current data, and battery capacity data in the current charging mode, respectively. This indicates the number of samples for each type of data after the moving average processing. , , These represent the voltage data, current data, and battery capacity data after moving average processing, respectively. One sample; , , These represent the voltage data, current data, and battery capacity data after moving average processing, respectively. One sample; Based on the voltage, current, and battery capacity data processed by the above moving average and their corresponding weighting coefficients, the Gaussian mixture model is constructed as shown in the following equation:

[0053] in: This represents the output of the Gaussian mixture model; The output of the Gaussian mixture model is then input into the multilayer perceptron, as shown in the following equation.

[0054] in: Represents a multilayer perceptron; This represents the output of the multilayer perceptron; These represent the parameters of a multilayer perceptron. Then the output of the multilayer perceptron is passed through The function calculates the probability output of the charging pile belonging to the current charging mode, as shown in the following formula:

[0055] in: This output indicates the probability that the charging station belongs to the current charging mode. Based on the probability output of all charging modes of the charging pile, the probability value of the charging pile belonging to each charging mode is obtained, as shown in the following formula:

[0056] in: These represent the probabilities of the charging station operating in fast, medium, and slow charging modes, respectively. This indicates the charging mode of the charging station.

[0057] In a preferred embodiment of this invention, the formula for calculating the error contribution of each charging mode is as follows:

[0058]

[0059]

[0060] in: , , These represent the error contribution rates for fast, medium, and slow charging modes, respectively. This represents the average charging data of the charging piles after the moving average processing. , , These represent the average voltage data after moving average processing for fast, medium, and slow charging modes, respectively. , , These represent the average current data after moving average processing for fast, medium, and slow charging modes, respectively. , , These represent the average battery capacity data after moving average processing for fast, medium, and slow charging modes, respectively.

[0061] In a preferred embodiment of this invention, the final charging error calculation formula for the charging pile is as follows:

[0062]

[0063]

[0064] in: , , These represent the final charging error of the charging station in fast, medium, and slow charging modes, respectively.

[0065] Example 2: A DC charging pile metering verification system includes a data acquisition module, a basic error calculation module, a charging mode classification module, and a final error calculation module; The data acquisition module is used to collect charging data of different models of electric vehicles on the same charging pile, and to preprocess the collected charging data. The basic error calculation module is used to collect charging data from all electric vehicle meters and calculate the basic charging error of the charging pile based on the charging data from each electric vehicle meter and the charging pile charging data. The charging mode classification module is used to classify charging pile charging data under different charging modes using an improved Gaussian mixture model. The final error calculation module is used to calculate the error contribution of each charging mode using the charging data of the charging pile under each charging mode, and then calculate the final charging error of the charging pile under each charging mode based on the error contribution of each charging mode and the basic charging error of the charging pile.

[0066] This system is used to implement the functions in Embodiment 1, and will not be described again here.

[0067] Example 3: This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the DC charging pile metering verification method as described in any embodiment of the present invention.

[0068] Example 4: This embodiment proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the DC charging pile metering verification method as described in any embodiment of the present invention.

[0069] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0070] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0071] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0072] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A DC charging pile measurement and verification method, characterized in that: The following steps are involved: Collect charging data of electric vehicles of different models on the same charging pile, and pre-process the collected charging data; Collect charging data from all electric vehicle meters, and calculate the basic charging error of the charging pile based on the charging data from each electric vehicle meter and the charging pile data; The charging data of charging piles under different charging modes are classified by using an improved Gaussian mixture model; The error contribution of each charging mode is calculated through the charging data of the charging pile under each charging mode, and then the final charging error of the charging pile under each charging mode is calculated based on the error contribution of each charging mode and the basic charging error of the charging pile.

2. A DC charging pile measurement and verification method according to claim 1, characterized in that: The preprocessing steps of the collected charging data are: The collected charging data is constructed into a sample set and normalized, as shown in the following formula: in: represents any sample in the sample set; represents a sample set; represents the sample mean; Build an autoencoder model, define the input layer, hidden layer and output layer of the autoencoder model, select the sigmoid activation function, use the root mean square error of the prediction residual as the loss function, and use the Adam optimizer to update the network weights; The normalized sample set is input into the autoencoder model for outlier cleaning, as shown in the following formula: in: Indicates the first The actual value of samples; represents the first Sample prediction value; Represents the standard deviation of the sample set; replace the sample mean with the outlier to complete the outlier cleaning.

3. A DC charging pile measurement and verification method according to claim 1, characterized in that: The specific calculation steps of the charging pile basic charging error are: Calculate the error between the charging data of the same charging pile and the charging data of each electric vehicle meter, and construct a normal distribution model , as shown in the following formula: in: represents the expectation of the error distribution; represents the variance of the error distribution; The error data with a distribution probability greater than 50% are selected to construct an error sample set and the error sample set is averaged. According to the central limit theorem, the mean is used as the basic charging error of the charging pile. , as shown in the following formula: in: Represents the number of samples in the error sample set; represents the first samples.

4. A DC charging pile measurement and verification method according to claim 3, characterized in that: The charging pile charging data includes voltage data, current data and battery capacity data, and the charging mode classification steps are as follows: A variable sliding window is set to perform sliding average processing on the voltage data, current data and battery capacity data in the charging data of the charging pile, as shown in the following formula: in: Represents the charging data of any type of charging pile after sliding average processing; Indicates the charging data length of the charging pile; Indicates the sliding window length; Indicates the charging data of this type of charging pile. samples; Indicates the number of sliding windows; Based on the voltage data, current data and battery capacity data after sliding average processing, the weight coefficients of the voltage data, current data and battery capacity data in different charging modes are calculated, as shown in the following formula: in: , , Respectively represent the weight coefficients of voltage data, current data and battery capacity data in the current charging mode; Indicates the number of samples of each type of data after sliding average processing; , , Respectively represent the voltage data, current data and battery capacity data after sliding average processing. samples; , , Respectively represent the voltage data, current data and battery capacity data after sliding average processing. samples; Based on the voltage data, current data and battery capacity data after the above sliding average processing and their corresponding weight coefficients, the Gaussian mixture model is constructed as shown in the following formula: in: represents the output of the Gaussian mixture model; Then input the output of the Gaussian mixture model into the multilayer perceptron, as shown in the following formula: in: represents a multi-layer perceptron; Represents the output of a multilayer perceptron; Represents the parameters of the multilayer perceptron; Then pass the output of the multi-layer perceptron through The function calculates the probability output that the charging pile belongs to the current charging mode, as shown in the following formula: in: Indicates the probability output that the charging pile belongs to the current charging mode; Based on the probability output of all charging modes of the charging pile, the probability value of the charging pile belonging to each charging mode is obtained, as shown in the following formula: in: Respectively represent the probability of the charging pile in fast, medium and slow charging modes; Indicates the charging mode of the charging pile.

5. A DC charging pile measurement and verification method according to claim 4, characterized in that: The error contribution calculation formula of each charging mode is: in: , , Respectively represent the error contribution of fast, medium and slow charging modes; Represents the mean value of charging pile charging data after sliding average processing; , , Respectively represent the mean values ​​of voltage data after sliding average processing belonging to fast, medium and slow charging modes; , , Respectively represent the mean current data after sliding average processing belonging to fast, medium and slow charging modes; , , Respectively represent the mean values ​​of battery capacity data after sliding average processing belonging to fast, medium and slow charging modes.

6. A DC charging pile measurement and verification method according to claim 5, characterized in that: The final charging error calculation formula of the charging pile is: in: , , Respectively represent the final charging errors of the charging pile in fast, medium and slow charging modes.

7. A DC charging pile measurement and verification system, characterized in that: It includes a data acquisition module, a basic error calculation module, a charging mode classification module and a final error calculation module; The data acquisition module is used to collect charging data of electric vehicles of different models on the same charging pile, and pre-process the collected charging data; The basic error calculation module is used to collect charging data from all electric vehicle metering meters, and calculate the basic charging error of the charging pile based on the charging data from the metering meter of each electric vehicle and the charging data from the charging pile; The charging mode classification module is used to classify charging data of charging piles under different charging modes through an improved Gaussian mixture model; The final error calculation module is used to calculate the error contribution of each charging mode through the charging data of the charging pile under each charging mode, and then calculate the final charging error of the charging pile under each charging mode based on the error contribution of each charging mode and the basic charging error of the charging pile.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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