Charging and feeding potential mining method and device, computer equipment and storage medium

Abstract

The invention discloses a charging and feeding potential mining method and device, computer equipment and a storage medium. The charging and feeding potential mining method comprises the steps that S1, vehicle using data of multiple electric vehicles are collected, and a Monte Carlo method is used for the vehicle using data to construct a behavior matrix of charging and feeding of the electric vehicles; s2, averagely dividing the behavior matrix into three sections, namely a training set, a verification set and a prediction set; s3, performing autoregression analysis on the data of the training set to obtain a prediction curve set; s4, analyzing an error between the prediction curve set and the check set, and performing upper limit time sequence prediction and lower limit time sequence prediction on the error to obtain a prediction interval set; s5, evaluating the charging and feeding potential of each time period of the next day according to the prediction interval set; and S6, setting the real-time charging subsidy and the feed subsidy of the next day according to the evaluation result. According to the charging and feeding potential mining method, the computer equipment and the storage medium, the charging and feeding potential of the electric vehicle can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle charging and feeding potential mining, and in particular to a charging and feeding potential mining method, device, computer equipment and storage medium. Background Art

[0002] With the increasing global awareness of environmental protection and the transformation of energy structure, the research and development and application of electric vehicles (EVs) have been rapidly developed as an effective means to reduce greenhouse gas emissions and reduce dependence on fossil fuels. Traditional electric vehicle charging methods mostly adopt a one-way energy flow mode, that is, from the power grid to the vehicle, which not only limits the role of electric vehicles in the power system, but also may cause grid overload during peak hours and affect power supply stability. In recent years, with the research progress of bidirectional charging and feeding technology (Vehicle-to-Grid, V2G), electric vehicles can not only be used as a means of transportation, but also as a distributed energy storage unit to participate in the energy scheduling of the power grid. V2G technology allows electric vehicles to charge during off-peak hours and feed the stored energy back to the grid when the grid demand is high, thereby improving the flexibility and efficiency of the entire power system, achieving optimal allocation of power resources, and effectively alleviating the pressure on the grid.

[0003] Therefore, how to improve the charging and feeding potential of electric vehicles, encourage users to charge their vehicles during off-peak hours, and contribute stored electricity when the grid needs it has become a key issue that needs to be solved urgently. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method, device, computer equipment and storage medium for mining charging and feeding potential, which can evaluate the charging and feeding potential of the next day based on historical data, and set personal real-time charging subsidies and feeding subsidies to improve the charging and feeding potential of electric vehicles.

[0005] The purpose of the present invention is achieved by the following technical solutions:

[0006] A method for mining charging and feeding potential, comprising:

[0007] S1. Collecting vehicle usage data of multiple electric vehicles, the vehicle usage data includes daily average mileage data, starting SOC data, and daily charging start time data, and constructing a charging and feeding behavior matrix of the electric vehicles using the Monte Carlo method for the vehicle usage data;

[0008] S2, dividing the behavior matrix into three sections, namely a training set, a verification set and a prediction set;

[0009] S3, performing autoregressive analysis on the data of the training set to obtain a prediction curve set;

[0010] S4, analyzing the errors between the prediction curve set and the verification set, performing upper limit time series prediction and lower limit time series prediction on the errors, and obtaining a prediction interval set;

[0011] S5. Evaluate the charging and feeding potential of each time period of the next day according to the prediction interval set;

[0012] S6. Set the next day’s real-time charging subsidy and power feed subsidy based on the evaluation results.

[0013] Further, the training set includes the vehicle usage data of the electric vehicle from time node 1 to time node 960, the verification set includes the vehicle usage data of the electric vehicle from time node 481 to time node 1440, and the prediction set includes the vehicle usage data of the electric vehicle from time node 1441 to time node 1536;

[0014] Among them, every 15 minutes is a time node.

[0015] Further, performing autoregressive analysis on the data of the training set to obtain a prediction curve set includes: determining an autoregressive analysis weight coefficient based on the training set data:

[0016]

[0017] weight i =γ i,best ,i=1,2,…480

[0018] Among them, y t is the predicted value of the time series at time t, μ is a constant term, p is the autocorrelation analysis order, the default value is 480, and γ i is the autocorrelation coefficient, which indicates the influence of the ith lag value on the current value, γ i,best is the autocorrelation coefficient when the prediction error is the smallest, yt' is the true value, weight i is the weight coefficient of the i-th lag value in the autoregression analysis.

[0019] Further, performing upper limit time series prediction and lower limit time series prediction on the error to obtain a prediction interval set specifically includes: determining an upper limit time series prediction weight coefficient and a lower limit time series prediction weight coefficient based on the verification set and the prediction curve set data:

[0020]

[0021] y t ∈[y t,d ,y t,u ]

[0022]

[0023] Among them, y t,u is the upper limit forecast value of the time series at time t, y t,d is the lower limit forecast value of the time series at time t, μ u is the constant term for upper bound time series forecast, μ d is the constant term for the lower bound time series forecast, γ i,u is the autocorrelation coefficient of the upper limit time series forecast, indicating the impact of the ith lag value on the current upper limit, γ i,d is the autocorrelation coefficient of the lower limit time series forecast, indicating the impact of the ith lag value on the current upper limit, ε t-j is the error of the jth lag value, θ j,u is the upper bound correlation coefficient of the jth lag value error, θ j,d is the lower bound correlation coefficient of the jth lag value error, γ i,u,best is the upper limit correlation coefficient when the prediction error is the smallest, γ i,d,bes is the lower limit correlation coefficient when the prediction error is the smallest, weight i,u is the upper limit weight coefficient of the ith lag value in the autoregression analysis, weight i,d is the lower limit weight coefficient of the i-th lag value in the autoregression analysis.

[0024] Furthermore, evaluating the charging and feeding potential of each time period of the next day according to the prediction interval set specifically includes:

[0025] The evaluation of the charging and feeding potential of each period of the next day according to the prediction interval set specifically includes:

[0026] According to the factor evaluation data, a state potential factor, a power price potential factor and a willingness potential factor are set, wherein the factor evaluation data at least includes one or more of the charging and feeding behaviors of the electric vehicle at each moment of the next day predicted by S1, S2, S3 and S4, the SOC at each moment, the travel state of the electric vehicle at each moment, the charging and feeding expenditure and the user willingness;

[0027]

[0028] Among them, δ k,t,1 is the state potential factor, when the predicted charging and feeding behavior of the electric vehicle is idle or charging, it is assigned a value of 1; when the predicted charging and feeding behavior of the electric vehicle is traveling, it is assigned a value of 0; when the predicted charging and feeding behavior of the electric vehicle is feeding, it is assigned a value of -1;

[0029]

[0030] Among them, δ k,t,2 is the electricity price potential factor, p t is the time-of-use electricity price, pk,min is the psychological charging price of the kth user, p k,max The psychological feed-in electricity price for the kth user;

[0031]

[0032] Among them, δ k,t,3 is the willingness potential factor;

[0033]

[0034] Among them, feed t is the feeding potential at time t, charge t is the charging potential at time t, k is the electric vehicle number, K is the total number of electric vehicles, and C is the battery capacity of the electric vehicle.

[0035] Furthermore, the next day's real-time charging subsidy and power feed subsidy are set according to the evaluation results, including:

[0036] By adjusting the user charging and feeding subsidy at time t to change p t , thereby changing δ k,t,2 , so that the charging and feeding potential at each moment is kept within the preset range.

[0037] A device for mining charging and feeding potential, comprising:

[0038] A data collection module is used to collect vehicle usage data of multiple electric vehicles, wherein the vehicle usage data includes daily average mileage data, starting SOC data, and daily charging start time data, and a Monte Carlo method is used to construct a behavior matrix of electric vehicle charging and feeding based on the vehicle usage data;

[0039] A matrix module, used to divide the behavior matrix into three sections, namely a training set, a verification set and a prediction set;

[0040] An autoregressive analysis module, used for performing autoregressive analysis on the data of the training set to obtain a prediction curve set;

[0041] A prediction module is used to analyze the error between the prediction curve set and the verification set, perform upper limit time series prediction and lower limit time series prediction on the error, and obtain a prediction interval set;

[0042] An evaluation module, used to evaluate the charging and feeding potential of each time period of the next day according to the prediction interval set;

[0043] The subsidy module is used to set the next day's real-time charging subsidy and feed subsidy based on the evaluation results.

[0044] Further, the training set includes the vehicle usage data of the electric vehicle from time node 1 to time node 960, the verification set includes the vehicle usage data of the electric vehicle from time node 481 to time node 1440, and the prediction set includes the vehicle usage data of the electric vehicle from time node 1441 to time node 1536;

[0045] Among them, every 15 minutes is a time node.

[0046] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned charging and feeding potential mining method when executing the computer program.

[0047] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned charging and feeding potential mining method.

[0048] Compared with the prior art, the beneficial effects of the present invention are: the ARIMA time series prediction method is used, which is different from the traditional method in that it can handle situations where there are a large number of cars and a variety of types. Specifically, it first constructs a behavior matrix based on the vehicle usage data of electric vehicles. Secondly, in order to solve the problem of inaccurate prediction caused by the small amount of data, it also cuts the behavior matrix, and uses autoregressive prediction and time series prediction to calibrate the prediction weights in turn, and then relaxes the constraints of the time series prediction again, thereby further improving the accuracy of the prediction weights. Finally, the row state potential factor, electricity price potential factor and willingness potential factor are introduced to reduce the prediction error, so that personal subsidies can be accurately set and the charging and feeding potential of electric vehicles can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of the method for mining charging and feeding potential of the present invention;

[0050] Figure 2 It is a curve chart of the original data of time and charging power, the ARIMA method predicted data, the AR-ARIMAU method predicted data and the AR-ARIMAD method predicted data. DETAILED DESCRIPTION

[0051] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Preferred embodiments of the present invention are provided in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0052] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0054] Embodiment 1

[0055] Reference Figure 1 A method for mining charging and feeding potential includes: S1, collecting vehicle usage data of multiple electric vehicles, the vehicle usage data including daily average mileage data, starting SOC data and daily charging start time data, and using the Monte Carlo method to construct a charging and feeding behavior matrix of the electric vehicle; S2, dividing the behavior matrix into three sections, namely a training set, a verification set and a prediction set; S3, performing autoregressive analysis on the data of the training set to obtain a prediction curve set; S4, analyzing the error between the prediction curve set and the verification set, performing upper limit time series prediction and lower limit time series prediction on the error, and obtaining a prediction interval set; S5, evaluating the charging and feeding potential of each time period of the next day according to the prediction interval set; S6, setting the next day's real-time charging subsidy and feeding subsidy according to the evaluation results.

[0056] Among them, electric vehicles need to have V2G function, that is, charging and feeding function. Specifically, electric vehicles include electric private cars, electric taxis, electric buses, etc.

[0057] In the above-mentioned charging and feeding potential mining method, the ARIMA time series prediction method is used. Unlike traditional methods, it can handle situations where there are a large number of diverse types of vehicles. Specifically, it first constructs a behavior matrix based on the vehicle usage data of electric vehicles. Secondly, in order to solve the problem of inaccurate prediction caused by the small amount of data, it also cuts the behavior matrix and uses autoregressive prediction and time series prediction to calibrate the prediction weights in turn, and then relaxes the constraints of the time series prediction again to further improve the accuracy of the prediction weights. Finally, the row state potential factor, electricity price potential factor and willingness potential factor are introduced to reduce the prediction error, so that personal subsidies can be accurately set to improve the charging and feeding potential of electric vehicles.

[0058] In addition, from Figure 2It can be seen that the prediction accuracy of the upper limit time series prediction method (AR-ARIMAU method) and the lower limit time series prediction method (AR-ARIMAD method) is 8.4% higher than that of the ordinary time series prediction method (ARIMA method). In the present invention, the upper limit time series prediction and the lower limit time series prediction can be used to further accurately set personal subsidies and improve the charging and feeding potential of electric vehicles.

[0059] In this embodiment, the behavior matrix is ​​as follows:

[0060] [Training set, validation set, prediction set]

[0061] Among them, the training set contains the usage data of electric vehicles from time node 1 to time node 960, the verification set contains the usage data of electric vehicles from time node 481 to time node 1440, and the prediction set contains the usage data of electric vehicles from time node 1441 to time node 1536; among them, every 15 minutes is a time node.

[0062] In this embodiment, performing autoregressive analysis on the data of the training set to obtain a prediction curve set includes: determining an autoregressive analysis weight coefficient based on the training set data:

[0063]

[0064] weight i =γ i,best ,i=1,2,…480

[0065] Among them, y t is the predicted value of the time series at time t, μ is a constant term, p is the autocorrelation analysis order, the default value is 480, and γ i is the autocorrelation coefficient, which indicates the influence of the ith lag value on the current value, γ i,best is the autocorrelation coefficient when the prediction error is the smallest, yt' is the true value, weight i is the weight coefficient of the i-th lag value in the autoregression analysis.

[0066] In this embodiment, performing upper limit time series prediction and lower limit time series prediction on the error to obtain the prediction interval set specifically includes: determining the upper limit time series prediction weight coefficient and the lower limit time series prediction weight coefficient based on the verification set and the prediction curve set data:

[0067]

[0068] y t ∈[y t,d ,y t,u ]

[0069]

[0070] Among them, y t,u is the upper limit forecast value of the time series at time t, y t,d is the lower limit forecast value of the time series at time t, μ u is the constant term for upper bound time series forecast, μ d is the constant term for the lower bound time series forecast, γ i,u is the autocorrelation coefficient of the upper limit time series forecast, indicating the impact of the ith lag value on the current upper limit, γ i,d is the autocorrelation coefficient of the lower limit time series forecast, indicating the impact of the ith lag value on the current upper limit, ε t-j is the error of the jth lag value, θ j,u is the upper bound correlation coefficient of the jth lag value error, θ j,d is the lower bound correlation coefficient of the jth lag value error, γ i,u,best is the upper limit correlation coefficient when the prediction error is the smallest, γ i,d,bes is the lower limit correlation coefficient when the prediction error is the smallest, weight i,u is the upper limit weight coefficient of the ith lag value in the autoregression analysis, weight i,d is the lower limit weight coefficient of the i-th lag value in the autoregression analysis.

[0071] In this embodiment, evaluating the charging and feeding potential of each time period of the next day according to the prediction interval set specifically includes:

[0072] According to the factor evaluation data, a state potential factor, a power price potential factor and a willingness potential factor are set, wherein the factor evaluation data at least includes one or more of the charging and feeding behaviors of the electric vehicle at each moment of the next day predicted by S1, S2, S3 and S4, the SOC at each moment, the travel state of the electric vehicle at each moment, the charging and feeding expenditure and the user willingness;

[0073]

[0074] Among them, δ k,t,1 is the state potential factor, when the predicted charging and feeding behavior of the electric vehicle is idle or charging, it is assigned a value of 1; when the predicted charging and feeding behavior of the electric vehicle is traveling, it is assigned a value of 0; when the predicted charging and feeding behavior of the electric vehicle is feeding, it is assigned a value of -1;

[0075]

[0076] Among them, δ k,t,2 is the electricity price potential factor, p t is the time-of-use electricity price, p k,min is the psychological charging price of the kth user, p k,max The psychological feed-in electricity price for the kth user;

[0077]

[0078] Among them, δ k,t,3 is the willingness potential factor;

[0079]

[0080] Among them, feed t is the feeding potential at time t, charge t is the charging potential at time t, k is the electric vehicle number, K is the total number of electric vehicles, and C is the battery capacity of the electric vehicle.

[0081] In this embodiment, the process of setting the next day's real-time charging subsidy and feeding subsidy is as follows: by adjusting the user's charging and feeding subsidy at time t to change p t , thereby changing δ k,t,2 , so that the charging and feeding potential at each moment is kept within the preset range. Specifically, the preset range refers to load pressure-feeding potential < expected grid overload level.

[0082] Embodiment 2

[0083] A device for mining charging and feeding potential comprises: a data collection module for collecting vehicle usage data of multiple electric vehicles, the vehicle usage data comprising daily average mileage data, initial SOC data and daily charging start time data, and using the Monte Carlo method to construct a charging and feeding behavior matrix of the electric vehicles; a matrix module for evenly dividing the behavior matrix into three sections, namely a training set, a verification set and a prediction set; an autoregressive analysis module for performing autoregressive analysis on the data of the training set to obtain a prediction curve set; a prediction module for analyzing the error between the prediction curve set and the verification set, performing upper limit time series prediction and lower limit time series prediction on the error to obtain a prediction interval set; an evaluation module for evaluating the charging and feeding potential of each time period of the next day according to the prediction interval set; and a subsidy module for setting a real-time charging subsidy and a feeding subsidy for the next day according to the evaluation result. In this embodiment, the training set includes the usage data of the electric vehicle from time node 1 to time node 960, the verification set includes the usage data of the electric vehicle from time node 481 to time node 1440, and the prediction set includes the usage data of the electric vehicle from time node 1441 to time node 1536; wherein every 15 minutes is a time node.

[0084] Embodiment 3

[0085] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for mining charging and feeding potential in the first embodiment are implemented.

[0086] Embodiment 4

[0087] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for mining charging and feeding potential in the first embodiment.

[0088] The above are only embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for mining charging and feeding potential, characterized in that: include: S1. Collecting vehicle usage data of multiple electric vehicles, the vehicle usage data includes daily average mileage data, starting SOC data, and daily charging start time data, and constructing a charging and feeding behavior matrix of the electric vehicles using the Monte Carlo method for the vehicle usage data; S2, dividing the behavior matrix into three sections, namely a training set, a verification set and a prediction set; S3, performing autoregressive analysis on the data of the training set to obtain a prediction curve set; S4, analyzing the errors between the prediction curve set and the verification set, performing upper limit time series prediction and lower limit time series prediction on the errors, and obtaining a prediction interval set; S5. Evaluate the charging and feeding potential of each time period of the next day according to the prediction interval set; S6. Set the next day’s real-time charging subsidy and power feed subsidy based on the evaluation results.

2. The method for mining charging and feeding potential according to claim 1, characterized in that: The training set includes the vehicle usage data of the electric vehicle from time node 1 to time node 960, the verification set includes the vehicle usage data of the electric vehicle from time node 481 to time node 1440, and the prediction set includes the vehicle usage data of the electric vehicle from time node 1441 to time node 1536; Among them, every 15 minutes is a time node.

3. The method for mining charging and feeding potential according to claim 1, characterized in that: Performing autoregressive analysis on the data of the training set to obtain a prediction curve set includes: determining an autoregressive analysis weight coefficient based on the training set data: weight i =γ i,best ,i=1,2,…480 Among them, y t is the predicted value of the time series at time t, μ is a constant term, p is the autocorrelation analysis order, the default value is 480, and γ i is the autocorrelation coefficient, which indicates the influence of the ith lag value on the current value, γ i,best is the autocorrelation coefficient when the prediction error is the smallest, yt' is the true value, weight i is the weight coefficient of the i-th lag value in the autoregression analysis.

4. The method for mining charging and feeding potential according to claim 1, characterized in that: Performing upper limit time series prediction and lower limit time series prediction on the error to obtain a prediction interval set specifically includes: determining an upper limit time series prediction weight coefficient and a lower limit time series prediction weight coefficient based on the verification set and the prediction curve set data: and t ∈[and t,d ,and t,u ] Among them, y t,u is the upper limit forecast value of the time series at time t, y t,d is the lower limit forecast value of the time series at time t, μ u is the constant term for upper bound time series forecast, μ d is the constant term for the lower bound time series forecast, γ i,u is the autocorrelation coefficient of the upper limit time series forecast, indicating the impact of the ith lag value on the current upper limit, γ i,d is the autocorrelation coefficient of the lower limit time series forecast, indicating the impact of the ith lag value on the current upper limit, ε t-j is the error of the jth lag value, θ j,u is the upper bound correlation coefficient of the jth lag value error, θ j,d is the lower bound correlation coefficient of the jth lag value error, γ i,u,best is the upper limit correlation coefficient when the prediction error is the smallest, γ i,d,bes is the lower limit correlation coefficient when the prediction error is the smallest, weight i,u is the upper limit weight coefficient of the ith lag value in the autoregression analysis, weight i,d is the lower limit weight coefficient of the i-th lag value in the autoregression analysis.

5. The method for mining charging and feeding potential according to claim 1, characterized in that: The evaluation of the charging and feeding potential of each period of the next day according to the prediction interval set specifically includes: According to the factor evaluation data, a state potential factor, a power price potential factor and a willingness potential factor are set, wherein the factor evaluation data at least includes one or more of the charging and feeding behaviors of the electric vehicle at each moment of the next day predicted by S1, S2, S3 and S4, the SOC at each moment, the travel state of the electric vehicle at each moment, the charging and feeding expenditure and the user willingness; Among them, δ k,t,1 is the state potential factor, when the predicted charging and feeding behavior of the electric vehicle is idle or charging, it is assigned a value of 1; when the predicted charging and feeding behavior of the electric vehicle is traveling, it is assigned a value of 0; when the predicted charging and feeding behavior of the electric vehicle is feeding, it is assigned a value of -1; Among them, δ k,t,2 is the electricity price potential factor, p t is the time-of-use electricity price, p k,min is the psychological charging price of the kth user, p k,max The psychological feed-in electricity price for the kth user; Among them, δ k,t,3 is the willingness potential factor; Among them, feed t is the feeding potential at time t, charge t is the charging potential at time t, k is the electric vehicle number, K is the total number of electric vehicles, and C is the battery capacity of the electric vehicle.

6. The method for mining charging and feeding potential according to claim 5, characterized in that: The specific process of setting the next day's real-time charging subsidy and feed subsidy is as follows: By adjusting the user charging and feeding subsidy at time t to change p t , thereby changing δ k,t,2 , so that the charging and feeding potential at each moment is kept within the preset range.

7. A device for mining charging and feeding potential, characterized in that: include: A data collection module is used to collect vehicle usage data of multiple electric vehicles, wherein the vehicle usage data includes daily average mileage data, starting SOC data, and daily charging start time data, and a Monte Carlo method is used to construct a behavior matrix of electric vehicle charging and feeding based on the vehicle usage data; A matrix module, used to divide the behavior matrix into three sections, namely a training set, a verification set and a prediction set; An autoregressive analysis module, used for performing autoregressive analysis on the data of the training set to obtain a prediction curve set; A prediction module is used to analyze the error between the prediction curve set and the verification set, perform upper limit time series prediction and lower limit time series prediction on the error, and obtain a prediction interval set; An evaluation module, used to evaluate the charging and feeding potential of each time period of the next day according to the prediction interval set; The subsidy module is used to set the next day's real-time charging subsidy and feed subsidy based on the evaluation results.

8. The charging and feeding potential mining device according to claim 7, characterized in that: The training set includes the vehicle usage data of the electric vehicle from time node 1 to time node 960, the verification set includes the vehicle usage data of the electric vehicle from time node 481 to time node 1440, and the prediction set includes the vehicle usage data of the electric vehicle from time node 1441 to time node 1536; Among them, every 15 minutes is a time node.

9. A computer 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 computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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