Electric vehicle scheduling method and device considering sharing of EV batteries, equipment and medium

Abstract

The application discloses an electric vehicle scheduling method and device considering shared EV batteries, equipment and a medium. The method comprises the following steps: obtaining user historical trip chain data, battery swap station battery swap data and new energy historical data, and obtaining historical period road traffic conditions; calculating a centralized new energy device grid feeding curve within a preset time; determining a time sequence relationship coefficient of the number of shared batteries required by the battery swap station to provide for electric vehicle battery swap; establishing a shared EV battery operation mode; constructing a light-vehicle-EV battery complementary model; taking battery swap station revenue and power grid load as optimization objectives, and obtaining an optimal electric vehicle scheduling strategy for scheduling electric vehicles by considering shared EV batteries. The application realizes battery vehicle-grid dual use by introducing a scheduling strategy of shared EV batteries, improves new energy utilization, and optimizes the benefits of users, battery swap stations and power grids and carbon emission reduction effect.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more particularly to electric vehicle scheduling methods, apparatus, devices, and media considering shared EV batteries. Background Technology

[0002] With the increasing number of electric vehicles and the expanding scale of their charging, the impact of charging behavior on the power grid is becoming increasingly significant. Along with the large-scale integration of distributed renewable energy sources, the volatility of future distribution network loads will become increasingly pronounced. Against this backdrop, electric vehicles, as a flexible and adjustable energy storage resource, can participate in grid regulation through a rational and orderly charging and feeding strategy. However, existing energy storage power stations are expensive to build, electric vehicle battery utilization is low, and load fluctuations are large. Simultaneously, the increasing maturity of digital grid technology and the significantly improved perception capabilities of electric vehicle status and user behavior provide strong support for the practical application of vehicle-to-grid (V2G) interaction technologies.

[0003] In existing technologies, such as the optimized scheduling method for guiding orderly charging of electric vehicles disclosed in patent CN202211168563.2, a multi-objective optimization function for orderly charging of electric vehicles is solved by establishing a reward-penalty tiered carbon price model, a dynamic time-of-use electricity price model, and an electric vehicle electricity price regulation model to guide electric vehicle users to charge in an orderly manner. However, most existing technologies still mainly consider guiding electric vehicles to charge in an orderly manner and treat electric vehicles and their batteries as tightly coupled objects, failing to effectively break the binding relationship between batteries and vehicles. This limits the independent mobility and flexible scheduling capabilities of batteries, resulting in certain limitations in power allocation and renewable energy consumption. In addition, existing technologies usually do not fully utilize the deep coupling analysis of the power generation fluctuation characteristics of renewable energy sources such as photovoltaics and the distribution patterns of vehicle traffic. They lack collaborative modeling of photovoltaic output, vehicle traffic density, and battery swapping demand, making it difficult to maximize the utilization rate of renewable energy and achieve the optimal balance of benefits among the grid, battery swapping stations, and users. Summary of the Invention

[0004] To address at least one of the problems existing in the prior art, this invention provides an electric vehicle scheduling method that considers shared EV batteries, which can provide an optimal electric vehicle scheduling strategy and effectively suppress load fluctuations.

[0005] To achieve the objective of this invention, the present invention provides an electric vehicle scheduling method considering shared EV batteries, comprising:

[0006] Acquire user historical travel chain data, battery swapping station data, and new energy historical data; obtain road traffic conditions for historical time periods based on historical travel chain data.

[0007] Predict the regional solar irradiance within a preset time period based on historical data of new energy sources, and calculate the feed grid curve of centralized new energy devices within the preset time period based on the regional solar irradiance.

[0008] Establish a vehicle flow density matrix and a vector of the number of shared batteries required by the battery swapping station for electric vehicles. Determine the time-series relationship coefficient of the number of shared batteries required by the battery swapping station for electric vehicles. Based on the time-series relationship coefficient, determine the main influencing factors of the number of shared batteries required by the battery swapping station for electric vehicles.

[0009] By constructing a shared EV battery operation mode, the EV battery can be used for both vehicle and network purposes;

[0010] By constructing a complementary model of light-vehicle-EV battery, the number of shared batteries that the battery swapping station needs to provide for electric vehicle battery swapping at each time is determined based on the user's battery swapping potential and the time-series relationship coefficient, thereby determining the proportion of shared batteries used by the battery swapping station for the grid.

[0011] Taking into account the output of new energy devices, the number of batteries available for EV swapping at swapping stations, the number of batteries available for grid feeding at swapping stations, and the number of price-sensitive EV users near swapping stations, the initial vehicle dispatching instructions, i.e. the initial electric vehicle dispatching strategy, are determined.

[0012] Using the revenue of battery swapping stations and the grid load as optimization objectives, an optimal electric vehicle scheduling strategy considering shared EV batteries is obtained through iterative optimization, and electric vehicles are scheduled using the optimal electric vehicle scheduling strategy.

[0013] The present invention also provides a scheduling device that takes into account shared EV batteries.

[0014] The present invention also provides a computer device.

[0015] The present invention also provides a computer-readable storage medium.

[0016] Compared with the prior art, the present invention can achieve at least the following beneficial effects:

[0017] (1) This invention considers the impact of traffic conditions on the battery swapping behavior of battery swapping stations, thereby obtaining the optimal ratio of solar-vehicle-EV shared battery coordination. The resulting strategy can improve the utilization rate of new energy sources while optimizing the benefits and carbon emission reduction effects for users, battery swapping stations and the power grid.

[0018] (2) This invention no longer considers binding the EV battery to the electric vehicle's travel time, enhances the battery's dual-use capability with the vehicle and the network, and improves scheduling flexibility.

[0019] (3) This invention considers a new type of electric vehicle power supply mode - battery swapping mode, which has a certain forward-looking nature. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an electric vehicle scheduling method considering shared EV batteries provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram illustrating the operating mechanism of an electric vehicle scheduling method considering shared EV batteries provided in an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the module composition of the scheduling device in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0024] Please see Figure 1 The present invention provides an electric vehicle scheduling method considering shared EV batteries, comprising:

[0025] S1. Collect users' historical travel chain data, battery swapping station data, and new energy historical data, and summarize the road traffic situation for historical periods based on users' historical travel chain data.

[0026] In this step, summarizing road traffic conditions for historical time periods based on users' historical travel chain data specifically includes:

[0027] For any given vehicle, it possesses historical travel chain data, such as: vehicle , Traveling between node 3 and node 4 at any given time can be denoted as =(3,4), where The data collection time interval Indicates the first vehicle The travel location at any given time, where the node is a road network node or an electric network node.

[0028] S2. Based on historical data of new energy sources, predict the regional solar irradiance within a preset time period, and calculate the feed curve of centralized new energy devices within the preset time period based on the regional solar irradiance.

[0029] In one embodiment of the present invention, the preset time is set to 24 hours; in other embodiments, it may be set to other times as needed.

[0030] In one embodiment of the present invention, the new energy device is a photovoltaic power generation device (theoretically it can be replaced by wind power, but photovoltaic is more common and more practical), and the grid feed curve of the centralized new energy device at a preset time is calculated, specifically including:

[0031] The power output of new energy sources is as follows:

[0032] ;

[0033] in, for At all times, the output power of new energy sources for Photovoltaic power output at all times.

[0034] The photovoltaic power output is as follows:

[0035] ;

[0036] ;

[0037] ;

[0038] in, for The photovoltaic grid-connected voltage at any given time, This is the short-circuit current. and For time-varying correction parameters, Open circuit voltage, for The maximum power point current at a given moment. for The maximum power point voltage at any given time.

[0039] Feed curve with The time is used as the horizontal axis, and the photovoltaic power output is used as the vertical axis.

[0040] S3. Establish the vehicle flow density matrix and the vector of the number of shared batteries required by the battery swapping station for electric vehicles. Determine the time-series relationship coefficient of the vehicle flow density matrix to determine the number of shared batteries required by the battery swapping station for electric vehicles. Based on the time-series relationship coefficient, determine the main influencing factors of the number of shared batteries required by the battery swapping station for electric vehicles.

[0041] In this step, a vehicle flow density matrix and a vector representing the number of shared batteries required by the battery swapping station for electric vehicles are established. The time-series relationship coefficients of the required number of shared batteries are calculated to determine the main influencing factors, including:

[0042] A traffic flow density matrix is ​​established based on historical road traffic conditions. The expression for the traffic flow density matrix is ​​as follows:

[0043] ;

[0044] ;

[0045] in, for Traffic density matrix at any given time. for time Node to The number of vehicles traveling through the node. , , For the number of nodes, , They represent the first vehicle real-time travel location The first and second digits.

[0046] The shared battery count vector is determined based on the number of battery swapping stations and the number of vehicles traveling at each station. The expression for the shared battery count vector that a battery swapping station needs to provide for electric vehicle battery swapping is as follows:

[0047] ;

[0048] in, for A vector representing the number of shared batteries that a battery swapping station needs to provide for electric vehicle battery swapping at any given time. for Time number The number of vehicles operating at the battery swapping station. This represents the number of battery swapping stations.

[0049] Determination of time series relationship coefficients:

[0050] ;

[0051] in, Let be a function representing the time series relation coefficients. for The function (0 < <1), The closer the value is to 1, the stronger the correlation. for time Node to The number of vehicles traveling through the node. for Time number The number of vehicles operating at the battery swapping station. , , This represents the total number of time periods. for The mean, for The mean.

[0052] The correlation is determined based on the time series relationship coefficient, and then the numbering is determined based on the correlation. The main influencing factors for the number of shared batteries required for a battery swapping station to be provided for electric vehicles include, in one embodiment of the present invention, the user's battery swapping potential and vehicle traffic density.

[0053] In one embodiment of the present invention, due to the time delay error between the vehicle flow density matrix and the battery swapping number vector, the time-difference calculation is performed on the timing relationship coefficient function, and the calculation formula is as follows:

[0054] ;

[0055] in, Let represent a mathematical function, and let represent finding the parameter that maximizes the probability. This represents the number of time intervals for timing errors. , In order to make The value corresponding to the maximum value Node to Number of vehicles traveling through the node, and their serial numbers. Number of vehicles operating at the battery swapping station.

[0056] S4. Establish a shared EV battery operation mode to achieve dual use of EV batteries for vehicles and the network.

[0057] EV battery dual-use refers to the ability of EV batteries to both power electric vehicles and feed power into the grid.

[0058] In this step, the shared EV battery operating mode specifically includes:

[0059] Electric vehicle users, battery aggregators, and battery swapping station operators have signed an agreement: battery aggregators will provide EV batteries to electric vehicle users and battery swapping station operators, but the other two parties must commit to accepting the dispatch of battery operators without affecting their normal travel.

[0060] S5. Construct a complementary model of light-vehicle-EV battery to determine the number of shared batteries that the battery swapping station needs to provide for electric vehicle battery swapping at each time point based on the user's battery swapping potential and the time-series relationship coefficient, thereby determining the proportion of shared batteries used by the battery swapping station for the grid.

[0061] This step specifically includes:

[0062] The formula for calculating a user's battery swapping potential is as follows:

[0063] ;

[0064] ;

[0065] in, For the first The closer a battery swapping potential is to 1 user, the greater the likelihood of it being possible. For battery swapping time, For the first The idle time of the vehicle corresponding to each user. sigmoid For activation function, For the first The remaining battery power of the vehicle corresponding to each user. Parameters used to determine whether a user is a potential battery swapping user, when A value of 1 indicates a potential battery swapping user.

[0066] At this point, let's assume the number is... The battery swapping station is the first The nearest battery swapping station for each user is calculated to obtain the number. The percentage of shared batteries that can be used in the battery swapping station for the feeder network is calculated using the following formula:

[0067] ;

[0068] in, for Time-based power station The proportion of shared batteries that can be used in the feeder network. for Time number The number of vehicles operating at the battery swapping station. Number The number of available batteries at a battery swapping station, when the first When the number of available batteries at the nearest battery swapping station for a user is 0, the next nearest battery swapping station is selected for recalculation, and so on; the number of shared batteries that a battery swapping station needs to provide for electric vehicle battery swapping is... ,in The number of battery-swapping vehicles is determined by traffic flow. This refers to the number of battery-swapping vehicles determined by battery swapping potential; It is the total number of users. For time series relationship coefficients.

[0069] S6. Taking into account the output of new energy devices, the number of batteries available for EV swapping at the swapping station, the number of batteries available for grid feeding at the swapping station, and the number of price-sensitive EV users near the swapping station, determine the initial vehicle dispatching instruction, i.e., the initial electric vehicle dispatching strategy.

[0070] In this step, the output of the new energy device will be used to charge the shared batteries at the battery swapping station; the number of batteries available for EV swapping at the station will determine the number of vehicles that can be swapped; the number of batteries available for grid feeding at the station will affect the grid load; and the number of price-sensitive EV users near the station will determine the specific users available for dispatch. Therefore, by comprehensively considering the above four factors, an initial electric vehicle dispatching strategy that considers shared EV batteries can be determined.

[0071] S7. Taking the revenue of battery swapping stations (considering aggregators and battery swapping stations as a single entity) and grid load (indirectly reflecting the total emission reduction) as optimization objectives, the objective function is:

[0072] ;

[0073]

[0074] in, and The objective function is denoted by , where represents the total revenue of the battery swapping station and the grid load (which indirectly reflects the total emission reduction), respectively. for The power supply of the battery swapping station at any given time is calculated based on the proportion of batteries fed into the grid. for Electricity price at any time for Car charging power at all times for The charging power of the battery swapping station at all times for Constant daily workload. Clearly, The larger the station, the higher its revenue. The smaller the value, the smaller the grid load, and the better the emission reduction effect.

[0075] S8. The objective function is calculated using the particle swarm optimization algorithm. After iterative optimization, the optimal electric vehicle scheduling strategy considering shared EV batteries is obtained. The electric vehicles are then scheduled using the optimal electric vehicle scheduling strategy.

[0076] In this step, the electric vehicle scheduling strategy considering shared EV batteries specifically includes: using a multi-objective particle swarm optimization algorithm to determine whether the user's vehicle dispatching instruction meets preset constraints (including travel time constraints, which can be determined based on existing technology and will not be elaborated here). If not, optimization continues; if it meets the constraints, it is determined whether convergence has been achieved or the upper limit of the number of iterations has been reached. If convergence has been achieved or the upper limit of the number of iterations has been reached, the user's vehicle dispatching instruction is output; otherwise, optimization continues. The multi-objective particle swarm optimization algorithm calculates which vehicles are suitable for battery swapping and which battery swapping station to go to for battery swapping. Its convergence result is infinitely close to the optimal electric vehicle scheduling strategy.

[0077] The electric vehicle dispatch strategy includes battery aggregators calculating which vehicle will go to which battery swapping station and which vehicle will not be swapped, and then reducing charging fees based on the dispatching costs of the required vehicles.

[0078] Please see Figure 2 The electric vehicle scheduling method considering shared EV batteries proposed in the foregoing embodiments of the present invention includes the following specific operation process:

[0079] like Figure 2 As shown by the arrows in the information flow diagram, battery aggregators sign contracts with electric vehicle users and battery swapping stations respectively. Based on the grid regulation requirements and combined with the optimal electric vehicle scheduling strategy obtained through the multi-objective particle swarm optimization algorithm, the battery aggregators issue vehicle dispatching instructions to electric vehicle users.

[0080] like Figure 2 As indicated by the energy flow arrows, the power grid charges the battery swapping stations; the battery swapping stations, in turn, supply power to the power grid; and electric vehicle users go to the corresponding battery swapping stations to swap batteries according to the control instructions issued by the battery aggregator.

[0081] In one embodiment of the present invention, an electric vehicle scheduling device considering shared EV batteries is provided; see [link to relevant documentation]. Figure 3 It includes the following modules:

[0082] The data collection module is used to acquire users' historical travel chain data, battery swapping station data, and historical new energy data;

[0083] The feed grid curve determination module is used to predict the regional irradiance within a preset time period based on historical data of new energy sources, and to calculate the feed grid curve of centralized new energy devices within the preset time period based on the regional irradiance.

[0084] The time-series relationship coefficient determination module is used to establish the vehicle flow density matrix and the vector of the number of shared batteries required by the battery swapping station to provide for electric vehicle battery swapping, determine the time-series relationship coefficient of the number of shared batteries required by the battery swapping station to provide for electric vehicle battery swapping, and determine the main influencing factors of the number of shared batteries required by the battery swapping station to provide for electric vehicle battery swapping based on the time-series relationship coefficient.

[0085] The operation mode construction module is used to achieve dual-use of EV batteries for both vehicle and network applications by sharing the EV battery operation mode;

[0086] The shared battery ratio acquisition module is used to determine the number of shared batteries that the battery swapping station needs to provide for electric vehicle battery swapping at each time based on the user's battery swapping potential and the time-series relationship coefficient through the light-vehicle-EV battery complementary model, thereby determining the shared battery ratio of the battery swapping station for grid feeding.

[0087] The initial strategy determination module is used to comprehensively consider the output of new energy devices, the number of batteries available for EV swapping at the swapping station, the number of batteries available for the grid at the swapping station, and the number of price-sensitive EV users near the swapping station to determine the initial vehicle dispatching instructions, i.e., the initial electric vehicle dispatching strategy.

[0088] The optimization module is used to obtain the optimal electric vehicle scheduling strategy that takes the revenue of the battery swapping station and the grid load as optimization objectives through iterative optimization.

[0089] The scheduling module is used to schedule electric vehicles using the optimal electric vehicle scheduling strategy.

[0090] In the optimization module, during the iteration process, it is determined whether the obtained electric dispatching strategy meets the constraints and whether further iteration is needed before issuing dispatching instructions to the user.

[0091] In one embodiment of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described in the embodiment.

[0092] In one embodiment of the present invention, a computer-readable storage medium is provided having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of the method described in the foregoing embodiments.

[0093] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined in this invention may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for scheduling electric vehicles considering shared EV batteries, characterized in that, include: Based on the acquired historical data of new energy sources, the regional solar irradiance is predicted within a preset time period, and the feed grid curve of the centralized new energy device within the preset time period is calculated based on the regional solar irradiance. Establish a vehicle flow density matrix and a vector of the number of shared batteries required by the battery swapping station for electric vehicles. Determine the time-series relationship coefficient of the number of shared batteries required by the battery swapping station for electric vehicles. Based on the time-series relationship coefficient, determine the main influencing factors of the number of shared batteries required by the battery swapping station for electric vehicles. By constructing a shared EV battery operation mode, the EV battery can be used for both vehicle and network purposes; By constructing a complementary model of light-vehicle-EV battery, the number of shared batteries that the battery swapping station needs to provide for electric vehicle battery swapping at each time is determined based on the user's battery swapping potential and the time-series relationship coefficient, thereby determining the proportion of shared batteries used by the battery swapping station for the grid. Taking into account the output of new energy devices, the number of batteries available for EV swapping at swapping stations, the number of batteries available for grid feeding at swapping stations, and the number of price-sensitive EV users near swapping stations, the initial vehicle dispatching instructions, i.e. the initial electric vehicle dispatching strategy, are determined. Using the revenue of battery swapping stations and the grid load as optimization objectives, the optimal electric vehicle scheduling strategy considering shared EV batteries is obtained through iterative optimization, and the electric vehicles are scheduled using the optimal electric vehicle scheduling strategy. The horizontal axis of the feed curve is At any given time, the vertical axis represents the photovoltaic output power, which is determined based on the photovoltaic grid-connected voltage, time-varying correction parameters, open-circuit voltage, and short-circuit current; the user's battery swapping potential is determined based on the battery swapping time, the idle time of the user's vehicle, and the remaining battery power of the user's vehicle. Assumption Number The battery swapping station is the first The nearest battery swapping station for each user is calculated to obtain the number. The percentage of shared batteries that can be used in the battery swapping station for the feeder network is calculated using the following formula: in, for Time-based power station The proportion of shared batteries that can be used in the feeder network. for Time number The number of vehicles operating at the battery swapping station. Number The number of available batteries at the battery swapping station; the number of shared batteries required at the battery swapping station to provide for electric vehicle battery swapping. ,in The number of battery-swapping vehicles is determined by traffic flow. This refers to the number of battery-swapping vehicles determined by battery swapping potential. It is the total number of users. These are the time-series correlation coefficients. The parameters used to determine whether a user is a potential battery swapping user; the objective function established with the revenue of the battery swapping station and the grid load as optimization objectives is as follows: in, and Let be the objective function, and let represent the total revenue of the battery swapping station and the grid load, respectively. for Power supply to the battery swapping station at all times for Electricity price at any time for Car charging power at all times for The charging power of the battery swapping station at all times for Daily workload at all times This represents the total number of time periods. for The power output of new energy sources at any given time is the photovoltaic power output.

2. The electric vehicle scheduling method considering shared EV batteries according to claim 1, characterized in that, Based on the acquired historical travel chain data, road traffic conditions for historical periods are obtained. A traffic density matrix is ​​then established based on these historical traffic conditions. The number of shared batteries is determined based on the number of battery swapping stations and the number of vehicles traveling at each station. Correlation is determined based on time-series relationship coefficients, and battery numbering is determined based on these correlations. The main influencing factors for the number of shared batteries required for electric vehicle battery swapping stations.

3. The electric vehicle scheduling method considering shared EV batteries according to claim 1, characterized in that, In the shared EV battery operation mode, the relationship between electric vehicle users, battery aggregators, and battery swapping station operators is as follows: battery aggregators provide EV batteries to electric vehicle users and battery swapping station operators, but the other two need to commit to accepting the dispatch of battery operators without affecting their normal travel.

4. A method for scheduling electric vehicles considering shared EV batteries according to any one of claims 1-3, characterized in that, The optimal electric vehicle scheduling strategy is obtained by iterative optimization using a multi-objective particle swarm optimization algorithm.

5. A scheduling device considering shared EV batteries, characterized in that, To implement the method according to any one of claims 1-3, the method comprises the following modules: The data collection module is used to acquire users' historical travel chain data, battery swapping station data, and historical new energy data; The feed grid curve determination module is used to predict the regional irradiance within a preset time period based on historical data of new energy sources, and to calculate the feed grid curve of centralized new energy devices within the preset time period based on the regional irradiance. The time-series relationship coefficient determination module is used to establish the vehicle flow density matrix and the vector of the number of shared batteries required by the battery swapping station to provide for electric vehicle battery swapping, determine the time-series relationship coefficient of the number of shared batteries required by the battery swapping station to provide for electric vehicle battery swapping, and determine the main influencing factors of the number of shared batteries required by the battery swapping station to provide for electric vehicle battery swapping based on the time-series relationship coefficient. The operation mode construction module is used to achieve dual-use of EV batteries for both vehicle and network applications by sharing the EV battery operation mode; The shared battery ratio acquisition module is used to determine the number of shared batteries that the battery swapping station needs to provide for electric vehicle battery swapping at each time based on the user's battery swapping potential and time-series relationship coefficient through the light-vehicle-EV battery complementary model, thereby determining the shared battery ratio of the battery swapping station for the grid. The initial strategy determination module is used to comprehensively consider the output of new energy devices, the number of batteries available for EV swapping at the swapping station, the number of batteries available for the grid at the swapping station, and the number of price-sensitive EV users near the swapping station to determine the initial vehicle dispatching instructions, i.e., the initial electric vehicle dispatching strategy. The optimization module is used to obtain the optimal electric vehicle scheduling strategy that takes the revenue of the battery swapping station and the grid load as optimization objectives through iterative optimization. The scheduling module is used to schedule electric vehicles using the optimal electric vehicle scheduling strategy.

6. 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, it implements the steps of the method according to any one of claims 1-3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-3.

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