Photovoltaic power output scenario generation method and device, equipment and storage medium

Abstract

The application provides a photovoltaic output scene generation method and device, equipment and a storage medium. Firstly, the ideal irradiance and the measured irradiance are subjected to a difference value and zero removal operation to serve as training data. Secondly, based on a Gaussian process regression model, the RBFKernel and ScaleKernel kernel function parameters are optimized by using the training data. Then, the extreme weather events affecting the photovoltaic output are modeled, and the extreme weather events are inserted on the annual time sequence. Next, based on the annual time sequence with the inserted extreme weather events, the Gaussian process regression model is used to fit and generate a complete annual irradiance difference scene, which is superimposed with the ideal annual irradiance scene to obtain an actual annual irradiance scene. Finally, according to the mapping relationship between the historical irradiance and the photovoltaic output, the annual photovoltaic output scene is obtained. The application has the advantages that the influence of meteorological factors on the photovoltaic output is considered, and multiple photovoltaic output scenes considering different meteorological conditions can be generated to provide power system dispatching with corresponding strategies.

Description

Technical Field

[0001] This application belongs to the field of power system planning, specifically to a method, apparatus, equipment, and storage medium for generating photovoltaic power output scenarios. Background Technology

[0002] In recent years, the scale of new energy grid connection has been increasing. Unlike traditional thermal power generation, wind and photovoltaic power generation, as the main forms of new energy power generation, are highly susceptible to external meteorological factors, resulting in intermittent and fluctuating power output. With the intensification of global warming, extreme weather events in my country are becoming more frequent, affecting wider areas, and exhibiting greater intensity, thus strengthening the coupling relationship between meteorology and the power grid. As the penetration rate of new energy into the power grid continues to increase, ensuring the stable operation of the grid and the high-level absorption of new energy are pressing issues. This problem is partly caused by the disturbances to the power grid due to the uncertainty of new energy output; therefore, research on methods for predicting or generating new energy output scenarios is necessary. Currently, much research focuses on methods based on historical new energy output data, such as data-driven modeling methods for generating new energy output scenarios, including statistical learning and artificial intelligence methods. However, these methods are typically for short-term predictions and cannot provide sufficient long-term new energy output scenarios for power system dispatch. Summary of the Invention

[0003] Considering the increasingly close coupling between meteorology and the power grid, and the fact that renewable energy output is highly susceptible to meteorological factors, this application proposes a method for generating photovoltaic (PV) output scenarios. First, the ideal irradiance and measured irradiance are subtracted and rounded to zero, and used as training data. Second, based on a Gaussian process regression model, the parameters of the RBFKernel and ScaleKernel kernel functions are optimized using the training data. Then, extreme meteorological events affecting PV output are modeled and inserted into the annual time series. Finally, based on the annual time series with inserted extreme meteorological events, a complete annual irradiance difference scenario is generated by fitting the Gaussian process regression model and superimposed on the ideal annual irradiance scenario. Based on the mapping relationship between historical irradiance and PV output, the annual PV output scenario is obtained.

[0004] To achieve the above objectives, the technical solution adopted in this application is as follows:

[0005] Firstly, this application provides a method for generating photovoltaic power output scenarios, the specific steps of which are as follows:

[0006] S1: Analyze the characteristics of extreme weather events affecting photovoltaic power output, model them, and insert the extreme weather events into the annual time series;

[0007] S2: The annual time series of extreme weather events is used as input data for a pre-trained Gaussian process regression model. The Gaussian process regression model generates an annual irradiance difference scenario and overlays it with an ideal annual irradiance scenario, which is then mapped to the annual photovoltaic power output scenario.

[0008] Furthermore, the method also includes:

[0009] The ideal irradiance and the measured irradiance were subtracted and rounded to zero, and then used as training data.

[0010] The parameters of the RBFKernel and ScaleKernel kernel functions are optimized using the training data to obtain a well-trained Gaussian process regression model.

[0011] Furthermore, step S1 analyzes the characteristics of extreme weather events affecting photovoltaic output, models them, and inserts the extreme weather events into the annual time series.

[0012] Furthermore, the specific steps of step S2 are as follows:

[0013] The annual time series data of extreme weather events are used as input data for a pre-trained Gaussian process regression model. The model generates an annual irradiance difference scene and overlays it with an ideal annual irradiance scene.

[0014]

[0015] σ 2 (X')=K(X',X')-K(X,X') T [K(X,X)+σ 2 I] -1 K(X,X')

[0016] In the formula, X is the input variable of the training sample, y is the output variable of the training sample, which refers to the irradiance difference at each time point; K(X,X') is the covariance matrix of the input variable and the test point, representing the correlation between the input variable and the test point; σ 2 Let I be the variance of Gaussian noise that is independent and identically distributed with respect to the training samples, I be the identity matrix, and f(X') be the predicted output at the test point. To predict the mean, σ 2 (X') represents the prediction variance, and finally the annual irradiance scenario is mapped to the annual photovoltaic power output scenario.

[0017] Secondly, embodiments of this application provide a photovoltaic power output scene generation device, comprising:

[0018] The acquisition module is used to analyze the characteristics of extreme weather events that affect photovoltaic output, model them, and insert the extreme weather events into the annual time series.

[0019] The input / output module is used to take the annual time series of extreme weather events as input data for a pre-trained Gaussian process regression model, generate an annual irradiance difference scenario and overlay it with an ideal annual irradiance scenario, and finally map it to an annual photovoltaic power output scenario.

[0020] The Gaussian process regression model is obtained by optimizing the parameters of the RBFKernel and ScaleKernel kernel functions using training data, which is based on ideal irradiance and measured irradiance.

[0021] Optionally, the device further includes: a training module, used to perform difference and zero-removal operations on the ideal irradiance and the measured irradiance to obtain training data; and to optimize the parameters of the kernel functions RBFKernel and ScaleKernel of the Gaussian process regression model using the training data to obtain a trained Gaussian process regression model.

[0022] Optionally, the training module is used for:

[0023] Determine the local latitude and longitude, and calculate the ideal irradiance using the solar constant, Earth-Sun distance correction factor, solar altitude angle, zenith angle, and solar declination parameters; then calculate the difference between the ideal irradiance and the local measured irradiance, and use the difference as training data after removing zeros.

[0024] Optionally, the training module is used to: optimize the parameters of the kernel functions RBFKernel and ScaleKernel of the Gaussian process regression model using training data to obtain a trained Gaussian process regression model, including:

[0025] The hyperparameters of the kernel function in the Gaussian process regression model are trained using the processed training data.

[0026] The properties of a Gaussian process regression model are determined by the mean function and the covariance function:

[0027]

[0028] The covariance function is a combination of ScaleKernel and RBFKernel:

[0029]

[0030] m(x) is the mean function of the Gaussian process regression model, k(x,x') is the covariance function of the Gaussian process model, and the two hyperparameters θ scale and θ rbf These factors respectively affect the amplitude and extent of fluctuations in the generated samples.

[0031] Optionally, the input / output module is used to output based on the following formula:

[0032]

[0033] σ 2 (X')=K(X',X')-K(X,X') T [K(X,X)+σ 2 I] -1 K(X,X')

[0034] In the formula, X is the input variable of the training sample, y is the output variable of the training sample, which refers to the irradiance difference at each time point; K(X,X') is the covariance matrix of the input variable and the test point, representing the correlation between the input variable and the test point; σ 2 Let I be the variance of Gaussian noise that is independent and identically distributed with respect to the training samples, I be the identity matrix, and f(X') be the predicted output at the test point. To predict the mean, σ 2 (X') represents the prediction variance, and finally the annual irradiance scenario is mapped to the annual photovoltaic power output scenario.

[0035] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method described in any of the above embodiments.

[0036] A computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, perform the steps of the method described in any of the above embodiments.

[0037] Beneficial effects:

[0038] This application proposes a data-mechanism fusion-based method for generating photovoltaic (PV) output scenarios, taking into account extreme weather conditions. Considering the increasingly complex coupling between meteorological factors and the power grid, a data-mechanism fusion-based PV output scenario generation method is proposed. First, the ideal irradiance and measured irradiance are subtracted and rounded to zero, and used as training data. Second, based on a Gaussian process regression model, the parameters of the RBFKernel and ScaleKernel kernel functions are optimized using the training data. Then, extreme weather events affecting PV output are modeled and inserted into the annual time series. The annual time series with inserted extreme weather events is used as input data for a pre-trained Gaussian process regression model. The model generates an annual irradiance difference scenario, which is then superimposed on the ideal annual irradiance scenario. Finally, based on the mapping relationship between historical irradiance and PV output, the annual PV output scenario is obtained. The advantages of this application are: considering the impact of meteorological factors on PV output, multiple long-term PV output scenarios considering different meteorological conditions can be generated to enable power system scheduling to formulate corresponding strategies. Attached Figure Description

[0039] Figure 1 A flowchart illustrating the steps of a data-mechanism fusion-based photovoltaic power output scenario generation method that takes extreme weather conditions into account in this application;

[0040] Figure 2 This is a schematic diagram illustrating step B of this application, which involves inserting extreme weather events into an annual time series.

[0041] Figure 3 A comparison image of the annual irradiance scene generated for this application and historical annual irradiance scenes;

[0042] Figure 4 The annual photovoltaic power output scenario generated for this application. Detailed Implementation

[0043] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments:

[0044] This application proposes a data-mechanism fusion-based method for generating photovoltaic power output scenarios that takes into account extreme weather conditions, such as... Figure 1 As shown, the method of this application specifically includes the following steps:

[0045] Step A: Model the extreme weather events that affect photovoltaic power output and insert these events into the annual time series.

[0046] Step B: The annual time series data of the extreme weather events is used as input data for a pre-trained Gaussian process regression model. The model generates an annual irradiance difference scenario and overlays it with the ideal annual irradiance scenario. Finally, based on the mapping relationship between historical irradiance and photovoltaic output, the annual photovoltaic output scenario is obtained. The Gaussian process regression model is obtained by optimizing the parameters of the RBFKernel and ScaleKernel kernel functions using training data, which is based on ideal and measured irradiance.

[0047] In this embodiment of the application, the data processing operation for the Gaussian process regression model training is as follows:

[0048] First, calculate the ideal irradiance using the following formula:

[0049] G = kG0sinh = kG0cosθ z =kG0(sinδsinφ+cosδcosφcosω)

[0050] In the formula, G0 is the solar constant, taken as 1368 (W / m³). 2 ), k is the Earth-Sun distance correction factor, h is the solar altitude angle, θ z It is the zenith angle (θ) z =90°-h), where δ is the solar declination, φ is the local latitude (positive for north), and ω is the solar hour angle. Among these, the solar constant G0 and the local latitude φ are known, while the Earth-Sun distance correction factor k, solar declination δ, and solar hour angle ω change continuously with the date and time.

[0051] If we ignore the influence of different times of day on the Earth-Sun distance, the Earth-Sun distance correction factor can be approximately calculated as follows:

[0052]

[0053] In the formula, r0 represents the average distance between the Earth and the Sun, r represents the actual distance between the Earth and the Sun, and N represents the accumulated days.

[0054] If we ignore the influence of different times of day on the solar declination, the solar declination δ can be approximated using the Cooper equation:

[0055]

[0056] The solar hour angle can be calculated using the following formula:

[0057] ω=(tt / 60-12)×15°

[0058] tt=C T +(4×(L Loc -L st ))+E Q

[0059] In the formula, tt is true solar time, and C T When it is time zone, L Loc The local longitude, L st It is the standard meridian longitude of the local time zone, E Q It's the time difference.

[0060] Then, measured irradiance data were obtained through the NASAPOWER platform, and the two data points were interpolated and zero-removed to serve as training data.

[0061] G train =GG measured

[0062] The hyperparameter optimization operation of the Gaussian process kernel function in this application is as follows:

[0063] The hyperparameters of the kernel function in the Gaussian process regression model are trained using the processed training data.

[0064] A Gaussian process is a set of any finite number of random variables that all have a joint Gaussian distribution, and its properties are completely determined by the mean function and the covariance function.

[0065]

[0066] Here, we choose a combination of the two covariance functions: ScaleKernel and RBFKernel.

[0067]

[0068] m(x) is the mean function of the Gaussian process model, k(x,x') is the covariance function of the Gaussian process model, and the two hyperparameters θ scale and θ rbf These factors respectively affect the amplitude and degree of fluctuation in the generated samples.

[0069] The specific steps of step A in this application are as follows:

[0070] The data obtained by subtracting the calculated ideal irradiance from the measured irradiance over the years represents the impact of meteorological conditions at each point in time on the irradiance at that time. A larger difference indicates a greater impact of the meteorological conditions on the irradiance. Therefore, based on historical meteorological event records, the difference data for corresponding time periods are found to model the degree of impact of extreme meteorological events on irradiance, and this model is then applied according to… Figure 2 The extreme weather events should be appropriately inserted into the annual time series, and the inserted time points should conform to the patterns of extreme weather events and the inserted extreme weather events should conform to the local weather conditions.

[0071] The specific steps of step B in this application are as follows:

[0072] The annual time series data of extreme weather events are used as input data for a pre-trained Gaussian process regression model. The model generates an annual irradiance difference scene and overlays it with an ideal annual irradiance scene.

[0073]

[0074] σ 2 (X')=K(X',X')-K(X,X') T [K(X,X)+σ 2 I] -1 K(X,X')

[0075] In the formula, X is the input variable of the training samples, referring to the time points with a step size of 1 hour and a total duration of 8760 hours; y is the output variable of the training samples, referring to the irradiance corresponding to each time point; K(X,X') is the covariance matrix between the input variables and the test points, representing the correlation between the input variables and the test points; σ 2 Let I be the variance of Gaussian noise that is independent and identically distributed with respect to the training samples, I be the identity matrix, and f(X') be the predicted output at the test point. To predict the mean, σ 2 (X') represents the prediction variance.

[0076] Then, it is mapped to the annual photovoltaic output scenario. The mapping relationship between photovoltaic output and irradiance can be represented by a direct proportional function. The comparison diagram between the annual irradiance scenario generated in this application and the historical annual irradiance scenario is shown below. Figure 3 The annual irradiance scenario generated by this patented method, based on the assumption of multiple extreme weather events, firstly ensures the regularity of irradiance on a daily time scale, i.e., irradiance is zero before sunrise and after sunset; secondly, it basically conforms to the fluctuation pattern of irradiance on an annual time scale; finally, the generated irradiance scenario also exhibits irradiance fluctuations due to changes in atmospheric transparency; the annual photovoltaic output scenario generated by this application is as follows: Figure 4 The parameter was obtained by fitting historical data and was 0.08618466. The annual irradiance scenario was then mapped to the annual photovoltaic output scenario through a positive proportional relationship.

[0077] Based on a similar inventive concept, embodiments of this application provide a photovoltaic power output scene generation device, including:

[0078] The acquisition module is used to analyze the characteristics of extreme weather events that affect photovoltaic output, model them, and insert the extreme weather events into the annual time series.

[0079] The input / output module is used to take the annual time series of extreme weather events as input data for a pre-trained Gaussian process regression model, generate an annual irradiance difference scenario and overlay it with an ideal annual irradiance scenario, and finally map it to an annual photovoltaic power output scenario.

[0080] The Gaussian process regression model is obtained by optimizing the parameters of the RBFKernel and ScaleKernel kernel functions using training data, which is based on ideal irradiance and measured irradiance.

[0081] Optionally, the device further includes: a training module, used to perform difference and zero-removal operations on the ideal irradiance and the measured irradiance to obtain training data; and to optimize the parameters of the kernel functions RBFKernel and ScaleKernel of the Gaussian process regression model using the training data to obtain a trained Gaussian process regression model.

[0082] Optionally, the training module is used for:

[0083] Determine the local latitude and longitude, and calculate the ideal irradiance using the solar constant, Earth-Sun distance correction factor, solar altitude angle, zenith angle, and solar declination parameters; then calculate the difference between the ideal irradiance and the local measured irradiance, and use the difference as training data after removing zeros.

[0084] Optionally, the training module is used to: optimize the parameters of the kernel functions RBFKernel and ScaleKernel of the Gaussian process regression model using training data to obtain a trained Gaussian process regression model, including:

[0085] The hyperparameters of the kernel function in the Gaussian process regression model are trained using the processed training data.

[0086] The properties of a Gaussian process regression model are determined by the mean function and the covariance function:

[0087]

[0088] The covariance function is a combination of ScaleKernel and RBFKernel:

[0089]

[0090] m(x) is the mean function of the Gaussian process regression model, k(x,x') is the covariance function of the Gaussian process model, and the two hyperparameters θ scale and θ rbf These factors respectively affect the amplitude and extent of fluctuations in the generated samples.

[0091] Optionally, the input / output module is used to output based on the following formula:

[0092]

[0093] σ 2 (X')=K(X',X')-K(X,X') T [K(X,X)+σ 2 I] -1 K(X,X')

[0094] In the formula, X is the input variable of the training sample, y is the output variable of the training sample, which refers to the irradiance difference at each time point; K(X,X') is the covariance matrix of the input variable and the test point, representing the correlation between the input variable and the test point; σ 2 Let I be the variance of Gaussian noise that is independent and identically distributed with respect to the training samples, I be the identity matrix, and f(X') be the predicted output at the test point. To predict the mean, σ 2 (X') represents the prediction variance, and finally, the annual irradiance scenario is mapped to the annual photovoltaic output scenario. Furthermore, this application embodiment also provides a computer device characterized by including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor is able to perform... Figure 1 The steps of the method are described.

[0095] Furthermore, embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, are executable. Figure 1 The steps of the method are described.

[0096] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Python.

[0097] This application is described with reference to flowchart illustrations of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each step in the flowchart, and combinations of steps in the flowchart, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the steps in the flowchart. Figure 1 A device for a function specified in one or more processes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 Steps of a specified function in one or more processes.

[0100] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0101] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0102] The above description is merely a preferred embodiment of this application and is not intended to limit this application in any other way. Any modifications or equivalent changes made based on the technical essence of this application shall still fall within the scope of protection claimed by this application.

Claims

1. A method for generating photovoltaic power output scenarios, characterized in that... The specific steps are as follows: We analyze the characteristics of extreme weather events that affect photovoltaic power output, model them, and insert these extreme weather events into the annual time series. The annual time series of extreme weather events is used as input data for a pre-trained Gaussian process regression model to generate an annual irradiance difference scenario and overlay it with an ideal annual irradiance scenario. Finally, it is mapped to an annual photovoltaic power output scenario. The Gaussian process regression model is obtained by optimizing the parameters of the RBFKernel and ScaleKernel kernel functions using training data. The training data is based on ideal irradiance and measured irradiance. The method further includes: The ideal irradiance and the measured irradiance were compared and rounded to zero, and then used as training data. The parameters of the kernel functions RBFKernel and ScaleKernel of the Gaussian process regression model are optimized using the training data to obtain the trained Gaussian process regression model. The process of subtracting and rounding the ideal irradiance from the measured irradiance, and then using the resulting data as training data, includes: Determine the local latitude and longitude, and calculate the ideal irradiance using the solar constant, Earth-Sun distance correction factor, solar altitude angle, zenith angle, and solar declination parameters; The difference between the ideal irradiance and the local measured irradiance is calculated, and the difference is rounded down to zero and used as training data. The parameters of the kernel functions RBFKernel and ScaleKernel of the Gaussian process regression model are optimized using the training data to obtain a well-trained Gaussian process regression model, including: The hyperparameters of the kernel function in the Gaussian process regression model are trained using the processed training data. The properties of a Gaussian process regression model are determined by the mean function and the covariance function: ； The covariance function is a combination of ScaleKernel and RBFKernel: ； It is the mean function of the Gaussian process regression model. It is the covariance function of the Gaussian process model, with two hyperparameters. and These respectively affect the amplitude and degree of fluctuation in the generated samples; Based on annual time series data interpolated from extreme weather events, a complete annual irradiance difference scenario is generated by fitting a Gaussian process regression model and then overlaid with an ideal annual irradiance scenario to obtain the actual annual irradiance scenario, including: ； ； ； ； In the formula, These are the input variables of the training samples. This is the output variable of the training samples, which here refers to the difference in irradiance at each time point; This is the covariance matrix of the input variables and test points, representing the correlation between the input variables and test points; The variance of Gaussian noise that is independent and identically distributed with respect to the training samples. It is the identity matrix. The predicted output for the test points. To predict the mean, To predict variance, the annual irradiance scenario is finally mapped to the annual photovoltaic output scenario.

2. A photovoltaic power output scene generation device, characterized in that, include: The acquisition module is used to analyze the characteristics of extreme weather events that affect photovoltaic output, model them, and insert the extreme weather events into the annual time series. The input / output module is used to take the annual time series of extreme weather events as input data for a pre-trained Gaussian process regression model, generate an annual irradiance difference scenario and overlay it with an ideal annual irradiance scenario, and finally map it to an annual photovoltaic power output scenario. The Gaussian process regression model is obtained by optimizing the parameters of the RBFKernel and ScaleKernel kernel functions using training data, which is based on ideal irradiance and measured irradiance. The training module is used to perform difference and zero-removal operations between the ideal irradiance and the measured irradiance, and use the results as training data. The parameters of the kernel functions RBFKernel and ScaleKernel of the Gaussian process regression model are optimized using the training data to obtain the trained Gaussian process regression model. The training module is used for: Determine the local latitude and longitude, and calculate the ideal irradiance using the solar constant, Earth-Sun distance correction factor, solar altitude angle, zenith angle, and solar declination parameters; The difference between the ideal irradiance and the local measured irradiance is calculated, and the difference is rounded down to zero and used as training data. The training module is used to: optimize the parameters of the kernel functions RBFKernel and ScaleKernel of the Gaussian process regression model using training data to obtain a trained Gaussian process regression model, including: The hyperparameters of the kernel function in the Gaussian process regression model are trained using the processed training data. The properties of a Gaussian process regression model are determined by the mean function and the covariance function: ； The covariance function is a combination of ScaleKernel and RBFKernel: ； It is the mean function of the Gaussian process regression model. It is the covariance function of the Gaussian process model, with two hyperparameters. and These respectively affect the amplitude and degree of fluctuation in the generated samples. The input / output module is used to output based on the following formula: ； ； ； ； In the formula, These are the input variables of the training samples. This is the output variable of the training samples, which here refers to the difference in irradiance at each time point; This is the covariance matrix of the input variables and test points, representing the correlation between the input variables and test points; The variance of Gaussian noise that is independent and identically distributed with respect to the training samples. It is the identity matrix. The predicted output for the test points. To predict the mean, To predict variance, the annual irradiance scenario is finally mapped to the annual photovoltaic output scenario.

3. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in claim 1.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, perform the steps of the method as described in claim 1.

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