Photovoltaic typical output scene clustering method considering feature weight optimization

Abstract

The invention belongs to the technical field of photovoltaic power generation, and particularly discloses a photovoltaic typical output scene clustering method considering feature weight optimization. The method comprises the following steps: firstly, establishing a function relationship between particle positions and various feature weights; then various feature weights are given to photovoltaic output data of corresponding features, and various feature optimal weights with the best clustering quality of the photovoltaic output data are found out through a particle swarm optimization algorithm; then, the optimal weights of the various features are endowed with photovoltaic output data of the corresponding features, and the photovoltaic output data are clustered; the clustering center is a photovoltaic typical output scene. According to the method, important features in the photovoltaic output data can be effectively identified, the influence of noise features on the data clustering effect is reduced, and the clustering effect is better than that of a traditional clustering method.

Description

Technical Field

[0001] The present application belongs to the field of photovoltaic power generation technology, and more specifically, to a photovoltaic typical output scenario clustering method considering feature weight optimization. Background Art

[0002] Typical photovoltaic output scenario extraction technology is to extract representative output patterns from historical photovoltaic output data for use in photovoltaic power generation prediction, power system planning and operation optimization.

[0003] So far, domestic and foreign experts and scholars have done a lot of research on the extraction technology of typical photovoltaic output scenes. In order to deal with massive and repetitive historical scenes, researchers introduced clustering algorithms, built scene reduction models, and extracted typical photovoltaic output scenes. For example, the K-Means algorithm based on the Huffman tree is used to optimize the selection of cluster centers of photovoltaic output sequences, and the Unsupervised K-Means algorithm is used to automatically determine the number of typical photovoltaic scenes. However, current related research focuses on the optimization of cluster center selection and the number of scenes, and cannot identify the importance of each feature in the data. The clustering accuracy is easily affected by noise features. Summary of the invention

[0004] In response to the above defects or improvement needs of the prior art, the present application provides a photovoltaic typical output scenario clustering method considering feature weight optimization, which aims to solve the technical problem that the clustering effect of the existing clustering method is easily interfered by noise characteristics.

[0005] To achieve the above objectives, in a first aspect, the present application provides a photovoltaic typical output scenario clustering method considering feature weight optimization, including: Establish the functional relationship between particle position and various feature weights; Assigning various feature weights to the photovoltaic output data of corresponding features, and finding the optimal weights of various features with the best clustering quality of the photovoltaic output data through a particle swarm optimization algorithm; Assigning optimal weights of the various features to the photovoltaic output data of the corresponding features, and clustering the photovoltaic output data; The cluster center is a typical photovoltaic output scenario.

[0006] Preferably, the optimal weights of various features with the best clustering quality of the photovoltaic output data are found through a particle swarm optimization algorithm, specifically: Initialize the population, obtain the fitness value of each particle, and measure the individual optimal position and the global optimal position based on the fitness value; Iteratively update the position of each particle in the population, obtain the fitness value of each particle under the current position state, and update the individual optimal position and the global optimal position; After the iteration stop condition is met, various feature weights corresponding to the global optimal position at this time are obtained based on the functional relationship as various feature optimal weights.

[0007] Preferably, the fitness value in the particle swarm optimization algorithm includes the objective function of the photovoltaic output data clustering method at the current particle position.

[0008] Preferably, the fitness value in the particle swarm optimization algorithm is for:

[0009] in, is a penalty function, which is used to make the sum of all weights approach 1 during the population iteration process; is the objective function of the clustering method:

[0010]

[0011]

[0012]

[0013] Used to describe whether a point is between clusters or within clusters, with 0 for between clusters and 1 for within clusters; represents weight; Used to measure the distance between a point and the cluster center; Indicates photovoltaic output data; represents the cluster center; Indicates the cluster number, Indicates the serial number of the data vector in the photovoltaic output data set, Represents the ordinal number of the feature in the data vector; represents the total number of clusters, Represents the total amount of data vectors in the dataset, Represents the total number of features in the data vector; Divided into A collection of .

[0014] Preferably, the penalty function for:

[0015]

[0016] in, is the preset minimum value; and are the first penalty coefficient and the second penalty coefficient respectively, Represents the maximum value function.

[0017] Preferably, the optimal weights of the various features are assigned to the photovoltaic output data of the corresponding features, and the photovoltaic output data are clustered, specifically: The K-Means clustering algorithm is used to cluster the photovoltaic output data, where the objective function is:

[0018]

[0019]

[0020]

[0021] Used to describe whether a point is between clusters or within clusters, with 0 for between clusters and 1 for within clusters; represents weight; Used to measure the distance between a point and the cluster center; Indicates photovoltaic output data; represents the cluster center; Indicates the cluster number, Indicates the serial number of the data vector in the photovoltaic output data set, Represents the ordinal number of the feature in the data vector; represents the total number of clusters, Represents the total amount of data vectors in the dataset, Represents the total number of features in the data vector; Divided into A collection of .

[0022] Preferably, the functional relationship between the particle position and various feature weights is Specifically:

[0023] in, Indicates the particle position during the current iteration; Indicates the number of Feature weights; Indicates the number of feature weights.

[0024] Preferably, the method further includes a dimensionality reduction step of photovoltaic output data, specifically: Sort the PV output data by time series; A plurality of feature types are selected, and features are extracted from the photovoltaic output data per unit time to obtain a feature vector set per unit time.

[0025] Preferably, the multiple feature types include but are not limited to multiple combinations of mean, standard deviation, maximum value, kurtosis, skewness and sample entropy.

[0026] In a second aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0027] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art: (1) This application introduces the feature weights of photovoltaic output data into the clustering method, and then combines the particle swarm optimization algorithm with the clustering algorithm. The clustering quality of the photovoltaic output data guides the optimization of the particle swarm, thereby obtaining the optimal weights of the features. The optimal weights are then assigned to the photovoltaic output data for clustering, thereby overcoming the interference of noise characteristics in the photovoltaic output data. Ultimately, a representative photovoltaic typical output scenario can be formed, providing important data support for the output decision-making of the water-photovoltaic complementary system.

[0028] (2) The clustering method of the present application assigns different weights to different features of photovoltaic output data, thereby highlighting the influence of advantageous features and overcoming the interference of noise features. Through comparative experiments, it is found that the clustering method of the present application has better clustering effect than the traditional clustering method.

[0029] (3) This application reduces high-dimensional photovoltaic output data to low-dimensional photovoltaic output data through a dimensionality reduction step, which can not only reduce the clustering complexity, but also reduce the impact of noise characteristics on clustering. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the typical photovoltaic output scenario clustering method provided in an embodiment of the present application.

[0031] Figure 2 It is a schematic diagram of the clustering iteration process of typical photovoltaic output scenarios provided in an embodiment of the present application.

[0032] FIG3 (a) is a schematic diagram of the photovoltaic output clustering results in region a provided in an embodiment of the present application.

[0033] FIG3( b ) is a schematic diagram of the photovoltaic output clustering results in region b provided in an embodiment of the present application.

[0034] FIG3 ( c ) is a schematic diagram of the photovoltaic output clustering results in region c provided in an embodiment of the present application.

[0035] FIG4( a ) is a typical output curve of region a provided in an embodiment of the present application.

[0036] FIG4( b ) is a typical output curve of region b provided in an embodiment of the present application.

[0037] FIG4( c ) is a typical output curve of region c provided in an embodiment of the present application.

[0038] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0040] The terms "first", "second", etc. in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of objects. For example, a first feature and a second feature are used to distinguish different types of features rather than to describe a specific order of features.

[0041] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0042] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two. For example, a plurality of feature types means two or more than two feature types, etc.

[0043] For ease of understanding, the English abbreviations and related technical terms involved in the embodiments of the present application are first explained and described below.

[0044] Particle Swarm Optimization (PSO), an optimization algorithm based on swarm intelligence, was proposed by Kennedy and Eberhart in 1995. It simulates the social behavior of biological groups such as bird flocks and fish schools, and finds the optimal solution through collaboration and information sharing between individuals.

[0045] CH index (Calinski-Harabasz Index), CH index evaluates clustering effect by calculating the ratio of inter-class dispersion to intra-class dispersion. The larger the value, the better the clustering effect.

[0046] SC index (Silhouette Coefficient), SC index evaluates the clustering effect by calculating the silhouette coefficient of each sample. The silhouette coefficient reflects the closeness of the sample to its own cluster and the separation from other clusters. The value range is [-1,1][−1,1]. The larger the value, the better the clustering effect.

[0047] DB index (Davies-Bouldin Index), DB index evaluates clustering effect by calculating the ratio of intra-cluster dispersion to inter-cluster separation. The smaller the value, the better the clustering effect.

[0048] K-Means, a classic clustering algorithm, is used to divide a data set into k clusters. Its core idea is to minimize the distance between each data point and the center of its cluster through iterative optimization.

[0049] K-Means++, an improved version of K-Means, improves the clustering effect by improving the selection of initial centroids to make the initial centroids as dispersed as possible.

[0050] BI-K-means, a hierarchical K-Means algorithm, recursively divides the data set into two parts until the specified number of clusters k is reached.

[0051] DBSCAN, a density-based clustering algorithm, divides high-density areas into clusters and identifies noise points in low-density areas.

[0052] HAC, a hierarchical clustering algorithm, constructs a clustering hierarchy tree (dendrogram) in a bottom-up (agglomerative method) or top-down (divisive method) manner.

[0053] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0054] In this embodiment, the photovoltaic output data comes from three photovoltaic power stations a, b and c in a certain province in my country. Figure 1 As shown, the specific steps include: 1. First, select features to reduce the dimension of photovoltaic data: First, the photovoltaic output data is sorted by time series. The output data of the three photovoltaic power stations from January 1, 2022 to December 31, 2022 are sorted. Each power station corresponds to a photovoltaic daily output sequence set, and an output data is taken with 15 minutes as a unit time, that is, each daily output sequence has 96 data points, and each daily output sequence has 96-dimensional data. Directly using these high-dimensional sequences for clustering will lead to reduced model operation efficiency, poor clustering effect, low discrimination and other problems. Therefore, six features such as mean, standard deviation, maximum value, kurtosis, skewness and sample entropy are selected to extract features from 96 data, thereby obtaining 6 eigenvalues, which are used to represent a photovoltaic daily output sequence, that is, 96 dimensions are reduced to 6 dimensions.

[0055] 2. Associate the particle position with the weights of the six features using the function Describe this relationship, for example:

[0056] in, Indicates the particle position during the current iteration; Indicates the number of Feature weights; Indicates the number of The initial value of each feature weight is obtained by default.

[0057] 3. Assign 6 feature weights to the photovoltaic output data of corresponding features, and find out the optimal weights of the 6 features with the best clustering quality of the photovoltaic output data through the particle swarm optimization algorithm; 3.1. Initialize the population, select the population size as 50, the maximum number of iterations as 35, and set the individual learning factor and social learning factor to 2.0; obtain the fitness value of each particle, and measure the individual optimal position and the global optimal position based on the fitness value; 3.2 Iteratively update the positions of 50 particles in the population, obtain the fitness value of each particle under the current position state, and update the individual optimal position and the global optimal position; Among them, the particle position update formula is:

[0058]

[0059] in, For inertia, particle location, For particles speed, for Second and Iterations, is the time difference, , They are individual learning factor and social learning factor, both of which are set to 2 in this embodiment. and is a random number between 0 and 1, For individuals The optimal location, is the optimal weighted position.

[0060] Through the function The particle position is converted into 6 feature weights, and then the 6 feature weights are assigned to the photovoltaic output data to calculate the particle fitness value: The fitness value The objective function of the photovoltaic output data clustering method at the current particle position is as follows:

[0061] in, is a penalty function, which is used to make the sum of all weights approach 1 during the population iteration process; The objective function of the clustering method is: Penalty Function for:

[0062]

[0063] in, is the preset minimum value; and are the first penalty coefficient and the second penalty coefficient respectively, Represents the maximum value function.

[0064] Objective Function for:

[0065]

[0066]

[0067]

[0068] Used to describe whether a point is between clusters or within clusters, with 0 for between clusters and 1 for within clusters; represents weight; Used to measure the distance between a point and the cluster center; Indicates photovoltaic output data; represents the cluster center; Indicates the cluster number, Indicates the serial number of the data vector in the photovoltaic output data set, Represents the ordinal number of the feature in the data vector; Indicates the total number of clusters, which is 4 in this embodiment. Represents the total amount of data vectors in the dataset, represents the total number of features in the data vector, which is 6 in this embodiment; Divided into A collection of .

[0069] 3.3 After 35 iterations, the iteration stop condition is met, based on the function The 6 feature weights corresponding to the global optimal position at this time are obtained as the 6 feature optimal weights.

[0070] In this embodiment, the iteration process is as follows Figure 2 As shown in the figure, after 10 generations of iteration, the fitness function reaches a better level, and after 25 generations, the algorithm gradually reaches the optimal level. This shows that the proposed algorithm has a faster convergence speed and can quickly find the optimal solution.

[0071] 4. Assign the optimal weights of the six features to the photovoltaic output data of the corresponding features, and cluster the photovoltaic output data: In this embodiment, clustering is performed based on the improved K-Means algorithm, and the weight information of the feature data is integrated into the objective function of the K-Means algorithm. Therefore, the objective function is changed to:

[0072]

[0073]

[0074]

[0075] Used to describe whether a point is between clusters or within clusters, with 0 for between clusters and 1 for within clusters; represents weight; Used to measure the distance between a point and the cluster center; Indicates photovoltaic output data; represents the cluster center; Indicates the cluster number, Indicates the serial number of the data vector in the photovoltaic output data set, Represents the ordinal number of the feature in the data vector; Indicates the total number of clusters, which is 4 in this embodiment. Represents the total amount of data vectors in the dataset, represents the total number of features in the data vector, which is 6 in this embodiment; Divided into A collection of .

[0076] As shown in Figure 3 (a), Figure 3 (b), and Figure 3 (c), the results of photovoltaic output clustering in the three locations a, b, and c in this embodiment. They all contain 4 clusters, the horizontal axis represents time, with a scale of 15 minutes, a total of 96 time periods, and the vertical axis represents photovoltaic output power, in kilowatts (kW). Taking the data of b as an example, the four clusters show obvious separation in the relationship between photovoltaic output power and time, which shows that the algorithm of this application successfully divides photovoltaic output sequences with similar output characteristics into the same cluster. Within each cluster, the photovoltaic output power change trend of the curve is relatively consistent, showing a high similarity of data points within the cluster. For example, the output power of cluster 1 is relatively low and stable most of the time, while the curves of clusters 2 and 4 reach higher output power during the noon period.

[0077] As shown in Figure 4 (a), Figure 4 (b), and Figure 4 (c), the typical photovoltaic output curves of the three places a, b, and c in this embodiment are respectively the cluster centers corresponding to the clusters in Figure 3 (a), Figure 3 (b), and Figure 3 (c), which represent the typical output scenarios of photovoltaic power stations in various places. It can be seen that the typical output scenarios of photovoltaic power stations in the three places have a certain degree of separation, which well represents the daily output conditions under various scenarios and can serve as the data basis for output decision-making of the water-photovoltaic complementary system.

[0078] Comparative test: In the comparative experiment, 5 traditional clustering methods were selected for comparison with the clustering method of the present application. The comparison results are shown in Table 1. In Table 1, PSO-WKM represents the clustering method of the present application.

[0079] Table 1

[0080] Among the six algorithms selected in this comparative test: PSO-WKM, K-Means, K-Means++ and BI-K-Means are partitioning clustering algorithms; DBSCAN is a density-based clustering algorithm; HAC is a hierarchical clustering algorithm. In Table 1, the CH index of the PSO-WKM algorithm proposed in this application is 43.8% and 81.6% higher than that of DBSCAN and HAC on average, which shows that the partitioning clustering algorithm is more suitable for the clustering of photovoltaic daily output sequences. Among the partitioning algorithms, PSO-WKM is 2.3%~7.8% higher than other K-Means algorithms in CH index, 1.8%~9.3% higher in SC index, and 0.7%~29.5% lower in DB index. This shows that the weight optimization function of the clustering method of this application can find noise features well, thereby reducing its impact on clustering accuracy and improving the clustering effect. In general, the clustering method proposed in this application is superior to the compared models in all indicators of each data set. This is because it can automatically optimize the weight factor and accurately extract the representative features of the photovoltaic daily output sequence.

[0081] Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, such as Figure 5 The system shown includes: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The processor can call the logic instructions in the memory to execute the method in the above embodiment.

[0082] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0083] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0084] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0085] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0086] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0087] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0088] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0089] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A photovoltaic typical output scene clustering method considering feature weight optimization, characterized in that: include: Establish the functional relationship between particle position and various feature weights; Assigning various feature weights to the photovoltaic output data of corresponding features, and finding the optimal weights of various features with the best clustering quality of the photovoltaic output data through a particle swarm optimization algorithm; Assigning optimal weights of the various features to the photovoltaic output data of the corresponding features, and clustering the photovoltaic output data; The cluster center is a typical photovoltaic output scenario.

2. The photovoltaic typical output scene clustering method according to claim 1, characterized in that: The particle swarm optimization algorithm is used to find the optimal weights of various features with the best clustering quality of the photovoltaic output data, specifically: Initialize the population, obtain the fitness value of each particle, and measure the individual optimal position and the global optimal position based on the fitness value; Iteratively update the position of each particle in the population, obtain the fitness value of each particle under the current position state, and update the individual optimal position and the global optimal position; After the iteration stop condition is met, various feature weights corresponding to the global optimal position at this time are obtained based on the functional relationship as various feature optimal weights.

3. The photovoltaic typical output scene clustering method according to claim 2 is characterized in that: The fitness value in the particle swarm optimization algorithm includes the objective function of the photovoltaic output data clustering method at the current particle position.

4. The photovoltaic typical output scene clustering method according to claim 1, 2 or 3, characterized in that: The fitness value in the particle swarm optimization algorithm for: in, is a penalty function, which is used to make the sum of all weights approach 1 during the population iteration process; The objective function of the clustering method is: Used to describe whether a point is between clusters or within clusters, with 0 for between clusters and 1 for within clusters; represents weight; Used to measure the distance between a point and the cluster center; Indicates photovoltaic output data; represents the cluster center; Indicates the cluster number, Indicates the serial number of the data vector in the photovoltaic output data set, Represents the ordinal number of the feature in the data vector; represents the total number of clusters, Represents the total amount of data vectors in the dataset, Represents the total number of features in the data vector; Divided into A collection of .

5. The photovoltaic typical output scene clustering method according to claim 4 is characterized in that: The penalty function for: in, is the preset minimum value; and are the first penalty coefficient and the second penalty coefficient respectively, Represents the maximum value function.

6. The photovoltaic typical output scene clustering method according to claim 1, characterized in that: The optimal weights of the various features are assigned to the photovoltaic output data of the corresponding features, and the photovoltaic output data are clustered, specifically: The K-Means clustering algorithm is used to cluster the photovoltaic output data, where the objective function is: Used to describe whether a point is between clusters or within clusters, with 0 for between clusters and 1 for within clusters; represents weight; Used to measure the distance between a point and the cluster center; Indicates photovoltaic output data; represents the cluster center; Indicates the cluster number, Indicates the serial number of the data vector in the photovoltaic output data set, Represents the ordinal number of the feature in the data vector; represents the total number of clusters, Represents the total amount of data vectors in the dataset, Represents the total number of features in the data vector; Divided into A collection of .

7. The photovoltaic typical output scene clustering method according to claim 1, characterized in that: The functional relationship between the particle position and various feature weights Specifically: in, Indicates the particle position during the current iteration; Indicates the number of Feature weights; Indicates the number of Feature weights.

8. The photovoltaic typical output scene clustering method according to claim 1, characterized in that: It also includes the dimensionality reduction steps of photovoltaic output data, specifically: Sort the PV output data by time series; A plurality of feature types are selected, and features are extracted from the photovoltaic output data per unit time to obtain a feature vector set per unit time.

9. The photovoltaic typical output scene clustering method according to claim 8, characterized in that: The multiple feature types include but are not limited to multiple combinations of mean, standard deviation, maximum value, kurtosis, skewness and sample entropy.

10. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 9.

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