GIS equipment opening and closing abnormity monitoring method and system based on voiceprint recognition

Abstract

The invention relates to a GIS equipment opening and closing abnormity monitoring method and system based on voiceprint recognition. The method comprises the following steps: collecting audio signals of abnormal opening and closing actions of GIS equipment in operation, classifying the audio signals according to abnormal types, and constructing an audio sample library; performing feature extraction on the audio sample library based on the Mel-frequency cepstrum coefficient to obtain MFCC feature vectors corresponding to various abnormal types; and training a classifier by using the MFCC feature vector, and generating a classification model so as to realize anomaly classification and recognition of the GIS equipment based on the classification model. By analyzing the sound characteristics of the GIS equipment in the opening and closing action process, whether the action of the GIS equipment is abnormal or not can be identified under the condition that the equipment is not contacted, and the specific type of the abnormality is judged, so that the real-time monitoring and fault early warning of the operation state of the GIS equipment are realized.

Description

Technical Field

[0001] The present application relates to the technical field of audio data processing, and in particular to a method and system for monitoring the abnormal opening and closing of GIS equipment based on voiceprint recognition. Background Art

[0002] GIS (Gas Insulated Switchgear) equipment consists of circuit breakers, disconnectors, grounding switches, current transformers, voltage transformers, lightning arresters, busbars, connectors and outgoing terminals. These devices or components are all enclosed in a metal grounded casing, which is filled with SF6 insulating gas at a certain pressure, so it is also called SF6 fully enclosed combination electrical appliances. The main function of GIS equipment is to control and adjust current and voltage when the power system is operating normally to ensure the stable operation of the power system. At the same time, when a fault occurs in the power system, GIS equipment can quickly cut off the fault current, prevent the accident from expanding, and protect the safety of the power system. Therefore, it is widely used in the power system.

[0003] Due to the complex operating mechanism of GIS equipment, faults such as inadequate opening and closing, idling of energy storage motors, and too low speed of circuit breakers often occur during opening and closing operations. Traditional abnormal state monitoring mostly relies on mechanical vibration monitoring or electrical parameter monitoring, but these methods have limited ability to identify certain acoustic anomalies. Summary of the invention

[0004] Based on this, it is necessary to provide a GIS equipment opening and closing abnormality monitoring method and system based on voiceprint recognition that can improve the accuracy of abnormality identification in response to the above technical problems.

[0005] In the first aspect, the present application provides a method for monitoring abnormal opening and closing of GIS equipment based on voiceprint recognition. The method comprises:

[0006] Collect audio signals of abnormal opening and closing actions of GIS equipment in operation, classify audio signals according to abnormal types, and build an audio sample library;

[0007] Extract features from the audio sample library based on the Mel-frequency cepstral coefficients to obtain MFCC feature vectors corresponding to various abnormal types;

[0008] The MFCC feature vector is used to train the classifier and generate a classification model to realize the abnormal classification and identification of GIS equipment based on the classification model.

[0009] In one embodiment, feature extraction is performed on the audio sample library based on the Mel-frequency cepstral coefficients to obtain MFCC feature vectors corresponding to various abnormal types, including:

[0010] Perform pre-emphasis processing on the audio sample library to obtain the initial signal;

[0011] The initial signal is processed by short-time Fourier transform to obtain the frequency domain signal;

[0012] The spectral signal is mapped to the Mel scale through a set of triangular filters to obtain the Mel energy;

[0013] Mel energy is processed using discrete cosine transform to obtain MFCC feature vectors.

[0014] In one embodiment, training a classifier using MFCC feature vectors to generate a classification model includes:

[0015] The MFCC feature vectors of the same abnormal type are clustered to obtain the clustering results corresponding to each abnormal type. Each MFCC feature vector is weighted according to the clustering results. The classifier is trained using the MFCC feature vectors labeled with the abnormal type and weight to obtain the classification model.

[0016] In one embodiment, clustering is performed on MFCC feature vectors of the same abnormal type to obtain clustering results corresponding to each abnormal type, and weighting each MFCC feature vector according to the clustering results includes:

[0017] The MFCC feature vectors of the same anomaly type are clustered, the cluster center is determined from the MFCC feature vector, and the MFCC feature vector is weighted according to the Euclidean distance between the cluster center and each MFCC feature vector of the same anomaly type.

[0018] In one embodiment, clustering MFCC feature vectors of the same abnormal type, and determining cluster centers from the MFCC feature vectors includes:

[0019] The DBSCAN algorithm is used to remove noise in the MFCC feature vectors of the same abnormal type to obtain processed sample data; the K-means algorithm is used to cluster the sample data to obtain a unique cluster center; wherein the K value in the K-means algorithm is 1.

[0020] In one embodiment, the method includes:

[0021] There is a negative correlation between Euclidean distance and weight.

[0022] In the second aspect, the present application also provides a GIS equipment opening and closing abnormality monitoring system based on voiceprint recognition. The system includes:

[0023] The sample acquisition module is used to collect audio signals of abnormal opening and closing actions of GIS equipment in operation, classify the audio signals according to the abnormal types, and build an audio sample library;

[0024] The feature extraction module is used to extract features from the audio sample library based on the Mel frequency cepstral coefficients to obtain the MFCC feature vectors corresponding to various abnormal types;

[0025] The classification and recognition module is used to train the classifier using the MFCC feature vector and generate a classification model to realize the abnormal classification and recognition of GIS equipment based on the classification model.

[0026] In a third aspect, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the above-mentioned GIS equipment opening and closing abnormality monitoring method based on voiceprint recognition are implemented.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-mentioned GIS equipment opening and closing abnormality monitoring method based on voiceprint recognition.

[0028] In a fifth aspect, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned GIS equipment opening and closing abnormality monitoring method based on voiceprint recognition are implemented.

[0029] The above-mentioned GIS equipment opening and closing abnormal monitoring method and system based on voiceprint recognition collects audio signals of abnormal opening and closing actions of GIS equipment in operation, classifies audio signals according to abnormal types, and constructs an audio sample library; extracts features from the audio sample library based on Mel-frequency cepstral coefficients to obtain MFCC feature vectors corresponding to various abnormal types; uses MFCC feature vectors to train a classifier and generate a classification model to achieve abnormal classification and identification of GIS equipment based on the classification model. By analyzing the sound characteristics of GIS equipment during the opening and closing action, it is possible to identify whether its action is abnormal without contacting the equipment, and determine the specific type of abnormality, thereby achieving real-time monitoring of the operating status of GIS equipment and fault warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of a method for monitoring abnormal opening and closing of GIS equipment based on voiceprint recognition in one embodiment;

[0031] Figure 2 The present invention is a structural block diagram of a GIS equipment opening and closing abnormality monitoring system based on voiceprint recognition in one embodiment. DETAILED DESCRIPTION

[0032] 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.

[0033] The present application embodiment provides a method for monitoring abnormal opening and closing of GIS equipment based on voiceprint recognition, such as Figure 1 As shown, the following steps are included:

[0034] Step 102, collecting audio signals of abnormal opening and closing actions of the running GIS equipment, classifying the audio signals according to the abnormal types, and constructing an audio sample library.

[0035] Acoustic sensors such as microphones are used to collect data on the opening and closing actions of running GIS equipment, and obtain audio signals of GIS equipment during opening and closing actions under different abnormal or fault conditions. The collected audio signals are classified and sorted according to the abnormal types, including abnormal voltage of energy storage motor, idling of energy storage motor, low speed of circuit breaker, abnormal voltage of drive motor, inadequate opening and closing of switches, etc., and an audio sample library is established.

[0036] Step 104: extract features from the audio sample library based on the Mel-frequency cepstral coefficients to obtain MFCC feature vectors corresponding to various abnormal types.

[0037] The MFCC (Mel-frequency Cepstral Coefficients) feature vectors are extracted from the audio signal in time frame order to form a two-dimensional feature map (time × frequency). This feature map can well reflect the changing trend of the audio signal in the time domain and frequency domain.

[0038] Based on the feature extraction method of Mel-frequency cepstral coefficients, characteristic spectra of various abnormal sounds are generated to provide data support for subsequent pattern recognition.

[0039] Step 106, using the MFCC feature vector to train a classifier and generate a classification model, so as to implement abnormal classification and identification of GIS equipment based on the classification model.

[0040] The MFCC feature vector of each audio signal is saved with corresponding label information (such as abnormality type, device status, etc.) for abnormal audio detection and classification model training. By comparing the MFCC feature vectors of different abnormal types, the classifier is trained to achieve automatic audio classification and recognition. According to the characteristics of abnormal audio, combined with historical data, fault mode recognition and diagnosis are performed to help improve the maintenance and management efficiency of equipment.

[0041] The classifier may be SVM (Support Vector Machine), CNN (Convolutional Neural Network), or the like.

[0042] This embodiment can effectively generate feature maps of various abnormal sounds by collecting and classifying audio data in the power system and combining it with the MFCC feature extraction method. This method can capture the frequency domain characteristics of audio signals, facilitate the construction of subsequent classification models and the identification of fault modes, and provide important data support for the intelligent operation and maintenance of power system equipment.

[0043] In one embodiment, step 104 includes: performing pre-emphasis processing on the audio sample library to obtain an initial signal; processing the initial signal using short-time Fourier transform to obtain a frequency domain signal; mapping the spectrum signal to the Mel scale through a set of triangular filters to obtain Mel energy; processing the Mel energy using discrete cosine transform to obtain an MFCC feature vector.

[0044] De-noise the original audio signal in the audio sample library, such as using a bandpass filter to filter out high-frequency and low-frequency noise. Use a pre-emphasis filter to increase the amplitude of the high-frequency component to compensate for the attenuation of the high-frequency part caused by the recording device or the environment, and obtain the original signal. The typical pre-emphasis filter is:

[0045] y[n]=x[n]-α·x[n-1]

[0046] Wherein, y[n] is the initial signal after pre-emphasis, x[n] is the original audio signal, and α is the pre-emphasis coefficient, which is generally set to a value between 0.9 and 1, and is set to 0.95 in this embodiment.

[0047] The initial signal is divided into multiple frames (usually each frame is 20-40ms long), and the local features of the audio signal are processed by short-time Fourier transform (STFT). A Hamming window is added to each frame to reduce the spectrum leakage problem introduced by the frame processing. Each frame signal is subjected to fast Fourier transform (FFT) to obtain the frequency domain signal.

[0048] The power spectrum of each frame is:

[0049]

[0050] Where X[k] is the kth frequency component and N is the number of sampling points per frame.

[0051] The spectrum is mapped to the Mel scale through a set of triangular filters. For example, the center frequency f(m) of the mth Mel filter is given by:

[0052]

[0053] Among them, f(m) is the Mel scale, f linear This is the frequency scale.

[0054] The power spectrum of each filter is weighted and summed to obtain the energy value on each filter, which is called Mel energy.

[0055] Take the logarithm of the Mel energy:

[0056] Log-Mel(m)=log(Mel(m))

[0057] Wherein, Mel(m) is the Mel energy.

[0058] Perform discrete cosine transform (DCT) on Log-Mel(m), extract the first 12 to 13 coefficients, and get the final MFCC feature vector. This process can be expressed as:

[0059]

[0060] Where M is the number of Mel filters and n is the index of the extracted cepstral coefficients.

[0061] In one embodiment, step 106 includes: clustering MFCC feature vectors of the same abnormal type, obtaining clustering results corresponding to each abnormal type, weighting each MFCC feature vector according to the clustering results, training a classifier using the MFCC feature vectors labeled with the abnormal type and weight, and obtaining a classification model.

[0062] Considering that GIS equipment is large, it is difficult to collect audio signals in a complete laboratory environment. Therefore, the audio signal may contain noise that cannot be accurately removed, resulting in reduced classification accuracy of the classifier. In response to this problem, this embodiment performs a preliminary analysis on the MFCC feature vector before inputting it into the classifier, clusters the MFCC feature vectors of the same abnormal type, determines the correlation between the MFCC feature vectors, and weights the MFCC feature vectors according to the strength of the correlation, annotates the corresponding weight of each MFCC feature vector, and inputs it into the classifier together with the anomaly type marked, so that the classifier learns more accurate abnormal audio features, thereby completing abnormal identification and classification more accurately.

[0063] In one embodiment, in step 106, clustering is performed on MFCC feature vectors of the same abnormal type, clustering results corresponding to each abnormal type are obtained, and weighting each MFCC feature vector according to the clustering results includes: clustering the MFCC feature vectors of the same abnormal type, determining the cluster center from the MFCC feature vectors, and weighting the MFCC feature vectors according to the Euclidean distance between the cluster center and each MFCC feature vector of the same abnormal type. The Euclidean distance is negatively correlated with the weight.

[0064] This embodiment clusters the MFCC feature vectors corresponding to each abnormal type, determines the cluster center corresponding to each abnormal type, and then calculates the Euclidean distance between all MFCC feature vectors corresponding to a certain abnormal type and the cluster center corresponding to the abnormal type, and weights all MFCC feature vectors of the abnormal type according to the Euclidean distance. The larger the Euclidean distance, the smaller the weight. For all MFCC feature vectors corresponding to the same abnormal type, normalization processing needs to be performed. The same method weights the MFCC feature vectors under all abnormal types.

[0065] In one embodiment, clustering is performed on MFCC feature vectors of the same abnormal type in step 106, and determining the cluster center from the MFCC feature vectors includes: removing noise in the MFCC feature vectors of the same abnormal type by using a DBSCAN algorithm to obtain processed sample data; clustering the sample data by using a K-means algorithm to obtain a unique cluster center; wherein the value of K in the K-means algorithm is 1.

[0066] The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is a density-based clustering algorithm that can identify clusters of any shape and is robust to noise data. The K-means (k-means) algorithm divides the data set into K clusters in an iterative manner, so that the similarity between data points within a cluster is high, while the similarity between data points between clusters is low. In this embodiment, the MFCC feature vectors are first clustered using the DBSCAN algorithm, and the output sample data can be obtained after clustering. At the same time, the noise can be removed by using the DBSCAN algorithm output. The K-means algorithm is suitable for clusters with compact structure and high separation, but it will be seriously affected by noise and outliers in the data; while the DBSCAN algorithm can identify noise and outliers well. This embodiment combines the two algorithms, and by first removing the noise determined after clustering by the DBSCAN clustering algorithm, the influence of noise on the selection of cluster centers and the iterative effect of the K-means algorithm can be reduced.

[0067] Considering that the data with noise still has certain abnormal characteristics, and the amount of abnormal audio signal data of GIS equipment is limited, in order to make full use of the data, after determining the cluster center, the noise will still be input into the classifier as training data. Due to the design of weights, the adverse effect of noise on the classification results of the classifier is reduced, thereby improving the validity of the data.

[0068] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0069] Based on the same inventive concept, the embodiment of the present application also provides a GIS equipment opening and closing abnormality monitoring system based on voiceprint recognition for implementing the above-mentioned GIS equipment opening and closing abnormality monitoring method based on voiceprint recognition. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the GIS equipment opening and closing abnormality monitoring system based on voiceprint recognition provided below can refer to the limitations of the GIS equipment opening and closing abnormality monitoring method based on voiceprint recognition above, and will not be repeated here.

[0070] In one embodiment, Figure 2 As shown, a GIS equipment opening and closing abnormal monitoring system based on voiceprint recognition is provided, including:

[0071] The sample acquisition module 202 is used to collect audio signals of abnormal opening and closing actions of GIS equipment in operation, classify the audio signals according to the abnormal types, and build an audio sample library;

[0072] A feature extraction module 204 is used to extract features from the audio sample library based on the Mel-frequency cepstral coefficients to obtain MFCC feature vectors corresponding to various abnormal types;

[0073] The classification and identification module 206 is used to train a classifier using the MFCC feature vector to generate a classification model, so as to implement abnormal classification and identification of GIS equipment based on the classification model.

[0074] In one embodiment, the feature extraction module 204 is further used to perform pre-emphasis processing on the audio sample library to obtain an initial signal;

[0075] The initial signal is processed by short-time Fourier transform to obtain the frequency domain signal;

[0076] The spectral signal is mapped to the Mel scale through a set of triangular filters to obtain the Mel energy;

[0077] Mel energy is processed using discrete cosine transform to obtain MFCC feature vectors.

[0078] In one embodiment, training a classifier using MFCC feature vectors to generate a classification model includes:

[0079] The MFCC feature vectors of the same abnormal type are clustered to obtain the clustering results corresponding to each abnormal type. Each MFCC feature vector is weighted according to the clustering results. The classifier is trained using the MFCC feature vectors labeled with the abnormal type and weight to obtain the classification model.

[0080] In one embodiment, the classification and identification module 206 is further used to cluster the MFCC feature vectors of the same abnormal type, determine the cluster center from the MFCC feature vectors, and assign weights to the MFCC feature vectors according to the Euclidean distance between the cluster center and each MFCC feature vector of the same abnormal type, wherein the Euclidean distance is negatively correlated with the weight.

[0081] In one embodiment, the classification and identification module 206 is also used to remove noise in the MFCC feature vectors of the same abnormal type through the DBSCAN algorithm to obtain processed sample data; cluster the sample data through the K-means algorithm to obtain a unique cluster center; wherein the K value in the K-means algorithm is 1.

[0082] Each module in the above-mentioned GIS equipment opening and closing abnormal monitoring system based on voiceprint recognition can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0083] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in all the above method embodiments when executing the computer program.

[0084] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in all the above method embodiments are implemented.

[0085] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in all the above method embodiments when executed by a processor.

[0086] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0087] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processors involved in each embodiment provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

[0088] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring abnormal opening and closing of GIS equipment based on voiceprint recognition, characterized in that: The method comprises: Collect audio signals of abnormal opening and closing actions of GIS equipment in operation, classify the audio signals according to the abnormal types, and build an audio sample library; Extract features from the audio sample library based on Mel-frequency cepstral coefficients to obtain MFCC feature vectors corresponding to various abnormal types; The MFCC feature vector is used to train a classifier to generate a classification model, so as to realize abnormal classification and identification of the GIS equipment based on the classification model.

2. The method according to claim 1, characterized in that The feature extraction of the audio sample library based on the Mel frequency cepstral coefficient to obtain the MFCC feature vectors corresponding to various abnormal types includes: Performing pre-emphasis processing on the audio sample library to obtain an initial signal; Processing the initial signal by short-time Fourier transform to obtain a frequency domain signal; Mapping the spectrum signal to the Mel scale through a set of triangular filters to obtain Mel energy; The Mel energy is processed by discrete cosine transform to obtain the MFCC feature vector.

3. The method according to claim 1, characterized in that The method of using the MFCC feature vector to train a classifier and generate a classification model comprises: The MFCC feature vectors of the same abnormal type are clustered to obtain clustering results corresponding to each abnormal type, each MFCC feature vector is weighted according to the clustering result, and the classifier is trained using the MFCC feature vectors labeled with the abnormal type and weight to obtain the classification model.

4. The method according to claim 3, characterized in that The clustering process is performed on the MFCC feature vectors of the same abnormal type to obtain the clustering results corresponding to each abnormal type, and weighting each MFCC feature vector according to the clustering results includes: Clustering is performed on the MFCC feature vectors of the same abnormal type, a cluster center is determined from the MFCC feature vectors, and the MFCC feature vectors are weighted according to the Euclidean distance between the cluster center and each of the MFCC feature vectors of the same abnormal type.

5. The method according to claim 4, characterized in that Clustering the MFCC feature vectors for the same abnormal type and determining the cluster center from the MFCC feature vectors includes: The DBSCAN algorithm is used to remove noise in the MFCC feature vectors of the same abnormal type to obtain processed sample data; the sample data is clustered by the K-means algorithm to obtain the unique cluster center; wherein the K value in the K-means algorithm is 1.

6. The method according to claim 4, characterized in that The method comprises: There is a negative correlation between the Euclidean distance and the weight.

7. A GIS equipment opening and closing abnormal monitoring system based on voiceprint recognition, characterized in that: The system comprises: The sample acquisition module is used to collect audio signals of abnormal opening and closing actions of GIS equipment in operation, classify the audio signals according to the abnormal types, and build an audio sample library; A feature extraction module is used to extract features from the audio sample library based on Mel frequency cepstral coefficients to obtain MFCC feature vectors corresponding to various abnormal types; The classification and recognition module is used to train a classifier using the MFCC feature vector to generate a classification model, so as to realize abnormal classification and recognition of the GIS equipment based on the classification model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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