On-load tap-changer mechanical state detection method and related device

Abstract

The invention provides a method for detecting the mechanical state of an on-load tap-changer and a related device, and relates to the technical field of electric power. The method comprises the steps of obtaining a vibration electric signal of a to-be-detected on-load tap-changer, preprocessing the vibration electric signal to obtain a frequency spectrum signal corresponding to the vibration electric signal, further determining an observation sequence corresponding to the frequency spectrum signal according to the frequency spectrum signal, and then respectively inputting the observation sequence into different mechanical state detection models to detect the mechanical state of the to-be-detected on-load tap-changer. And obtaining a detection result output by each mechanical state detection model, and determining the mechanical state of the to-be-detected on-load tap-changer according to the detection results, thereby improving the detection accuracy and reliability.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method for detecting the mechanical state of an on-load tap changer and a related device. Background Art

[0002] In power systems, the on-load tap changer (OLTC) is the only moving component in the on-load tap-changing transformer and a key component. Accurate and timely operation of the OLTC effectively reduces and avoids large voltage fluctuations. Furthermore, it optimizes load distribution across power equipment and fully utilizes the relationship between active and reactive power output, ensuring safe and reliable operation of the power system and enhancing the flexibility of grid dispatch.

[0003] In the related art, the mechanical state of the on-load tap changer is detected by using wavelet transform and wavelet coefficient "ridge distribution map". Specifically, when detecting the mechanical state of the on-load tap changer, the vibration electrical signal corresponding to the on-load tap changer to be detected is obtained, and the wavelet coefficient corresponding to the vibration electrical signal is obtained based on wavelet transform and a "ridge distribution map" is constructed. The corresponding features of the "ridge distribution map" are further compared and analyzed with the "ridge" pattern in the established working mode library. By determining whether the "ridge" pattern has changed, the mechanical state of the on-load tap changer is determined, and the detection of the mechanical state of the on-load tap changer based on wavelet transform and wavelet coefficient "ridge distribution map" is realized. The working mode library contains the mapping relationship between the "ridge distribution map" of the vibration electrical signal of the on-load tap changer under different simulated working conditions and its working mode.

[0004] However, the inventors have found that the above method has the problem of low detection accuracy. Summary of the Invention

[0005] The present application provides a method for detecting the mechanical state of an on-load tap changer and a related device, so as to solve the problem of low accuracy in detecting the mechanical state of an on-load tap changer in the related art.

[0006] In a first aspect, the present application provides a method for detecting the mechanical state of an on-load tap changer, comprising: obtaining a vibration electrical signal of the on-load tap changer to be detected; preprocessing the vibration electrical signal to obtain a spectrum signal corresponding to the vibration electrical signal; determining, based on the spectrum signal, an observation sequence corresponding to the spectrum signal; inputting the observation sequences into different mechanical state detection models respectively to obtain detection results output by each mechanical state detection model, and determining the mechanical state of the on-load tap changer to be detected based on the detection results.

[0007] In one possible implementation, an observation sequence corresponding to the spectrum signal is determined based on the spectrum signal, including: selecting N timestamps in the time period corresponding to the spectrum signal based on preset rules; for each of the N timestamps, performing signal feature extraction processing on the target spectrum signal corresponding to the timestamp to obtain an observation vector corresponding to the target spectrum signal; and obtaining an observation sequence based on the observation vector.

[0008] In one possible implementation, signal feature extraction processing is performed on the target spectrum signal corresponding to the timestamp to obtain an observation vector corresponding to the target spectrum signal, including: performing interval processing on the target spectrum signal corresponding to the timestamp to obtain a discrete spectrum corresponding to the target spectrum signal; normalizing the discrete spectrum to obtain a normalized discrete spectrum; and performing vector quantization processing on the normalized discrete spectrum to obtain an observation vector.

[0009] In one possible implementation, the observation sequences are input into different mechanical state detection models to obtain detection results output by each mechanical state detection model, and the mechanical state of the on-load tap changer to be detected is determined based on the detection results. This includes: inputting the observation sequences into different mechanical state detection models to obtain observation probability values corresponding to the observation sequences output by each mechanical state detection model; determining the mechanical state detection model corresponding to the maximum observation probability value as a target mechanical state detection model; and determining the mechanical state of the on-load tap changer to be detected based on the target mechanical state detection model based on a mapping relationship between the mechanical state detection models and the mechanical states of the on-load tap changer.

[0010] In a possible implementation, after determining the mechanical state of the on-load tap changer to be detected according to the detection result, the method further includes: adjusting the tap position of the on-load tap changer to be detected according to the mechanical state.

[0011] In a possible implementation, different machine state detection models have different corresponding characteristic parameters.

[0012] In one possible implementation, different mechanical state detection models are obtained in the following manner: obtaining training test data samples, the training test data samples include simulated vibration electrical signals of a simulated on-load tap changer under different working conditions; preprocessing the simulated vibration electrical signals corresponding to each working condition in the training test data samples to obtain simulated spectrum signals corresponding to the simulated vibration electrical signals; determining a simulated observation sequence corresponding to the simulated spectrum signals based on the simulated spectrum signals; inputting the simulated observation sequence into the mechanical state detection model to be trained for model training to obtain the mechanical state detection models under the working conditions corresponding to the simulated vibration electrical signals, and thereby obtaining different mechanical state detection models.

[0013] In a second aspect, the present application provides a device for detecting the mechanical state of an on-load tap changer, comprising:

[0014] An acquisition module, used for acquiring a vibration electrical signal of the on-load tap changer to be detected;

[0015] A preprocessing module is used to preprocess the vibration electrical signal to obtain a spectrum signal corresponding to the vibration electrical signal;

[0016] A determination module, used for determining an observation sequence corresponding to the spectrum signal according to the spectrum signal;

[0017] The detection module is used to input the observation sequence into different mechanical state detection models respectively, obtain the detection results output by each mechanical state detection model, and determine the mechanical state of the on-load tap changer to be detected based on the detection results.

[0018] In one possible implementation, the determination module is specifically used to: select N timestamps in the time period corresponding to the spectrum signal based on preset rules; for each of the N timestamps, perform signal feature extraction processing on the target spectrum signal corresponding to the timestamp to obtain an observation vector corresponding to the target spectrum signal; and obtain an observation sequence based on the observation vector.

[0019] In one possible implementation, the determination module is further used to: perform interval processing on the target spectrum signal corresponding to the timestamp to obtain a discrete spectrum corresponding to the target spectrum signal; perform normalization processing on the discrete spectrum to obtain a normalized discrete spectrum; and perform vector quantization processing on the normalized discrete spectrum to obtain an observation vector.

[0020] In one possible implementation, the detection module is specifically configured to: input observation sequences into different mechanical state detection models to obtain observation probability values corresponding to the observation sequences output by each mechanical state detection model; determine the mechanical state detection model corresponding to the maximum observation probability value as the target mechanical state detection model; and determine the mechanical state of the on-load tap changer to be detected based on the target mechanical state detection model, based on a mapping relationship between the mechanical state detection model and the mechanical state of the on-load tap changer.

[0021] In one possible implementation, the device for detecting the mechanical state of the on-load tap changer further includes an adjustment module (not shown). The adjustment module is configured to, after determining the mechanical state of the on-load tap changer to be detected based on the detection result, adjust the tap position of the on-load tap changer to be detected based on the mechanical state.

[0022] In a possible implementation, different machine state detection models have different corresponding characteristic parameters.

[0023] In one possible implementation, different mechanical state detection models are obtained in the following manner: obtaining training test data samples, the training test data samples include simulated vibration electrical signals of a simulated on-load tap changer under different working conditions; preprocessing the simulated vibration electrical signals corresponding to each working condition in the training test data samples to obtain simulated spectrum signals corresponding to the simulated vibration electrical signals; determining a simulated observation sequence corresponding to the simulated spectrum signals based on the simulated spectrum signals; inputting the simulated observation sequence into the mechanical state detection model to be trained for model training to obtain the mechanical state detection models under the working conditions corresponding to the simulated vibration electrical signals, and thereby obtaining different mechanical state detection models.

[0024] In a third aspect, the present application provides an electronic device comprising a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; and the processor executes the computer-executable instructions stored in the memory to implement the method provided in the first aspect above.

[0025] In a fourth aspect, the present application provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method provided in the first aspect above.

[0026] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method provided in the first aspect above.

[0027] The present application provides a method and related device for detecting the mechanical state of an on-load tap changer. The method obtains a vibration electrical signal of the on-load tap changer to be detected, pre-processes the vibration electrical signal, obtains a spectrum signal corresponding to the vibration electrical signal, further determines an observation sequence corresponding to the spectrum signal based on the spectrum signal, and then inputs the observation sequence into different mechanical state detection models to obtain a detection result output by each mechanical state detection model. The mechanical state of the on-load tap changer to be detected is determined based on the detection result. The present application performs spectrum analysis on the vibration electrical signal of the on-load tap changer to be detected, obtains a spectrum signal corresponding to the vibration electrical signal, further inputs the observation sequence corresponding to the spectrum signal into different mechanical state detection models to obtain a detection result output by each mechanical state detection model, and determines the mechanical state of the on-load tap changer to be detected based on the detection result, thereby improving the accuracy and reliability of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0029] Figure 1A schematic diagram of the circuit connection relationship of the parallel on-load tap changers provided in an embodiment of the present application;

[0030] Figure 2 Schematic diagram of the process of detecting the mechanical state of the on-load tap changer provided in the embodiment of the present application Figure 1 ;

[0031] Figure 3 Schematic diagram of the process of detecting the mechanical state of the on-load tap changer provided in the embodiment of the present application Figure 2 ;

[0032] Figure 4 Schematic diagram of the process of detecting the mechanical state of the on-load tap changer provided in the embodiment of the present application Figure 3 ;

[0033] Figure 5 A flowchart illustrating a method for obtaining different mechanical state detection models provided in an embodiment of the present application;

[0034] Figure 6 A schematic diagram of a process for constructing a mechanical state detection model provided in an embodiment of the present application;

[0035] Figure 7 A schematic structural diagram of a device for detecting the mechanical state of an on-load tap changer provided in an embodiment of the present application;

[0036] Figure 8 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application.

[0037] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0038] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0039] Based on the problems existing in the related art, the embodiment of the present application performs spectrum analysis on the vibration electrical signal of the on-load tap changer to be detected to obtain a spectrum signal corresponding to the vibration electrical signal, and further inputs the observation sequences corresponding to the spectrum signal into different mechanical state detection models to obtain the detection results output by each mechanical state detection model. Based on the detection results, the mechanical state of the on-load tap changer to be detected is determined to improve the accuracy and reliability of the detection.

[0040] The following first describes in detail the application scenarios to which the embodiments of the present application are applicable.

[0041] The on-load tap changer mechanical state detection method provided in the embodiments of the present application is applicable to detecting the mechanical state of a single on-load tap changer, i.e., a scenario where a single on-load tap-changing transformer is controlled by a single on-load tap changer, and is also applicable to detecting the mechanical state of multiple parallel on-load tap changers, i.e., a scenario where a single on-load tap-changing transformer is controlled by multiple parallel on-load tap changers. During the on-load tap changer mechanical state detection process, the on-load tap-changing transformer is in operation, i.e., the on-load tap changer is in operation.

[0042] It should be noted that in the detection scenario of the mechanical state of parallel on-load tap-changers, multiple on-load tap-changers are connected in parallel in the circuit, working together to control the same on-load tap-changer. It can be understood that by connecting multiple on-load tap-changers in parallel, the power supply reliability can be improved, the voltage regulation effect can be optimized, and the load distribution capability can be enhanced. Figure 1 The circuit connection relationship of parallel-connected on-load tap-changers is explained.

[0043] Figure 1 This is a schematic diagram of the circuit connection relationship of the parallel on-load tap changers provided in the embodiment of the present application. Figure 1 As shown, one end of a plurality of on-load tap-changers connected in parallel in the same on-load tap-changing transformer is connected to a power bus, and the other end of the plurality of on-load tap-changers connected in parallel is connected to the same load bus.

[0044] The method for detecting the mechanical state of an on-load tap changer provided in the embodiments of the present application is described in detail below with reference to specific embodiments.

[0045] Figure 2 Schematic diagram of the process of detecting the mechanical state of the on-load tap changer provided in the embodiment of the present application Figure 1 .like Figure 2 As shown, the specific implementation of the method for detecting the mechanical state of the on-load tap changer may include the following steps:

[0046] S201: Acquire a vibration electrical signal of the on-load tap changer to be detected.

[0047] Exemplarily, the vibration electrical signal is collected by a vibration measurement sensor.

[0048] Exemplarily, the vibration measurement sensor may be a piezoelectric acceleration sensor.

[0049] For example, the vibration measuring sensor may be fixed to the magnetic base by means of bolts at the bottom, so as to be adsorbed on the outside of the on-load tap changer body to be detected by means of the magnetic base.

[0050] It's understood that when a vibration measurement sensor senses vibration, the piezoelectric material's lattice structure deforms, causing the relative displacement of the positive and negative charge centers, generating charges on the material's surface. These charges are then converted into an electrical vibration signal proportional to the vibration acceleration within the vibration measurement sensor.

[0051] In a possible implementation, a vibration measurement sensor is used to collect mechanical vibration signals of the on-load tap changer to be tested during operation, and the collected mechanical vibration signals are converted into vibration electrical signals.

[0052] Illustratively, the vibration electrical signal may be a voltage signal, a current signal, a resistance signal, a capacitance signal, an inductance signal, and the like.

[0053] S202 , preprocessing the vibration electrical signal to obtain a frequency spectrum signal corresponding to the vibration electrical signal.

[0054] Exemplarily, the pre-processing may include amplification processing, filtering processing, and conversion processing.

[0055] In one possible implementation method, the vibration electrical signal is first amplified by an amplifier to increase the level range of the vibration electrical signal, and the amplified vibration electrical signal is further filtered by a filter to remove noise and interference in the vibration electrical signal and improve the quality of the vibration electrical signal. The filtered vibration electrical signal is then converted by a data acquisition card to obtain a spectrum signal corresponding to the vibration electrical signal.

[0056] It should be noted that in the embodiment of the present application, the order of amplifying and filtering the vibration electrical signal is not limited and can be determined according to actual application requirements.

[0057] S203: Determine an observation sequence corresponding to the spectrum signal according to the spectrum signal.

[0058] In a possible implementation, feature extraction processing is performed on the spectrum signal to obtain an observation sequence corresponding to the spectrum signal.

[0059] S204 , inputting the observation sequence into different mechanical state detection models respectively, obtaining the detection results output by each mechanical state detection model, and determining the mechanical state of the on-load tap changer to be detected based on the detection results.

[0060] Exemplarily, the mechanical state detection model may be an HMM model.

[0061] Illustratively, the characteristic parameters corresponding to each mechanical state detection model in different mechanical state detection models are different, and the characteristic parameters corresponding to each mechanical state detection model are respectively used to represent and identify a specific mechanical state of the on-load tap changer.

[0062] Exemplarily, the characteristic parameters may be a state transition probability matrix, a state probability vector, and an observation probability matrix.

[0063] It is understandable that the detection results output by each mechanical state detection model may be different.

[0064] Exemplarily, the detection result may include characteristic parameters of the corresponding mechanical state detection model, and observation probability values, etc.

[0065] For example, the mechanical state of the on-load tap changer to be tested may be a normal state or a fault state, wherein the fault state may be a brake failure state, an abnormal arc state, a loose fastener state, or a spring kinetic energy deficiency state.

[0066] In an embodiment of the present application, a vibration electrical signal of an on-load tap changer to be detected is obtained by performing a spectral analysis on the vibration electrical signal, and a spectrum signal corresponding to the vibration electrical signal is obtained. Based on the spectrum signal, an observation sequence corresponding to the spectrum signal is further determined. The observation sequence is then input into different mechanical state detection models to obtain a detection result output by each mechanical state detection model. Based on the detection result, the mechanical state of the on-load tap changer to be detected is determined. In an embodiment of the present application, a spectral analysis of the vibration electrical signal of an on-load tap changer to be detected is performed to obtain a spectrum signal corresponding to the vibration electrical signal. Based on the spectrum signal, an observation sequence corresponding to the spectrum signal is then input into different mechanical state detection models to obtain a detection result output by each mechanical state detection model. Based on the detection result, the mechanical state of the on-load tap changer to be detected is determined, thereby improving the accuracy and reliability of detection.

[0067] The following combination Figure 3 The specific implementation method of step S203 of determining the observation sequence corresponding to the spectrum signal according to the spectrum signal is described in detail.

[0068] Figure 3 Schematic diagram of the process of detecting the mechanical state of the on-load tap changer provided in the embodiment of the present application Figure 2 .like Figure 3As shown, the specific implementation of determining the observation sequence corresponding to the spectrum signal according to the spectrum signal in the detection method may include the following steps:

[0069] S301 : Select N time stamps in a time period corresponding to a spectrum signal based on a preset rule.

[0070] Exemplarily, the preset rules may be equal interval selection and feature point selection, etc. This application does not limit the preset rules, and they may be determined according to actual application requirements.

[0071] In a possible implementation, starting from the start time corresponding to the spectrum signal, a timestamp is selected at intervals of a preset duration to obtain N timestamps.

[0072] Exemplarily, the preset duration may be 10 seconds.

[0073] It should be noted that the embodiment of the present application does not limit the size of the preset time length, and it can be determined according to actual application requirements.

[0074] S302 : For each of the N time stamps, perform signal feature extraction processing on the target spectrum signal corresponding to the time stamp to obtain an observation vector corresponding to the target spectrum signal.

[0075] It's understandable that power equipment failures typically cause changes in the vibration signal's frequency, primarily manifesting as the generation of new frequency components and / or an increase in the amplitude of existing frequencies. Therefore, by performing signal feature extraction based on spectrum analysis on the spectrum corresponding to the vibration signal, the accuracy of on-load tap-changer mechanical status detection can be improved.

[0076] Exemplarily, the signal feature extraction process may include binning, normalization, and vector quantization.

[0077] Optionally, a possible implementation method of performing signal feature extraction processing on the target spectrum signal corresponding to the timestamp to obtain the observation vector corresponding to the target spectrum signal may be: performing interval processing on the target spectrum signal corresponding to the timestamp to obtain the discrete spectrum corresponding to the target spectrum signal; normalizing the discrete spectrum to obtain the normalized discrete spectrum; and performing vector quantization processing on the normalized discrete spectrum to obtain the observation vector.

[0078] The following describes in detail the implementation methods of the interval processing, normalization processing and vector quantization processing.

[0079] 1) Perform interval processing on the target spectrum signal corresponding to the timestamp:

[0080] For example, assuming that the sampling frequency corresponding to the target spectrum signal is , when the target spectrum signal corresponding to the timestamp is processed in intervals, the power spectrum density (PSD) obtained is Within the range, The interval is divided into n frequency bands, and the discrete spectrum in each frequency band is obtained.

[0081] For example, the discrete spectrum can be expressed by the following formula:

[0082]

[0083] in, Indicates the The discrete spectrum of frequency bands, Indicates the The upper cutoff frequency of the frequency band, Indicates the The lower cutoff frequency of the frequency band.

[0084] For example, when the sampling frequency is 10 kHz, the [0, 5 kHz] interval can be divided into 12 frequency bands, and the specific frequency bands can be: 0~250 Hz, 250~500 Hz, 500~800 Hz, 800~1100 Hz, 1100~1400 Hz, 1400~1800 Hz, 1800~2200 Hz, 2200~2700 Hz, 2700~3200 Hz, 3200~3800 Hz, 3800~4400 Hz, and 4400~5000 Hz.

[0085] It can be understood that a sequence of discrete spectrums in each frequency band in chronological order is determined as a discrete spectrum corresponding to the target spectrum signal.

[0086] For example, the discrete spectrum can be expressed as:

[0087]

[0088] 2) Normalize the discrete spectrum:

[0089] Exemplarily, a maximum-minimum normalization method is used to first determine the discrete spectrum minimum and the discrete spectrum maximum in the discrete spectrum, and then calculate the normalized discrete spectrum.

[0090] For example, the normalized discrete spectrum can be expressed as:

[0091]

[0092] in, , represents the i-th normalized discrete spectrum value, represents the minimum discrete spectrum value in the discrete spectrum before normalization, Indicates the maximum discrete spectrum value in the discrete spectrum before normalization.

[0093] 3) Perform vector quantization on the normalized discrete spectrum:

[0094] In one possible implementation, the interval [0, 1] is divided into M equal regions, and the first region to the Mth region are sequentially assigned index values such as 1, 2, ..., M. When the normalized discrete spectrum value falls into a certain region, the index value of the region is used as the vector quantization result.

[0095] For example, When ≤1 / 40, the vector quantization value is 1, 1 / 40< When ≤2 / 40, the vector quantization value is 2, and so on.

[0096] It can be understood that the vector quantization result is a discretized expression of the feature vector, so that the extracted signal features can be represented by a finite number of discrete values, and finally a feature vector represented by an integer value is obtained, that is, the observation vector.

[0097] S303: Obtain an observation sequence based on the observation vector.

[0098] In one possible implementation, all observation vectors corresponding to the timestamps are arranged in chronological order to obtain an observation sequence.

[0099] For example, the observation sequence can be expressed as:

[0100]

[0101] Where T represents the length of the observation sequence, Represents the observation value corresponding to time t.

[0102] In an embodiment of the present application, N timestamps are selected based on preset rules in a time period corresponding to the spectrum signal, and signal feature extraction processing is performed on the target spectrum signal corresponding to each of the N timestamps to obtain an observation vector corresponding to the target spectrum signal. Further, an observation sequence is obtained based on the observation vector, thereby improving the accuracy of mechanical state detection of the on-load tap changer.

[0103] The following combination Figure 4 The specific implementation method of inputting the observation sequence into different mechanical state detection models in step S204, obtaining the detection results output by each mechanical state detection model, and determining the mechanical state of the on-load tap changer to be detected based on the detection results is described in detail.

[0104] Figure 4Schematic diagram of the process of detecting the mechanical state of the on-load tap changer provided in the embodiment of the present application Figure 3 .like Figure 4 As shown, in this detection method, the observation sequence is input into different mechanical state detection models respectively, the detection results output by each mechanical state detection model are obtained, and the specific implementation method of determining the mechanical state of the on-load tap changer to be detected based on the detection results may include the following steps:

[0105] S401 : Input observation sequences into different mechanical state detection models respectively to obtain observation probability values corresponding to the observation sequences output by each mechanical state detection model.

[0106] Exemplarily, if different mechanical state detection models include a first mechanical state detection model, a second mechanical state detection model, and a third mechanical state detection model, the observation sequences are respectively input into the first mechanical state detection model, the second mechanical state detection model, and the third mechanical state detection model to obtain the observation probability values corresponding to the observation sequences output by the first mechanical state detection model, the second mechanical state detection model, and the third mechanical state detection model, respectively.

[0107] Exemplarily, the observation probability values corresponding to the observation sequences outputted by the first mechanical state detection model, the second mechanical state detection model, and the third mechanical state detection model may be different.

[0108] S402 : Determine the mechanical state detection model corresponding to the maximum observation probability value as the target mechanical state detection model.

[0109] S403 : Based on the mapping relationship between the mechanical state detection model and the mechanical state of the on-load tap changer, and according to the target mechanical state detection model, determine the mechanical state of the on-load tap changer to be detected.

[0110] Exemplarily, the mapping relationship between the mechanical state detection model and the mechanical state of the on-load tap changer may be stored in a database.

[0111] Table 1 shows the mapping relationship between the mechanical state detection model provided in the embodiment of the present application and the mechanical state of the on-load tap changer.

[0112] Table 1

[0113]

[0114] As shown in Table 1, each mechanical state detection model corresponds to a mechanical state of the on-load tap changer. The characteristic parameters of different mechanical state detection models corresponding to different mechanical states of the on-load tap changer are different.

[0115] For example, when the target mechanical state detection model is mechanical state detection model 1 in Table 1, the mechanical state of the on-load tap changer to be detected is determined to be a normal state. When the target mechanical state detection model is mechanical state detection model 2 in Table 1, the mechanical state of the on-load tap changer to be detected is determined to be a brake failure state, that is, it is determined that the on-load tap changer to be detected is in a fault state, and so on.

[0116] In an embodiment of the present application, observation sequences are input into different mechanical state detection models to obtain observation probability values corresponding to the observation sequences output by each mechanical state detection model. The mechanical state detection model corresponding to the maximum observation probability value is determined as the target mechanical state detection model. Based on the mapping relationship between the mechanical state detection model and the mechanical state of the on-load tap changer, the mechanical state of the on-load tap changer to be detected is determined according to the target mechanical state detection model. In an embodiment of the present application, observation probability values are extracted from the observation sequence corresponding to the vibration electrical signal based on multiple different mechanical state detection models that each focus on identifying a mechanical state of the on-load tap changer. The observation probability value is further maximized to ensure that the mechanical state detection model that best matches the current observation sequence is selected, thereby improving the detection accuracy of the mechanical state of the on-load tap changer.

[0117] Optionally, in the method for detecting the mechanical state of an on-load tap changer provided in an embodiment of the present application, in another possible implementation manner, after the mechanical state detection model corresponding to the maximum observation probability value is determined as the target mechanical state detection model, the mechanical state of the on-load tap changer to be detected can be determined based on the mapping relationship between the characteristic parameter range of the mechanical state detection model and the mechanical state of the on-load tap changer and according to the target characteristic parameters corresponding to the target mechanical state detection model.

[0118] Table 2 shows the mapping relationship between the characteristic parameter range of the mechanical state detection model provided by the embodiment of the present application and the mechanical state of the on-load tap changer.

[0119] As shown in Table 2, the characteristic parameter range of each mechanical state detection model corresponds to a mechanical state of the on-load tap changer.

[0120] Table 2

[0121]

[0122] Exemplarily, the characteristic parameter range may be the range between two corresponding characteristic parameters when a change in the characteristic parameter is less than a preset threshold during the model training process of the multi-machine state detection model.

[0123] For example, the characteristic parameters of the target mechanical state detection model are between When the mechanical state of the on-load tap-changer to be detected is within the corresponding range, it is determined that the mechanical state of the on-load tap-changer to be detected is normal. When the characteristic parameters of the target mechanical state detection model are between If the value is within the corresponding range, it is determined that the mechanical state of the on-load tap-changer to be detected is a brake failure state, and so on.

[0124] Optionally, in the method for detecting the mechanical state of an on-load tap changer provided in an embodiment of the present application, after determining the mechanical state of the on-load tap changer to be detected according to the detection result, the method further includes: adjusting the tap position of the on-load tap changer to be detected according to the mechanical state.

[0125] It is understandable that by adjusting the tap position of the on-load tap changer to be tested, the transformation ratio of power equipment such as the on-load tap-changing transformer can be changed to achieve precise control of the output voltage of the on-load tap-changing transformer, thereby ensuring the stability of the load-end voltage.

[0126] It can be understood that in the detection scenario of the mechanical state of parallel on-load tap-changers, on the one hand, when the system load or voltage fluctuates, each parallel on-load tap-changer can independently adjust the tap position according to its own settings and system requirements, thereby changing the transformer ratio, achieving precise control of the output voltage, and ensuring the stability of the load end voltage; on the other hand, when there is an on-load tap-changer in a faulty state among the parallel on-load tap-changers, the on-load tap-changer in the faulty state can be controlled to be closed to ensure the normal operation of the electrical equipment.

[0127] It can be understood that in the method for detecting the mechanical state of an on-load tap changer provided in the embodiment of the present application, in the scenario of parallel on-load tap changers, by obtaining multi-channel sensor signals that can reflect the operating status of the electrical equipment, that is, the vibration electrical signal corresponding to each on-load tap changer, the extraction of mechanical state characteristics can be diversified and more effective, while ensuring the stable operation of the vibration measurement sensor, and improving the accuracy of detection.

[0128] Optionally, among the different mechanical state detection models provided in the embodiments of the present application, each mechanical state detection model corresponds to a different characteristic parameter.

[0129] Exemplarily, the characteristic parameters corresponding to each mechanical state detection model are similar to those described above and are not described in detail here.

[0130] The following combination Figure 5 The acquisition methods of different mechanical state detection models provided in the embodiments of the present application are described in detail.

[0131] Figure 5 Schematic diagram of the process of obtaining different mechanical state detection models provided in the embodiment of the present application. Figure 5 As shown, the acquisition method of different mechanical state detection models includes the following steps:

[0132] S501 : Acquire training test data samples, where the training test data samples include simulated vibration electrical signals of a simulated on-load tap changer under different working conditions.

[0133] For example, the different operating conditions may be a normal operating condition of the on-load tap changer, a loose spring condition, a worn contact condition, a condition where the dial is difficult to fit into the slot, and the like.

[0134] In one possible implementation, a piezoelectric accelerometer is used to collect mechanical vibration signal waveforms, i.e., simulated vibration electrical signals, during multiple switching processes of a simulated switch under normal operating conditions, spring loosening conditions, contact wear conditions, and conditions where the dial is difficult to enter the slot. Multiple sets of test data samples are collected for each state.

[0135] For example, the simulated vibration electrical signals under different working conditions may be multiple groups of simulated vibration signals. This application does not limit the number of simulated vibration electrical signals under the same working condition, and the number may be determined based on actual application requirements.

[0136] S502 , preprocessing the simulated vibration electrical signal corresponding to each working condition in the training test data sample to obtain a simulated frequency spectrum signal corresponding to the simulated vibration electrical signal.

[0137] In this step, the specific implementation method of preprocessing the analog vibration electrical signal to obtain the analog spectrum signal corresponding to the analog vibration electrical signal is similar to the above and will not be repeated here.

[0138] S503: Determine a simulated observation sequence corresponding to the simulated spectrum signal according to the simulated spectrum signal.

[0139] The specific implementation of this step is similar to the above and will not be repeated here.

[0140] S504 , inputting the simulated observation sequence into the mechanical state detection model to be trained for model training, obtaining the mechanical state detection model under the working condition corresponding to the simulated vibration electrical signal, and further obtaining different mechanical state detection models.

[0141] In one possible implementation, model training is performed based on a parameter estimation algorithm, such as the Baum-Welch algorithm, to obtain characteristic parameters of the mechanical state detection model corresponding to various operating conditions. Specifically, the parameter estimation algorithm, based on a recursive approach, iteratively updates the characteristic parameters of the mechanical state detection model, achieving a local maximum in the probability of generating an observation sequence. It then derives an intermediate variable formula to update the characteristic parameters of the mechanical state detection model, gradually optimizing the mechanical state detection model. When the observation probability converges, the training of the mechanical state detection model is considered complete.

[0142] The embodiment of the present application does not limit the size of the preset threshold, which can be determined according to actual application requirements.

[0143] The specific implementation steps of the mechanical state detection model are as follows:

[0144] S1, data preparation.

[0145] That is, the observation sequence is obtained.

[0146] S2, initialize the characteristic parameters of the mechanical state detection model.

[0147] 1) Initialize the state probability vector

[0148] For example, the state probability vector can be expressed as:

[0149]

[0150] in, represents the state probability vector, represents the probability that the model is in state i at the initial moment, L represents the number of states, and .

[0151] For example, the state probability vector can be randomly initialized. The embodiment of the present application does not limit the method of initializing the state probability vector, as long as That's it.

[0152] 2) Initialize the state transition probability matrix

[0153] For example, the state transition probability matrix can be expressed as:

[0154]

[0155] Among them, A represents the state transition probability matrix, represents the probability of transitioning from state i to state j, and holds for all i.

[0156] For example, the state transition probability matrix can be randomly initialized. The embodiment of the present application does not limit the method of initializing the state transition probability matrix, as long as That's it.

[0157] 3) Initialize the observation probability matrix

[0158] For example, the observation probability matrix can be expressed as:

[0159]

[0160] Where B represents the observation probability matrix, represents the probability of observing symbol k in state j, M represents the number of observed symbols, and holds for all j.

[0161] It should be noted that k represents the discrete values implied by the spectrum signal after vector quantization processing, that is, the observation values in the observation sequence, and these discrete values are the specific values of the observation symbol k.

[0162] For example, the observation probability matrix can be randomly initialized. The embodiment of the present application does not limit the method of initializing the observation probability matrix, as long as That's it.

[0163] S3, define and calculate the forward probability and backward probability.

[0164] 1) Define and calculate forward probability

[0165] Exemplarily, the forward probability is used to represent all possible paths from the initial state to the current state and the corresponding observation probabilities.

[0166] For example, define the forward probability At time t, the mechanical state detection model is in state i and observes the first t observation symbols probability.

[0167] It is understandable that Corresponding to different values of the observation symbol k, and mapping different values of k to the corresponding observation symbol .

[0168] For example, the forward probability can be calculated recursively, such as:

[0169] Initialize the forward probability: At t=1, the corresponding forward probability can be expressed as:

[0170]

[0171] in, Indicates the first observation symbol;

[0172] Recursively calculate the forward probability: When t=2, 3, ..., T, the corresponding forward probability can be expressed as:

[0173]

[0174] Where T represents the length of the observation sequence.

[0175] 2) Define and calculate backward probability

[0176] Exemplarily, the backward probability is used to represent the information transfer from the current state to the end of the observation sequence.

[0177] For example, define the backward probability At time t, the mechanical state detection model is in state i and observes the subsequent Tt observation symbols probability.

[0178] For example, the backward probability can be calculated recursively, such as:

[0179] Initialize the backward probability: at t=T, the corresponding backward probability This holds true for all i;

[0180] Recursively calculate the backward probability: When t=T-1, T-2, ..., 1, the corresponding backward probability can be expressed as:

[0181]

[0182] S4, calculate intermediate variables.

[0183] 1) Represents the characteristic parameters of the detection model under a given observation sequence Q and the current mechanical state The observation probability of being in state i at time t and in state j at time t+1 under the condition of .

[0184] For example, It can be expressed by the following formula:

[0185]

[0186] 2) Represents the characteristic parameters of the detection model in the given observation sequence Q and the current mechanical state The probability of being in state i at time t under the condition of .

[0187] For example, It can be expressed by the following formula:

[0188]

[0189] S5, update model parameters.

[0190] 1) Update the initial state probability vector :

[0191] 2) Update the state transition probability matrix A:

[0192]

[0193] 3) Update the state transition probability matrix B:

[0194]

[0195] S6, repeating steps S3 to S5 until the mechanical state detection model converges.

[0196] For example, the forward probability, backward probability, and intermediate variables are repeatedly calculated and the characteristic parameters of the mechanical state detection model are updated until the change in the characteristic parameters is less than a preset threshold or the maximum number of iterations is reached. At this point, the mechanical state detection model is considered to have converged and training is completed.

[0197] It can be understood that based on the above-mentioned model training method, by training the mechanical state detection model to be trained based on the simulated vibration electrical signals under different working conditions, the mechanical state detection models of the on-load tap changer under different working conditions, i.e., the mechanical state detection models under different mechanical states, and the characteristic parameters corresponding to the mechanical state detection models can be obtained.

[0198] In an embodiment of the present application, by obtaining training test data samples, the simulated vibration electrical signals corresponding to each working condition in the training test data samples are preprocessed to obtain a simulated spectrum signal corresponding to the simulated vibration electrical signals, and based on the simulated spectrum signal, a simulated observation sequence corresponding to the simulated spectrum signal is determined, and the simulated observation sequence is input into the mechanical state detection model to be trained for model training, thereby obtaining a mechanical state detection model under the working condition corresponding to the simulated vibration electrical signals, and further obtaining different mechanical state detection models. In an embodiment of the present application, by training the mechanical state detection model to be trained based on the simulated vibration electrical signals of the simulated on-load tap changer under different working conditions, a mechanical state detection model corresponding to each working condition, i.e., each mechanical state, can be obtained, and then the mechanical state of the on-load tap changer to be detected is detected based on the mechanical state detection model corresponding to each mechanical state, thereby improving the accuracy of the detection and, at the same time, improving the robustness of the mechanical state detection model.

[0199] Figure 6 This is a flow chart of the construction of a mechanical state detection model provided in the embodiment of the present application. Figure 6 As shown in FIG, the process of constructing a mechanical state detection model includes performing signal feature extraction processing on the spectrum signal corresponding to the vibration electrical signal to obtain an observation sequence, and further performing model training based on the observation sequence to obtain a trained mechanical state detection model and characteristic parameters of the mechanical state detection model.

[0200] The specific implementation method of each step is similar to the above and will not be repeated here.

[0201] It can be understood that by performing model training based on the vibration electrical signals under different mechanical state working conditions, the mechanical state detection model corresponding to the different mechanical state working conditions and the characteristic parameters of the mechanical state detection model can be obtained.

[0202] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0203] Figure 7 This is a schematic diagram of the structure of the detection device for the mechanical state of the on-load tap changer provided in the embodiment of the present application. Figure 7 As shown, the device 70 for detecting the mechanical state of an on-load tap changer includes an acquisition module 710 , a pre-processing module 720 , a determination module 730 and a detection module 740 .

[0204] The acquisition module 710 is used to acquire the vibration electrical signal of the on-load tap changer to be detected;

[0205] A preprocessing module 720 is used to preprocess the vibration electrical signal to obtain a spectrum signal corresponding to the vibration electrical signal;

[0206] A determination module 730 is configured to determine an observation sequence corresponding to the spectrum signal based on the spectrum signal;

[0207] The detection module 740 is configured to input the observation sequence into different mechanical state detection models respectively, obtain the detection results output by each mechanical state detection model, and determine the mechanical state of the on-load tap changer to be detected based on the detection results.

[0208] In one possible implementation, the determination module 730 is specifically used to: select N timestamps in the time period corresponding to the spectrum signal based on preset rules; for each of the N timestamps, perform signal feature extraction processing on the target spectrum signal corresponding to the timestamp to obtain an observation vector corresponding to the target spectrum signal; and obtain an observation sequence based on the observation vector.

[0209] In one possible implementation, the determination module 730 is further used to: perform interval processing on the target spectrum signal corresponding to the timestamp to obtain a discrete spectrum corresponding to the target spectrum signal; perform normalization processing on the discrete spectrum to obtain a normalized discrete spectrum; and perform vector quantization processing on the normalized discrete spectrum to obtain an observation vector.

[0210] In one possible implementation, the detection module 740 is specifically configured to: input observation sequences into different mechanical state detection models to obtain observation probability values corresponding to the observation sequences output by each mechanical state detection model; determine the mechanical state detection model corresponding to the maximum observation probability value as the target mechanical state detection model; and determine the mechanical state of the on-load tap changer to be detected based on the target mechanical state detection model, based on a mapping relationship between the mechanical state detection model and the mechanical state of the on-load tap changer.

[0211] In one possible implementation, the device for detecting the mechanical state of the on-load tap changer further includes an adjustment module (not shown). The adjustment module is configured to, after determining the mechanical state of the on-load tap changer to be detected based on the detection result, adjust the tap position of the on-load tap changer to be detected based on the mechanical state.

[0212] In a possible implementation, different machine state detection models have different corresponding characteristic parameters.

[0213] In one possible implementation, different mechanical state detection models are obtained in the following manner: obtaining training test data samples, the training test data samples include simulated vibration electrical signals of a simulated on-load tap changer under different working conditions; preprocessing the simulated vibration electrical signals corresponding to each working condition in the training test data samples to obtain simulated spectrum signals corresponding to the simulated vibration electrical signals; determining a simulated observation sequence corresponding to the simulated spectrum signals based on the simulated spectrum signals; inputting the simulated observation sequence into the mechanical state detection model to be trained for model training to obtain the mechanical state detection models under the working conditions corresponding to the simulated vibration electrical signals, and thereby obtaining different mechanical state detection models.

[0214] The device for detecting the mechanical state of an on-load tap changer provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar, and are not described in detail in this embodiment.

[0215] Figure 8 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application. Figure 8 As shown, the electronic device 80 provided in this embodiment includes: at least one processor 801 and a memory 802. Optionally, the device 80 also includes a communication component 803. The processor 801, the memory 802 and the communication component 803 are connected via a bus 804.

[0216] During the specific implementation process, at least one processor 801 executes the computer-executable instructions stored in the memory 802, so that the at least one processor 801 performs the above method.

[0217] The specific implementation process of the processor 801 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0218] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules in the processor.

[0219] The memory may include a high-speed memory (Random Access Memory, referred to as RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0220] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of presentation, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0221] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0222] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0223] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0224] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0225] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0226] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0227] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0228] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0229] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0230] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for detecting the mechanical state of an on-load tap changer, characterized in that: include: Obtaining the vibration electrical signal of the on-load tap-changer to be tested; Preprocessing the vibration electrical signal to obtain a frequency spectrum signal corresponding to the vibration electrical signal; determining, according to the spectrum signal, an observation sequence corresponding to the spectrum signal; The observation sequences are respectively input into different mechanical state detection models to obtain detection results output by each mechanical state detection model, and the mechanical state of the on-load tap changer to be detected is determined based on the detection results.

2. The detection method according to claim 1, wherein The determining, based on the spectrum signal, an observation sequence corresponding to the spectrum signal includes: In the time period corresponding to the spectrum signal, N timestamps are selected based on a preset rule; For each of the N time stamps, performing signal feature extraction processing on the target spectrum signal corresponding to the time stamp to obtain an observation vector corresponding to the target spectrum signal; The observation sequence is obtained according to the observation vector.

3. The detection method according to claim 2, characterized in that The performing signal feature extraction processing on the target spectrum signal corresponding to the timestamp to obtain an observation vector corresponding to the target spectrum signal includes: Performing interval processing on the target spectrum signal corresponding to the timestamp to obtain a discrete spectrum corresponding to the target spectrum signal; performing normalization processing on the discrete spectrum to obtain a normalized discrete spectrum; Vector quantization is performed on the normalized discrete spectrum to obtain the observation vector.

4. The detection method according to claim 1, wherein Inputting the observation sequence into different mechanical state detection models respectively to obtain detection results output by each mechanical state detection model, and determining the mechanical state of the on-load tap changer to be detected based on the detection results, includes: Inputting the observation sequences into the different mechanical state detection models respectively to obtain the observation probability value corresponding to the observation sequence output by each mechanical state detection model; The mechanical state detection model corresponding to the maximum observation probability value is determined as the target mechanical state detection model; Based on the mapping relationship between the mechanical state detection model and the mechanical state of the on-load tap changer, the mechanical state of the on-load tap changer to be detected is determined according to the target mechanical state detection model.

5. The detection method according to any one of claims 1 to 4, characterized in that After determining the mechanical state of the on-load tap changer to be tested according to the test result, the method further includes: The tap position of the on-load tap changer to be detected is adjusted according to the mechanical state.

6. The detection method according to any one of claims 1 to 4, characterized in that The characteristic parameters corresponding to each of the different mechanical state detection models are different.

7. The detection method according to any one of claims 1 to 4, characterized in that The different mechanical state detection models are obtained in the following manner: Acquire training test data samples, wherein the training test data samples include simulated vibration electrical signals of a simulated on-load tap changer under different working conditions; Preprocessing the simulated vibration electrical signal corresponding to each working condition in the training test data sample to obtain a simulated spectrum signal corresponding to the simulated vibration electrical signal; Determining a simulated observation sequence corresponding to the simulated spectrum signal according to the simulated spectrum signal; The simulated observation sequence is input into the mechanical state detection model to be trained for model training, so as to obtain the mechanical state detection model under the working condition corresponding to the simulated vibration electrical signal, and further obtain the different mechanical state detection models.

8. A device for detecting the mechanical state of an on-load tap changer, characterized in that: include: An acquisition module, used for acquiring a vibration electrical signal of the on-load tap changer to be detected; A preprocessing module, configured to preprocess the vibration electrical signal to obtain a frequency spectrum signal corresponding to the vibration electrical signal; a determination module, configured to determine, based on the spectrum signal, an observation sequence corresponding to the spectrum signal; The detection module is configured to input the observation sequence into different mechanical state detection models respectively, obtain a detection result output by each mechanical state detection model, and determine the mechanical state of the on-load tap changer to be detected based on the detection result.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

11. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 7 when being executed by a processor.

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