Main transformer maintenance-free oil chromatography online monitoring method, device and computer equipment

Abstract

The present application relates to a method, device and computer equipment for online monitoring of maintenance-free oil chromatography of a main transformer. The method comprises: obtaining insulating oil gas separation data corresponding to the transformer; the insulating oil gas separation data is obtained by separating the insulating oil gas through the vacuum degassing device of the transformer's online monitoring device; using the initial gas detection parameters to perform gas concentration analysis on the insulating oil gas separation data to obtain the concentration values ​​of each gas in the transformer; according to each gas concentration value, self-learning the initial gas detection parameters to obtain the gas component adjustment parameters; using the gas component adjustment parameters as the initial gas detection parameters, returning to execute the step of performing gas concentration analysis on the insulating oil gas separation data using the initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer, until the gas concentration values ​​converge. The use of this method can reduce the risk of equipment failure of the transformer and also help improve the accuracy and real-time performance of monitoring.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, device, computer equipment, storage medium and computer program product for online monitoring of maintenance-free oil chromatography of a main transformer. Background Art

[0002] With the development of power grid technology, transformers are one of the important equipment in substations. Transformers will inevitably accumulate various defects in the long-term operation of the power grid. The process of these defects developing into failures can be divided into two categories: gradual failures and sudden failures. Gradual failures are mainly manifested in that during the normal operation of the transformer, due to insulation aging, moisture, etc., the insulation performance gradually decreases, eventually leading to the occurrence of failures. Although the probability of such failures is very high, they have certain regularity and can be discovered and avoided in time through online monitoring. Timely and accurate prediction of possible transformer failures is not only a hot topic for power grid operation and maintenance personnel, but also the key to ensuring the safe and stable operation of the power grid.

[0003] In traditional technology, stable dedicated gas detectors are used to monitor the content of hydrogen, methane, acetylene, ethylene, ethane, carbon dioxide, trace water, total hydrocarbons, and total combustible gas in the insulating oil of oil-filled high-voltage electrical equipment such as transformers / reactors. However, the traditional method requires collecting samples in the laboratory for offline analysis, which makes it impossible to obtain gas content data in real time during the operation of the transformer. Such delays can cause potential faults to be ignored or delayed in detection, thereby increasing the risk of equipment failure of the transformer. Summary of the invention

[0004] Based on this, it is necessary to provide a main transformer maintenance-free oil chromatography online monitoring method, device, computer equipment, computer-readable storage medium and computer program product that can reduce the risk of equipment failure of the transformer in response to the above technical problems.

[0005] In a first aspect, the present application provides a main transformer maintenance-free oil chromatography online monitoring method, comprising:

[0006] Obtaining insulating oil and gas separation data corresponding to the transformer; the insulating oil and gas separation data is obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer;

[0007] Performing gas concentration analysis on the insulating oil-gas separation data using initial gas detection parameters to obtain concentration values ​​of various gases in the transformer;

[0008] According to each of the gas concentration values, the initial gas detection parameters are self-learned to obtain the gas component adjustment parameters;

[0009] The gas component adjustment parameter is used as the initial gas detection parameter, and the step of using the initial gas detection parameter to perform gas concentration analysis on the insulating oil and gas separation data to obtain the concentration value of each gas in the transformer is returned to execute until the gas concentration values ​​converge.

[0010] In a second aspect, the present application also provides a main transformer maintenance-free oil chromatography online monitoring device, comprising:

[0011] A separation data acquisition module is used to acquire insulating oil and gas separation data corresponding to the transformer; the insulating oil and gas separation data is obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer;

[0012] A gas concentration calculation module, used to perform gas concentration analysis on the insulating oil-gas separation data using initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer;

[0013] A component parameter learning module, used for self-learning the initial gas detection parameters according to the gas concentration values ​​to obtain the gas component adjustment parameters;

[0014] The gas concentration calculation module is also used to use the gas component adjustment parameters as the initial gas detection parameters, return to execute the step of using the initial gas detection parameters to perform gas concentration analysis on the insulating oil and gas separation data to obtain the concentration values ​​of each gas in the transformer, until the gas concentration values ​​converge.

[0015] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0016] Obtaining insulating oil and gas separation data corresponding to the transformer; the insulating oil and gas separation data is obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer;

[0017] Performing gas concentration analysis on the insulating oil-gas separation data using initial gas detection parameters to obtain concentration values ​​of various gases in the transformer;

[0018] According to each of the gas concentration values, the initial gas detection parameters are self-learned to obtain the gas component adjustment parameters;

[0019] The gas component adjustment parameter is used as the initial gas detection parameter, and the step of using the initial gas detection parameter to perform gas concentration analysis on the insulating oil and gas separation data to obtain the concentration value of each gas in the transformer is returned to execute until the gas concentration values ​​converge.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0021] Obtaining insulating oil and gas separation data corresponding to the transformer; the insulating oil and gas separation data is obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer;

[0022] Performing gas concentration analysis on the insulating oil-gas separation data using initial gas detection parameters to obtain concentration values ​​of various gases in the transformer;

[0023] According to each of the gas concentration values, the initial gas detection parameters are self-learned to obtain the gas component adjustment parameters;

[0024] The gas component adjustment parameter is used as the initial gas detection parameter, and the step of using the initial gas detection parameter to perform gas concentration analysis on the insulating oil and gas separation data to obtain the concentration value of each gas in the transformer is returned to execute until the gas concentration values ​​converge.

[0025] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0026] Obtaining insulating oil and gas separation data corresponding to the transformer; the insulating oil and gas separation data is obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer;

[0027] Performing gas concentration analysis on the insulating oil-gas separation data using initial gas detection parameters to obtain concentration values ​​of various gases in the transformer;

[0028] According to each of the gas concentration values, the initial gas detection parameters are self-learned to obtain the gas component adjustment parameters;

[0029] The gas component adjustment parameter is used as the initial gas detection parameter, and the step of using the initial gas detection parameter to perform gas concentration analysis on the insulating oil and gas separation data to obtain the concentration value of each gas in the transformer is returned to execute until the gas concentration values ​​converge.

[0030] The above-mentioned main transformer maintenance-free oil chromatography online monitoring method, device, computer equipment, storage medium and computer program product obtain the insulating oil gas separation data corresponding to the transformer; the insulating oil gas separation data is obtained by separating the insulating oil gas through the vacuum degassing device of the transformer's online monitoring device; the insulating oil gas separation data is analyzed for gas concentration using initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer; the initial gas detection parameters are self-learned according to the gas concentration values ​​to obtain the gas component adjustment parameters; the gas component adjustment parameters are used as the initial gas detection parameters, and the step of performing gas concentration analysis on the insulating oil gas separation data using the initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer is returned to execute until the gas concentration values ​​converge.

[0031] The vacuum degassing device of the online monitoring device is used to separate the oil and gas in the insulating oil, which realizes the efficient acquisition of the gas components inside the transformer, avoids the tedious steps of extracting, processing and analyzing samples in traditional methods, and saves a lot of time and labor costs; the initial gas detection parameters are used for gas concentration analysis, which can not only quickly obtain the concentration values ​​of each gas component, but also continuously optimize the detection parameters through self-learning, realize the self-adaptation and self-adjustment of the monitoring system, and further continuously iterate until the gas concentration value converges, so that the monitoring system can respond to the changes in the gas inside the transformer in a timely and accurate manner, with higher sensitivity and accuracy, which not only helps to discover potential fault signs and warn of possible equipment problems in advance, but also provides important reference information for operation and maintenance personnel to guide them to formulate more effective maintenance strategies. In summary, this method not only reduces the risk of equipment failure of the transformer, but also helps to automate and intelligentize the gas monitoring of the main transformer, improves the accuracy and real-time nature of monitoring, helps to improve the safety and stability of the power system, reduces operation and maintenance costs, and extends the life of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 It is an application environment diagram of a main transformer maintenance-free oil chromatography online monitoring method in one embodiment;

[0034] Figure 2 A schematic diagram of a flow chart of a main transformer maintenance-free oil chromatography online monitoring method in one embodiment;

[0035] Figure 3 A schematic flow chart of a method for obtaining gas concentration values ​​in one embodiment;

[0036] Figure 4 A schematic flow chart of a method for obtaining a gas concentration analysis value in one embodiment;

[0037] Figure 5 A schematic diagram of a flow chart of a method for obtaining an insulating oil and gas photoelectric signal in an embodiment;

[0038] Figure 6 It is a flow chart of a method for obtaining the insulating oil gas sound wave frequency and the insulating oil gas sound wave amplitude in one embodiment;

[0039] Figure 7 A schematic flow chart of a method for adjusting parameters of a gas component in one embodiment;

[0040] Figure 8 It is a structural block diagram of a main transformer maintenance-free oil chromatography online monitoring device in one embodiment;

[0041] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0042] 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.

[0043] The embodiment of the present application provides a main transformer maintenance-free oil chromatography online monitoring method, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the insulating oil gas separation data corresponding to the transformer through the terminal 102; the insulating oil gas separation data is obtained by separating the insulating oil gas through the vacuum degassing device of the online monitoring device of the transformer; the insulating oil gas separation data is analyzed for gas concentration using the initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer; according to each gas concentration value, the initial gas detection parameters are self-learned to obtain the gas component adjustment parameters; the gas component adjustment parameters are used as the initial gas detection parameters, and the steps of analyzing the gas concentration of the insulating oil gas separation data using the initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer are returned until the gas concentration values ​​converge. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable device may be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0044] In an exemplary embodiment, Figure 2 As shown in the figure, a main transformer maintenance-free oil chromatography online monitoring method is provided, and the method is applied to Figure 1 The server in the example is used to illustrate, including the following steps 202 to 208. Among them:

[0045] Step 202, obtaining insulating oil-gas separation data corresponding to the transformer.

[0046] The insulating oil and gas separation data may be the composition data of the oil and gas in the transformer insulating oil obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer.

[0047] Specifically, a vacuum degassing device of a special online monitoring device is set in the transformer. The function of the vacuum degassing device of this online monitoring device is to separate the oil and gas in the insulating oil through a special separation technology. When the transformer is working, various gases may exist in the insulating oil, such as hydrogen, methane, acetylene, ethylene, ethane, carbon dioxide, trace water, total hydrocarbons, total combustible gas content, etc. The vacuum degassing device of the online monitoring device uses its special structure and material properties to produce selectivity in terms of the size and polarity of oil and gas molecules, so that it can effectively separate oil and gas and obtain insulating oil oil-gas separation data.

[0048] Step 204: Use the initial gas detection parameters to perform gas concentration analysis on the insulating oil-gas separation data to obtain the concentration values ​​of each gas in the transformer.

[0049] The initial gas detection parameters may be initial parameters of a gas detection component in the transformer.

[0050] The gas concentration value may be the concentration value of various gases in the transformer.

[0051] Specifically, the initial gas detection parameters of the gas detection component are set, and the initial gas detection parameters may include the molecular size, polarity, and transmission rate of the vacuum degassing device of the online monitoring device. The initial gas detection parameters are applied to the insulating oil gas separation data, and the insulating oil gas separation data are processed and calculated using the principles and techniques of gas analysis to obtain the concentration values ​​of various gases in the transformer. In the process of processing and calculating the insulating oil gas separation data, various gas analysis methods, such as chromatography, mass spectrometry, etc., are used to ensure accurate measurement of various gas components and gas concentration values.

[0052] Step 206, self-learning the initial gas detection parameters according to the gas concentration values ​​to obtain the gas component adjustment parameters.

[0053] The gas component adjustment parameters may be new parameters after adjusting the parameters in the gas detection component.

[0054] Specifically, a self-learning algorithm or model is established. This model can be based on machine learning or artificial intelligence technology, and can automatically adjust the parameters of the gas detection component according to the existing gas concentration value and the corresponding initial gas detection parameters to meet the accuracy of optimizing gas analysis. In actual operation, optimization methods such as back propagation algorithm and genetic algorithm can be used to gradually adjust the parameters of the gas detection component through continuous iteration and optimization, so that the disturbance of the parameters of the model gas detection component is continuously reduced. This process requires a lot of data support and computing resources to ensure that the model can achieve higher accuracy and stability in continuous learning and optimization. Finally, through this self-learning method, the gas component adjustment parameters suitable for the current working state of the transformer can be obtained.

[0055] Step 208, using the gas component adjustment parameters as initial gas detection parameters, returning to execute the step of using the initial gas detection parameters to perform gas concentration analysis on the insulating oil-gas separation data to obtain the concentration values ​​of each gas in the transformer, until the gas concentration values ​​converge.

[0056] Specifically, if the gas concentration values ​​have not converged, the gas component adjustment parameters adjusted through self-learning are applied to the initial gas detection parameters, and the gas concentration analysis of the insulating oil and gas separation data is performed using this new parameter combination to obtain the concentration values ​​of each gas in the transformer. Check whether the obtained gas concentration values ​​have converged, that is, whether they have reached a stable state. If the gas concentration values ​​have not reached convergence, the adjusted gas component adjustment parameters are used again as the initial gas detection parameters, and the above steps are repeated until the gas concentration values ​​converge.

[0057] In the above-mentioned main transformer maintenance-free oil chromatography online monitoring method, the insulating oil-gas separation data corresponding to the transformer is obtained; the insulating oil-gas separation data is obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer; the insulating oil-gas separation data is analyzed for gas concentration using initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer; the initial gas detection parameters are self-learned according to the gas concentration values ​​to obtain the gas component adjustment parameters; the gas component adjustment parameters are used as the initial gas detection parameters, and the step of performing gas concentration analysis on the insulating oil-gas separation data using the initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer is returned to execute until the gas concentration values ​​converge.

[0058] The vacuum degassing device of the online monitoring device is used to separate the oil and gas in the insulating oil, which realizes the efficient acquisition of the gas components inside the transformer, avoids the tedious steps of extracting, processing and analyzing samples in traditional methods, and saves a lot of time and labor costs; the initial gas detection parameters are used for gas concentration analysis, which can not only quickly obtain the concentration values ​​of each gas component, but also continuously optimize the detection parameters through self-learning, realize the self-adaptation and self-adjustment of the monitoring system, and further continuously iterate until the gas concentration value converges, so that the monitoring system can respond to the changes in the gas inside the transformer in a timely and accurate manner, with higher sensitivity and accuracy, which not only helps to discover potential fault signs and warn of possible equipment problems in advance, but also provides important reference information for operation and maintenance personnel to guide them to formulate more effective maintenance strategies. In summary, this method not only reduces the risk of equipment failure of the transformer, but also helps to automate and intelligentize the gas monitoring of the main transformer, improves the accuracy and real-time nature of monitoring, helps to improve the safety and stability of the power system, reduces operation and maintenance costs, and extends the life of equipment.

[0059] In an exemplary embodiment, Figure 3 As shown, the initial gas detection parameters are used to perform gas concentration analysis on the insulating oil-gas separation data to obtain the concentration values ​​of each gas in the transformer, including steps 302 to 306. Among them:

[0060] Step 302: Determine a gas concentration analysis method based on the insulating oil gas separation data.

[0061] Among them, the gas concentration analysis method can be an analysis method for the concentration of various gases in the insulating oil and gas separation data, including a single gas concentration analysis method and a multi-gas concentration analysis method. The single gas concentration analysis method can be an analysis method in which each gas is relatively isolated and does not consider the influence of each other, while the multi-gas concentration analysis method can be a joint analysis of the concentrations of multiple gases. By comprehensively considering the concentration changes of multiple gases, the operating status of the transformer and the health status of the insulating oil can be more accurately evaluated.

[0062] Specifically, the insulating oil gas separation data is preprocessed, including data cleaning and denoising, to ensure the quality and reliability of the data. According to the characteristics of the gas composition and the analysis requirements, a suitable initial gas concentration analysis method is selected, which may involve chromatography, mass spectrometry, spectral analysis and other technologies; and a corresponding gas concentration analysis model or algorithm is established based on the initial gas concentration analysis method, and the gas concentration analysis method suitable for the current scenario is determined in combination with the data characteristics and actual conditions.

[0063] Step 304: According to the gas concentration analysis method, the gas detection component of the transformer is controlled to perform gas concentration analysis on the insulating oil gas separation data to obtain a gas concentration analysis value.

[0064] Among them, the gas detection component can be a device for detecting the gas concentration of various gases in the transformer. The gas detection component includes a micro photoacoustic cell. The micro photoacoustic cell integrates a laser and a microphone, which can increase the resonant frequency of the photoacoustic cell to the sub-ultrasonic band, effectively avoiding external interference. The photocell does not require consumables such as chromatographic columns and carrier gases, and the equipment adopts a multiple temperature control design. For hydrocarbon gases, a micro photoacoustic cell is used independently to solve the problem of gas cross-interference.

[0065] The gas concentration analysis value may be analysis data obtained after performing concentration analysis on insulating oil and gas separation data, but is not the final concentration value of various gases in the transformer.

[0066] Specifically, according to the selected gas concentration analysis method, the operating parameters of the gas detection component of the control transformer are determined, such as the analysis frequency, sampling interval, etc. Then, these parameters are applied to the gas detection component so that it can monitor and analyze the insulating oil gas separation data in real time according to the set method. The gas detection component processes and calculates the insulating oil gas separation data according to the set operating parameters to obtain the corresponding gas concentration analysis value. The analysis results are verified and adjusted to ensure accurate and reliable gas concentration analysis values.

[0067] Step 306: Perform concentration change analysis on each gas concentration analysis value to obtain each gas concentration value.

[0068] Specifically, the gas concentration analysis values ​​are analyzed in time series to detect the changing trend of gas concentration, which includes the use of statistical methods, trend analysis techniques, etc. to identify possible periodic changes, mutations or trends. The detected concentration changes are analyzed to determine their impact on the transformer operating status and possible causes, and the gas concentration values ​​are obtained based on the results of the concentration change analysis.

[0069] In this embodiment, the gas concentration analysis value can be accurately obtained by controlling the gas detection component of the transformer to perform gas concentration analysis on the insulating oil gas separation data. Subsequently, the concentration change analysis of these analysis values ​​is performed to obtain the concentration values ​​of various gases. The benefit of this method is that it can accurately monitor and analyze the concentrations of various gases inside the transformer, which helps to timely discover possible problems or abnormal conditions in the operation of the transformer, so as to take corresponding measures to deal with them and ensure the safe and stable operation of the transformer. At the same time, two methods of single gas concentration analysis and multi-gas concentration analysis are provided to make the analysis results more comprehensive and accurate.

[0070] In an exemplary embodiment, Figure 4 As shown, according to the gas concentration analysis method, the gas detection component of the control transformer performs gas concentration analysis on the insulating oil gas separation data to obtain the gas concentration analysis value, including steps 402 to 406. Wherein:

[0071] Step 402, using initial gas detection parameters, setting the wavelength of the detection beam of the gas detection component.

[0072] Among them, the wavelength of the detection light beam can be the wavelength of the laser emitted by the laser, which is used to irradiate various gases in the transformer. The laser has its own real-time monitoring and control of optical power, and automatically corrects the attenuation and drift that may be caused by long-term operation of the laser; the laser integrates a closed and self-constant pressure active photoacoustic cell and is equipped with a narrow linewidth semiconductor laser and its control circuit, a cantilever beam microphone and a precision amplification circuit, a digital filter and a phase-locked demodulation circuit, an ultra-precision power supply, data processing and storage, communication, and control, including a fully metal shielded and automatically constant temperature sealing structure.

[0073] Specifically, according to the insulating oil gas separation data and the gas concentration analysis method, determine the appropriate light beam wavelength range, which involves the absorption characteristics of different gases, and then set the light source of the gas detection component according to the initial gas detection parameters and the light beam wavelength range, and select the appropriate detection beam wavelength according to the initial gas detection parameters and the insulating oil gas separation data. According to the selected detection beam wavelength, adjust the optical components in the detection system to ensure that the light beam can remain stable and accurate during the transmission and detection process in the system. Verify and adjust the set detection beam wavelength to ensure that it matches the absorption characteristics of the selected gas component.

[0074] Step 404, controlling the gas detection component to detect the wavelength of the light beam, performing gas concentration analysis on the insulating oil and gas separation data, and obtaining an insulating oil and gas photoelectric signal.

[0075] The insulating oil and gas photoelectric signal may be an optical signal and an electrical signal generated when the detection light beam is irradiated to various gases in the transformer.

[0076] Specifically, the gas detection component is controlled to transmit the wavelength of the detection light beam to the insulating oil gas separation data representing various gases, and the gas absorption spectrum characteristics are used for detection. The changes in the absorption spectrum are analyzed through the insulating oil gas separation data, and the concentrations of various gases in the insulating oil are quantitatively measured. According to the changes in the photoelectric signal detected after the measurement, the insulating oil gas photoelectric signal is generated.

[0077] Step 406, determining the gas concentration analysis value according to the insulating oil gas photoelectric signal.

[0078] Specifically, the insulating oil gas photoelectric signal is converted into the corresponding gas concentration value by using a previously established calibration model or standard curve, and the calibration model or standard curve can be obtained by measuring and analyzing gas samples of different concentrations based on experimental data and theoretical models. The converted gas concentration value is verified and adjusted to ensure its accuracy and reliability, and the obtained gas concentration analysis value is the result determined according to the insulating oil gas photoelectric signal.

[0079] In this embodiment, by using the initial gas detection parameters, the wavelength of the detection beam of the gas detection component is set, so as to control the gas detection component to perform gas concentration analysis on the insulating oil and gas separation data. In this way, the photoelectric signals of the insulating oil and gas can be obtained, and then the gas concentration analysis value can be determined based on these signals. The benefit of this method is that it can accurately analyze and monitor the gas composition inside the transformer, which helps to promptly discover possible problems or abnormal conditions in the operation of the transformer, and provide key information for the health status of the transformer to ensure its safe and stable operation. By controlling the wavelength of the light beam, gas analysis can be carried out in a targeted manner, thereby improving the accuracy and precision of the analysis, making the obtained gas concentration analysis value more reliable.

[0080] In an exemplary embodiment, Figure 5 As shown, the gas detection component is controlled to detect the wavelength of the light beam, and the gas concentration analysis is performed on the insulating oil and gas separation data to obtain the insulating oil and gas photoelectric signal, including steps 502 to 504. Among them:

[0081] Step 502, controlling the gas detection component to detect the wavelength of the light beam, performing sound signal detection on the insulating oil and gas separation data, and obtaining the insulating oil and gas sound wave frequency and the insulating oil and gas sound wave amplitude.

[0082] The insulating oil gas sound wave frequency and the insulating oil gas sound wave amplitude are respectively the frequency and amplitude of the sound wave generated by the periodic change of the internal temperature of the transformer gas due to absorption of the detection light beam.

[0083] Specifically, the gas detection component is controlled to transmit the wavelength of the detection light beam to the insulating oil-gas separation data, and a sound sensing device such as an acoustic wave sensor or a microphone is used to detect and capture the sound signal caused by the periodic expansion and compression of the gas in the transformer due to the periodic change of the internal temperature caused by the absorption of the detection light beam. The captured sound signal is further subjected to frequency analysis and amplitude measurement to obtain the insulating oil-gas sound wave frequency and the insulating oil-gas sound wave amplitude.

[0084] Step 504 , performing spectral analysis on the insulating oil-gas separation data according to the insulating oil-gas acoustic wave frequency and the insulating oil-gas acoustic wave amplitude to obtain the insulating oil-gas photoelectric signal.

[0085] Specifically, characteristic information of the insulating oil-gas acoustic wave frequency and the insulating oil-gas acoustic wave amplitude are extracted to obtain the insulating oil-gas acoustic wave characteristics and the insulating oil-gas acoustic wave characteristics, and the insulating oil-gas acoustic wave characteristics and the insulating oil-gas acoustic wave characteristics are used to perform spectral analysis on the insulating oil-gas separation data to extract optical information of various gases in the transformer. The results of the spectral analysis are combined with the insulating oil-gas separation data to obtain the photoelectric signal of the insulating oil-gas.

[0086] In this embodiment, the sound signal detection is performed on the insulating oil and gas separation data by controlling the gas detection component to detect a specific light beam wavelength, thereby obtaining the sound wave frequency and amplitude information of the insulating oil and gas. Then, based on these sound wave frequencies and amplitudes, the insulating oil and gas separation data is spectrally analyzed to obtain the photoelectric signal of the insulating oil and gas. The benefit of this method is that it can obtain the information of the insulating oil and gas more comprehensively and accurately by combining sound signals and optical analysis. The sound wave frequency and amplitude provide important indicators about the state of the insulating oil and gas, while the spectral analysis further extracts the optical information related to the gas composition. The photoelectric signal obtained in this way can more accurately reflect the concentration and composition of the gas inside the transformer, providing important data support for subsequent fault diagnosis and prevention.

[0087] In an exemplary embodiment, Figure 6 As shown, the gas detection component is controlled to detect the wavelength of the light beam, and the sound signal detection is performed on the insulating oil and gas separation data to obtain the insulating oil and gas sound wave frequency and the insulating oil and gas sound wave amplitude, including steps 602 to 606. Among them:

[0088] Step 602, controlling the gas detection component to detect the wavelength of the light beam, performing sound signal detection on the insulating oil and gas separation data, and obtaining the insulating oil and gas temperature change information.

[0089] The insulating oil and gas temperature change information may be a situation where the wavelength of the detection light beam is irradiated into various gases in the transformer, causing the temperature of the gas to change.

[0090] Specifically, the gas detection component is controlled to transmit the detection light beam wavelength to the insulating oil and gas separation data, and a sound sensing device, such as an acoustic sensor or a microphone, is used to detect and capture the sound signal in the insulating oil, determine the temperature change data of the insulating oil and gas, and associate the acquired temperature change data with the corresponding timestamp to form the insulating oil and gas temperature change information.

[0091] Step 604, calculating the expansion amount of the insulating oil and the compression amount of the insulating oil according to the temperature change information of the insulating oil and the gas.

[0092] The insulating oil gas expansion amount may be any gas in the transformer, and the periodic change of temperature may cause the gas around the gas to generate periodic expansion amount.

[0093] The insulating oil gas compression amount may be any gas in the transformer, and the periodic change of temperature may cause the gas around the gas to generate periodic compression amount.

[0094] Specifically, according to the temperature change data of the insulating oil and gas, the expansion coefficient and compression coefficient of the insulating oil and gas under temperature change in the current scenario are calculated using physical principles such as thermal expansion and the ideal gas state equation. According to the initial volume and temperature of the insulating oil and gas, the molar mass of the insulating oil and gas, and other parameters, the volume of the insulating oil and gas at different temperatures is calculated using relevant formulas such as the ideal gas state equation, and compared with the initial volume to obtain the volume change of the insulating oil and gas. The expansion or compression of the insulating oil and gas is further calculated by combining the volume change of the insulating oil and gas, the expansion coefficient and compression coefficient of the insulating oil and gas under temperature change in the current scenario.

[0095] Step 606, determining the frequency and amplitude of the sound wave of the insulating oil and gas according to the expansion amount of the insulating oil and gas and the compression amount of the insulating oil and gas.

[0096] Specifically, the relationship model between the frequency and amplitude of sound waves and the expansion and compression amounts is derived by using the provided data on the expansion and compression amounts of insulating oil and gas, combined with the acoustic principle and knowledge of gas physics. The changes in the expansion and compression amounts are converted into corresponding changes in the frequency and amplitude of sound waves using the established model, and the converted changes in the frequency and amplitude of sound waves are analyzed and calculated to determine the frequency and amplitude of sound waves of insulating oil and gas at different temperatures.

[0097] In this embodiment, by controlling the gas detection component to detect a specific light beam wavelength, the insulating oil and gas separation data is subjected to sound signal detection to obtain the temperature change information of the insulating oil and gas. Using this temperature change information, the expansion and compression of the insulating oil and gas can be calculated, so as to know the state change of the gas in the transformer. Subsequently, according to the changes in the expansion and compression, the sound wave frequency and amplitude of the insulating oil and gas are determined. The advantage of this method is that the temperature change information of the insulating oil and gas can be obtained in real time through sound signal detection, and the expansion and compression of the gas can be accurately calculated in combination with the temperature change information, and then the sound wave frequency and amplitude can be determined, providing comprehensive monitoring and evaluation for the operating status of the transformer. Through this comprehensive analysis method, the dynamic changes of the gas inside the transformer can be better understood and grasped, problems can be discovered in time and measures can be taken to ensure the safe and stable operation of the transformer.

[0098] In an exemplary embodiment, Figure 7 As shown, the initial gas detection parameters include the attenuation of the detection component and the drift of the detection component; according to the gas concentration values, the initial gas detection parameters are self-learned to obtain the gas component adjustment parameters, including steps 702 to 706. Among them:

[0099] Step 702: According to the gas concentration value, the attenuation and drift of the detection component are adjusted to obtain initial component adjustment parameters.

[0100] The attenuation and drift of the detection component may be the attenuation and drift that occur when the laser of the detection component is working.

[0101] Specifically, the gas concentration value is input into a pre-set algorithm or model, and the attenuation and drift of the detection component are adjusted through the pre-set algorithm or model. The adjustment process involves calibration methods, compensation techniques, etc. to ensure that the detection component can accurately reflect the changes in the actual gas concentration. Based on the adjusted attenuation and drift, the initial component adjustment parameters are generated, including sensor sensitivity, zero calibration value, etc.

[0102] Step 704: adjust the component resonant frequency of the gas detection component according to the initial component adjustment parameter to obtain the gas component adjustment parameter.

[0103] The component resonant frequency may be the resonant frequency of a photoacoustic cell in a laser.

[0104] Specifically, based on the existing initial component adjustment parameters, including sensor sensitivity, zero point calibration value, etc., determine the method and strategy for adjusting the component resonant frequency of the gas detection component; based on the determined method and strategy, adjust the relevant components or parameters of the gas detection component, such as adjusting the circuit elements or mechanical structure, to change the resonant frequency of the component to obtain the gas component adjustment parameters; verify and adjust the gas component adjustment parameters to ensure that they are consistent with the expected values; and apply the verified gas component adjustment parameters to the gas detection component.

[0105] In this embodiment, the attenuation and drift of the detection component are adjusted using the gas concentration value, thereby obtaining the initial component adjustment parameters, and the component resonant frequency of the gas detection component can be further adjusted to obtain the required gas component adjustment parameters. The advantage of this method is that it dynamically adjusts the parameters of the detection component to better adapt to the changes in the gas composition inside the transformer. By adjusting the attenuation and drift, the detection error can be reduced and the accuracy and stability of the detection can be improved; and by adjusting the component resonant frequency, the detection component can respond more sensitively to changes in the gas composition, further improving the accuracy and reliability of the detection. Therefore, this method can effectively improve the performance of the gas monitoring system inside the transformer and provide reliable technical support for timely detection of potential problems and prevention of failures.

[0106] It should be understood that, although the steps in the flowcharts involved in the above 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 is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may 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 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.

[0107] Based on the same inventive concept, the embodiment of the present application also provides a main transformer maintenance-free oil chromatogram online monitoring device for implementing the above-mentioned main transformer maintenance-free oil chromatogram online monitoring method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more main transformer maintenance-free oil chromatogram online monitoring devices provided below can refer to the above-mentioned limitations on a main transformer maintenance-free oil chromatogram online monitoring method, which will not be repeated here.

[0108] In an exemplary embodiment, Figure 8 As shown, a main transformer maintenance-free oil chromatography online monitoring device is provided, including: a separation data acquisition module 802, a gas concentration calculation module 804 and a component parameter learning module 806, wherein:

[0109] The separation data acquisition module 802 is used to obtain the insulating oil and gas separation data corresponding to the transformer; the insulating oil and gas separation data is obtained by separating the insulating oil and gas through the vacuum degassing device of the transformer's online monitoring device;

[0110] The gas concentration calculation module 804 is used to perform gas concentration analysis on the insulating oil-gas separation data using the initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer;

[0111] The component parameter learning module 806 is used to self-learn the initial gas detection parameters according to the gas concentration values ​​to obtain the gas component adjustment parameters;

[0112] The gas concentration calculation module 804 is also used to use the gas component adjustment parameters as initial gas detection parameters, return to execute the steps of using the initial gas detection parameters to perform gas concentration analysis on the insulating oil and gas separation data to obtain the concentration values ​​of each gas in the transformer, until the gas concentration values ​​converge.

[0113] In one embodiment, the gas concentration calculation module 804 is also used to determine a gas concentration analysis method based on the insulating oil gas separation data; the gas concentration analysis method includes a single gas concentration analysis method and a multiple gas concentration analysis method; according to the gas concentration analysis method, the gas detection component of the transformer is controlled to perform a gas concentration analysis on the insulating oil gas separation data to obtain a gas concentration analysis value; and each gas concentration analysis value is subjected to a concentration change analysis to obtain each gas concentration value.

[0114] In one embodiment, the gas concentration calculation module 804 is also used to use the initial gas detection parameters to set the detection beam wavelength of the gas detection component; control the gas detection component to detect the beam wavelength, perform gas concentration analysis on the insulating oil and gas separation data, and obtain the insulating oil and gas photoelectric signal; determine the gas concentration analysis value based on the insulating oil and gas photoelectric signal.

[0115] In one embodiment, the gas concentration calculation module 804 is also used to control the gas detection component to detect the wavelength of the light beam, perform sound signal detection on the insulating oil-gas separation data, and obtain the insulating oil-gas sound wave frequency and the insulating oil-gas sound wave amplitude; according to the insulating oil-gas sound wave frequency and the insulating oil-gas sound wave amplitude, perform spectral analysis on the insulating oil-gas separation data to obtain the insulating oil-gas photoelectric signal.

[0116] In one embodiment, the gas concentration calculation module 804 is also used to control the gas detection component to detect the wavelength of the light beam, perform sound signal detection on the insulating oil and gas separation data, and obtain the insulating oil and gas temperature change information; calculate the insulating oil and gas expansion and the insulating oil and gas compression according to the insulating oil and gas temperature change information; determine the insulating oil and gas sound wave frequency and the insulating oil and gas sound wave amplitude according to the insulating oil and gas expansion and the insulating oil and gas compression.

[0117] In one embodiment, the component parameter learning module 806 is also used to adjust the attenuation and drift of the detection component according to the gas concentration value to obtain the initial component adjustment parameters; according to the initial component adjustment parameters, the component resonant frequency of the gas detection component is adjusted to obtain the gas component adjustment parameters.

[0118] Each module in the above-mentioned main transformer maintenance-free oil chromatography online monitoring device 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 the above modules.

[0119] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a main transformer maintenance-free oil chromatography online monitoring method is implemented.

[0120] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0121] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0122] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0123] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0124] 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 regulations.

[0125] 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 this 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), magnetoresistive 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 to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0126] 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.

[0127] 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 maintenance-free online monitoring method for a main transformer, characterized in that: The method comprises: Obtaining insulating oil and gas separation data corresponding to the transformer; the insulating oil and gas separation data is obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer; Using the initial gas detection parameters to perform gas concentration analysis on the insulating oil gas separation data to obtain the gas concentration values ​​in the transformer, specifically including: determining a gas concentration analysis method according to the insulating oil gas separation data; the gas concentration analysis method includes a single gas concentration analysis method and a multiple gas concentration analysis method; according to the gas concentration analysis method, controlling the gas detection component of the transformer to perform gas concentration analysis on the insulating oil gas separation data to obtain a gas concentration analysis value; the gas detection component includes a micro photoacoustic cell; the micro photoacoustic cell integrates a laser and a microphone; performing concentration change analysis on each of the gas concentration analysis values ​​to obtain each of the gas concentration values; Wherein, the initial gas detection parameters include the attenuation of the detection component and the drift of the detection component; the attenuation of the detection component and the drift of the detection component are the attenuation and drift conditions that occur when the laser of the gas detection component is working; According to each of the gas concentration values, the initial gas detection parameters are self-learned to obtain the gas detection component adjustment parameters, specifically including: establishing a self-learning model; wherein the self-learning model is based on a back propagation algorithm or a genetic algorithm, and automatically adjusts the parameters of the gas detection component according to the existing gas concentration value and the corresponding initial gas detection parameters, and gradually adjusts the parameters of the gas detection component through continuous iteration and optimization, so that the disturbance of the parameters of the gas detection component is continuously reduced; the gas concentration value is input into the self-learning model, and the attenuation of the detection component and the drift of the detection component are adjusted by the self-learning model to obtain the initial component adjustment parameters; the initial component adjustment parameters include sensor sensitivity and zero point calibration value; according to the initial component adjustment parameters, a method and strategy for adjusting the component resonant frequency of the gas detection component is determined; according to the method and strategy for adjusting the component resonant frequency of the gas detection component, the component resonant frequency of the gas detection component is adjusted to obtain the gas detection component adjustment parameters; the gas detection component adjustment parameters are verified to ensure that the gas detection component adjustment parameters are consistent with the expected values, and the verified gas detection component adjustment parameters are applied to the gas detection component; The verified gas detection component adjustment parameters are used as the initial gas detection parameters, and the step of performing gas concentration analysis on the insulating oil-gas separation data using the initial gas detection parameters to obtain the concentration values ​​of each gas in the transformer is returned to be executed until the gas concentration values ​​converge.

2. The method according to claim 1, characterized in that According to the gas concentration analysis method, controlling the gas detection component of the transformer to perform gas concentration analysis on the insulating oil and gas separation data to obtain a gas concentration analysis value includes: Using the initial gas detection parameters, setting the detection beam wavelength of the gas detection component; Controlling the gas detection component to detect the wavelength of the light beam, performing gas concentration analysis on the insulating oil and gas separation data, and obtaining an insulating oil and gas photoelectric signal; The gas concentration analysis value is determined according to the insulating oil gas photoelectric signal.

3. The method according to claim 2, characterized in that The controlling the gas detection component to detect the wavelength of the light beam, performing gas concentration analysis on the insulating oil and gas separation data, and obtaining the insulating oil and gas photoelectric signal, comprises: Controlling the gas detection component to detect the wavelength of the light beam, performing sound signal detection on the insulating oil and gas separation data, and obtaining the insulating oil and gas sound wave frequency and the insulating oil and gas sound wave amplitude; The insulating oil-gas separation data is spectrally analyzed according to the insulating oil-gas sound wave frequency and the insulating oil-gas sound wave amplitude to obtain the insulating oil-gas photoelectric signal.

4. The method according to claim 3, characterized in that The controlling the gas detection component to detect the wavelength of the light beam, performing sound signal detection on the insulating oil and gas separation data, and obtaining the insulating oil and gas sound wave frequency and the insulating oil and gas sound wave amplitude, comprises: Controlling the gas detection component to detect the wavelength of the light beam, performing sound signal detection on the insulating oil and gas separation data, and obtaining the insulating oil and gas temperature change information; Calculating the expansion amount of the insulating oil and the compression amount of the insulating oil according to the temperature change information of the insulating oil and the gas; The frequency of the insulating oil gas sound wave and the amplitude of the insulating oil gas sound wave are determined according to the expansion amount of the insulating oil gas and the compression amount of the insulating oil gas.

5. A maintenance-free online monitoring device for a main transformer, characterized in that: The device comprises: A separation data acquisition module is used to acquire insulating oil and gas separation data corresponding to the transformer; the insulating oil and gas separation data is obtained by separating the insulating oil and gas through a vacuum degassing device of an online monitoring device of the transformer; A gas concentration calculation module is used to perform gas concentration analysis on the insulating oil and gas separation data using initial gas detection parameters to obtain gas concentration values ​​of various gases in the transformer, specifically including: determining a gas concentration analysis method according to the insulating oil and gas separation data; the gas concentration analysis method includes a single gas concentration analysis method and a multiple gas concentration analysis method; according to the gas concentration analysis method, controlling the gas detection component of the transformer to perform gas concentration analysis on the insulating oil and gas separation data to obtain gas concentration analysis values; the gas detection component includes a micro photoacoustic cell; the micro photoacoustic cell integrates a laser and a microphone; performing concentration change analysis on each of the gas concentration analysis values ​​to obtain each of the gas concentration values; wherein the initial gas detection parameters include an attenuation amount of the detection component and a drift amount of the detection component; the attenuation amount of the detection component and the drift amount of the detection component are the attenuation and drift conditions that occur when the laser of the gas detection component is working; The component parameter learning module is used to self-learn the initial gas detection parameters according to each gas concentration value to obtain the gas detection component adjustment parameters, specifically including: establishing a self-learning model; wherein the self-learning model is based on a back propagation algorithm or a genetic algorithm, and automatically adjusts the parameters of the gas detection component according to the existing gas concentration value and the corresponding initial gas detection parameter, and gradually adjusts the parameters of the gas detection component through continuous iteration and optimization, so that the disturbance of the parameters of the gas detection component is continuously reduced; the gas concentration value is input into the self-learning model, and the attenuation of the detection component is adjusted by the self-learning model and adjusting the drift amount of the detection component to obtain initial component adjustment parameters; the initial component adjustment parameters include sensor sensitivity and zero calibration value; according to the initial component adjustment parameters, determining a method and strategy for adjusting the component resonant frequency of the gas detection component; according to the method and strategy for adjusting the component resonant frequency of the gas detection component, adjusting the component resonant frequency of the gas detection component to obtain gas detection component adjustment parameters; verifying the gas detection component adjustment parameters to ensure that the gas detection component adjustment parameters are consistent with expected values, and applying the verified gas detection component adjustment parameters to the gas detection component; The gas concentration calculation module is also used to use the verified gas detection component adjustment parameters as the initial gas detection parameters, return to execute the step of using the initial gas detection parameters to perform gas concentration analysis on the insulating oil and gas separation data to obtain the concentration values ​​of each gas in the transformer, until the gas concentration values ​​converge.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. 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 4 are implemented.

8. 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 4 are implemented.

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