Method and system for researching optimal parameter combination of power equipment operation

Abstract

The invention provides a method and system for researching an optimal parameter combination of power equipment operation, and the method comprises the steps: obtaining to-be-monitored target power equipment in response to a power monitoring instruction; when the to-be-monitored target power equipment operates according to the parameter combination, acquiring the actual output power of the to-be-monitored target power equipment; calculating the energy conversion efficiency of the target power equipment to be monitored according to the parameter combination and the actual output power; and according to the energy conversion efficiency and a preset energy conversion efficiency threshold, determining an optimal operation parameter combination of the to-be-monitored target power equipment. According to the optimal parameter combination of the operation of the target power equipment provided by the method and the system, the influence of environmental factors on the monitoring result is reduced, and the operation state of the equipment is comprehensively evaluated from the mechanical dimension and the electrical dimension, so that the comprehensiveness and the reliability of monitoring are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power acoustic monitoring, and more particularly to a method and system for studying an optimal parameter combination for operating electric power equipment. Background Art

[0002] Multi-parameter information is a type of information that combines multiple different types of parameters to comprehensively evaluate and monitor the operating status of power equipment. Power acoustics is a method that applies acoustic technology to the monitoring and fault diagnosis of power equipment.

[0003] The continuous expansion and increasing complexity of power systems have placed higher demands on real-time monitoring and fault diagnosis of power equipment. Traditional power monitoring methods primarily rely on monitoring power parameters. However, as power equipment ages and the operating environment becomes more complex, single power parameter monitoring can no longer fully reflect the operating status of the equipment. Furthermore, environmental factors such as temperature and humidity significantly impact the operating status of power equipment. Traditional monitoring methods often overlook the coordinated monitoring of environmental parameters, impacting the reliability of monitoring results. Therefore, how to improve the comprehensiveness and accuracy of monitoring by selecting appropriate parameters for operating power equipment is a technical problem that urgently needs to be addressed. Summary of the Invention

[0004] In order to solve the technical problem in the existing technology that single power parameter monitoring cannot fully reflect the operating status of the equipment and is not accurate, the present invention provides a method and system for studying the optimal parameter combination for the operation of power equipment, so as to improve the comprehensiveness and accuracy of power monitoring, optimize resource allocation and improve monitoring efficiency.

[0005] According to one aspect of the present invention, a method for studying an optimal parameter combination for operation of an electric power device is provided, comprising:

[0006] Responding to the power monitoring instruction, obtaining a target power device to be monitored;

[0007] When the target power equipment to be monitored operates according to the parameter combination, obtaining the actual output power of the target power equipment to be monitored, wherein the parameter combination is any parameter combination extracted from a predetermined parameter combination set, each parameter combination including a plurality of power parameters and acoustic parameters;

[0008] Calculating the energy conversion efficiency of the target power equipment to be monitored based on the parameter combination and the actual output power;

[0009] An optimal combination of operating parameters of the target power equipment to be monitored is determined based on the energy conversion efficiency and a preset energy conversion efficiency threshold.

[0010] According to another aspect of the present invention, a system for studying an optimal parameter combination for operation of an electric power device is provided, the system comprising:

[0011] A target device module, configured to obtain a target power device to be monitored in response to a power monitoring instruction;

[0012] a data acquisition module, configured to obtain the actual output power of the target power equipment to be monitored when the target power equipment to be monitored operates according to a parameter combination, wherein the parameter combination is any parameter combination extracted from a predetermined set of parameter combinations, each parameter combination including a plurality of power parameters and acoustic parameters;

[0013] a data calculation module, configured to calculate the energy conversion efficiency of the target power equipment to be monitored based on the parameter combination and the actual output power;

[0014] The result output module is used to determine the optimal operating parameter combination of the target power equipment to be monitored based on the energy conversion efficiency and a preset energy conversion efficiency threshold.

[0015] According to yet another aspect of the present invention, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for reading the executable instructions from the memory and executing the instructions to implement the method described in any one of the above aspects of the present invention.

[0017] The method and system for studying the optimal parameter combination for the operation of electric power equipment described in the present invention include: obtaining the target electric power equipment to be monitored in response to an electric power monitoring instruction; obtaining the actual output power of the target electric power equipment to be monitored when the target electric power equipment to be monitored operates according to the parameter combination; calculating the energy conversion efficiency of the target electric power equipment to be monitored based on the parameter combination and the actual output power; and determining the optimal parameter combination for the operation of the target electric power equipment to be monitored based on the energy conversion efficiency and a preset energy conversion efficiency threshold. The optimal parameter combination for the operation of the target electric power equipment provided by the method and system is a parameter sampling value of the operation of the electric power equipment obtained based on the optimal environmental parameters, which can ensure the consistency and stability of the monitoring environment, reduce the impact of environmental factors on the monitoring results, and improve the reliability of the monitoring data; further, the use of the optimal parameter combination to operate the target electric power equipment can eliminate redundant and unnecessary data and improve the efficiency of monitoring; the optimal parameter combination includes power parameters and acoustic parameters, which can comprehensively evaluate the operating status of the equipment from both mechanical and electrical dimensions, thereby ensuring the comprehensiveness and reliability of monitoring and improving the accuracy of fault diagnosis. In addition, the optimal parameter combination is determined from the initial multi-parameter combination set through the energy conversion criterion. By summarizing multiple effective initial multi-parameter combinations, a variety of choices are provided, increasing the flexibility of optimizing the optimal parameter combination for subsequent target power equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0019] Figure 1 A flowchart of a method for studying an optimal parameter combination for operation of an electric power device according to a preferred embodiment of the present invention;

[0020] Figure 2 A schematic structural diagram of a system for studying optimal parameter combinations for power equipment operation according to a preferred embodiment of the present invention;

[0021] Figure 3 Schematic diagram of the structure of an electronic device according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0022] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0023] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0024] Exemplary Methods

[0025] Figure 1 Flowchart of the method for studying the optimal parameter combination of power equipment operation according to the preferred embodiment of the present invention. Figure 1 As shown, the method for studying the optimal parameter combination for operating power equipment according to this preferred embodiment starts from step 101 .

[0026] In step 101 , in response to a power monitoring instruction, target power equipment to be monitored is acquired.

[0027] The present invention aims to improve the comprehensiveness and reliability of power equipment monitoring by determining the optimal parameter combination for the operation of the power equipment. Therefore, it is first necessary to determine the target object for power monitoring, that is, the target power equipment, according to the power monitoring instruction.

[0028] In step 102, when the target power equipment to be monitored operates according to the parameter combination, the actual output power of the target power equipment to be monitored is obtained, wherein the parameter combination is any parameter combination extracted from a predetermined parameter combination set, and each parameter combination includes several power parameters and acoustic parameters.

[0029] Preferably, the step of obtaining the target power equipment to be monitored in response to the power monitoring instruction further includes generating a parameter combination set, wherein:

[0030] Determining optimal environmental parameters based on historical data of the custom environmental parameters, wherein the environmental parameters include temperature and humidity;

[0031] Acquire an initial power device set whose operating environment satisfies the optimal environment parameters, wherein the initial power device set includes a plurality of initial power devices;

[0032] For each initial power device, acoustic parameters and power parameters are sampled separately according to a preset monitoring frequency, wherein the acoustic parameters include mechanical vibration sound, partial discharge sound and mechanical friction sound, and the power parameters include current, voltage and power;

[0033] Determining the maximum value of the acoustic parameter of each initial power device according to the acoustic parameter sampling value;

[0034] generating an acoustic parameter set according to maximum values ​​of acoustic parameters of all initial power devices in the initial power device set;

[0035] generating a power parameter set according to power parameter sampling values ​​of all initial power devices in the initial power device set;

[0036] Obtaining a plurality of optimal acoustic parameters according to the acoustic parameter set and preset acoustic parameter standard values, wherein the acoustic parameter standard values ​​include mechanical vibration sound standard values, partial discharge sound standard values, and mechanical friction sound standard values;

[0037] According to the power parameter set, obtaining a plurality of optimal power parameters;

[0038] A parameter combination set is generated according to a plurality of optimal acoustic parameters and optimal electrical parameters, wherein each parameter combination includes a mechanical vibration sound, a partial discharge sound, a mechanical friction sound, a current, a voltage and a power.

[0039] Preferably, determining the optimal environmental parameters based on the historical data of the customized environmental parameters includes:

[0040] Obtain historical temperature and humidity data sets for multiple historical detection periods;

[0041] Acquire a detection frequency according to the historical temperature dataset and the historical humidity dataset;

[0042] Based on the detection frequency, using a comprehensive sensor to collect data to generate a temperature data set and a humidity data set, wherein the comprehensive sensor includes a temperature sensor and a humidity sensor;

[0043] Denoising the temperature dataset and the humidity dataset to obtain a denoised temperature dataset and a denoised humidity dataset;

[0044] Calculate the temperature correlation between the historical temperature dataset and the denoised temperature dataset, and the humidity correlation between the historical humidity dataset and the denoised humidity dataset respectively;

[0045] When the temperature correlation or the humidity correlation is less than or equal to zero, returning to the step of collecting data using a comprehensive sensor based on the detection frequency to generate a temperature data set and a humidity data set, until both the temperature correlation and the humidity correlation are greater than zero;

[0046] When the temperature correlation and the humidity correlation are both greater than zero, the denoised temperature dataset and the denoised humidity dataset are confirmed as the standard temperature dataset and the standard humidity dataset;

[0047] The optimal environmental parameters are determined based on the standard temperature and humidity data sets.

[0048] In this preferred embodiment, the historical temperature dataset refers to a collection of all temperature data recorded at different time points over multiple past detection periods. The historical humidity dataset refers to a collection of all humidity data recorded at different time points over multiple past detection periods. The temperature sensor and humidity sensor are devices for detecting temperature and humidity, respectively, and are capable of converting temperature and humidity into electrical signals.

[0049] The multiple historical detection periods refer to periods during which environmental parameter detection was performed at different points in the past. The step of denoising the temperature and humidity datasets to obtain the denoised temperature and humidity datasets comprises denoising the temperature and humidity datasets using a median filter to obtain the denoised temperature and humidity datasets. The median filter method described in this embodiment of the present invention is prior art and will not be described in detail here.

[0050] Importantly, the steps of calculating the temperature correlation between the historical temperature dataset and the denoised temperature dataset, and calculating the humidity correlation between the historical humidity dataset and the denoised humidity dataset are to evaluate the consistency and correlation between the current detection data and the historical data. By calculating the correlation, the reliability of the current detection data can be ensured. Only when the correlation value is not zero can the current data be confirmed as standard data, thereby improving the accuracy of environmental parameter confirmation. Temperature correlation refers to the strength of the linear relationship between the historical temperature dataset and the denoised temperature dataset. The acquisition of humidity correlation is consistent with the acquisition of temperature correlation, and can achieve the same effect, which will not be repeated here.

[0051] Preferably, acquiring the detection frequency according to the historical temperature dataset and the historical humidity dataset includes:

[0052] Visualizing the historical temperature data set and the historical humidity data set to obtain a temperature curve and a humidity curve, wherein the horizontal axis of the temperature curve is time and the vertical axis is the temperature value, and the horizontal axis of the humidity curve is time and the vertical axis is the humidity value;

[0053] The curve fluctuation rate of the temperature curve and the humidity curve is calculated using a pre-built curve fluctuation rate formula, wherein the curve fluctuation rate formula is as follows:

[0054]

[0055] Where σ represents the volatility of the curve, N represents the total number of data points on the curve, k represents the index variable for summation, and x k represents the kth data point, and μ represents the average value of all data on the curve;

[0056] Determine the curve volatility type according to the curve volatility σ and the custom curve volatility threshold, wherein when σ is not less than σ0, it indicates that the curve volatility is high; otherwise, it indicates low;

[0057] The detection frequency is determined according to the type of the curve fluctuation rate.

[0058] It should be explained that the step of visualizing the historical temperature dataset and the historical humidity dataset is: visualizing the historical temperature dataset and the historical humidity dataset using a visualization tool, such as Excel.

[0059] It should be explained that a high volatility refers to significant data variability, requiring a higher detection frequency to capture these changes. A low volatility refers to minimal data variability, requiring a lower detection frequency to conserve resources and costs. The step of determining the detection frequency based on the high and low volatility rates is intended to adjust the detection frequency of the data acquisition equipment based on the high and low volatility rates, thereby optimizing the data acquisition strategy and improving the accuracy and efficiency of data analysis.

[0060] Preferably, the temperature correlation between the historical temperature dataset and the denoised temperature dataset is calculated, comprising:

[0061] Calculating a historical temperature average value based on the historical temperature data set, and calculating a denoised temperature average value based on the denoised temperature data set;

[0062] The temperature correlation between the historical temperature dataset and the denoised temperature dataset is calculated using a pre-built Pearson correlation coefficient formula, the historical temperature average value, and the denoised temperature average value. The Pearson correlation coefficient formula is as follows:

[0063]

[0064] Among them, r represents temperature dependence, Y i represents the i-th temperature value in the denoised temperature dataset, X i represents the i-th historical temperature value in the historical temperature dataset, X represents the average value of the historical temperature dataset, and Y represents the average value of the denoised temperature dataset.

[0065] It should be explained that the steps of obtaining the average value of the historical temperature data set and obtaining the average value of the denoised temperature data set are: calculating the average value using the following formula:

[0066]

[0067] Where J represents the mean, l represents the number of data points in the dataset, o represents the index of the data point, and J o represents the oth data point.

[0068] It is understood that the Pearson correlation coefficient formula is a statistical indicator formula used to measure the strength of the linear relationship between two variables. In the embodiments of the present invention, the Pearson correlation coefficient formula is used to calculate the temperature correlation between the historical temperature dataset and the denoised temperature dataset. The historical temperature average refers to the average of all temperature values ​​in the historical temperature dataset. The denoised temperature average refers to the average of all temperature values ​​in the denoised temperature dataset.

[0069] It should be explained that the steps of generating a power parameter set based on the power parameter sampling values ​​of all the initial power equipment in the initial power equipment set are: installing current transformers and voltage transformers on the initial power equipment, and monitoring the initial power equipment using a preset monitoring frequency to obtain voltage signals and current signals, calculating the power through the voltage signals and current signals, and confirming the current, voltage and power as power parameters.

[0070] Preferably, determining the maximum value of the acoustic parameter of each initial power device according to the acoustic parameter sampling value includes:

[0071] generating three time-domain signal sets according to the acoustic parameter sampling values, namely a mechanical vibration acoustic signal set, a partial discharge acoustic signal set, and a mechanical friction acoustic signal set;

[0072] Perform fast Fourier transform on each time domain signal set to obtain the corresponding frequency domain signal set;

[0073] Plotting the frequency domain signal set into a spectrum diagram, wherein the horizontal axis of the frequency domain diagram is frequency and the vertical axis is amplitude;

[0074] Extracting a maximum peak from the spectrum graph, wherein the maximum peak is the maximum value extracted from the spectrum graph for the first time, and values ​​extracted subsequently are all smaller than the maximum value;

[0075] According to the maximum peak value obtained from each time domain signal set, the maximum value of the acoustic parameter of each initial power device is obtained by summarizing.

[0076] It should be clarified that multiple time-domain signal sets refer to datasets of raw sound acquired from multiple sensors. Fast Fourier transform (FFT) is an efficient algorithm used to convert time-domain signals into frequency-domain signals, making it easier to analyze the signal's frequency components. A frequency-domain signal set refers to a set of frequency-domain data obtained by performing a FFT on a time-domain signal set. A preset monitoring frequency refers to a pre-set monitoring frequency used to monitor initial power equipment.

[0077] Preferably, the step of obtaining a plurality of optimal acoustic parameters according to the acoustic parameter set and preset acoustic parameter standard values ​​includes:

[0078] For each maximum value of the acoustic parameter in the acoustic parameter set, the Euclidean distance between the maximum value and the preset standard value of the acoustic parameter is calculated, and the calculation formula is:

[0079]

[0080] Where D represents the Euclidean distance, G0 represents the standard value of mechanical vibration sound, G represents the maximum peak value of mechanical vibration sound, B0 represents the standard value of partial discharge sound, B represents the maximum peak value of partial discharge sound, M0 represents the standard value of mechanical friction sound, and M represents the maximum peak value of mechanical friction sound.

[0081] Sort the calculated Euclidean distances in ascending order to obtain an ascending distance set;

[0082] A low-order distance group is extracted from the ascending distance set, and the maximum value of the acoustic parameter corresponding to the low-order distance group is confirmed as the optimal acoustic parameter, wherein the low-order distance group includes several smallest Euclidean distances in the ascending distance set.

[0083] It should be explained that the acoustic parameter standard value refers to the parameter value obtained by taking the average value of multiple measurements of the normally operating mechanical equipment. The ascending distance set refers to the set obtained by sorting the Euclidean distance set in ascending order.

[0084] For example, the standard value of mechanical vibration sound is 50, the standard value of partial discharge sound is 30, and the standard value of mechanical friction sound is 20. By collecting acoustic parameters of the initial power equipment, the acoustic parameter set {(48, 29, 18), (52, 31, 19), (51, 28, 21), (50, 30, 22)} is obtained. The Euclidean distance formula is used to calculate the Euclidean distance between each acoustic parameter in the acoustic parameter set and the standard acoustic parameter to obtain the Euclidean distance set (3, 3.16, 2.65, 2). The Euclidean distance set is sorted in ascending order to obtain the ascending distance set (2, 2.65, 3, 3.16). The low-order distance group (2, 2.65, 3) is extracted from the ascending distance set, and the acoustic parameters corresponding to the low-order distance group are confirmed as multiple optimal acoustic parameters {(50, 30, 22), (51, 28, 21), (48, 29, 18)}.

[0085] Preferably, obtaining a plurality of optimal power parameters according to the power parameter set includes:

[0086] For the n groups of sampled values ​​of power parameters in the power parameter set, the average value a of the sampled values ​​of the i-th group of power parameters is calculated respectively. i and standard deviation b i ;

[0087] Generate a mean value set and a standard deviation set of power parameters according to the mean values ​​and standard deviations of the samples of n groups of power parameters;

[0088] According to the mean value set and the standard deviation set, a statistical matrix is ​​generated, and its expression is:

[0089]

[0090] Where T represents the statistical matrix;

[0091] Summing the elements in each row vector of the statistical matrix to obtain a mean sum value;

[0092] Generate a mean sum value set according to all mean sum values ​​in the statistical matrix;

[0093] Sorting the mean sum values ​​in the mean sum value set in descending order to obtain a sequence sum value set;

[0094] Extracting a high-order sum value group from the sequence and value set, wherein the high-order sum value group refers to a subset consisting of several items in the sequence and value set with the largest average sum value;

[0095] The sampled values ​​of several groups of power parameters corresponding to the high-order sum value group are used as the corresponding optimal power parameters.

[0096] It should be explained that the mean value set refers to the set of mean values ​​of the sampled values ​​of each group of power parameters in the power parameter set. The standard deviation set refers to the set of standard deviations of the sampled values ​​of each group of power parameters in the power parameter set. The row vector refers to the data vector represented by each row in the statistical matrix. The mean sum value refers to the mean value and standard deviation extracted from the row vector in the statistical matrix, and the sum of the mean value and the standard deviation is calculated. The sequence sum value set refers to the set of the mean square sum values ​​of all power parameters arranged in descending order. The high order sum value group refers to the subset consisting of the first three items with the largest mean square sum value in the sequence sum value set.

[0097] For example, the power parameter set is as follows:

[0098] {(220,2,1100),(221,6,1120),(219,4,1080),(222,7,1130)}

[0099] The mean and standard deviation of the sampling values ​​of each group of power parameters in the power parameter set are calculated to obtain the mean value set (440, 449, 434, 453) and the standard deviation set (474, 483, 467, 486). The sum of the mean and standard deviation is calculated to obtain the mean sum value set (914, 932, 901, 939). The mean sum values ​​in the mean sum value set are sorted in descending order to obtain the sequence sum value set (939, 932, 914, 901). The high-order sum value group (939, 932, 914) is extracted from the sequence sum value set, and the corresponding power parameters in the high-order sum value group are confirmed as multiple optimal power parameters {(222, 7, 1130), (221, 6, 1120), (220, 2, 1100)}.

[0100] In step 103 , the energy conversion efficiency of the target power equipment to be monitored is calculated according to the parameter combination and the actual output power.

[0101] Preferably, the energy conversion efficiency of the target power equipment to be monitored is calculated based on the parameter combination and the actual output power, and the calculation formula is:

[0102]

[0103] Where, represents the energy conversion efficiency, P represents the actual output power, I represents the current parameter in the parameter combination, and V represents the voltage parameter in the parameter combination.

[0104] In step 104 , an optimal combination of operating parameters of the target power equipment to be monitored is determined based on the energy conversion efficiency and a preset energy conversion efficiency threshold.

[0105] Preferably, the determining of the optimal operating parameter combination of the target power equipment to be monitored based on the energy conversion efficiency and a preset energy conversion efficiency threshold comprises:

[0106] An initial multi-parameter combination set is generated based on all parameter combinations in the parameter combination set that satisfy the energy conversion criterion, wherein the expression of the energy conversion criterion is:

[0107]

[0108] Where, is the energy conversion efficiency threshold;

[0109] The initial multi-parameter combinations are concentrated, and the initial multi-parameter combination corresponding to the maximum energy conversion efficiency is determined as the optimal parameter combination for the operation of the target power equipment to be monitored, wherein the maximum energy conversion efficiency.

[0110] For example, multiple optimal acoustic parameters {(50, 30, 22), (51, 28, 21), (48, 29, 18)}, multiple optimal power parameters {(222, 7, 1130), (221, 6, 1120), (220, 2, 1100)} are fused to obtain a parameter combination set as shown below:

[0111] {(50,30,22,222,7,1130),(50,30,22,221,6,1120),(50,30,22,220,2,1100),(51,28,21,222,7,1130),(51,28,21,221,6,1120),(51,28,21,220,2,1100),(48,29,18,222,7,1130),(48,29,18,221,6,1120),(48,29,18,220,2,1100)}.

[0112] The optimal parameter combination for the operation of the target power equipment provided by the method for studying the optimal parameter combination for the operation of power equipment described in this preferred embodiment is a parameter sampling value of the power equipment operation obtained based on the optimal environmental parameters. Using the optimal parameter combination to operate the target power equipment can eliminate redundant and unnecessary data. Moreover, the optimal parameter combination includes power parameters and acoustic parameters, which can comprehensively evaluate the operating status of the equipment from both mechanical and electrical dimensions, thereby ensuring the comprehensiveness and reliability of monitoring and improving the accuracy of fault diagnosis. Furthermore, the optimal parameter combination is determined from the initial multi-parameter combination set through the energy conversion criterion. By summarizing multiple valid initial multi-parameter combinations, a variety of options are provided, increasing the flexibility of subsequent optimization of the optimal parameter combination for the operation of the target power equipment.

[0113] Exemplary Systems

[0114] The figure is a schematic diagram of the structure of a system for studying the optimal parameter combination of power equipment operation according to a preferred embodiment of the present invention. Figure 2 As shown, the system 200 for studying the optimal parameter combination of power equipment operation according to this preferred embodiment includes:

[0115] The target device module 201 is configured to obtain a target power device to be monitored in response to a power monitoring instruction;

[0116] a data acquisition module 202 for acquiring actual output power of the target power equipment to be monitored when the target power equipment to be monitored operates according to a parameter combination, wherein the parameter combination is any parameter combination extracted from a predetermined set of parameter combinations, each parameter combination including a plurality of power parameters and acoustic parameters;

[0117] A data calculation module 203 is configured to calculate the energy conversion efficiency of the target power equipment to be monitored based on the parameter combination and the actual output power;

[0118] The result output module 204 is configured to determine an optimal combination of operating parameters of the target power equipment to be monitored based on the energy conversion efficiency and a preset energy conversion efficiency threshold.

[0119] Preferably, the system further comprises an initial set module for generating a parameter combination set, wherein:

[0120] Determining optimal environmental parameters based on historical data of the custom environmental parameters, wherein the environmental parameters include temperature and humidity;

[0121] Acquire an initial power device set whose operating environment satisfies the optimal environment parameters, wherein the initial power device set includes a plurality of initial power devices;

[0122] For each initial power device, acoustic parameters and power parameters are sampled separately according to a preset monitoring frequency, wherein the acoustic parameters include mechanical vibration sound, partial discharge sound and mechanical friction sound, and the power parameters include current, voltage and power;

[0123] Determining the maximum value of the acoustic parameter of each initial power device according to the acoustic parameter sampling value;

[0124] generating an acoustic parameter set according to maximum values ​​of acoustic parameters of all initial power devices in the initial power device set;

[0125] generating a power parameter set according to power parameter sampling values ​​of all initial power devices in the initial power device set;

[0126] Obtaining a plurality of optimal acoustic parameters according to the acoustic parameter set and preset acoustic parameter standard values, wherein the acoustic parameter standard values ​​include mechanical vibration sound standard values, partial discharge sound standard values, and mechanical friction sound standard values;

[0127] According to the power parameter set, obtaining a plurality of optimal power parameters;

[0128] A parameter combination set is generated according to a plurality of optimal acoustic parameters and optimal electrical parameters, wherein each parameter combination includes a mechanical vibration sound, a partial discharge sound, a mechanical friction sound, a current, a voltage and a power.

[0129] Preferably, the initial set module determines the optimal environmental parameters based on the custom environmental parameters and historical data of the environmental parameters, including:

[0130] Obtain historical temperature and humidity data sets for multiple historical detection periods;

[0131] Acquire a detection frequency according to the historical temperature dataset and the historical humidity dataset;

[0132] Based on the detection frequency, using a comprehensive sensor to collect data to generate a temperature data set and a humidity data set, wherein the comprehensive sensor includes a temperature sensor and a humidity sensor;

[0133] Denoising the temperature dataset and the humidity dataset to obtain a denoised temperature dataset and a denoised humidity dataset;

[0134] Calculate the temperature correlation between the historical temperature dataset and the denoised temperature dataset, and the humidity correlation between the historical humidity dataset and the denoised humidity dataset respectively;

[0135] When the temperature correlation or the humidity correlation is less than or equal to zero, returning to the step of collecting data using a comprehensive sensor based on the detection frequency to generate a temperature data set and a humidity data set, until both the temperature correlation and the humidity correlation are greater than zero;

[0136] When the temperature correlation and the humidity correlation are both greater than zero, the denoised temperature dataset and the denoised humidity dataset are confirmed as the standard temperature dataset and the standard humidity dataset;

[0137] The optimal environmental parameters are determined based on the standard temperature and humidity data sets.

[0138] Preferably, the initial collection module obtains the detection frequency according to the historical temperature data set and the historical humidity data set, including:

[0139] Visualizing the historical temperature data set and the historical humidity data set to obtain a temperature curve and a humidity curve, wherein the horizontal axis of the temperature curve is time and the vertical axis is the temperature value, and the horizontal axis of the humidity curve is time and the vertical axis is the humidity value;

[0140] The curve fluctuation rate of the temperature curve and the humidity curve is calculated using a pre-built curve fluctuation rate formula, wherein the curve fluctuation rate formula is as follows:

[0141]

[0142] Where σ represents the volatility of the curve, N represents the total number of data points on the curve, k represents the index variable for summation, and x k represents the kth data point, and μ represents the average value of all data on the curve;

[0143] Determine the curve volatility type according to the curve volatility σ and the custom curve volatility threshold, wherein when σ is not less than σ0, it indicates that the curve volatility is high; otherwise, it indicates low;

[0144] The detection frequency is determined according to the type of the curve fluctuation rate.

[0145] Preferably, the initial set module calculates the temperature correlation between the historical temperature data set and the denoised temperature data set, including

[0146] Calculating a historical temperature average value based on the historical temperature data set, and calculating a denoised temperature average value based on the denoised temperature data set;

[0147] The temperature correlation between the historical temperature dataset and the denoised temperature dataset is calculated using a pre-built Pearson correlation coefficient formula, the historical temperature average value, and the denoised temperature average value. The Pearson correlation coefficient formula is as follows:

[0148]

[0149] Among them, r represents temperature dependence, Y i represents the i-th temperature value in the denoised temperature dataset, X i represents the i-th historical temperature value in the historical temperature dataset, X represents the average value of the historical temperature dataset, and Y represents the average value of the denoised temperature dataset.

[0150] Preferably, the initial set module determines the maximum value of the acoustic parameter of each initial power device according to the acoustic parameter sampling value, including:

[0151] generating three time-domain signal sets according to the acoustic parameter sampling values, namely a mechanical vibration acoustic signal set, a partial discharge acoustic signal set, and a mechanical friction acoustic signal set;

[0152] Perform fast Fourier transform on each time domain signal set to obtain the corresponding frequency domain signal set;

[0153] Plotting the frequency domain signal set into a spectrum diagram, wherein the horizontal axis of the frequency domain diagram is frequency and the vertical axis is amplitude;

[0154] Extracting a maximum peak from the spectrum graph, wherein the maximum peak is the maximum value extracted from the spectrum graph for the first time, and values ​​extracted subsequently are all smaller than the maximum value;

[0155] According to the maximum peak value obtained from each time domain signal set, the maximum value of the acoustic parameter of each initial power device is obtained by summarizing.

[0156] Preferably, the initial set module obtains several optimal acoustic parameters according to the acoustic parameter set and preset acoustic parameter standard values, including:

[0157] For each maximum value of the acoustic parameter in the acoustic parameter set, the Euclidean distance between the maximum value and the preset standard value of the acoustic parameter is calculated, and the calculation formula is:

[0158]

[0159] Where D represents the Euclidean distance, G0 represents the standard value of mechanical vibration sound, G represents the maximum peak value of mechanical vibration sound, B0 represents the standard value of partial discharge sound, B represents the maximum peak value of partial discharge sound, M0 represents the standard value of mechanical friction sound, and M represents the maximum peak value of mechanical friction sound.

[0160] Sort the calculated Euclidean distances in ascending order to obtain an ascending distance set;

[0161] A low-order distance group is extracted from the ascending distance set, and the maximum value of the acoustic parameter corresponding to the low-order distance group is confirmed as the optimal acoustic parameter, wherein the low-order distance group includes several smallest Euclidean distances in the ascending distance set.

[0162] Preferably, the initial set module obtains several optimal power parameters according to the power parameter set, including:

[0163] For the n groups of sampled values ​​of power parameters in the power parameter set, the average value a of the sampled values ​​of the i-th group of power parameters is calculated respectively. i and standard deviation b i ;

[0164] Generate a mean value set and a standard deviation set of power parameters according to the mean values ​​and standard deviations of the samples of n groups of power parameters;

[0165] According to the mean value set and the standard deviation set, a statistical matrix is ​​generated, and its expression is:

[0166]

[0167] Where T represents the statistical matrix;

[0168] Summing the elements in each row vector of the statistical matrix to obtain a mean sum value;

[0169] Generate a mean sum value set according to all mean sum values ​​in the statistical matrix;

[0170] Sorting the mean sum values ​​in the mean sum value set in descending order to obtain a sequence sum value set;

[0171] Extracting a high-order sum value group from the sequence and value set, wherein the high-order sum value group refers to a subset consisting of several items in the sequence and value set with the largest average sum value;

[0172] The sampled values ​​of several groups of power parameters corresponding to the high-order sum value group are used as the corresponding optimal power parameters.

[0173] Preferably, the data calculation module 203 calculates the energy conversion efficiency of the target power equipment to be monitored according to the parameter combination and the actual output power, and the calculation formula is:

[0174]

[0175] Where, represents the energy conversion efficiency, P represents the actual output power, I represents the current parameter in the parameter combination, and V represents the voltage parameter in the parameter combination.

[0176] Preferably, the result output module 204 determines the optimal operating parameter combination of the target power equipment to be monitored according to the energy conversion efficiency and a preset energy conversion efficiency threshold, including:

[0177] An initial multi-parameter combination set is generated based on all parameter combinations in the parameter combination set that satisfy the energy conversion criterion, wherein the expression of the energy conversion criterion is:

[0178]

[0179] Where, is the energy conversion efficiency threshold;

[0180] The initial multi-parameter combinations are concentrated, and the initial multi-parameter combination corresponding to the maximum energy conversion efficiency is determined as the optimal parameter combination for the operation of the target power equipment to be monitored, wherein the maximum energy conversion efficiency.

[0181] The system for studying the optimal parameter combination for the operation of power equipment described in this preferred embodiment and the method for studying the optimal parameter combination for the operation of power equipment have the same steps of screening parameter combinations from a parameter combination set as the optimal parameter combination for the operation of the target power equipment, and the technical effects achieved are also the same, which will not be repeated here.

[0182] Exemplary electronic devices

[0183] Figure 3 1 is a schematic diagram of the structure of an electronic device according to a preferred embodiment of the present invention. The electronic device can be either or both of the first device and the second device, or a standalone device independent of them. The standalone device can communicate with the first device and the second device to receive collected input signals from them. Figure 3FIG2 is a block diagram of an electronic device according to an embodiment of the present disclosure. Figure 3 As shown, the electronic device includes one or more processors 301 and a memory 302 .

[0184] The processor 301 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0185] The memory 302 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may run the program instructions to implement the energy consumption anomaly diagnosis method based on the enterprise energy consumption space of the various embodiments disclosed above and / or other desired functions. In one example, the electronic device may further include: an input device 303 and an output device 304, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0186] In addition, the input device 303 may also include, for example, a keyboard, a mouse, and the like.

[0187] The output device 304 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0188] Of course, to simplify, Figure 3 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0189] Exemplary computer program products and computer-readable storage media

[0190] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the method of studying the optimal parameter combination for the operation of power equipment according to various embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0191] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0192] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method for studying the optimal parameter combination for the operation of power equipment according to various embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0193] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0194] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0195] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. For system embodiments, since they largely correspond to method embodiments, their description is relatively simple. For relevant parts, references to the description of the method embodiments are sufficient.

[0196] The block diagrams of the devices, devices, equipment, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0197] The apparatus and method of the present disclosure may be implemented in many ways. For example, the apparatus and method of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers recording media that store programs for executing the method according to the present disclosure.

[0198] It should also be noted that, in the apparatus, equipment and method of the present disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present disclosure. The above description of the disclosed aspects is provided to enable any technician in this field to make or use the present disclosure. Various modifications to these aspects will be very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown here, but to the widest range consistent with the principles and novel features disclosed herein.

[0199] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for studying the optimal parameter combination of power equipment operation, characterized in that: The method comprises: Responding to the power monitoring instruction, obtaining a target power device to be monitored; When the target power equipment to be monitored operates according to the parameter combination, obtaining the actual output power of the target power equipment to be monitored, wherein the parameter combination is any parameter combination extracted from a predetermined parameter combination set, each parameter combination including a plurality of power parameters and acoustic parameters; Calculating the energy conversion efficiency of the target power equipment to be monitored based on the parameter combination and the actual output power; An optimal combination of operating parameters of the target power equipment to be monitored is determined based on the energy conversion efficiency and a preset energy conversion efficiency threshold.

2. The method according to claim 1, characterized in that The step of obtaining the target power equipment to be monitored in response to the power monitoring instruction also includes generating a parameter combination set, wherein: Determining optimal environmental parameters based on historical data of the custom environmental parameters, wherein the environmental parameters include temperature and humidity; Acquire an initial power device set whose operating environment satisfies the optimal environment parameters, wherein the initial power device set includes a plurality of initial power devices; For each initial power device, acoustic parameters and power parameters are sampled separately according to a preset monitoring frequency, wherein the acoustic parameters include mechanical vibration sound, partial discharge sound and mechanical friction sound, and the power parameters include current, voltage and power; Determining the maximum value of the acoustic parameter of each initial power device according to the acoustic parameter sampling value; generating an acoustic parameter set according to maximum values ​​of acoustic parameters of all initial power devices in the initial power device set; generating a power parameter set according to power parameter sampling values ​​of all initial power devices in the initial power device set; Obtaining a plurality of optimal acoustic parameters according to the acoustic parameter set and preset acoustic parameter standard values, wherein the acoustic parameter standard values ​​include mechanical vibration sound standard values, partial discharge sound standard values, and mechanical friction sound standard values; According to the power parameter set, obtaining a plurality of optimal power parameters; A parameter combination set is generated according to a plurality of optimal acoustic parameters and optimal electrical parameters, wherein each parameter combination includes a mechanical vibration sound, a partial discharge sound, a mechanical friction sound, a current, a voltage and a power.

3. The method according to claim 2, characterized in that The step of determining the optimal environmental parameters based on the customized environmental parameters and historical data of the environmental parameters includes: Obtain historical temperature and humidity data sets for multiple historical detection periods; Acquire a detection frequency according to the historical temperature dataset and the historical humidity dataset; Based on the detection frequency, using a comprehensive sensor to collect data to generate a temperature data set and a humidity data set, wherein the comprehensive sensor includes a temperature sensor and a humidity sensor; Denoising the temperature dataset and the humidity dataset to obtain a denoised temperature dataset and a denoised humidity dataset; Calculate the temperature correlation between the historical temperature dataset and the denoised temperature dataset, and the humidity correlation between the historical humidity dataset and the denoised humidity dataset respectively; When the temperature correlation or the humidity correlation is less than or equal to zero, returning to the step of collecting data using a comprehensive sensor based on the detection frequency to generate a temperature data set and a humidity data set, until both the temperature correlation and the humidity correlation are greater than zero; When the temperature correlation and the humidity correlation are both greater than zero, the denoised temperature dataset and the denoised humidity dataset are confirmed as the standard temperature dataset and the standard humidity dataset; The optimal environmental parameters are determined based on the standard temperature and humidity data sets.

4. The method according to claim 3, characterized in that The acquiring the detection frequency according to the historical temperature dataset and the historical humidity dataset includes: Visualizing the historical temperature data set and the historical humidity data set to obtain a temperature curve and a humidity curve, wherein the horizontal axis of the temperature curve is time and the vertical axis is the temperature value, and the horizontal axis of the humidity curve is time and the vertical axis is the humidity value; The curve fluctuation rates of the temperature curve and the humidity curve are calculated using a pre-built curve fluctuation rate formula, wherein the curve fluctuation rate formula is as follows: In the formula, σ represents the volatility of the curve, N represents the total number of data points on the curve, k represents the index variable of the summation, and x k represents the kth data point, and μ represents the average value of all data on the curve; Determine the curve volatility type according to the curve volatility σ and the custom curve volatility threshold, wherein when σ is not less than σ0, it indicates that the curve volatility is high; otherwise, it indicates low; The detection frequency is determined according to the type of the curve fluctuation rate.

5. The method according to claim 3, characterized in that The temperature correlation between the historical temperature dataset and the denoised temperature dataset is calculated, including Calculating a historical temperature average value based on the historical temperature data set, and calculating a denoised temperature average value based on the denoised temperature data set; The temperature correlation between the historical temperature dataset and the denoised temperature dataset is calculated using a pre-built Pearson correlation coefficient formula, the historical temperature average value, and the denoised temperature average value. The Pearson correlation coefficient formula is as follows: Among them, r represents temperature dependence, Y i represents the i-th temperature value in the denoised temperature dataset, X i represents the i-th historical temperature value in the historical temperature dataset, X represents the average value of the historical temperature dataset, and Y represents the average value of the denoised temperature dataset.

6. The method according to claim 2, characterized in that Determining the maximum value of the acoustic parameter of each initial power device according to the acoustic parameter sampling value includes: generating three time-domain signal sets according to the acoustic parameter sampling values, namely a mechanical vibration acoustic signal set, a partial discharge acoustic signal set, and a mechanical friction acoustic signal set; Perform fast Fourier transform on each time domain signal set to obtain the corresponding frequency domain signal set; Plotting the frequency domain signal set into a spectrum diagram, wherein the horizontal axis of the frequency domain diagram is frequency and the vertical axis is amplitude; Extracting a maximum peak from the spectrum graph, wherein the maximum peak is the maximum value extracted from the spectrum graph for the first time, and values ​​extracted subsequently are all smaller than the maximum value; According to the maximum peak value obtained from each time domain signal set, the maximum value of the acoustic parameter of each initial power device is obtained by summarizing.

7. The method according to claim 6, characterized in that The step of obtaining a plurality of optimal acoustic parameters according to the acoustic parameter set and preset acoustic parameter standard values ​​includes: For each maximum value of the acoustic parameter in the acoustic parameter set, the Euclidean distance between the maximum value and the preset standard value of the acoustic parameter is calculated, and the calculation formula is: Where D represents the Euclidean distance, G0 represents the standard value of mechanical vibration sound, G represents the maximum peak value of mechanical vibration sound, B0 represents the standard value of partial discharge sound, B represents the maximum peak value of partial discharge sound, M0 represents the standard value of mechanical friction sound, and M represents the maximum peak value of mechanical friction sound. Sort the calculated Euclidean distances in ascending order to obtain an ascending distance set; A low-order distance group is extracted from the ascending distance set, and the maximum value of the acoustic parameter corresponding to the low-order distance group is confirmed as the optimal acoustic parameter, wherein the low-order distance group includes several smallest Euclidean distances in the ascending distance set.

8. The method according to claim 2, characterized in that The step of obtaining a plurality of optimal power parameters according to the power parameter set includes: For the n groups of sampled values ​​of power parameters in the power parameter set, the average value a of the sampled values ​​of the i-th group of power parameters is calculated respectively. i and standard deviation b i ; Generate a mean value set and a standard deviation set of power parameters according to the mean values ​​and standard deviations of the samples of n groups of power parameters; According to the mean value set and the standard deviation set, a statistical matrix is ​​generated, and its expression is: Where T represents the statistical matrix; Summing the elements in each row vector of the statistical matrix to obtain a mean sum value; Generate a mean sum value set according to all mean sum values ​​in the statistical matrix; Sorting the mean sum values ​​in the mean sum value set in descending order to obtain a sequence sum value set; Extracting a high-order sum value group from the sequence and value set, wherein the high-order sum value group refers to a subset consisting of several items in the sequence and value set with the largest average sum value; The sampled values ​​of several groups of power parameters corresponding to the high-order sum value group are used as the corresponding optimal power parameters.

9. The method according to claim 1, characterized in that The energy conversion efficiency of the target power equipment to be monitored is calculated based on the parameter combination and the actual output power, and the calculation formula is: Where, represents the energy conversion efficiency, P represents the actual output power, I represents the current parameter in the parameter combination, and V represents the voltage parameter in the parameter combination.

10. The method according to claim 1, characterized in that The determining of the optimal operating parameter combination of the target power equipment to be monitored based on the energy conversion efficiency and a preset energy conversion efficiency threshold comprises: An initial multi-parameter combination set is generated based on all parameter combinations in the parameter combination set that satisfy the energy conversion criterion, wherein the expression of the energy conversion criterion is: Where, is the energy conversion efficiency threshold; The initial multi-parameter combinations are concentrated, and the initial multi-parameter combination corresponding to the maximum energy conversion efficiency is determined as the optimal parameter combination for the operation of the target power equipment to be monitored, wherein the maximum energy conversion efficiency.

11. A system for studying the optimal parameter combination of power equipment operation, characterized in that: The system comprises: A target device module, configured to obtain a target power device to be monitored in response to a power monitoring instruction; a data acquisition module, configured to obtain the actual output power of the target power equipment to be monitored when the target power equipment to be monitored operates according to a parameter combination, wherein the parameter combination is any parameter combination extracted from a predetermined set of parameter combinations, each parameter combination including a plurality of power parameters and acoustic parameters; a data calculation module, configured to calculate the energy conversion efficiency of the target power equipment to be monitored based on the parameter combination and the actual output power; The result output module is used to determine the optimal operating parameter combination of the target power equipment to be monitored based on the energy conversion efficiency and a preset energy conversion efficiency threshold.

12. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 10.

13. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 10.

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