Water turbine fault diagnosis method and device, electronic equipment and storage medium

Abstract

The invention provides a water turbine fault diagnosis method and device, electronic equipment and a storage medium, and belongs to the technical field of water turbine fault diagnosis, and the method comprises the steps: synchronously collecting an audio signal and a vibration signal of a running water turbine; expanding the original data set of the collected audio signals and vibration signals to obtain an expanded data set; constructing a neural network model; training a neural network model by using the extended data set to obtain a fault diagnosis model; audio signals and vibration signals of the running water turbine are input into the final fault diagnosis model, a fault diagnosis result is output to expand a collected original data set, sufficient sample support is provided for training of a neural network model, overfitting of the neural network model is prevented, the original data adopt multi-source signals, and the fault diagnosis accuracy is improved. The diagnosis precision of the fault diagnosis model can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of water turbine generator sets, and in particular to a water turbine fault diagnosis method, device, electronic equipment and storage medium. Background Art

[0002] With the vigorous development of hydropower, large and medium-sized hydropower stations in China have been built and put into operation one after another, and hydropower accounts for an increasingly larger proportion of electricity supply. The turbine generator set is the core equipment of the hydropower station, responsible for converting water flow energy into electrical energy. In order to ensure the safe and stable operation of the hydropower station, it is necessary to effectively carry out the status monitoring and fault diagnosis research of the hydropower unit. The application of fault diagnosis technology can reduce the fault operation time of the unit, accurately locate the fault, promptly detect the early signs of fault, provide scientific and reasonable maintenance solutions, and prepare for a rainy day. The abnormal or fault status information of most hydropower units is hidden in the audio signal and vibration signal of the equipment.

[0003] At present, the common method is to study the fault diagnosis method of hydraulic turbines based on vibration signals or audio signals. Neural network models have also been widely used in the field of hydraulic turbine fault diagnosis. However, the fault identification algorithm based on the neural network model requires a large amount of fault data support when applied, and the hydraulic turbine fault data samples are scarce in practice, resulting in a serious sample imbalance between fault data and normal data, which easily causes the neural network-based fault diagnosis model to over-fit. In addition, the accuracy of hydraulic turbine fault diagnosis using a single signal is not high, resulting in the problem of inaccurate output results of the fault diagnosis model for hydraulic turbines. Summary of the invention

[0004] In view of the above problems, the present invention provides a turbine fault diagnosis method, device, electronic device and storage medium, which solve the problems in the prior art that turbine fault data samples are scarce, resulting in serious sample imbalance between fault data and normal data, which easily causes the neural network-based fault diagnosis model to over-fit, and the accuracy of turbine fault diagnosis using a single signal is not high, resulting in inaccurate turbine diagnostic output results of the fault diagnosis model. The collected original data set is expanded to provide sufficient sample support for the training of the neural network model to prevent the neural network model from overfitting. The original data uses multi-source signals to improve the diagnostic accuracy of the fault diagnosis model.

[0005] An embodiment of the present invention provides a method for diagnosing faults of a hydraulic turbine, comprising:

[0006] Synchronously collect audio signals and vibration signals of running turbines;

[0007] Expanding the original data sets of the collected audio signals and vibration signals to obtain expanded data sets;

[0008] Build a neural network model;

[0009] The neural network model is trained using the expanded data set to obtain a fault diagnosis model;

[0010] The audio signal and vibration signal of the running turbine are input into the final fault diagnosis model, and the fault diagnosis result is output.

[0011] In some embodiments, an acquisition card with a synchronization function is used to synchronously acquire the audio signal and the vibration signal of the running water turbine.

[0012] In some embodiments, the step of expanding the data set of the collected audio signal and vibration signal to obtain an expanded data set includes:

[0013] Flipping and / or inverting the audio signal and the vibration signal in the original data set;

[0014] Applying the same phase rotation to the flipped and / or inverted audio and vibration signals;

[0015] Add Gaussian noise of the same variance to the phase-inverted audio and vibration signals;

[0016] The processed audio signal and vibration signal are stored in the original data set to obtain an expanded data set.

[0017] In some embodiments, constructing a neural network model includes:

[0018] Set up two input layers, one for audio signals and the other for vibration signals;

[0019] A convolutional layer is set after each input layer. One convolutional layer is used to extract the time-frequency features of the audio signal, and another convolutional layer is used to extract the frequency features of the vibration signal.

[0020] Set the ReLU activation function after each convolutional layer to increase nonlinearity;

[0021] A maximum pooling layer is set after each ReLU activation function to reduce the number of parameters and extract important features;

[0022] A feature fusion layer is set after the maximum pooling layer to fuse the features of the audio signal and the vibration signal;

[0023] A fully connected layer is set after the feature fusion layer to map the fused features to the category space;

[0024] An output layer is set after the fully connected layer, and the output layer converts the output into a probability distribution using a Softmax function.

[0025] In some embodiments, including:

[0026] A Dropout layer is set between the fully connected layer and the output layer.

[0027] In some embodiments, the method of using the expanded data set to train a neural network model to obtain a fault diagnosis model includes:

[0028] Divide the augmented dataset into training set, validation set, and test set;

[0029] Add the training set and validation set to the neural network model;

[0030] Define the loss function of the neural network model;

[0031] By iterating the training set, the parameters of the neural network model are continuously updated to minimize the loss function, and the neural network model is verified using the validation set to prevent overfitting;

[0032] Use the test set to evaluate the neural network model, calculate the accuracy and recall rate indicators of the neural network model, and obtain the evaluation results;

[0033] According to the evaluation results, the model is tuned to obtain the fault diagnosis model.

[0034] In some embodiments, including:

[0035] The cross entropy loss function is used to measure the difference between the probability distribution predicted by the neural network model and the true label:

[0036]

[0037] Where L is the loss function, y i is the i-th true label, N is a natural number greater than 0, y , i is the probability predicted by the neural network model.

[0038] An embodiment of the present invention provides a hydraulic turbine fault diagnosis device, comprising:

[0039] An acquisition module, used for synchronously acquiring audio signals and vibration signals of a running turbine;

[0040] An expansion module, used to expand the original data set of the collected audio signal and vibration signal to obtain an expanded data set;

[0041] Building blocks for building neural network models;

[0042] A training module, used to train a neural network model using an expanded data set to obtain a fault diagnosis model;

[0043] The diagnosis module is used to input the audio signal and vibration signal of the running turbine into the final fault diagnosis model and output the fault diagnosis result.

[0044] An embodiment of the present invention provides an electronic device, including:

[0045] A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the methods described above is executed.

[0046] An embodiment of the present invention provides a storage medium, wherein a computer program stored in the storage medium can be executed by one or more processors and can be used to implement any of the methods described above.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The audio signal and vibration signal of the running turbine are synchronously collected; the original data set of the collected audio signal and vibration signal is expanded to obtain an expanded data set; a neural network model is constructed; the neural network model is trained using the expanded data set to obtain a fault diagnosis model; the audio signal and vibration signal of the running turbine are input into the final fault diagnosis model, and the fault diagnosis result is output to expand the collected original data set, thereby providing sufficient sample support for the training of the neural network model and preventing the neural network model from overfitting. In addition, the original data uses multi-source signals, which can improve the diagnostic accuracy of the fault diagnosis model. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0050] Figure 1 A schematic diagram of a flow chart of a method for diagnosing faults of a hydraulic turbine provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of the structure of a hydraulic turbine fault diagnosis device provided by an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0054] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0055] If similar descriptions of "first\second\third" appear in the application documents, the following instructions are added. In the following description, the terms "first\second\third" involved are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0057] Based on the problems existing in the related art, an embodiment of the present invention provides a method for diagnosing faults of a hydraulic turbine, and the executor of the method can be an electronic device. The electronic device can be various types of terminals such as a laptop computer, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, a portable gaming device), and can also be implemented as a server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (CDNs), and big data and artificial intelligence platforms.

[0058] In some embodiments, the functions implemented by the diagnostic method provided by the embodiments of the present invention may be implemented by a processor of an electronic device calling a program code, wherein the program code may be stored in a computer storage medium.

[0059] The embodiment of the present invention provides a method for diagnosing faults of a hydraulic turbine. Figure 1 A schematic diagram of a flow chart of a hydraulic turbine fault diagnosis method provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, including:

[0060] Step S1: synchronously collecting audio signals and vibration signals of a running turbine;

[0061] In the embodiment of the present invention, an acoustic emission sensor is installed at the turbine to collect the audio signal of the running turbine, and a vibration sensor is set on the turbine to collect the vibration signal of the running turbine. In order to ensure the synchronization of the audio signal and the vibration signal, an acquisition card with a synchronization function is used to synchronously collect the audio signal and the vibration signal of the running turbine.

[0062] Step S2: expanding the original data set of the collected audio signal and vibration signal to obtain an expanded data set;

[0063] In some embodiments, the step S2 comprises:

[0064] Step S21: flipping and / or inverting the audio signal and the vibration signal in the original data set;

[0065] Step S22: applying the same phase rotation to the flipped and / or inverted audio signal and vibration signal;

[0066] Step S23: Adding Gaussian noise of the same variance to the phase-inverted audio signal and the vibration signal;

[0067] Step S24: storing the processed audio signal and vibration signal into the original data set to obtain an expanded data set.

[0068] In the embodiment of the present invention, flipping is flipping the signal along the horizontal axis, and inversion refers to flipping along the vertical axis. The signal may be flipped or inverted alone, or the signal may be flipped and then inverted to increase the diversity of the signal. After the signal is flipped and / or inverted, processing may be stopped, or the signal may be phase rotated to further increase the diversity of the signal. After the signal is phase rotated, processing may be stopped, or Gaussian noise may be added to the signal to simulate interference in an actual environment to further increase the diversity of the signal. The processed audio signal and vibration signal are stored in the original data set to obtain an expanded data set.

[0069] Step S3: constructing a neural network model;

[0070] In some embodiments, step S3 includes:

[0071] Step S31: setting two input layers, one input layer for audio signals and the other input layer for vibration signals;

[0072] Step S32: a convolution layer is set after each input layer, one convolution layer is used to extract the time-frequency features of the audio signal, and another convolution layer is used to extract the frequency features of the vibration signal;

[0073] Step S33: Set a ReLU activation function after each convolutional layer to increase nonlinearity;

[0074] Step S34: Setting a maximum pooling layer after each ReLU activation function to reduce the number of parameters and extract important features;

[0075] Step S35: a feature fusion layer is set after the maximum pooling layer to fuse the features of the audio signal and the vibration signal;

[0076] Step S36: a fully connected layer is set after the feature fusion layer to map the fused features to the category space;

[0077] Step S37: An output layer is set after the fully connected layer, and the output layer uses a Softmax function to convert the output into a probability distribution.

[0078] In an embodiment of the present invention, two branch networks are constructed, one branch network processes audio signals, and the other branch network processes vibration signals. The extracted audio features and vibration features are then passed through a feature fusion layer, the fused features are input into a fully connected layer, the fused features are mapped to a category space, and a diagnosis result is obtained. Finally, the diagnosis result is input into an output layer, and the output layer uses a Softmax function to convert the output into a probability distribution.

[0079] In some embodiments, including:

[0080] A Dropout layer is set between the fully connected layer and the output layer.

[0081] In the embodiment of the present invention, overfitting is reduced by setting a Dropout layer.

[0082] Step S4: training a neural network model using the expanded data set to obtain a fault diagnosis model;

[0083] In some embodiments, the step S4 comprises:

[0084] Step S41: Divide the expanded data set into a training set, a validation set and a test set;

[0085] Step S42: adding the training set and the validation set to the neural network model;

[0086] Step S43: defining the loss function of the neural network model;

[0087] In some embodiments, including:

[0088] The cross entropy loss function is used to measure the difference between the probability distribution predicted by the neural network model and the true label:

[0089]

[0090] Where L is the loss function, yi is the i-th true label, N is a natural number greater than 0, y , i is the probability predicted by the neural network model.

[0091] Step S44: by iterating the training set, continuously updating the parameters of the neural network model to minimize the loss function, and using the validation set to validate the neural network model to prevent overfitting;

[0092] Step S45: Use the test set to evaluate the neural network model, calculate the accuracy and recall rate indicators of the neural network model, and obtain the evaluation results;

[0093] Step S46: According to the evaluation results, the model is tuned to obtain a fault diagnosis model.

[0094] In an embodiment of the present invention, the expanded data set is divided into a training set, a validation set and a test set, wherein the ratio of the training set, the validation set and the test set can be 7:2:1. The neural network model is iteratively trained through the training set, and the parameters of the neural network model are continuously updated to fit the neural network model, and then the neural network model is verified through the validation set to prevent the neural network model from overfitting. The neural network model is evaluated using the test set, and the accuracy and recall rate indicators of the neural network model are calculated to obtain the evaluation result of the neural network model, and the parameters of the model are adjusted according to the evaluation result to obtain a trained fault diagnosis model.

[0095] Step S5: input the audio signal and vibration signal of the running turbine into the final fault diagnosis model, and output the fault diagnosis result.

[0096] In summary, by synchronously collecting the audio signal and vibration signal of the running turbine; expanding the original data set of the collected audio signal and vibration signal to obtain an expanded data set; building a neural network model; using the expanded data set to train the neural network model to obtain a fault diagnosis model; inputting the audio signal and vibration signal of the running turbine into the final fault diagnosis model, and outputting the fault diagnosis result to expand the collected original data set, sufficient sample support is provided for the training of the neural network model to prevent the neural network model from overfitting, and the original data uses multi-source signals, which can improve the diagnostic accuracy of the fault diagnosis model.

[0097] Based on the foregoing embodiments, an embodiment of the present invention provides a hydraulic turbine fault diagnosis device, wherein each module included in the device and each unit included in each module can be implemented by a processor in a computer device; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0098] The embodiment of the present invention provides a hydraulic turbine fault diagnosis device. Figure 2 A schematic diagram of the structure of a hydraulic turbine fault diagnosis device provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, including:

[0099] An acquisition module, used for synchronously acquiring audio signals and vibration signals of a running turbine;

[0100] An expansion module, used to expand the original data set of the collected audio signal and vibration signal to obtain an expanded data set;

[0101] Building blocks for building neural network models;

[0102] A training module, used to train a neural network model using an expanded data set to obtain a fault diagnosis model;

[0103] The diagnosis module is used to input the audio signal and vibration signal of the running turbine into the final fault diagnosis model and output the fault diagnosis result.

[0104] It should be noted that, in the embodiment of the present invention, if the above-mentioned diagnostic method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0105] Accordingly, an embodiment of the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the diagnostic method provided in the above embodiment.

[0106] An embodiment of the present invention provides an electronic device; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to realize the connection and communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include a standard wired interface and a wireless interface. The processor 401 is configured to execute the program of the diagnostic method stored in the memory to implement the steps in the diagnostic method provided in the above embodiment.

[0107] It should be noted that the description of the above storage medium and electronic device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of the present invention, please refer to the description of the method embodiments of the present invention for understanding.

[0108] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0109] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0110] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0111] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0112] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0113] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read On ly Memory), disks or optical disks, etc. Various media that can store program codes.

[0114] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product, which is stored in a storage medium and includes several instructions for a controller to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0115] The above is only an embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for diagnosing faults of a hydraulic turbine, characterized in that: include: Synchronously collect audio signals and vibration signals of running turbines; Expanding the original data sets of the collected audio signals and vibration signals to obtain expanded data sets; Build a neural network model; The neural network model is trained using the expanded data set to obtain a fault diagnosis model; The audio signal and vibration signal of the running turbine are input into the final fault diagnosis model, and the fault diagnosis result is output.

2. The method according to claim 1, characterized in that An acquisition card with synchronization function is used to synchronously acquire the audio signal and vibration signal of the running turbine.

3. The method according to claim 1, characterized in that The method of expanding the data set of the collected audio signal and vibration signal to obtain an expanded data set includes: Flipping and / or inverting the audio signal and the vibration signal in the original data set; Applying the same phase rotation to the flipped and / or inverted audio and vibration signals; Add Gaussian noise of the same variance to the phase-inverted audio and vibration signals; The processed audio signal and vibration signal are stored in the original data set to obtain an expanded data set.

4. The method according to claim 3, characterized in that: The construction of the neural network model includes: Set up two input layers, one for audio signals and the other for vibration signals; A convolutional layer is set after each input layer. One convolutional layer is used to extract the time-frequency features of the audio signal, and another convolutional layer is used to extract the frequency features of the vibration signal. Set the ReLU activation function after each convolutional layer to increase nonlinearity; A maximum pooling layer is set after each ReLU activation function to reduce the number of parameters and extract important features; A feature fusion layer is set after the maximum pooling layer to fuse the features of the audio signal and the vibration signal; A fully connected layer is set after the feature fusion layer to map the fused features to the category space; An output layer is set after the fully connected layer, and the output layer converts the output into a probability distribution using a Softmax function.

5. The method according to claim 4, characterized in that include: A Dropout layer is set between the fully connected layer and the output layer.

6. The method according to claim 1, characterized in that The method of using the expanded data set to train the neural network model to obtain the fault diagnosis model includes: Divide the augmented dataset into training set, validation set, and test set; Add the training set and validation set to the neural network model; Define the loss function of the neural network model; By iterating the training set, the parameters of the neural network model are continuously updated to minimize the loss function, and the neural network model is verified using the validation set to prevent overfitting; Use the test set to evaluate the neural network model, calculate the accuracy and recall rate indicators of the neural network model, and obtain the evaluation results; According to the evaluation results, the model is tuned to obtain the fault diagnosis model.

7. The method according to claim 6, characterized in that include: The cross entropy loss function is used to measure the difference between the probability distribution predicted by the neural network model and the true label: Where L is the loss function, y i is the i-th true label, N is a natural number greater than 0, y , i is the probability predicted by the neural network model.

8. A hydraulic turbine fault diagnosis device, characterized in that: include: An acquisition module, used for synchronously acquiring audio signals and vibration signals of a running turbine; An expansion module, used to expand the original data set of the collected audio signal and vibration signal to obtain an expanded data set; Building blocks for building neural network models; A training module, used to train a neural network model using an expanded data set to obtain a fault diagnosis model; The diagnosis module is used to input the audio signal and vibration signal of the running turbine into the final fault diagnosis model and output the fault diagnosis result.

9. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is executed.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method as claimed in any one of claims 1 to 7.

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