Degradation degree prediction method, device and equipment of wind turbine generator transmission system and computer readable medium
By collecting vibration data in the wind turbine drive system and using the time series prediction model to predict the degree of degradation, the problem of difficult-to-predict the transmission system degradation trend is solved, and the power generation efficiency is improved.
Patent Information
- Application Number
- CN202411674033.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-21
- Publication Date
- 2025-05-06
AI Technical Summary
In the harsh environment and unstable stress, the fault detection frequency and complex structure of the wind turbine drive system are high, which makes it difficult to predict the degradation trend and affects the power generation efficiency.
By collecting vibration data in the wind turbine drive system, input data is generated, and time series prediction models, such as feedback neural network model or ELM neural network model, prediction of the degree of deterioration is performed.
It realizes effective prediction of the deterioration trend of the entire life cycle of the wind turbine transmission system, reduces fault downtime, and improves the power generation efficiency of the wind farm.
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Figure CN119939206A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of wind power technology, and in particular to a method, device, equipment and computer-readable medium for predicting the degree of degradation of a wind turbine transmission system. Background Art
[0002] Wind power is the green engine of the global economy and an important means to achieve "carbon peak and carbon neutrality". In 2022, China's wind power industry operated smoothly. As of the end of December, the national wind power installed capacity was 365.44 million kilowatts, an increase of 11.2% year-on-year. The transmission system is a key component of the wind turbine, and its role is to transmit the power generated by wind energy to the generator.
[0003] However, the research on the transmission system of wind turbines in my country is still in its initial stage, and the operating environment of most units is very harsh. Wind power plants are mostly located in remote uninhabited areas or offshore, which are easily affected by various extreme climates. In addition, due to the intermittent nature of wind energy, the force of the units is unstable. Because the fault detection frequency of the transmission system is high, the unit structure is complex, the replacement is difficult, and the downtime is long, the degradation trend prediction of the transmission system is particularly critical among the various systems of wind turbines. Summary of the invention
[0004] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.
[0005] Some embodiments of the present application propose a method, device, electronic device and computer-readable medium for predicting the degree of degradation of a wind turbine transmission system to solve the technical problems mentioned in the above background technology section.
[0006] As a first aspect of the present application, some embodiments of the present application provide a method for predicting the degree of degradation of a wind turbine transmission system, comprising: collecting vibration data in the wind turbine transmission system; generating input data based on the vibration data; inputting the input data into a degradation degree prediction model so that the degradation degree prediction model outputs a prediction result.
[0007] Furthermore, the degradation degree prediction model is a time series prediction model.
[0008] Furthermore, the degradation degree prediction model is a feedback neural network model.
[0009] Furthermore, the degradation degree prediction model is an ELM neural network model.
[0010] Further, the vibration data includes: main bearing vibration data, gearbox vibration data, generator vibration data;
[0011] Wherein, the main bearing vibration data at least includes first type vibration data collected from the main shaft in the wind turbine transmission system;
[0012] The gearbox vibration data at least includes second type vibration data collected from gearboxes at various levels in the wind turbine transmission system;
[0013] The generator vibration data at least includes third type vibration data collected from the bearing of the motor shaft of the generator in the wind turbine transmission system.
[0014] Furthermore, the method for predicting the degree of degradation of the wind turbine transmission system also includes: constructing the degradation degree prediction model; wherein, constructing the degradation degree prediction model includes: obtaining a data set of vibration data for training the model; extracting feature vectors based on the data set; dividing the data set into a training set and a test set; and using the training set to train the degradation degree prediction model.
[0015] Furthermore, the extracting of feature vectors based on the data set includes: using an EWT algorithm to obtain a series of singular values of extracted IMF components as feature quantities.
[0016] As the second aspect of the present application, some embodiments of the present application provide a device for predicting the degree of degradation of a wind turbine transmission system, comprising: an acquisition module for collecting vibration data in the wind turbine transmission system; a generation module for generating input data based on the vibration data; and a prediction module for inputting the input data into a degradation degree prediction model so that the degradation degree prediction model outputs a prediction result.
[0017] As the third aspect of the present application, some embodiments of the present application provide an electronic device, comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.
[0018] As a fourth aspect of the present application, some embodiments of the present application provide a computer-readable medium on which a computer program is stored, wherein when the program is executed by a processor, the method described in any implementation of the above-mentioned first aspect is implemented.
[0019] The beneficial effects of the present application are: providing a method, device, equipment and computer-readable medium for predicting the degree of degradation of a wind turbine transmission system that can effectively predict the degradation trend over the entire life cycle. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The illustrative embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application.
[0021] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.
[0022] In the attached picture:
[0023] Figure 1 It is a schematic diagram of the main steps of a method for predicting the degree of degradation according to an embodiment of the present application;
[0024] Figure 2 is a schematic diagram of some steps in a method for predicting the degree of degradation according to an embodiment of the present application;
[0025] Figure 3 It is a main flow chart of a method for predicting the degree of degradation according to an embodiment of the present application;
[0026] Figure 4 It is a time domain diagram of EWT signal decomposition in a degradation degree prediction method according to an embodiment of the present application;
[0027] Figure 5 It is a frequency domain diagram of EWT signal decomposition in a degradation degree prediction method according to an embodiment of the present application;
[0028] Figure 6 This is a schematic diagram of the variation trend of singular values in a method for predicting the degree of degradation according to an embodiment of the present application;
[0029] Figure 7 It is a schematic diagram of the absolute value change of the prediction error of the strong and weak predictors in the degradation degree prediction method according to an embodiment of the present application;
[0030] Figure 8 is a schematic diagram of degradation trend according to an embodiment of the present application;
[0031] Fig. 9 is a schematic diagram of the prediction error of the ELM_Adaboost model according to an embodiment of the present application;
[0032] Fig.10 is a schematic diagram of an ELM model prediction error according to an embodiment of the present application;
[0033] Fig.11 is a schematic diagram of a degradation degree prediction device according to an embodiment of the present application;
[0034] Fig.12 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0035] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.
[0036] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.
[0037] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.
[0038] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".
[0039] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.
[0040] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.
[0041] Reference Figure 1 As shown, the method for predicting the degree of degradation of the wind turbine transmission system of the present application mainly includes the following steps:
[0042] S101: Construct a degradation degree prediction model.
[0043] S102: Collect vibration data in the wind turbine transmission system.
[0044] S103: Generate input data according to the vibration data.
[0045] S104: Inputting the input data into a degradation degree prediction model so that the degradation degree prediction model outputs a prediction result
[0046] Reference Figure 2As shown, as the specific steps of step S101 of this application:
[0047] S201: Acquire a data set of vibration data for training a model.
[0048] S202: Extract feature vectors based on the data set.
[0049] S203: Divide the data set into a training set and a test set.
[0050] S204: Using the training set to train a degradation degree prediction model.
[0051] Specifically, the degradation degree prediction model is a time series prediction model; more specifically, the degradation degree prediction model is a feedback neural network model, such as an ELM (Extreme Learning Machines) neural network.
[0052] As a specific solution, the vibration data includes: main bearing vibration data, gearbox vibration data, and generator vibration data. Among them, the main bearing vibration data at least includes the first type of vibration data collected from the main shaft in the wind turbine transmission system; the gearbox vibration data at least includes the second type of vibration data collected from the gearboxes at all levels in the wind turbine transmission system; the generator vibration data at least includes the third type of vibration data collected from the bearing of the motor shaft of the generator in the wind turbine transmission system.
[0053] Specifically, the step of extracting feature vectors based on the data set specifically includes: using EWT (Empirical Wavelet Transform) algorithm to obtain a series of singular values of extracted IMF (Intrinsic Mode Functions) components as feature quantities.
[0054] More specifically, the method for predicting the degree of degradation of the wind turbine transmission system of the present application mainly includes the following steps from the perspective of model construction:
[0055] Step 1: Data Collection
[0056] Bearing vibration data is collected through vibration sensors placed at the main bearings, gearboxes at various levels, bearings at both ends of the generator, etc.
[0057] Step 2: Feature parameter extraction
[0058] The EWT algorithm is used to obtain a series of IMF components, and the singular values of the IMF components are calculated and extracted as feature quantities.
[0059] Specifically, in step 2, the singular values of the IMF components are extracted as feature quantities, the feature quantities are stored in an array and labeled, and the data set is divided after normalization.
[0060] As a more specific solution, the three largest peaks are used in step 2 to determine the filter passband.
[0061] The specific principle of the empirical wavelet transform in step 2 is as follows:
[0062] Assume that the Fourier spectrum support interval is defined in the range [0, π] and is divided into N consecutive segments, ω n is the boundary between the segments. n As the center, define a width of 2τ n The transition phase T n .
[0063] The empirical wavelet is defined as n Bandpass filter of interval. The empirical scaling function and the empirical wavelet are defined by equation (1) and (2) respectively.
[0064]
[0065] The most commonly used β(x) that meets the requirements is:
[0066] β(x)=x 4 (35-84x+70x 2 -20x 3 ) (3)
[0067] Step 3: Establishment of degradation trend prediction model
[0068] Divide the processed data set into test set and training set, build the ELM neural network model, set sample weights and the number of hidden layer neurons, set network training parameters, initialize the network, predict data and calculate errors, and start a new round of training until the error reaches the threshold.
[0069] As a preferred solution, an adaptive enhancement algorithm is applied to the ELM neural network model. If the previous basic classification sample is misclassified, its weight will be increased, while the weight of the correctly classified sample will be reduced and used for the next classifier training. A weak classifier is added in each round. In this process, the weak classifier is iterated, trained, and combined and adjusted until a certain error rate or the maximum number of iterations is reached, thus obtaining a strong classifier.
[0070] The ELM_Adaboost algorithm can be used to build a time series degradation trend prediction model, which can more accurately predict the degradation trend of the bearings in the wind turbine transmission system throughout their life cycle.
[0071] The specific principle of ELM in step 3 is as follows:
[0072] For any N different samples (x i ,t i ),in have The standard SLFNs with hidden nodes and activation function g(x) is modeled as:
[0073]
[0074] In the formula, the weight vector connecting the i-th hidden layer unit and the input unit is w i =[w i1 ,w i2 ,…,w in ] T , the weight vector connecting the i-th hidden layer unit and the output unit is β i =[β i1 ,β i2 ,…,β in ] T , b i is the threshold of the i-th hidden unit. i ·x j Indicates w i and x j The inner product of .
[0075] To make the above SLFNs represent these N samples approximately without error, that is, Then there exists β i , w i and b i ,make:
[0076]
[0077] The above equation can be written compactly as:
[0078] Hβ=T (6)
[0079] in,
[0080]
[0081] Where H is the hidden layer output matrix of the neural network.
[0082] when When , SLFNs can approximate these training samples with zero error; but in most cases The corresponding β i , w i and b i may not exist. Therefore, you need to find a specific and make:
[0083]
[0084] When H is unknown, a gradient-based learning algorithm is generally used to find the minimum value of ||Hβ=T||.
[0085] The advantage of ELM is that the input weight w i and hidden layer bias b i Any value can be assigned, and the output matrix H remains unchanged. From equation (8), we can see that training a SLFN is equivalent to finding the least squares solution of the linear system Hβ=T.
[0086] Compared with the prior art, the present application provides a method for identifying bearing degradation in a doubly-fed asynchronous wind generator transmission system. It uses a vibration sensor to measure bearing vibration data, builds a degradation trend prediction model, identifies the degree of bearing degradation in the doubly-fed asynchronous wind generator transmission system, avoids losses caused by fault shutdown, and improves the power generation efficiency of the wind farm.
[0087] Reference Figures 3 to 10 As shown, as a specific implementation scheme, the degradation degree prediction method of the present application specifically includes the following steps:
[0088] Step 1, data collection: Collect bearing vibration data through vibration sensors.
[0089] Step 2, feature parameter extraction: Use the empirical wavelet transform method and use the three maximum peaks to determine the filter passband. The time domain diagram of the three components IMF1 to IMF3 obtained by decomposition is as follows: Figure 4 , the frequency domain diagram is as follows Figure 5 .
[0090] Calculate the singular value of the IMF component as the characteristic quantity, and its change trend is as follows Figure 6 As shown. The feature vectors are stored in an array and labeled, and the data set is divided after normalization. The first 70 samples of the data set are extracted as the training set. Each sample has multiple features, and each feature is a numerical value. Similarly, the remaining samples are extracted as the test set.
[0091] Step 3: Establishment of degradation trend prediction model: In the specific experiment, it is divided into the following steps.
[0092] (1) The feature vector extracted in the previous step is used as the test set and training set. The weight distribution of the initial training sample is as follows:
[0093] D(i)=(w1,w2,w3,…,w n ) (9)
[0094] The weight of each sample is Establish an ELM neural network and set the number of hidden layer neurons to 20.
[0095] (2) During this ELM network training, the output prediction data y i , and calculate the error on D at this time.
[0096]
[0097] At the same time, adjust the upper error limit.
[0098] (3) Calculate the weight of the weak classifier in the final strong classifier.
[0099]
[0100] (4) Update the weight distribution.
[0101]
[0102] Where, t j is the true value, y k is the predicted value, Z k is the normalization constant.
[0103] (5) After all training is completed, the weight α of the weak classifier t On this basis, all ELMs are integrated to obtain a strong classifier.
[0104]
[0105] Among them, f(y k ,α k ) is the weak classifier in the kth round of training, and a total of K rounds were conducted. Set K=10.
[0106] Compare the previous training set and output the error between the predicted value and the true value. As shown in the figure
[0107] Depend on Figure 7 It can be seen that through the adaptive boosting algorithm, ELM_Adaboost is used as a strong predictor, and its accuracy is higher than ELM. By comparing with the true value, the change trend of the degradation index (i.e., singular value) of the strong and weak predictors and the true value is as follows Figure 8 shown.
[0108] The error between the two predictors at each time point is Fig. 9 , 10 The calculation of each error parameter is shown in Table 1.
[0109] From Table 1 and Figure 5 , 6It can be concluded that the prediction model based on ELM_AdaBoost has higher accuracy and fit than the model based on ELM, and the degree of bearing deterioration can be known early and effectively.
[0110] Table 1 Error summary
[0111]
[0112] Reference Fig.11 As shown, the device for predicting the degree of degradation of a wind turbine transmission system of the present application includes: a collection module, a generation module and a prediction module.
[0113] Among them, the acquisition module is used to collect vibration data in the wind turbine transmission system; the generation module generates input data based on the vibration data; the prediction module inputs the input data into a degradation degree prediction model so that the degradation degree prediction model outputs a prediction result.
[0114] like Fig.12 As shown, the electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 are also stored. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.
[0115] Typically, the following devices may be connected to the I / O interface 805: input devices 806 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 809. The communication device 809 may allow the electronic device 800 to communicate with other devices wirelessly or by wire to exchange data. Although Fig.12 The electronic device 800 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Fig.12 Each block shown in the figure may represent one device, or may represent multiple devices as required.
[0116] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 809, or installed from the storage device 808, or installed from the ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.
[0117] It should be noted that the computer-readable medium described above in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer 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 of the above.
[0118] In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.
[0119] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperTextTransferProtocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an adhoc peer-to-peer network), as well as any currently known or future developed network.
[0120] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.
[0121] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).
[0122] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, which contains one or more executable instructions for implementing a specified logical function.
[0123] It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.
[0124] For example, two boxes shown in succession may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.
[0125] The units described in some embodiments of the present disclosure may be implemented in software or hardware, and the units described may also be arranged in a processor.
[0126] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.
[0127] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.
Claims
1. A method for predicting the degradation degree of a wind turbine transmission system, comprising: Collect vibration data in wind turbine drive systems; generating input data based on the vibration data; The input data is input into a degradation degree prediction model so that the degradation degree prediction model outputs a prediction result.
2. The method for predicting the degree of deterioration of a wind turbine transmission system according to claim 1, characterized in that: The degradation degree prediction model is a time series prediction model.
3. The method for predicting the degree of degradation of a wind turbine transmission system according to claim 2, characterized in that: The degradation degree prediction model is a feedback neural network model.
4. The method for predicting the degree of degradation of a wind turbine transmission system according to claim 3 is characterized in that: The degradation degree prediction model is an ELM neural network model.
5. The method for predicting the degree of degradation of a wind turbine transmission system according to claim 4, characterized in that: The vibration data includes: main bearing vibration data, gearbox vibration data, generator vibration data; Wherein, the main bearing vibration data at least includes first type vibration data collected from the main shaft in the wind turbine transmission system; The gearbox vibration data at least includes second type vibration data collected from gearboxes at various levels in the wind turbine transmission system; The generator vibration data at least includes third type vibration data collected from the bearing of the motor shaft of the generator in the wind turbine transmission system.
6. The method for predicting the degree of degradation of a wind turbine transmission system according to claim 5, characterized in that: The method for predicting the degree of degradation of the wind turbine transmission system further includes: Constructing the degradation degree prediction model; Wherein, constructing the degradation degree prediction model comprises: Obtain a dataset of vibration data for training the model; extracting a feature vector based on the data set; Dividing the data set into a training set and a test set; The training set is used to train the degradation degree prediction model.
7. The method for predicting the degree of degradation of a wind turbine transmission system according to claim 6, characterized in that: in, The extracting a feature vector based on the data set comprises: The EWT algorithm is used to obtain a series of singular values of extracted IMF components as feature quantities.
8. A device for predicting the degree of degradation of a wind turbine transmission system, comprising: A collection module, used to collect vibration data in the wind turbine transmission system; A generating module, generating input data according to the vibration data; The prediction module inputs the input data into a degradation degree prediction model so that the degradation degree prediction model outputs a prediction result.
9. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the processors implement the method according to any one of claims 1 to 7.
10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.