Lithium battery fault identification method and system based on recurrence plot and improved AlexNet model, and storage medium

Abstract

The invention relates to the technical field of deep learning, in particular to a lithium battery fault recognition method and system based on a recurrence plot and an improved AlexNet model, and a storage medium. The method comprises the following steps: acquiring one-dimensional time sequence data from various sensors of the lithium battery; preprocessing the one-dimensional time sequence data to generate original fault waveform data; converting the original fault waveform data into a recurrence plot; the recurrence plot is input into a pre-trained improved AlexNet model for feature extraction, the improved AlexNet model processes feature vectors in the recurrence plot through a multi-head self-attention mechanism, and the processed feature vectors are input into a classification layer for fault type prediction of the battery; and obtaining a battery fault type prediction result output by the classification layer. The objective of the invention is to solve the problem of how to predict possible faults in a lithium battery.

Description

Technical Field

[0001] The present application relates to the field of deep learning technology, and in particular to a lithium battery fault identification method, system and storage medium based on a recursive graph and an improved AlexNet model. Background Art

[0002] As one of the most important energy storage devices today, lithium-ion batteries are widely used in many fields, from consumer electronics to electric vehicles to large-scale energy storage systems. This type of battery has the advantages of high energy density, long cycle life, and high charge and discharge efficiency. However, with the diversification and complexity of its use scenarios, lithium-ion batteries are prone to various faults after long-term use, such as capacity decay, internal short circuit, overcharge, over-discharge, thermal runaway, etc. These faults will not only seriously affect the life and performance of lithium batteries, but may also cause safety accidents, especially in electric vehicles and energy storage systems, where the consequences of lithium battery failure are particularly serious. Therefore, how to accurately, real-time and effectively identify lithium battery failures has become an important topic in battery research and application.

[0003] At present, the traditional identification of lithium battery faults usually relies on artificially set thresholds or simple physical models, which are difficult to adapt to fault characteristics under complex working conditions and have the defect of low fault identification accuracy. In addition, the traditional identification of lithium battery faults is based on the symptoms of faults that have occurred, and lacks the ability to warn of early and hidden faults. In particular, when the internal problems of the battery have not yet manifested as abnormal external parameters, early detection cannot be performed, and there is a defect of detection lag.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a lithium battery fault identification method based on a recursive graph and an improved AlexNet model, aiming to solve the problem of how to predict possible faults in lithium batteries.

[0006] To achieve the above objectives, the present application provides a lithium battery fault identification method based on a recursive graph and an improved AlexNet model, the method comprising:

[0007] Collect one-dimensional time series data from various sensors of lithium batteries;

[0008] Preprocessing the one-dimensional time series data to generate original fault waveform data;

[0009] Converting the original fault waveform data into a recurrence graph;

[0010] Inputting the recursive graph into a pre-trained improved AlexNet model for feature extraction, wherein the improved AlexNet model processes feature vectors in the recursive graph through a multi-head self-attention mechanism, and inputs the processed feature vectors into a classification layer for battery fault type prediction;

[0011] Obtain the battery failure type prediction result output by the classification layer.

[0012] Optionally, the step of converting the original fault waveform data into a recursive graph comprises:

[0013] Calculating the Euclidean distance between each point in the original fault waveform data;

[0014] Calculating a difference between a preset threshold and the Euclidean distance, and generating a recursive graph according to the difference;

[0015] Wherein, each element R in the recursive graph ij The definition is as follows:

[0016] R ij =Θ(∈-d ij )

[0017] Where Θ is a step function, when ∈≥d ij When R ij =1, otherwise R ij =0.

[0018] Optionally, the convolutional layers in the improved AlexNet model are frozen, and the hyperparameters in the fully connected layers in the improved AlexNet model are fine-tuned.

[0019] Optionally, the calculation formula for predicting the fault type of the battery in the classification layer is as follows:

[0020]

[0021] in, is the predicted battery failure type, M is the feature vector output by the multi-head self-attention mechanism, W is the weight matrix, and b is the bias term.

[0022] Optionally, after the step of obtaining the battery fault type prediction result output by the classification layer, the step further includes:

[0023] A battery health status report is generated, which includes the current operating status of the battery, potential failure risks, and maintenance recommendations.

[0024] Optionally, the preprocessing includes denoising and normalization processing.

[0025] Optionally, the normalization formula is:

[0026]

[0027] Among them, m i It is a data point in the one-dimensional time series data. max(M) and min(M) represent the maximum and minimum values ​​of the time series data respectively. M is the input time series.

[0028] In addition, to achieve the above-mentioned purpose, the present application also provides a lithium battery fault identification system, which includes: a memory, a processor, and a lithium battery fault identification program based on a recursive graph and an improved AlexNet model stored in the memory and executable on the processor. When the lithium battery fault identification program based on the recursive graph and the improved AlexNet model is executed by the processor, the steps of the lithium battery fault identification method based on the recursive graph and the improved AlexNet model as described above are implemented.

[0029] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, on which is stored a lithium battery fault identification program based on a recursive graph and an improved AlexNet model. When the lithium battery fault identification program based on the recursive graph and the improved AlexNet model is executed by a processor, the steps of the lithium battery fault identification method based on the recursive graph and the improved AlexNet model as described above are implemented.

[0030] This application has at least the following beneficial effects:

[0031] 1. Traditional lithium battery fault identification often relies on artificially set thresholds or simple physical models, which are difficult to adapt to fault characteristics under complex working conditions, resulting in low fault identification accuracy. Therefore, this application introduces recursive graphs and improved deep learning models (AlexNet), combined with a multi-head attention mechanism, to enhance the ability to extract complex fault features and improve the accuracy of fault identification.

[0032] 2. Traditional fault detection methods are usually based on the symptoms of faults that have already occurred and lack the ability to warn of early and hidden faults. In particular, when internal battery problems have not yet manifested as abnormal external parameters, it is difficult for existing methods to perform early detection. Therefore, this application can effectively identify early signs of faults through the timing feature capture capability of the recursive graph, thereby achieving early fault detection.

[0033] 3. With the diversification of lithium battery application scenarios, the working environment of batteries is becoming increasingly complex, such as drastic changes in temperature and load. Traditional methods have shown obvious limitations in dealing with complex working conditions. Therefore, this application introduces a multi-head attention mechanism to enhance the ability to capture local important features, so that it can still maintain high accuracy of fault detection under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of the architecture of the hardware operating environment of the lithium battery fault identification system involved in the embodiment of the present application;

[0035] Figure 2 This is a flow chart of a first embodiment of a lithium battery fault identification method based on a recursive graph and an improved AlexNet model of the present application;

[0036] Figure 3 This is a schematic diagram of the improved AlexNet model architecture involved in the embodiments of the present application;

[0037] Figure 4 This is a flow chart of a second embodiment of the lithium battery fault identification method based on a recursive graph and an improved AlexNet model of the present application.

[0038] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0039] In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary 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 limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0040] As an implementation plan, Figure 1 This is a schematic diagram of the architecture of the hardware operating environment of the lithium battery fault identification system involved in the embodiment of the present application.

[0041] like Figure 1 As shown, the lithium battery fault identification system may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0042] Those skilled in the art will understand that Figure 1 The lithium battery fault identification system architecture shown in the figure does not constitute a limitation on the lithium battery fault identification system, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0043] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a lithium battery fault identification program based on a recursive graph and an improved AlexNet model. Among them, the operating system is a program for managing and controlling the hardware and software resources of the lithium battery fault identification system, the lithium battery fault identification program based on the recursive graph and the improved AlexNet model, and the operation of other software or programs.

[0044] exist Figure 1 In the lithium battery fault identification system shown, the user interface 1003 is mainly used to connect to the terminal and communicate data with the terminal; the network interface 1004 is mainly used for the background server and communicates data with the background server; the processor 1001 can be used to call the lithium battery fault identification program based on the recursive graph and the improved AlexNet model stored in the memory 1005.

[0045] In this embodiment, the lithium battery fault identification system includes: a memory 1005, a processor 1001, and a lithium battery fault identification program based on a recursive graph and an improved AlexNet model stored in the memory and executable on the processor, wherein:

[0046] When the processor 1001 calls the lithium battery fault identification program based on the recursive graph and the improved AlexNet model stored in the memory 1005, the following operations are performed:

[0047] Collect one-dimensional time series data from various sensors of lithium batteries;

[0048] Preprocessing the one-dimensional time series data to generate original fault waveform data;

[0049] Converting the original fault waveform data into a recurrence graph;

[0050] Inputting the recursive graph into a pre-trained improved AlexNet model for feature extraction, wherein the improved AlexNet model processes feature vectors in the recursive graph through a multi-head self-attention mechanism, and inputs the processed feature vectors into a classification layer for battery fault type prediction;

[0051] Obtain the battery failure type prediction result output by the classification layer.

[0052] When the processor 1001 calls the lithium battery fault identification program based on the recursive graph and the improved AlexNet model stored in the memory 1005, the following operations are performed:

[0053] Calculating the Euclidean distance between each point in the original fault waveform data;

[0054] The difference between the preset threshold and the Euclidean distance is calculated, and a recursive graph is generated according to the difference.

[0055] When the processor 1001 calls the lithium battery fault identification program based on the recursive graph and the improved AlexNet model stored in the memory 1005, the following operations are performed:

[0056] A battery health status report is generated, which includes the current operating status of the battery, potential failure risks, and maintenance recommendations.

[0057] Based on the hardware architecture of the lithium battery fault identification system using the above-mentioned deep learning technology, an embodiment of the lithium battery fault identification method based on the recursive graph and the improved AlexNet model of the present application is proposed.

[0058] First embodiment

[0059] Reference Figure 2 In the first embodiment, the lithium battery fault identification method based on the recursive graph and the improved AlexNet model includes the following steps:

[0060] Step S10, collecting one-dimensional time series data from various sensors of the lithium battery;

[0061] In this embodiment, the system first collects key one-dimensional time series data in the lithium battery.

[0062] Optionally, the one-dimensional time series data includes parameters such as voltage, current and temperature.

[0063] Step S20, preprocessing the one-dimensional time series data to generate original fault waveform data;

[0064] Furthermore, the one-dimensional time series data is preprocessed to eliminate noise interference and unify the data scale, thereby obtaining high-quality original fault waveform data.

[0065] Optionally, the preprocessing method includes denoising and normalization processing.

[0066] Further and optionally, the formula for the normalization process is:

[0067]

[0068] Among them, m iIt is a data point in the one-dimensional time series data. max(M) and min(M) represent the maximum and minimum values ​​of the time series data respectively. M is the input time series.

[0069] Step S30, converting the original fault waveform data into a recursive graph;

[0070] In this embodiment, the recursive graph is used for feature extraction. The system converts one-dimensional time series data into a two-dimensional image to show the similarity and repeatability of the data at different time points. The recursive graph can effectively capture the potential fault characteristics in battery operation, especially in the process of gradual development of the fault, it can reveal the early signs of the fault.

[0071] It should be noted that the recursive graph can effectively capture the timing characteristics of the lithium battery during operation, help extract key time-dependent information in the fault evolution process, and improve the ability to identify early faults and hidden faults.

[0072] Step S40, inputting the recursive graph into a pre-trained improved AlexNet model for feature extraction, wherein the improved AlexNet model processes feature vectors in the recursive graph through a multi-head self-attention mechanism, and inputs the processed feature vectors into a classification layer for battery fault type prediction;

[0073] In this embodiment, the converted recursive graph is input into the pre-trained improved AlexNet model for feature extraction.

[0074] The AlexNet model is a convolutional neural network whose structure is mainly composed of convolutional layers, pooling layers, local response normalization layers, fully connected layers and activation function modules.

[0075] Convolutional Layers: Convolutional layers are responsible for extracting local features in the recursive graph, such as edges, textures, etc. The convolution operation performs element-by-element weighted summation of the sliding convolution kernel and the input image area to achieve feature extraction. The specific convolution calculation formula is:

[0076]

[0077] Where I is the input recursive graph, W is the convolution kernel, and x, y are the positions in the feature map. The convolution operation extracts local features from the recursive graph by sliding the convolution kernel.

[0078] Activation Function: After the convolutional layer, AlexNet uses ReLU (Rectified Linear Unit) as the activation function to introduce nonlinearity so that the network can learn complex patterns. The calculation formula of ReLU is as follows:

[0079] f(x)=max(0,x)

[0080] The nonlinear characteristics of ReLU enable the network to learn richer features in different feature spaces, while avoiding the gradient vanishing problem of traditional activation functions (such as sigmoid and tanh). max(0,x): means taking the maximum value between the input value x and 0. If x is less than 0, the ReLU function outputs 0; if x is greater than 0, it directly outputs x.

[0081] Pooling Layers: Pooling layers are used to downsample feature maps to reduce computational complexity. The most commonly used is Max Pooling, whose formula is:

[0082]

[0083] Among them, R represents the size of the pooling window.

[0084] It should be noted that the improved AlexNet model after pre-training needs to be pre-trained on a large-scale image dataset to enable it to have good image feature extraction capabilities.

[0085] In this embodiment, refer to Figure 3 The schematic diagram of the improved AlexNet model architecture is shown. In order to further enhance the feature extraction capability of the model under complex working conditions, this embodiment makes improvements on the traditional AlexNet model architecture and further provides an improved architecture feature of the AlexNet model:

[0086] The Multihead Self-Attention (MSA) mechanism is introduced in the output layer of the model. MSA can focus on different areas in the recursive graph at the same time through multiple parallel attention heads, and automatically identify the most critical features for fault identification. The Multihead Self-Attention mechanism helps the model capture more representative fault modes under complex working conditions and improve the accuracy and robustness of fault detection.

[0087] The calculation steps of the multi-head self-attention mechanism are as follows:

[0088] First, each attention head calculates the attention weight based on the input query vector (Query), key vector (Key) and value vector (Value), the formula is as follows:

[0089]

[0090] where Q, K, and V are query, key, and value vectors, respectively, and d kis the dimension of the vector. Through the self-attention mechanism, the model can assign different weights to different regions in the recurrence graph, thereby capturing the fault features more accurately.

[0091] In the second step, multiple attention heads process different feature subspaces in parallel, and finally concatenate the outputs of these heads and generate the final output through linear transformation. The formula is as follows:

[0092] MSA(Q,K,V)=Concat(head1,…,head h )W O

[0093] Among them, head i is the output of each attention head, w o is the linear transformation matrix.

[0094] Step S50, obtaining the battery fault type prediction result output by the classification layer.

[0095] In this embodiment, the battery failure type prediction result output by the classification layer is obtained, so as to analyze whether the lithium battery has a potential failure risk according to the battery failure type prediction result.

[0096] In the technical solution provided in this embodiment, by introducing a recursive graph and an improved deep learning model (AlexNet), combined with a multi-head attention mechanism, the ability to extract complex fault features is enhanced and the accuracy of fault identification is improved; through the timing feature capture capability of the recursive graph, early signs of faults can be effectively identified, thereby achieving early fault detection; in addition, by introducing a multi-head attention mechanism, the ability to capture important local features is enhanced, so that it can still maintain high-accuracy fault detection under complex working conditions.

[0097] Second embodiment

[0098] As an optional embodiment, this embodiment provides a method for generating a recursive graph, based on the first embodiment, referring to Figure 4 , the step S20 specifically includes:

[0099] Step S21, calculating the Euclidean distance between each point in the original fault waveform data;

[0100] Specifically, in this embodiment, the Euclidean distance between each point in the time series data is first calculated, which is defined as follows:

[0101] d ij =||m i -m j ||

[0102] Among them, m i and m jrepresents the i-th and j-th data points in the time series data, and ||·|| represents the Euclidean distance.

[0103] Step S22, calculating the difference between a preset threshold and the Euclidean distance, and generating a recursive graph according to the difference;

[0104] Wherein, each element R in the recursive graph ij The definition is as follows:

[0105] R ij =Θ(∈-d ij )

[0106] Where Θ is a step function, when ∈≥d ij When R ij =1, otherwise R ij =0.

[0107] Third embodiment

[0108] As an optional embodiment, in this embodiment, based on the first embodiment, the first few convolutional layers of the improved AlexNet model are frozen through the transfer learning method, and the hyperparameters in the fully connected layers in the improved AlexNet model are fine-tuned.

[0109] Since lithium battery fault detection is different from general image classification tasks, the latter layers of the model (such as the fully connected layers) are fine-tuned to adapt to specific fault detection tasks. After freezing the first few convolutional layers, only the subsequent fully connected layers are fine-tuned. The calculation formula of the fine-tuned features through the fully connected layer is:

[0110]

[0111] in, is the predicted battery failure type, M is the feature vector output by the multi-head self-attention mechanism, W is the weight matrix, and b is the bias term.

[0112] Fourth embodiment

[0113] As an optional embodiment, in this embodiment, based on the first embodiment, after step S40, the following further includes:

[0114] Step S50, generating a battery health status report, wherein the battery health status report includes the current operating status of the battery, potential failure risks, and maintenance recommendations.

[0115] In this embodiment, after the system completes the prediction of the battery fault detection results, it generates a battery health status report, which includes the current operating status of the battery, potential fault risks and maintenance recommendations, to help users optimize battery usage, prevent further deterioration of the fault, and extend the battery life.

[0116] In addition, it can be understood by a person skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing related software through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the lithium battery fault identification system to implement the process steps of the embodiment of the above method.

[0117] Therefore, the present application also provides a computer-readable storage medium, which stores a lithium battery fault identification program based on a recursive graph and an improved AlexNet model. When the lithium battery fault identification program based on the recursive graph and the improved AlexNet model is executed by a processor, the various steps of the lithium battery fault identification method based on the recursive graph and the improved AlexNet model described in the above embodiment are implemented.

[0118] The computer-readable storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which may store program codes.

[0119] It should be noted that since the storage medium provided in the embodiment of the present application is the storage medium used to implement the method of the embodiment of the present application, based on the method introduced in the embodiment of the present application, the person skilled in the art can understand the specific structure and deformation of the storage medium, so it is not repeated here. All storage media used in the method of the embodiment of the present application belong to the scope of protection of this application.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0121] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0125] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0126] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A lithium battery fault identification method based on recursive graph and improved AlexNet model, characterized in that: The method comprises the following steps: Collect one-dimensional time series data from various sensors of lithium batteries; Preprocessing the one-dimensional time series data to generate original fault waveform data; Converting the original fault waveform data into a recurrence graph; Inputting the recursive graph into a pre-trained improved AlexNet model for feature extraction, wherein the improved AlexNet model processes feature vectors in the recursive graph through a multi-head self-attention mechanism, and inputs the processed feature vectors into a classification layer for battery fault type prediction; Obtain the battery failure type prediction result output by the classification layer.

2. The method according to claim 1, characterized in that The step of converting the original fault waveform data into a recursive graph comprises: Calculating the Euclidean distance between each point in the original fault waveform data; Calculating a difference between a preset threshold and the Euclidean distance, and generating a recursive graph according to the difference; Wherein, each element R in the recursive graph ij The definition is as follows: R ij =Θ(∈-d ij ) Where Θ is a step function, when ∈≥d ij When R ij =1, otherwise R ij =0.

3. The method according to claim 1, characterized in that The convolutional layers in the improved AlexNet model are frozen, and the hyperparameters in the fully connected layers in the improved AlexNet model are fine-tuned.

4. The method according to claim 1, characterized in that The calculation formula for predicting the fault type of the battery in the classification layer is as follows: in, is the predicted battery failure type, M is the feature vector output by the multi-head self-attention mechanism, W is the weight matrix, and b is the bias term.

5. The method according to claim 1, characterized in that After the step of obtaining the battery fault type prediction result output by the classification layer, the method further includes: A battery health status report is generated, which includes the current operating status of the battery, potential failure risks, and maintenance recommendations.

6. The method according to claim 1, characterized in that The preprocessing includes denoising and normalization processing.

7. The method according to claim 6, characterized in that The formula for the normalization process is: Among them, m i It is a data point in the one-dimensional time series data. max(M) and min(M) represent the maximum and minimum values ​​of the time series data respectively. M is the input time series.

8. A lithium battery fault identification system, characterized in that: The lithium battery fault identification system includes: a memory, a processor, and a lithium battery fault identification program based on a recursive graph and an improved AlexNet model stored in the memory and executable on the processor. When the lithium battery fault identification program based on a recursive graph and an improved AlexNet model is executed by the processor, the steps of the lithium battery fault identification method based on a recursive graph and an improved AlexNet model as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a lithium battery fault identification program based on a recursive graph and an improved AlexNet model. When the lithium battery fault identification program based on a recursive graph and an improved AlexNet model is executed by a processor, the steps of the lithium battery fault identification method based on a recursive graph and an improved AlexNet model as described in any one of claims 1 to 7 are implemented.

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