Lithium battery abnormal sample detection model training method and related device

Abstract

The invention belongs to the technical field of lithium battery anomaly detection, and discloses a training method of a lithium battery anomaly sample detection model and a related device. The training method comprises the following steps: based on formation equipment in a lithium battery production process, collecting resource data and calculating to obtain the distribution of the resource data; based on the initially trained lithium battery abnormal sample detection model, obtaining training sample data and calculating to obtain the distribution of the training sample data; and calculating a difference value between the distribution of the resource data and the distribution of the training sample data, and if the difference value is greater than or equal to a preset threshold value, performing incremental training on the initially trained lithium battery abnormal sample detection model based on the resource data and the training sample data. According to the technical scheme of the invention, when the battery data generates domain offset, iterative updating of the model can be realized through incremental training, the generalization ability of the model is enhanced, and the reliability of a model detection result is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium battery anomaly detection, and in particular relates to a training method and related devices for a lithium battery anomaly sample detection model. Background Art

[0002] With the widespread application of lithium batteries in electric vehicles, energy storage systems and other fields, real-time monitoring of their health status has become particularly important.

[0003] At present, in order to improve the efficiency and accuracy of detection, in the production process of lithium batteries, companies generally use lithium battery abnormal sample detection models to monitor the health status of batteries; explanatory, the lithium battery abnormal sample detection model can select a suitable machine learning or deep learning model according to the characteristics of the data and the requirements of the detection task (for example, support vector machines, neural networks, long short-term memory networks and other models can be used for abnormal sample detection), and then use the pre-collected sample data to train the model. After reaching the preset convergence conditions and passing the performance test, the trained lithium battery abnormal sample detection model can be obtained.

[0004] The above-mentioned trained lithium battery abnormal sample detection model can detect abnormal lithium battery samples during the battery production process, and has good detection efficiency and accuracy; however, when the raw materials fluctuate or the equipment conditions fluctuate, the data domain of the lithium battery will also shift accordingly. In this case, the above-mentioned trained lithium battery abnormal sample detection model is difficult to continue to use, and the generalization ability of the model needs to be further strengthened. Summary of the invention

[0005] The purpose of the present invention is to provide a training method and related device for a lithium battery abnormal sample detection model to solve one or more of the above-mentioned technical problems. The technical solution provided by the present invention is specifically an incremental training solution for a lithium battery abnormal sample detection model with high generalization ability. When the battery data generates domain shift, the model can be iteratively updated through incremental training, thereby enhancing the generalization ability of the model and ensuring the reliability of the model detection results.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for training a lithium battery abnormal sample detection model, comprising the following steps: Based on the formation equipment in the lithium battery production process, resource data is collected and the distribution of resource data is calculated; wherein the resource data includes voltage data and current data; Based on the initially trained lithium battery abnormal sample detection model, the training sample data is obtained and the distribution of the training sample data is calculated; The difference value between the distribution of the resource data and the distribution of the training sample data is calculated. If the difference value is greater than or equal to a preset threshold, the initially trained lithium battery abnormal sample detection model is incrementally trained based on the resource data and the training sample data to obtain the final trained lithium battery abnormal sample detection model.

[0007] A further improvement of the training method of the present invention is that: In the step of collecting resource data and calculating the distribution of the resource data, the triggering condition for calculating the distribution of the resource data is a change in the raw material batch or continuous production for a preset period of time.

[0008] A further improvement of the training method of the present invention is that: The step of calculating and obtaining the distribution of resource data comprises: Divide the resource data into short sequences, and establish a window-based statistical probability model for each short sequence. Based on the statistical probability model, the probability distribution law is obtained and used as the distribution of the resource data; The steps for calculating the length of the short sequence are as follows: calculating the sum of the charging time and the discharging time, dividing it by a preset coefficient and rounding it up; in the statistical probability model, multiple continuous windows are defined, and the probability distribution law is calculated by counting the number of data in each window; the number of windows in each short sequence is consistent.

[0009] A further improvement of the training method of the present invention is that: The step of calculating and obtaining the distribution of training sample data comprises: The training sample data is divided into short sequences, and a window-based statistical probability model is established for each short sequence. The probability distribution law is obtained based on the statistical probability model and used as the distribution of the training sample data; The steps for calculating the length of the short sequence are as follows: calculating the sum of the charging time and the discharging time, dividing it by a preset coefficient and rounding it up; in the statistical probability model, multiple continuous windows are defined, and the probability distribution law is calculated by counting the number of data in each window; the number of windows in each short sequence is consistent.

[0010] A further improvement of the training method of the present invention is that: In the step of calculating the difference between the distribution of resource data and the distribution of training sample data, the difference value calculation expression is: ; In the formula, error represents the difference value; P train Represents the distribution of training sample data; P source represents the distribution of resource data; ||·||1 represents the 1-norm.

[0011] A further improvement of the training method of the present invention is that: The step of incrementally training the initially trained lithium battery abnormal sample detection model based on the resource data and the training sample data comprises: Using random non-repetitive sampling, sampling is performed from the resource data and the training sample data respectively, and the sampling results of the training sample data are replaced with the sampling results of the resource data to obtain new training sample data; Based on the new training sample data, the lithium battery abnormal sample detection model is retrained in a gradient descent manner; Among them, when the difference between the loss when the retrained lithium battery abnormal sample detection model converges and the loss when the initially trained lithium battery abnormal sample detection model converges is greater than a predetermined threshold, the retraining is invalid training, otherwise it is valid training; when invalid training occurs, the parameters of the lithium battery abnormal sample detection model remain unchanged.

[0012] In a second aspect, the present invention provides a training system for a lithium battery abnormal sample detection model, comprising: A first distribution acquisition module is used to collect resource data and calculate the distribution of resource data based on the formation equipment in the lithium battery production process; wherein the resource data includes voltage data and current data; A second distribution acquisition module is used to acquire training sample data and calculate the distribution of the training sample data based on the initially trained lithium battery abnormal sample detection model; The discrimination and incremental training module is used to calculate the difference between the distribution of the resource data and the distribution of the training sample data. If the difference is greater than or equal to a preset threshold, the initially trained lithium battery abnormal sample detection model is incrementally trained based on the resource data and the training sample data to obtain the final trained lithium battery abnormal sample detection model.

[0013] A further improvement of the training system of the present invention is that: In the step of collecting resource data and calculating the distribution of the resource data executed by the first distribution acquisition module, the triggering condition for calculating the distribution of the resource data is a change in the raw material batch or continuous production for a preset period of time.

[0014] A further improvement of the training system of the present invention is that: The step of the first distribution acquisition module performing calculation to obtain the distribution of resource data includes: Divide the resource data into short sequences, and establish a window-based statistical probability model for each short sequence. Based on the statistical probability model, the probability distribution law is obtained and used as the distribution of the resource data; The steps for calculating the length of the short sequence are as follows: calculating the sum of the charging time and the discharging time, dividing it by a preset coefficient and rounding it up; in the statistical probability model, multiple continuous windows are defined, and the probability distribution law is calculated by counting the number of data in each window; the number of windows in each short sequence is consistent.

[0015] A further improvement of the training system of the present invention is that: The step of the second distribution acquisition module performing calculation to obtain the distribution of training sample data includes: The training sample data is divided into short sequences, and a window-based statistical probability model is established for each short sequence. The probability distribution law is obtained based on the statistical probability model and used as the distribution of the training sample data; The steps for calculating the length of the short sequence are as follows: calculating the sum of the charging time and the discharging time, dividing it by a preset coefficient and rounding it up; in the statistical probability model, multiple continuous windows are defined, and the probability distribution law is calculated by counting the number of data in each window; the number of windows in each short sequence is consistent.

[0016] A further improvement of the training system of the present invention is that: In the step of calculating the difference between the distribution of resource data and the distribution of training sample data in the discrimination and incremental training module, the difference value calculation expression is: ; In the formula, error represents the difference value; P train Represents the distribution of training sample data; P source represents the distribution of resource data; ||·||1 represents the 1-norm.

[0017] A further improvement of the training system of the present invention is that: The step of the discrimination and incremental training module performing incremental training on the initially trained lithium battery abnormal sample detection model based on the resource data and the training sample data comprises: Using random non-repetitive sampling, sampling is performed from the resource data and the training sample data respectively, and the sampling results of the training sample data are replaced with the sampling results of the resource data to obtain new training sample data; Based on the new training sample data, the lithium battery abnormal sample detection model is retrained in a gradient descent manner; Among them, when the difference between the loss when the retrained lithium battery abnormal sample detection model converges and the loss when the initially trained lithium battery abnormal sample detection model converges is greater than a predetermined threshold, the retraining is invalid training, otherwise it is valid training; when invalid training occurs, the parameters of the lithium battery abnormal sample detection model remain unchanged.

[0018] In a third aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a training method for a lithium battery abnormal sample detection model as described in any one of the first aspect of the present invention is implemented.

[0019] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the training method of the lithium battery abnormal sample detection model as described in any one of the first aspect of the present invention is implemented.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a training method for a lithium battery abnormal sample detection model, which is an incremental training scheme designed to improve the generalization ability of the lithium battery abnormal sample detection model. This scheme not only focuses on the accuracy and efficiency in traditional model training, but also focuses on solving the problem of model inapplicability caused by battery data domain offset. It can significantly improve the generalization ability of the model and ensure the continued effectiveness and reliability of the model in practical applications.

[0021] In addition, in the preferred technical solution of the present invention, the trigger conditions for calculating the distribution of resource data are given, which ensures the training efficiency and computing power cost.

[0022] Furthermore, in the preferred technical solution of the present invention, the training steps, trigger conditions and judgment conditions of the incremental training are specifically given, which can reasonably complete the incremental training, improve the generalization ability of the model, and ensure the effectiveness and reliability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 It is a flowchart of a method for training a lithium battery abnormal sample detection model in an embodiment of the present invention.

[0025] Figure 2 It is a flowchart of a method for training a lithium battery abnormal sample detection model in a specific embodiment of the present invention.

[0026] Figure 3 yes Figure 2 A schematic diagram of the implementation principle of the illustrated embodiment.

[0027] Figure 4 It is a schematic diagram of a training system for a lithium battery abnormal sample detection model in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments and technical solutions are only part of the embodiments of the present invention, not all of the embodiments.

[0029] All other embodiments obtained by those of ordinary skill in the art without creative work based on the technical solutions disclosed in the embodiments of the present invention belong to the scope of protection of the present invention. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] See also Figure 1 The present invention discloses a method for training a lithium battery abnormal sample detection model, comprising the following steps: Step 1, based on the formation equipment in the lithium battery production process, collecting resource data and calculating the distribution of the resource data; wherein the resource data includes voltage data and current data; Step 2: Based on the initially trained lithium battery abnormal sample detection model, obtain training sample data and calculate the distribution of the training sample data; Step 3, calculate the difference between the distribution of the resource data and the distribution of the training sample data. If the difference is greater than or equal to a preset threshold, perform incremental training on the initially trained lithium battery abnormal sample detection model based on the resource data and the training sample data to obtain the final trained lithium battery abnormal sample detection model.

[0031] The technical solution proposed in the embodiment of the present invention focuses on enhancing the generalization ability of the lithium battery abnormal sample detection model through an incremental training method. This solution not only maintains the high standards of the model in terms of accuracy and efficiency, but also specifically solves the model failure problem caused by battery data domain offset (i.e., changes in data distribution over time or conditions). Specifically, the existing traditional model training is often based on static data sets. Once the training is completed, it may not be able to effectively identify the newly emerging data patterns; the present invention adopts incremental learning, allowing the model to continuously receive new data and make fine adjustments after initial training, so as to adapt to changes in the data domain and maintain the timeliness of the model. In summary, by continuously learning and adapting to new data, the model can better generalize to unseen data scenarios and reduce the degradation of model performance caused by data domain offset; the solution ensures the effectiveness of the model in long-term operation, which is particularly important for application scenarios such as lithium battery health management and fault prediction, and improves the value and reliability of practical applications. In addition, compared with frequent retraining of the entire model, incremental training only locally updates the new data, which greatly saves computing resources and time costs.

[0032] See also Figure 2 and Figure 3 In a specific embodiment of the present invention, a training method for a lithium battery abnormal sample detection model is provided, comprising: Prerequisites: Establish a lithium battery abnormal sample detection model and train it with training sample data to obtain an initially trained lithium battery abnormal sample detection model; Explanatoryally, a buffer zone can be divided on the server and named as a training zone; the training sample data in the training zone is used to train the lithium battery abnormal sample detection model; further explanatoryally, the present invention does not involve model construction, and the focus is on incremental model training. The model can be any type of neural network-based anomaly detection model, such as LSTM (long short-term memory network)-based, Transformer (self-attention mechanism)-based, Autoencoder (unsupervised learning)-based models, etc.; the necessary premise is that the model can be trained, and its parameters are not fixed, that is, it can be trained by the gradient descent method in deep learning, and the average loss of the model when the training converges is recorded as L0.

[0033] 1) Data collection, including: 1.1) Deploy sensors on the formation cabinets for lithium battery production, and use edge computers to collect data such as voltage and current as resource data, which is then uploaded to the central server; 1.2) Set up a buffer zone on the central server, named resource zone, which is used to receive resource data uploaded by edge computers. These resource data are used as the data source for incremental training; 2) Data distribution calculation, including: 2.1) Set up a timing program to calculate the distribution of resource data according to the trigger conditions. Explanatoryally, due to the large amount of calculation, two trigger conditions are set. Among them, condition 1 is to calculate the distribution of resource data in the buffer when the raw material batch changes; condition 2 is to calculate the distribution of resource data in the buffer when production continues for half a month; 2.2) Obtain the distribution of training sample data used by the initially trained lithium battery abnormal sample detection model. The calculation method is as follows: For long sequences, they must be divided into short sequences; specifically, the sum of the charging time and the discharging time (calculated in minutes) can be calculated according to the charging and discharging time, divided by the preset coefficient m and rounded up (usually 60), and the resulting number is the length of each short sequence. In practice, the entire section generally lasts 120 minutes, so there are basically no exceptions. Furthermore, when an exception occurs, if the long sequence exceeds 120 minutes, the part above 120 minutes can be deleted; if it is less than 120 minutes, the sequence can be padded to 120 minutes, and the non-existent data segment can be supplemented with the termination voltage of the discharge process.

[0034] A window-based statistical probability model is established for each short sequence, that is, continuous windows are defined, the number of data in each window is counted and the probability distribution law is calculated. It must be ensured that the number of windows for each short sequence is consistent, and the number of windows can be expressed as n.

[0035] The obtained probability distribution law is called training distribution, and its form is expressed as: ; In the formula, each row represents the probability distribution law of a short sequence, for example, p 1 n Represents the distribution law of the first short sequence, where n is the number of windows and m is the number of short sequences, usually 60.

[0036] 3) Data distribution comparison, including: The difference between the distributions is calculated using the following formula, which is the 1-norm of the matrix obtained by the difference between the two, expressed as, ; In the formula, error represents the difference value; P train Represents the distribution of training sample data; P source Indicates the distribution of resource data.

[0037] The steps for obtaining the threshold used in the comparison include: recording the distribution of resource data obtained for the first time without changing the raw material batch and on the 30th day of continuous production as P 01 , the distribution of the training sample data used when building the model for the first time is recorded as P 00 , the error calculated according to the above formula is E 0 is used as the threshold.

[0038] During the comparison process, after the trigger condition is met and the resource data distribution is calculated, the error calculated according to the above formula is E 1, E 1 If more than E 0, it means that the data distribution in the current resource area is too different from that in the training area and incremental training is needed. Otherwise, incremental training is not needed.

[0039] In the preferred technical solution of the embodiment of the present invention, the calculation trigger conditions of the distribution of resource data and the trigger conditions of incremental training are specifically given, which can reasonably complete the incremental training and ensure the training efficiency and computing power cost.

[0040] In one embodiment of the present invention, the specific steps of incremental training include: Sample the resource data in the resource area, and select 20% of them by random non-repetitive sampling, and this part of the data must not exceed 20% of the number of data samples in the training area; The training sample data in the training area are sampled, and the sampling number is equal to the sampling number of the resource data in the resource area, and the sampling is performed according to random non-repetitive sampling; The sampled data in the resource data is replaced with the sampled data in the training sample data, which means that part of the data in the training area will be removed, and the sampled data in the resource area is added, and the model is retrained in a gradient descent manner; further, the loss of the current model training convergence is recorded as L1, and the loss of the model at this time is checked. If L1 exceeds L0 by 20%, the training is invalid, otherwise it is called valid training; when invalid training occurs, the model parameters remain unchanged.

[0041] In the technical solution provided by the embodiment of the present invention, in the face of continuous changes in the data domain, an incremental training mechanism is introduced, which means that the model does not need to be retrained from scratch, but can be locally updated or fine-tuned using newly collected lithium battery data. This training method significantly reduces the training time, while maintaining the model's sensitivity to the latest data distribution and improving generalization capabilities. In order to further enhance the model's resistance to domain shift, the present invention automatically adjusts model parameters by monitoring changes in data distribution. This dynamic adjustment mechanism ensures that the model can still maintain high-accuracy anomaly detection capabilities when raw materials, equipment conditions, etc. change. In order to ensure the reliability of the model detection results, the accuracy of the detection results can be further improved by comparing losses during the incremental training process. In summary, the incremental training solution for the lithium battery abnormal sample detection model provided by the embodiment of the present invention solves the problem of reduced model generalization ability caused by data domain shift, realizes efficient iterative updates and continuous reliability assurance of the model, and provides strong technical support for lithium battery anomaly detection.

[0042] The following are device embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0043] See also Figure 4 In an embodiment of the present invention, a training system for a lithium battery abnormal sample detection model is provided, comprising: A first distribution acquisition module is used to collect resource data and calculate the distribution of resource data based on the formation equipment in the lithium battery production process; wherein the resource data includes voltage data and current data; A second distribution acquisition module is used to acquire training sample data and calculate the distribution of the training sample data based on the initially trained lithium battery abnormal sample detection model; The discrimination and incremental training module is used to calculate the difference between the distribution of the resource data and the distribution of the training sample data. If the difference is greater than or equal to a preset threshold, the initially trained lithium battery abnormal sample detection model is incrementally trained based on the resource data and the training sample data to obtain the final trained lithium battery abnormal sample detection model.

[0044] In one embodiment of the present invention, a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used to perform the operation of the training method of the lithium battery abnormal sample detection model.

[0045] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM (Random Access Memory) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the training method for the abnormal sample detection model of lithium batteries in the above embodiment.

[0046] 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 take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program codes.

[0047] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the 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 the 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.

[0048] 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 including 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.

[0049] 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 The steps for the functions specified in one or more boxes.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A training method for a lithium battery abnormal sample detection model, characterized in that: The following steps are involved: Based on the formation equipment in the lithium battery production process, resource data is collected and the distribution of resource data is calculated; wherein the resource data includes voltage data and current data; Based on the initially trained lithium battery abnormal sample detection model, the training sample data is obtained and the distribution of the training sample data is calculated; The difference value between the distribution of the resource data and the distribution of the training sample data is calculated. If the difference value is greater than or equal to a preset threshold, the initially trained lithium battery abnormal sample detection model is incrementally trained based on the resource data and the training sample data to obtain the final trained lithium battery abnormal sample detection model.

2. The training method for a lithium battery abnormal sample detection model according to claim 1, characterized in that: In the step of collecting resource data and calculating the distribution of the resource data, the triggering condition for calculating the distribution of the resource data is a change in the raw material batch or continuous production for a preset period of time.

3. The training method for a lithium battery abnormal sample detection model according to claim 1, characterized in that: The step of calculating and obtaining the distribution of resource data comprises: Divide the resource data into short sequences, and establish a window-based statistical probability model for each short sequence. Based on the statistical probability model, the probability distribution law is obtained and used as the distribution of the resource data; The steps for calculating the length of the short sequence are as follows: calculating the sum of the charging time and the discharging time, dividing it by a preset coefficient and rounding it up; in the statistical probability model, multiple continuous windows are defined, and the probability distribution law is calculated by counting the number of data in each window; the number of windows in each short sequence is consistent.

4. The training method for a lithium battery abnormal sample detection model according to claim 1, characterized in that: The step of calculating and obtaining the distribution of training sample data comprises: The training sample data is divided into short sequences, and a window-based statistical probability model is established for each short sequence. The probability distribution law is obtained based on the statistical probability model and used as the distribution of the training sample data; The steps for calculating the length of the short sequence are as follows: calculating the sum of the charging time and the discharging time, dividing it by a preset coefficient and rounding it up; in the statistical probability model, multiple continuous windows are defined, and the probability distribution law is calculated by counting the number of data in each window; the number of windows in each short sequence is consistent.

5. The training method for a lithium battery abnormal sample detection model according to claim 1, characterized in that: In the step of calculating the difference between the distribution of resource data and the distribution of training sample data, the difference value calculation expression is: ； In the formula, error represents the difference value; P train Represents the distribution of training sample data; P source represents the distribution of resource data; ||·||1 represents the 1-norm.

6. The training method for a lithium battery abnormal sample detection model according to claim 1, characterized in that: The step of incrementally training the initially trained lithium battery abnormal sample detection model based on the resource data and the training sample data comprises: Using random non-repetitive sampling, sampling is performed from the resource data and the training sample data respectively, and the sampling results of the training sample data are replaced with the sampling results of the resource data to obtain new training sample data; Based on the new training sample data, the lithium battery abnormal sample detection model is retrained in a gradient descent manner; Among them, when the difference between the loss when the retrained lithium battery abnormal sample detection model converges and the loss when the initially trained lithium battery abnormal sample detection model converges is greater than a predetermined threshold, the retraining is invalid training, otherwise it is valid training; when invalid training occurs, the parameters of the lithium battery abnormal sample detection model remain unchanged.

7. A training system for a lithium battery abnormal sample detection model, characterized in that: include: A first distribution acquisition module is used to collect resource data and calculate the distribution of resource data based on the formation equipment in the lithium battery production process; wherein the resource data includes voltage data and current data; A second distribution acquisition module is used to acquire training sample data and calculate the distribution of the training sample data based on the initially trained lithium battery abnormal sample detection model; The discrimination and incremental training module is used to calculate the difference between the distribution of the resource data and the distribution of the training sample data. If the difference is greater than or equal to a preset threshold, the initially trained lithium battery abnormal sample detection model is incrementally trained based on the resource data and the training sample data to obtain the final trained lithium battery abnormal sample detection model.

8. The training system for a lithium battery abnormal sample detection model according to claim 7, characterized in that: In the step of collecting resource data and calculating the distribution of the resource data executed by the first distribution acquisition module, the triggering condition for calculating the distribution of the resource data is a change in the raw material batch or continuous production for a preset period of time.

9. The training system for a lithium battery abnormal sample detection model according to claim 7, characterized in that: The step of the first distribution acquisition module performing calculation to obtain the distribution of resource data includes: Divide the resource data into short sequences, and establish a window-based statistical probability model for each short sequence. Based on the statistical probability model, the probability distribution law is obtained and used as the distribution of the resource data; The steps for calculating the length of the short sequence are as follows: calculating the sum of the charging time and the discharging time, dividing it by a preset coefficient and rounding it up; in the statistical probability model, multiple continuous windows are defined, and the probability distribution law is calculated by counting the number of data in each window; the number of windows in each short sequence is consistent.

10. The training system for a lithium battery abnormal sample detection model according to claim 7, characterized in that: The step of the second distribution acquisition module performing calculation to obtain the distribution of training sample data includes: The training sample data is divided into short sequences, and a window-based statistical probability model is established for each short sequence. The probability distribution law is obtained based on the statistical probability model and used as the distribution of the training sample data; The steps for calculating the length of the short sequence are as follows: calculating the sum of the charging time and the discharging time, dividing it by a preset coefficient and rounding it up; in the statistical probability model, multiple continuous windows are defined, and the probability distribution law is calculated by counting the number of data in each window; the number of windows in each short sequence is consistent.

11. The training system for a lithium battery abnormal sample detection model according to claim 7, characterized in that: In the step of calculating the difference between the distribution of resource data and the distribution of training sample data in the discrimination and incremental training module, the difference value calculation expression is: ； In the formula, error represents the difference value; P train Represents the distribution of training sample data; P source represents the distribution of resource data; ||·||1 represents the 1-norm.

12. The training system for a lithium battery abnormal sample detection model according to claim 7, characterized in that: The step of the discrimination and incremental training module performing incremental training on the initially trained lithium battery abnormal sample detection model based on the resource data and the training sample data comprises: Using random non-repetitive sampling, sampling is performed from the resource data and the training sample data respectively, and the sampling results of the training sample data are replaced with the sampling results of the resource data to obtain new training sample data; Based on the new training sample data, the lithium battery abnormal sample detection model is retrained in a gradient descent manner; Among them, when the difference between the loss when the retrained lithium battery abnormal sample detection model converges and the loss when the initially trained lithium battery abnormal sample detection model converges is greater than a predetermined threshold, the retraining is invalid training, otherwise it is valid training; when invalid training occurs, the parameters of the lithium battery abnormal sample detection model remain unchanged.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the training method of the lithium battery abnormal sample detection model according to any one of claims 1 to 6 is implemented.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the training method of the lithium battery abnormal sample detection model according to any one of claims 1 to 6 is implemented.

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