CN114089206A - Method, system, medium and device for predicting service life of battery redundancy module - Google Patents
Method, system, medium and device for predicting service life of battery redundancy module Download PDFInfo
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Abstract
The invention provides a method, a system, a medium and equipment for predicting the service life of a battery redundancy module, wherein the method comprises the following steps: collecting operating data of battery redundancy modules with different known lives, and preprocessing the operating data to obtain sample data; extracting one part from the sample data as training data and extracting the other part as test data; selecting a corresponding neural network structure and a hidden layer structure based on the operation data to obtain a corresponding neural network model; respectively training and testing the neural network model by using the sample data and the test data to obtain a neural network prediction model; embedding the neural network prediction model into the executable program to obtain a service life prediction program, and inputting the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program to output the predicted service life. The invention can effectively predict the service life of the battery redundancy module, maximize the service cycle of the battery redundancy module, and avoid resource waste, thereby reducing the cost.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method, a system, a medium and equipment for predicting the service life of a battery redundancy module.
Background
Data security is an important ring of security production in the industry today, and Battery Backup Units (BBUs) are mostly equipped in today's various types of storage products to ensure stable operation of the system and data availability and security. However, the service life prediction of the BBU has been a problem that is difficult to solve, and in order to ensure data security, the BBU is often actively replaced before the service life of the BBU is reached, which causes a great deal of waste of material resources and unnecessary increase of cost.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method, a system, a medium, and a device for predicting a lifetime of a battery redundancy module, so as to solve the problem in the prior art that the lifetime of the battery redundancy module cannot be predicted well, so that the battery redundancy module is replaced too early, thereby wasting material resources.
Based on the above purpose, the present invention provides a method for predicting the life of a battery redundancy module, comprising the following steps:
collecting operating data of battery redundancy modules with different known lives, and preprocessing the operating data to obtain sample data;
extracting one part from the sample data as training data and extracting the other part as test data;
selecting a corresponding neural network structure and a hidden layer structure based on the operation data to obtain a corresponding neural network model;
respectively training and testing the neural network model by using the sample data and the test data to obtain a neural network prediction model;
embedding the neural network prediction model into the executable program to obtain a service life prediction program, and inputting the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program to output the predicted service life.
In some embodiments, training and testing the neural network model using the sample data and the test data, respectively, to obtain the neural network prediction model comprises:
training the neural network model by using the sample data to obtain a neural network training model;
testing the neural network training model by using the test data, and judging whether the test result meets the preset requirement;
and responding to the test result not meeting the preset requirement, continuing training and correspondingly testing the neural network training model until the final test result meets the preset requirement, and taking the neural network training model corresponding to the final test result as a neural network prediction model.
In some embodiments, embedding the neural network prediction model in the executable program to obtain a life prediction program, and inputting the prediction data of the battery redundancy module whose life is to be predicted into the life prediction program to output the predicted life thereof comprises:
embedding the neural network prediction model into an executable program to obtain a service life prediction program, and inputting the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program in a parameter mode through a command line to output the predicted service life.
In some embodiments, the method further comprises:
and setting a life guard threshold value for the corresponding battery redundancy module based on the output predicted life value, and sending out an early warning notice that the life is about to be terminated based on the life guard threshold value.
In some embodiments, operational data for a battery redundancy module of known life includes: temperature, voltage, current, elapsed time, and number of failures during operation of a battery redundancy module of known life.
In some embodiments, pre-processing the operational data comprises:
the run data was pre-processed using Z-score normalization.
In some embodiments, the selected neural network structure is a deep neural network structure and the selected hidden layer structure is six hidden layers.
In another aspect of the present invention, a system for predicting a lifetime of a battery redundancy module is further provided, including:
the system comprises a preprocessing module, a data acquisition module and a data processing module, wherein the preprocessing module is configured to acquire operating data of battery redundancy modules with different known lives and preprocess the operating data to obtain sample data;
the data extraction module is configured to extract one part from the sample data as training data and extract the other part as test data;
the neural network model selection module is configured for selecting a corresponding neural network structure and a hidden layer structure based on the operation data to obtain a corresponding neural network model;
the training and testing module is configured to utilize the sample data and the test data to respectively train and test the neural network model so as to obtain a neural network prediction model; and
and the service life prediction module is configured for embedding the neural network prediction model into the executable program to obtain a service life prediction program, and inputting the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program to output the predicted service life.
In yet another aspect of the present invention, a computer-readable storage medium is also provided, storing computer program instructions, which when executed by a processor, implement the above-described method.
In yet another aspect of the present invention, a computer device is further provided, which includes a memory and a processor, the memory storing a computer program, which when executed by the processor performs the above method.
The invention has at least the following beneficial technical effects:
1. the invention constructs a set of life prediction method of the storage equipment battery redundancy module by utilizing a deep learning technology in a standardized process, integrates data acquisition, data preprocessing and training and testing of a neural network model, can realize real-time and periodic life prediction of the battery redundancy module, and can be used as an effective auxiliary means for the service cycle of the battery redundancy module and equipment updating;
2. on the premise of ensuring the stable operation and data safety of the storage system, the service cycle of the battery redundancy module is maximized, so that the resource waste is avoided, the service life of the battery redundancy module is prolonged as much as possible, and the cost is reduced;
3. by embedding the neural network prediction model into the executable program, automatic deployment is realized, the neural network prediction model can be applied to multiple platforms, and the application universality is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by using the drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating a method for predicting the life of a battery redundancy module according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system for predicting the life of a battery redundancy module according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a computer-readable storage medium for implementing a method for predicting a lifetime of a battery redundancy module according to an embodiment of the present invention;
fig. 4 is a schematic hardware configuration diagram of a computer device for performing a method for predicting a lifetime of a battery redundancy module according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following embodiments of the present invention are described in further detail with reference to the accompanying drawings.
It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two non-identical entities with the same name or different parameters, and it is understood that "first" and "second" are only used for convenience of expression and should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements does not include all of the other steps or elements inherent in the list.
In view of the above objects, a first aspect of the embodiments of the present invention provides an embodiment of a method for predicting a lifetime of a battery redundancy module. Fig. 1 is a schematic diagram illustrating an embodiment of a method for predicting the life of a battery redundancy module according to the present invention. As shown in fig. 1, the embodiment of the present invention includes the following steps:
step S10, collecting operation data of battery redundancy modules with different known lives, and preprocessing the operation data to obtain sample data;
step S20, extracting one part from the sample data as training data and extracting the other part as test data;
s30, selecting a corresponding neural network structure and a hidden layer structure based on the operation data to obtain a corresponding neural network model;
step S40, training and testing the neural network model by using the sample data and the test data respectively to obtain a neural network prediction model; and
and step S50, embedding the neural network prediction model into the executable program to obtain a life prediction program, and inputting the prediction data of the battery redundancy module with the life to be predicted into the life prediction program to output the predicted life.
The embodiment of the invention constructs a set of service life prediction method of the storage equipment battery redundancy module by utilizing a deep learning technology in a standardized process, integrates data acquisition, data preprocessing and training and testing of a neural network model, can realize real-time and periodic service life prediction of the battery redundancy module, and can be used as an effective auxiliary means for the service cycle of the battery redundancy module and equipment updating; on the premise of ensuring the stable operation and data safety of the storage system, the service cycle of the battery redundancy module is maximized, so that the resource waste is avoided, the service life of the battery redundancy module is prolonged as much as possible, and the cost is reduced; by embedding the neural network prediction model into the executable program, automatic deployment is realized, the neural network prediction model can be applied to multiple platforms, and the application universality is improved.
In some embodiments, training and testing the neural network model using the sample data and the test data, respectively, to obtain the neural network prediction model comprises: training the neural network model by using the sample data to obtain a neural network training model; testing the neural network training model by using the test data, and judging whether the test result meets the preset requirement; and responding to the test result not meeting the preset requirement, continuing training and correspondingly testing the neural network training model until the final test result meets the preset requirement, and taking the neural network training model corresponding to the final test result as a neural network prediction model.
In this embodiment, the ratio of the training data to the test data may be 4:1, and the training and testing are performed for multiple iterations with 50 pieces of data as a group (batch size). When the neural network training model is tested, test data can be input into the neural network training model after each training to obtain an output prediction result, and then the prediction result is compared with the test data, so that the prediction accuracy of the neural network training model trained at this time can be obtained, and the test result is obtained. The preset requirement may be a preset prediction accuracy, for example, the prediction accuracy may be set to 70% or more, thereby avoiding the over-fitting and under-fitting situations.
In some embodiments, embedding the neural network prediction model in the executable program to obtain a life prediction program, and inputting the prediction data of the battery redundancy module whose life is to be predicted into the life prediction program to output the predicted life thereof comprises: embedding the neural network prediction model into an executable program to obtain a service life prediction program, and inputting the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program in a parameter mode through a command line to output the predicted service life.
In the embodiment, the neural network prediction model is embedded into the executable program, and the prediction data of the battery redundancy module with the service life to be predicted is input into the program in a parameter form through the command line, so that the predicted service life is output, the automatic deployment is convenient to realize, the neural network prediction model is applied to multiple platforms, and the application universality is improved.
In some embodiments, the method further comprises: and setting a life guard threshold value for the corresponding battery redundancy module based on the output predicted life value, and sending out an early warning notice that the life is about to be terminated based on the life guard threshold value.
In this embodiment, assuming that the life warning threshold is set to be 5 hours, when the predicted life of the output battery redundancy module is 5 hours, an early warning notification that the life of the battery redundancy module is about to be terminated may be sent out, and after receiving the notification, a manager may timely detect and replace the battery redundancy module, so as to prevent the battery redundancy module from stopping working due to the end of life.
In some embodiments, pre-processing the operational data comprises: the operation data is preprocessed.
In this example, Z-score normalization (zero-mean normalization), also called standard deviation normalization, is used to normalize the data by giving the mean (mean) and standard deviation (standard deviation) of the raw data. The processed data were in accordance with the standard normal distribution, i.e. mean 0 and standard deviation 1. The Z-score normalization method is applicable to situations where the maximum and minimum values of attribute A are unknown, or where there is outlier data that is outside of the range of values. The steps of normalization are: 1) calculating an arithmetic mean (mathematical expectation) xi and a standard deviation si of each variable (index); 2) and (3) carrying out standardization treatment: zij is (xij-xi)/si, wherein zij is a standardized variable value, and xij is an actual variable value; 3) and exchanging the signs before the inverse indexes. The normalized variable values fluctuate around 0, with values greater than 0 indicating a higher than average level and values less than 0 indicating a lower than average level.
In some embodiments, operational data for a battery redundancy module of known life includes: temperature, voltage, current, elapsed time, and number of failures during operation of a battery redundancy module of known life.
In some embodiments, the selected neural network structure is a deep neural network structure and the selected hidden layer structure is six hidden layers.
Alternative neural network structures include CNN (convolutional neural network), RNN (cyclic neural network), DNN (deep neural network), etc., and in the above embodiments, a mature back-propagation DNN network structure is selected according to the collected operational data. While neural networks are based on extensions of the perceptron, DNN can be understood as neural networks with many hidden layers. DNNs are sometimes also called Multi-Layer perceptrons (MLPs). From the DNN, which is divided by the positions of different layers, the neural network layers inside the DNN can be divided into three categories: an input layer, a hidden layer, and an output layer. Typically the first layer is the input layer, the last layer is the output layer, and the number of layers in between are all hidden layers. According to various characteristics of the operating data such as temperature, voltage, current, used time and failure frequency, the structure of the six hidden layers is selected based on the complexity of the six hidden layers.
An exemplary embodiment of a method for predicting the life of a battery redundancy module of the present invention is as follows:
(1) data acquisition: based on scripts written in the C language, BBUs (battery redundancy modules) real-time operating data of known different lifetimes, including temperature, voltage, current, age, number of failures, and the like, are periodically collected. The data is stored in dump form and can be stored to a local or database as required to be used as training and testing data for supervised learning. In practice, most storage devices already have this data collection function.
(2) Data preprocessing: and a common z-score Standardization preprocessing (Standardization preprocessing) mode is adopted, so that the data dimension and numerical influence are removed, the data distribution is optimized, and the convergence speed of model training is improved.
(3) Model building and training:
a) and (3) algorithm selection:
the framework adopts a mature counter-propagating DNN network (deep neural network), and adopts a 6-hidden-layer structure of N64 |128 |256 |128 |64 |32 |1 according to the existing data volume, so that the nonlinear simulation capability of the framework is fully ensured. Wherein N is the characteristic number of input data, and the tail output layer outputs the predicted service life.
b) Loss function establishment and optimization method:
the framework adopts Euclidean distance (Euclidean distance) of a predicted value and an original value as a loss function reference, and uses an Adam optimizer as gradient descent optimization (gradient optimization), so that transverse oscillation during gradient descent is reduced, and faster convergence speed is ensured.
c) Activation function:
the frame adopts Relu activation function, avoids the situation that the gradient disappears, and ensures the stability of training.
d) Model training and testing:
the data set was as follows 4: the ratio of 1 is divided into a training set and a testing set, and the training and testing are carried out by taking 50 pieces of data as a group (batch size) for a plurality of times of iteration. The accuracy rate of the training set and the test set is ensured to be maintained above 70%, and over-fitting and under-fitting conditions are avoided.
(4) And (3) post-treatment: the model is embedded into an executable program, and prediction data can be transmitted in a parameter mode in a command line mode in Linux or Windows systems, and the predicted service life can be output in a return value mode.
(5) Expanding and implementing: the BBU can be predicted and detected regularly, and the life warning threshold value can be established according to experience or relevant basis.
In a second aspect of the embodiments of the present invention, a system for predicting a lifetime of a battery redundancy module is also provided. Fig. 2 is a schematic diagram of an embodiment of a system for predicting the life of a battery redundancy module according to the present invention. As shown in fig. 2, a life prediction system of a battery redundancy module includes: the preprocessing module 10 is configured to collect operation data of battery redundancy modules with different known lives, and preprocess the operation data to obtain sample data; a data extraction module 20 configured to extract one part from the sample data as training data and another part as test data; a neural network model selection module 30 configured to select a corresponding neural network structure and hidden layer structure based on the operation data to obtain a corresponding neural network model; a training and testing module 40 configured to train and test the neural network model using the sample data and the test data, respectively, to obtain a neural network prediction model; and a life prediction module 50 configured to embed the neural network prediction model into the executable program to obtain a life prediction program, and input the prediction data of the battery redundancy module whose life is to be predicted into the life prediction program to output the predicted life thereof.
In a third aspect of the embodiments of the present invention, a computer-readable storage medium is further provided, and fig. 3 is a schematic diagram of a computer-readable storage medium implementing a method for predicting a lifetime of a battery redundancy module according to an embodiment of the present invention. As shown in fig. 3, the computer-readable storage medium 3 stores computer program instructions 31. The computer program instructions 31 when executed by a processor implement the steps of:
collecting operating data of battery redundancy modules with different known lives, and preprocessing the operating data to obtain sample data;
extracting one part from the sample data as training data and extracting the other part as test data;
selecting a corresponding neural network structure and a hidden layer structure based on the operation data to obtain a corresponding neural network model;
respectively training and testing the neural network model by using the sample data and the test data to obtain a neural network prediction model; and
embedding the neural network prediction model into the executable program to obtain a service life prediction program, and inputting the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program to output the predicted service life.
In some embodiments, training and testing the neural network model using the sample data and the test data, respectively, to obtain the neural network prediction model comprises: training the neural network model by using the sample data to obtain a neural network training model; testing the neural network training model by using the test data, and judging whether the test result meets the preset requirement; and responding to the test result not meeting the preset requirement, continuing training and correspondingly testing the neural network training model until the final test result meets the preset requirement, and taking the neural network training model corresponding to the final test result as a neural network prediction model.
In some embodiments, embedding the neural network prediction model in the executable program to obtain a life prediction program, and inputting the prediction data of the battery redundancy module whose life is to be predicted into the life prediction program to output the predicted life thereof comprises: embedding the neural network prediction model into an executable program to obtain a service life prediction program, and inputting the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program in a parameter mode through a command line to output the predicted service life.
In some embodiments, the steps further comprise: and setting a life guard threshold value for the corresponding battery redundancy module based on the output predicted life value, and sending out an early warning notice that the life is about to be terminated based on the life guard threshold value.
In some embodiments, operational data for a battery redundancy module of known life includes: temperature, voltage, current, elapsed time, and number of failures during operation of a battery redundancy module of known life.
In some embodiments, pre-processing the operational data comprises: the run data was pre-processed using Z-score normalization.
In some embodiments, the selected neural network structure is a deep neural network structure and the selected hidden layer structure is six hidden layers.
It is to be understood that all the embodiments, features and advantages set forth above with respect to the method for predicting the life of a battery redundancy module according to the present invention are equally applicable to the system for predicting the life of a battery redundancy module and the storage medium according to the present invention, without conflicting with each other.
In a fourth aspect of the embodiments of the present invention, there is further provided a computer device, including a memory 402 and a processor 401 as shown in fig. 4, where the memory 402 stores therein a computer program, and the computer program, when executed by the processor 401, implements the method of any one of the above embodiments.
Fig. 4 is a schematic hardware structure diagram of an embodiment of a computer device for executing a method for predicting the life of a battery redundancy module according to the present invention. Taking the computer device shown in fig. 4 as an example, the computer device includes a processor 401 and a memory 402, and may further include: an input device 403 and an output device 404. The processor 401, the memory 402, the input device 403 and the output device 404 may be connected by a bus or other means, and fig. 4 illustrates an example of a connection by a bus. The input device 403 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the life prediction system of the battery redundancy module. The output device 404 may include a display device such as a display screen.
The memory 402, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules corresponding to the method for predicting the life of a battery redundancy module in the embodiments of the present application. The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by use of a life prediction method of the battery redundancy module, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to local modules via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor 401 executes various functional applications of the server and data processing by running the nonvolatile software programs, instructions and modules stored in the memory 402, that is, implements the life prediction method of the battery redundancy module of the above-described method embodiment.
Finally, it should be noted that the computer-readable storage medium (e.g., memory) herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of example, and not limitation, nonvolatile memory can include Read Only Memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which can act as external cache memory. By way of example and not limitation, RAM is available in a variety of forms such as synchronous RAM (DRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to comprise, without being limited to, these and other suitable types of memory.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as software or hardware depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein may be implemented or performed with the following components designed to perform the functions herein: a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and/or any other such configuration.
The foregoing is an exemplary embodiment of the present disclosure, but it should be noted that various changes and modifications could be made herein without departing from the scope of the present disclosure as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the disclosed embodiments described herein need not be performed in any particular order. Furthermore, although elements of the disclosed embodiments of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated.
It should be understood that, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly supports the exception. It should also be understood that "and/or" as used herein is meant to include any and all possible combinations of one or more of the associated listed items. The numbers of the embodiments disclosed in the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, of embodiments of the invention is limited to these examples; within the idea of an embodiment of the invention, also technical features in the above embodiment or in different embodiments may be combined and there are many other variations of the different aspects of the embodiments of the invention as described above, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present invention are intended to be included within the scope of the embodiments of the present invention.
Claims (10)
1. A method for predicting the service life of a battery redundancy module is characterized by comprising the following steps:
collecting operating data of battery redundancy modules with different known lives, and preprocessing the operating data to obtain sample data;
extracting one part from the sample data as training data and extracting the other part as test data;
selecting a corresponding neural network structure and a hidden layer structure based on the operation data to obtain a corresponding neural network model;
respectively training and testing the neural network model by using the sample data and the test data to obtain a neural network prediction model; and
and embedding the neural network prediction model into an executable program to obtain a service life prediction program, and inputting the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program to output the predicted service life.
2. The method of claim 1, wherein training and testing the neural network model using the sample data and the test data, respectively, to obtain a neural network prediction model comprises:
training the neural network model by using the sample data to obtain a neural network training model;
testing the neural network training model by using the test data, and judging whether a test result meets a preset requirement;
and responding to the test result not meeting the preset requirement, continuing training and correspondingly testing the neural network training model until the final test result meets the preset requirement, and taking the neural network training model corresponding to the final test result as the neural network prediction model.
3. The method of claim 1, wherein embedding the neural network predictive model into an executable program to obtain a life prediction program, and inputting the predicted data of the battery redundancy module whose life is to be predicted into the life prediction program to output its predicted life comprises:
embedding the neural network prediction model into an executable program to obtain a life prediction program, and inputting prediction data of a battery redundancy module with the life to be predicted into the life prediction program in a parameter mode through a command line to output the predicted life.
4. The method of claim 1, further comprising:
and setting a life guard threshold value for the corresponding battery redundancy module based on the output predicted life value, and sending out early warning notice that the life is about to be terminated based on the life guard threshold value.
5. The method of claim 1, wherein the operational data for the known life battery redundancy module comprises: temperature, voltage, current, age, and number of failures during operation of the known life battery redundancy module.
6. The method of claim 1, wherein pre-processing the operational data comprises:
the run data was pre-processed using Z-score normalization.
7. The method of claim 1, wherein the selected neural network structure is a deep neural network structure and the selected hidden layer structure is six hidden layers.
8. A system for predicting the life of a redundant battery module, comprising:
the system comprises a preprocessing module, a data acquisition module and a data processing module, wherein the preprocessing module is configured to acquire operating data of battery redundancy modules with different known lives and preprocess the operating data to obtain sample data;
the data extraction module is configured to extract one part from the sample data as training data and extract the other part as test data;
the neural network model selection module is configured to select a corresponding neural network structure and a hidden layer structure based on the operation data to obtain a corresponding neural network model;
the training and testing module is configured to utilize the sample data and the test data to respectively train and test the neural network model so as to obtain a neural network prediction model; and
and the service life prediction module is configured to embed the neural network prediction model into an executable program to obtain a service life prediction program, and input the prediction data of the battery redundancy module with the service life to be predicted into the service life prediction program to output the predicted service life.
9. A computer-readable storage medium, characterized in that computer program instructions are stored which, when executed by a processor, implement the method according to any one of claims 1-7.
10. A computer device comprising a memory and a processor, characterized in that the memory has stored therein a computer program which, when executed by the processor, performs the method according to any one of claims 1-7.
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