A lithium battery test data analysis method and system based on industrial computer

Abstract

The present invention provides a lithium battery test data analysis method and system based on an industrial computer in the field of lithium battery test data analysis technology, the method comprising: step S1, the industrial computer configures the data information of each lithium battery test data to be analyzed, reads the configuration information of the machine, and inputs the data information and the configuration information into the word length calculation model to obtain the word length; step S2, based on idle computer resources and word length calculation of the maximum data load; step S3, based on the first storage space occupied by each field in each lithium battery test data, calculate the second storage space occupied by a lithium battery test data, and calculate the total number of data based on the second storage space and the total file size; step S4, based on the maximum data load and the total number of data, determine the number of single data analysis, read the lithium battery test data corresponding to the number of data analysis for analysis. The advantage of the present invention is that it greatly improves the analysis efficiency of the industrial computer in lithium battery test data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery test data analysis, and in particular to a lithium battery test data analysis method and system based on an industrial computer. Background Art

[0002] Lithium batteries need to undergo a series of tests during the production process and generate corresponding test data. By loading and analyzing the test data, we can obtain information such as the life cycle, quality, and parameters of the lithium battery. Since the lithium battery testing cycle is relatively long and the data sampling frequency is relatively high, a large amount of test data is often generated.

[0003] Traditionally, the analysis and processing of large amounts of lithium battery test data requires high-configuration servers or the construction of a large data server cluster to support it. However, lithium battery manufacturers are often unwilling to spend a lot of money to purchase servers for basic data analysis, but instead hope that the configuration of industrial computers can also undertake basic data analysis. However, when loading large amounts of lithium battery test data, industrial computers may encounter problems such as insufficient memory, slow opening time, and interface freezes, which in turn affects the analysis efficiency of lithium battery test data. These problems will greatly affect the production, testing, and delivery of lithium batteries.

[0004] Therefore, how to provide a lithium battery test data analysis method and system based on an industrial computer to improve the analysis efficiency of lithium battery test data analysis by the industrial computer has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a lithium battery test data analysis method and system based on an industrial computer, so as to improve the analysis efficiency of lithium battery test data analysis performed by the industrial computer.

[0006] In a first aspect, the present invention provides a lithium battery test data analysis method based on an industrial computer, comprising the following steps:

[0007] Step S1, obtaining a large amount of historical test data, and computer resources consumed by industrial computers with different configurations to analyze each of the historical test data, and constructing a data set based on each of the historical test data and the computer resources;

[0008] Step S2, creating a word length calculation model based on a neural network, and training the word length calculation model using the data set;

[0009] Step S3, the industrial computer configures the data information of each lithium battery test data to be analyzed through the interactive interface, reads the configuration information of the local computer, and inputs the data information and configuration information into the word length calculation model to obtain the word length;

[0010] Step S4, the industrial computer monitors the current idle computer resources, and calculates the maximum data load based on the idle computer resources and the word length;

[0011] Step S5, the industrial computer calculates the second storage space occupied by one piece of lithium battery test data based on the first storage space occupied by each field in each lithium battery test data, and calculates the total number of data based on the second storage space and the total file size of each lithium battery test data;

[0012] Step S6: The industrial computer determines the number of single data analyses based on the maximum data load, the total number of data items, and the data information, and reads the lithium battery test data corresponding to the data analysis number through memory mapping technology for analysis.

[0013] Furthermore, the step S1 is specifically as follows:

[0014] A large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each of the historical test data are obtained. After preprocessing the historical test data and computer resources by data cleaning and format conversion, the data type, configuration information of the industrial computer, and word length of each of the historical test data and computer resources are annotated, and a data set is constructed based on the annotated historical test data and computer resources.

[0015] Furthermore, the step S2 is specifically as follows:

[0016] A word length calculation model is created based on a neural network, a loss function of the word length calculation model is set, the data set is divided into a training set and a verification set according to a preset ratio, the word length calculation model is trained by the training set until the loss value of the loss function is less than a preset loss threshold; the trained word length calculation model is then verified by the verification set to determine whether the word length calculation accuracy is greater than a preset accuracy threshold, if so, the training is terminated; if not, the training set is expanded to continue the training.

[0017] Furthermore, in step S3, the data information at least includes data type, data field to be analyzed, data loading amount and paging information.

[0018] Furthermore, the step S6 is specifically as follows:

[0019] The industrial computer selects the minimum value from the maximum data load, the total number of data items and the data load carried by the data information as the single data analysis quantity, reads the lithium battery test data corresponding to the data analysis quantity through memory mapping technology for analysis, and releases the corresponding computer resources after the analysis of all the lithium battery test data is completed.

[0020] In a second aspect, the present invention provides a lithium battery test data analysis system based on an industrial computer, comprising the following modules:

[0021] A data set construction module is used to obtain a large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each of the historical test data, and to construct a data set based on each of the historical test data and the computer resources;

[0022] A word length calculation model training module, used for creating a word length calculation model based on a neural network, and training the word length calculation model using the data set;

[0023] The word length acquisition module is used for the industrial computer to configure the data information of each lithium battery test data to be analyzed through the interactive interface, read the configuration information of the local computer, and input the data information and configuration information into the word length calculation model to obtain the word length;

[0024] A data maximum load calculation module is used for the industrial computer to monitor the current idle computer resources and calculate the data maximum load based on the idle computer resources and word length;

[0025] A data total number calculation module is used for the industrial computer to calculate the second storage space occupied by one piece of lithium battery test data based on the first storage space occupied by each field in each lithium battery test data, and calculate the total number of data based on the second storage space and the total file size of each lithium battery test data;

[0026] The lithium battery test data analysis module is used for the industrial computer to determine the number of single data analyses based on the maximum data load, the total number of data items and data information, and reads the lithium battery test data corresponding to the data analysis number through memory mapping technology for analysis.

[0027] Furthermore, the data set construction module is specifically used for:

[0028] A large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each of the historical test data are obtained. After preprocessing the historical test data and computer resources by data cleaning and format conversion, the data type, configuration information of the industrial computer, and word length of each of the historical test data and computer resources are annotated, and a data set is constructed based on the annotated historical test data and computer resources.

[0029] Furthermore, the word length calculation model training module is specifically used for:

[0030] A word length calculation model is created based on a neural network, a loss function of the word length calculation model is set, the data set is divided into a training set and a verification set according to a preset ratio, the word length calculation model is trained by the training set until the loss value of the loss function is less than a preset loss threshold; the trained word length calculation model is then verified by the verification set to determine whether the word length calculation accuracy is greater than a preset accuracy threshold, if so, the training is terminated; if not, the training set is expanded to continue the training.

[0031] Furthermore, in the word length acquisition module, the data information at least includes data type, data field to be analyzed, data loading amount and paging information.

[0032] Furthermore, the lithium battery test data analysis module is specifically used for:

[0033] The industrial computer selects the minimum value from the maximum data load, the total number of data items and the data load carried by the data information as the single data analysis quantity, reads the lithium battery test data corresponding to the data analysis quantity through memory mapping technology for analysis, and releases the corresponding computer resources after the analysis of all the lithium battery test data is completed.

[0034] The advantages of the present invention are:

[0035] By acquiring a large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each historical test data, a data set is constructed based on each historical test data and computer resources, and the word length calculation model created based on the neural network is trained through the data set; then the industrial computer configures the data information of each lithium battery test data to be analyzed through an interactive interface, reads the configuration information of the local computer, inputs the data information and configuration information into the word length calculation model to obtain the word length, monitors the current idle computer resources, and calculates the maximum data load based on the idle computer resources and the word length; then, based on the first storage space occupied by each field in each lithium battery test data, the second storage space occupied by a lithium battery test data is calculated, and the total number of data is calculated based on the second storage space and the total file size of each lithium battery test data; finally, the number of single data analyses is determined based on the maximum data load, the total number of data and the data information, and the number of single data analyses is determined based on the maximum data load, the total number of data and the data information. The lithium battery test data corresponding to the data analysis quantity is read through the memory mapping technology for analysis; that is, the word length of the lithium battery test data corresponding to the data information processed by the current industrial computer is calculated based on the pre-trained word length calculation model, the maximum data load is calculated based on idle computer resources and word length, and the second storage space occupied by a lithium battery test data is calculated based on the first storage space occupied by each field, and then the total number of data is calculated, and then combined with the data load in the data information, the maximum data load, the total number of data and the minimum value of the data load are taken as the single data analysis quantity, so as to avoid the workload of a single data analysis exceeding the load of the industrial computer, so that the industrial computer can efficiently complete the current analysis task, and directly read the lithium battery test data for analysis in combination with the memory mapping technology, and release the corresponding computer resources in time after the analysis is completed so as to continue to execute subsequent tasks, which ultimately greatly improves the analysis efficiency of the industrial computer in lithium battery test data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with embodiments with reference to the accompanying drawings.

[0037] Figure 1 The present invention is a flow chart of a lithium battery test data analysis method based on an industrial computer.

[0038] Figure 2 The present invention is a structural schematic diagram of a lithium battery test data analysis system based on an industrial computer. DETAILED DESCRIPTION

[0039] The technical solution in the embodiment of the present application has the following overall idea: the word length of the lithium battery test data of the corresponding data information processed by the current industrial computer is calculated based on the pre-trained word length calculation model, the maximum data load is calculated based on idle computer resources and word length, the second storage space occupied by a lithium battery test data is calculated based on the first storage space occupied by each field, and then the total number of data is calculated, and then combined with the data load in the data information, the maximum data load, the total number of data and the minimum value of the data load are taken as the number of single data analysis, so as to avoid the workload of a single data analysis exceeding the load of the industrial computer, so that the industrial computer can efficiently complete the current analysis task, directly read the lithium battery test data for analysis in combination with the memory mapping technology, and release the corresponding computer resources in time after the analysis is completed so as to continue to execute subsequent tasks, thereby improving the analysis efficiency of the industrial computer in lithium battery test data analysis.

[0040] Please refer to Figure 1 to Figure 2 As shown, a preferred embodiment of a lithium battery test data analysis method based on an industrial computer of the present invention comprises the following steps:

[0041] Step S1, obtaining a large amount of historical test data, and computer resources consumed by industrial computers with different configurations to analyze each of the historical test data, and constructing a data set based on each of the historical test data and the computer resources; the computer resources include at least a CPU and a memory;

[0042] Step S2, creating a word length calculation model based on a neural network, and training the word length calculation model through the data set; word length is the number of binary bits that a computer can process in parallel at one time, and is an important indicator for measuring computer performance, that is, analyzing the computer resources consumed by a unit of data volume;

[0043] Step S3, the industrial computer configures the data information of each lithium battery test data to be analyzed through the interactive interface, reads the configuration information of the local computer, and inputs the data information and configuration information into the word length calculation model to obtain the word length;

[0044] Step S4, the industrial computer monitors the current idle computer resources, and calculates the maximum data load based on the idle computer resources and the word length;

[0045] Step S5, the industrial computer calculates the second storage space occupied by one piece of lithium battery test data based on the first storage space occupied by each field in each lithium battery test data, and calculates the total number of data based on the second storage space and the total file size of each lithium battery test data;

[0046] Step S6: The industrial computer determines the number of data to be analyzed at a time based on the maximum data load, the total number of data items, and the data information, and reads the lithium battery test data corresponding to the number of data to be analyzed through memory mapping technology. If the lithium battery test data cannot be analyzed in one time, the lithium battery test data will be loaded and analyzed in batches (pages).

[0047] The step S1 is specifically as follows:

[0048] A large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each of the historical test data are obtained. After preprocessing the historical test data and computer resources by data cleaning and format conversion, the data type, configuration information of the industrial computer, and word length of each of the historical test data and computer resources are annotated, and a data set is constructed based on the annotated historical test data and computer resources.

[0049] The step S2 is specifically as follows:

[0050] A word length calculation model is created based on a neural network, a loss function of the word length calculation model is set, the data set is divided into a training set and a verification set according to a preset ratio, the word length calculation model is trained by the training set until the loss value of the loss function is less than a preset loss threshold; the trained word length calculation model is then verified by the verification set to determine whether the word length calculation accuracy is greater than a preset accuracy threshold, if so, the training is terminated; if not, the training set is expanded to continue the training.

[0051] The training progress is measured by the loss function during the training process, and the calculation accuracy of the word length is verified by the verification set, so as to effectively ensure the reliability of the calculation results of the word length calculation model. Since the configuration information of industrial computers is varied, even a small configuration difference may have a great impact on the word length, which cannot be simply calculated and expressed by a linear function. Therefore, the word length calculation model created by the neural network is used to calculate the word length.

[0052] In the step S3, the data information at least includes data type, data field to be analyzed, data loading amount and paging information.

[0053] The step S6 is specifically as follows:

[0054] The industrial computer selects the minimum value from the maximum data load, the total number of data items, and the data load carried by the data information as the single data analysis quantity, reads the lithium battery test data corresponding to the data analysis quantity through memory mapping technology for analysis, and releases the corresponding computer resources after all the lithium battery test data analysis is completed. Memory mapping technology is a technology that maps the content of a disk file or other device to the virtual address space of a process; under this mechanism, the data of the file or device is cached in the memory, and a continuous virtual address is allocated in the address space of the process, so that the process can directly access the data like accessing the memory, without the need for traditional file I / O operations, which not only simplifies file operations, but also improves data access and processing efficiency, and is particularly suitable for processing files with large data volumes; at the same time, memory mapping technology also supports multiple processes to share the same data, thereby achieving efficient inter-process communication.

[0055] A preferred embodiment of a lithium battery test data analysis system based on an industrial computer of the present invention includes the following modules:

[0056] A data set construction module is used to obtain a large amount of historical test data and computer resources consumed by industrial computers with different configurations to analyze each of the historical test data, and to construct a data set based on each of the historical test data and computer resources; the computer resources include at least CPU and memory;

[0057] A word length calculation model training module is used to create a word length calculation model based on a neural network, and train the word length calculation model through the data set; word length is the number of binary bits that a computer can process in parallel at one time, and is an important indicator for measuring computer performance, that is, the computer resources consumed by analyzing a unit of data volume;

[0058] The word length acquisition module is used for the industrial computer to configure the data information of each lithium battery test data to be analyzed through the interactive interface, read the configuration information of the local computer, and input the data information and configuration information into the word length calculation model to obtain the word length;

[0059] A data maximum load calculation module is used for the industrial computer to monitor the current idle computer resources and calculate the data maximum load based on the idle computer resources and word length;

[0060] A data total number calculation module is used for the industrial computer to calculate the second storage space occupied by one piece of lithium battery test data based on the first storage space occupied by each field in each lithium battery test data, and calculate the total number of data based on the second storage space and the total file size of each lithium battery test data;

[0061] The lithium battery test data analysis module is used for the industrial computer to determine the number of data to be analyzed at a time based on the maximum data load, the total number of data items, and the data information, and read the lithium battery test data corresponding to the data analysis number through the memory mapping technology for analysis. If the lithium battery test data cannot be analyzed in a single time, the lithium battery test data will be loaded and analyzed in batches (pages).

[0062] The data set construction module is specifically used for:

[0063] A large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each of the historical test data are obtained. After preprocessing the historical test data and computer resources by data cleaning and format conversion, the data type, configuration information of the industrial computer, and word length of each of the historical test data and computer resources are annotated, and a data set is constructed based on the annotated historical test data and computer resources.

[0064] The word length calculation model training module is specifically used for:

[0065] A word length calculation model is created based on a neural network, a loss function of the word length calculation model is set, the data set is divided into a training set and a verification set according to a preset ratio, the word length calculation model is trained by the training set until the loss value of the loss function is less than a preset loss threshold; the trained word length calculation model is then verified by the verification set to determine whether the word length calculation accuracy is greater than a preset accuracy threshold, if so, the training is terminated; if not, the training set is expanded to continue the training.

[0066] The training progress is measured by the loss function during the training process, and the calculation accuracy of the word length is verified by the verification set, so as to effectively ensure the reliability of the calculation results of the word length calculation model. Since the configuration information of industrial computers is varied, even a small configuration difference may have a great impact on the word length, which cannot be simply calculated and expressed by a linear function. Therefore, the word length calculation model created by the neural network is used to calculate the word length.

[0067] In the word length acquisition module, the data information at least includes data type, data field to be analyzed, data loading amount and paging information.

[0068] The lithium battery test data analysis module is specifically used for:

[0069] The industrial computer selects the minimum value from the maximum data load, the total number of data items, and the data load carried by the data information as the single data analysis quantity, reads the lithium battery test data corresponding to the data analysis quantity through memory mapping technology for analysis, and releases the corresponding computer resources after all the lithium battery test data analysis is completed. Memory mapping technology is a technology that maps the content of a disk file or other device to the virtual address space of a process; under this mechanism, the data of the file or device is cached in the memory, and a continuous virtual address is allocated in the address space of the process, so that the process can directly access the data like accessing the memory, without the need for traditional file I / O operations, which not only simplifies file operations, but also improves data access and processing efficiency, and is particularly suitable for processing files with large data volumes; at the same time, memory mapping technology also supports multiple processes to share the same data, thereby achieving efficient inter-process communication.

[0070] In summary, the advantages of the present invention are:

[0071] By acquiring a large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each historical test data, a data set is constructed based on each historical test data and computer resources, and the word length calculation model created based on the neural network is trained through the data set; then the industrial computer configures the data information of each lithium battery test data to be analyzed through an interactive interface, reads the configuration information of the local computer, inputs the data information and configuration information into the word length calculation model to obtain the word length, monitors the current idle computer resources, and calculates the maximum data load based on the idle computer resources and the word length; then, based on the first storage space occupied by each field in each lithium battery test data, the second storage space occupied by a lithium battery test data is calculated, and the total number of data is calculated based on the second storage space and the total file size of each lithium battery test data; finally, the number of single data analyses is determined based on the maximum data load, the total number of data and the data information, and the number of single data analyses is determined based on the maximum data load, the total number of data and the data information. The lithium battery test data corresponding to the data analysis quantity is read through the memory mapping technology for analysis; that is, the word length of the lithium battery test data corresponding to the data information processed by the current industrial computer is calculated based on the pre-trained word length calculation model, the maximum data load is calculated based on idle computer resources and word length, and the second storage space occupied by a lithium battery test data is calculated based on the first storage space occupied by each field, and then the total number of data is calculated, and then combined with the data load in the data information, the maximum data load, the total number of data and the minimum value of the data load are taken as the single data analysis quantity, so as to avoid the workload of a single data analysis exceeding the load of the industrial computer, so that the industrial computer can efficiently complete the current analysis task, and directly read the lithium battery test data for analysis in combination with the memory mapping technology, and release the corresponding computer resources in time after the analysis is completed so as to continue to execute subsequent tasks, which ultimately greatly improves the analysis efficiency of the industrial computer in lithium battery test data analysis.

[0072] Although the specific implementation modes of the present invention are described above, those skilled in the art should understand that the specific implementation modes described are only illustrative and are not intended to limit the scope of the present invention. Equivalent modifications and changes made by those skilled in the art in accordance with the spirit of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A lithium battery test data analysis method based on an industrial computer, characterized in that: The steps include: Step S1, obtaining a large amount of historical test data, and computer resources consumed by industrial computers with different configurations to analyze each of the historical test data, and constructing a data set based on each of the historical test data and the computer resources; Step S2, creating a word length calculation model based on a neural network, and training the word length calculation model using the data set; Step S3, the industrial computer configures the data information of each lithium battery test data to be analyzed through the interactive interface, reads the configuration information of the local computer, and inputs the data information and configuration information into the word length calculation model to obtain the word length; Step S4, the industrial computer monitors the current idle computer resources, and calculates the maximum data load based on the idle computer resources and the word length; Step S5, the industrial computer calculates the second storage space occupied by one piece of lithium battery test data based on the first storage space occupied by each field in each lithium battery test data, and calculates the total number of data based on the second storage space and the total file size of each lithium battery test data; Step S6: The industrial computer determines the number of single data analyses based on the maximum data load, the total number of data items, and the data information, and reads the lithium battery test data corresponding to the data analysis number through memory mapping technology for analysis.

2. A lithium battery test data analysis method based on an industrial computer as claimed in claim 1, characterized in that: The step S1 is specifically as follows: A large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each of the historical test data are obtained. After preprocessing the historical test data and computer resources by data cleaning and format conversion, the data type, configuration information of the industrial computer, and word length of each of the historical test data and computer resources are annotated, and a data set is constructed based on the annotated historical test data and computer resources.

3. The method for analyzing lithium battery test data based on an industrial computer according to claim 1, characterized in that: The step S2 is specifically as follows: A word length calculation model is created based on a neural network, a loss function of the word length calculation model is set, the data set is divided into a training set and a verification set according to a preset ratio, the word length calculation model is trained by the training set until the loss value of the loss function is less than a preset loss threshold; the trained word length calculation model is then verified by the verification set to determine whether the word length calculation accuracy is greater than a preset accuracy threshold, if so, the training is terminated; if not, the training set is expanded to continue the training.

4. The method for analyzing lithium battery test data based on an industrial computer according to claim 1, characterized in that: In the step S3, the data information at least includes data type, data field to be analyzed, data loading amount and paging information.

5. The method for analyzing lithium battery test data based on an industrial computer according to claim 1, characterized in that: The step S6 is specifically as follows: The industrial computer selects the minimum value from the maximum data load, the total number of data items and the data load carried by the data information as the single data analysis quantity, reads the lithium battery test data corresponding to the data analysis quantity through memory mapping technology for analysis, and releases the corresponding computer resources after the analysis of all the lithium battery test data is completed.

6. A lithium battery test data analysis system based on an industrial computer, characterized in that: Includes the following modules: A data set construction module is used to obtain a large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each of the historical test data, and to construct a data set based on each of the historical test data and the computer resources; A word length calculation model training module, used for creating a word length calculation model based on a neural network, and training the word length calculation model using the data set; The word length acquisition module is used for the industrial computer to configure the data information of each lithium battery test data to be analyzed through the interactive interface, read the configuration information of the local computer, and input the data information and configuration information into the word length calculation model to obtain the word length; A data maximum load calculation module is used for the industrial computer to monitor the current idle computer resources and calculate the data maximum load based on the idle computer resources and word length; A data total number calculation module is used for the industrial computer to calculate the second storage space occupied by one piece of lithium battery test data based on the first storage space occupied by each field in each lithium battery test data, and calculate the total number of data based on the second storage space and the total file size of each lithium battery test data; The lithium battery test data analysis module is used for the industrial computer to determine the number of single data analyses based on the maximum data load, the total number of data items and data information, and reads the lithium battery test data corresponding to the data analysis number through memory mapping technology for analysis.

7. A lithium battery test data analysis system based on an industrial computer as claimed in claim 6, characterized in that: The data set construction module is specifically used for: A large amount of historical test data and the computer resources consumed by industrial computers with different configurations to analyze each of the historical test data are obtained. After preprocessing the historical test data and computer resources by data cleaning and format conversion, the data type, configuration information of the industrial computer, and word length of each of the historical test data and computer resources are annotated, and a data set is constructed based on the annotated historical test data and computer resources.

8. The lithium battery test data analysis system based on an industrial computer as claimed in claim 6, characterized in that: The word length calculation model training module is specifically used for: A word length calculation model is created based on a neural network, a loss function of the word length calculation model is set, the data set is divided into a training set and a verification set according to a preset ratio, the word length calculation model is trained by the training set until the loss value of the loss function is less than a preset loss threshold; the trained word length calculation model is then verified by the verification set to determine whether the word length calculation accuracy is greater than a preset accuracy threshold, if so, the training is terminated; if not, the training set is expanded to continue the training.

9. A lithium battery test data analysis system based on an industrial computer as claimed in claim 6, characterized in that: In the word length acquisition module, the data information at least includes data type, data field to be analyzed, data loading amount and paging information.

10. The lithium battery test data analysis system based on an industrial computer as claimed in claim 6, characterized in that: The lithium battery test data analysis module is specifically used for: The industrial computer selects the minimum value from the maximum data load, the total number of data items and the data load carried by the data information as the single data analysis quantity, reads the lithium battery test data corresponding to the data analysis quantity through memory mapping technology for analysis, and releases the corresponding computer resources after the analysis of all the lithium battery test data is completed.

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