Battery test data processing method and system, control device and storage medium
By monitoring path information and judging exceptions in battery test data using preset rules and identification algorithms, the problem of cumbersome processing of abnormal data in the prior art is solved, and automated processing is realized, improving efficiency and accuracy.
Patent Information
- Application Number
- CN202411962416.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2025-04-25
AI Technical Summary
The prior art is difficult to automatically identify and process abnormal data in battery test data, resulting in cumbersome manual search and processing and inefficient efficiency.
By monitoring the input path information, the test data to be processed is determined, and based on the preset rules and identification algorithm, determine whether there is an abnormality in the data, and perform corresponding preset processing operations.
It realizes automatic identification and processing of abnormal data, improves the efficiency and accuracy of data processing, and reduces the occurrence of manual errors.
Smart Images
Figure CN119881653A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of data processing, and specifically provides a battery test data processing method, system, control device and storage medium. Background Art
[0002] With the vigorous development of new energy technologies, batteries have become an indispensable and important part of the global economy and science and technology. Their high energy density, long life, excellent temperature adaptability and environmental protection characteristics have made them attract much attention in the new energy industry.
[0003] When it comes to battery safety, performance and stability, comprehensive testing is crucial. Through testing, key indicators such as battery charge and discharge performance, cycle life, and safety performance can be evaluated to ensure reliable operation under various working conditions. At the same time, testing also helps to discover potential safety hazards and defects, improve products in a timely manner, and improve battery quality and reliability, thereby ensuring customer safety and normal operation of equipment.
[0004] In the process of testing batteries, testers need to process a large amount of test data and frequently consult numerous files, and manually identify and remove abnormal data, which not only greatly increases their workload, but also consumes valuable time resources. Especially when dealing with a large amount of similar data, manual inspection is easily affected by factors such as fatigue and lack of concentration, resulting in misjudgments or wrong judgments. Therefore, how to quickly identify and remove abnormal data, so as to help technicians accurately analyze abnormal data, is a technical challenge that needs to be solved urgently.
[0005] Accordingly, the art needs a new battery test data processing method, system, control device and storage medium solution to solve the above problems. Summary of the invention
[0006] In order to overcome the above-mentioned defects, the present application is proposed to provide a battery test data processing method, system, control device and storage medium that solves or at least partially solves the technical problem that the prior art cannot automatically identify and process battery test data anomalies.
[0007] In a first aspect, the present application provides a battery test data processing method, the method comprising: monitoring input path information; in response to a preset operation being triggered, determining the test data to be processed based on the monitored path information; judging whether there is abnormal data in the test data to be processed; and performing a preset processing operation on the test data based on a result of the judgment.
[0008] In a technical solution of the above-mentioned battery test data processing method, each of the test data corresponds to a temperature value, wherein the temperature value is the test environment temperature value when the battery is tested, and judging whether there is abnormal data in the test data based on preset rules includes: grouping the test data based on several continuous and different preset temperature value ranges, wherein the temperature values corresponding to the test data belonging to the same group are all within the same preset temperature range; for any group of the test data, judging whether there is abnormal data in the group based on preset judgment rules; traversing each group of the test data to obtain all the abnormal data.
[0009] In a technical solution of the above-mentioned battery test data processing method, each of the preset temperature value ranges corresponds to a preset threshold value, and for any group of the test data, based on preset judgment rules, judging whether there is abnormal data in the group includes: determining suspicious data in the test data based on a preset recognition algorithm and the numerical value represented by the test data; judging whether the difference between the numerical value represented by the suspicious data and the numerical value represented by the test data adjacent to the suspicious data is greater than the preset threshold value corresponding to the group where the suspicious data is located; if so, determining that the suspicious data is abnormal data.
[0010] In a technical solution of the above-mentioned battery test data processing method, each of the preset temperature value ranges corresponds to a preset threshold value, and for any group of the test data, based on a preset judgment rule, judging whether there is abnormal data in the group includes: evenly dividing the number of tests in the group into a number of secondary groups, wherein the number of the test data in each secondary group does not exceed a preset number; for the test data in each secondary group, based on a preset recognition algorithm and the numerical value represented by the test data, determining suspicious data in the test data; judging whether the difference between the numerical value represented by the suspicious data and the numerical value represented by the test data adjacent to the suspicious data is greater than the preset threshold value corresponding to the group where the suspicious data is located; if so, determining that the suspicious data is abnormal data.
[0011] In a technical solution of the above-mentioned battery test data processing method, the preset processing operation on the test data based on the result of the judgment includes: deleting the abnormal data in the test data to obtain updated test data; storing the abnormal information to the location pointed to by the path information, wherein the abnormal information includes at least one of the abnormal data, the difference, and the test data adjacent to the abnormal data.
[0012] In a technical solution of the above-mentioned battery test data processing method, the method also includes: based on the updated test data, generating a curve diagram for characterizing the updated test data; and / or recording the total number of the deleted abnormal data, and if the total number is greater than a preset value, outputting a preset prompt message.
[0013] In a technical solution of the above-mentioned battery test data processing method, determining the test data to be processed based on the monitored path information includes: determining the file pointed to by the path information based on the path information; and determining the target file from all the pointed files based on preset retrieval rules, wherein the target file at least contains the test data.
[0014] In a second aspect, the present application provides a battery test data processing system, the system comprising: an acquisition module, the acquisition module being configured to monitor input path information; a processing module, the processing module being configured to: in response to a preset operation being triggered, determine the test data to be processed based on the monitored path information; determine whether there is abnormal data in the test data to be processed; and based on the result of the judgment, perform a preset processing operation on the test data.
[0015] In a third aspect, a control device is provided, which includes a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the battery test data processing method described in any one of the technical solutions of the above-mentioned battery test data processing method.
[0016] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the battery test data processing method described in any one of the technical solutions of the above-mentioned battery test data processing method.
[0017] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:
[0018] In the technical solution of the present application, after the input path information is monitored, the test data to be processed can be obtained based on the path information, thereby automatically determining whether there is abnormal data in the test data, and performing preset processing operations on the test data. Through the present application, it is possible to automatically and accurately query the test data, identify abnormal data and perform corresponding processing, avoid the uncertainty in the manual search, identification and processing process, and improve the efficiency of processing.
[0019] Furthermore, in implementing the technical solution of the present application, the file at the location pointed to by the path information is first determined, and then the file at the location is further accurately searched based on the search rule. Through the present application, the target file containing the test data to be processed can be automatically and quickly selected from a number of files.
[0020] Furthermore, in the implementation of the technical solution of the present application, by collecting the temperature values of the test environment when testing the battery, the test data is grouped, and the test data with temperature values falling in the same range are divided into the same group, taking into account the impact of the test environment temperature on the battery-related parameters. Since the battery parameters are highly correlated with temperature, if the temperature value difference is large, the test data difference will also be large. Therefore, the test data belonging to the same temperature range is analyzed as a whole. On the one hand, it can avoid the accuracy of data judgment affected by excessive temperature differences, and on the other hand, it can also improve data processing efficiency.
[0021] Furthermore, in the technical solution of the present application, the suspicious data is first identified by using a recognition algorithm, and then the difference between the suspicious data and the adjacent test data is determined, wherein each group of grouped test data corresponds to a threshold value, and if the difference value is greater than the threshold value, the suspicious data is determined to be abnormal data. The recognition accuracy is improved by combining the recognition algorithm with the interpolation analysis between adjacent data points. Moreover, each group of grouped test data can be further divided into secondary groups to improve data processing efficiency and recognition accuracy.
[0022] Furthermore, in the implementation of the technical solution of the present application, abnormal data can be cleared from the original test data, and the cleared abnormal data and its adjacent data can be additionally saved; the number of cleared abnormal data can also be recorded, and if too much abnormal data is cleared, prompt information can be output to achieve an early warning effect to avoid the risks that may be caused by clearing a large amount of data; corresponding curve diagrams can also be generated based on the test data after the abnormal data is cleared, so as to intuitively display the distribution of the data and provide a more detailed basis for data analysis. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] The disclosure of the present application will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. In addition, similar numbers in the drawings are used to represent similar components, among which:
[0024] Figure 1 is a flowchart of the main steps of a battery test data processing method according to an embodiment of the present application;
[0025] Figure 2This is a flowchart of the main steps of determining whether test data is abnormal according to an embodiment of the present application;
[0026] Figure 3 is a flowchart of a method for processing battery test data according to an embodiment of the present application;
[0027] Figure 4 is a schematic diagram of a program flow for determining whether test data is abnormal according to an embodiment of the present application;
[0028] Figure 5 is a schematic diagram of a preset interface according to an embodiment of the present application;
[0029] Figure 6 is a schematic diagram of the relationship between voltage and capacity in lithium battery test data according to an embodiment of the present application;
[0030] Figure 7 It is a schematic diagram of the main structure block diagram of a battery test data processing system according to an embodiment of the present application.
[0031] List of reference numerals:
[0032] 11: Acquisition module; 12: Processing module. DETAILED DESCRIPTION
[0033] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.
[0034] In the description of the present application, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, memory, and may also include software parts, such as program code, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program code, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and may include only A, only B or A and B. The singular terms "one" and "the" may also include plural forms.
[0035] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a method for processing battery test data according to an embodiment of the present application. Figure 1 As shown, the battery test data processing method in the embodiment of the present application mainly includes the following steps S1 to S4.
[0036] Step S1, monitoring input path information;
[0037] In this embodiment, the path information refers to the information of the path of the target file in the input box below “input folder path”.
[0038] In one embodiment, the path information input in the preset interface can be monitored, wherein the preset interface can be an input interface specified by the user, or can be an input window display interface of a tool for receiving external input, such as the attached Figure 5 The schematic diagram of the preset interface shown in the figure, the preset interface can be attached Figure 5 All display interfaces shown.
[0039] Step S2: in response to a preset operation being triggered, determining test data to be processed based on the monitored path information;
[0040] In this embodiment, the preset operation may be a preset button being selected, or an arbitrary / specified key being pressed. Figure 5 In the example, the preset operation can also be to verify that the path information meets the conditions. For example, after verifying that the input path information is not an error path, the preset operation is considered to be triggered. Test data refers to data generated when testing the battery, which may include but is not limited to battery cell temperature, charge / discharge voltage, charge / discharge current, etc.
[0041] In one embodiment, step S2 may further specifically include steps S21 and S22:
[0042] Step S21, based on the path information, determining the file pointed to by the path information;
[0043] In this embodiment, the path information includes the location or address of the file. It should be understood that the path may include multiple files, and the types of the multiple files may also be different from each other.
[0044] Step S22: based on the preset search rules, determine the target file from all the pointed files;
[0045] In this embodiment, since the path information may contain several different types of files, it is necessary to determine the file containing the test data to be processed from multiple files based on the preset search rules. The preset search rules can be based on file identifiers such as file name, file extension, file type, etc., and can also be based on file attributes such as date, size, permissions, etc. In one embodiment, the preset search rules can also include drilling down to retrieve a portion of data in the target file. For example, if the target file is an Excel file, the preset search rules can also include retrieving data in a sheet table on a page in the file.
[0046] continue Figure 1 In the illustrated embodiment, step S3 includes: determining whether there is abnormal data in the test data;
[0047] In this embodiment, a machine learning method such as an abnormal data identification model can be used, or a statistical method such as a Z-Score test can be used to identify abnormal test data in the test data. In addition, in the process of using a model or algorithm to identify abnormal data, all test data can be input as a whole, or the test data can be grouped and each group can be input as a whole. By flexibly adjusting the input strategy, both the recognition efficiency and the recognition accuracy can be improved.
[0048] In one embodiment, see the attached Figure 2 , Figure 2 FIG. 1 is a flow chart of the main steps of determining whether test data is abnormal according to an embodiment of the present application. Figure 2 As shown, step S3 may specifically include steps S31 to S33.
[0049] Step S31, grouping the test data based on a number of continuous and different preset temperature value ranges;
[0050] In the present embodiment, it should be clear that each test data corresponds to a temperature value, that is, the collected and recorded test data is accompanied by the temperature value during the test, which is the test environment temperature value when the battery is tested. Specifically, the test environment temperature value can be the temperature of the battery environment, or the temperature of the battery cell environment. Since the heat generated by the battery cell itself will also affect the temperature of the surrounding environment, the test environment temperature value can also be the temperature of the battery cell itself.
[0051] In one embodiment, the test data can be divided into 4 groups based on 25℃-35℃ (including 25℃ but not including 35℃), 35℃-45℃ (including 35℃ but not including 45℃), 45℃-60℃ (including 45℃ but not including 60℃), and 60℃ and above.
[0052] Step S32: for any group of test data, determine whether there is abnormal data in the group based on preset judgment rules;
[0053] In this embodiment, a group is input as a whole, and a machine learning method such as an abnormal data identification model can be used, or a statistical method such as a Z-Score test can be used to identify abnormal test data in the test data.
[0054] Step S33, traverse each set of test data to obtain all abnormal data;
[0055] In this embodiment, each set of test data is regarded as a whole for analysis, so as to obtain all abnormal data.
[0056] In the attached Figure 2 In one implementation of the embodiment shown, each set of preset temperature ranges corresponds to a preset threshold value, for example, 25°C-35°C corresponds to a threshold value of 0.01, 35°C-45°C corresponds to a threshold value of 0.02, 45°C-60°C corresponds to a threshold value of 0.03, and 60°C and above corresponds to a threshold value of 0.04. It should be understood that the specific values of the threshold values corresponding to each set of temperature ranges are only exemplary, and different types of test data such as charging voltage, charging current, discharging voltage, and discharging current also have different threshold values. In practical applications, more accurate threshold values should be flexibly set based on historical data or other rules to improve the accuracy of abnormality judgment.
[0057] Combined with Figure 4 The schematic diagram of the program flow for determining whether the test data is abnormal is shown, step S32 may specifically include steps S321 to S323:
[0058] Step S321, determining suspicious data in the test data based on a preset recognition algorithm and the values represented by the test data;
[0059] In this embodiment, the preset identification algorithm may be a Z-Score test algorithm, or may be a Tukey test algorithm, or may be a box plot algorithm. Data points identified solely based on the algorithm are recorded as suspicious data rather than being directly defined as abnormal data.
[0060] Step S322: determine whether the difference between the value represented by the suspicious data and the value represented by the test data adjacent to the suspicious data is greater than a preset threshold corresponding to the group where the suspicious data is located.
[0061] In this embodiment, if there is a contextual relationship between the positions of the data points, such as data belonging to the same row or column in an Excel table, the difference between the suspicious data and the adjacent data is directly calculated; if there is no contextual relationship between the positions of the data points, a contextual relationship can be established based on the preset test time, data entry time, the value represented by the test data, etc. The difference between the suspicious data and the previous adjacent test data can be calculated, and the difference between the suspicious data and the next adjacent test data can also be calculated.
[0062] Step S323: If yes, determine that the suspicious data is abnormal data;
[0063] In another embodiment, based on the above embodiment, step S320 may be further included, and step S320 is located before step S321, specifically: the test number is evenly divided into a number of secondary groups, wherein the number of test data in each secondary group does not exceed a preset number. Correspondingly, step S321 is: for the test data in each secondary group, based on the preset recognition algorithm and the numerical value represented by the test data, suspicious data is determined in the test data. Secondary groups are further divided in each group of grouped test data to improve data processing efficiency and recognition accuracy.
[0064] continue Figure 1 In the illustrated embodiment, step S4 includes: performing a preprocessing operation on the test data based on the result of the determination;
[0065] In this embodiment, if it is determined that there is abnormal data in the test data, the abnormal data may be cleared and the test data to be processed may be updated, or the abnormal data may be marked and the abnormal data may be corrected, which is not limited in this embodiment.
[0066] In one embodiment, in combination with Figure 3 The program flow diagram of the battery test data processing method shown in FIG. 1 , step S4 specifically includes steps S41 and S42:
[0067] Step S41, deleting abnormal data from the test data to obtain updated test data;
[0068] Step S42: Store the exception information to the location pointed to by the path information.
[0069] In this embodiment, the abnormal information includes at least one of the abnormal data, the difference between the abnormal data and adjacent data, and adjacent test data.
[0070] In one embodiment, if the target file is an Excel file, the exception information may further include the column name or row name where the exception data is located, that is, the test object name or number.
[0071] In one embodiment, the path information input in the preset interface can be monitored, for example, the path information input in the attached Figure 5 The input box below "Output Folder Path" contains the information of the storage path of the newly created file. If the target file is an Excel file, the storage path is preferably set to create a new sheet page in the target file to store the exception information.
[0072] In one embodiment, the battery test data processing method provided by the present invention may further include:
[0073] Based on the updated test data, the following Figure 6 The curve diagram shown is used to characterize the updated test data; and / or, recording the total number of deleted abnormal data, if the total number is greater than a preset value, outputting a preset prompt message.
[0074] So far, the battery test data processing method of the present application has been fully described. It should be pointed out that although the various steps are described in a specific order in the above embodiment, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in other orders, and these changes are within the scope of protection of the present application.
[0075] Furthermore, the present application also provides a battery test data processing system.
[0076] See attached Figure 7 , Figure 7 FIG. 1 is a main structural block diagram of a battery test data processing system according to an embodiment of the present application. Figure 7As shown, the battery test data processing system in the embodiment of the present application mainly includes an acquisition module 11 and a processing module 12. In some embodiments, one or more of the acquisition module 11 and the processing module 12 can be combined into one module. In some embodiments, the acquisition module 11 can be configured to monitor the input path information. The processing module 12 can be configured to: in response to the preset operation being triggered, determine the test data to be processed based on the monitored path information; determine whether there is abnormal data in the test data to be processed; based on the result of the judgment, perform a preset processing operation on the test data. In one embodiment, the description of the specific implementation function of the acquisition module 11 can refer to step S1. In one embodiment, the description of the specific implementation function of the processing module 12 can refer to steps S2-S4. In one embodiment, the battery test data processing system may also include an output module, which is configured to: generate a curve diagram for characterizing the updated test data based on the updated test data; and / or record the total number of the abnormal data deleted, and if the total number is greater than a preset value, output a preset prompt message.
[0077] The battery test data processing system is used to perform Figure 1 The battery test data processing method embodiment shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technicians in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the battery test data processing system can refer to the contents described in the embodiment of the battery test data processing method, which will not be repeated here.
[0078] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.
[0079] Furthermore, the present application also provides a control device. In a control device embodiment according to the present application, the control device includes a processor and a storage device, the storage device can be configured to store a program for executing the battery test data processing method of the above method embodiment, and the processor can be configured to execute the program in the storage device, which includes but is not limited to the program for executing the battery test data processing method of the above method embodiment. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The control device can be a control device device formed by various electronic devices.
[0080] Furthermore, the present application also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium can be configured to store a program for executing the battery test data processing method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned battery test data processing method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.
[0081] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present application, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.
[0082] It is understood by those skilled in the art that each module in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present application, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present application.
[0083] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.
Claims
1. A battery test data processing method, characterized in that: The method comprises: Monitor input path information; In response to a preset operation being triggered, determining test data to be processed based on the monitored path information; Determine whether there is abnormal data in the test data to be processed; Based on the result of the determination, a preset processing operation is performed on the test data.
2. The battery test data processing method according to claim 1, characterized in that: Each of the test data corresponds to a temperature value, wherein the temperature value is a test environment temperature value when the battery is tested, and judging whether there is abnormal data in the test data based on a preset rule includes: The test data are grouped based on a plurality of continuous and different preset temperature value ranges, wherein the temperature values corresponding to the test data belonging to the same group are all within the same preset temperature range; For any group of the test data, judging whether there is abnormal data in the group based on preset judgment rules; Each set of the test data is traversed to obtain all the abnormal data.
3. The battery test data processing method according to claim 2, characterized in that: Each of the preset temperature value ranges corresponds to a preset threshold value. For any group of the test data, judging whether there is abnormal data in the group based on a preset judgment rule includes: Determining suspicious data in the test data based on a preset recognition algorithm and a value represented by the test data; Determine whether a difference between a value represented by the suspicious data and a value represented by test data adjacent to the suspicious data is greater than a preset threshold corresponding to the group to which the suspicious data belongs; If so, the suspicious data is determined to be abnormal data.
4. The battery test data processing method according to claim 2, characterized in that: Each of the preset temperature value ranges corresponds to a preset threshold value. For any group of the test data, judging whether there is abnormal data in the group based on a preset judgment rule includes: Divide the number of tests in the group into a number of secondary groups evenly, wherein the number of test data in each secondary group does not exceed a preset number; For each of the test data in the secondary group, based on a preset recognition algorithm and a value represented by the test data, determining suspicious data in the test data; Determine whether a difference between a value represented by the suspicious data and a value represented by test data adjacent to the suspicious data is greater than a preset threshold corresponding to the group to which the suspicious data belongs; If so, the suspicious data is determined to be abnormal data.
5. The battery test data processing method according to claim 3 or 4, characterized in that: The performing of a preset processing operation on the test data based on the result of the judgment includes: Deleting the abnormal data from the test data to obtain updated test data; The exception information is stored in the location pointed to by the path information, wherein the exception information includes at least one of the exception data, the difference, and test data adjacent to the exception data.
6. The battery test data processing method according to claim 5, characterized in that: The method further comprises: Based on the updated test data, generating a curve diagram for representing the updated test data; and / or, The total number of the deleted abnormal data is recorded, and if the total number is greater than a preset value, a preset prompt message is output.
7. The battery test data processing method according to claim 1, characterized in that: The determining of the test data to be processed based on the monitored path information includes: Based on the path information, determining the file pointed to by the path information; Based on preset retrieval rules, a target file is determined from all the pointed files, wherein the target file at least contains the test data.
8. A battery test data processing system, characterized in that: The system comprises: An acquisition module, wherein the acquisition module is configured to monitor input path information; A processing module, wherein the processing module is configured to: in response to a preset operation being triggered, determine the test data to be processed based on the monitored path information; determine whether there is abnormal data in the test data to be processed; and perform a preset processing operation on the test data based on a result of the determination.
9. A control device, comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by the processor to execute the battery test data processing method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the battery test data processing method according to any one of claims 1 to 7.