Battery data processing method, device and system, computer equipment and storage medium

Abstract

The invention relates to a battery data processing method, device and system, computer equipment and a storage medium. The method comprises the following steps: in response to a battery analysis demand for a target battery, determining an expected analysis item of the target battery; selecting an analysis parameter according to the expected analysis item; acquiring battery detection data corresponding to the analysis parameters; and performing data analysis on the battery detection data to obtain a battery analysis result of the target battery. By adopting the method, the user can be helped to conveniently and quickly obtain the battery analysis result of the target battery, and the convenience of battery data analysis is effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of data analysis, and in particular to a battery data processing method, apparatus, system, computer equipment, storage medium and computer program product. Background Art

[0002] Since human society entered the electrical age, electric energy has been widely used in various fields of human life, such as power, lighting, chemistry, and communication, and has played a huge role in people's daily lives, and its importance is no less than that of water and food. As a device for energy conversion and electric energy storage, batteries can convert chemical or physical energy into electric energy through reactions, thus becoming the main storage carrier of electric energy and accounting for a large proportion of electric energy use.

[0003] As an important energy storage tool and the power source of power products, the performance of batteries will directly affect the quality of power products and thus the user experience. Therefore, in the process of battery development, use and after-sales, analyzing and evaluating battery data is an effective way to improve the efficiency of battery or battery product development and the safety of battery use.

[0004] General battery analysis systems need to rely on big data platforms and other platforms, such as data middle-end components, to analyze and process battery data. The threshold for users is high and the convenience is poor. Summary of the invention

[0005] Based on this, it is necessary to provide a battery data processing method, device, computer equipment, computer-readable storage medium and computer program product that can conveniently and quickly analyze and process battery data in response to the above technical problems.

[0006] In a first aspect, the present application provides a battery data processing method, the method comprising:

[0007] In response to a battery analysis requirement for a target battery, determining a desired analysis item for the target battery;

[0008] Selecting analysis parameters according to the desired analysis items;

[0009] Obtaining battery detection data corresponding to the analysis parameters;

[0010] The battery detection data is analyzed to obtain a battery analysis result of the target battery.

[0011] In the above embodiment, by integrating the analysis functions of multiple battery analysis projects into a battery data analysis system, when the user needs to analyze the battery data, there is no need to rely on the collaborative processing of the data platform. Various analysis requirements for battery data can be realized in one battery analysis system, which can help users quickly and easily obtain the battery analysis results of the target battery, effectively improving the convenience of battery data analysis.

[0012] In some embodiments, the selecting of analysis parameters according to the desired analysis items includes:

[0013] Determining, according to the expected analysis item, analysis parameters matching the expected analysis item and analysis methods for the analysis parameters;

[0014] The performing data analysis on the battery detection data to obtain a battery analysis result of the target battery includes:

[0015] The analysis method is used to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery.

[0016] In the above embodiment, by predetermining the analysis parameters and analysis methods corresponding to the analysis items, when it is necessary to perform data analysis on the battery data, the corresponding analysis parameters and analysis methods can be quickly matched according to the desired analysis items selected by the user, and the battery detection data can be analyzed based on the corresponding analysis methods. This can better manage the various analysis items in the battery data analysis system, and also effectively improve the efficiency of the battery data analysis.

[0017] In some embodiments, in response to a battery analysis requirement for a target battery, determining a desired analysis item for the target battery includes:

[0018] In response to a battery analysis requirement for a target battery, obtaining a preconfigured analysis item list;

[0019] A desired analysis item selected from the analysis item candidate list is acquired.

[0020] In the above embodiment, all analysis items on the battery data analysis system can be centrally displayed through the analysis item list, so that the user can select the desired analysis items that meet the battery analysis requirements.

[0021] In some of the embodiments, the expected analysis items include at least one of an abnormal battery analysis item, a battery life assessment item, a battery failure warning item, a battery portrait item, and a root cause analysis item.

[0022] In the above embodiment, by integrating at least one analysis item among the abnormal battery analysis item, the battery life assessment item, the battery fault warning item, the battery portrait item and the root cause analysis item in the battery data analysis system, the battery data analysis system can be made into an efficient and flexible battery data analysis tool provided to users, so as to lower the user's usage threshold and improve the user's convenience.

[0023] In some embodiments, the desired analysis project is a battery portrait project; the analysis method is a battery portrait analysis method;

[0024] The using the analysis method to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery includes:

[0025] The battery detection data is subjected to battery profiling using at least one of the battery usage condition dimension, the battery usage environment dimension and the battery status dimension, using the corresponding battery profiling analysis method to obtain a battery profiling result of the target battery.

[0026] In the above embodiment, by integrating the battery portrait analysis project into the battery data analysis system, when the user has a portrait requirement for the target battery, a battery portrait that meets the user's actual analysis requirement dimensions can be generated. The battery portrait analysis project can help the user quickly understand the battery profile and obtain a quick and comprehensive analysis result, thereby improving the user's convenience.

[0027] In some embodiments, the expected analysis project is a root cause analysis project; the analysis method is a root cause analysis method;

[0028] The using the analysis method to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery includes:

[0029] Performing data detection and analysis on the battery detection data to obtain a detection and analysis result of the target battery, wherein the detection and analysis result includes at least one of a life detection result, a fault detection result, or an alarm detection result;

[0030] When the life detection result of the target battery is life decay, or the fault detection result is a battery fault, or the alarm detection result is a battery alarm, obtaining a portrait result of the target battery;

[0031] A root cause analysis is performed according to the image result of the target battery to obtain a root cause analysis result, wherein the root cause analysis result includes at least one of a life attenuation cause, a failure cause, or an alarm cause.

[0032] In the above embodiment, by integrating the root cause analysis project on the battery data analysis system, when the user has a root cause analysis demand for the target battery, the problem existing in the battery can be quickly detected and determined, and the construction of a battery portrait system can be triggered from multiple dimensions related to the battery problem. The root cause analysis of the problem is performed based on the battery portrait system, so that the user can quickly know the cause of the battery problem, thereby improving the user's convenience.

[0033] In some embodiments, determining, according to the expected analysis item, analysis parameters matching the expected analysis item and analysis methods for the analysis parameters includes:

[0034] Determining, according to the expected analysis item, candidate analysis parameters matching the expected analysis item and candidate analysis methods for the candidate analysis parameters;

[0035] Visually displaying the candidate analysis parameters and the candidate analysis methods on a user terminal;

[0036] In response to an edit event for the candidate analysis parameters and the candidate analysis methods, acquiring update content of the candidate analysis parameters and the candidate analysis methods;

[0037] The candidate analysis parameters and the candidate analysis methods are updated based on the updated content to obtain analysis parameters matching the desired analysis items and analysis methods for the analysis parameters.

[0038] In the above embodiment, by displaying the candidate analysis parameters and candidate analysis methods matched by the system on the user terminal, when the analysis parameters and analysis methods currently stored in the system do not meet the user's usage requirements, the user can be provided with the opportunity to customize and modify the analysis parameters and analysis methods, so that the analysis parameters and analysis methods of the final expected analysis project can be more in line with the user's usage requirements, thereby improving the analysis accuracy of the expected analysis project and enhancing the user's convenience.

[0039] In some embodiments, determining, according to the expected analysis item, analysis parameters matching the expected analysis item and analysis methods for the analysis parameters includes:

[0040] In a case where the desired analysis item is an unconfigured item, in response to a registration event for the desired analysis item, an analysis parameter configured for the desired analysis item and an analysis method for the analysis parameter are acquired.

[0041] In the above embodiment, when the desired analysis project required by the user does not exist in the battery data analysis system, the user can flexibly introduce new external analysis projects into the battery data analysis system through the project registration function of the battery data analysis system, thereby making the battery data analysis system more practical and open.

[0042] In some embodiments, obtaining battery detection data corresponding to the analysis parameters includes:

[0043] Obtaining an analysis parameter identifier of the analysis parameter;

[0044] Matching the analysis parameter identifier with the parameter identifier of the battery data corresponding to the target battery;

[0045] If at least one analysis parameter identifier fails to match the parameter identifier of the battery data, determining the analysis parameter identifier that failed to match;

[0046] Based on the unsuccessfully matched analysis parameter identifier, the supplementary battery detection data corresponding to the unsuccessfully matched analysis parameter identifier is acquired.

[0047] In the above embodiment, by setting corresponding parameter identifiers for analysis parameters, it is possible to quickly determine whether there is missing battery detection data based on the parameter identifiers, and obtain corresponding supplementary battery detection data, thereby providing a data basis for the smooth execution of subsequent battery data analysis.

[0048] In some embodiments, the acquiring, based on the unmatched analysis parameter identifier, the supplementary battery detection data corresponding to the unmatched analysis parameter identifier includes:

[0049] Determining a parameter generation item for generating the supplementary battery detection data based on the unmatched analysis parameter identifier;

[0050] Determining a parameter generation method matching the parameter generation project according to the parameter generation project;

[0051] Obtaining battery data corresponding to the parameter generation method;

[0052] The parameter generation method is used to perform data generation processing on the battery data to obtain supplementary battery detection data corresponding to the unmatched analysis parameter identifier.

[0053] In the above embodiment, by configuring a parameter generation project in the battery data analysis system, when it is necessary to generate intermediate analysis parameters required by a specific analysis algorithm, the battery data analysis system can quickly generate the intermediate analysis parameters required by the specific analysis algorithm based on the existing battery data through the parameter generation project, thereby providing a data basis for the subsequent completion of the desired analysis projects required by the user, and further improving the user's convenience.

[0054] In some of these embodiments, the parameter generation project includes a feature value generation project;

[0055] The using the parameter generation method to perform data generation processing on the battery data to obtain supplementary battery detection data corresponding to the unmatched analysis parameter identifier includes:

[0056] Obtain the eigenvalue generation method corresponding to the eigenvalue generation project;

[0057] The characteristic value generation method is used to perform characteristic value data generation processing on the battery data to obtain a characteristic value generation result.

[0058] In the above embodiment, by configuring a characteristic value generation project in the battery data analysis system, when it is necessary to generate characteristic value parameters required by a specific analysis algorithm, the battery data analysis system can quickly generate the characteristic value parameters required by the specific analysis algorithm based on the existing battery data through the characteristic value generation project, thereby providing a data basis for the subsequent completion of the desired analysis projects required by the user, and further improving the user's convenience.

[0059] In some embodiments, the method further comprises:

[0060] In response to a data preprocessing requirement for the battery detection data, determining a desired preprocessing item for the target battery;

[0061] Determine a preprocessing method matching the expected preprocessing item according to the expected preprocessing item;

[0062] Using the preprocessing method to perform data preprocessing operations on the battery detection data;

[0063] The battery detection data is updated based on the preprocessed data obtained after performing the data preprocessing operation.

[0064] In the above embodiment, by integrating the preprocessing project into the battery data analysis system, the battery data can be accurately detected and efficiently processed from the data level, so that the corrected battery data can better meet the needs of users and improve the user's convenience.

[0065] In some embodiments, the method further comprises:

[0066] In response to the visual display instruction for the target battery, determining a desired display item for the target battery;

[0067] Determining, according to the desired display item, visual display parameters matching the desired display item, and a visual display method for the visual display parameters;

[0068] Obtaining battery data corresponding to the visual display parameter;

[0069] The visual display method is used to perform a data display operation on the battery data, and the battery data is displayed on the user terminal in a preset display form.

[0070] In the above embodiment, by integrating the display project into the battery data analysis system, it can help users better understand the data overview of the battery data.

[0071] In some embodiments, the method further comprises:

[0072] In response to a data import event for a target battery, determining a data import type of the target battery;

[0073] Determining a data import method that matches the data import type according to the data import type;

[0074] The data import method is used to import the battery data of the target battery into a local database for storage.

[0075] In the above embodiment, by importing the battery data that needs to be analyzed into the local database of the battery data analysis system, analysis and processing can be performed based on the battery data stored in the local database according to the analysis method corresponding to the analysis item, thereby improving the compatibility and fluency of the data analysis and processing process.

[0076] In some embodiments, the data import type includes at least one of real-time data import, offline data import, and model mapping import, and the data import method includes at least one of real-time data import method, offline data import method, and model mapping import method;

[0077] The method of importing the battery data of the target battery into a local database for storage includes at least one of the following methods:

[0078] The first one:

[0079] Determine a data import interface pre-configured with a data acquisition device of the target battery;

[0080] Acquiring real-time battery data of the target battery based on the data import interface;

[0081] Importing the real-time battery data into a local database for storage;

[0082] Second type:

[0083] According to the mapping relationship between the battery data model of the target battery and the data model of the local database, mapping the battery data to the local database for storage;

[0084] The third type:

[0085] In response to an import instruction triggered by the offline battery data of the target battery, the offline battery data of the target battery is imported into a local database for storage.

[0086] In the above embodiment, by configuring the battery data analysis system with a data import function that supports multiple data import types, the user's data import needs can be met from all aspects, effectively improving the user's convenience in using the battery data analysis system.

[0087] In some embodiments, the method further comprises:

[0088] In response to a data processing requirement for the battery data in the local database, determining a data processing item for the battery data;

[0089] Determining a data processing method that matches the data processing project according to the data processing project;

[0090] Using the data processing method to perform data processing operations on the battery data;

[0091] The battery detection data is updated based on the processed data obtained after executing the data processing operation.

[0092] In the above embodiment, by pre-integrating the data processing project in the battery data analysis system and configuring the corresponding data processing method for the data processing project, when there is a data processing demand, the battery data can be processed according to the data processing method corresponding to the data processing project, so that the battery data meets the data usage requirements in subsequent applications.

[0093] In some embodiments, the method further comprises:

[0094] In response to a result display requirement for the target battery, determining a target analysis dimension and a result display dimension of the target battery, wherein the result display dimension includes at least one of a two-dimensional dimension or a three-dimensional dimension;

[0095] Obtain comprehensive battery data of the target battery and a result display method corresponding to the result display dimension;

[0096] Using the result presentation method, a comprehensive analysis result of the target battery is generated and presented based on the comprehensive battery data and the target analysis dimension.

[0097] In the above embodiment, a comprehensive result display function is integrated into the battery data analysis system, which can generate multi-dimensional comprehensive analysis results that conform to the battery when the user needs to perform multi-dimensional visual analysis of the battery, provide the user with data visual analysis in various dimensions, and improve the user's convenience.

[0098] The evaluation report generation function is used to comprehensively evaluate the battery based on the results of various analyses of the battery data and output a corresponding evaluation report. Users have the need to generate battery evaluation reports in some actual usage scenarios, such as after-sales scenarios and research and analysis scenarios.

[0099] In some embodiments, the method further comprises:

[0100] In response to an evaluation report generation instruction for the target battery, determining a battery evaluation type of the target battery;

[0101] Determine the battery evaluation data and report template required to generate a battery evaluation report of the target battery based on the battery evaluation type;

[0102] A battery evaluation report is generated according to a report template corresponding to the evaluation type and the battery evaluation data.

[0103] In the above embodiment, an evaluation report generation function is integrated into the battery data analysis system, which can provide users with a comprehensive evaluation report that meets their actual needs while analyzing and processing the battery data, thereby further lowering the user's usage threshold and improving user convenience.

[0104] In a second aspect, the present application further provides a battery data processing device, the device comprising:

[0105] An event response module, configured to determine a desired analysis item of the target battery in response to a battery analysis requirement for the target battery;

[0106] An analysis parameter selection module, used for selecting analysis parameters according to the desired analysis items;

[0107] A data acquisition module, used to acquire battery detection data corresponding to the analysis parameters;

[0108] The analysis module is used to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery.

[0109] In a third aspect, the present application further provides a battery data processing system, the system comprising:

[0110] A data scanning module, for determining the desired analysis items of the target battery in response to the battery analysis requirements for the target battery; selecting analysis parameters according to the desired analysis items; acquiring battery detection data corresponding to the analysis parameters; performing data analysis on the battery detection data to obtain the battery analysis results of the target battery;

[0111] The local storage module is used to store the battery detection data.

[0112] In a fourth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0113] In a fifth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the above method when executed by a processor.

[0114] The battery data processing method, device, system, computer equipment, storage medium and computer program product mentioned above, the battery data analysis system includes multiple analysis items, which can analyze the battery data. When using, the user can select the desired analysis items for the target battery from multiple analysis items according to the battery analysis requirements for the target battery. The battery data analysis system selects analysis parameters according to the desired analysis items selected by the user, obtains the battery detection data corresponding to the analysis parameters, and performs data analysis on the battery detection data to obtain the battery analysis results of the target battery. By integrating the analysis functions of multiple battery analysis items into a battery data analysis system, when the user needs to analyze the battery data, he does not need to rely on the collaborative processing of the data platform. Various analysis requirements for battery data can be realized in one battery analysis system, which can help users quickly and easily obtain the battery analysis results of the target battery, effectively improving the convenience of battery data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 A diagram of an application environment of a battery data processing method according to one or more embodiments;

[0116] Figure 2 is a flowchart of a battery data processing method according to one or more embodiments;

[0117] Figure 3 is a flowchart of a battery data processing method according to one or more embodiments;

[0118] Figure 4 A schematic flow chart of the steps of performing data analysis on battery detection data using an analysis method according to one or more embodiments to obtain a battery analysis result of a target battery;

[0119] Figure 5 A schematic diagram of a flow chart of determining analysis parameters matching expected analysis items according to expected analysis items and analysis method steps for the analysis parameters according to one or more embodiments;

[0120] Figure 6 A schematic diagram of a flow chart of steps for obtaining battery detection data corresponding to analysis parameters according to one or more embodiments;

[0121] Figure 7 A flowchart of the steps of obtaining supplementary battery detection data corresponding to an analysis parameter identifier that has not been successfully matched based on an analysis parameter identifier that has not been successfully matched according to one or more embodiments;

[0122] Figure 8 is a flowchart of a battery data processing method according to one or more embodiments;

[0123] Fig. 9 is a flowchart of a battery data processing method according to one or more embodiments;

[0124] Fig.10 is a flowchart of a battery data processing method according to one or more embodiments;

[0125] Fig.11 is a flowchart of a battery data processing method according to one or more embodiments;

[0126] Fig.12 is a flowchart of a battery data processing method according to one or more embodiments;

[0127] Fig.13 is a flowchart of a battery data processing method according to one or more embodiments;

[0128] Fig.14 is a structural block diagram of a battery data analysis system according to one or more embodiments;

[0129] Fig.15 is a flowchart of a battery data processing method according to one or more embodiments;

[0130] Fig.16 is a structural block diagram of a battery data processing device according to another one or more embodiments;

[0131] Fig.17The figure is a diagram of the internal structure of a computer device according to one or more embodiments. DETAILED DESCRIPTION

[0132] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0133] Battery data has a high reference value for all business stages, including battery development, use, and after-sales. Taking the power battery of an electric vehicle as an example, during the development process, by analyzing and evaluating the battery data of the power battery, it can provide users with effective reference data when developing the power battery, thereby improving the development efficiency of the power battery. During use, analyzing and evaluating the battery data of the power battery can quickly determine the use status of the power battery, and provide timely reminders to users on how to use the power battery product. In the after-sales process, analyzing and evaluating the battery usage data of the power battery can provide certain data support for power batteries when fault tracing and root cause analysis are required.

[0134] At present, the commonly used battery data analysis method is usually to use a battery analysis system to evaluate and analyze the input battery data. However, most battery analysis systems can only perform simple and fixed battery analysis. When multiple analysis functions need to be realized, they often rely on multiple platforms, such as big data platforms and application platforms. The collaboration between them can only be completed, which invisibly raises the user's usage threshold and reduces the convenience of using the battery analysis system.

[0135] In order to lower the threshold for using the battery analysis system and improve its ease of use, the analysis functions of multiple battery analysis projects can be integrated into the battery analysis system. When users have battery data analysis needs, they only need to select the corresponding desired analysis project from the multiple analysis projects of the battery data analysis system according to the actual battery analysis needs. The battery data analysis system can select the analysis parameters according to the desired analysis project, obtain the battery detection data corresponding to the analysis parameters, perform data analysis on the battery detection data, and obtain battery analysis results that meet user needs. By integrating the analysis functions of multiple battery analysis projects into a battery data analysis system, users do not need to rely on data platform collaborative processing when they need to analyze battery data. Various analysis needs for battery data can be realized in one battery analysis system, which can help users quickly and easily obtain the battery analysis results of the target battery, effectively improving the convenience of battery data analysis.

[0136] The battery data processing method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the battery data analysis system 102 is connected to the user terminal 104 for communication. The data storage system can store the data that the battery data analysis system 102 needs to process. The data storage system can be integrated in the battery data analysis system 102, or it can be placed on the cloud or other network servers. The user selects the desired analysis project from multiple analysis projects of the battery data analysis system 102 based on the user terminal 104. The battery data analysis system 102 responds to the user's battery analysis requirements for the target battery, determines the desired analysis project of the target battery, selects analysis parameters according to the desired analysis project, obtains the battery detection data corresponding to the analysis parameters, performs data analysis on the battery detection data, and obtains the battery analysis results of the target battery. .

[0137] Among them, the battery data analysis system 102 is a system for analyzing and processing battery detection data according to the desired analysis items selected by the user. The battery data analysis system 102 can quickly analyze the battery detection data to obtain the corresponding battery analysis results. The battery data analysis system 102 can be integrated on the user terminal 104 for user use. The integrated form of the battery data analysis system 102 is related to the category of the user terminal 104 in which it is integrated. For example, if the user terminal 104 is a portable mobile terminal such as a smart phone or a smart watch, the state of the battery data analysis system 102 integrated on the user terminal 104 can be a data analysis application. If the user terminal 104 is an intelligent terminal device, such as a personal computer, a laptop computer, etc., the state of the battery data analysis system 102 integrated on the user terminal 104 can be a data analysis tool software.

[0138] The user terminal 104 is a terminal carrier for running the battery data analysis system 102, and the user uses the battery data analysis system 102 based on the user terminal 104. The user terminal 104 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The portable wearable device can be a smart watch, a smart bracelet, etc.

[0139] It is understandable that the target battery can be any kind of electric energy storage carrier, such as a power battery, an energy storage battery, a mobile power bank, etc. For ease of understanding, the following description will be given by taking a power battery as the target battery.

[0140] In some embodiments, Figure 2 As shown, a battery data processing method is provided, and the method is applied to Figure 1 The battery data analysis system in the example is used to illustrate, including the following steps:

[0141] Step 202 : In response to a battery analysis requirement for a target battery, desired analysis items for the target battery are determined.

[0142] Among them, the analysis project is a project in the battery data analysis system that has the function of analyzing and processing battery detection data, and can analyze and process the battery detection data through certain analysis logic. In actual use, the battery data analysis system can analyze and process the battery detection data through the analysis logic corresponding to the analysis project, and then obtain the battery analysis results that can provide data reference value for the user's actual business.

[0143] The desired analysis project is an analysis project selected by the user from various analysis projects of the battery data analysis system in response to the battery analysis requirements of the target battery. After the user starts the battery data analysis system, the corresponding desired analysis project can be selected from multiple analysis projects based on the system interface of the battery data analysis system. For example, the system interface of the battery data analysis system is configured with selection buttons corresponding to each achievable analysis project. The user can click the selection button corresponding to the desired analysis project based on the system interface of the battery data analysis system to trigger the selection of the desired analysis project from multiple analysis projects of the battery data analysis system. For another example, the system interface of the battery data analysis system is configured with a multi-level selection menu, including but not limited to an analysis project selection submenu, an editing tool submenu, a help submenu, etc. The user can first click the analysis project selection submenu based on the system interface of the battery data analysis system, and then enter the analysis project selection submenu and click the selection button corresponding to the desired analysis project to trigger the selection of the desired analysis project from multiple analysis projects of the battery data analysis system.

[0144] Multiple analysis projects can be integrated into the battery data analysis system. The number and type of integrated analysis projects can be determined according to the actual application scenarios of battery data analysis performed by the user.

[0145] In some application scenarios where the space occupied by the battery data analysis system needs to be considered, in order to reduce the configuration memory of the battery data analysis system, corresponding analysis items can be configured according to the actual usage requirements of the user.

[0146] For example, in some of the embodiments, if the user is a research and development personnel who needs to analyze and process battery data during the battery development stage, the analysis projects that need to be integrated into the battery data analysis system can be determined based on the analysis projects that the user frequently uses when performing battery development business, such as battery profiling projects, abnormal battery analysis projects, etc., to facilitate the user to provide battery analysis reference data when determining development information such as battery performance and battery development quality corresponding to the battery to be developed.

[0147] In some embodiments, if the user is a battery product user and needs to analyze and process the battery data during the battery usage phase, the analysis items that need to be integrated into the battery data analysis system can be determined based on the analysis items that are frequently used when the battery is in operation, such as battery life assessment items, battery failure warning items, battery portrait items, and cruising range analysis items, etc. This is to provide certain reference data for users when using battery products.

[0148] In some embodiments, if the user is an after-sales person of a battery product and needs to analyze and process the battery data in the after-sales stage of the battery, the analysis items that need to be integrated in the battery data analysis system can be determined based on the analysis items that are frequently used in the after-sales stage of the battery, such as battery life evaluation items, battery image items, root cause analysis items, etc. This is to provide certain data support for the user when performing after-sales processing.

[0149] It can be understood that the above embodiments are merely simple examples of business functions that may need to be used in various application scenarios. In actual application, the business functions at each stage can be configured and used according to actual conditions.

[0150] In order to improve the operational convenience of the battery data analysis system, in other embodiments, various analysis items that need to be used when the battery is in various stages are integrated into the battery data analysis system, so that users can quickly select the desired analysis items according to actual conditions and complete the analysis and processing of the battery data through the battery data analysis system.

[0151] In some optional embodiments, a user starts running a battery data analysis system based on a user terminal, triggers a battery analysis requirement for a target battery through a system interface of the battery data analysis system, and the battery data analysis system determines desired analysis items for the target battery in response to the battery analysis requirement for the target battery.

[0152] In one of the embodiments, in response to a battery analysis requirement for a target battery, determining desired analysis items for the target battery includes: in response to the battery analysis requirement for the target battery, obtaining a preconfigured analysis item list, and obtaining desired analysis items selected from the analysis item candidate list.

[0153] The analysis project list includes all analysis projects integrated in the battery data analysis system. Through the analysis project list, all analysis projects in the battery data analysis system can be centrally displayed, so that users can select the desired analysis project that meets the battery analysis requirements.

[0154] In some optional embodiments, the battery data analysis system is pre-configured with an analysis project list, which includes all analysis projects currently integrated in the battery data analysis system. In actual use, the user triggers the battery analysis requirement for the target battery through the system interface of the battery data analysis system, and the battery data analysis system responds to the battery analysis requirement for the target battery by calling the pre-configured analysis project list and displaying it. The user selects the desired analysis project from the analysis project candidate list, and the battery data analysis system obtains the desired analysis project selected from the analysis project candidate list.

[0155] Step 204: Select analysis parameters according to the desired analysis items.

[0156] Analysis parameters are the types of battery data parameters that need to be used when performing corresponding data analysis on an analysis project. For example, when the analysis project is an abnormal battery analysis project, the corresponding analysis parameters may be the battery's current, voltage, capacitance, etc. When the analysis project is a battery life assessment project, the corresponding analysis parameters may be battery usage time, cell throughput, etc. After determining the analysis parameters for the desired analysis project, the battery data analysis system can obtain the corresponding battery detection data based on the analysis parameters.

[0157] In some embodiments, the battery data analysis system selects analysis parameters according to the expected analysis items, including: determining analysis parameters corresponding to the expected analysis items according to the expected analysis items and a preset parameter mapping table, wherein the preset parameter mapping table is used to characterize the correspondence between the analysis items and the analysis parameters.

[0158] Step 206, obtaining battery detection data corresponding to the analysis parameters.

[0159] Among them, battery detection data can be divided into separate application data and integrated data according to the data type. Among them, separate application data refers to battery data generated by an application device or an application scenario. For example, in order to perform electrochemical impedance analysis on batteries or battery products, R&D personnel will use electrochemical impedance detection equipment to detect batteries or battery products and collect corresponding electrochemical impedance data. At this time, the electrochemical impedance data can be considered as separate application data. For another example, in order to perform battery capacity analysis on batteries or battery products, the battery capacity data collected by R&D personnel after using battery capacity detection equipment to detect batteries or battery products can also be considered as separate application data.

[0160] Integrated data refers to battery data obtained by integrating battery data generated by multiple application devices or application scenarios. For example, for the same battery or battery product, R&D personnel can perform electrochemical impedance detection, charge and discharge testing, battery capacity detection, production process data recording, production material data recording, etc. on the battery or battery product to obtain multiple battery data. In order to facilitate centralized management, the above multiple battery data can be integrated to obtain the battery data of the battery or battery product. Optionally, when the integrated data is obtained, the integrated data can be processed according to actual needs, such as data cleaning processing, data replication processing, etc.

[0161] Battery data can be divided into raw battery data and intermediate processing state battery data according to the data processing status. Raw battery data can be battery data obtained after testing the battery or battery product without further analysis and processing, such as electrochemical impedance data, battery capacity test data, etc. Intermediate processing state battery data can be battery data obtained by further analyzing and processing the raw battery data after obtaining it, such as battery data obtained by calculating characteristic values ​​and index parameters of the battery.

[0162] In some of the embodiments, after performing battery data analysis on the original battery data, the user can retain and store the obtained processing results, so that when further battery data analysis is needed, the intermediate processing state battery data can be directly used without repeatedly processing the original battery data again. This can effectively reduce the resource utilization rate during battery analysis and processing and improve the efficiency of battery analysis and processing.

[0163] The battery data analysis system needs to obtain the battery detection data corresponding to the analysis parameters from the target data source for subsequent analysis and processing. In some optional embodiments, the target data source may be a default data source specified by the user on the battery data analysis system. The user starts running the battery data analysis system based on the user terminal, triggers the analysis project selection operation through the system interface of the battery data analysis system, and selects the desired analysis project to be used from the multiple analysis projects integrated in the battery data analysis system. After the user selects the desired analysis project, the battery data analysis system can directly load the battery data to be analyzed and processed from the default data source. For example, the user can specify the data repository of the user terminal as the default data source. After the user selects the desired analysis project, the battery data analysis system can directly obtain the battery detection data corresponding to the analysis parameters from the data repository of the user terminal.

[0164] In other optional embodiments, the target data source can be a real-time data source specified by the user according to the actual storage location of the battery data after the user selects the desired analysis project. The user starts the battery data analysis system based on the user terminal, triggers the analysis project selection operation through the system interface of the battery data analysis system, and selects the desired analysis project to be used from the multiple analysis projects integrated by the battery data analysis system. The battery data analysis system generates and displays the target data source selection page in response to the desired analysis project selection operation, and the user selects the target data source based on the target data source selection page. The battery data analysis system obtains the battery detection data corresponding to the analysis parameters from the target data source based on the target data source selected by the user. For example, if the user needs to analyze the battery impedance detection data of the battery, the device that generates the battery impedance detection data, such as the impedance detection device, the electrochemical workstation and other devices, can be determined as the target data source.

[0165] In some of the embodiments, in order to improve the accuracy of obtaining battery detection data and reduce the possibility of invalid analysis and processing due to data acquisition errors, the battery data analysis system can generate a data confirmation prompt message and display it to the user after the battery detection data to be obtained is selected. The data confirmation prompt message contains data information of the battery detection data to be obtained, which is convenient for the user to determine whether the battery detection data to be obtained is the battery detection data that needs to be analyzed and processed, thereby improving the accuracy of obtaining the battery detection data.

[0166] In some embodiments, the battery detection data corresponding to the analysis parameters obtained by the battery data analysis system may be all the data of the target battery. For example, the user may store the battery data corresponding to the analysis parameters in the target data source according to the battery analysis requirements of the target battery, and the battery data analysis system may directly obtain all the battery data as battery detection data for subsequent data analysis. It may also be partial battery data of the target battery. For example, the user may also store the comprehensive battery data of the target battery in the target data source. After selecting the analysis parameters, the battery data analysis system obtains the battery detection data corresponding to the analysis parameters from the comprehensive battery data of the target battery according to the analysis parameters.

[0167] Step 208: Analyze the battery detection data to obtain a battery analysis result of the target battery.

[0168] In some optional embodiments, after the battery data analysis system acquires the battery detection data corresponding to the analysis parameters, it performs data analysis on the battery detection data according to the analysis logic of the desired analysis item to obtain the battery analysis result of the target battery.

[0169] In the above-mentioned battery data processing method, the battery data analysis system includes multiple analysis items that can analyze the battery data. When using it, the user can select the desired analysis item for the target battery from the multiple analysis items according to the battery analysis requirements for the target battery. The battery data analysis system selects the analysis parameters according to the desired analysis items selected by the user, obtains the battery detection data corresponding to the analysis parameters, and performs data analysis on the battery detection data to obtain the battery analysis results of the target battery. By integrating the analysis functions of multiple battery analysis items into a battery data analysis system, when the user needs to analyze the battery data, he does not need to rely on the collaborative processing of the data platform. Various analysis requirements for battery data can be realized in one battery analysis system, which can help users quickly and easily obtain the battery analysis results of the target battery, effectively improving the convenience of battery data analysis.

[0170] The battery data analysis system can realize data analysis functions for multiple analysis projects. The key point is that the battery data analysis system can analyze the battery detection data according to the analysis logic of each analysis project.

[0171] In order to better manage the analysis logic corresponding to each analysis project, in some embodiments, such as Figure 3 A battery data processing method is provided, wherein the step 204 of selecting analysis parameters according to the desired analysis items may include:

[0172] Step 302: determining, according to the expected analysis item, analysis parameters matching the expected analysis item and analysis methods for the analysis parameters.

[0173] Among them, the analysis method is a specific analysis method used by the battery data analysis system when performing data analysis on the battery detection data based on the expected analysis project. The analysis method may include the specific steps of the analysis logic corresponding to the analysis project, and the analysis algorithm to be used when executing the specific analysis steps. It is understandable that different analysis projects will have different specific analysis steps in the corresponding analysis method, and the analysis algorithms to be used when executing the specific analysis steps will also be different.

[0174] In some optional embodiments, the designer generates the analysis method corresponding to the analysis item in advance according to the specific analysis logic corresponding to the analysis item and the analysis algorithm to be used when executing the specific analysis logic. After the battery data analysis system determines the expected analysis item, it can determine the analysis parameters matching the expected analysis item and the analysis method for the analysis parameters according to the expected analysis item.

[0175] In some of the embodiments, the designer generates a preset mapping table in advance according to the correspondence between the analysis items, the analysis parameters and the analysis methods, and configures the preset mapping table in the battery data analysis system. After determining the desired analysis items for the target battery, the battery data analysis system matches the desired analysis items with the analysis items in the preset mapping table, and determines the analysis parameters and analysis methods corresponding to the successfully matched analysis items as the analysis parameters corresponding to the desired analysis items, and the analysis methods for the analysis parameters.

[0176] Step 208 of analyzing the battery detection data to obtain the battery analysis result of the target battery may include:

[0177] Step 304: Use an analysis method to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery.

[0178] In some optional embodiments, after acquiring the battery detection data corresponding to the analysis parameters, the battery data analysis system may perform data analysis on the battery detection data according to the specific analysis steps and analysis algorithms in the analysis method to obtain the battery analysis results of the target battery.

[0179] In the above embodiment, by predetermining the analysis parameters and analysis methods corresponding to the analysis items, when it is necessary to perform data analysis on the battery data, the corresponding analysis parameters and analysis methods can be quickly matched according to the desired analysis items selected by the user, and the battery detection data can be analyzed based on the corresponding analysis methods. This can better manage the various analysis items in the battery data analysis system, and also effectively improve the efficiency of the battery data analysis.

[0180] In some embodiments, the desired analysis items that a user can select from the battery data analysis system may include at least one of an abnormal battery analysis item, a battery life assessment item, a battery failure warning item, a battery portrait item, and a root cause analysis item.

[0181] Among them, the abnormal battery analysis project is an analysis project that conducts preliminary problem analysis and judgment on the battery through various indicator parameters of the battery. The battery data analysis system performs abnormal battery data analysis on the battery detection data, and can quickly conduct preliminary analysis and judgment on some basic problems of the battery, such as making preliminary judgments based on the indicator parameters such as current, voltage, temperature in the battery to determine whether there is a problem with the battery, or to determine which battery cell in the battery pack has a problem. Another example is abnormal detection of impedance data in the electrochemical impedance spectrum. In some embodiments, the analysis results obtained by the battery data analysis system for abnormal battery data analysis on the battery detection data can also be used as intermediate data for providing parameters for subsequent in-depth analysis and business application of the battery, so as to make battery research and development and application more targeted and efficient. The abnormal battery analysis project can be a preliminary battery analysis project. After the abnormal battery analysis is performed on the battery, if the analysis results show that the various indicators of the battery or battery product are in good condition, the user can choose not to conduct subsequent in-depth analysis, such as selecting the battery portrait project to perform battery portrait analysis on the battery detection data, or selecting the root cause analysis project to perform root cause analysis on the battery detection data.

[0182] The battery life assessment project is an analysis project that predicts and evaluates the battery life through the battery life analysis parameters. The battery data analysis system performs battery life assessment and analysis on the battery test data, which can timely predict and evaluate the battery life, grasp the real-time health status of the battery, and is also conducive to the life cycle management of the battery.

[0183] The battery fault warning project is an analysis project that uses the battery fault warning parameters to detect and warn of possible battery faults in the battery. The battery data analysis system performs battery fault warning analysis on the battery detection data, and can issue warning prompts when the battery may fail or has failed, thereby improving the safety of battery use.

[0184] The battery portrait project is an analysis project that uses the battery portrait parameters to profile the battery or battery products in one or more dimensions. The battery data analysis system needs to implement the battery portrait analysis function based on data statistical methods. By integrating the battery portrait project, the battery data analysis system can, for example, analyze the battery data corresponding to the battery usage conditions to obtain a statistical portrait of charging and discharging to help the battery build fast charging and slow charging habits; analyze the battery data of the battery driving conditions to obtain a driving behavior portrait; analyze the battery data of the battery usage environment to obtain a portrait of the battery's external environment; perform statistical analysis on the battery data of battery fault alarms and warnings to obtain a battery status portrait, etc. Battery portrait analysis is an important means of battery data analysis, which can cover multiple dimensions, such as battery materials, battery production processes, usage environment, usage conditions, etc.

[0185] The root cause analysis project is an analysis project that analyzes the causes of problems with batteries or battery products through the root cause analysis parameters of the battery. By integrating the root cause analysis project, the battery data analysis system can promptly analyze and determine the causes of problems with batteries or battery products when problems occur, and provide data parameters for the prevention of battery failures. In some embodiments, the root cause analysis function can be used in conjunction with the battery portrait function. When the battery data analysis system performs battery portrait analysis on the target battery, it can build a corresponding battery portrait system for the target battery by generating battery portraits in multiple dimensions. When performing root cause analysis later, for example, when a battery has a lifespan decay, the battery portrait system can be used to perform a comprehensive root cause analysis of the battery based on the portraits of the battery's materials, processes, models, charging and discharging conditions, usage, and other dimensions in the portrait system, so as to achieve a fast and comprehensive analysis effect.

[0186] It can be understood that the above-mentioned battery life assessment projects, battery fault warning projects, battery portrait projects and root cause analysis projects can all be considered as in-depth analysis projects of batteries. The analysis methods required to achieve in-depth analysis of batteries are generally composed of specific analysis algorithms. The calculation dimensions of specific analysis algorithms are generally higher than the algorithm dimensions used in the previous abnormal battery analysis. Therefore, the specific analysis algorithms required to implement battery life assessment projects, battery fault warning projects, battery portrait projects and root cause analysis projects are also highly professional, such as highly professional algorithms such as battery life assessment and fault warning.

[0187] In some optional embodiments, the above analysis items can be integrated into the battery data analysis system according to the actual usage requirements of the user, so that the battery data analysis system becomes an efficient and flexible battery data analysis tool provided to the user, thereby lowering the user's usage threshold and improving the user's convenience.

[0188] The analysis items mentioned above are all important analysis items in the current battery data analysis process. The analysis process of each of the above analysis items will be explained below.

[0189] In some embodiments, when the expected analysis item is an abnormal battery analysis item, the analysis method corresponding to the expected analysis item is the abnormal battery analysis method. Using the analysis method to analyze the battery detection data to obtain the battery analysis result of the target battery includes:

[0190] The abnormal battery analysis method is used to perform data analysis on the battery detection data to obtain the abnormal battery analysis result of the target battery. The battery detection data is index data obtained according to the index parameters of the target battery.

[0191] The abnormal battery analysis method is used to analyze the index data to be analyzed so as to preliminarily screen the problems that may exist in the battery data. The battery data analysis system can determine whether there is an abnormal index state in the battery through the abnormal battery analysis method.

[0192] In some optional embodiments, the analysis parameters corresponding to the abnormal battery analysis project are indicator parameters of the battery, such as indicator parameters such as capacitance, current, voltage, and battery operating temperature. When the analysis project selected by the user is an abnormal battery analysis project, the battery data analysis system obtains indicator data corresponding to the indicator parameters, i.e., current data, capacitance data, voltage data, and battery operating temperature data of the target battery, compares the indicator data with preset abnormal standard data, determines that the indicator state corresponding to the indicator data that meets the abnormal condition is an abnormal state, and obtains the abnormal battery analysis result of the target battery.

[0193] In the above embodiment, by integrating the abnormal battery analysis project on the battery data analysis system, when the user has the need to perform problem analysis or fault detection on the battery, the user can first use the abnormal battery analysis project to perform an initial screening of possible problems with the battery. If the abnormal battery analysis results show that the status of various indicators of the battery are good, the subsequent in-depth data analysis process can be omitted, which effectively reduces the computational complexity of battery data analysis and detection and improves the efficiency of battery data analysis processing.

[0194] In some embodiments, when the expected analysis project is a battery life assessment project, the analysis method corresponding to the expected analysis project is a battery life assessment method. Using the analysis method to analyze the battery detection data to obtain the battery analysis result of the target battery includes:

[0195] The battery life evaluation method is used to perform data analysis on the battery detection data to obtain a battery life evaluation analysis result of the target battery. The battery detection data is battery data obtained according to the life evaluation parameters of the target battery.

[0196] Among them, the battery life assessment method is to use the corresponding life assessment algorithm to perform data analysis on the battery detection data to obtain an analysis method for predicting the battery service life.

[0197] In some optional embodiments, the analysis parameters corresponding to the battery life assessment project are battery life assessment parameters, such as battery life assessment parameters such as battery usage time, battery usage conditions, and battery cell throughput. When the analysis project selected by the user is a battery life assessment project, the battery data analysis system obtains battery data corresponding to the life assessment parameters, i.e., battery usage time data, battery usage condition data, and battery cell throughput data of the target battery, and uses the corresponding battery life assessment algorithm to perform data analysis on the battery data to obtain a battery life assessment result for the target battery.

[0198] It is understandable that the battery life evaluation algorithm can be any algorithm that can predict and evaluate the battery life. For example, in some embodiments, the battery life evaluation algorithm can determine the battery decay rate based on a pre-constructed relationship curve between the number of cycles and the decay rate, and then predict the battery life based on the decay rate. In other embodiments, the battery life evaluation algorithm can also predict the battery life based on the battery life evaluation parameters according to a pre-configured life estimation model.

[0199] In the above embodiment, by integrating the battery life assessment project into the battery data analysis system, when the user has a need to predict the battery life, the battery data analysis system can be used to timely predict and assess the battery service life, grasp the real-time health status of the battery, and also facilitate the battery life cycle management.

[0200] In some embodiments, when the expected analysis item is a battery fault warning item, the analysis method corresponding to the expected analysis item is a fault warning analysis method. Using the analysis method to analyze the battery detection data to obtain the battery analysis result of the target battery includes:

[0201] The battery detection data is analyzed using a fault warning analysis method to obtain a fault warning analysis result of the target battery. The battery detection data is battery data obtained according to the fault warning parameters of the target battery.

[0202] Among them, the fault warning analysis method is an analysis method that uses a corresponding fault warning algorithm to perform data analysis on battery detection data to obtain a battery fault warning analysis result.

[0203] In some optional embodiments, the analysis parameters corresponding to the battery fault warning project are fault warning parameters of the battery, such as fault warning parameters such as battery construction materials, battery construction structure, and battery operating parameters. When the analysis project selected by the user is a battery fault warning project, the battery data analysis system obtains battery data corresponding to the fault warning parameters, i.e., the construction material data, construction structure data, and operating parameter data of the target battery, and uses the corresponding battery fault warning algorithm to perform data analysis on the battery data to obtain a battery fault warning analysis result of the target battery.

[0204] In the above embodiment, by integrating the battery fault warning project on the battery data analysis system, when the user has the need to predict or detect battery faults, the battery data analysis system can be used to timely perform battery fault warning analysis on the battery detection data, and can issue warning prompts when the battery may fail or has already failed, thereby improving the safety of battery use.

[0205] It is understandable that the battery fault warning algorithm can be any algorithm that can detect and warn the battery fault. For example, in some embodiments, the battery fault warning algorithm can be based on the pre-set fault detection threshold and fault warning threshold, to perform fault detection and fault warning confirmation on the fault warning parameters of the battery. When the fault warning parameter of the battery exceeds the fault detection threshold, it is determined that the battery has a fault. When the fault warning parameter of the battery exceeds the fault warning threshold, it is determined that the probability of a fault in the battery reaches the warning condition. In other embodiments, the battery fault warning algorithm can also be based on a pre-configured fault warning model, to perform fault classification and identification on the fault warning parameters of the battery, to determine the faults in the battery, and whether a fault warning is required.

[0206] In some embodiments, when the expected analysis project is a battery portrait project, the analysis method corresponding to the expected analysis project is the battery portrait analysis method. Using the analysis method to analyze the battery detection data to obtain the battery analysis result of the target battery includes:

[0207] The battery detection data is subjected to battery profiling using at least one of the battery usage condition dimension, the battery usage environment dimension and the battery status dimension, using a corresponding battery profiling analysis method to obtain a battery profiling result of the target battery.

[0208] Among them, battery portrait analysis, as an important means of battery data analysis, can cover multiple dimensions.

[0209] The battery operating condition dimension is used to analyze the operating conditions of the battery during use. The battery operating condition is one of the important factors affecting the battery. Taking power batteries as an example, the frequent start and stop of electric vehicles, high power density discharge, and repeated load changes will affect the battery materials of the power battery, thereby affecting the battery life.

[0210] In some embodiments, when it is necessary to perform a battery portrait on the battery detection data from the dimension of battery usage conditions, the battery data analysis system first obtains the battery detection data corresponding to the dimension of battery usage conditions. It can be understood that the battery detection data corresponding to the dimension of battery usage conditions can be the start-stop data, discharge data, variable load data, etc. during battery use. Then the battery data analysis system obtains the portrait method corresponding to the dimension of battery usage conditions to perform a battery portrait on the battery detection data, and obtains the battery portrait result of the target battery in the dimension of battery usage conditions.

[0211] In some of the embodiments, the battery data analysis system can perform a battery profile of the target battery based on the battery operating temperature, and then provide data parameters for establishing fast charging and slow charging habits for the target battery.

[0212] The battery use environment dimension is used to analyze the environment in which the battery is used. Like the battery use condition dimension, the battery use environment dimension is also one of the important factors affecting the battery. Taking power batteries as an example, the products used by the power batteries, the temperature and humidity of the use environment, etc. will affect the battery life of the power batteries.

[0213] In some embodiments, when it is necessary to perform a battery portrait on the battery detection data from the dimension of the battery use environment, the battery data analysis system first obtains the battery detection data corresponding to the battery use environment dimension. It can be understood that the battery detection data corresponding to the battery use environment dimension can be the ambient temperature, ambient humidity, ambient charging status, etc. when the battery is used. Then the battery data analysis system obtains the portrait method corresponding to the battery use environment dimension to perform a battery portrait on the battery detection data, and obtains the battery portrait result of the target battery in the battery use environment dimension.

[0214] The battery status dimension is an analysis dimension for analyzing the overall status of the target battery. By constructing a battery portrait of the overall battery status, users can have a more comprehensive understanding of the real-time status of the battery.

[0215] In some embodiments, when it is necessary to perform a battery portrait on the battery detection data from the battery status dimension, the battery data analysis system first obtains the battery detection data corresponding to the battery status dimension. It can be understood that the battery detection data corresponding to the battery status dimension can be battery fault alarm analysis result data, early warning statistical analysis results, life prediction analysis results, etc. Then the battery data analysis system obtains the portrait method corresponding to the battery status to perform a battery portrait on the battery detection data, and obtains the battery portrait result of the target battery in the battery status dimension.

[0216] It is understandable that in actual use, a battery portrait of a certain dimension may be generated according to the user's usage needs, or a battery portrait of multiple dimensions or all dimensions may be generated.

[0217] In the above embodiment, by integrating the battery portrait analysis project into the battery data analysis system, when the user has a portrait requirement for the target battery, a battery portrait that meets the user's actual analysis requirement dimensions can be generated. The battery portrait analysis project can help the user quickly understand the battery profile and obtain a quick and comprehensive analysis result, thereby improving the user's convenience.

[0218] In some embodiments, when the expected analysis item is a root cause analysis item, the analysis method corresponding to the expected analysis item is a root cause analysis method of the battery. Figure 4 As shown, the battery detection data is analyzed using an analysis method to obtain the battery analysis results of the target battery, including:

[0219] Step 402, performing data detection and analysis on the battery detection data to obtain a detection and analysis result of the target battery, wherein the detection and analysis result includes at least one of a life detection result, a fault detection result or an alarm detection result.

[0220] The data detection and analysis is a detection and analysis process of at least one of life analysis, fault detection analysis, and fault level analysis on the battery. The life detection result obtained by performing life analysis on the battery can predict the service life of the battery. The fault detection result obtained by performing fault prediction analysis on the battery can determine the fault that the battery has sent. The alarm detection result obtained by performing fault alarm analysis on the battery can determine whether the fault existing in the battery has reached the alarm level.

[0221] In some optional embodiments, when the battery data analysis system determines that the expected analysis item is a root cause analysis item, it obtains battery detection data for root cause analysis, performs data detection and analysis on the battery detection data, and obtains detection and analysis results of the target battery, wherein the detection and analysis results include at least one of life detection results, fault detection results, and alarm detection results.

[0222] In some of the embodiments, life analysis, fault prediction analysis and fault warning analysis of the battery can be achieved through the battery life assessment project and battery fault warning project integrated in the battery data analysis system.

[0223] Step 404 , when the life detection result of the target battery is life decay, or the fault detection result is battery fault, or the alarm detection result is battery alarm, obtain at least one of the following: a portrait result of the target battery.

[0224] Among them, when the life detection result of the target battery is life attenuation, it means that the internal structure of the battery may have changed, resulting in the battery's storage capacity being affected. When the fault detection result of the target battery is battery fault, it means that the battery has failed. When the alarm detection result of the target battery is battery alarm, it means that the fault in the battery has reached the alarm level and is relatively serious.

[0225] The above three detection results can all reflect problems with the battery. Therefore, when the target detection result includes at least one of the above three detection results, the root cause analysis of the problem in the battery can be performed.

[0226] In some optional embodiments, the battery data analysis system obtains the target battery portrait result when determining that the target battery life detection result is life decay, or the fault detection result is a battery fault, or the alarm detection result is a battery alarm. It is understandable that the target battery portrait result can be obtained by analyzing the battery portrait project integrated in the battery data analysis system.

[0227] Step 406, performing a root cause analysis based on the target battery portrait result to obtain a root cause analysis result, wherein the root cause analysis result includes at least one of a life attenuation cause, a failure cause, or an alarm cause.

[0228] Among them, since root cause analysis is a comprehensive analysis method in battery data analysis, when performing root cause analysis on a target battery, the portrait result used is often a battery portrait system of the target battery.

[0229] In some of the embodiments, when it is necessary to perform root cause analysis on the battery life decay problem, the battery data analysis system can construct a battery portrait system that includes portrait dimensions related to the battery life, such as battery materials, battery production processes, use environment, and use conditions. The battery data analysis system performs root cause analysis on the battery life decay problem based on the battery portrait system related to the battery life in the target battery to obtain the cause of the battery life decay.

[0230] In some embodiments, when it is necessary to perform root cause analysis on battery failure problems and fault alarms, the battery data analysis system can construct a battery portrait system including battery usage environment, battery usage conditions, battery status, and other portrait dimensions related to possible battery failures. The battery data analysis system performs root cause analysis on the battery failure problem based on the battery portrait system related to the battery failure in the target battery, and obtains at least one of the battery failure cause or alarm cause.

[0231] It is understandable that in actual use, according to the actual use requirements of the user, the battery data analysis system may perform root cause analysis on only one battery problem, or may perform root cause analysis on multiple or all battery problems at the same time.

[0232] In the above embodiment, by integrating the root cause analysis project on the battery data analysis system, when the user has a root cause analysis demand for the target battery, the problem existing in the battery can be quickly detected and determined, and the construction of a battery portrait system can be triggered from multiple dimensions related to the battery problem. The root cause analysis of the problem is performed based on the battery portrait system, so that the user can quickly know the cause of the battery problem, thereby improving the user's convenience.

[0233] Since some of the expected analysis projects include in-depth analysis of batteries, as external factors such as data structure and business characteristics are updated and iterated, the analysis methods used in the corresponding expected analysis projects also need to be updated and iterated to improve the accuracy of battery analysis.

[0234] In some embodiments, Figure 5As shown, according to the expected analysis project, the analysis parameters matching the expected analysis project and the analysis method for the analysis parameters are determined, including:

[0235] Step 502: Determine, according to the expected analysis item, candidate analysis parameters that match the expected analysis item and candidate analysis methods for the candidate analysis parameters.

[0236] Among them, the candidate analysis parameters and the candidate analysis methods for the candidate analysis parameters can be considered as the analysis parameters and analysis methods that have not been updated, and the basic analysis parameters and basic analysis methods of the expected analysis items pre-configured in the system by the designer. It can also be considered as the historical analysis parameters and historical analysis methods of the expected analysis items stored in the battery data analysis system before the current analysis. Although the historical analysis parameters and historical analysis methods may have been updated for many rounds, they may still not match the user needs of the current analysis.

[0237] In some optional embodiments, the battery data analysis system determines, based on the desired analysis item, candidate analysis parameters stored in the current system that match the desired analysis item and candidate analysis methods for the candidate analysis parameters.

[0238] Step 504: Visually display the candidate analysis parameters and candidate analysis methods on the user terminal.

[0239] In some optional embodiments, the battery data analysis system visualizes the candidate analysis parameters and candidate analysis methods that match the expected analysis project on a user terminal, and the user can view the candidate analysis parameters and candidate analysis methods that match the current expected analysis project based on the user terminal to determine whether the candidate analysis parameters and candidate analysis methods match the current usage requirements.

[0240] Step 506 : in response to the editing event for the candidate analysis parameters and the candidate analysis methods, obtaining updated content of the candidate analysis parameters and the candidate analysis methods.

[0241] The editing event is an editing event triggered by the user based on the candidate analysis parameters and candidate analysis methods displayed on the user terminal interface. The user modifies and edits the candidate analysis parameters and candidate analysis methods displayed on the user terminal according to his / her own usage requirements, triggering an editing event for the candidate analysis parameters and candidate analysis methods.

[0242] For example, in some of the embodiments, when the user determines that the life assessment parameters corresponding to the current battery life assessment project and the life assessment algorithm in the battery life assessment method do not meet the current usage requirements and cannot accurately assess the battery life of the target battery, the life assessment parameters and the life assessment algorithm in the battery life assessment method can be modified and edited to obtain new life assessment parameters and life assessment algorithm. The updated life assessment parameters and the battery life assessment method can then be used to evaluate the life of the target battery, thereby achieving a more accurate life assessment effect.

[0243] In some optional embodiments, if the user determines that the displayed candidate analysis parameters and candidate analysis methods do not match the current use requirements, the user can modify and edit the candidate analysis parameters and candidate analysis methods using editing tools provided on the battery data analysis system. The battery data analysis system responds to the editing event triggered by the user for the candidate analysis parameters and candidate analysis methods, and obtains the updated content generated by the user's editing of the candidate analysis parameters and candidate analysis methods.

[0244] Step 508: Update the candidate analysis parameters and candidate analysis methods based on the updated content to obtain analysis parameters that match the expected analysis items and analysis methods for the analysis parameters.

[0245] In some optional embodiments, after obtaining the updated content for the candidate analysis parameters and the candidate analysis methods, the battery data analysis system updates the candidate analysis parameters and the candidate analysis methods based on the updated content, and obtains the analysis parameters that match the expected analysis items and the analysis methods for the analysis parameters. For example, when the analysis method is a fault warning method, the warning threshold in the fault warning method can be adjusted accordingly; when the analysis method is a battery life assessment method, the model parameters of the assessment model in the life assessment method can be adjusted accordingly.

[0246] In the above embodiment, by displaying the candidate analysis parameters and candidate analysis methods matched by the system on the user terminal, when the analysis parameters and analysis methods currently stored in the system do not meet the user's usage requirements, the user can be provided with the opportunity to customize and modify the analysis parameters and analysis methods, so that the analysis parameters and analysis methods of the final expected analysis project can be more in line with the user's usage requirements, thereby improving the analysis accuracy of the expected analysis project and enhancing the user's convenience.

[0247] The selection and modification of the above analysis parameters and analysis methods are all operations performed on the analysis parameters and analysis methods corresponding to the expected analysis items configured in the battery data analysis system. However, the battery analysis scenarios are complex and changeable, and sometimes the expected analysis items that users want to implement may be analysis items that are not configured in the battery data analysis system.

[0248] Based on this, in some embodiments, determining analysis parameters matching the expected analysis items and analysis methods for the analysis parameters according to the expected analysis items includes:

[0249] In a case where the desired analysis item is an unconfigured item, in response to a registration event for the desired analysis item, an analysis parameter configured for the desired analysis item and an analysis method for the analysis parameter are acquired.

[0250] In some optional embodiments, when the user does not find the configured expected analysis items in the battery data analysis system for the analysis needs of the target battery, the user can add the expected analysis items to the system through the custom item registration function of the battery data analysis system. In response to the registration event for the expected analysis item, the battery data analysis system obtains the analysis parameters and analysis methods for the analysis parameters configured by the user for the expected analysis item based on the battery data analysis system, and binds the analysis parameters and the analysis methods for the analysis parameters with the expected analysis items that the user expects to add, so as to obtain the newly added expected analysis items. Subsequently, the battery data analysis system can perform data analysis on the battery detection data of the target battery according to the analysis parameters and analysis methods that match the expected analysis items, and obtain battery analysis results that meet the user's usage needs.

[0251] In the above embodiment, when the desired analysis project required by the user does not exist in the battery data analysis system, the user can flexibly introduce new external analysis projects into the battery data analysis system through the project registration function of the battery data analysis system, thereby making the battery data analysis system more practical and open.

[0252] Since the analysis method required for in-depth battery analysis is generally composed of specific analysis algorithms, the calculation dimension of the specific analysis algorithm is generally higher than the algorithm dimension used in the previous abnormal battery analysis. Therefore, when conducting in-depth analysis of the battery, the battery data required by the specific analysis algorithm may be intermediate data, such as characteristic value data that may be used in battery life assessment and battery fault warning.

[0253] Based on this, in some embodiments, such as Figure 6 As shown, the battery detection data corresponding to the analysis parameters is obtained, including:

[0254] Step 602, obtaining the analysis parameter identifier of the analysis parameter.

[0255] The analysis parameter identifier is used to identify the parameter type to which the analysis parameter belongs. Taking the life assessment parameter as an example, the life assessment parameter may include battery usage time, battery usage conditions, battery cell throughput and other life assessment parameters. The battery usage time, battery usage conditions, battery cell throughput and other parameters will be set with corresponding parameter identifiers. The analysis parameter identifier can help the battery data analysis system obtain the required battery detection data from a large amount of battery data.

[0256] In some optional embodiments, after determining the analysis parameters corresponding to the desired analysis items, the battery data analysis system obtains the analysis parameter identifiers of each parameter in the analysis parameters.

[0257] In some of the embodiments, the battery data analysis system pre-configures an identification mapping table of analysis parameters and analysis parameter identifiers for each desired analysis item. When the battery data analysis system determines the analysis parameters corresponding to the desired analysis item, the analysis parameter identifier of the analysis parameter can be directly obtained according to the identification mapping table.

[0258] Step 604: Match the analysis parameter identifier with the parameter identifier of the battery data corresponding to the target battery.

[0259] In some optional embodiments, the battery data analysis system matches the determined analysis parameter identifier with the parameter identifier of the battery data corresponding to the target battery.

[0260] Step 606: if at least one analysis parameter identifier fails to match the parameter identifier of the battery data, determine the analysis parameter identifier that failed to match.

[0261] In some optional embodiments, the battery data analysis system matches the determined analysis parameter identifiers with the parameter identifiers of the battery data corresponding to the target battery. If all analysis parameter identifiers can be matched to the corresponding battery data, the battery data required for the current data analysis is considered complete. In the event that at least one analysis parameter identifier fails to match the parameter identifier of the battery data, it means that the battery data required for the current data analysis is missing and the data analysis process of the desired analysis project cannot be completed. The battery data analysis system determines the analysis parameter identifiers that have not been successfully matched.

[0262] Step 608: Based on the unmatched analysis parameter identifier, obtain the supplementary battery detection data corresponding to the unmatched analysis parameter identifier.

[0263] In some optional embodiments, the battery data analysis system obtains supplementary battery detection data corresponding to the unmatched analysis parameter identifiers based on the unmatched analysis parameter identifiers, and completes the battery data required for data analysis through the supplementary battery detection data, thereby facilitating the subsequent completion of the data analysis process corresponding to the desired analysis project.

[0264] In some of the embodiments, the battery data analysis system may generate a data supplement prompt based on the analysis parameter identifier that has not been successfully matched, and display the data supplement prompt on the user terminal to inform the user that the battery data needs to be supplemented, and obtain the supplementary battery detection data supplemented by the user.

[0265] In the above embodiment, by setting corresponding parameter identifiers for analysis parameters, it is possible to quickly determine whether there is missing battery detection data based on the parameter identifiers, and obtain corresponding supplementary battery detection data, thereby providing a data basis for the smooth execution of subsequent battery data analysis.

[0266] In order to further improve the convenience of the battery data analysis system, in some embodiments, such as Figure 7 As shown, based on the unmatched analysis parameter identifier, obtaining the supplementary battery detection data corresponding to the unmatched analysis parameter identifier includes:

[0267] Step 702: Determine a parameter generation item for generating supplementary battery detection data based on the unmatched analysis parameter identifier.

[0268] Among them, the parameter generation project is a pre-configured project for generating battery data corresponding to the intermediate analysis parameters required by a specific analysis algorithm. By pre-configuring the parameter generation project in the battery data analysis system, the user only needs to provide the original battery data, that is, the battery data obtained directly after testing the battery or battery product without further analysis and processing, and the battery data analysis system can obtain the battery data corresponding to the intermediate analysis parameters through the parameter generation project. It can be understood that different specific analysis algorithms require different intermediate analysis parameters, so the types of parameter generation projects will also vary according to the specific analysis algorithms. For example, if indicator parameters are needed in the battery life assessment algorithm, the corresponding parameter generation project is the indicator parameter generation project. If battery characteristic values ​​are needed in the battery fault warning algorithm, the corresponding parameter generation project is the characteristic value parameter generation project.

[0269] In some optional embodiments, the battery data analysis system determines, based on the unsuccessfully matched analysis parameter identifiers, a parameter generation item for generating the supplementary battery detection data required for generating the desired analysis item.

[0270] Step 704: Determine a parameter generation method that matches the parameter generation project according to the parameter generation project.

[0271] Among them, the parameter generation method is a method for generating battery data corresponding to intermediate analysis parameters required for use by a specific analysis algorithm. By processing the existing battery data in the battery data analysis system using the parameter generation method, the battery data corresponding to the intermediate analysis parameters required for use by the specific analysis algorithm can be obtained. The parameter generation method matches the parameter generation project. For example, when the parameter generation project is an indicator parameter generation project, the parameter generation method is an indicator parameter generation method that matches the indicator parameter generation project. When the parameter generation project is a characteristic value parameter generation project, the parameter generation method is a characteristic value generation method that matches the characteristic value parameter generation project.

[0272] In some optional embodiments, the battery data analysis system determines a parameter generation method that matches the parameter generation project based on the determined parameter generation project. It can be understood that the specific matching method for matching the parameter generation project with the parameter generation method is basically the same as the method for matching the expected analysis project with the analysis method in the previous text, and will not be repeated here.

[0273] Step 706, obtaining battery data corresponding to the parameter generation method.

[0274] In some optional embodiments, the battery data analysis system obtains battery data corresponding to the parameter generation method. The battery data corresponding to the parameter generation method may be the original battery data stored in the battery data analysis system, or may be the battery data corresponding to the intermediate parameters already stored in the battery data analysis system. For example, when generating characteristic value parameters, relevant index parameters may need to be used. At this time, if relevant index data is stored in the battery data analysis system, the index data may be obtained to generate characteristic value parameters.

[0275] Step 708: Perform data generation processing on the battery data using a parameter generation method to obtain supplementary battery detection data corresponding to the unmatched analysis parameter identifier.

[0276] In some optional embodiments, the battery data analysis system uses a certain parameter generation method to perform data generation processing on the acquired battery data, obtains supplementary battery detection data corresponding to the unmatched analysis parameter identifier, and sets a corresponding analysis parameter identifier for the supplementary battery detection data.

[0277] In the above embodiment, by configuring a parameter generation project in the battery data analysis system, when it is necessary to generate intermediate analysis parameters required by a specific analysis algorithm, the battery data analysis system can quickly generate the intermediate analysis parameters required by the specific analysis algorithm based on the existing battery data through the parameter generation project, thereby providing a data basis for the subsequent completion of the desired analysis projects required by the user, and further improving the user's convenience.

[0278] The parameter generation process is described below by taking the parameter generation project as a feature value generation project as an example.

[0279] In some embodiments, the parameter generation project includes a feature value generation project. Using the parameter generation method to perform data generation processing on the battery data to obtain supplementary battery detection data corresponding to the unmatched analysis parameter identifier includes:

[0280] The characteristic value generation method corresponding to the characteristic value generation item is obtained, and the characteristic value generation method is used to perform characteristic value data generation processing on the battery data to obtain a characteristic value generation result.

[0281] Among them, the eigenvalue is an important parameter in the field of battery data analysis and can be considered as a specific indicator parameter of the battery. In the battery field, eigenvalues ​​may be used for specific algorithms or business analysis. The implementation of the eigenvalue generation project depends on the eigenvalue calculation function configured in the battery data analysis system.

[0282] The characteristic value generation method is used to perform data generation processing on battery data to obtain the characteristic value required for the desired analysis project. It can be understood that the characteristic value generation method is generated according to the specific algorithm steps of the corresponding characteristic value algorithm.

[0283] In some optional embodiments, in order to improve user convenience, designers can pre-set a characteristic value generation method generated according to a common characteristic value algorithm in the battery data analysis system. After determining that the parameter generation project is a characteristic value generation project, the battery data analysis system can select a corresponding characteristic value generation method from the pre-configured internal characteristic value generation methods, perform characteristic value data generation processing on the battery data, and obtain a characteristic value generation result.

[0284] In some optional embodiments, different eigenvalue generation methods are required in different business analysis scenarios or algorithms. In order to improve the openness and universality of the battery data analysis system and reduce the development pressure of system developers, in addition to the common eigenvalue generation methods pre-set in the battery data analysis system as mentioned above, the battery data analysis system can also provide the function of integrating external eigenvalue generation methods.

[0285] If the eigenvalue generation method required by the user does not exist in the battery data analysis system, the user can integrate the eigenvalue generation method generated by himself or obtained externally into the battery data analysis system through the corresponding algorithm integration scheduling framework. The battery data analysis system obtains the external integrated eigenvalue generation method through the corresponding algorithm integration scheduling framework, performs eigenvalue data generation processing on the battery data based on the eigenvalue generation method, and obtains the eigenvalue generation result.

[0286] In the above embodiment, by configuring a characteristic value generation project in the battery data analysis system, when it is necessary to generate characteristic value parameters required by a specific analysis algorithm, the battery data analysis system can quickly generate the characteristic value parameters required by the specific analysis algorithm based on the existing battery data through the characteristic value generation project, thereby providing a data basis for the subsequent completion of the desired analysis projects required by the user, and further improving the user's convenience.

[0287] Before performing the data analysis steps of the desired analysis items according to the battery data, in order to improve the accuracy of the battery analysis, the battery data can also be subjected to corresponding pre-processing so that the processed battery data can better meet the analysis needs of the user. The following will describe several pre-processing processes that can be used for battery data.

[0288] In some embodiments, Figure 8 As shown, the battery data processing method may further include the following steps:

[0289] Step 802 : Determine desired preprocessing items of a target battery in response to a data preprocessing requirement for battery detection data.

[0290] Among them, data preprocessing is a data processing process that detects anomalies in battery data from the data level and performs preliminary processing on abnormal data in the battery data to make it better meet the user's usage needs or the requirements of subsequent battery data analysis. Data-level anomalies may include, for example, data value range anomalies, data collection rule anomalies, etc. The expected preprocessing project of the target battery is a project that processes data-level anomalies of battery data, such as data missing investigation projects, abnormal data exclusion projects, etc. The implementation of the preprocessing project depends on the data preprocessing function configured in the battery data analysis system.

[0291] The data preprocessing requirement for battery detection data can be triggered by the user according to his own usage requirements, or it can be triggered by a trigger event pre-set by the designer. For example, the data preprocessing requirement can be triggered after the battery data is loaded into the battery data analysis system.

[0292] In some optional embodiments, the battery data analysis system determines desired preprocessing items for preprocessing the battery detection data in response to a data preprocessing requirement for the battery detection data.

[0293] Step 804: determine a preprocessing method that matches the expected preprocessing item according to the expected preprocessing item.

[0294] Among them, the preprocessing method is a data processing method used to detect and process abnormalities at the data level of battery detection data to obtain battery detection data that meets the user's usage requirements.

[0295] In some optional embodiments, after determining the desired preprocessing items, the battery data analysis system continues to determine the preprocessing method that matches the desired preprocessing items according to the desired preprocessing items. It is understandable that different desired preprocessing items will result in different matching preprocessing methods.

[0296] Step 806: Use a preprocessing method to perform data preprocessing operations on the battery detection data.

[0297] In some optional embodiments, the battery data analysis system uses a determined preprocessing method to perform data preprocessing operations on the battery detection data to obtain battery detection data that meets the user's usage requirements.

[0298] In some of the embodiments, when the expected preprocessing item is an abnormal data exclusion item, the preprocessing method may be a threshold detection method. The battery data analysis system uses the threshold detection method to perform data preprocessing operations on the battery detection data, and compares the battery detection data with a pre-set data threshold. If there is battery detection data greater than the set data threshold, the battery detection data is considered to be abnormal data. For example, according to the data properties of the battery's EIS impedance data, the data threshold for the battery's EIS impedance data is set to 100,000 mΩ. When the battery data analysis system detects the EIS impedance data, if the data is greater than 100,000 mΩ, it means that the data obviously does not conform to the data properties of the impedance data, and the data is determined to be abnormal data.

[0299] In some embodiments, when the desired preprocessing project is a data missing troubleshooting project, the preprocessing method may be a data acquisition format detection method. The battery data analysis system uses the data acquisition format detection method to perform data preprocessing operations on the battery detection data, and compares the data acquisition format of the battery detection data with the preset data acquisition format. If the data acquisition format of the battery detection data does not conform to the preset data acquisition format, it can be considered that the battery detection data has data missing. If there is a jump acquisition point in the data table, the battery detection data can be determined as abnormal data.

[0300] Step 808 , updating the battery detection data based on the preprocessed data obtained after performing the data preprocessing operation.

[0301] In some optional embodiments, the battery data analysis system updates the pre-processed data after performing the data pre-processing operation into the battery detection data.

[0302] In the above embodiment, by integrating the preprocessing project into the battery data analysis system, the battery data can be accurately detected and efficiently processed from the data level, so that the corrected battery data can better meet the needs of users and improve the user's convenience.

[0303] In some embodiments, Fig. 9 As shown, the battery data processing method may further include the following steps:

[0304] Step 902 : Determine desired display items of the target battery in response to a visual display instruction for the target battery.

[0305] Among them, the display project is a project used to visually display the battery data of the target battery to the user. The battery data analysis system can display the battery data to the user in various display methods by realizing the exhibition project. For example, when the battery detection data is visually displayed, the battery detection data can be displayed in a variety of display methods such as list, chart or custom display. The list display function includes but is not limited to horizontal table display, vertical table display, all data table display, and some key data table display. The chart display function includes but is not limited to bar charts, line charts, area charts, pie charts, XY scatter charts, etc. The custom display method can be customized according to the user's actual usage scenario. The data visualization display function can realize the effect of list browsing and chart visual analysis of battery data. Visually displaying the battery data before analyzing the battery data can quickly and conveniently display the data overview, so that the user has a certain understanding of the battery data from a visual perspective. When analyzing the battery data, using the data visualization display function to visualize the data in the middle of the analysis can help users more clearly understand the progress and situation of the data analysis and processing. It can be understood that the realization of the demonstration project depends on the data visualization analysis function configured on the battery data analysis system.

[0306] Different battery data need to be displayed, and the corresponding display items are also different. For example, when the battery detection data of the target battery needs to be displayed, the expected display item can be the detection data display item. When the battery data needs to be displayed. When the analysis results of the target battery need to be displayed, the expected display items can be the abnormal battery analysis result display item, the life assessment result display item, the fault warning result display item, the battery portrait display item, the root cause analysis result display item, etc.

[0307] The visual display instructions for the target battery can be triggered by the user according to his own usage needs, or can be triggered by a trigger event pre-set by the designer. For example, it can be triggered after each analysis result is obtained, after the battery data is loaded into the battery data analysis system, etc.

[0308] In some optional embodiments, the battery data analysis system determines a desired display item for the target battery in response to a visual display instruction for the target battery.

[0309] Step 904: Determine, according to the desired display item, visual display parameters that match the desired display item, and a visual display method for the visual display parameters.

[0310] Among them, both the visual display parameters and the visual display methods correspond to the expected display items. For example, when the expected display item is the detection data display item, the corresponding visual display parameters are the battery detection parameters, and the visual display method is the detection data display method. When the expected display item is the life evaluation result display item, the corresponding visual display parameters are the life evaluation result parameters, and the visual display method is the life evaluation result display method.

[0311] In some optional embodiments, after determining the desired display item, the battery data analysis system determines the visual display parameters that match the desired display item and the visual display method for the visual display parameters according to the desired display item. In particular, just like the method in which the battery data system determines the corresponding analysis parameters and analysis methods according to the desired analysis item, the battery data analysis system can also determine the visual display parameters and visual display methods that match the desired display item according to a pre-generated mapping relationship table.

[0312] Step 906, obtaining battery data corresponding to the visual display parameters.

[0313] In some optional embodiments, the battery data analysis system obtains corresponding battery data according to the determined visual display parameters. For example, if the visual display parameter is a battery detection parameter, the battery detection data is obtained as the battery data for visual display. If the visual display parameter is a life evaluation result parameter, the life evaluation result obtained after performing battery life evaluation analysis is obtained as the battery data for visual display.

[0314] Step 908: Use a visual display method to perform a data display operation on the battery data, and display the battery data on the user terminal in a preset display format.

[0315] In some optional embodiments, the battery data analysis system performs a data display operation on the battery data using a visual display method that matches the desired display item, and displays the battery data on the user terminal in a preset display format.

[0316] When performing data pre-processing, the battery detection data can be visually displayed before data pre-processing, which can quickly and conveniently display the data overview, allowing users to have a certain understanding of the battery data from a visual perspective.

[0317] In some of the embodiments, the battery data analysis system uses a visual display method to perform data display operations on the battery data, and displays the battery data in a preset display format on the user terminal, which may include: parsing battery detection data, extracting table elements, loading the table elements into a table and displaying the table, the table elements include at least one of a list title, a row title and a table field, and the battery detection data is tabular data stored in text form.

[0318] In some embodiments, the battery data analysis system uses a visual display method to perform data display operations on the battery data, and displays the battery data in a preset display form on the user terminal, which may include: parsing the battery data, extracting table elements, and loading the table elements into the table, the table elements include at least one of the list title, the row title and the table field; the battery data is tabular data stored in text format. Aggregate the data of at least one column of the table or the data of the specified row in at least one column to obtain the target aggregation result. Generate a chart corresponding to the target aggregation result and display the chart. When visually displaying the battery data, the battery data can be aggregated according to the corresponding aggregation requirements, and then a visual chart is generated and displayed based on the processing results after the aggregation processing, which enables users to quickly extract key data information from the chart when browsing the visual chart, thereby improving the user's convenience. Using this method, offline files (such as CSV) files can be loaded into the battery data processing system for visual analysis and preview of charts.

[0319] In some embodiments, the battery data loaded by the battery data analysis system may be battery data generated or stored in a table format. In this case, the battery data analysis system uses a visual display method to perform a data display operation on the battery data, and displays the battery data in a preset display form on the user terminal, which may include: loading battery data in a table format and displaying the table. The battery data is displayed in the original table form on the system page of the battery data analysis system, and the user can directly browse the battery data visually based on the user terminal, so as to quickly understand the data overview of the battery data.

[0320] In some embodiments, when the battery data is generated or stored in a table format, the user may also need to understand the specific conditions of the battery or battery product through the aggregated battery data in some battery data analysis scenarios. Based on this, the battery data analysis system uses a visual display method to perform data display operations on the battery data, and displays the battery data in a preset display form on the user terminal, which may include: loading battery data in a table format, aggregating the data of at least one column of the table or the data of a specified row in at least one column to obtain a target aggregation result. Generate a chart corresponding to the target aggregation result and display the chart.

[0321] In some embodiments, in order to further improve user convenience, the battery data analysis system can perform data display operations on the battery data using a visual display method. When the battery data is displayed in a preset display form on the user terminal, it can also provide a conditional query function based on the list visualization or chart visualization. For example, the user can click a query button on the system page of the battery data analysis system based on a visual list or a visual chart to trigger a conditional query operation on the currently displayed visual list or visual chart, thereby helping the user to filter out specific data sets from a large amount of battery data, such as filtering out data on charging conditions.

[0322] The above-mentioned embodiments are distance explanations of several possible situations of visual display of battery detection data before data preprocessing when performing data preprocessing. It can be seen from the above-mentioned corresponding embodiments that the battery data analysis system integrates display items that can provide visual browsing of battery data lists, visual analysis of charts, specific data query capabilities, and the ability to provide multi-dimensional aggregation, analysis and display for visual charts, helping users better understand the data overview of battery data from various angles.

[0323] Furthermore, based on the demonstration project integrated in the battery data analysis system, there are also multiple ways to pre-process the battery detection data.

[0324] In some of the embodiments, after the battery data analysis system generates corresponding visualization data based on the battery data, it can use the data preprocessing method to detect anomalies in the visualized table, chart, or original battery data. When abnormal data is detected in the visualized table, chart, or original battery data, an abnormal prompt information is generated based on the abnormal data to remind the user of the data anomaly. Based on the data anomaly reminder, the user modifies or deletes the abnormal data in the visualized table, chart, or original battery data, triggering the generation of a modification instruction or a deletion instruction. In response to the modification instruction or the deletion instruction, the battery data analysis system modifies or deletes the abnormal data to obtain corrected battery data.

[0325] In some of the embodiments, the battery data analysis system detects data in a visualization table, chart, or raw battery data using a set data threshold. If data in the visualization table, chart, or raw battery data is greater than the set data threshold, the data may be considered abnormal data.

[0326] In some of the embodiments, the battery data analysis system detects the visualization table, chart or original battery data through the set data collection format. If the collection format of the visualization table, chart or original battery data does not conform to the set data collection format, the data can be considered as abnormal data.

[0327] In some embodiments, when the battery data analysis system detects abnormal data in a visualization table or chart, it can directly perform color conversion or font conversion on the abnormal data to remind the user of data abnormality. The user can modify or delete the abnormal data according to the color or font of each data in the visualization table or chart to improve the accuracy of data preprocessing.

[0328] In some of the embodiments, when the battery data analysis system detects abnormal data in a visual table, chart, or original battery data, it can generate an error link based on the position of the abnormal data in the visual table, chart, or original battery data, and generate abnormal prompt information based on the error link to remind the user of the data abnormality. The user can quickly locate the position of the abnormal data in the visual table, chart, or original battery data based on the error link in the abnormal prompt information, and modify or delete the abnormal data to achieve the effect of improving the accuracy and processing efficiency of data preprocessing.

[0329] In the above-mentioned embodiments, by using the display items and data preprocessing items in coordination on the battery data analysis system, the user can be provided with the ability to edit and process the data on the basis of visually browsing the battery data, and the battery data can be accurately detected and efficiently processed at the data level, so that the corrected battery data can better meet the needs of the user and improve the user's convenience.

[0330] The data import function is used to import the battery data that needs to be analyzed into the local database of the battery data analysis system. Before the battery data analysis system analyzes and processes the battery data, it is necessary to formally import the battery data into the local database of the battery data analysis system, so that the battery data analysis system can analyze and process the battery data stored in the local database according to the analysis method corresponding to the analysis project, thereby improving the compatibility and fluency of the data analysis and processing process.

[0331] In some embodiments, Fig.10 As shown, the battery data processing method may further include:

[0332] Step 1002: In response to a data import event for a target battery, determine a data import type of the target battery.

[0333] Among them, in order to support the import of multi-source heterogeneous data, the battery data analysis system can import data into the local database through a variety of data import methods. Therefore, when importing data, due to different import methods, there can be multiple data import types, such as real-time data import, offline data import, model mapping import, etc.

[0334] In some optional embodiments, the battery data analysis system determines the data import type of the target battery in response to a data import event for the target battery.

[0335] In some of the embodiments, the user may trigger a data import event for a target battery after completing data preprocessing of the battery detection data.

[0336] In some of the embodiments, the battery data analysis system may automatically trigger a data import event for a target battery after accessing a target data source.

[0337] Step 1004: Determine a data import method that matches the data import type according to the data import type.

[0338] In some optional embodiments, after determining the data import type, the battery data analysis system determines a data import method that matches the data import type according to the data import type.

[0339] Step 1006: Use a data import method to import the battery data of the target battery into a local database for storage.

[0340] In some optional embodiments, the battery data analysis system uses a matching data import method to obtain the battery data of the target battery, and imports the battery data of the target battery into a local database for storage. By formally importing the battery data into the local database of the battery data analysis system, the battery data analysis system can perform analysis and processing based on the battery data stored in the local database according to the analysis method corresponding to the analysis project, thereby improving the compatibility and fluency of the data analysis and processing process.

[0341] The following will use the data import types of real-time data import, offline data import, and model mapping import to illustrate the data import process.

[0342] In some embodiments, the data import type is real-time data import, and the data import method corresponding to the data import type is a real-time data import method. Using the data import method to import the battery data of the target battery into the local database for storage includes:

[0343] Determine a data import interface pre-configured with a data acquisition device of a target battery, obtain real-time battery data of the target battery based on the data import interface, and import the real-time battery data into a local database for storage.

[0344] Among them, the data import interface is a docking interface pre-configured for the battery data analysis system and various battery data sources that may need to import data in real time, and is used to import real-time battery data generated by the battery data source into the battery data analysis system.

[0345] In some embodiments, the preset data import interface may be an API and an SDK. By providing an API and an SDK for system docking, the real-time data import function can be expanded for the battery data analysis system, that is, FTP protocol data access, platform data docking in the form of http / https protocol, and terminal data docking in the form of tcp protocol can be realized through the API and SDK provided by the system.

[0346] In actual use, in some optional embodiments, when the battery data analysis system determines that the data import type of the target battery is real-time data import, it calls a pre-configured data import interface to import the battery data generated in real time by the target data source into the local database.

[0347] In some embodiments, the data import type is model mapping import, and the data import method corresponding to the data import type is the model mapping import method. Using the data import method to import the battery data of the target battery into the local database for storage includes:

[0348] According to the mapping relationship between the battery data model of the target battery and the data model of the local database, the battery data is mapped to the local database for storage.

[0349] The data model of battery data is a model used to describe, organize and operate battery data, and can be considered as a storage form of battery data. The data model of the local database is also a tool used by the local database to abstractly store real-world battery data. The mapping relationship between the data model of battery data and the data model of the local database refers to the establishment of a corresponding relationship between data elements of the two data models. By pre-building the mapping relationship between the data models of various battery data and the data models of the local database, rapid access to multi-source heterogeneous data can be achieved.

[0350] In some optional embodiments, when the battery data analysis system determines that the data import type of the target battery is model mapping import, it calls the mapping relationship between the data model of the battery data and the data model of the local database, and maps the battery data to the local database for storage. Through the mapping relationship between the data model of the battery data and the data model of the local database, the battery data analysis system can directly map the battery data to be imported to the system local database for storage, so that even if the data model of the battery data to be imported is flexible and changeable, the data to be imported can be compatible with the battery data analysis system.

[0351] In order to improve the openness and practicality of the battery data analysis system, the battery data analysis system can open the configuration function of the mapping relationship between data models to users.

[0352] Based on this, in some embodiments, when a user needs to add a new data source for providing battery data, the battery data analysis system can trigger a data source configuration instruction. The battery data analysis system responds to the data source configuration instruction and configures a mapping relationship between at least one data model of battery data and a data model of a local database in the system. Through the model custom configuration method, the data model of battery data can be compatible with the battery data analysis system even if it is flexible and changeable, thereby realizing rapid access to multi-source heterogeneous data in the configuration mode, effectively lowering the user's usage threshold.

[0353] In some embodiments, the data import type is offline data import, and the data import method corresponding to the data import type is an offline data import method. Using the data import method to import the battery data of the target battery into the local database for storage includes:

[0354] In response to an import instruction triggered by the offline battery data of the target battery, the offline battery data of the target battery is imported into a local database for storage.

[0355] In some optional embodiments, when it is determined that the data import type of the target battery is offline data import, the battery data analysis system imports the battery data into the local database in response to an import instruction triggered by the offline battery data of the target battery.

[0356] It can be understood that in order to improve the stability of battery data import, in some embodiments, the data import function of the battery data analysis system preferentially supports offline data import. Due to the complexity and diversity of battery data collection methods, the data formats of the collected battery data are also diverse. The data import function of the battery data analysis system can support the import of battery data in a variety of different data formats. The data formats supported by the battery data analysis system include but are not limited to txt, xls, csv, dat and other data formats.

[0357] In the above embodiment, by configuring the battery data analysis system with a data import function that supports multiple data import types, the user's data import needs can be met from all aspects, effectively improving the user's convenience in using the battery data analysis system.

[0358] The data processing function is used to process battery data according to corresponding data processing rules. Since the data processing function is to detect anomalies in battery data from the application level, it is different from the universal anomaly detection and processing of battery data by the data preprocessing function. The anomaly detection and processing of battery data by the data processing function is to perform application detection from the actual application level of battery data to meet the use requirements of data analysis of battery data in subsequent applications. Therefore, when the battery data analysis system executes the data processing function, it is necessary to first import the battery data into the local database.

[0359] In some embodiments, Fig.11 As shown, the battery data processing method also includes:

[0360] Step 1102 , in response to a data processing requirement for battery data in a local database, determining a data processing item for the battery data.

[0361] Among them, the data processing project is a functional project used to realize the data processing function. By pre-configuring the data processing project in the battery data analysis system, the battery data can be detected for abnormalities at the application level so that it can meet the use requirements of data analysis of battery data in subsequent applications. The data processing project can be determined according to the data processing requirements of the battery data.

[0362] The data processing requirements of battery data are used to determine the judgment conditions for what kind of data processing needs to be performed on battery data. The data processing requirements can be determined based on data quality requirements and business analysis requirements. Data quality requirements are data processing judgment conditions set based on algorithm characteristics, and business analysis requirements are data processing judgment conditions set based on business characteristics.

[0363] In some embodiments, a part of the data analysis algorithm has clear requirements for data quality. If the corresponding data analysis is directly performed on the battery data that does not meet the data quality requirements, it is easy to affect the data analysis results. Take the EIS impedance data of the battery as an example. When performing impedance analysis on the battery based on the EIS impedance data, the frequency, real part and imaginary part of the impedance data are required attributes. The missing data in these fields needs to be deleted.

[0364] In some embodiments, when a user analyzes and processes battery data, due to business characteristics, there may be a situation where data processing of the battery data is required so that the data for subsequent data analysis and processing is data that meets the business characteristics.

[0365] In some embodiments, if battery analysis processing needs to be performed on certain indicator data in the battery data according to business characteristics, the indicator data needs to be screened out from the battery data. For example, if the voltage data, current data and other indicator data in the battery data need to be used for subsequent analysis and processing according to business characteristics, the battery data analysis system needs to screen out the corresponding voltage data, current data and the like from a large amount of battery data for subsequent analysis and processing.

[0366] In some embodiments, if the target attribute of each battery data needs to be determined according to the business characteristics, and the battery analysis and processing is subsequently performed according to the target attribute, it is necessary to perform attribute classification operations on the battery data. For example, if the voltage data of batteries generated in each batch needs to be analyzed according to the business characteristics, it is necessary to classify the voltage data from the battery data according to the batch, and then analyze and process the voltage data corresponding to each batch.

[0367] In some optional embodiments, the battery data analysis system determines a data processing item for the battery data in response to a data processing demand for the battery data in a local database.

[0368] Step 1104: Determine a data processing method that matches the data processing project according to the data processing project.

[0369] The data processing method is a processing method generated according to the data processing rules. It can be understood that the data processing method corresponds to the data processing project. By processing the battery data through the data processing method, the battery data can meet the use requirements of the data in subsequent use.

[0370] In some optional embodiments, after determining the data processing project of the battery data, the battery data analysis system searches for a data processing method corresponding to the data processing project according to the data processing project, and determines the data processing method as the data processing method when processing the battery data.

[0371] It is understandable that the data processing items can be pre-configured by the designer on the battery data analysis system. The designer determines the commonly used data processing items according to the usage scenario of the battery data analysis system, binds the data processing items with the data processing methods corresponding to the data processing items, and pre-configures them on the battery data analysis system.

[0372] Step 1106, using a data processing method to perform a data processing operation on the battery data.

[0373] After determining the data processing method to be used for data processing of the battery data, the battery data analysis system obtains the battery data to be processed from the local database, performs data processing operations on the battery data using the data processing method, and obtains processed battery data.

[0374] Step 1108, updating the battery detection data based on the processed data obtained after performing the data processing operation.

[0375] The battery data analysis system updates the battery detection data based on the processed battery data, so that the updated battery detection data can better meet the requirements of subsequent data analysis.

[0376] In the above embodiment, by pre-integrating the data processing project in the battery data analysis system and configuring the corresponding data processing method for the data processing project, when there is a data processing demand, the battery data can be processed according to the data processing method corresponding to the data processing project, so that the battery data meets the data usage requirements in subsequent applications.

[0377] In addition to pre-configuring the data processing method in the battery data analysis system, in order to improve the universality of the battery data analysis system, in some embodiments, the battery data analysis system can also obtain a mounting script for processing the battery data, obtain the battery data from a local database, execute the mounting script on the battery data to process the data, and obtain the processed battery data.

[0378] Due to the diversity of user data analysis needs, the methods for processing battery data may be different in different analysis scenarios. The battery data analysis system cannot store all data processing methods. Therefore, if there is no data processing method in the battery data analysis system that can process battery data to meet user needs, users can obtain or construct data processing methods that meet their own needs from the outside. When the battery data needs to be processed, the mounting script of the acquired and constructed data processing method can be mounted in the battery data analysis system to complete the configuration process of the data processing method in the battery data analysis system. By implementing the function of mounting script configuration on the battery data analysis system, the degree of data processing restrictions of the battery data analysis system is effectively reduced, which can improve the universality of the battery data analysis system and the convenience of user use.

[0379] Similarly, in order to improve the openness and practicality of the battery data analysis system, the battery data analysis system can open the configuration functions of data processing methods and mounting scripts to users. In some embodiments, when the user needs to add a new data processing method, the user can trigger the configuration instructions for the data processing project through the battery data analysis system, configure a new data processing method for the data processing project, or mount a new data processing script. The battery data analysis system responds to the configuration instructions of the data processing project and configures at least one data processing method, or at least one mounting script of the data processing method in the system. By means of custom configuration of methods, open data processing capabilities can be implemented for the battery data analysis system, which can adapt to various business needs and effectively improve the universality of the battery data analysis system.

[0380] The processing efficiency of the data processing process is also an important factor affecting the user experience. In some embodiments, in order to improve the data processing efficiency of the battery data analysis system, the battery data analysis system uses a data processing method to process the battery data. When implementing the data processing project, the data processing time can be calculated based on the processing data volume of the battery data. When the data processing time is greater than the preset processing time, it means that the data processing process takes a long time. The battery data analysis system can call a certain number of processing threads according to the data processing time, and process the battery data through parallel computing to achieve the effect of improving the data processing efficiency of the battery data analysis system.

[0381] In some embodiments, the battery data analysis system can directly compare the processing data volume of the battery data with a preset processing data volume threshold. When the processing data volume is greater than the preset processing data volume threshold, a certain number of processing threads can be called according to the processing data volume of the battery data, and the battery data can be processed through parallel computing to achieve the effect of improving the data processing efficiency of the battery data analysis system.

[0382] Through the above series of pre-processing, the battery data used in data analysis can already meet the analysis requirements well, and the obtained battery analysis results can also meet the analysis needs of users.

[0383] In order to further conduct a comprehensive analysis of the battery analysis results, such as Fig.12 As shown, in some embodiments, the battery data processing method further includes:

[0384] Step 1202 : In response to a result display requirement for a target battery, determine a target analysis dimension and a result display dimension of the target battery, wherein the result display dimension includes at least one of a two-dimensional dimension and a three-dimensional dimension.

[0385] Among them, the result display demand of the target battery refers to the display demand for comprehensive result display of the target battery. Different from the battery life assessment result display, battery fault warning result display, root cause analysis result display, etc. recorded in the previous article, the result display demand of the target battery needs to realize the original data of the battery data, such as the original collection data, various indicator data, material and process configuration data, and the processing result data generated after battery data analysis and processing, such as characteristic values, battery portraits, battery life estimation, fault warning, alarm and other battery-related data for multi-dimensional joint analysis.

[0386] The result display requirements of the target battery rely on the data multi-dimensional analysis function configured on the battery data analysis system. Although the data multi-dimensional analysis function is also a function for multi-dimensional comprehensive analysis of batteries or battery products, it is different from the battery portrait function in that in addition to providing multi-dimensional comprehensive analysis capabilities, the data multi-dimensional analysis function can also provide users with data visual analysis of various dimensions according to user needs, such as two-dimensional and three-dimensional data visual analysis.

[0387] The target analysis dimension refers to the analysis dimension that needs to be included when constructing a multi-dimensional comprehensive analysis result. A multi-dimensional comprehensive analysis result will contain data analysis results corresponding to multiple target analysis dimensions. For example, before realizing the multi-dimensional data analysis function, the battery data analysis system implements multiple other functions to obtain various data analysis results, such as a characteristic value system composed of various characteristic values ​​of the battery, or a battery portrait system, battery life assessment results, fault warning results, fault alarm results, etc. By combining the data analysis results corresponding to each target analysis dimension, users can quickly establish a comprehensive understanding of the battery. It can be understood that the number of target analysis dimensions is determined according to all battery-related data categories stored in the local database of the battery data analysis system when performing multi-dimensional data analysis.

[0388] For example, if the local database stores the battery characteristic value system, battery portrait system, battery index data analysis results, etc. of the battery to be analyzed, then the target analysis dimensions for multi-dimensional data analysis are battery characteristic values, battery portraits, and battery index data status. If the local database also stores, for example, battery material data, battery process data, etc., then the target analysis dimensions for multi-dimensional data analysis may also include configuration overview dimensions.

[0389] The result display dimension is a dimensional parameter used to characterize the display format of the comprehensive analysis results of the target battery. The result display dimension may include at least one of a two-dimensional dimension or a three-dimensional dimension. When displaying the comprehensive analysis results of the target battery, the battery data analysis system may only generate analysis results in two dimensions or only generate analysis results in three dimensions. It may also display some of the analysis results, such as battery cell analysis results, in two dimensions according to actual needs, and display other analysis results, such as battery application analysis results, in three dimensions.

[0390] In actual use, the battery data analysis system determines the target analysis dimension and result display dimension of the target battery in response to the result display requirement for the target battery.

[0391] Step 1204, obtaining comprehensive battery data of the target battery and a result display method corresponding to the result display dimension.

[0392] Among them, the battery comprehensive data refers to the battery comprehensive data related to the battery corresponding to the battery data stored at the current moment in the local database of the battery data analysis system. The battery comprehensive data may include, but is not limited to, battery detection data that has not been analyzed after being imported into the local database (such as various indicator parameters, material and process configuration data in the original collected data), various analysis result data of battery data analysis according to the expected analysis items (such as abnormal battery analysis result data, battery life assessment result data, battery fault warning result data, battery portrait result data, root cause analysis result data, etc.), and various intermediate parameters generated when performing battery data analysis (such as battery characteristic value data, indicator data, etc.).

[0393] The result display method is a method used to display the comprehensive battery data of the target battery in the corresponding result display dimension.

[0394] In actual use, after determining the target analysis dimensions of the target battery, the battery data analysis system obtains the corresponding battery comprehensive data of the battery and the result display method corresponding to the result display dimension from the local database.

[0395] Step 1206, using the result display method, generates and displays the comprehensive analysis results of the target battery according to the battery comprehensive data and the target analysis dimension.

[0396] When the battery data analysis system displays the comprehensive results of the target battery, it will allocate corresponding dimensional analysis data from the battery comprehensive data to each target analysis dimension based on the target dimension. For example, the configuration data related to battery materials, battery processes, etc. in the battery comprehensive data can be allocated to the configuration overview dimension in the target analysis dimension, and the battery portrait result data obtained and stored by the battery data analysis system after performing battery portrait analysis on the battery data can be allocated to the battery portrait dimension in the target analysis dimension.

[0397] In some optional embodiments, the battery data analysis system allocates the battery comprehensive data according to the target analysis dimension, allocates corresponding dimensional analysis data to each target analysis dimension, and then uses the result display method to process the dimensional analysis data corresponding to each target analysis dimension, generates a comprehensive analysis result of the target battery of the corresponding display dimension, and displays the comprehensive analysis result to the user in the corresponding dimension.

[0398] In the above embodiment, a comprehensive result display function is integrated into the battery data analysis system, which can generate multi-dimensional comprehensive analysis results that conform to the battery when the user needs to perform multi-dimensional visual analysis of the battery, provide the user with data visual analysis in various dimensions, and improve the user's convenience.

[0399] The evaluation report generation function is used to comprehensively evaluate the battery based on the results of various analyses of the battery data and output a corresponding evaluation report. Users have the need to generate battery evaluation reports in some actual usage scenarios, such as after-sales scenarios and research and analysis scenarios.

[0400] In some embodiments, Fig.13 As shown, the battery data processing method also includes:

[0401] Step 1302 : In response to an evaluation report generation instruction for a target battery, determine a battery evaluation type of the target battery.

[0402] Among them, the evaluation report generation instruction is an instruction used to instruct the battery data analysis system to generate a battery evaluation report of the corresponding evaluation type. The evaluation report generation instruction is triggered by the user based on the comprehensive analysis results displayed on the battery data analysis system page. The user triggers the generation of the evaluation report generation instruction based on the comprehensive analysis results according to his or her actual needs, such as research purposes or application needs. The evaluation report instruction carries the evaluation type.

[0403] In some embodiments, the evaluation type of the battery evaluation report may be a single-dimensional evaluation type, corresponding to each target analysis dimension in the comprehensive analysis result. For example, if the target analysis dimension is battery life evaluation or battery failure warning, the evaluation type may be a corresponding battery life evaluation evaluation type or battery failure warning evaluation type, etc. The user may directly trigger a corresponding evaluation report generation instruction based on each target analysis dimension in the comprehensive analysis result to generate a corresponding battery evaluation report.

[0404] In some embodiments, the evaluation type of the battery evaluation report may also be a comprehensive evaluation type, which is generated by combining the analysis results of at least two target analysis dimensions in the comprehensive analysis results. For example, the analysis results of battery life evaluation and battery failure warning may be combined to generate a corresponding battery evaluation report according to actual user needs.

[0405] In some optional embodiments, the user triggers the generation of an evaluation report generation instruction including an evaluation type based on the multi-dimensional analysis results displayed on the battery data analysis system page. The battery data analysis system responds to the evaluation report generation instruction and obtains the evaluation type of the battery evaluation report according to the evaluation report generation instruction.

[0406] Step 1304 , determining the battery evaluation data and report template required to generate a battery evaluation report of the target battery based on the battery evaluation type.

[0407] Among them, the evaluation data is the specific data content used to generate a battery evaluation report, which may include battery detection data that has not been analyzed after being imported into the local database (such as various indicator parameters, material and process configuration data in the original collected data), various analysis result data of battery data analysis according to the expected analysis items (such as abnormal battery analysis result data, battery life evaluation result data, battery fault warning result data, battery portrait result data, root cause analysis result data, etc.), and at least one of the various intermediate parameters generated when performing battery data analysis (such as battery characteristic value data, indicator data, etc.).

[0408] The report template is a standard format template used to specify the specific format of the battery evaluation report. In order to improve the report generation quality of the battery evaluation report, designers or users can configure corresponding report templates for each evaluation type in the battery data analysis system according to actual scenario requirements. When the battery data analysis system generates a battery evaluation report, it only needs to fill in the evaluation data according to the standard format of the report template to obtain a high-quality battery evaluation report with a unified format.

[0409] In some embodiments, in order to improve the practicality and openness of the system, the battery data analysis system also provides report template addition and modification and update functions. When the user needs to add a report template or modify and update the original report template, the user can call the corresponding addition tool or modification tool in the battery data analysis system to complete the template addition or update.

[0410] In some embodiments, if the evaluation type of the evaluation report is a single-dimensional evaluation type, the evaluation data generated for the evaluation report is also the evaluation data corresponding to the single dimension. For example, when the evaluation type is a battery life evaluation type, the corresponding evaluation data is also the battery life evaluation result data of the battery.

[0411] In some of the embodiments, if the evaluation type of the evaluation report is a comprehensive evaluation type, the evaluation data generated for the evaluation report is also the corresponding comprehensive evaluation data. For example, when the evaluation type is a comprehensive evaluation type formed by the combination of battery life evaluation and battery fault warning, the corresponding evaluation data also includes the battery life evaluation result data and battery fault warning result data of the battery.

[0412] In some optional embodiments, the battery data analysis system determines the evaluation data required to generate a battery evaluation report of the evaluation type and the report template corresponding to the evaluation type based on the evaluation type of the acquired battery evaluation report, obtains the evaluation data and report module from the local database, and provides a data basis for the subsequent generation of the battery evaluation report.

[0413] Step 1306 , generating a battery evaluation report according to the report template corresponding to the evaluation type and the battery evaluation data.

[0414] The battery data analysis system fills the evaluation data into the corresponding position in the template according to the fixed standard format of the report template to generate a battery evaluation report.

[0415] In the above embodiment, an evaluation report generation function is integrated into the battery data analysis system, which can provide users with a comprehensive evaluation report that meets their actual needs while analyzing and processing the battery data, thereby further lowering the user's usage threshold and improving user convenience.

[0416] The battery data analysis system is an execution subject for executing each method step in the above-mentioned battery data processing method. In some embodiments, the battery data analysis system includes a data scanning module and a local storage module.

[0417] The data scanning module is used to respond to the battery analysis requirements for the target battery, determine the expected analysis items of the target battery; select analysis parameters according to the expected analysis items; obtain battery detection data corresponding to the analysis parameters; perform data analysis on the battery detection data to obtain the battery analysis results of the target battery.

[0418] The local storage module is used to store battery detection data.

[0419] In order to explain in detail the combined use of various functions in the battery data analysis system, in some embodiments, an overall architecture of a battery data analysis system is provided, such as Fig.14 As shown, the battery data processing method is applied to Fig.14 Taking the battery data analysis system as an example, the joint application of various functions in the battery data analysis system is specifically explained.

[0420] Among them, the battery data analysis system includes external data sources, data access modules, data processing modules, local storage modules, data scanning modules, eigenvalue calculation modules, data visual analysis modules, as well as the interactive development framework, process orchestration framework, algorithm scheduling framework, parallel computing framework, log analysis and other auxiliary analysis tools provided in the system.

[0421] The external data source is used to provide battery data, and specifically can provide, for example, material data, production process-related data, GB32960-related data, EIS data, electrochemical data, external platform data, and other end-side data.

[0422] The data access module is connected to the external data source to obtain the battery data to be analyzed and processed from the external data source. The data access module is provided with units such as data import, data model configuration, data mapping configuration, external API, data docking API / SDK, task scheduling API, etc.

[0423] The data processing module is connected to the data access module and the local storage module. The data processing module can process the battery data accessed by the data access module or the data stored in the local storage module, and store the processed battery data in the local storage module. The data processing module is provided with units such as data detection, data cleaning, data aggregation, data calculation, rule configuration, script mounting, data reporting, external API, task scheduling API, etc.

[0424] The local storage module is connected to the data processing module, data scanning module, eigenvalue calculation module, and data visual analysis module respectively. The local storage module is used to store various analysis and processing data generated by the battery data analysis system during the analysis phase. The local storage module is provided with units such as configuration data, source data, battery portrait, data set, algorithm result data, and feature set.

[0425] The eigenvalue calculation module is connected to the local storage module, the data scanning module and the data visual analysis module respectively, and is used to calculate the eigenvalue of the battery. The eigenvalue calculation module is provided with units such as algorithm integration, visual analysis, external API, task scheduling API, and eigenvalue design.

[0426] The data scanning module is connected to the local storage module, the eigenvalue calculation module and the data visual analysis module respectively, and is used to realize complex calculation functions such as indicator data analysis and battery detection analysis. The data scanning module is equipped with units such as algorithm registration, rule definition, process scheduling, external API, task scheduling API, and algorithm scheduling framework.

[0427] The data visual analysis module is connected to the local storage module, the data scanning module, and the characteristic value calculation module respectively to realize the visual analysis of the battery data. The data visual analysis module is provided with units such as life assessment, fault warning, two-dimensional correlation analysis, three-dimensional correlation analysis, battery assessment, and expert advice.

[0428] When the above modules are used in combination, interactive development framework, process orchestration framework, algorithm scheduling framework, parallel computing framework, log analysis and other auxiliary analysis tools can also be used for auxiliary operation to achieve corresponding functions.

[0429] Based on the system architecture of the above battery data analysis system, such as Fig.15 As shown, the specific process of realizing the integrated functions of the battery data analysis system includes the following steps:

[0430] Step 1501, the battery data analysis system loads the data to be imported (such as CSV files) into the software system for list browsing and chart visual analysis capabilities. Among them, the chart visualization provides conditional query to filter out specific data sets, such as selecting charging condition data; for the chart visual analysis, multi-dimensional aggregation (such as calculating the average value of battery cell voltage data over a period of time) and analysis display are provided. The purpose of this step is to understand the data overview, and the system module to be used is the data visual analysis module.

[0431] In step 1502, the R&D personnel determines whether data preprocessing is required based on the visual analysis in step 1501. If data preprocessing is not required, step 1504 is directly executed.

[0432] Step 1503: If data preprocessing is required, the R&D personnel modify or delete the data based on the visual interface and perform data editing on the data. The data editing process is completed by the data processing module in the battery data analysis system.

[0433] Step 1504: The battery data analysis system imports the battery data into a local database.

[0434] The battery data analysis system gives priority to supporting offline data file import, and the supported data formats include common file formats such as txt, xls, xlsx, csv, dat, etc. In order to support multi-source heterogeneous data import, the battery data analysis system provides data model mapping capabilities, and the data model to be imported is mapped to the local storage database model, so that the data model to be imported can be flexible and diverse, and can be compatible with the technical solution. The data is imported into the local storage database to realize the multi-source heterogeneous data import of the configuration mode. The functional module that the battery data analysis system needs to use to import data from external data sources into the local database is the data access module. The data access module of the battery data analysis system provides system docking API and SDK, so it can be expanded to real-time data access, that is, the API and SDK provided by this software system can be used to realize FTP protocol data access, platform data docking in the form of http\https protocol, and end-side data docking in the form of tcp protocol.

[0435] Step 1505: Process the battery data to make the data meet subsequent usage requirements.

[0436] When the battery data analysis system processes the battery data, it needs to use the data processing module. At the same time, the battery data analysis system provides open data processing capabilities to adapt to various business needs and improve the universality of the analysis system. The battery data analysis system mainly realizes open data processing capabilities by configuring data processing rules and mounting processing scripts. The configuration rules mainly configure target attributes, indicators, thresholds, etc., which are completed through the rule configuration unit in the data processing module. The mounting script mainly mounts the externally developed data processing script, which is completed through the script mounting unit in the data processing module. The battery data analysis system preferentially supports python scripts, and scripts in other languages ​​can be extended. Whether it is the configuration method or the mounting script method, the battery data analysis system provides an execution and scheduling framework. The execution and scheduling framework is mainly used to run algorithms, including data input, output, and operation modules. For those that take a long time, the battery data analysis system provides multi-threaded or parallel computing acceleration capabilities, mainly supporting single-machine multi-core vertical acceleration operation. The data processing of the configuration method uses the data processing module, and the data import of the mounting script method uses the algorithm scheduling framework. Both methods need to read and write data from the local storage module.

[0437] Step 1506, perform eigenvalue calculation and judgment on the battery data after data processing. If eigenvalue calculation is required, use an eigenvalue calculation algorithm to calculate the eigenvalue of the battery data, and store the calculated eigenvalue in the local storage module.

[0438] The eigenvalue calculation function of the battery data analysis system requires the joint implementation of multiple functional modules. Among them, if the eigenvalue algorithm for eigenvalue calculation is a preset eigenvalue algorithm, it is necessary to use the algorithm integration unit in the eigenvalue calculation module to implement it. If the eigenvalue algorithm for eigenvalue calculation is an external eigenvalue calculation algorithm, it is necessary to use the eigenvalue calculation module and the algorithm scheduling framework to jointly implement it. This also reflects the openness and universality of the battery data analysis system. The calculated eigenvalues ​​are stored in the local storage database, so the storage and reading of eigenvalues ​​require the use of a local storage module. It should be noted that eigenvalues ​​usually have business attributes or algorithm attributes, such as eigenvalues ​​in the electrochemical field, eigenvalues ​​in the SOH field, and eigenvalues ​​in the EIS field. At the same time, one or more eigenvalues ​​can generate new eigenvalues ​​through secondary calculations. Based on the hierarchical and field-based characteristics of eigenvalues, the organization and storage of eigenvalues ​​need to be carried out according to the system, so the corresponding feature set storage location can be provided in the local database. At the same time, the battery data analysis system provides eigenvalue visual analysis capabilities, which is implemented through the data visual analysis module, to provide a reference for algorithm evaluation by eigenvalue algorithm developers. Another purpose of this step is to generate more eigenvalues ​​and indicator parameters through algorithm calculations, providing more dimensions for battery analysis, enabling a more comprehensive analysis of the battery and discovering battery problems as comprehensively as possible.

[0439] Step 1507: If eigenvalue calculation is not required, then perform index data analysis.

[0440] In order to conduct a preliminary screening of problems in the battery, the basic indicator data of the battery can be preliminarily analyzed using data analysis methods to preliminarily determine whether there is a problem with the battery or battery components. The analysis results can be used as a reference for subsequent in-depth data analysis and business applications, so that research and development and application are more targeted and more efficient. The calculation accuracy and results of this step are relatively important. In order to achieve good accuracy, the analysis algorithm needs to be continuously optimized. The algorithm scheduling, data visualization and other functions in the battery data analysis system support data optimization and tuning very well. To realize indicator data analysis, you need to directly use the data scanning module. During the data scanning process, if the data volume is large and parallel acceleration is required, the data scanning module will use a parallel computing framework. The data input and storage in the indicator data analysis require the use of the local storage module.

[0441] Step 1508: Perform battery detection analysis on the battery data based on the indicator data analysis results.

[0442] Battery detection and analysis mainly further analyzes the data based on the indicator data analysis to find battery problems. It mainly uses algorithms with high battery professionalism to calculate and analyze data, such as battery life assessment, fault warning and other algorithms. This step is mainly implemented through algorithm registration, algorithm arrangement, and then using the scheduling framework for automated process calculation. In the algorithm registration link, in addition to registering and introducing new algorithms, you can also edit existing algorithms according to the data status and business characteristics, such as adjusting warning thresholds, data slicing, model parameters, etc. For in-depth data analysis, in addition to pre-setting common algorithms for use, the battery data analysis system can also flexibly and quickly introduce new algorithms through algorithm registration, which makes the battery data analysis system highly practical and open. This step mainly uses the data scanning module. If parallel acceleration is required, the data scanning module will call the parallel computing framework. In addition, the data reading and storage involved in this step rely on the local storage module.

[0443] Step 1509, use the battery profiling function to perform comprehensive statistical analysis on the battery.

[0444] Battery profiling is performed through statistical data analysis to build a battery profiling system. Battery profiling is very important in battery analysis. For example, when a battery has a lifespan degradation, the root cause analysis can be conducted based on the battery's material, process, model, charge and discharge conditions, and usage. The battery profiling function is mainly implemented using the data processing module, while the data reading and storage involved use the local storage module.

[0445] Step 1510, obtaining analysis data of multiple dimensions related to the battery from the local storage module, and performing multi-dimensional visual analysis of the battery data.

[0446] This step is to analyze the various indicators and parameters of the original collected data of the battery, configuration data such as materials and processes, characteristic values, portraits, life estimation, fault warnings and alarms, etc. Through this multi-dimensional joint analysis, on the one hand, it is possible to conduct a comprehensive analysis and root cause analysis of the alarms and warnings, life conditions, and at the same time, it is possible to have a more comprehensive understanding of the battery. In this step, three-dimensional and two-dimensional data visual analysis can be provided. Multi-dimensional data visual analysis mainly uses the local storage module and the data visual analysis module.

[0447] Step 1511, generating a battery evaluation report based on the analysis results.

[0448] This step is to conduct a comprehensive evaluation of the battery based on the results of the previous multi-dimensional data analysis. The corresponding evaluation report can be output according to the research objectives or application needs. It mainly uses the data visual analysis module, and the data reading and storage rely on the local storage module.

[0449] The above process is described using an EIS impedance data in CSV format as an example.

[0450] First, load the CSV data into the battery data analysis system, and view the CSV data through the lists and charts provided by the system. If the data is found to be abnormal, such as a field exceeding the normal range, or some collection points have jumped, use the data editing tool provided by the battery data analysis system to process the data, and write the edited data back to the CSV file. Use the data import function provided by the battery data analysis system to import the CSV data into the database on the local machine. The battery data analysis system comes with a lightweight, installation-free database. The data visual analysis module provided by the battery data analysis system can be used to analyze the data imported into the local database to understand the source data.

[0451] The data imported into the local database is processed according to business characteristics and algorithm requirements. For example, in EIS impedance analysis, frequency, real part, and imaginary part are required attributes. Missing data in these fields is deleted and the edited data is written back to the local database.

[0452] Some analyses require specific indicators. For example, analysis of battery impedance anomalies or lithium deposition requires the use of capacitors. In this case, the characteristic value calculation module is used to calculate the capacitance, generate the characteristic value of the capacitance, and write the generated data back to the local database for storage.

[0453] Currently, the local machine database contains original EIS data and indicator data (such as the capacitance calculated in 6). These data can be initially screened and analyzed as needed. For example, the imaginary part can be initially screened, and the mean value - 3 * standard deviation can be used as the reference line. The original EIS data and threshold line can be analyzed through the data visual analysis module to understand the source data and indicator conditions.

[0454] Determine whether battery detection and analysis is needed based on source data and indicators, such as EIS data outliers, lithium plating inspection, trend anomaly analysis, etc. Battery detection and analysis will generate corresponding analysis result data, which will be written into the local database for storage as the basis for subsequent comprehensive analysis.

[0455] After executing the battery detection and analysis, in addition to the EIS raw data, the local database also contains the calculated indicator data analysis results and the abnormal result data of the battery detection and analysis. At this time, you can use the data statistical analysis method to build a battery portrait, such as building abnormal trend, outlier, lithium precipitation and other abnormal portraits. At the same time, it also supports the import of materials, configurations, 32960 and other more data into the local database storage through the data import capability provided by this software, so as to build a battery portrait with richer dimensions.

[0456] After the battery portrait processing is completed, the local database will have EIS raw data, indicator data, abnormal inspection data, and battery portrait data. Data visual analysis can be used to present these data for multi-dimensional battery data analysis. The data visual analysis module of the software also provides the output of a battery evaluation report, which outputs the analysis results into a report.

[0457] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the various embodiments described above may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps. The description of each embodiment above tends to emphasize the differences between the various embodiments, and the same or similarities can be referenced to each other. For the sake of brevity, this article will not repeat them.

[0458] In some embodiments, Fig.16 As shown, a battery data processing device 1600 is provided, including: an event response module 1601, an analysis parameter selection module 1602, a data acquisition module 1603 and an analysis module 1604, wherein:

[0459] The event response module 1601 is used to determine the expected analysis items of the target battery in response to the battery analysis requirement for the target battery.

[0460] The analysis parameter selection module 1602 is used to select analysis parameters according to the desired analysis items.

[0461] The data acquisition module 1603 is used to acquire the battery detection data corresponding to the analysis parameters.

[0462] The analysis module 1604 is used to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery.

[0463] The battery data processing device and the battery data analysis system mentioned above include multiple analysis items that can analyze the battery data. When using the battery data processing device, the user can select the desired analysis item for the target battery from the multiple analysis items according to the battery analysis requirements for the target battery. The battery data analysis system selects the analysis parameters according to the desired analysis items selected by the user, obtains the battery detection data corresponding to the analysis parameters, and performs data analysis on the battery detection data to obtain the battery analysis results of the target battery. By integrating the analysis functions of multiple battery analysis items into a battery data analysis system, when the user needs to analyze the battery data, he does not need to rely on the collaborative processing of the data platform. Various analysis requirements for battery data can be realized in one battery analysis system, which can help users quickly and easily obtain the battery analysis results of the target battery, effectively improving the convenience of battery data analysis.

[0464] Each module in the above battery data analysis system can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0465] In some embodiments, a computer device is provided. The computer device may be a server or terminal integrated with a battery data analysis system. The internal structure diagram thereof may be as follows: Fig.17 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as battery data, processing logic of business functions, and processing results of business functions. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a battery data processing method is implemented.

[0466] Those skilled in the art will understand that Fig.17 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0467] In some embodiments, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the specific steps in the above-mentioned battery data processing method when executing the computer program.

[0468] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the specific steps in the above-mentioned battery data processing method are implemented.

[0469] In some embodiments, a computer program product is provided, including a computer program, which implements the specific steps in the above-mentioned battery data processing method when executed by a processor.

[0470] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0471] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0472] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0473] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A battery data processing method, characterized in that: The method comprises: In response to a battery analysis requirement for a target battery, determining a desired analysis item for the target battery; Selecting analysis parameters according to the desired analysis items; Obtaining battery detection data corresponding to the analysis parameters; The battery detection data is analyzed to obtain a battery analysis result of the target battery.

2. The method according to claim 1, characterized in that The step of selecting analysis parameters according to the desired analysis items includes: Determining, according to the expected analysis item, analysis parameters matching the expected analysis item and analysis methods for the analysis parameters; The performing data analysis on the battery detection data to obtain a battery analysis result of the target battery includes: The analysis method is used to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery.

3. The method according to claim 1 or 2, characterized in that: The step of determining, in response to a battery analysis requirement for a target battery, desired analysis items for the target battery comprises: In response to a battery analysis requirement for a target battery, obtaining a preconfigured analysis item list; A desired analysis item selected from the analysis item candidate list is acquired.

4. The method according to any one of claims 1 to 3, characterized in that: The expected analysis items include: at least one of an abnormal battery analysis item, a battery life assessment item, a battery failure warning item, a battery portrait item, and a root cause analysis item.

5. The method according to claim 2, characterized in that: The expected analysis project is a battery image project; the analysis method is a battery image analysis method; The using the analysis method to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery includes: The battery detection data is subjected to battery profiling using at least one of the battery usage condition dimension, the battery usage environment dimension and the battery status dimension, using the corresponding battery profiling analysis method to obtain a battery profiling result of the target battery.

6. The method according to claim 2, characterized in that The expected analysis project is a root cause analysis project; the analysis method is a root cause analysis method; The using the analysis method to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery includes: Performing data detection and analysis on the battery detection data to obtain a detection and analysis result of the target battery, wherein the detection and analysis result includes at least one of a life detection result, a fault detection result, or an alarm detection result; When the life detection result of the target battery is life decay, or the fault detection result is a battery fault, or the alarm detection result is a battery alarm, obtaining a portrait result of the target battery; A root cause analysis is performed according to the image result of the target battery to obtain a root cause analysis result, wherein the root cause analysis result includes at least one of a life attenuation cause, a failure cause, or an alarm cause.

7. The method according to any one of claims 2 to 6, characterized in that: The step of determining, according to the expected analysis item, analysis parameters matching the expected analysis item and an analysis method for the analysis parameters comprises: Determining, according to the expected analysis item, candidate analysis parameters matching the expected analysis item and candidate analysis methods for the candidate analysis parameters; Visually displaying the candidate analysis parameters and the candidate analysis methods on a user terminal; In response to an edit event for the candidate analysis parameters and the candidate analysis methods, acquiring update content of the candidate analysis parameters and the candidate analysis methods; The candidate analysis parameters and the candidate analysis methods are updated based on the updated content to obtain analysis parameters matching the desired analysis items and analysis methods for the analysis parameters.

8. The method according to any one of claims 2 to 7, characterized in that: The step of determining, according to the expected analysis item, analysis parameters matching the expected analysis item and an analysis method for the analysis parameters comprises: In a case where the desired analysis item is an unconfigured item, in response to a registration event for the desired analysis item, an analysis parameter configured for the desired analysis item and an analysis method for the analysis parameter are acquired.

9. The method according to any one of claims 2 to 8, characterized in that: The obtaining of battery detection data corresponding to the analysis parameter includes: Obtaining an analysis parameter identifier of the analysis parameter; Matching the analysis parameter identifier with the parameter identifier of the battery data corresponding to the target battery; If at least one analysis parameter identifier fails to match the parameter identifier of the battery data, determining the analysis parameter identifier that failed to match; Based on the unsuccessfully matched analysis parameter identifier, the supplementary battery detection data corresponding to the unsuccessfully matched analysis parameter identifier is acquired.

10. The method according to claim 9, characterized in that The acquiring, based on the unmatched analysis parameter identifier, the supplementary battery detection data corresponding to the unmatched analysis parameter identifier comprises: Determining a parameter generation item for generating the supplementary battery detection data based on the unmatched analysis parameter identifier; Determining a parameter generation method matching the parameter generation project according to the parameter generation project; Obtaining battery data corresponding to the parameter generation method; The parameter generation method is used to perform data generation processing on the battery data to obtain supplementary battery detection data corresponding to the unmatched analysis parameter identifier.

11. The method according to claim 10, characterized in that The parameter generation project includes a feature value generation project; The using the parameter generation method to perform data generation processing on the battery data to obtain supplementary battery detection data corresponding to the unmatched analysis parameter identifier includes: Obtain the eigenvalue generation method corresponding to the eigenvalue generation project; The characteristic value generation method is used to perform characteristic value data generation processing on the battery data to obtain a characteristic value generation result.

12. The method according to any one of claims 1 to 11, characterized in that: The method further comprises: In response to a data preprocessing requirement for the battery detection data, determining a desired preprocessing item for the target battery; Determine a preprocessing method matching the expected preprocessing item according to the expected preprocessing item; Using the preprocessing method to perform data preprocessing operations on the battery detection data; The battery detection data is updated based on the preprocessed data obtained after performing the data preprocessing operation.

13. The method according to any one of claims 1 to 12, characterized in that: The method further comprises: In response to the visual display instruction for the target battery, determining a desired display item for the target battery; Determining, according to the desired display item, visual display parameters matching the desired display item, and a visual display method for the visual display parameters; Obtaining battery data corresponding to the visual display parameter; The visual display method is used to perform a data display operation on the battery data, and the battery data is displayed on the user terminal in a preset display form.

14. The method according to any one of claims 1 to 13, characterized in that: The method further comprises: In response to a data import event for a target battery, determining a data import type of the target battery; Determining a data import method that matches the data import type according to the data import type; The data import method is used to import the battery data of the target battery into a local database for storage.

15. The method according to claim 14, characterized in that The data import type includes at least one of real-time data import, offline data import and model mapping import, and the data import method includes at least one of real-time data import method, offline data import method and model mapping import method; The method of importing the battery data of the target battery into a local database for storage includes at least one of the following methods: The first one: Determine a data import interface pre-configured with a data acquisition device of the target battery; Acquiring real-time battery data of the target battery based on the data import interface; Importing the real-time battery data into a local database for storage; Second type: According to the mapping relationship between the battery data model of the target battery and the data model of the local database, mapping the battery data to the local database for storage; The third type: In response to an import instruction triggered by the offline battery data of the target battery, the offline battery data of the target battery is imported into a local database for storage.

16. The method according to claim 14 or 15, characterized in that The method further comprises: In response to a data processing requirement for the battery data in the local database, determining a data processing item for the battery data; Determining a data processing method that matches the data processing project according to the data processing project; Using the data processing method to perform data processing operations on the battery data; The battery detection data is updated based on the processed data obtained after executing the data processing operation.

17. The method according to any one of claims 1 to 16, characterized in that: The method further comprises: In response to a result display requirement for the target battery, determining a target analysis dimension and a result display dimension of the target battery, wherein the result display dimension includes at least one of a two-dimensional dimension or a three-dimensional dimension; Obtain comprehensive battery data of the target battery and a result display method corresponding to the result display dimension; Using the result presentation method, a comprehensive analysis result of the target battery is generated and presented based on the comprehensive battery data and the target analysis dimension.

18. The method according to claim 17, characterized in that The method further comprises: In response to an evaluation report generation instruction for the target battery, determining a battery evaluation type of the target battery; Determine the battery evaluation data and report template required to generate a battery evaluation report of the target battery based on the battery evaluation type; A battery evaluation report is generated according to a report template corresponding to the evaluation type and the battery evaluation data.

19. A battery data processing device, characterized in that: The device comprises: An event response module, configured to determine a desired analysis item of the target battery in response to a battery analysis requirement for the target battery; An analysis parameter selection module, used for selecting analysis parameters according to the desired analysis items; A data acquisition module, used to acquire battery detection data corresponding to the analysis parameters; The analysis module is used to perform data analysis on the battery detection data to obtain a battery analysis result of the target battery.

20. A battery data processing system, characterized in that: The system comprises: A data scanning module, for determining the desired analysis items of the target battery in response to the battery analysis requirements for the target battery; selecting analysis parameters according to the desired analysis items; acquiring battery detection data corresponding to the analysis parameters; performing data analysis on the battery detection data to obtain the battery analysis results of the target battery; The local storage module is used to store the battery detection data.

21. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 18 are implemented.

22. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 18 are implemented.

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