Lithium battery test data comprehensive operation method, system, device and medium

Abstract

This invention relates to the field of comprehensive utilization technology of lithium battery test data, and discloses a method, system, equipment, and medium for comprehensive calculation of lithium battery test data. In the finished product testing stage, a unique identifier is generated for each battery cell and bound to the test task; raw test data is collected in real time and stored in a strong association with the identifier, forming a record with the individual battery as the primary key; a hierarchical association model is constructed to represent the relationship between finished product packages or modules and individual battery cells; in response to traceability query requests, the set of identifiers of all associated individual batteries is determined according to the model, and the corresponding test data is extracted and comprehensively calculated using these identifiers as keys; the traceability analysis results are visualized and downloaded. This invention solves the problems of lacking an individual-level data association model and being unable to efficiently trace individual battery test data, improving data utilization efficiency and the ability to locate quality problems.

Description

Technical Field

[0001] This invention relates to the field of comprehensive utilization technology of lithium battery test data, and in particular to a method, system, device and medium for comprehensive calculation of lithium battery test data. Background Technology

[0002] With the booming development of the new energy vehicle industry, lithium batteries, as a core power source, generate massive amounts of multi-source and heterogeneous test data during their research, development, production, and testing. These data come from diverse sources, including charge / discharge testing equipment, environmental simulation systems, and internal resistance meters from different manufacturers, with varying data formats, sampling frequencies, and storage methods. Traditional data processing methods often rely on manual entry or decentralized file management, leading to severe data silos, low data utilization, and difficulty in supporting systematic performance evaluation, cross-project comparisons, and continuous product improvement of lithium batteries.

[0003] To address the aforementioned issues, existing invention patent document CN118410224A discloses an intelligent comprehensive calculation method, system, device, and medium for lithium battery test data. This includes: extracting and filtering multi-category data from a server database; integrating the result data with related system data and storing it in a ClickHouse database and a MySQL database for unified management; processing the integrated data using various methods such as single-factor analysis, multi-factor analysis, trend analysis, correlation analysis, and dynamic analysis to generate analysis results for different task modules; building a Pack electrical performance simulation model based on the analysis results and lithium battery application environment factors; and visualizing and querying the analyzed data. This solution, to a certain extent, achieves automated data collection and multi-source integration, improves the unified management level and basic analysis capabilities of the data, and reduces data maintenance costs.

[0004] However, the aforementioned solutions primarily target experimental data management during the R&D phase, focusing on test items and methods. They lack a comprehensive lifecycle data organization and correlation model centered on individual batteries (such as single cells or unique identifiers of finished batteries). Specifically, this solution cannot efficiently trace and comprehensively calculate the raw data, test equipment status, test environment parameters, and judgment results of each individual battery cell during the finished product testing process, starting from the final battery product (such as a battery pack or module). Due to the lack of this individual-level data correlation and traceability capability, existing comprehensive calculation methods struggle to perform in-depth correlation mining and closed-loop analysis of lithium battery finished product test data. This severely restricts the depth and breadth of data comprehensive calculations and hinders the efficient localization of quality issues and the improvement of product improvement capabilities. Summary of the Invention

[0005] The purpose of this invention is to provide a comprehensive calculation method, system, device and medium for lithium battery test data, which solves the problem in the prior art of lacking a full life cycle data organization and correlation calculation model with individual batteries as the core, and the inability to efficiently trace the test data of individual cells from the finished battery.

[0006] To achieve the above objectives, the present invention provides a method for comprehensive calculation of lithium battery test data, comprising the following steps: In the lithium battery finished product testing stage, a unique battery individual identification code is generated for each battery cell under test, and this identification code is bound to the test task; During the test execution, the raw test data of the battery unit is collected in real time, and the raw test data is strongly associated with the individual battery identifier code and stored to form a test data record with the individual battery as the primary key. Construct a hierarchical structure association model for battery products, wherein the hierarchical structure association model records the compositional relationship between the finished battery pack or module and the individual identifier codes of the multiple individual batteries contained therein; In response to a user-input traceability query request, which includes the finished product identification code or individual battery identification code of the target battery, the set of all battery individual identification codes associated with the target battery is determined according to the hierarchical structure association model. Using each identifier in the battery individual identifier set as the primary key, the corresponding original test data is extracted from the test data record, and the extracted data is comprehensively calculated to generate traceability analysis results. The traceability analysis results are presented in a visual format, and a data download service is provided.

[0007] The extracted data undergoes comprehensive calculations, specifically including: The system performs trend calculations on data from the same battery at different test time points, compares the same test data of different individual batteries in the same finished product package, calculates the deviation between the test data of abnormal battery individuals and the statistical data of batteries in the same batch and model, and automatically re-judges the test data of each individual battery according to the preset pass / fail threshold.

[0008] In the lithium battery finished product testing stage, a unique individual battery identification code is generated for each battery cell under test, and this identification code is bound to the testing task. Specifically, this includes: The individual battery identification code is a one-dimensional code, two-dimensional code, or RFID serial number generated according to a preset coding rule based on the battery production batch, model, production date, and serial number. The individual battery identification code is entered into the testing system by a barcode scanner or RFID reader and associated with the test work order, test equipment number, test software version, and operator information.

[0009] Specifically, the real-time acquisition of raw test data from the battery cells includes: The raw test data includes charge / discharge voltage, current, capacity, energy, internal resistance, temperature, and test timestamp; the acquisition method uses a real-time data interface that communicates with the test equipment, and the sampling frequency is dynamically configured according to the test items; each data record is accompanied by the individual battery identification code of the current battery.

[0010] The construction of a hierarchical structure model for battery products specifically includes: In the battery pack or module assembly process, a parent-child relationship table is established by scanning the individual battery identification code of each cell and the finished product identification code of the finished pack or module. This table is stored in a relational database and allows users to query the list of individual battery identification codes of all cells under a given cell by the finished product identification code, and to query the finished product identification code of a given cell by the individual battery identification code.

[0011] Specifically, responding to a user-input traceability query request includes: The traceability query request is input through a visual interface, supporting single or batch queries. When the query object is a finished battery pack, the individual battery identification codes of all individual batteries under the finished product are obtained from the hierarchical structure association model. When the query object is an individual battery, the product identification code to which it belongs is queried upwards or its own data is queried directly.

[0012] A comprehensive computing system for lithium battery test data includes: The identification management module is used to generate a unique individual battery identification code for each battery cell under test during the lithium battery finished product testing process, and to bind the identification code to the test task; The data acquisition module is communicatively connected to the identification management module. It is used to receive the individual battery identification code and collect the original test data of the battery unit in real time during the test execution process. The original test data is strongly associated with the individual battery identification code and stored to form a test data record with the individual battery as the primary key. The hierarchical association module is used to construct a hierarchical structure association model for battery products. The hierarchical structure association model records the composition relationship between the finished battery pack or module and the individual identifier codes of the multiple individual batteries it contains. The traceability query module is communicatively connected to the hierarchical association module and the data acquisition module, respectively, and is used to respond to the traceability query request input by the user. The traceability query request includes the finished product identification code or the individual identification code of the target battery. Based on the hierarchical structure association model, the module determines the set of all battery individual identification codes associated with the target battery, and extracts the corresponding test data records from the data acquisition module based on the set of battery individual identification codes. The comprehensive calculation module is communicatively connected to the traceability query module and is used to perform comprehensive calculations on the test data records extracted by the traceability query module to generate traceability analysis results. The output module is communicatively connected to the integrated computing module and is used to display the traceability analysis results in a visual form and provide data download services.

[0013] A lithium battery test data comprehensive computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the lithium battery test data comprehensive computing method.

[0014] A lithium battery test data processing medium stores a computer program, which, when executed by a processor, implements the steps of the lithium battery test data processing method.

[0015] This invention discloses a method, system, device, and medium for comprehensive calculation of lithium battery test data. First, during the finished product testing phase, a unique identifier is generated for each battery cell and bound to the testing task. Raw test data is collected in real time and stored in a strong association with this identifier, forming a record with the individual battery as the primary key. Simultaneously, a hierarchical association model is constructed to record the relationship between finished product packs / modules and individual battery cells. In response to user traceability query requests, the set of all associated individual battery identifiers is determined based on this model. Using this set as the key, corresponding test data is extracted for comprehensive calculation (including trend calculation, comparison calculation, deviation calculation, and automatic re-judgment). Finally, the traceability analysis results are visualized and available for download.

[0016] This invention establishes a test data organization model with individual battery identification codes as the core and a finished product-cell hierarchical association model, which enables accurate traceability and comprehensive calculation of the entire life cycle from finished battery products to individual battery test data. It effectively solves the problems of lacking individual-level data association models and being unable to efficiently trace individual battery test data, and significantly improves the ability to deeply utilize lithium battery finished product test data and the efficiency of locating quality problems. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0018] Figure 1 This is a flowchart of the steps in the lithium battery test data comprehensive calculation method according to the first embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the lithium battery test data comprehensive calculation system according to the second embodiment of the present invention.

[0020] In the diagram: 201 - Identification Management Module, 202 - Data Acquisition Module, 203 - Hierarchical Association Module, 204 - Traceability Query Module, 205 - Comprehensive Calculation Module, 206 - Output Module. Detailed Implementation

[0021] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0022] First embodiment: Please refer to Figure 1 This invention provides a method for comprehensive calculation of lithium battery test data, comprising the following steps: S101: In the lithium battery finished product testing stage, a unique battery individual identification code is generated for each battery cell to be tested, and this identification code is bound to the test task; Specifically, in the lithium battery finished product testing stage, a unique individual battery identification code is generated for each battery cell under test. This identification code can be in the form of a barcode, QR code, or RFID serial number. Its encoding rules are based on a combination of information such as the battery's production batch, model, production date, and serial number, ensuring that each battery cell has a unique and identifiable identity throughout its entire life cycle. Before the test task starts, the identification code is entered into the testing system using a barcode scanner or RFID reader and associated with the current test work order, test equipment number, test software version, and operator information. This binding operation can be achieved using the data interface of the existing Manufacturing Execution System (MES) or test management platform, using the identification code as the primary key basis for all subsequent data records. Through the above processing, each piece of test data establishes a one-to-one correspondence with a specific battery cell from the source, providing the prerequisite for subsequent test data organization, storage, and traceability calculations centered on individual batteries.

[0023] S102: During the test execution process, the original test data of the battery unit is collected in real time, and the original test data is strongly associated with the individual battery identifier code and stored to form a test data record with the individual battery as the primary key; Specifically, during test execution, the sampling frequency is dynamically configured according to the test requirements through a real-time data interface (such as SCPI, Modbus, or OPC UA, or other industry standard protocols) communicating with the device under test. For example, 1Hz is used for high-rate charging and discharging, and 0.1Hz is used for static testing, to collect raw test data of the battery cells in real time. The raw test data includes at least charging and discharging voltage, current, capacity, energy, internal resistance, temperature, and test timestamp. These parameters are all standard data collection items in the field of lithium battery testing. Before each collected data record is written to the storage system, the individual battery identifier of the current test battery is forcibly attached, that is, this identifier is used as a fixed dimension field of the data record. Subsequently, the data is written to a time-series database (such as InfluxDB) or a columnar database (such as ClickHouse), forming a test data record with the individual battery identifier as one of the primary keys. This strongly correlated storage method allows any subsequent query for a specific battery to be quickly retrieved directly using the identifier as an index, without the need for cross-table joins or fuzzy matching, thus laying the data foundation for comprehensive calculation and traceability analysis centered on the individual battery.

[0024] S103: Construct a hierarchical structure association model for battery products, wherein the hierarchical structure association model records the compositional relationship between the finished battery pack or module and the individual identifier codes of the multiple individual batteries it contains; Specifically, during the battery pack or module assembly process, a barcode scanner sequentially scans the individual battery identifier of each cell and the finished product identifier of the entire pack or module. A parent-child relationship table is then established using a Manufacturing Execution System (MES) or a dedicated data entry module. This relationship table contains at least the finished product identifier, the individual cell identifier of the child cell, assembly time, and workstation information, and is stored in a relational database (such as MySQL). This hierarchical relationship model supports bidirectional queries: on the one hand, the individual cell identifier of a finished product can be used to quickly retrieve a list of individual battery identifiers for all its cells; on the other hand, the individual cell identifier of any cell can be used to reverse-engineer its corresponding finished product identifier. This model utilizes existing database foreign key relationships and indexing techniques to achieve a complete hierarchical mapping of battery products from individual cells to finished products using a concise relational table structure. This provides a structured query path for locating and aggregating test data of all associated individual cells based on the finished product identifier in subsequent steps.

[0025] S104: In response to a traceability query request input by the user, the traceability query request includes the finished product identification code or individual battery identification code of the target battery, and determine the set of all battery individual identification codes associated with the target battery according to the hierarchical structure association model; Specifically, a visual query interface, such as a graphical user interface or a web page, is provided to receive traceability query requests input by users. These requests include two types: one is the finished product identification code of the target battery (such as the serial number of a battery pack or module), and the other is the individual battery identification code of the target battery cell. When a user inputs the finished product identification code, a query operation is performed in the relational database based on the hierarchical structure association model established in step S103 to obtain the individual battery identification codes of all lower-level individual batteries associated with that finished product identification code, forming an identification code set. When a user inputs the individual battery identification code, they can choose to query upwards to the finished product identification code to which that individual battery belongs, or directly use the individual battery identification code itself as a set for subsequent calculations. To improve query flexibility, single identification code queries are supported, as well as batch importing of multiple identification codes for batch traceability. Through this method, utilizing the directed parent-child relationship of the hierarchical structure association model, the user's high-level query requirements are automatically mapped to a complete list of low-level battery individual identification codes, providing an accurate set of retrieval keys for extracting corresponding test data records in subsequent steps, avoiding the inefficiency and errors of manually piecing together information across systems.

[0026] S105: Using each identifier in the battery individual identifier code set as the primary key, extract the corresponding original test data from the test data record, and perform comprehensive calculations on the extracted data to generate traceability analysis results; Specifically, firstly, each individual battery identifier in the aforementioned identifier set is used as the primary key to retrieve data from the test data records stored in step S102. Since the test data records are forcibly accompanied by the individual battery identifier field, and the database has indexed this field, all original test data corresponding to each identifier can be efficiently extracted, including voltage, current, capacity, energy, internal resistance, temperature, and timestamp. Subsequently, comprehensive calculations are performed on the extracted data to generate traceability analysis results. The comprehensive calculation specifically includes the following four aspects: First, trend calculation, which performs longitudinal analysis on the data of the same battery at different test time points, such as calculating the capacity decay rate and internal resistance growth rate, reflecting the change law of the battery performance over time; second, comparison calculation, which performs horizontal comparison on the same test data of different individual batteries under the same finished package or module, calculating statistical quantities such as range and standard deviation to evaluate battery consistency; third, deviation calculation, which analyzes the deviation between the test data of abnormal battery individuals and the statistical mean or normal range of batteries of the same batch and model to locate the degree of deviation; and fourth, automatic re-judgment, which re-judges the test data of each individual battery according to preset qualification judgment thresholds, such as discharge capacity not less than 80% of the nominal value and internal resistance not exceeding the specified upper limit, and marks out-of-tolerance data. This invention realizes multi-level comprehensive calculation from individual trend, intra-group comparison, batch deviation to qualification re-judgment. Finally, the calculation results are organized in the form of structured data, such as JSON or data tables, as traceability analysis results for subsequent steps to display and output.

[0027] S106: Display the traceability analysis results in a visual format and provide a data download service.

[0028] Specifically, visualization technologies, such as open-source chart libraries like ECharts and Highcharts, are used to display the traceability analysis results in a graphical interface. Specifically, a tree structure or list format is used to display the hierarchical relationship between the finished battery pack and its individual cells. When a user clicks on any individual cell node, the test curve for that cell is dynamically loaded in the right-hand area, such as the charge / discharge voltage-capacity curve and the internal resistance-time series graph. Simultaneously, the same data curves of other individual cells within the same finished battery pack can be overlaid on the same coordinate axis for intuitive comparison. For trend calculation results, a line graph shows the change in capacity decay rate or internal resistance growth rate over time; for deviation calculation results, a scatter plot or box plot shows the relationship between abnormal data and batch statistical distribution; for automatic re-judgment results, abnormal batteries are highlighted with color markings (e.g., green for qualified, red for unqualified) and alarm icons. Furthermore, a data download service is provided. Users can export the current traceability analysis results to common file formats such as Excel, CSV, or PDF via interface buttons. The download content includes the original test data, intermediate calculation results, and final judgment conclusions. The aforementioned visualization and data download services utilize existing mature web front-end and back-end technologies, such as HTTP file transfer and data stream output. These common technologies are integrated into a traceability analysis process centered on the individual battery identification code, providing users with a complete closed-loop experience from querying to displaying and exporting. Through this step, users do not need professional data processing skills to intuitively obtain traceability analysis results of the entire lifecycle of lithium batteries, from finished products to individual cells, effectively supporting the identification of quality issues and product improvement decisions.

[0029] Second embodiment: Please refer to Figure 2 This invention provides a comprehensive computing system for lithium battery test data, comprising: The identification management module 201 is used to generate a unique individual battery identification code for each battery cell under test during the lithium battery finished product testing process, and to bind the identification code to the test task. The data acquisition module 202 is communicatively connected to the identification management module 201. It is used to receive the individual battery identification code and collect the original test data of the battery unit in real time during the test execution process. It stores the original test data and the individual battery identification code in a strong association to form a test data record with the individual battery as the primary key. The hierarchical association module 203 is used to construct a hierarchical structure association model for battery products. The hierarchical structure association model records the composition relationship between the finished battery pack or module and the individual identifier codes of the multiple individual batteries it contains. The traceability query module 204 is communicatively connected to the hierarchical association module 203 and the data acquisition module 202, respectively, and is used to respond to the traceability query request input by the user. The traceability query request includes the finished product identification code or the individual identification code of the target battery. Based on the hierarchical structure association model, the module determines the set of all battery individual identification codes associated with the target battery, and extracts the corresponding test data records from the data acquisition module 202 based on the set of battery individual identification codes. The comprehensive calculation module 205 is communicatively connected to the traceability query module 204 and is used to perform comprehensive calculations on the test data records extracted by the traceability query module 204 to generate traceability analysis results. The output module 206 is communicatively connected to the integrated computing module 205 and is used to display the traceability analysis results in a visual form and provide data download services.

[0030] Specifically, firstly, the identification management module 201 generates a unique individual battery identification code for each battery unit under test during the finished product testing phase and binds it to the test task. The data acquisition module 202 communicates with the identification management module 201, receives the individual battery identification codes from the identification management module 201, collects raw test data in real time during test execution, and stores each data record strongly associated with the identification code, forming a test data record with the individual battery as the primary key. The hierarchical association module 203 independently constructs a parent-child relationship model between the finished battery pack or module and the identification codes of the individual batteries it contains. When the user inputs a traceability query request through the visual interface, the traceability query module 204 communicates with both the hierarchical association module 203 and the data acquisition module 202: the traceability query module 204 first determines the set of all individual battery identification codes associated with the target battery based on the hierarchical structure association model in the hierarchical association module 203, then extracts the corresponding test data record from the data acquisition module 202 based on this set, and transmits the extracted data to the comprehensive calculation module 205. The integrated calculation module 205 communicates with the traceability query module 204, performing trend calculations, comparison calculations, deviation calculations, and automatic re-judgments on the received test data records to generate traceability analysis results. Finally, the output module 206 communicates with the integrated calculation module 205, displaying the traceability analysis results in visual formats such as charts and curves, and providing data download services. Through the orderly collaboration between these modules, the system automatically completes a closed loop from identifier binding, data collection, hierarchical modeling to traceability query, integrated calculation, and result output. This achieves accurate traceability and integrated calculation throughout the entire lifecycle, from finished battery products to individual battery test data, effectively solving the problem of lacking individual-level data association models and significantly improving data utilization efficiency and the ability to locate quality problems.

[0031] Third embodiment: This invention provides a lithium battery test data comprehensive computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the lithium battery test data comprehensive computing method.

[0032] Fourth embodiment: This invention provides a lithium battery test data comprehensive calculation medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the lithium battery test data comprehensive calculation method.

[0033] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims

1. A comprehensive calculation method for lithium battery test data, characterized in that, Includes the following steps: In the lithium battery finished product testing stage, a unique battery individual identification code is generated for each battery cell under test, and this identification code is bound to the test task; During the test execution, the raw test data of the battery unit is collected in real time, and the raw test data is strongly associated with the individual battery identifier code and stored to form a test data record with the individual battery as the primary key. Construct a hierarchical structure association model for battery products, wherein the hierarchical structure association model records the compositional relationship between the finished battery pack or module and the individual identifier codes of the multiple individual batteries contained therein; In response to a user-input traceability query request, which includes the finished product identification code or individual battery identification code of the target battery, the set of all battery individual identification codes associated with the target battery is determined according to the hierarchical structure association model. Using each identifier in the battery individual identifier set as the primary key, the corresponding original test data is extracted from the test data record, and the extracted data is comprehensively calculated to generate traceability analysis results. The traceability analysis results are presented in a visual format, and a data download service is provided.

2. The lithium battery test data comprehensive calculation method as described in claim 1, characterized in that, The extracted data undergoes comprehensive calculations, specifically including: The system performs trend calculations on data from the same battery at different test time points, compares the same test data of different individual batteries in the same finished product package, calculates the deviation between the test data of abnormal battery individuals and the statistical data of batteries in the same batch and model, and automatically re-judges the test data of each individual battery according to the preset pass / fail threshold.

3. The lithium battery test data comprehensive calculation method as described in claim 1, characterized in that, In the lithium battery finished product testing phase, a unique individual battery identification code is generated for each battery cell under test, and this identification code is bound to the testing task. Specifically, this includes: The individual battery identification code is a one-dimensional code, two-dimensional code, or RFID serial number generated according to a preset coding rule based on the battery production batch, model, production date, and serial number. The individual battery identification code is entered into the testing system by a barcode scanner or RFID reader and associated with the test work order, test equipment number, test software version, and operator information.

4. The lithium battery test data comprehensive calculation method as described in claim 1, characterized in that, Real-time acquisition of raw test data from battery cells, specifically including: The raw test data includes charge / discharge voltage, current, capacity, energy, internal resistance, temperature, and test timestamp; the acquisition method uses a real-time data interface that communicates with the test equipment, and the sampling frequency is dynamically configured according to the test items; each data record is accompanied by the individual battery identification code of the current battery.

5. The lithium battery test data comprehensive calculation method as described in claim 1, characterized in that, Constructing a hierarchical structure model for battery products, specifically including: In the battery pack or module assembly process, a parent-child relationship table is established by scanning the individual battery identification code of each cell and the finished product identification code of the finished pack or module. This table is stored in a relational database and allows users to query the list of individual battery identification codes of all cells under a given cell by the finished product identification code, and to query the finished product identification code of a given cell by the individual battery identification code.

6. The method of claim 1, wherein the test data is integrated by a plurality of test data integration methods. In response to a user-input traceability query request, specifically including: The traceability query request is input through a visual interface, supporting single or batch queries. When the query object is a finished battery pack, the individual battery identification codes of all individual batteries under the finished product are obtained from the hierarchical structure association model. When the query object is an individual battery, the product identification code to which it belongs is queried upwards or its own data is queried directly.

7. A lithium battery test data comprehensive calculation system, applied to the lithium battery test data comprehensive calculation method as described in claim 1, characterized in that, include: The identification management module is used to generate a unique individual battery identification code for each battery cell under test during the lithium battery finished product testing process, and to bind the identification code to the test task; The data acquisition module is communicatively connected to the identification management module. It is used to receive the individual battery identification code and collect the original test data of the battery unit in real time during the test execution process. The original test data is strongly associated with the individual battery identification code and stored to form a test data record with the individual battery as the primary key. The hierarchical association module is used to construct a hierarchical structure association model for battery products. The hierarchical structure association model records the composition relationship between the finished battery pack or module and the individual identifier codes of the multiple individual batteries it contains. The traceability query module is communicatively connected to the hierarchical association module and the data acquisition module, respectively, and is used to respond to the traceability query request input by the user. The traceability query request includes the finished product identification code or the individual identification code of the target battery. Based on the hierarchical structure association model, the module determines the set of all battery individual identification codes associated with the target battery, and extracts the corresponding test data records from the data acquisition module based on the set of battery individual identification codes. The comprehensive calculation module is communicatively connected to the traceability query module and is used to perform comprehensive calculations on the test data records extracted by the traceability query module to generate traceability analysis results. The output module is communicatively connected to the integrated computing module and is used to display the traceability analysis results in a visual form and provide data download services.

8. A lithium battery test data processing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the lithium battery test data comprehensive calculation method as described in any one of claims 1 to 6.

9. A lithium battery test data processing medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements the steps of the lithium battery test data comprehensive calculation method as described in any one of claims 1 to 6.

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