Battery parameter monitoring method, battery parameter monitoring system and computer equipment

Abstract

The invention discloses a battery parameter monitoring method, a battery parameter monitoring system, computer equipment and a computer readable storage medium. The method comprises the following steps: in response to a received configuration instruction, configuring an exception judgment standard associated with a preset process scene element; and according to the abnormity judgment standard, carrying out abnormity judgment on the collected electrical performance parameters of the battery, and determining the abnormal condition of the battery. And according to the received target process scene elements and the query time interval, querying and displaying a corresponding target abnormal condition from the abnormal conditions of the battery. Thus, by configuring the abnormity judgment standard associated with the preset process scene element, flexible adaptation of the abnormity judgment standard can be realized, and the problem that a traditional monitoring method is insufficient in pertinence can be solved. Moreover, through accurate query screening and result display, rapid tracing of abnormal data can be realized, and data support is provided for production process optimization.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and in particular to battery parameter monitoring methods, battery parameter monitoring systems, computer equipment, and computer-readable storage media. Background Technology

[0002] In related technologies, battery parameter monitoring systems often use fixed thresholds or manual verification to determine battery anomalies. This approach fails to flexibly adapt to diverse process scenarios involving different production lines, processes, and product models, resulting in low monitoring efficiency and incomplete anomaly identification. Summary of the Invention

[0003] This application provides a battery parameter monitoring method, a battery parameter monitoring system, a computer device, and a computer-readable storage medium.

[0004] This application provides a battery parameter monitoring method, the method comprising: In response to the received configuration instructions, configure the anomaly judgment criteria associated with the preset process scenario elements; According to the aforementioned anomaly determination criteria, the collected electrical performance parameters of the battery are assessed for anomalies to determine the abnormal condition of the battery. Based on the received target process scenario elements and query time interval, query and display the corresponding target abnormal situation from the abnormal situation of the battery.

[0005] In response to received configuration commands, anomaly detection criteria associated with preset process scenario elements are configured. Next, based on these criteria, the collected battery electrical performance parameters are analyzed for anomalies to determine the battery's abnormal condition. Finally, based on the received target process scenario elements and the query time range, the corresponding target anomaly is queried and displayed from the battery's anomalies. This approach, by configuring anomaly detection criteria associated with preset process scenario elements, allows for flexible adaptation of anomaly detection standards, thus addressing the lack of specificity in traditional monitoring methods. Furthermore, precise query filtering and result display enable rapid tracing of abnormal data, providing data support for production process optimization.

[0006] In some implementations, the preset process scenario elements include at least one of the following: battery parameter type, production line, production process, and model information. The parameter type includes voltage, internal resistance, capacity decay coefficient, or voltage drop rate.

[0007] Thus, the preset process scenario elements include at least one of the following: battery parameter type, production line, production process, and model information. Parameter types include voltage, internal resistance, capacity decay coefficient, or voltage drop rate. By clearly defining the specific composition of the preset process scenario elements and the core scope of parameter types, a clear basis for configuring anomaly judgment criteria is provided. This allows for the configuration of targeted anomaly judgment criteria to meet the differentiated needs of different product models and production line processes, avoiding misjudgments and omissions.

[0008] In some implementations, the anomaly determination criteria include master table configuration data, and the configuration of anomaly determination criteria associated with preset process scenario elements in response to received configuration instructions includes: Based on the received first trigger signal, configure the main table information of the anomaly judgment standard, and generate the main table configuration data. The first target field of the main table information includes at least one of the parameter type, the name of the anomaly judgment standard, the production line, the production process, and the model information. The main table configuration data includes the code in the anomaly judgment standard, and the code in the anomaly judgment standard is generated based on the production process and the model information.

[0009] Thus, based on the received first trigger signal, the master table information for configuring anomaly judgment criteria is generated, and master table configuration data is produced. The first target field of the master table information includes at least one of the following: parameter type, name of the anomaly judgment criterion, production line, production process, and model information. The master table configuration data includes the code in the anomaly judgment criterion, which is generated based on the production process and model information. In this way, by clearly defining the first target field, the configured master table configuration data can be standardized, thereby enabling the construction of master table configuration data with a clear correspondence to the preset process scenario elements. This facilitates subsequent rapid querying, reuse, and maintenance, reducing management costs.

[0010] In some embodiments, the method further includes: Based on the received second trigger signal for the main table configuration data, verify the complete status of the second target field of the main table configuration data, and the existence of historical anomaly judgment criteria that are the same as the preset process scenario elements of the anomaly judgment criteria and have been enabled. If the second target field is complete and there is no historical anomaly judgment standard that is the same as the preset process scenario element of the anomaly judgment standard and has been enabled, save the anomaly judgment standard.

[0011] Thus, based on the received second trigger signal for the main table configuration data, the integrity of the second target field in the main table configuration data is verified, as well as the existence of historical anomaly judgment standards that are identical to the preset process scenario elements of the anomaly judgment criteria and have been enabled. Then, if the second target field is complete and there are no historical anomaly judgment standards identical to the preset process scenario elements of the anomaly judgment criteria that have been enabled, the anomaly judgment standard is saved. In this way, by checking the integrity of the second target field and whether the anomaly judgment standard is repeatedly enabled, invalid configuration of the anomaly judgment standard due to data loss can be avoided, improving configuration efficiency. Furthermore, it can also avoid confusion in the anomaly judgment logic, ensuring that the anomaly judgment standard takes effect according to a unique standard when executed, improving the accuracy of anomaly judgment.

[0012] In some implementations, the anomaly determination criteria include sub-table configuration data, and the configuration of anomaly determination criteria associated with preset process scenario elements in response to received configuration instructions includes: Based on the received third trigger signal, configure the rule details information of the anomaly judgment criteria and generate sub-table configuration data. The rule details information includes at least one of test time interval, priority, parameter name and standard threshold information. The priority is non-repeatable numerical data, and the standard threshold information is used to define the compliance threshold interval of the parameter name.

[0013] Thus, based on the received third trigger signal, the rule details for configuring the anomaly judgment criteria are configured, and sub-table configuration data is generated. The rule details include at least one of the following: test time interval, priority, parameter name, and standard threshold information. The priority is non-repeatable numerical data, and the standard threshold information is used to define the compliance threshold range for the parameter name. In this way, through explicit rule details, the configured sub-table configuration data can be standardized, thereby enabling the construction of sub-table configuration data with a clear correspondence to the preset process scenario elements, facilitating subsequent rapid querying, reuse, and maintenance, and reducing management costs.

[0014] In some embodiments, the method further includes: Based on the received fourth trigger signal for the sub-table configuration data, verify the validity status of the main table of the anomaly judgment criteria and the integrity of the third target field of the sub-table configuration data, wherein the third target field includes priority and / or parameter name; If the main table is in a disabled state and the third target field is complete, save the configuration data of the sub-table.

[0015] Thus, based on the received fourth trigger signal for the sub-table configuration data, the validity status of the main table for anomaly judgment criteria and the integrity of the third target field of the sub-table configuration data are verified. The third target field includes priority and / or parameter name. Then, if the main table is in a disabled state and the third target field is complete, the sub-table configuration data is saved. This way, by prohibiting modification of sub-table constraints while the main table is enabled, the judgment logic chaos caused by sudden changes in detailed rules during the execution of the anomaly judgment criteria can be avoided, ensuring the consistency of the anomaly judgment criteria during battery screening and reducing the risk of misjudgment and missed judgment. Furthermore, by verifying the integrity of the third target field, it can be ensured that every piece of sub-table configuration data can be implemented, improving the overall quality of the anomaly judgment criteria.

[0016] In some embodiments, the method further includes: Based on the received fifth trigger signal, the validity status of the anomaly determination criterion is switched, and the validity status includes an enabled state and a disabled state.

[0017] Thus, based on the received fifth trigger signal, the validity status of the anomaly judgment standard is switched, including an enabled state and a disabled state. In this way, by switching the validity status of the anomaly judgment standard, the anomaly judgment standard can be quickly adapted to the needs of production process adjustments, product iterations, etc. Old rules can be temporarily disabled rather than deleted, and can be directly enabled when they need to be reused later, which greatly reduces the cost of repeated configuration.

[0018] In some embodiments, the method further includes: When the anomaly determination criteria are in the disabled state, the main table configuration data and / or the sub-table configuration data are edited according to the received sixth trigger signal; Based on the received seventh trigger signal, perform logical deletion processing on the main table configuration data and / or the sub-table configuration data.

[0019] Thus, when the anomaly detection criteria are disabled, the main table configuration data and / or sub-table configuration data are edited based on the received sixth trigger signal. Based on the received seventh trigger signal, the main table configuration data and / or sub-table configuration data are logically deleted. By restricting the editing or deletion of the main table configuration data and / or sub-table configuration data when enabled, logical abrupt changes in the anomaly detection criteria during operation can be avoided, ensuring the consistency of the criteria during cell screening and reducing the risk of misjudgments or omissions due to tampering with the anomaly detection criteria.

[0020] In some embodiments, the method further includes: Based on the received eighth trigger signal and the first query condition, the anomaly determination standard is queried. The first query condition includes the name of the anomaly determination standard, the model information, the production process, and / or the validity status of the anomaly determination standard.

[0021] Thus, based on the received eighth trigger signal and the first query condition, the anomaly judgment standard is queried. The first query condition includes the name, model information, production process, and / or validity status of the anomaly judgment standard. By covering the name, model information, production process, and validity status of the first query condition, fuzzy queries and combinations of multiple conditions can be supported, thereby significantly improving the accuracy and speed of the query.

[0022] In some embodiments, determining the abnormality of the battery by judging the collected electrical performance parameters according to the anomaly judgment criteria includes: Obtain the electrochemical parameters of the target battery during the test time interval; Outlier removal was performed on the electrochemical parameters. Calculate the target judgment value based on the electrochemical parameters after outlier removal and the test time interval; The abnormal condition of the target battery is determined based on the target judgment value and the standard threshold information.

[0023] In this way, the electrochemical parameters of the target battery within the test time interval are obtained. Next, outlier removal processing is performed on the electrochemical parameters. Then, based on the outlier-removed electrochemical parameters and the test time interval, a target judgment value is calculated. Finally, based on the target judgment value and standard threshold information, the abnormality of the target battery is determined. In this way, outlier removal processing can eliminate interference from invalid data. The comparison between the target judgment value and standard threshold information enables the quantification and implementation of the judgment standard. Furthermore, the combination of outlier removal processing and the comparison between the target judgment value and standard threshold information can significantly improve the identification rate of abnormal cells, thereby effectively reducing the missed and false positives of anomalies.

[0024] In some implementations, the step of querying and displaying the corresponding target anomaly from the abnormal conditions of the battery based on the received target process scenario elements and query time interval includes: Based on the received ninth trigger signal, verify the completeness of the obtained second query condition. The second query condition includes the target process scenario element and the target time interval. The target process scenario element includes at least one of the target parameter type, target production line, target production process and target model information. If the second query condition is complete, a query is performed based on the second query condition to determine the target abnormal condition corresponding to the second query condition from the abnormal conditions of the battery.

[0025] Thus, based on the received ninth trigger signal, the completeness of the acquired second query condition is verified. The second query condition includes target process scenario elements and target time intervals. The target process scenario elements include target parameter type, target production line, target production process, and target model information. Next, if the second query condition is complete, a query is performed based on the second query condition to determine the target abnormal situation corresponding to the second query condition from among the abnormal situations of the battery. In this way, by verifying the completeness of the second query condition, invalid queries due to missing conditions can be avoided.

[0026] This application also provides a battery parameter monitoring system, which includes a battery parameter monitoring device, and the battery parameter monitoring device includes a rule configuration module, a rule execution module, and a result query module. The rule configuration module is configured to respond to received configuration instructions and configure anomaly judgment criteria associated with preset process scenario elements; The rule execution module is configured to determine the abnormality of the battery by performing anomaly determination on the collected electrical performance parameters of the battery according to the anomaly determination criteria. The result query module is configured to query and display the corresponding target abnormal situation from the abnormal situations of the battery based on the received target process scenario elements and query time interval.

[0027] Therefore, a battery parameter monitoring system is provided, comprising a battery parameter monitoring device, which includes a rule configuration module, a rule execution module, and a result query module. The rule configuration module responds to received configuration commands and configures anomaly judgment criteria associated with preset process scenario elements. The rule execution module, based on the anomaly judgment criteria, performs anomaly judgment on the collected battery electrical performance parameters to determine the battery's abnormal condition. The result query module, based on the received target process scenario elements and query time interval, queries and displays the corresponding target anomaly from the battery's anomaly conditions. Thus, by configuring anomaly judgment criteria associated with preset process scenario elements, flexible adaptation of anomaly judgment criteria can be achieved, thereby solving the problem of insufficient targeting in traditional monitoring methods. Furthermore, through precise query filtering and result display, rapid traceability of abnormal data can be achieved, providing data support for production process optimization.

[0028] This application also provides a computer device in which a computer program is stored in a memory, and the processor executes the computer program to implement the steps of the above method.

[0029] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0030] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description

[0031] The above and additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is one of the flowcharts illustrating a battery parameter monitoring method according to certain embodiments of this application; Figure 2 This is a second schematic flowchart of a battery parameter monitoring method according to certain embodiments of this application; Figure 3 This is the third flowchart of a battery parameter monitoring method according to certain embodiments of this application; Figure 4 This is the fourth flowchart of a battery parameter monitoring method according to certain embodiments of this application; Figure 5 This is the fifth flowchart of a battery parameter monitoring method according to certain embodiments of this application; Figure 6 This is a flowchart of a battery parameter monitoring method according to certain embodiments of this application, number six. Figure 7 This is the seventh flowchart of a battery parameter monitoring method according to certain embodiments of this application; Figure 8 This is the eighth flowchart of a battery parameter monitoring method according to certain embodiments of this application; Figure 9 This is the ninth flowchart of a battery parameter monitoring method according to certain embodiments of this application; Figure 10 This is the tenth flowchart of a battery parameter monitoring method according to certain embodiments of this application; Figure 11 This is a schematic diagram of the structure of a battery parameter monitoring device according to certain embodiments of this application. Detailed Implementation

[0032] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of this application, and should not be construed as limiting the embodiments of this application.

[0033] In large-scale battery production, different factories naturally differ in production line configuration and equipment precision. Even within the same factory, each production process—from wafer fabrication and formation to packaging—has its own emphasis on battery performance. This, coupled with multiple series and specifications of product models, creates a complex and diverse range of differentiated processes. However, battery parameter monitoring systems in related technologies generally employ a one-size-fits-all fixed threshold judgment model. This means setting uniform upper and lower limits for parameters such as voltage and internal resistance, without considering the errors of different production line equipment, the focus of different process inspections, and the differences in performance benchmarks between different battery models. For example, directly applying the voltage threshold from the formation process to the packaging process, or using the internal resistance standard for battery model A for battery model B, leads to some normal cells being misjudged as abnormal due to standard mismatch, while cells with genuine potential risks are not identified because the threshold is too wide. Meanwhile, some systems rely on manual verification for auxiliary judgment. Faced with the detection data of thousands or even tens of thousands of cells per hour in mass production, it is not only difficult for humans to achieve real-time synchronous verification, but also easy to make judgment errors due to visual fatigue and differences in experience. It is impossible to cover all cell data and it is also difficult to capture hidden problems such as self-discharge abnormalities and small internal resistance fluctuations.

[0034] This combination of fixed thresholds and manual verification cannot flexibly adapt to the personalized needs of different process scenarios, directly resulting in low efficiency of the monitoring process. The anomaly identification is neither comprehensive nor accurate. After a large number of hidden abnormal cells enter the market, it will not only cause customer complaints such as poor differential pressure, but may also affect the operational stability of terminal equipment and even create safety hazards.

[0035] Based on the above issues, please refer to Figure 1 This application provides a battery parameter monitoring method, the method including: 011: In response to the received configuration command, configure the anomaly judgment criteria associated with the preset process scenario elements; 012: Based on the anomaly judgment criteria, the collected battery electrical performance parameters are judged to determine the abnormality of the battery. 013: Based on the received target process scenario elements and query time range, query and display the corresponding target anomaly from the abnormal conditions of the battery.

[0036] This application also provides a computer device, including a memory and a processor. The battery parameter monitoring method of this application can be implemented by the computer device of this application. Specifically, the memory stores a computer program, and the processor is used to configure anomaly judgment criteria associated with preset process scenario elements in response to received configuration instructions. Based on the anomaly judgment criteria, the processor performs anomaly judgment on the collected battery electrical performance parameters to determine the battery's abnormal condition. Furthermore, based on the received target process scenario elements and query time intervals, the processor queries and displays the corresponding target anomaly condition from the battery's abnormal conditions.

[0037] This application also provides a battery parameter monitoring system, which includes a battery parameter monitoring device. In some embodiments, the battery parameter monitoring device includes a rule configuration module, a rule execution module, and a result query module. The battery parameter monitoring method of this application can be implemented by the battery parameter monitoring device of this application. Specifically, the rule configuration module is used to configure anomaly judgment criteria associated with preset process scenario elements in response to received configuration instructions. The rule execution module is used to perform anomaly judgment on the collected battery electrical performance parameters according to the anomaly judgment criteria to determine the abnormal situation of the battery. The result query module is used to query and display the corresponding target anomaly situation from the abnormal situations of the battery based on the received target process scenario elements and query time interval.

[0038] Specifically, configuration instructions refer to rule setting signals triggered through the system interface, which are used to initiate the configuration process for anomaly judgment criteria.

[0039] The preset process scenario elements refer to the set of core parameters used to distinguish different production scenarios, including battery parameter types, production lines, production processes, and model information. Specifically, parameter types refer to the categories of indicators for battery quality compliance and abnormal conditions in the Battery Parameter Monitoring System 1000, such as self-discharge anomalies reflecting fault types, internal resistance reflecting the magnitude of the battery's internal impedance, voltage, and the K-value reflecting the battery's self-discharge rate. Production lines refer to the specific production lines used for battery production; different production lines have different production precision and equipment parameters. Production processes refer to specific steps in the production process, such as open-circuit voltage testing; different processes require monitoring different parameters. Model information refers to the specific product model of the battery, used to distinguish battery products of different specifications and applications.

[0040] Anomaly detection criteria refer to a set of standardized rules used to determine whether electrical performance parameters are compliant, and are divided into main table configuration data and sub-table configuration data.

[0041] Electrical performance parameters refer to the electrical characteristics data exhibited by the battery during the production process, and are the basis for judging whether the battery quality is qualified.

[0042] The target process scenario element refers to the specific scenario conditions specified by the user when querying abnormal results.

[0043] The query time range refers to the time range within which the user specifies the data to be queried for abnormal data.

[0044] Users send configuration commands via the terminal, inputting relevant preset process scenario elements to define corresponding anomaly judgment criteria. Upon receiving the configuration command, the system generates and stores structured anomaly judgment criteria. This allows the rule configuration module to respond to received configuration commands, such as adding or modifying judgment criteria, deeply binding preset process scenario elements with anomaly judgment criteria. This means that differentiated parameter screening criteria can be configured for different production lines, processes, and battery models.

[0045] Subsequently, the rule execution module acquires the battery electrical performance parameters uploaded by the production equipment in real time through the data acquisition interface, and calls the preset anomaly judgment criteria to verify the electrical performance parameters of each battery one by one. In this way, the rule execution module can determine anomalies in the collected battery production process electrical performance parameters based on the anomaly judgment criteria directly generated by the rule configuration module, such as comparing the actual value of the parameter with the preset threshold, and the deviation of the calculated parameter from the mean, to determine whether a single battery has an anomaly.

[0046] Furthermore, users can input target process scenario elements and query time ranges through the results query module. After verifying the completeness of the query conditions, the module retrieves the corresponding target anomalies from the database and displays them to the user in a visual format, such as a list. It also supports subsequent data export and analysis. In this way, the results query module can output data based on target process scenario elements and query time ranges as search criteria, with the data source being the judgment results of the rule execution module. This allows for the rapid acquisition of abnormal battery data within a specific scenario by accurately locating the scenario and time range.

[0047] In summary, the battery parameter monitoring method and system provided in this application configure anomaly judgment criteria associated with preset process scenario elements in response to received configuration instructions. Then, based on the anomaly judgment criteria, anomalies are judged in the collected battery electrical performance parameters to determine the battery's abnormal condition. Finally, based on the received target process scenario elements and query time interval, the corresponding target anomaly is queried and displayed from the battery's abnormal conditions. Thus, by configuring anomaly judgment criteria associated with preset process scenario elements, flexible adaptation of anomaly judgment criteria can be achieved, thereby solving the problem of insufficient targeting in traditional monitoring methods. Furthermore, through precise query filtering and result display, rapid tracing of abnormal data can be achieved, providing data support for production process optimization.

[0048] In some implementations, the preset process scenario elements include at least one of the following: battery parameter type, production line, production process, and model information. The parameter type includes voltage, internal resistance, capacity decay coefficient, or voltage drop rate.

[0049] Specifically, voltage refers to the potential difference between the positive and negative electrodes of a battery, and is a fundamental electrical parameter reflecting the battery's energy storage and output status. Whether its value is within the preset range is directly related to whether the battery can work normally. Abnormal voltage may indicate obvious failure risks such as separator damage, poor electrode contact, and electrolyte abnormalities.

[0050] Internal resistance refers to the impedance encountered when current flows inside a battery, reflecting the integrity and conductivity of the battery's internal structure. Excessive internal resistance usually stems from problems such as poor soldering of the tabs, aging of electrode materials, and internal short circuits within the cell. It is one of the main causes of poor charge-discharge voltage differences and is a key indicator affecting battery performance.

[0051] The capacity decay coefficient refers to the ratio of the actual usable capacity to the initial rated capacity after multiple charge-discharge cycles. It is a core parameter for measuring the long-term cycle stability and lifespan of a battery. An excessive capacity decay coefficient indicates a rapid decline in the battery's energy storage capacity, suggesting potential failure risks such as material degradation and structural aging.

[0052] The rate of voltage drop refers to the extent to which the voltage of a battery decreases per unit time when the battery is at rest, directly reflecting the battery's self-discharge level. An excessively rapid rate of voltage drop indicates abnormal battery self-discharge, which may be due to insufficient electrolyte purity, abnormal electrode surface reactions, etc., and is an important reason for poor voltage differential after long-term storage.

[0053] Thus, the preset process scenario elements include at least one of the following: battery parameter type, production line, production process, and model information. Parameter types include voltage, internal resistance, capacity decay coefficient, or voltage drop rate. By clearly defining the specific composition of the preset process scenario elements and the core scope of parameter types, a clear basis for configuring anomaly judgment criteria is provided. This allows for the configuration of targeted anomaly judgment criteria to meet the differentiated needs of different product models and production line processes, avoiding misjudgments and omissions.

[0054] Please see Figure 2 In some implementations, the anomaly determination criteria include master table configuration data. Step 011 (in response to the received configuration instruction, configuring the anomaly determination criteria associated with preset process scenario elements) includes: 0111: Based on the received first trigger signal, configure the main table information for the anomaly judgment criteria and generate the main table configuration data.

[0055] In some implementations, the rule configuration module is also used to configure the main table information of the anomaly judgment criteria based on the received first trigger signal and generate main table configuration data.

[0056] In some implementations, the processor is also configured to configure the master table information of the anomaly determination criteria based on the received first trigger signal, and generate master table configuration data.

[0057] Specifically, the first trigger signal refers to the user-initiated main table information configuration operation command, such as clicking the "Create Main Table" button, which is used to start the process of entering and configuring main table fields.

[0058] The main table information refers to the set of basic scenario information for anomaly judgment criteria. It is used to define the scope of application of the rules and directly associates the applicable scenarios and usage attributes of the anomaly judgment criteria.

[0059] The first target field refers to the elements that must be configured in the main table information, including parameter type, name of the anomaly judgment standard, production line, production process and model information.

[0060] The code refers to the unique identifier of the anomaly judgment standard. It is generated based on the production process and model information and is used to quickly identify the core applicable attributes of the rule, so as to facilitate management, query and reuse.

[0061] The user initiates a main table configuration operation through the system interface, generating the first trigger signal. After receiving the signal, the first configuration subunit pops up the main table configuration interface. Based on the actual production quality control needs, the user selects at least one item from the first target field for configuration: selects the parameter type to specify the core monitoring indicators, selects the production line and production process to define the applicable production links, selects the model information to specify the applicable product range, and manually enters the name of the anomaly judgment standard to complete the configuration of the first target field.

[0062] Next, during user configuration, the system automatically extracts production process and model information, and combines this with preset coding rules, such as factory code, product model, production process, and three-digit serial number, to generate a unique code, ensuring that each piece of configuration data in the main table has a unique identifier. In some implementations, the code consists of a factory code, product model, production process, and three-digit serial number. The factory code is associated with the factory to which the currently configured production line belongs, and the three-digit serial number increments sequentially according to the configuration order within the same process scenario, ensuring that the code is globally unique and that the factory, model, and process corresponding to the rules can be quickly located through the code.

[0063] Finally, after all the primary target fields are configured, the system integrates this information to form the main table configuration data, which is then bound to the anomaly judgment criteria. This provides basic support for subsequent operations such as detailed configuration of sub-table rules, enabling / disabling rules, and querying. At the same time, it ensures that the main table configuration data accurately corresponds to the preset process scenario elements, giving the anomaly judgment criteria a clear applicable boundary.

[0064] Thus, based on the received first trigger signal, the master table information for configuring anomaly judgment criteria is generated, and master table configuration data is produced. The first target field of the master table information includes at least one of the following: parameter type, name of the anomaly judgment criterion, production line, production process, and model information. The master table configuration data includes the code in the anomaly judgment criterion, which is generated based on the production process and model information. In this way, by clearly defining the first target field, the configured master table configuration data can be standardized, thereby enabling the construction of master table configuration data with a clear correspondence to the preset process scenario elements. This facilitates subsequent rapid querying, reuse, and maintenance, reducing management costs.

[0065] Please see Figure 3 In some implementations, the method further includes: 0112: Based on the received second trigger signal for the main table configuration data, verify the complete status of the second target field of the main table configuration data, and the existence of historical anomaly judgment criteria that are the same as the preset process scenario elements of the anomaly judgment criteria and have been enabled. 0113: If the second target field is complete and there is no historical anomaly judgment standard that is the same as the preset process scenario element of the anomaly judgment standard and has been enabled, save the anomaly judgment standard.

[0066] In some implementations, the rule configuration module is further configured to verify the completeness of the second target field of the main table configuration data and the existence of historical anomaly judgment criteria that are identical to the preset process scenario elements of the anomaly judgment criteria and have been enabled, based on the received second trigger signal for the main table configuration data. If the second target field is complete and there are no historical anomaly judgment criteria that are identical to the preset process scenario elements of the anomaly judgment criteria and have been enabled, the anomaly judgment criteria are saved.

[0067] In some implementations, the processor is further configured to, based on the received second trigger signal for the main table configuration data, verify the integrity of the second target field of the main table configuration data, and the existence of historical anomaly judgment criteria that are identical to the preset process scenario elements of the anomaly judgment criteria and have been enabled. If the second target field is complete and no historical anomaly judgment criteria identical to the preset process scenario elements of the anomaly judgment criteria and have been enabled exist, the processor saves the anomaly judgment criteria.

[0068] Specifically, the second trigger signal refers to the user-initiated command to save the main table configuration data, such as clicking the "Save Main Table" button on the system interface, which is the trigger condition for starting the pre-save verification process.

[0069] The second target field refers to the mandatory field in the main table configuration data that ensures the effective operation of the rule. It is the key to binding the anomaly judgment criteria with the process scenario. If it is missing, the rule cannot take effect normally.

[0070] Historical anomaly judgment criteria refer to the anomaly judgment criteria of past configurations that have been stored in the system database. The scenario elements may overlap with the current configuration.

[0071] Saving refers to the operation of persistently storing the verified master table configuration data into the system. The saved data will serve as the basis for subsequent anomaly detection and rule management.

[0072] After the user completes the field filling in of the main table configuration data, a second trigger signal is triggered, and the system immediately starts the dual verification process.

[0073] First, the system performs a completeness check on the target fields in the main table. The system automatically checks whether all target fields, such as parameter type, anomaly criteria name, production line, production process, and model information, are filled in. If any field is missing, the system immediately displays a prompt: "Please enter XXX (missing field name)," terminating the save process and re-triggering it only after the user completes the field.

[0074] Subsequently, based on the preset process scenario elements in the current main table, the system retrieves historical anomaly judgment criteria from the system database. If a historical anomaly judgment criterion with completely identical scenario elements exists and is in an enabled state, the system displays an alarm message: "This type of anomaly judgment criterion already exists; please do not add it again," and terminates the saving process. If this type of anomaly judgment criterion does not exist, or the anomaly judgment criterion is in a disabled state, the verification passes.

[0075] After the above verification is passed, the configuration data of the main table, the creator or modifier, and the creation time or modification time are integrated into complete data, stored in the system database, and a mapping relationship between the code and the main table data is established to provide data support for subsequent sub-table configuration, rule query, and execution call.

[0076] Thus, based on the received first trigger signal, the master table information for configuring anomaly judgment criteria is generated, and master table configuration data is produced. The first target field of the master table information includes at least one of the following: parameter type, name of the anomaly judgment criterion, production line, production process, and model information. The master table configuration data includes the code in the anomaly judgment criterion, which is generated based on the production process and model information. In this way, by clearly defining the first target field, the configured master table configuration data can be standardized, thereby enabling the construction of master table configuration data with a clear correspondence to the preset process scenario elements. This facilitates subsequent rapid querying, reuse, and maintenance, reducing management costs.

[0077] Please see Figure 4 In some implementations, the anomaly determination criteria include sub-table configuration data. Step 011 (in response to a received configuration instruction, configuring anomaly determination criteria associated with preset process scenario elements) includes: 0114: Based on the received third trigger signal, configure the rule details of the anomaly judgment criteria and generate sub-table configuration data.

[0078] In some implementations, the rule configuration module is also used to configure the rule details information of the anomaly judgment criteria based on the received third trigger signal and generate sub-table configuration data.

[0079] In some implementations, the processor is also used to generate sub-table configuration data based on the received third trigger signal, the rule details of the configuration anomaly determination criteria.

[0080] Specifically, the third trigger signal refers to the specific operation signal that the user triggers to configure the sub-table rules details. For example, clicking the "Add Sub-table Item" button in the system is a key trigger condition for starting the sub-table field entry and configuration process.

[0081] The rule details refer to the core content of the sub-table configuration data, which is the specific basis for anomaly judgment and includes at least one of the following: test time interval, priority, parameter name, and standard threshold information.

[0082] The test time interval refers to the time range within which the data used for anomaly detection is limited, ensuring the timeliness and relevance of data collection and avoiding invalid data from different time periods from interfering with the detection results. For example, a time range of 10 hours before the test and 5 hours after the test is set for a certain battery cell.

[0083] Priority refers to the order in which multiple rules are executed. It uses numerical data that cannot be repeated; the smaller the number, the higher the execution priority, ensuring that the rule execution logic is clear and conflict-free. For example, rule 1 has a priority of 1, and rule 2 has a priority of 2.

[0084] The parameter name refers to the specific electrical performance parameter identifier used by the sub-table configuration data. It corresponds to the parameter type in the main table and clarifies the specific monitoring indicator targeted by the sub-table rule. For example, the OCVS voltage corresponds to the voltage type in the main table.

[0085] Standard threshold information refers to the compliance boundaries corresponding to the defined parameter name, including the parameter's standard value, upper limit value, and lower limit value, which serves as the quantitative basis for determining whether a parameter is abnormal. For example, the standard value of OCVS voltage is 3.2V, the upper limit value is 3.3V, and the lower limit value is 3.1V.

[0086] After completing the basic configuration of the main table, users can trigger a third trigger signal to refine the judgment rules if needed, thus entering the sub-table rule detail configuration interface. Users select at least one item from the rule details information for configuration based on preset process scenario elements and actual judgment requirements: configure the test time interval to specify the effective data collection period; select parameter names to lock in specific judgment indicators; set standard threshold information to define compliance boundaries; if multiple sub-table rules exist, each rule must be configured with a unique priority value to specify the execution order. If "OR" relationship rules need to be configured, they can be defined through field identifiers to ensure that the corresponding judgment logic is triggered when any rule is met. After all detailed fields are configured, the system integrates this information to generate sub-table configuration data and establishes a connection with the corresponding main table configuration data, forming a complete anomaly judgment standard and providing a specific execution basis for subsequent anomaly judgments.

[0087] Thus, based on the received third trigger signal, the rule details for configuring the anomaly judgment criteria are configured, and sub-table configuration data is generated. The rule details include at least one of the following: test time interval, priority, parameter name, and standard threshold information. The priority is non-repeatable numerical data, and the standard threshold information is used to define the compliance threshold range for the parameter name. In this way, through explicit rule details, the configured sub-table configuration data can be standardized, thereby enabling the construction of sub-table configuration data with a clear correspondence to the preset process scenario elements, facilitating subsequent rapid querying, reuse, and maintenance, and reducing management costs.

[0088] Please see Figure 5 In some implementations, the method further includes: 0115: Based on the received fourth trigger signal for the sub-table configuration data, verify the validity status of the main table in the anomaly judgment criteria, and the integrity of the third target field of the sub-table configuration data; 0116: Save the sub-table configuration data when the main table is in a disabled state and the third target field is complete.

[0089] In some implementations, the rule configuration module is further configured to verify the validity status of the main table for anomaly judgment criteria and the integrity of the third target field of the sub-table configuration data based on the received fourth trigger signal for the sub-table configuration data. It also saves the sub-table configuration data if the main table is in a disabled state and the third target field is complete.

[0090] In some implementations, the processor is further configured to verify the validity status of the main table and the integrity of the third target field of the sub-table configuration data based on the received fourth trigger signal for the sub-table configuration data, according to the anomaly determination criteria. And, if the main table is in a disabled state and the third target field is complete, to save the sub-table configuration data.

[0091] Specifically, the fourth trigger signal refers to the specific signal that the user triggers the sub-table configuration data saving operation. For example, clicking the system's "Save Sub-table" button is a key trigger condition for starting the sub-table saving verification process, which connects with the third trigger signal for configuring sub-table details.

[0092] The validity status refers to the enabled or disabled status of the main table, which is divided into two types: enabled (the rule is being executed) and disabled (the rule is not being executed). It is the core basis for controlling the editing permissions of sub-tables.

[0093] The third target field refers to the mandatory field in the sub-table configuration data that ensures the validity of the judgment logic, including priority and / or parameter name. If this third target field is missing, the sub-table rules cannot be executed normally.

[0094] Thus, based on the received fourth trigger signal for the sub-table configuration data, the validity status of the main table for anomaly judgment criteria and the integrity of the third target field of the sub-table configuration data are verified. The third target field includes priority and / or parameter name. Then, if the main table is in a disabled state and the third target field is complete, the sub-table configuration data is saved. This way, by prohibiting modification of sub-table constraints while the main table is enabled, the judgment logic chaos caused by sudden changes in detailed rules during the execution of the anomaly judgment criteria can be avoided, ensuring the consistency of the anomaly judgment criteria during battery screening and reducing the risk of misjudgment and missed judgment. Furthermore, by verifying the integrity of the third target field, it can be ensured that every piece of sub-table configuration data can be implemented, improving the overall quality of the anomaly judgment criteria.

[0095] Please see Figure 6 In some implementations, the method further includes: 0117: Based on the received fifth trigger signal, switch the validity status of the anomaly judgment standard.

[0096] In some implementations, the rule configuration module is also used to switch the validity status of the anomaly determination criteria based on the received fifth trigger signal.

[0097] In some implementations, the processor is also configured to switch the validity state of the exception determination criterion based on the received fifth trigger signal.

[0098] Specifically, the fifth trigger signal refers to the specific operation signal that the user triggers the state switch of the abnormal judgment standard, such as clicking the system enable or disable button, which is the trigger condition for starting the state change process, and is related to the configuration data of the main table and the configuration data of the sub-table.

[0099] When a user needs to adjust the effective status of an anomaly judgment standard, the system locates the target anomaly judgment standard and triggers the fifth trigger signal. The system first identifies the current validity status of the anomaly judgment standard. If the current status is disabled, after the user clicks the enable button, the system updates the validity status to enabled, and automatically records the name of the person who modified the status and the modification time, updating the main table configuration data of the rule. After the status change, the rule immediately participates in the anomaly judgment of battery parameters, and the system will restrict the editing operations of the main table configuration data and sub-table configuration data of the target anomaly judgment standard. If the current status is enabled, after the user clicks the disable button, the system updates the validity status to disabled, and simultaneously records the person who modified the status and the modification time. After the status change, the rule suspends participation in anomaly judgment, and the system removes the editing restrictions on the main table configuration data and sub-table configuration data, allowing the user to modify the rule details or delete the rule as needed. In some implementations, if the user repeatedly triggers the same status switching signal, such as clicking "enable" again for an already enabled rule, the system will not perform the status change operation to avoid invalid and redundant processing.

[0100] Thus, based on the received fifth trigger signal, the validity status of the anomaly judgment standard is switched, including an enabled state and a disabled state. In this way, by switching the validity status of the anomaly judgment standard, the anomaly judgment standard can be quickly adapted to the needs of production process adjustments, product iterations, etc. Old rules can be temporarily disabled rather than deleted, and can be directly enabled when they need to be reused later, which greatly reduces the cost of repeated configuration.

[0101] Please see Figure 7 In some implementations, the method further includes: 0118: When the anomaly judgment criteria are disabled, the configuration data of the main table and / or the configuration data of the sub-table are edited according to the received sixth trigger signal; 0119: Based on the received seventh trigger signal, perform logical deletion processing on the configuration data of the main table and / or the configuration data of the sub-table.

[0102] In some implementations, the rule configuration module is also used to edit the main table configuration data and / or sub-table configuration data according to the received sixth trigger signal when the anomaly judgment criteria are disabled, and to logically delete the main table configuration data and / or sub-table configuration data according to the received seventh trigger signal.

[0103] In some implementations, the processor is further configured to, when the anomaly detection criteria are disabled, edit the main table configuration data and / or the sub-table configuration data according to the received sixth trigger signal, and logically delete the main table configuration data and / or the sub-table configuration data according to the received seventh trigger signal.

[0104] Specifically, the sixth trigger signal refers to the signal that the user triggers the editing operation of the main table configuration data or the sub-table configuration data. For example, clicking the system "Edit" button is the trigger condition for starting the data modification process, and it is only effective when triggered in the disabled state.

[0105] Editing refers to the operation of modifying, supplementing or adjusting the configuration data of the main table and / or the configuration data of the sub-table. After modification, the corresponding save and verification process must be followed.

[0106] The seventh trigger signal refers to the signal that the user triggers the deletion operation of the main table configuration data / sub-table configuration data, such as clicking the system "Delete" button. This is the key trigger condition for starting the logical deletion process and can only be triggered when the system is disabled.

[0107] Logical deletion is not a physical removal of data. Instead, it marks the data deletion status, such as by setting a deletion flag, so that the data no longer participates in the normal business process of the system. The data itself is still retained in the system, supporting subsequent traceability and recovery of the deletion.

[0108] When a user triggers the sixth trigger signal, the system first checks if the rule's current status is disabled. If it is not disabled, an error message is displayed and the operation is terminated. If the verification passes, the system unlocks editing permissions for the main table and / or sub-tables, allowing the user to modify the scenario information in the main table or the judgment details in the sub-table.

[0109] When a user triggers the seventh trigger signal, the system also checks whether the rule's status is disabled and whether the rule's current status is disabled. If it is not disabled, an error message is displayed and the operation is terminated. If the verification passes, the system performs a logical deletion operation, preventing it from participating in normal business processes such as rule querying and judgment. Simultaneously, the system automatically records the person who modified the deletion and the modification time, leaving a data trace. Deleted data can be traced through the backend, and if it needs to be reused later, its valid status can be restored through permission operations.

[0110] Thus, when the anomaly detection criteria are disabled, the configuration data in the main table and / or sub-tables is edited based on the received sixth trigger signal. Based on the received seventh trigger signal, the configuration data in the main table and / or sub-tables is logically deleted. By restricting the editing or deletion of the main table and / or sub-table configuration data when enabled, logical abrupt changes in the anomaly detection criteria during operation can be avoided, ensuring the consistency of the criteria during cell screening and reducing the risk of misjudgments or omissions due to tampering with the anomaly detection criteria.

[0111] Please see Figure 8 In some implementations, the method further includes: 0120: Based on the received eighth trigger signal and the first query condition, query the anomaly judgment criteria.

[0112] In some implementations, the rule configuration module is also used to query the anomaly judgment criteria based on the received eighth trigger signal and the first query condition.

[0113] In some implementations, the processor is also configured to query the anomaly determination criteria based on the received eighth trigger signal and the first query condition.

[0114] Specifically, the eighth trigger signal refers to the specific signal that triggers the query operation of the abnormal judgment criteria by the user, such as clicking the system query button, which is a key trigger condition for starting the query process.

[0115] The first query condition refers to the set of filtering dimensions used to filter the target anomaly judgment criteria, including the name of the anomaly judgment criteria, model information, production process and / or the validity status of the anomaly judgment criteria, which can be flexibly adapted to different query needs.

[0116] Users can set the first query condition on the system's anomaly judgment criteria query interface according to their actual needs. For example, they can enter keywords in the name input box, select specific options corresponding to model information and production process, or select the validity status through the drop-down box. The setting of the first query condition supports single-dimensional or multi-dimensional combinations.

[0117] After the user completes the setting of the first query condition, the system triggers the eighth trigger signal. The system first verifies the legality of the first query condition. If the first query condition is legal, the system will traverse all the exception judgment criteria stored in the system based on the query condition and match the corresponding field information in the configuration data of the main table. That is, the exception judgment criteria that the name contains keywords, the model information is consistent, the production process is matched, and the validity status is consistent will be filtered out.

[0118] Query results are typically displayed in list format, including core information about the anomaly criteria, such as code, name, model, process, validity status, and creation time. Users can use the list to further locate the target anomaly criteria, providing an entry point for subsequent operations such as editing, enabling, reusing, or deleting. If no matching results are found, the system can provide prompts to help users adjust their search criteria and search again.

[0119] Thus, based on the received eighth trigger signal and the first query condition, the anomaly judgment standard is queried. The first query condition includes the name, model information, production process, and / or validity status of the anomaly judgment standard. By covering the name, model information, production process, and validity status of the first query condition, fuzzy queries and combinations of multiple conditions can be supported, thereby significantly improving the accuracy and speed of the query.

[0120] Please see Figure 9 In some implementations, step 012 (determining the abnormality of the battery by judging the collected electrical performance parameters according to the anomaly judgment criteria) includes: 0121: Obtain the electrochemical parameters of the target battery during the test time interval; 0122: Outlier removal processing for electrochemical parameters; 0123: Calculate the target judgment value based on the electrochemical parameters after outlier removal and the test time interval; 0124: Determine the abnormal situation of the target battery based on the target judgment value and standard threshold information.

[0121] In some implementations, the rule execution module is further configured to acquire the electrochemical parameters of the target battery within the test time interval, and to perform outlier removal processing on the electrochemical parameters. The rule execution module is also configured to calculate a target judgment value based on the outlier-removed electrochemical parameters and the test time interval, and to determine any abnormalities in the target battery based on the target judgment value and standard threshold information.

[0122] In some implementations, the processor is further configured to acquire the electrochemical parameters of the target battery within the test time interval, and to perform outlier removal processing on the electrochemical parameters. The processor is also configured to calculate a target judgment value based on the outlier-removed electrochemical parameters and the test time interval, and to determine any abnormalities in the target battery based on the target judgment value and standard threshold information.

[0123] Specifically, the target battery refers to a single battery or batch of batteries that are currently in the testing process and need to be judged for abnormalities; it is the object carrier of the judgment process.

[0124] The test time interval refers to a specific time range set for evaluating the stability of battery performance, used to collect continuous parameter data within that period.

[0125] Electrochemical parameters refer to the core electrical characteristics of a battery during the test period. They are the basic data source for judging battery quality, including voltage, internal resistance, K value, etc., which directly reflect the energy state and internal structural integrity of the battery.

[0126] Outlier removal refers to the process of screening and purifying the collected raw electrochemical parameters. A preset algorithm is used to remove extreme deviations caused by equipment malfunctions, testing errors, etc., ensuring that the parameters used in the calculations are accurate and valid. In some implementations, the preset algorithm may be based on standard deviation, median deviation, etc.

[0127] The target judgment value refers to the quantitative result obtained by calculating the effective electrochemical parameters after outlier removal. It is a bridge connecting the original data and the anomaly judgment, such as LSS value and standard deviation, and has clear physical meaning and comparable attributes.

[0128] Standard threshold information refers to the compliance boundary data defined in the anomaly judgment criteria, including the upper limit, lower limit or range of parameters, and is the core basis for judging whether the target judgment value is compliant.

[0129] An abnormal situation refers to a battery quality problem status determined by the system when the target judgment value exceeds the compliance range defined by the standard threshold information. This situation needs to be clearly marked and processed subsequently.

[0130] The system filters out the electrochemical parameters of the target battery within the specified time interval from the raw data uploaded from the production equipment, based on the test time interval configured in the anomaly judgment criteria sub-table. If no test time interval is configured, the system defaults to acquiring all electrochemical parameters of the single disk containing the target battery, ensuring that the data acquisition matches the judgment scenario.

[0131] Subsequently, a standardized algorithm was used to process the screened electrochemical parameters, identifying and removing extreme data that exceeded reasonable ranges, such as data deviating from the median by more than three standard deviations. This process, by eliminating outliers caused by chance, ensured the authenticity and representativeness of the remaining data, providing a reliable foundation for subsequent calculations.

[0132] Next, based on the parameter types configured in the main table of anomaly judgment criteria, the corresponding calculation method is selected. Taking LSS value calculation as an example, the system first calculates the median or mean of the electrochemical parameters after purification, and then divides the electrochemical parameter value of a single cell by the median or mean to obtain the target judgment value. If auxiliary calculation indicators such as standard deviation and outlier degree are involved, the calculation is completed simultaneously based on the electrochemical parameters after outlier removal.

[0133] Finally, the system compares the calculated target judgment value with the standard threshold information in the sub-table configuration data. If the target judgment value is within the compliance range defined by the standard threshold, the battery is determined to be normal. If it exceeds the compliance range or fails to meet other requirements in the rules, the battery is determined to be abnormal, and the abnormality type is marked, such as self-discharge abnormality, high internal resistance, etc., completing the entire abnormality judgment process.

[0134] In this way, the electrochemical parameters of the target battery within the test time interval are obtained. Next, outlier removal processing is performed on the electrochemical parameters. Then, based on the outlier-removed electrochemical parameters and the test time interval, a target judgment value is calculated. Finally, based on the target judgment value and standard threshold information, the abnormality of the target battery is determined. In this way, outlier removal processing can eliminate interference from invalid data. The comparison between the target judgment value and standard threshold information enables the quantification and implementation of the judgment standard. Furthermore, the combination of outlier removal processing and the comparison between the target judgment value and standard threshold information can significantly improve the identification rate of abnormal cells, thereby effectively reducing the missed and false positives of anomalies.

[0135] Please see Figure 10 In some implementations, step 013 (based on the received target process scenario elements and query time interval, querying and displaying the corresponding target anomaly from the battery's anomaly conditions) includes: 0131: Based on the received ninth trigger signal, verify the integrity of the obtained second query condition; 0132: If the second query condition is complete, perform a query based on the second query condition, and determine the target abnormal condition corresponding to the second query condition from the abnormal conditions of the battery.

[0136] In some implementations, the result query module is further configured to verify the completeness of the acquired second query condition based on the received ninth trigger signal, and, if the second query condition is complete, perform a query based on the second query condition to determine the target abnormal condition corresponding to the second query condition from among the abnormal conditions of the battery.

[0137] In some implementations, the processor is further configured to verify the completeness of the acquired second query condition based on the received ninth trigger signal, and if the second query condition is complete, to perform a query based on the second query condition to determine the target abnormal condition corresponding to the second query condition from among the abnormal conditions of the battery.

[0138] Specifically, the ninth trigger signal refers to the specific signal that triggers the user to perform a query operation for the target abnormal situation, such as clicking the system's "Query Results" button, which is the trigger condition for starting the query process and condition verification.

[0139] The second query condition refers to the core set of conditions used to filter out target anomalies, including target process scenario elements and target time intervals, which is the basis for ensuring the accuracy of query results.

[0140] The target process scenario elements refer to the scenario filtering dimensions specified by the user during the query. They correspond to the preset process scenario elements and include target parameter types, target production lines, target production processes, and target model information, which are used to define the scenario boundaries of the query results.

[0141] The target time interval refers to the user-specified period for querying abnormal situations, including the start and end times. It is used to filter abnormal data generated within a specific time period to ensure the time accuracy of the query results.

[0142] Integrity verification refers to the system checking the required fields of the second query condition to confirm that the core fields of the target process scenario elements and the target time interval have been filled with valid information, so as to avoid invalid query results due to missing conditions.

[0143] The target abnormal situation refers to the result set generated by the system after judging the abnormality of the battery's electrical performance parameters according to the abnormality judgment criteria. The result set includes information such as whether the battery is abnormal, the type of abnormality, and relevant parameter data.

[0144] Upon receiving the ninth trigger signal, the system enters the result query module interface and configures the second query conditions according to actual needs. For example, in the target process scenario element dimension, the target parameter type, target production line, target production process, and target model information are selected through a two-level selection page. In the target time interval dimension, the start and end times are manually selected or entered to specify the time range for the query. After completing the configuration of the second query conditions, the system immediately initiates a completeness check of the second query conditions. For example, it checks one by one whether valid options have been selected for the target parameter type, target production line, target production process, and target model information, and whether the start and end times of the target time interval have been filled in and are logically sound.

[0145] If any core condition is missing or invalid, the system will display an error message: "Please enter XXX (missing condition name)" and terminate the query, waiting for the user to complete the query. If all conditions are complete and valid, and the validation passes, the system will iterate through all target anomalies stored in the system based on the second query condition and match the corresponding result data. After the query is completed, the system usually displays the results in a list format, allowing users to view details, and also provides a data export function for subsequent analysis and archiving.

[0146] Thus, based on the received ninth trigger signal, the completeness of the acquired second query condition is verified. The second query condition includes target process scenario elements and target time intervals. The target process scenario elements include target parameter type, target production line, target production process, and target model information. Next, if the second query condition is complete, a query is performed based on the second query condition to determine the target abnormal situation corresponding to the second query condition from among the abnormal situations of the battery. In this way, by verifying the completeness of the second query condition, invalid queries due to missing conditions can be avoided.

[0147] Please see Figure 11 The present application also provides a battery parameter monitoring system 1000, which includes a battery parameter monitoring device 100, and the battery parameter monitoring device includes a rule configuration module 110, a rule execution module 120 and a result query module 130. The rule configuration module 110 is configured to, in response to a received configuration instruction, configure anomaly judgment criteria associated with preset process scenario elements; The rule execution module 120 is configured to determine the abnormality of the battery by judging the collected electrical performance parameters according to the abnormality judgment criteria. The result query module 130 is configured to query and display the corresponding target abnormal situation from the abnormal situations of the battery based on the received target process scenario elements and query time interval.

[0148] Specifically, the rule configuration module 110 can respond to received configuration instructions and configure anomaly judgment criteria associated with preset process scenario elements. The rule execution module 120 can also perform anomaly judgment on the collected battery electrical performance parameters according to the anomaly judgment criteria, that is, by obtaining the electrochemical parameters of the target battery within a specified test time interval, removing outliers, calculating the target judgment value, and comparing it with standard threshold information to determine the battery anomaly. The result query module 130 can receive the target process scenario elements and the query time interval, and after verifying the completeness of the query conditions, filter and display the corresponding target anomalies from the battery anomalies.

[0149] In some embodiments, the battery parameter monitoring system 1000 further includes an input device 200 and an output device 300. The input device 200 is used to receive various operation commands and data configuration information initiated by the user, including but not limited to configuration commands for entering preset process scenario elements to trigger anomaly judgment criteria, first query conditions, second query conditions, and various trigger signals. The input device 200 can be a keyboard, mouse, touchscreen, or other similar devices, supporting multiple interaction methods such as manual input, drop-down selection, and batch import, ensuring that users can efficiently complete operations such as scenario configuration, condition filtering, and function triggering.

[0150] The output device 300 can output key data and judgment results from the entire battery parameter monitoring process in a variety of ways, such as through a display, based on actual production needs. The display provides a visual interactive interface and data display window, supporting the visualization of operations and the presentation of results throughout the battery parameter monitoring process. Thus, through an intuitive interface layout and data visualization, the display can provide users with visual support for configuration operation guidance, real-time monitoring feedback, and historical data traceability, thereby improving the efficiency of anomaly judgment standard configuration, anomaly data query, and production process monitoring.

[0151] Therefore, a battery parameter monitoring system is provided, comprising a battery parameter monitoring device, which includes a rule configuration module, a rule execution module, and a result query module. The rule configuration module responds to received configuration commands and configures anomaly judgment criteria associated with preset process scenario elements. The rule execution module, based on the anomaly judgment criteria, performs anomaly judgment on the collected battery electrical performance parameters to determine the battery's abnormal condition. The result query module, based on the received target process scenario elements and query time interval, queries and displays the corresponding target anomaly from the battery's anomaly conditions. Thus, by configuring anomaly judgment criteria associated with preset process scenario elements, flexible adaptation of anomaly judgment criteria can be achieved, thereby solving the problem of insufficient targeting in traditional monitoring methods. Furthermore, through precise query filtering and result display, rapid traceability of abnormal data can be achieved, providing data support for production process optimization.

[0152] This application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of the detection method for the battery cell to be processed as described above.

[0153] It is understood that a computer program includes computer program code. Computer program code can be in the form of source code, object code, executable files, or some intermediate form. Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc.

[0154] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the above-described method.

[0155] In this specification, the terms "specifically," "furthermore," "particularly," "understandably," etc., refer to specific features, structures, materials, or characteristics described in connection with embodiments or examples that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0156] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of executable request code comprising one or more steps for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0157] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for monitoring battery parameters, characterized in that, The method includes: In response to the received configuration instructions, configure the anomaly judgment criteria associated with the preset process scenario elements; According to the aforementioned anomaly determination criteria, the collected electrical performance parameters of the battery are assessed for anomalies to determine the abnormal condition of the battery. Based on the received target process scenario elements and query time interval, query and display the corresponding target abnormal situation from the abnormal situation of the battery.

2. The battery parameter monitoring method according to claim 1, characterized in that, The preset process scenario elements include at least one of the following: battery parameter type, production line, production process, and model information. The parameter type includes voltage, internal resistance, capacity decay coefficient, or voltage drop rate.

3. The battery parameter monitoring method according to claim 2, characterized in that, The anomaly determination criteria include main table configuration data. The configuration of anomaly determination criteria associated with preset process scenario elements in response to received configuration instructions includes: Based on the received first trigger signal, configure the main table information of the anomaly judgment standard, and generate the main table configuration data. The first target field of the main table information includes at least one of the parameter type, the name of the anomaly judgment standard, the production line, the production process, and the model information. The main table configuration data includes the code in the anomaly judgment standard, and the code in the anomaly judgment standard is generated based on the production process and the model information.

4. The battery parameter monitoring method according to claim 3, characterized in that, The method further includes: Based on the received second trigger signal for the main table configuration data, verify the complete status of the second target field of the main table configuration data, and the existence of historical anomaly judgment criteria that are the same as the preset process scenario elements of the anomaly judgment criteria and have been enabled. If the second target field is complete and there is no historical anomaly judgment standard that is the same as the preset process scenario element of the anomaly judgment standard and has been enabled, save the anomaly judgment standard.

5. The battery parameter monitoring method according to claim 4, characterized in that, The anomaly determination criteria include sub-table configuration data. The configuration of anomaly determination criteria associated with preset process scenario elements in response to received configuration instructions includes: Based on the received third trigger signal, configure the rule details information of the anomaly judgment criteria and generate sub-table configuration data. The rule details information includes at least one of test time interval, priority, parameter name and standard threshold information. The priority is non-repeatable numerical data, and the standard threshold information is used to define the compliance threshold interval of the parameter name.

6. The battery parameter monitoring method according to claim 5, characterized in that, The method further includes: Based on the received fourth trigger signal for the sub-table configuration data, verify the validity status of the main table of the anomaly judgment criteria and the integrity of the third target field of the sub-table configuration data, wherein the third target field includes priority and / or parameter name; If the main table is in a disabled state and the third target field is complete, save the configuration data of the sub-table.

7. The battery parameter monitoring method according to any one of claims 3-6, characterized in that, The method further includes: Based on the received fifth trigger signal, the validity status of the anomaly determination criterion is switched, and the validity status includes an enabled state and a disabled state.

8. The battery parameter monitoring method according to claim 6, characterized in that, The method further includes: When the anomaly determination criteria are in the disabled state, the main table configuration data and / or the sub-table configuration data are edited according to the received sixth trigger signal; Based on the received seventh trigger signal, perform logical deletion processing on the main table configuration data and / or the sub-table configuration data.

9. The battery parameter monitoring method according to claim 5 or 6, characterized in that, The method further includes: Based on the received eighth trigger signal and the first query condition, the anomaly determination standard is queried. The first query condition includes the name of the anomaly determination standard, the model information, the production process, and / or the validity status of the anomaly determination standard.

10. The battery parameter monitoring method according to claim 5 or 6, characterized in that, The step of determining the abnormality of the battery by judging the collected electrical performance parameters according to the abnormality judgment criteria includes: Obtain the electrochemical parameters of the target battery during the test time interval; Outlier removal was performed on the electrochemical parameters. Calculate the target judgment value based on the electrochemical parameters after outlier removal and the test time interval; The abnormal condition of the target battery is determined based on the target judgment value and the standard threshold information.

11. The battery parameter monitoring method according to any one of claims 1-6, characterized in that, The step of querying and displaying corresponding target anomalies from the battery's anomalies based on the received target process scenario elements and query time interval includes: Based on the received ninth trigger signal, verify the completeness of the obtained second query condition. The second query condition includes the target process scenario element and the target time interval. The target process scenario element includes at least one of the target parameter type, target production line, target production process and target model information. If the second query condition is complete, a query is performed based on the second query condition to determine the target abnormal condition corresponding to the second query condition from the abnormal conditions of the battery.

12. A battery parameter monitoring system, characterized in that, The battery parameter monitoring system includes a battery parameter monitoring device, which includes a rule configuration module, a rule execution module, and a result query module. The rule configuration module is configured to respond to received configuration instructions and configure anomaly judgment criteria associated with preset process scenario elements; The rule execution module is configured to determine the abnormality of the battery by performing anomaly determination on the collected electrical performance parameters of the battery according to the anomaly determination criteria. The result query module is configured to query and display the corresponding target abnormal situation from the abnormal situations of the battery based on the received target process scenario elements and query time interval.

13. A computer device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-11.

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