Voltage layering identification method and related device

Abstract

The invention provides a voltage layering identification method and a related device, which are applied to a processing module in a battery energy storage system, the battery energy storage system comprises at least one battery module, and the battery module is composed of a plurality of battery cells; the method comprises the following steps: acquiring an energy storage time sequence parameter to determine charging and discharging data of a battery module; according to the voltage value sequence, calculating a voltage dispersion score under each cell grouping number; and performing hierarchical identification operation based on the voltage dispersion score to output a diagnosis result which is used for representing whether the battery energy storage system has a voltage hierarchical fault or not. Thus, the processing module obtains the charging and discharging data of the battery module by obtaining the energy storage time sequence parameters of the battery energy storage system in the operation process, and the voltage layering condition of the battery module is detected by analyzing the charging and discharging data, so that the abnormal trend can be found in the early stage of a fault, and the stability and safety of the system are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the field of new energy power system energy storage, and particularly relates to a voltage stratification identification method and a related device. BACKGROUND

[0002] At present, fault diagnosis of an energy storage system mainly depends on simple judgment of absolute value thresholds of voltage, current and the like.

[0003] However, the fault diagnosis method sets fixed thresholds to alarm parameter abnormalities, and can quickly respond to part of sudden faults, but the analysis dimension is single, lacks in-depth analysis of the internal voltage condition of a battery module, and it is difficult to effectively identify voltage stratification phenomena caused by connection loosening, inconsistent cell aging and the like. Since such a traditional method lacks the analysis capability of voltage spatial distribution, the actual application effect is limited, and it is difficult to meet the actual operation safety requirements of a battery energy storage system. SUMMARY

[0004] The application provides a voltage stratification identification method and a related device, so as to realize grouping of voltage sequences according to a physical topology structure, and calculation of voltage dispersion scores under different grouping quantities, thereby accurately identifying regular voltage stratification faults, realizing the transition from passive response threshold overrun to active early warning distribution mode abnormality, and significantly improving the early nature, accuracy and reliability of fault diagnosis.

[0005] In a first aspect, an embodiment of the application provides a voltage stratification identification method applied to a processing module in a battery energy storage system, the battery energy storage system comprising at least one battery module, the battery module being composed of a plurality of cells; the method comprising: obtaining energy storage time sequence parameters to determine the charge and discharge data of the battery module, wherein the energy storage time sequence parameters refer to voltage parameters, current parameters and corresponding time sequence information recorded in the normal operation process of the battery energy storage system, and the charge and discharge data are used to indicate the voltage value sequence of each battery module after completing a single charging cycle or discharging cycle; calculating voltage dispersion scores under each cell grouping quantity according to the voltage value sequence, wherein the cell grouping quantity is determined based on the physical topology structure of the battery module, and the voltage dispersion is used to quantify the dispersion degree of the voltage distribution in the battery module under any one cell grouping quantity; performing stratification identification operations based on the voltage dispersion scores to output diagnosis results, wherein the diagnosis results are used to represent whether the battery energy storage system has a voltage stratification fault.

[0006] In a second aspect, embodiments of this application provide an electronic device including a processor, a memory, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps as described in the first aspect of embodiments of this application.

[0007] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps in the first aspect of embodiments of this application.

[0008] Fourthly, embodiments of this application provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement some or all of the steps described in the first aspect of embodiments of this application.

[0009] As can be seen from the embodiments of this application, the processing module accurately identifies regular voltage stratification faults by grouping the voltage sequence according to the physical topology and calculating the voltage dispersion score under different group numbers. This effectively prevents more serious safety accidents that may be caused by overheating of connection points or accelerated local aging, ensuring the stable operation of the energy storage system. In addition, accurate fault location enables maintenance personnel to perform precise operations, thereby reducing unnecessary downtime and manual inspections and significantly improving maintenance efficiency. Furthermore, timely detection and handling of voltage stratification problems helps prevent some cells in the battery pack from being in adverse operating conditions for a long time (such as overcharging or over-discharging), promoting balanced operation of the battery pack, thereby delaying overall aging and extending the system's service life. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the structure of a battery energy storage system provided in an embodiment of this application; Figure 2 This is a schematic flowchart of a voltage layer identification method provided in an embodiment of this application; Figure 3 This is a schematic flowchart of another voltage layer identification method provided in an embodiment of this application; Figure 4 This is a schematic flowchart of another voltage layer identification method provided in the embodiments of this application; Figure 5This is a functional unit block diagram of a voltage stratification identification device provided in an embodiment of this application; Figure 6 This is a functional unit block diagram of another voltage stratification identification device provided in the embodiments of this application; Figure 7 This is a structural block diagram of a processing module provided in an embodiment of this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0013] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0014] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0015] Please see Figure 1 , Figure 1 This is a schematic diagram of a battery energy storage system provided in an embodiment of this application. Figure 1 As shown, the battery energy storage system 100 includes a processing module 110 and a battery module 120, wherein the battery module 120 is composed of multiple battery cells 121 (i.e., Figure 1 The battery modules 120 (cells 1 to n) are integrated with a voltage acquisition beam. Specifically, in the actual architecture of the battery energy storage system 100, multiple battery modules 120 can be combined into a battery cluster, and multiple battery clusters can be combined into a battery energy storage array. A single battery cluster is composed of multiple battery modules 120 (i.e., battery module 1, battery module 2 to battery module n) connected in series.Figure 1 This is a simplified structural diagram. To achieve the high voltage and large capacity required by the energy storage system, the electrical connection between cells 121 is typically in series, while within a module, they can be connected in parallel first and then in series. Battery modules 120 within the same battery cluster are all connected in series, while battery clusters can be connected in series or in parallel.

[0016] Furthermore, in the battery energy storage system 100, the processing module 110 is responsible for information processing, intelligent analysis, and decision-making. The battery module 120 is responsible for energy storage and release, and is also the object being monitored. Together, they form a closed-loop system. The battery module 120 provides data, and the processing module 110 analyzes the data and diagnoses the health status of the battery module 120.

[0017] Furthermore, the processing module 110 collects energy storage timing parameters through a data acquisition device installed on the battery module 120. Upon receiving the energy storage timing parameters, the processing module 110 executes a series of algorithms to diagnose potential cell voltage stratification faults within the entire battery energy storage system 100. If the processing module 110 diagnoses a voltage stratification fault that may be caused by loose connections, cell aging, or other reasons, it generates control commands or feedback messages to instruct relevant technicians to address the faults in the battery energy storage system 100. The processing module 110 can be any commercially available active unit or main control board (i.e., a microcontroller or digital signal processor), or a cloud server with data uploading capabilities. Typically, the battery energy storage system 100 can correspond to one or more processing modules 110. Each processing module 110 is responsible for collecting energy storage timing parameters from multiple cells 121 in one or more battery modules 120, thereby diagnosing whether each cell 121 has a corresponding fault.

[0018] Based on this, the present application provides a voltage layer identification method, and the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0019] Example 1: The framework of the voltage layer identification method in this application is described below.

[0020] Please see Figure 2 , Figure 2 This is a flowchart illustrating a voltage stratification identification method provided in an embodiment of this application. The method is applied to a processing module in a battery energy storage system. The battery energy storage system includes at least one battery module, and the battery module is composed of multiple battery cells. The method includes: Step S201: Obtain energy storage timing parameters to determine the charge and discharge data of the battery module.

[0021] Among them, energy storage time sequence parameters refer to the voltage parameters, current parameters and corresponding time sequence information recorded during the normal operation of the battery energy storage system. Charge and discharge data are used to indicate the voltage value sequence after each battery module completes a single charge cycle or discharge cycle.

[0022] In one possible embodiment, obtaining energy storage timing parameters to determine the charge and discharge data of the battery module includes: parsing the time series information corresponding to the voltage parameters and the time series information corresponding to the current parameters in the energy storage timing parameters to determine the charge and discharge state transition time; determining the voltage data set within each charging cycle or discharging cycle based on the charge and discharge state transition time and the voltage parameters in the energy storage timing parameters; and determining the voltage data set that meets the validity conditions as the charge and discharge data.

[0023] The charging / discharging state transition time is determined based on the change in current direction and the trend of voltage change. The validity conditions include the cycle duration condition and the preset voltage boundary condition. The cycle duration condition is used to indicate that the duration of a single charging cycle or discharging cycle is not less than the preset duration. The preset voltage boundary condition is set based on the normal operating voltage range of the battery. The preset voltage boundary condition is used to indicate that the highest voltage value after the completion of the charging cycle is higher than the preset first voltage value, and the lowest voltage value after the completion of the discharging cycle is lower than the preset second voltage value.

[0024] The charge / discharge state transition time is determined based on the changes in current direction and voltage trends. Specifically, it involves monitoring the time series information of current parameters to identify current direction change patterns. These patterns distinguish between the charging state corresponding to a charging cycle, the discharging state corresponding to a discharging cycle, and the stationary state. The current direction in the charging state is inflow into the battery module, while the current direction in the discharging state is outflow from the battery module. Simultaneously, the time series information of voltage parameters is analyzed to calculate the voltage change slope characteristics. These slope characteristics verify the continuity of the charge / discharge process; the voltage shows an upward trend during the charging cycle and a downward trend during the discharging cycle. Validity conditions include cycle duration conditions and preset voltage boundary conditions.

[0025] Preferably, the cycle duration condition is used to indicate that the duration of a single charging cycle or discharging cycle is not less than 0.5 hours. The preset voltage boundary condition is set based on the normal operating voltage range of the battery. The preset voltage boundary condition is used to indicate that the highest voltage value after the completion of the charging cycle is higher than 3.45V and the lowest voltage value after the completion of the discharging cycle is lower than 3.15V.

[0026] As can be seen in this example, the processing module improves the accuracy of state transition time identification by performing multi-parameter fusion verification during the data acquisition process, and the validity screening eliminates short-term fluctuations and noise, providing a high-quality and reliable data foundation for subsequent voltage stratification analysis.

[0027] In one possible embodiment, parsing the time series information corresponding to the voltage parameter and the time series information corresponding to the current parameter in the energy storage time series parameters to determine the charge / discharge state transition time includes: monitoring the time series information of the current parameter and identifying the current direction change pattern; analyzing the time series information of the voltage parameter and calculating the voltage change slope characteristics; and determining the charge / discharge state transition time based on the current direction change pattern and the voltage change slope characteristics.

[0028] The current direction change mode is used to distinguish the charging state corresponding to the charging cycle, the discharging state corresponding to the discharging cycle, and the resting state. The current direction corresponding to the charging state is flowing into the battery module, and the current direction corresponding to the discharging state is flowing out of the battery module. The voltage change slope feature is used to verify the continuity of the charging and discharging process. The voltage corresponding to the charging cycle shows an upward trend, and the voltage corresponding to the discharging cycle shows a downward trend.

[0029] The determination of the charging and discharging state transition time combines the synergistic analysis of current direction and voltage slope. For example, when the current changes from positive to negative (charging to discharging), the time T1 is recorded, and at the same time, the voltage slope changes from positive to negative, and the time T2 is recorded. If the time difference is less than a preset threshold (such as 1 minute), the weighted average is taken as the final transition time to ensure the accuracy of the time point.

[0030] As can be seen in this example, the processing module improves the accuracy of state transition time identification by performing multi-parameter fusion verification during the data acquisition process, avoids misjudgment of a single parameter, improves the robustness of state identification, and lays the foundation for subsequent data filtering.

[0031] Step S202: Calculate the voltage dispersion score for each cell group based on the voltage value sequence.

[0032] The number of cell groups is determined based on the physical topology of the battery module, and the voltage dispersion is used to quantify the degree of dispersion of the voltage distribution within the battery module under any given number of cell groups.

[0033] In one possible embodiment, the voltage dispersion score for each number of cell groups is calculated based on the voltage value sequence, including: setting multiple candidate group numbers associated with the physical topology of the battery module; for each candidate group number, determining whether the sequence length of the voltage value sequence is an integer multiple of the number of candidate groups currently being processed; if so, determining that the number of candidate groups currently being processed is the number of cell groups, and dividing the voltage value sequence into multiple continuous and uniform voltage groups according to the number of cell groups, with the cells in each voltage group being electrically adjacent; performing an extremum removal operation for each voltage group to update the voltage group; calculating the corresponding voltage average value for each updated voltage group; and calculating the voltage dispersion score corresponding to the number of cell groups currently being processed based on all voltage average values. If not, marking the number of candidate groups currently being processed as invalid, and continuing to traverse the next number of candidate groups until all candidate group numbers have been processed.

[0034] In this design, the cells within each voltage group are electrically adjacent. This means that the grouping is based on the actual physical layout of the battery module (such as series or parallel structure), ensuring that the cells within each group are directly connected electrically, thus accurately reflecting the voltage distribution characteristics inside the module. For example, for a module composed of multiple cells connected in series, the continuity of the cells must be maintained during grouping to avoid cross-group splitting, in order to accurately capture stratification caused by loose connections or aging. Preferably, the number of cells can range from 8 to 70, and the number of candidate groups covers common configurations such as 16, 24, and 32. The divisibility of the sequence length is verified by traversal to ensure that the grouping is uniform and conforms to topological constraints.

[0035] The voltage dispersion score is calculated using the standard deviation algorithm, whereby the dispersion score is equal to the square root of the sum of the squares of the differences between the average of all voltage groups and the overall average. This score quantifies the dispersion of the voltage distribution: a higher score indicates a greater voltage difference between groups and a more pronounced stratification phenomenon. Preferably, the dispersion score threshold is set to 0.002; stratification identification is triggered when the score exceeds this threshold. Furthermore, extreme value removal eliminates the influence of individual cell anomalies (such as sensor noise). For example, removing the highest and lowest voltage values ​​in each group before calculating the average improves the robustness of the algorithm and eliminates abnormal data caused by sensor noise. As can be seen, in this example, the grouping and discreteness calculation mechanism can effectively identify voltage stratification faults, and the accuracy of fault location is ensured by electrically connecting adjacent groups. Furthermore, the standard deviation algorithm is simple and efficient, reducing computational complexity and making it suitable for real-time processing; extreme value removal and threshold comparison enhance the method's adaptability to data noise and reduce false positives. Overall, this method improves the accuracy of fault diagnosis and its engineering practicality, providing reliable support for the safe operation and maintenance of energy storage systems.

[0036] In one possible embodiment, after calculating the voltage dispersion score corresponding to the number of cell groups currently being processed based on all voltage averages, the method further includes: arranging the voltage averages corresponding to each voltage group according to the grouping order corresponding to the number of cell groups to determine the average value sequence; calculating the statistical deviation score for each voltage group for all average value sequences; identifying voltage groups whose corresponding statistical deviation scores exceed a preset statistical threshold as abnormal voltage groups, and recording the abnormal group number and the corresponding statistical deviation score.

[0037] The statistical deviation score is calculated in the form of Z-scores to quantify the degree of deviation of the average value of each voltage group from the average value sequence. The abnormal group number is the position identifier of the abnormal voltage group in the voltage value sequence.

[0038] The statistical deviation score is calculated using the Z-score formula: Z = (X - μ) / σ, where X represents the average value of a single voltage group, μ represents the arithmetic mean of the average value sequence (i.e., the average of all group averages), and σ represents the standard deviation of the average value sequence. This formula, through standardization, converts the average value of each group into a deviation score relative to the overall sequence, expressed as a multiple of standard deviation. For example, if a group has a Z-score of 2.5, it means its average value is 2.5 standard deviations higher than the overall mean. Preferably, the preset statistical threshold parameter can be set to 2.5. When the Z-score exceeds this threshold, the voltage of that group is considered abnormal, possibly due to loose connections or cell aging. An abnormal group number records the starting position of that group in the voltage value sequence (e.g., number 1 represents the first group), facilitating precise fault location. This calculation method eliminates the influence of individual cell fluctuations, focusing on inter-group differences and improving the targeting of fault identification.

[0039] As can be seen in this example, the processing module effectively distinguishes between normal voltage fluctuations and abnormal stratification through statistical deviation quantification, reducing the false positive rate. Simultaneously, the design of abnormal grouping numbers provides intuitive fault location information, facilitating rapid intervention by maintenance personnel, thereby enhancing the safety and maintenance efficiency of the energy storage system. Overall, this method combines statistical principles and engineering practice, achieving a seamless transition from data to decision-making.

[0040] Step S203: Perform a hierarchical identification operation based on the voltage dispersion score to output the diagnostic results.

[0041] The diagnostic results are used to characterize whether voltage stratification faults exist in the battery energy storage system.

[0042] Voltage stratification faults refer to problems such as voltage distribution stratification caused by cell aging or loose connections.

[0043] In one possible embodiment, a hierarchical identification operation is performed based on the voltage dispersion score to output a diagnostic result, including: determining the voltage dispersion score with the largest value as the highest dispersion score among the voltage dispersion scores corresponding to the number of cell groups; comparing the highest dispersion score with a preset dispersion threshold; if the highest dispersion score exceeds the preset dispersion threshold, recording the number of cell groups corresponding to the highest dispersion score as the target number of groups; and outputting a diagnostic result indicating that there is a voltage stratification fault in the battery energy storage system.

[0044] The preset dispersion threshold is determined based on the historical normal data and historical fault data of the corresponding battery module, and the diagnostic results carry the target group number, abnormal group number and corresponding statistical deviation score.

[0045] The process of determining the preset dispersion threshold involves statistical analysis of historical operating data: extracting the voltage dispersion score distribution from the normal operation records of the battery module, calculating its mean or percentile (such as the 95th percentile) as a reference benchmark; simultaneously, adjusting the threshold to ensure sensitivity by combining data from historical fault cases (such as known loose connection events). For example, the threshold is often set to 0.002V, which can effectively distinguish between normal voltage fluctuations and stratified faults. The diagnostic results not only indicate the existence of the fault but also contain detailed information, namely, the number of target groups reflects the actual cell range configuration of the module (ranging from 8 to 70), the abnormal group number locates the fault group position, and the statistical deviation score (Z score) quantifies the degree of abnormality.

[0046] As can be seen, in this example, the processing module improves the accuracy and efficiency of the judgment by comparing the highest dispersion score with the threshold. Furthermore, the historical data-driven threshold setting enhances the adaptability of the method, making it suitable for different battery types and operating environments. In addition, the rich information in the final diagnostic results (such as the number of groups and deviation scores) enables precise operation and maintenance, reducing unnecessary downtime checks, thereby improving the overall economy and safety of the energy storage system.

[0047] As can be seen, in this embodiment, Example 1 ensures the accuracy of data acquisition through multi-parameter fusion verification and validity screening; it employs a physical topology-based grouping strategy and standard deviation algorithm to quantify voltage distribution dispersion, and combines Z-score statistics to identify abnormal groups, achieving early and accurate location of voltage stratification faults caused by cell aging, loose connections, etc. This method significantly improves diagnostic accuracy, robustness, and engineering practicality (no additional equipment required), supporting the transformation of energy storage systems from passive response to proactive early warning, and effectively ensuring the safe operation of the system.

[0048] Example 2: The voltage stratification identification method in this application will be described below with reference to the specific implementation of the statistical verification mechanism.

[0049] Please see Figure 3 , Figure 3 This is a flowchart illustrating another voltage stratification identification method provided in an embodiment of this application. The method is applied to a processing module in a battery energy storage system. The battery energy storage system includes at least one battery module, and the battery module is composed of multiple battery cells. The method includes: Step S301: Obtain energy storage timing parameters to determine the charging and discharging data of the battery module.

[0050] Among them, energy storage time series parameters refer to the voltage parameters, current parameters, and corresponding time series information recorded during the normal operation of the battery energy storage system. Charge and discharge data are used to indicate the voltage value sequence after each battery module completes a single charge cycle or discharge cycle. The specific implementation of this step is the same as in Example 1, including parsing the voltage and current time series to determine the charge and discharge state transition time, and applying validity conditions (such as cycle length not less than 0.5 hours, charging end voltage not less than 3.45V or discharging end voltage not higher than 3.15V) to filter data to ensure data quality.

[0051] Step S302: Calculate the voltage dispersion score for each cell group based on the voltage value sequence.

[0052] The number of cell groups is determined based on the physical topology of the battery module (typically ranging from 8 to 70), and voltage dispersion is used to quantify the degree of dispersion of voltage distribution within the battery module under any given number of cell groups. This step is achieved through grouping, extreme value removal, and standard deviation calculation, with details similar to Example 1.

[0053] Step S303: Perform a hierarchical identification operation based on the voltage dispersion score to output the diagnostic results.

[0054] The diagnostic results are used to characterize whether a voltage stratification fault exists in the battery energy storage system. If the diagnostic results indicate yes, the process jumps to step S304. This step includes determining the highest dispersion score (maximum value), comparing it with a preset threshold (preferably, the preset threshold can be 0.002), and outputting the results (including the number of target groups, abnormal group numbers, etc.).

[0055] Step S304: Identify the abnormal battery module based on the diagnostic results.

[0056] Among them, identifying abnormal battery modules refers to identifying specific battery modules marked as having voltage stratification faults from the diagnostic results, which will then serve as targets for subsequent verification.

[0057] Step S305: Perform multiple independent detection operations on the abnormal battery module and count the frequency of abnormal voltage groups in each independent detection operation.

[0058] The independent detection operation repeatedly executes steps S301 to S303, with each detection based on different charge-discharge cycle data to ensure data independence. For example, for the same abnormal module, voltage sequences are acquired, dispersion scores are calculated, and hierarchical identification is performed in multiple charge-discharge cycles. The frequency of occurrence is the proportion of abnormal voltage groups (e.g., groups with Z scores exceeding 2.5) appearing in multiple detections. For example, if 10 independent detections are performed and the abnormal group appears 8 times, the frequency is 80%.

[0059] Step S306: If the frequency of occurrence of abnormal voltage groups exceeds a preset frequency threshold, the diagnostic result is maintained; if the frequency of occurrence of abnormal voltage groups is less than or equal to the preset frequency threshold, the diagnostic result is updated to "no".

[0060] Specifically, the preset frequency threshold is preferably set to 85%, meaning that a persistent fault is only confirmed when the abnormal group appears stably in more than 85% of the detections; otherwise, it is considered an occasional fluctuation, and the updated result is no fault.

[0061] As can be seen, in this example, the statistical verification mechanism filters out temporary interference (such as sensor noise or transient load changes) through multiple independent checks (e.g., repeated data acquisition, dispersion calculation, and hierarchical identification), ensuring that only repetitive anomalies are confirmed, significantly reducing the risk of false positives and false negatives. At the same time, this method enhances the reliability of the diagnostic results, providing a stable basis for operational and maintenance decisions.

[0062] As can be seen, in this embodiment, the statistical verification mechanism and the core detection steps (S301-S303) form a closed loop, achieving an upgrade from single diagnosis to multiple verifications. Through frequency statistics, it is possible to distinguish between intermittent anomalies and stable faults, improving the accuracy of fault identification. Overall, this embodiment improves the early warning and stability of energy storage system faults, supports long-term safe operation, and reduces unnecessary maintenance costs.

[0063] Example 3: The voltage stratification identification method in this application will be described below with reference to the specific implementation of the early warning operation after fault diagnosis.

[0064] Please see Figure 4 , Figure 4 This is a flowchart illustrating another voltage stratification identification method provided in this application embodiment. The method is applied to a processing module in a battery energy storage system. The battery energy storage system includes at least one battery module, and the battery module is composed of multiple battery cells. The method includes: Step S401: Obtain energy storage timing parameters to determine the charging and discharging data of the battery module.

[0065] Among them, energy storage time-series parameters refer to the voltage parameters, current parameters, and corresponding time series information recorded during the normal operation of the battery energy storage system. Charge and discharge data are used to indicate the voltage value sequence after each battery module completes a single charge or discharge cycle. The specific implementation of this step is the same as in Example 1, ensuring data quality through parsing the time series data and validity screening.

[0066] Step S402: Calculate the voltage dispersion score for each cell group based on the voltage value sequence.

[0067] The number of cell groups is determined based on the physical topology of the battery module, and the voltage dispersion is used to quantify the degree of dispersion of the voltage distribution within the battery module under any given number of cell groups.

[0068] The number of cell groups is determined based on the physical topology of the battery module, and voltage dispersion is used to quantify the degree of dispersion of voltage distribution within the battery module. This step is achieved through grouping, extreme value removal, and standard deviation calculation, with details the same as in Example 1.

[0069] Step S403: Perform a hierarchical identification operation based on the voltage dispersion score to output the diagnostic results.

[0070] The diagnostic results are used to characterize whether the battery energy storage system has voltage stratification faults (such as voltage distribution stratification problems caused by cell aging or loose connections). This step includes determining the highest dispersion score, comparing it with a threshold, and outputting results containing the number of target groups, abnormal group numbers, and Z-scores.

[0071] Step S404: Based on the fault detection results, execute the corresponding early warning operation.

[0072] The specific implementation method of step S404 is as follows: In one possible embodiment, after maintaining the diagnostic results, the method further includes: determining the fault level based on the voltage dispersion score and occurrence frequency corresponding to each independent detection operation; generating a fault diagnosis report containing the target group number, abnormal group number, statistical deviation score, and fault level; generating processing suggestion information by matching a preset operation and maintenance strategy library according to the fault diagnosis report; and pushing the processing suggestion information and fault diagnosis report to the operation and maintenance management platform or associated devices in real time through a communication interface, and storing the fault diagnosis report in historical fault data.

[0073] Specifically, fault levels are classified based on a combination of dispersion score and the frequency of occurrence of abnormal groups. For example, a dispersion score between 0.002 and 0.005 with a frequency of 85%-90% is defined as a low-level fault (requiring attention); a score between 0.005 and 0.01 with a frequency of 90%-95% is a medium-level fault (requiring planned maintenance); and a score > 0.01 or a frequency > 95% is a high-level fault (requiring urgent handling). The level classification is based on statistical optimization of historical fault data to ensure it matches the risk level.

[0074] Furthermore, the report can be organized in a structured format, which includes fault location (abnormal group number), severity (Z score and fault level), module configuration (number of target groups), and detection timestamp, facilitating subsequent querying and analysis.

[0075] For example, the operation and maintenance strategy library is a predefined rule library, such as: low-level faults suggest "increase monitoring frequency", medium-level faults suggest "tighten connections during the next shutdown", and high-level faults suggest "stop immediately and replace the faulty cell". The matching process automatically selects the optimal strategy based on the fault level and anomaly type (such as Z-score distribution pattern).

[0076] As can be seen in this example, the processing module executes early warning operations through hierarchical management and policy library matching, achieving precise operation and maintenance, and improving resource utilization efficiency through hierarchical management. Furthermore, automated report generation and push shorten response time and reduce the risk of human error. At the same time, the accumulation of historical data lays the foundation for long-term system optimization, demonstrating the sustainability of intelligent operation and maintenance.

[0077] As can be seen, in this embodiment, the processing module achieves full-process coverage of "detection-diagnosis-disposal," from data acquisition to fault confirmation, and then to tiered early warning and handling suggestions. This not only improves the timeliness and accuracy of fault handling but also builds an adaptive operation and maintenance system through a strategy library and historical data, significantly enhancing the reliability and economy of the energy storage system and ultimately ensuring the safe and stable operation of the system.

[0078] The following are embodiments of the apparatus of this application. These embodiments of the apparatus and the embodiments of the method of this application belong to the same concept and are used to execute the methods described in the embodiments of this application. For ease of explanation, only the parts related to the apparatus embodiments of this application are shown in the embodiments of this application. For specific technical details not disclosed, please refer to the description of the embodiments of the method of this application, which will not be repeated here.

[0079] This application provides a voltage stratification identification device applied to a processing module in a battery energy storage system. Specifically, the voltage stratification identification device is used to execute the steps performed by the processing module in the voltage stratification identification method described above. The voltage stratification identification device provided in this application may include modules corresponding to the respective steps.

[0080] This application embodiment can divide the voltage stratification identification device into functional modules according to the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. The module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0081] When dividing each function into modules according to its corresponding function. Figure 5 This is a functional unit block diagram of a voltage stratification identification device provided in an embodiment of this application; the voltage stratification identification device is applied to... Figure 1 The processing module in the battery energy storage system shown includes at least one battery module, which is composed of multiple battery cells. The voltage stratification identification device 50 includes: a data acquisition unit 501, which acquires energy storage time-series parameters to determine the charge and discharge data of the battery module. The energy storage time-series parameters refer to the voltage parameters, current parameters, and corresponding time series information recorded during the normal operation of the battery energy storage system. The charge and discharge data are used to indicate the voltage value sequence after each battery module completes a single charge cycle or discharge cycle. A score calculation unit 502 calculates the voltage dispersion score for each number of cell groups based on the voltage value sequence. The number of cell groups is determined based on the physical topology of the battery module. The voltage dispersion is used to quantify the degree of dispersion of the voltage distribution within the battery module under any given number of cell groups. A result output unit 503 performs a stratification identification operation based on the voltage dispersion score to output a diagnostic result. The diagnostic result is used to characterize whether there is a voltage stratification fault in the battery energy storage system.

[0082] In one possible embodiment, in acquiring energy storage timing parameters to determine the charging and discharging data of the battery module, the data acquisition unit 501 is specifically used to: parse the time series information corresponding to the voltage parameter and the time series information corresponding to the current parameter in the energy storage timing parameters, and determine the charging and discharging state transition time, wherein the charging and discharging state transition time is determined based on the current direction change and voltage change trend; determine the voltage data set within each charging cycle or discharging cycle according to the charging and discharging state transition time and the voltage parameter in the energy storage timing parameters; determine the voltage data set that meets the validity conditions as charging and discharging data, the validity conditions including the cycle duration condition and the preset voltage boundary condition, the cycle duration condition is used to indicate that the duration corresponding to a single charging cycle or discharging cycle is not less than the preset duration, the preset voltage boundary condition is set based on the normal operating voltage range of the battery, and the preset voltage boundary condition is used to indicate that the highest voltage value after the completion of the charging cycle is higher than the preset first voltage value, and the lowest voltage value after the completion of the discharging cycle is lower than the preset second voltage value.

[0083] In one possible embodiment, in analyzing the time series information corresponding to the voltage parameter and the time series information corresponding to the current parameter in the energy storage time series parameters to determine the charge / discharge state transition time, the data acquisition unit 501 is specifically used to: monitor the time series information of the current parameter and identify the current direction change pattern, wherein the current direction change pattern is used to distinguish the charging state corresponding to the charging cycle, the discharging state corresponding to the discharging cycle, and the quiescent state, the current direction corresponding to the charging state is flowing into the battery module, and the current direction corresponding to the discharging state is flowing out of the battery module; analyze the time series information of the voltage parameter and calculate the voltage change slope feature, wherein the voltage change slope feature is used to verify the continuity of the charge / discharge process, the voltage corresponding to the charging cycle shows an upward trend, and the voltage corresponding to the discharging cycle shows a downward trend; and determine the charge / discharge state transition time based on the current direction change pattern and the voltage change slope feature.

[0084] In one possible embodiment, in calculating the voltage dispersion score for each number of cell groups based on the voltage value sequence, the score calculation unit 502 is specifically configured to: set multiple candidate group numbers associated with the physical topology of the battery module; for each candidate group number, determine whether the sequence length of the voltage value sequence is an integer multiple of the number of candidate groups currently being processed; if so, determine that the number of candidate groups currently being processed is the number of cell groups, and divide the voltage value sequence into multiple continuous and uniform voltage groups according to the number of cell groups, wherein the electrical connections of the cells within each voltage group are adjacent; perform an extremum removal operation for each voltage group to update the voltage group; calculate the corresponding voltage average value for each updated voltage group; and calculate the voltage dispersion score corresponding to the number of cell groups currently being processed based on all voltage average values. If not, mark the number of candidate groups currently being processed as invalid, and continue to traverse the next number of candidate groups until all candidate group numbers have been processed.

[0085] In one possible embodiment, after calculating the voltage dispersion score corresponding to the number of cell groups currently being processed based on all voltage averages, the score calculation unit 502 is further configured to: arrange the voltage averages corresponding to each voltage group according to the grouping order corresponding to the number of cell groups to determine the average value sequence; calculate the statistical deviation score for each voltage group for all average value sequences, wherein the statistical deviation score is calculated in the form of Z-scores to quantify the degree of deviation of the average value of each voltage group from the average value sequence; determine the voltage groups whose corresponding statistical deviation scores exceed a preset statistical threshold as abnormal voltage groups, and record the abnormal group number and the corresponding statistical deviation score, wherein the abnormal group number is the position identifier of the abnormal voltage group in the voltage value sequence.

[0086] In one possible embodiment, in performing a hierarchical identification operation based on voltage dispersion scores to output diagnostic results, the result output unit 503 is specifically configured to: determine the voltage dispersion score with the largest value as the highest dispersion score among the voltage dispersion scores corresponding to the number of cell groups; compare the highest dispersion score with a preset dispersion threshold, wherein the preset dispersion threshold is determined based on the historical normal data and historical fault data of the corresponding battery module; if the highest dispersion score exceeds the preset dispersion threshold, record the number of cell groups corresponding to the highest dispersion score as the target number of groups; and output a diagnostic result indicating that there is a voltage stratification fault in the battery energy storage system, the diagnostic result carrying the target number of groups, the abnormal group number, and the corresponding statistical deviation score.

[0087] In one possible embodiment, after performing a hierarchical identification operation based on the voltage dispersion score to output a diagnostic result, the result output unit 503 is further configured to: determine the abnormal battery module based on the diagnostic result; perform multiple independent detection operations for the abnormal battery module; count the frequency of occurrence of abnormal voltage groups in each independent detection operation; if the frequency of occurrence of abnormal voltage groups exceeds a preset frequency threshold, maintain the diagnostic result; if the frequency of occurrence of abnormal voltage groups is less than or equal to the preset frequency threshold, update the diagnostic result to "no".

[0088] In one possible embodiment, after maintaining the diagnostic results, the result output unit 503 is further configured to: determine the fault level based on the voltage dispersion score and occurrence frequency corresponding to each independent detection operation; generate a fault diagnosis report containing the number of target groups, abnormal group numbers, statistical deviation scores, and fault levels; generate processing suggestion information by matching a preset operation and maintenance strategy library according to the fault diagnosis report; and push the processing suggestion information and fault diagnosis report to the operation and maintenance management platform or associated devices in real time through a communication interface, and store the fault diagnosis report in historical fault data.

[0089] When using integrated units, such as Figure 6 As shown, Figure 6 This is a functional unit block diagram of another voltage stratification identification device provided in this application embodiment. Figure 6 In this document, the voltage stratification identification device 60 includes a processing module 602 and a communication module 601. The processing module 602 controls and manages the operation of the voltage stratification identification device 60, including, for example, the steps of the data acquisition unit 501, the score calculation unit 502, and the result output unit 503, and / or other processes for executing the techniques described herein. The communication module 601 supports interaction between the voltage stratification identification device and other devices. Figure 6 As shown, the voltage stratification identification device may include a storage module 603, which is used to store the program code and data of the voltage stratification identification device.

[0090] The processing module 602 may be a processor or processing module, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication module 601 may be a transceiver, RF circuitry, or a communication interface, etc. The storage module 603 may be a memory.

[0091] All relevant content in each scenario involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here. The voltage stratification identification device 60 described above can all perform the above... Figure 2 The voltage stratification identification method shown.

[0092] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0093] Figure 7 This is a structural block diagram of a processing module provided in an embodiment of this application. For example... Figure 7As shown, the processing module 70 may include one or more of the following components: a processor 701 and a memory 702 coupled to the processor 701, wherein the memory 702 may store one or more computer programs 703, and the one or more computer programs 703 may be configured to implement the methods described in the above embodiments when executed by one or more processors 701. The processing module 70 here is the processing module 110 in the above embodiments.

[0094] Processor 701 may include one or more processing cores. Processor 701 connects various parts within the processing module 70 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 702, and by calling data stored in memory 702. Optionally, processor 701 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 701 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 701 and may be implemented separately using a communication chip.

[0095] The memory 702 may include random access memory (RAM) or read-only memory (ROM). The memory 702 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 702 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described above. The data storage area may also store data created by the processing module 70 during use.

[0096] It is understood that the processing module 70 may include more or fewer structural elements than those shown in the above block diagram, and this is not limited here.

[0097] This application also provides a computer storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements some or all of the steps of any of the methods described in the above method embodiments.

[0098] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0099] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0101] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically comprised separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0103] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute partial steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, volatile memory, or non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM), etc., which are various media capable of storing program code.

[0104] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A voltage stratification identification method, characterized in that, A processing module applied in a battery energy storage system, the battery energy storage system including at least one battery module, the battery module being composed of multiple battery cells; the method includes: The energy storage timing parameters are obtained to determine the charge and discharge data of the battery module. The energy storage timing parameters refer to the voltage parameters, current parameters and corresponding time series information recorded during the normal operation of the battery energy storage system. The charge and discharge data are used to indicate the voltage value sequence of each battery module after completing a single charge cycle or discharge cycle. Based on the voltage value sequence, the voltage dispersion score is calculated for each number of cell groups, wherein the number of cell groups is determined based on the physical topology of the battery module, and the voltage dispersion is used to quantify the degree of dispersion of the voltage distribution within the battery module under any given number of cell groups. A stratification identification operation is performed based on the voltage dispersion score to output a diagnostic result, which is used to characterize whether the battery energy storage system has a voltage stratification fault.

2. The method according to claim 1, characterized in that, The step of obtaining the energy storage timing parameters to determine the charge and discharge data of the battery module includes: Analyze the time series information corresponding to the voltage parameter and the time series information corresponding to the current parameter in the energy storage timing parameters to determine the charging and discharging state transition time; Based on the charge / discharge state transition time and the voltage parameter in the energy storage timing parameters, determine the voltage data set for each charging cycle or the discharging cycle; The voltage data set that meets the validity conditions is determined as the charge / discharge data. The validity conditions include a cycle duration condition and a preset voltage boundary condition. The cycle duration condition is used to indicate that the duration of a single charge cycle or discharge cycle is not less than a preset duration. The preset voltage boundary condition is set based on the normal operating voltage range of the battery. The preset voltage boundary condition is used to indicate that the highest voltage value after the completion of the charge cycle is higher than a preset first voltage value, and the lowest voltage value after the completion of the discharge cycle is lower than a preset second voltage value.

3. The method according to claim 2, characterized in that, The step of analyzing the time series information corresponding to the voltage parameter and the time series information corresponding to the current parameter in the energy storage timing parameters to determine the charge / discharge state transition time includes: The time series information of the current parameter is monitored to identify the current direction change pattern. The current direction change pattern is used to distinguish the charging state corresponding to the charging cycle, the discharging state corresponding to the discharging cycle, and the resting state. The current direction corresponding to the charging state is flowing into the battery module, and the current direction corresponding to the discharging state is flowing out of the battery module. Analyze the time series information of the voltage parameters and calculate the voltage change slope characteristics. The voltage change slope characteristics are used to verify the continuity of the charging and discharging process. The voltage corresponding to the charging cycle shows an upward trend, and the voltage corresponding to the discharging cycle shows a downward trend. The charging / discharging state transition time is determined based on the current direction change pattern and voltage change slope characteristics.

4. The method according to any one of claims 1-3, characterized in that, The step of calculating the voltage dispersion score for each cell group based on the voltage value sequence includes: Set the number of candidate groups associated with the physical topology of the battery module; For each number of candidate groups, determine whether the sequence length of the voltage value sequence is an integer multiple of the number of candidate groups currently being processed; If so, the number of candidate groups currently being processed is determined to be the number of cell groups, and the voltage value sequence is divided into multiple consecutive and uniform voltage groups according to the number of cell groups, wherein the electrical connections of the cells in each voltage group are adjacent. An extremum removal operation is performed for each of the voltage groups to update the voltage groups; For each updated voltage group, calculate the corresponding average voltage value; Based on all the voltage average values, calculate the voltage dispersion score corresponding to the number of cell groups currently being processed; If not, mark the current number of candidate groups being processed as invalid, and continue to traverse the next number of candidate groups until all the candidate groups have been processed.

5. The method according to claim 4, characterized in that, After calculating the voltage dispersion score corresponding to the number of cell groups currently being processed based on all the voltage averages, the method further includes: Arrange the average voltage values ​​corresponding to each voltage group according to the grouping order corresponding to the number of battery cell groups, so as to determine the average value sequence; For all the mean value sequences, a statistical deviation score is calculated for each voltage group, wherein the statistical deviation score is calculated in Z-score form to quantify the degree of deviation of the mean value of each voltage group from the mean value sequence; Voltage groups whose statistical deviation scores exceed a preset statistical threshold are identified as abnormal voltage groups, and the abnormal group number and the corresponding statistical deviation score are recorded. The abnormal group number is the position identifier of the abnormal voltage group in the voltage value sequence.

6. The method according to claim 5, characterized in that, The hierarchical identification operation based on voltage dispersion score, to output diagnostic results, includes: Based on the voltage dispersion score corresponding to the number of each cell group, the voltage dispersion score with the largest value is determined as the highest dispersion score; The highest dispersion score is compared with a preset dispersion threshold, wherein the preset dispersion threshold is determined based on the historical normal data and historical fault data of the corresponding battery module; If the highest dispersion score exceeds the preset dispersion threshold, then the number of cell groups corresponding to the highest dispersion score is recorded as the target number of groups; The output represents a diagnostic result indicating that the battery energy storage system has the voltage stratification fault. The diagnostic result carries the number of target groups, the abnormal group number, and the corresponding statistical deviation score.

7. The method according to claim 6, characterized in that, After performing the hierarchical identification operation based on the voltage dispersion score to output diagnostic results, the method further includes: The abnormal battery module was identified based on the diagnostic results. Perform multiple independent detection operations on the abnormal battery module; The frequency of occurrence of the abnormal voltage group in each of the independent detection operations is statistically analyzed; If the frequency of occurrence of the abnormal voltage group exceeds a preset frequency threshold, the diagnostic result is maintained. If the frequency of occurrence of the abnormal voltage group is less than or equal to a preset frequency threshold, the diagnostic result is updated to "No".

8. The method according to claim 7, characterized in that, After maintaining the diagnostic result, the method further includes: The fault level is determined based on the voltage dispersion score and the frequency of occurrence corresponding to each independent detection operation. Generate a fault diagnosis report that includes the target group quantity, the abnormal group number, the statistical deviation score, and the fault level; Based on the fault diagnosis report, a preset operation and maintenance strategy library is matched to generate processing suggestion information; The processing suggestion information and the fault diagnosis report are pushed to the operation and maintenance management platform or associated equipment in real time through the communication interface, and the fault diagnosis report is stored in the historical fault data.

9. An electronic device, characterized in that, The method includes a processor, a memory, a communication interface, and one or more programs, said programs being stored in the memory and configured to be executed by the processor, said programs including instructions for performing the steps of the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, A computer program for storing electronic data interchange is provided, wherein the computer program causes a computer to perform the method as described in any one of claims 1-8.

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