Evaluation method, system and equipment of battery alarm function and medium

Abstract

The invention belongs to the technical field of batteries, and particularly relates to a method for evaluating a battery alarm function, which comprises the following steps of: acquiring operation data and a corresponding actual alarm signal when a battery state is abnormal from a cloud end, and generating a corresponding expected alarm signal according to the operation data and a claimed threshold provided by a manufacturer. Four states of correct alarm, false alarm, missing alarm and no alarm are identified according to the difference between the actual alarm signal and the expected alarm signal, so that the false alarm times and the missing alarm times are identified and counted by using a sliding window statistical algorithm. Furthermore, the false alarm rate is calculated according to the false alarm times and the actual alarm times, and the missing alarm rate is calculated according to the missing alarm times and the expected alarm times. And finally, judging whether the corresponding alarm function meets the standard of the supervision platform according to the missing report rate and the false report rate. According to the invention, the relative state of the claimed threshold and the implicit threshold of the battery alarm function can be evaluated according to the actual operation data of the battery so as to verify whether the alarm function is safe and reliable.

Description

Technical Field

[0001] This invention belongs to the field of battery technology, specifically relating to an evaluation method, system, device, and medium for battery alarm functions. Background Technology

[0002] Batteries, as electrical energy storage components, include but are not limited to lead-acid batteries, lithium-ion batteries (including but not limited to lithium iron phosphate batteries, ternary lithium batteries, lithium titanate batteries, etc.), sodium-ion batteries, lithium metal batteries, semi-solid-state batteries, and solid-state batteries. They are widely used in various power consumption scenarios, such as electric vehicles, electric ships, electric aircraft, and other electrified transportation vehicles, as well as energy storage systems such as energy storage power stations, data centers, and intelligent computing centers. Therefore, the performance of batteries directly determines the economic efficiency and safety stability of related equipment.

[0003] However, with various abnormal states occurring during battery operation, such as overheating, overvoltage, and malfunctions, the reliability of its alarm function has become a key factor in maintaining the safe operation of the equipment. However, due to the threshold matching issue of the battery alarm function—that is, the implicit threshold actually used to generate the alarm signal may deviate from the threshold publicly claimed by the manufacturer—the alarm signal may not accurately reflect the true fault state of the battery, leading to false alarms or missed alarms.

[0004] Therefore, to ensure the safety and reliability of the battery alarm function, it is necessary to conduct a systematic and targeted analysis of the battery alarm signals and ensure that they comply with the technical standards and safety specifications set by regulatory authorities through rigorous verification. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to propose a method for evaluating whether the battery alarm function meets the standards of the regulatory platform by using actual battery operating data. By detecting false alarms and missed alarms in the alarm signals, and further analyzing the relative state of the claimed threshold and the implicit threshold, the method can maintain the operational safety of the battery.

[0006] To achieve the above and other related objectives, this invention provides a method for evaluating a battery alarm function, comprising: acquiring alarm signals when the battery state is abnormal, including actual alarm signals and expected alarm signals; using a sliding window statistical algorithm to identify and count the number of false alarms and the number of missed alarms in the alarm signals; using a sliding window statistical algorithm to identify and count the actual number of alarms for the actual alarm signals, and determining the false alarm rate of the alarm signals based on the number of false alarms and the actual number of alarms; using a sliding window statistical algorithm to identify and count the expected number of alarms for the expected alarm signals, and determining the missed alarm rate of the alarm signals based on the number of missed alarms and the expected number of alarms; and judging whether the corresponding alarm function meets the standards of the regulatory platform based on the missed alarm rate and the false alarm rate of the alarm signals.

[0007] According to a specific embodiment of the present invention, the actual alarm signal is generated based on the battery's operating parameters and the implicit threshold used to measure whether the parameters are abnormal, while the expected alarm signal is generated based on the battery's operating parameters and the claimed threshold set by the manufacturer to measure whether the parameters are abnormal.

[0008] According to a specific embodiment of the present invention, judging whether the corresponding alarm function meets the standards of the regulatory platform based on the false alarm rate and false alarm rate of the alarm signal includes: using a clustering algorithm to identify the relative state of the implicit threshold and the claimed threshold based on the false alarm rate and false alarm rate of the alarm signal, and judging whether the corresponding alarm function meets the standards of the regulatory platform based on it.

[0009] According to a specific embodiment of the present invention, the clustering algorithm is a density-based clustering algorithm.

[0010] According to a specific embodiment of the present invention, the step of identifying the relative state of the implicit threshold and the claimed threshold using a clustering algorithm based on the false alarm rate and false alarm rate of the alarm signal includes: defining the minimum number of samples for the clustering algorithm as 2; identifying the optimal distance to separate dense clusters from sparse noise points as the neighborhood radius based on the minimum number of samples and the K-distance map of the false alarm rate and the false alarm rate; performing two-dimensional clustering on the false alarm rate and the false alarm rate based on the minimum number of samples and the neighborhood radius, and identifying the relative state of the implicit threshold and the claimed threshold based on the clustering results.

[0011] According to a specific embodiment of the present invention, the step of identifying and counting the number of false alarms and the number of missed alarms in the alarm signal using a sliding window statistical algorithm includes: within any time window, identifying the state of the actual alarm signal and the expected alarm signal respectively: if the actual alarm signal triggers an alarm and the expected alarm signal does not trigger an alarm, then incrementing the number of false alarms of the alarm signal by one; if the actual alarm signal does not trigger an alarm and the expected alarm signal triggers an alarm, then incrementing the number of missed alarms of the alarm signal by one.

[0012] According to a specific embodiment of the present invention, the method further includes: calculating the false alarm rate and false alarm rate of several battery alarm signals of the same model respectively, and calculating the average false alarm rate and the average false alarm rate based on the several false alarm rates and the several false alarm rates, so as to judge whether the alarm function of this model of battery meets the standards of the regulatory platform.

[0013] A battery alarm function evaluation system includes: a data collection module for acquiring alarm signals when the battery status is abnormal, including actual alarm signals and expected alarm signals; a false alarm / missed alarm statistics module for identifying and counting the number of false alarms and missed alarms in the alarm signals using a sliding window statistical algorithm; a false alarm rate calculation module for identifying and counting the actual number of alarms for the actual alarm signals using a sliding window statistical algorithm, and determining the false alarm rate of the alarm signals based on the number of false alarms and the actual number of alarms; a missed alarm rate calculation module for identifying and counting the expected number of alarms for the expected alarm signals using a sliding window statistical algorithm, and determining the missed alarm rate of the alarm signals based on the number of missed alarms and the expected number of alarms; and an alarm function evaluation module for judging whether the corresponding alarm function meets the standards of the regulatory platform based on the missed alarm rate and false alarm rate of the alarm signals.

[0014] An electronic device includes a processor coupled to a memory storing program instructions that, when executed by the processor, implement the method described above.

[0015] A computer-readable storage medium includes a program that, when run on a computer, causes the computer to perform the method described above.

[0016] This invention provides a method for evaluating battery alarm functions. It can assess the safety and reliability of corresponding alarm functions based on battery alarm signals. Specifically, it analyzes the false alarm rate and leakage rate of alarm signals, or the average false alarm rate and average leakage rate of the same alarm signal for several batteries of the same model. Based on this, it fully analyzes the relative state of the implicit threshold and claimed threshold used to generate alarm signals, and verifies whether the alarm function meets the standard specifications. This enables the regulatory platform to make correct assessments of batteries produced by manufacturers and ensure the safe operation of batteries. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a specific embodiment of a battery alarm function evaluation method provided by the present invention;

[0018] Figure 2 This is a schematic diagram of a specific embodiment of the alarm signal provided by the present invention;

[0019] Figure 3 This is a schematic diagram of another specific embodiment of the alarm signal provided by the present invention;

[0020] Figure 4 A schematic diagram of a specific embodiment of the battery alarm function evaluation system provided by the present invention;

[0021] Figure 5This is a structural block diagram of a specific embodiment of an electronic device provided by the present invention. Detailed Implementation

[0022] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0023] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0024] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, publicly known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0025] Example 1

[0026] Please see Figure 1 The battery alarm function evaluation method shown can be applied to power batteries in electrified transportation vehicles such as electric vehicles, electric ships, and electric aircraft, or energy storage batteries in energy storage systems such as energy storage power stations, data centers, and intelligent computing centers. Specifically, it includes:

[0027] Step S100: Obtain alarm signals when the battery status is abnormal, including actual alarm signals and expected alarm signals.

[0028] It should be noted that the expected alarm signal is generated based on the battery's actual operating parameters (such as current, voltage, temperature, and state of charge) and the manufacturer's claimed thresholds for measuring whether these parameters are abnormal. For example, if any operating parameter of the battery exceeds the corresponding claimed threshold, it indicates that the battery may be experiencing an abnormality, and an alarm should be triggered to alert maintenance personnel.

[0029] The actual alarm signal is generated based on the battery's operating parameters and the implicit threshold used to measure whether these parameters are abnormal (i.e., the actual judgment standard embedded in the algorithm or hardware logic). For example, the manufacturer may specify that the battery input voltage should not exceed 400V (the claimed threshold). However, in practical applications, due to the flexibility of thresholds, the implicit threshold used to measure whether the battery input voltage is abnormal may not necessarily be 400V; it could be 450V or 500V. Therefore, when the battery input voltage actually reaches 400V, an alarm should be triggered, but it may not, resulting in a discrepancy between the expected alarm signal and the actual alarm signal, failing to maintain consistency.

[0030] In response, the actual operating data of the battery can be obtained from the cloud, that is, the working parameters of the battery during actual operation, and corresponding expected alarm signals can be generated based on the claimed thresholds set by the manufacturer to measure whether the parameters are abnormal.

[0031] It is understandable that there are no major restrictions on the preprocessing of the actual battery operating data. For example, the data can be denoised and cleaned first, and then used with the claimed threshold to generate the corresponding expected alarm signal. Modifications and refinements made by those skilled in the art to the embodiments of the present invention without departing from the spirit of the present invention still fall within the scope of the invention application patent of the present invention.

[0032] The actual alarm signal is generated by real-time monitoring and diagnosis of the data during the actual operation of the battery, and the alarm is triggered accordingly. It is also uploaded to the cloud along with the actual operation data.

[0033] Therefore, in this embodiment, signals from these two different sources are used together as the battery alarm signal.

[0034] Furthermore, in order to reveal whether the claimed threshold and the implicit threshold of a certain alarm function of the battery are consistent, it is necessary to conduct a specific analysis of the actual alarm signal and the expected alarm signal.

[0035] Specifically, such as Figure 2 As shown, if at a certain moment the actual alarm signal indicates that an alarm has been triggered (represented by "1"), but the expected alarm signal indicates that an alarm has not been triggered (represented by "0"), this indicates that the battery's alarm function has triggered a false alarm. Conversely, if at a certain moment the actual alarm signal does not trigger an alarm (represented by "0"), but the expected alarm signal triggers an alarm (represented by "1"), this indicates that the battery's alarm function has missed an alarm.

[0036] Understandably, if both the actual alarm signal and the expected alarm signal are 1 at a certain moment, it means that the battery's alarm function has been correctly triggered and there has been no missed alarm. If both the actual alarm signal and the expected alarm signal are 0 at a certain moment, it means that the battery has not experienced any abnormality, and the corresponding alarm function has not been falsely triggered.

[0037] Therefore, based on the difference between the actual alarm signal and the expected alarm signal, the four states of correct alarm, false alarm, missed alarm, and no alarm can be identified, thereby counting the number of false alarms and missed alarms.

[0038] It is also understandable that the battery alarm function can be further divided into multi-level alarms, such as level 1 alarm, level 2 alarm, level 3 alarm, etc., which can be represented by "1", "2", "3" respectively.

[0039] The occurrence of false alarms can be represented as follows:

[0040] The occurrence of underreporting can be represented as follows:

[0041] Where AA(t) i ) and EA(t i () represent the actual alarm signal and the expected alarm signal at time t, respectively. i The value of FA(t) (the current state), and FA(t) i ) and MA(t i ) represent the time t respectively i Whether false alarms and false negatives occur, i.e., FA(t) i A value of 1 indicates a false alarm; similarly, MA(t) = 1. i A value of 1 indicates a missed detection. When FA(t) is 1, it means a false negative has occurred. i ) and MA(t i When FA(t) is 2, it indicates a correct alarm. i ) and MA(t i When the value is 0, it means there is no alarm. In these two states, it also means that there is neither a false alarm nor a missed alarm.

[0042] Step S200: Use a sliding window statistical algorithm to identify and count the number of false alarms and the number of missed alarms in the alarm signals.

[0043] To count the number of false alarms and missed alarms, a sliding time window algorithm can be used to process alarm signals. This involves determining whether a false alarm or missed alarm has occurred within a time window based on the status of the actual alarm signal and the expected alarm signal, thereby counting the number of false alarms and missed alarms.

[0044] Specifically, the "false alarm" state can be identified starting from the end time of the alarm signal (FA(t)). i The number of times () = 1) occurs, such as Figure 2 , 3 As shown. The time window continuously moves from the end time back to the start time. If an actual alarm signal triggers an alarm within any time window, but the expected alarm signal does not, FAS = 1 will be output, incrementing the false alarm count. Conversely, if no false alarm occurs, FAS = 0 will be output.

[0045] It should be noted that for any given time window, if a false alarm is detected, the point at which the false alarm occurred will be used as the new starting point of the time window; otherwise, the time window will only move forward by one window unit until the start time of the alarm signal is reached.

[0046] Therefore, the number of false alarms for an alarm signal can be expressed as:

[0047] Similarly, the "missed alarm" status (MA(t)) can be identified starting from the initial moment of the alarm signal. i The number of times () = 1) occurs, such as Figure 2 , 3 As shown. The time window moves continuously from the start time to the end time. If the actual alarm signal fails to trigger within any time window, but the expected alarm signal does, MAS = 1 will be output, incrementing the count of missed alarms. Conversely, if no missed alarm occurs, MAS = 0.

[0048] Similarly, for any given time window, if a missed alarm is detected, the point at which the missed alarm occurred is taken as the new starting point of the time window; otherwise, the time window moves forward by one window unit until the end of the alarm signal is reached.

[0049] Therefore, the number of missed alarm signals can be expressed as:

[0050] Step S300: Use a sliding window statistical algorithm to identify and count the actual number of alarms of the actual alarm signal, and determine the false alarm rate of the alarm signal based on the number of false alarms and the actual number of alarms.

[0051] Step S400: Use a sliding window statistical algorithm to identify and count the expected number of alarms for the expected alarm signal, and determine the false alarm rate of the alarm signal based on the number of missed alarms and the expected number of alarms.

[0052] To calculate the false alarm rate and the missed alarm rate, it is also necessary to determine the actual number of alarms and the expected number of alarms for the alarm signals. Accordingly, the alarm status of the actual alarm signals and the expected alarm signals can be identified and statistically analyzed using a sliding window statistical algorithm.

[0053] Specifically, for actual alarm signals, the alarm can be triggered within either the start or end time window. If an alarm is triggered, AAS = 1 is output; otherwise, AAS = 0 is output. Similarly, for anticipated alarm signals, the alarm can be triggered within each time window. If an alarm is triggered, EAS = 1 is output; otherwise, EAS = 0 is output. This process is used to statistically analyze the actual number of alarms and the anticipated number of alarms.

[0054] Correspondingly, the actual number of alarms can be expressed as:

[0055] The expected number of alarms can be expressed as:

[0056] Furthermore, the ratio of false alarms to actual alarms can be calculated as the false alarm rate of the alarm signal. Similarly, the ratio of missed alarms to expected alarms can be calculated as the missed alarm rate of the alarm signal. Accordingly, the accuracy and reliability of the alarm function can be analyzed based on the false alarm rate and the missed alarm rate.

[0057] Furthermore, to ensure the accuracy of the alarm function evaluation for a specific battery model, the false alarm rate and missed alarm rate for this alarm function of several batteries of that model can be statistically analyzed to calculate the average false alarm rate and the average missed alarm rate.

[0058] and

[0059] Among them, FAR m (a) and MAR m (a) represents the average false alarm rate and the average false alarm rate for battery model "m", respectively. FAR(a) and MAR(a) represent the false alarm rate and the false alarm rate for alarm function "a", respectively. N represents the quantity of batteries of this model.

[0060] Step S500: Based on the false alarm rate and missed alarm rate of the alarm signal, determine whether the corresponding alarm function meets the standards of the regulatory platform.

[0061] It is understandable that the false alarm rate and false negative rate calculated from the alarm signal of a single battery can be used to assess whether its corresponding alarm function meets the requirements of the regulatory platform. Alternatively, the average false alarm rate and average false negative rate calculated from the same alarm signal of several batteries of a certain model can be used to assess whether the alarm function of that battery model meets the requirements of the regulatory platform. For example, the regulatory platform will evaluate the batteries produced by the manufacturer, and will not only conduct random checks on one battery. Accordingly, the average false alarm rate and average false negative rate of a certain alarm function of several batteries can be used to assess whether the alarm function meets the standard. No excessive restrictions are imposed on this. Modifications and refinements made by those skilled in the art to the embodiments of the present invention without departing from the spirit of the present invention still fall within the scope of the invention application patent of the present invention.

[0062] As for how to judge whether the alarm function of a battery or a certain model of battery meets the requirements, the false alarm rate or average false alarm rate calculated from its alarm signal, as well as the missed alarm rate or average missed alarm rate, can be compared with the preset threshold to make a judgment.

[0063] In this embodiment, a density-based spatial clustering algorithm (DBSCAN) is used for two-dimensional clustering to determine the relative state of the implicit threshold and the claimed threshold. Based on this relative state, the alarm function is evaluated to determine whether it meets the requirements of the regulatory platform, as detailed below:

[0064] First, the minimum sample size for the clustering algorithm is defined as 2, allowing it to identify the smallest anomalous clusters, i.e., clusters with only two points exhibiting similar threshold deviations. Second, based on the minimum sample size and the K-distance plot of the false negative rate and false positive rate, the optimal distance separating dense clusters from sparse noise points is identified as the neighborhood radius. Finally, based on the determined minimum sample size and neighborhood radius, two-dimensional clustering is performed on the false negative rate / mean false negative rate and the false positive rate / mean false positive rate. The relative states of the implicit threshold and the claimed threshold are identified based on the clustering results, including whether the implicit threshold is consistent with the claimed threshold, whether the implicit threshold is more lenient than the claimed threshold, whether the implicit threshold is more stringent than the claimed threshold, and whether the alarm function is abnormal. This determines whether the battery or battery model meets the requirements.

[0065] It should be noted that the steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they contain the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0066] Example 2

[0067] Please see Figure 4 As shown, this application also provides an evaluation system for a battery alarm function, comprising:

[0068] The data collection module 10 is used to acquire alarm signals when the battery status is abnormal, including actual alarm signals and expected alarm signals.

[0069] The false alarm / missed alarm statistics module 20 is used to identify and count the number of false alarms and missed alarms in the alarm signal using a sliding window statistical algorithm.

[0070] The false alarm rate calculation module 30 is used to identify and count the actual number of alarms of the actual alarm signal using a sliding window statistical algorithm, and to determine the false alarm rate of the alarm signal based on the number of false alarms and the number of actual alarms.

[0071] The missed alarm rate calculation module 40 is used to identify and count the expected number of alarms of the expected alarm signal using a sliding window statistical algorithm, and to determine the missed alarm rate of the alarm signal based on the number of missed alarms and the expected number of alarms.

[0072] The alarm function evaluation module 50 is used to judge whether the corresponding alarm function meets the standards of the regulatory platform based on the false alarm rate and false alarm rate of the alarm signal.

[0073] It should be noted that the battery alarm function evaluation system provided in the above embodiments and the battery alarm function evaluation method provided in Embodiment 1 belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the battery alarm function evaluation method provided in Embodiment 1 can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above, and no limitation is imposed here.

[0074] Example 3

[0075] Please see Figure 5 As shown, this application also provides an electronic device, including a memory 2, a processor 1, and a program stored in the memory and executable on the processor, wherein the processor executes the steps of any of the methods described above.

[0076] The memory includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory can include both internal and external storage units of the electronic device. The memory can be used not only to store application software and various types of data installed on the electronic device, but also to temporarily store data that has been output or will be output.

[0077] In some embodiments, the processor may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory and calls data stored in the memory to perform various functions and process data of the electronic device. The processor executes the operating system and various installed application programs of the electronic device. The processor executes the application programs to implement the steps in the above method embodiments.

[0078] For example, the program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of program instruction segments capable of performing a specific function, which describe the execution process of the program in the electronic device.

[0079] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute some of the functions of the various embodiments of the present invention.

[0080] In summary, this invention provides a method for evaluating battery alarm functions. It can assess the safety and reliability of corresponding alarm functions based on battery alarm signals. Specifically, it analyzes the false alarm rate and leakage rate of alarm signals, or the average false alarm rate and average leakage rate of the same alarm signal from several batteries of the same model. Based on this analysis, it fully examines the relative state of the implicit threshold and claimed threshold used to generate the alarm signal, verifying whether the alarm function conforms to standards and specifications. This enables the regulatory platform to make correct assessments of batteries produced by manufacturers, ensuring the safe operation of batteries.

[0081] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for evaluating a battery alarm function, characterized in that, include: Acquire alarm signals when the battery status is abnormal, including actual alarm signals and expected alarm signals; The sliding window statistical algorithm is used to identify and count the number of false alarms and missed alarms in the alarm signals. The actual number of alarms for the actual alarm signal is identified and counted using a sliding window statistical algorithm, and the false alarm rate of the alarm signal is determined based on the number of false alarms and the actual number of alarms. The expected number of alarms for the expected alarm signal is identified and counted using a sliding window statistical algorithm, and the missed alarm rate of the alarm signal is determined based on the missed alarm count and the expected number of alarms. The failure rate and false alarm rate of the alarm signals are used to assess whether the corresponding alarm function meets the standards of the regulatory platform.

2. The method for evaluating the battery alarm function according to claim 1, characterized in that, The actual alarm signal is generated based on the battery's operating parameters and the implicit thresholds used to measure whether the parameters are abnormal. The expected alarm signal is generated based on the battery's operating parameters and the claimed thresholds set by the manufacturer to measure whether the parameters are abnormal.

3. The method for evaluating the battery alarm function according to claim 2, characterized in that, The evaluation of whether the corresponding alarm function meets the standards of the regulatory platform is based on the false alarm rate and missed alarm rate of the alarm signal, including: Based on the false alarm rate and false alarm rate of the alarm signal, a clustering algorithm is used to identify the relative state of the implicit threshold and the claimed threshold, and the corresponding alarm function is judged to meet the standards of the regulatory platform.

4. The method for evaluating the battery alarm function according to claim 3, characterized in that, The clustering algorithm used is a density-based clustering algorithm.

5. The method for evaluating the battery alarm function according to claim 3, characterized in that, The step of identifying the relative state of the implicit threshold and the claimed threshold using a clustering algorithm based on the false alarm rate and false alarm rate of the alarm signal includes: The minimum number of samples for the clustering algorithm is defined as 2; Based on the minimum number of samples and the K-distance graph of the false negative rate and the false positive rate, the optimal distance to separate dense clusters from sparse noise points is identified as the neighborhood radius. Based on the minimum number of samples and the neighborhood radius, two-dimensional clustering is performed on the false negative rate and the false positive rate, and the relative state of the implicit threshold and the claimed threshold is identified based on the clustering results.

6. The method for evaluating the battery alarm function according to claim 1, characterized in that, The steps for identifying and counting the number of false alarms and missed alarms in the alarm signals using the sliding window statistical algorithm include: Within any given time window, identify the status of the actual alarm signal and the expected alarm signal respectively: If the actual alarm signal triggers an alarm, but the expected alarm signal does not trigger an alarm, then the false alarm count of the alarm signal is incremented by one. If the actual alarm signal does not trigger an alarm, but the expected alarm signal does trigger an alarm, then the number of missed alarms for the alarm signal is incremented by one.

7. The method for evaluating the battery alarm function according to claim 1, characterized in that, Also includes: The false alarm rate and false alarm rate of alarm signals for several batteries of the same model are calculated separately. The average false alarm rate and the average false alarm rate are calculated based on the false alarm rate and the false alarm rate, which are used to judge whether the alarm function of this battery model meets the standards of the regulatory platform.

8. A battery alarm function evaluation system, characterized in that, include: The data collection module is used to acquire alarm signals when the battery status is abnormal, including actual alarm signals and expected alarm signals; The false alarm / missed alarm statistics module is used to identify and count the number of false alarms and missed alarms in the alarm signal using a sliding window statistical algorithm; The false alarm rate calculation module is used to identify and count the actual number of alarms of the actual alarm signal using a sliding window statistical algorithm, and to determine the false alarm rate of the alarm signal based on the number of false alarms and the number of actual alarms. The missed alarm rate calculation module is used to identify and count the expected number of alarms for the expected alarm signal using a sliding window statistical algorithm, and to determine the missed alarm rate of the alarm signal based on the number of missed alarms and the expected number of alarms. The alarm function evaluation module is used to judge whether the corresponding alarm function meets the standards of the regulatory platform based on the false alarm rate and false alarm rate of the alarm signal.

9. An electronic device, characterized in that, The method includes a processor coupled to a memory storing program instructions, which, when executed by the processor, implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Includes a program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 7.

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