Abnormality identification method and device, computer device, storage medium and program product

Abstract

This application relates to an anomaly identification method, apparatus, computer equipment, storage medium, and program product. The method includes: acquiring monitoring data of the battery system of a target vehicle within a monitoring time period; segmenting the monitoring data according to a preset State of Charge (SOC) classification range and a preset temperature range to obtain multiple data segments; and then determining whether the battery system is abnormal based on the multiple data segments. Segmenting the monitoring data facilitates the development of more detailed anomaly identification strategies for various vehicle types, thereby improving the accuracy of anomaly identification. Furthermore, segmenting the monitoring data according to temperature and SOC improves the efficiency and accuracy of data classification.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to an anomaly identification method, apparatus, computer equipment, storage medium, and program product. Background Technology

[0002] With the rapid popularization of new energy vehicles and the rapid development of the new energy vehicle industry, people have put forward higher requirements for the reliability and safety of new energy vehicle batteries. By statistically analyzing the operation monitoring data of electric vehicles, identifying abnormal behavior of vehicle power batteries and identifying potential abnormal conditions of batteries, the safety performance of electric vehicles can be effectively improved.

[0003] However, traditional anomaly detection methods suffer from low accuracy. Summary of the Invention

[0004] Therefore, it is necessary to provide an anomaly identification method, apparatus, computer equipment, storage medium, and program product that can improve the accuracy of anomaly identification in battery systems in response to the above-mentioned technical problems.

[0005] Firstly, this application provides an anomaly identification method, including:

[0006] Acquire monitoring data of the target vehicle's battery system during the monitoring period;

[0007] The monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments;

[0008] Based on the multiple data segments, determine whether the battery system is malfunctioning.

[0009] In one embodiment, determining whether the battery system is malfunctioning based on the plurality of data segments includes:

[0010] Determine the maximum and minimum voltage values ​​in each of the data segments;

[0011] Determine the average voltage value in each of the data segments; the average voltage value is the average of all voltage values ​​in the data segment except for the maximum voltage value and the minimum voltage value.

[0012] Based on the maximum voltage value, the minimum voltage value, and the average voltage value in each of the data segments, determine whether the battery system is abnormal.

[0013] In one embodiment, determining whether the battery system is abnormal based on the maximum voltage value, the minimum voltage value, and the average voltage value in each of the data segments includes:

[0014] For each data segment, based on the first occurrence frequency and the first frequency threshold corresponding to the maximum voltage value, it is determined whether there is a concentrated anomaly of high voltage cells in the battery system; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0015] Based on the second occurrence frequency and the second frequency threshold corresponding to the minimum voltage value, it is determined whether there is a concentrated abnormality of low voltage cells in the battery system; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment;

[0016] Based on the maximum voltage value, the minimum voltage value, and the average voltage value, determine whether the battery system has extreme voltage difference anomalies.

[0017] In one embodiment, determining whether the battery system has extreme voltage difference anomalies based on the maximum voltage value, the minimum voltage value, and the average voltage value includes:

[0018] The difference between the maximum voltage value and the average voltage value is determined as the first difference, and the difference between the average voltage value and the minimum voltage value is determined as the second difference;

[0019] Based on the first difference and the first preset difference, determine whether the battery system has an extreme voltage difference anomaly, and / or, based on the second difference and the second preset difference, determine whether the battery system has an extreme voltage difference anomaly.

[0020] In one embodiment, the method further includes:

[0021] The number of times the target vehicle's battery system experiences anomalies within a preset time period is determined, where the length of the preset time period is greater than the length of the monitoring time period.

[0022] Based on the number of anomalies, determine whether the sampling line connection of the battery system is normal.

[0023] In one embodiment, determining whether the sampling line connection of the battery system is normal based on the number of anomalies includes:

[0024] If the number of abnormal occurrences exceeds a preset threshold, then the sampling line of the battery system is determined to be loosely connected.

[0025] If the ratio of the number of anomalies to the total number of samples is greater than a preset ratio threshold, then the sampling line of the battery system is determined to be disconnected; the total number of samples is the number of samples within the preset time period.

[0026] Secondly, this application also provides an anomaly detection device, comprising:

[0027] The acquisition module is used to acquire monitoring data of the target vehicle's battery system during the monitoring period;

[0028] The segmentation module is used to segment the monitoring data according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments;

[0029] The first determining module is used to determine whether the battery system is abnormal based on the multiple data segments.

[0030] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0031] Acquire monitoring data of the target vehicle's battery system during the monitoring period;

[0032] The monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments;

[0033] Based on the multiple data segments, determine whether the battery system is malfunctioning.

[0034] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0035] Acquire monitoring data of the target vehicle's battery system during the monitoring period;

[0036] The monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments;

[0037] Based on the multiple data segments, determine whether the battery system is malfunctioning.

[0038] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0039] Acquire monitoring data of the target vehicle's battery system during the monitoring period;

[0040] The monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments;

[0041] Based on the multiple data segments, determine whether the battery system is malfunctioning.

[0042] The aforementioned anomaly identification method, device, computer equipment, storage medium, and program product acquire monitoring data of the target vehicle's battery system within a monitoring period. This data is then segmented according to preset State of Charge (SOC) classification intervals and preset temperature intervals, resulting in multiple data segments. Based on these multiple data segments, it is determined whether the battery system is abnormal. Segmenting the monitoring data facilitates the development of more refined anomaly identification strategies for various vehicle types, thereby improving the accuracy of anomaly identification. Furthermore, segmenting the monitoring data based on temperature and SOC improves the efficiency and accuracy of data classification. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a diagram illustrating the application environment of an anomaly detection method in one embodiment;

[0045] Figure 2 This is a flowchart illustrating an anomaly detection method in one embodiment;

[0046] Figure 3 This is a flowchart illustrating an anomaly detection method in another embodiment;

[0047] Figure 4 This is a flowchart illustrating an anomaly detection method in another embodiment;

[0048] Figure 5 This is a flowchart illustrating an anomaly detection method in another embodiment;

[0049] Figure 6 This is a flowchart illustrating an anomaly detection method in another embodiment;

[0050] Figure 7 This is a flowchart illustrating an anomaly detection method in another embodiment;

[0051] Figure 8 This is a structural block diagram of an anomaly detection device in one embodiment;

[0052] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] The anomaly identification method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 sends monitoring data of the target vehicle's battery system during the monitoring period to server 104, allowing server 104 to segment the monitoring data according to preset State of Charge (SOC) classification ranges and preset temperature ranges. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0055] In one embodiment, such as Figure 2 As shown, an anomaly identification method is provided, which can be applied to... Figure 1 Taking the server in the example of this, the explanation includes:

[0056] S201, acquire monitoring data of the target vehicle's battery system during the monitoring period.

[0057] In this embodiment, the server can pre-store all monitoring data of the target vehicle's battery system, thereby determining the corresponding monitoring data from all the monitoring data based on the monitoring time period; alternatively, the server can send a request carrying the monitoring time period to the terminal, and the terminal can send the corresponding monitoring data to the server based on the monitoring time period. The terminal can be an electronic device used for monitoring or storage, such as the target vehicle or a data acquisition device.

[0058] S202, the monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments.

[0059] In this embodiment of the application, the monitoring data is sliced ​​according to a preset SOC classification interval to obtain a first data slice, and the monitoring data is sliced ​​according to a preset temperature interval to obtain a second data slice, thereby obtaining multiple data segments based on the first data slice and the second data slice.

[0060] Optionally, the monitoring data within the monitoring period can be cleaned. Further, the cleaned monitoring data can be segmented according to preset State of Charge (SOC) classification intervals and preset temperature ranges to obtain multiple data segments. For example, for individual cell voltage and probe temperature data, voltage data within a preset voltage range and probe temperature data within a preset temperature range are retained, while data outside these ranges are completely removed. For instance, the preset voltage range is 2-5V, and the preset temperature range is -40-100℃. For example, the preset SOC classification interval can be 20%, dividing the monitoring data into 5 first data slices; the monthly temperature range can be 20℃, dividing the monitoring data into 7 second data slices, resulting in a total of 25 data segments.

[0061] S203 determines whether the battery system is malfunctioning based on multiple data segments.

[0062] In this embodiment of the application, each data segment is analyzed to determine whether there is abnormal data in the monitoring data of each data segment, thereby determining whether the battery system is abnormal.

[0063] Optionally, a normal data range corresponding to the monitored data can be preset. Based on this normal data range, it can be determined whether abnormal data exists in each data segment, and the number of data segments containing abnormal data. Furthermore, the number of data segments containing abnormal data can be used to determine whether the battery system is abnormal. For example, if the number of data segments containing abnormal data exceeds a preset threshold, the battery system is determined to be abnormal; if the number of data segments containing abnormal data does not exceed the preset threshold, the battery system is determined to be normal.

[0064] The aforementioned anomaly identification method, device, computer equipment, storage medium, and program product acquire monitoring data of the target vehicle's battery system within a monitoring period. This data is then segmented according to preset State of Charge (SOC) classification intervals and preset temperature intervals, resulting in multiple data segments. Based on these multiple data segments, it is determined whether the battery system is abnormal. Segmenting the monitoring data facilitates the development of more refined anomaly identification strategies for various vehicle types, thereby improving the accuracy of anomaly identification. Furthermore, segmenting the monitoring data based on temperature and SOC improves the efficiency and accuracy of data classification.

[0065] In one embodiment, one implementation of the above-described S203 is provided, such as... Figure 3As shown, the above "determining whether the battery system is abnormal based on multiple data segments" includes:

[0066] S301, determine the maximum and minimum voltage values ​​in each data segment.

[0067] In this embodiment, the voltage values ​​in each data segment are numbered and sorted according to their magnitude, thereby determining the maximum and minimum voltage values ​​in each data segment, as well as the data number corresponding to the maximum and minimum voltage values. Furthermore, the frequency of occurrence of the maximum and minimum voltage values ​​can be statistically analyzed.

[0068] S302, determine the average voltage value in each data segment; the average voltage value is the average of the voltage values ​​in the data segment excluding the maximum and minimum voltage values.

[0069] In this embodiment, for each data segment, the maximum and minimum voltage values ​​within the data segment are removed, and the average value of the remaining voltage values ​​is determined. The average voltage value can be expressed as U. mean For example, if a data segment includes voltage values ​​of 5V, 6V, 7V, 7V, 2V, 1V, and 7V, then after removing the maximum and minimum voltage values ​​within the data segment, the remaining voltage values ​​are 5V, 6V, 2V, and 1V, and the average voltage is 3.5V.

[0070] S303 determines whether the battery system is abnormal based on the maximum voltage value, minimum voltage value, and average voltage value in each data segment.

[0071] Optionally, a first voltage threshold, a second voltage threshold, and a voltage average range can be preset, thereby determining whether the battery system is abnormal by comparing the maximum voltage value with the first voltage threshold, the minimum voltage value with the second voltage threshold, and the voltage average value with the voltage average range.

[0072] For example, if the maximum voltage value is greater than a first voltage threshold, and / or the minimum voltage value is less than a second voltage threshold, and / or the average voltage value is not within the range of the average voltage value, the battery system is determined to be abnormal.

[0073] In the above-mentioned application embodiments, by analyzing the maximum voltage value, minimum voltage value and average voltage value in each data segment, it is determined whether the battery system is abnormal. The judgment of abnormality is accurate to each data segment, which improves the accuracy of abnormality determination.

[0074] In one embodiment, one implementation of S303 above is provided, such as... Figure 4 As shown, for each data segment, the above-mentioned "determining whether the battery system is abnormal based on the maximum voltage value, minimum voltage value, and average voltage value in each data segment" includes:

[0075] S401, determine whether there is a concentrated anomaly of high voltage cells in the battery system based on the first occurrence frequency corresponding to the maximum voltage value and the first frequency threshold; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0076] As an optional implementation, the number of maximum voltage values ​​in the data segment and the total amount of voltage values ​​in the data segment are determined. The ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment is determined as the first occurrence frequency, which can be represented as w1. Further, if the first occurrence frequency is greater than the first frequency threshold, it is determined that there is a concentrated abnormality of high voltage cells in the battery system. If the first occurrence frequency is not greater than the first frequency threshold, it is determined that there is no concentrated abnormality of high voltage cells in the battery system.

[0077] As another optional implementation, the difference between the maximum voltage value and the average voltage value of the data segment is determined and denoted as U. max When the first occurrence frequency is greater than the first frequency threshold, the U of the data segment is determined. max If the voltage difference exceeds the maximum difference threshold (e.g., 40mV), it indicates that there is a concentrated abnormality of high-voltage cells in the battery system.

[0078] As another optional implementation, the difference between the maximum voltage value and the average voltage value of the data segment is determined and denoted as U. max1 And determine the difference between the maximum voltage value and the average voltage value of the preceding data segment, denoted as U. max2 Further analysis is conducted to determine if the maximum voltage difference is gradually increasing. If it is increasing, it is determined that there is a concentrated anomaly in high-voltage cells within the battery system. For example, when U... max1 For U max2 When the voltage is 1.1 times the value, the maximum voltage difference is determined to gradually increase.

[0079] S402, determine whether there is a concentrated abnormality of low voltage cells in the battery system based on the second occurrence frequency and the second frequency threshold corresponding to the minimum voltage value; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0080] In this embodiment of the application, the number of minimum voltage values ​​in the data segment and the total amount of voltage values ​​in the data segment are determined. The ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment is determined as the second occurrence frequency. The second occurrence frequency can be represented as w2. Further, if the second occurrence frequency is greater than the second frequency threshold, it is determined that there is a concentrated abnormality of low voltage cells in the battery system. If the second occurrence frequency is not greater than the second frequency threshold, it is determined that there is no concentrated abnormality of low voltage cells in the battery system.

[0081] As another optional implementation, the difference between the maximum voltage value and the average voltage value of the data segment is determined and denoted as U. min When the first occurrence frequency is greater than the first frequency threshold, the U value of that data segment is determined. min If the difference is less than the minimum threshold, then it is determined that there is a concentrated anomaly in the high-voltage cells of the battery system.

[0082] As another optional implementation, the difference between the minimum voltage value and the average voltage value of the data segment is determined and denoted as U. min1 And determine the difference between the minimum voltage value and the average voltage value of the preceding data segment, denoted as U. min2 Further analysis is conducted to determine if the minimum voltage difference is gradually increasing. If it is increasing, it is determined that there is a concentrated anomaly in high-voltage cells within the battery system. For example, when U... min1 For U min2 When the voltage is 1.1 times the value, the minimum voltage difference is determined to gradually increase.

[0083] S403 determines whether there is an extreme voltage difference anomaly in the battery system based on the maximum voltage value, minimum voltage value, and average voltage value.

[0084] In this embodiment, the difference between the maximum voltage value and the average voltage value is determined, as well as the difference between the minimum voltage value and the average voltage value. Further, a target difference between the two differences can be determined, thereby determining whether the battery system exhibits extreme voltage difference anomalies based on the target difference and a preset difference threshold.

[0085] Optional, such as Figure 5 As shown, the above-mentioned "determining whether there is an extreme voltage difference anomaly in the battery system based on the maximum voltage value, minimum voltage value, and average voltage value" includes:

[0086] S501, the difference between the maximum voltage value and the average voltage value is determined as the first difference, and the difference between the average voltage value and the minimum voltage value is determined as the second difference.

[0087] S502, determine whether there is an extreme voltage difference abnormality in the battery system based on the first difference and the first preset difference, and / or determine whether there is an extreme voltage difference abnormality in the battery system based on the second difference and the second preset difference.

[0088] In this embodiment of the application, a first difference between the maximum voltage value and the average voltage value of the data segment is determined, denoted as U. max Determine the second difference between the minimum voltage value and the average voltage value of this data segment, denoted as U. min Furthermore, if it is determined that the first difference is greater than the first preset difference, and / or the second difference is less than the second preset difference, it is determined that the battery system has an extreme voltage difference abnormality; if it is determined that the first difference is not greater than the first preset difference, and the second difference is not less than the second preset difference, it is determined that the battery system does not have an extreme voltage difference abnormality.

[0089] In the above-mentioned embodiments, the presence of extreme voltage differences in the battery system is determined based on the maximum voltage value, the minimum voltage value, the average voltage value, and the preset threshold parameters, making the anomaly determination results more reliable and improving the accuracy of the anomaly determination results.

[0090] In one embodiment, such as Figure 6 As shown, the above-mentioned anomaly identification method also includes:

[0091] S204, determine the number of times the target vehicle's battery system experiences anomalies within a preset time period, where the length of the preset time period is greater than the length of the monitoring time period.

[0092] In this embodiment, the voltage range threshold within each data segment can be determined based on the individual battery data of multiple vehicles of the same model as the target vehicle. For example, the box plot method can be used to determine the maximum difference U. max-x and the lowest difference U min-x This allows the determination of the number of voltage anomalies in each data segment of the target vehicle based on the highest and lowest voltage differences. For example, an anomaly is defined as a first voltage difference greater than the highest difference or a second voltage difference less than the lowest difference.

[0093] S205, based on the number of abnormalities, determine whether the sampling line connection of the battery system is normal.

[0094] In this embodiment, if the number of abnormal occurrences exceeds a preset threshold, it can be determined that the sampling line connection of the battery system is abnormal; if the number of abnormal occurrences does not exceed the preset threshold, it can be determined that the sampling line connection of the battery system is normal.

[0095] In the above-mentioned embodiments, the sampling line connection of the battery system is determined to be normal based on the number of abnormalities of the target vehicle's battery system within a preset time period, thereby expanding the scope of determining battery system abnormalities and improving the comprehensiveness of abnormality determination.

[0096] In one embodiment, one implementation of the above-described S205 is provided, such as... Figure 7 As shown, the above "determining whether the sampling line connection of the battery system is normal based on the number of anomalies" includes:

[0097] S601, if the number of abnormal occurrences exceeds the preset threshold, then the sampling line of the battery system is determined to be loosely connected.

[0098] In this embodiment, if the number of anomalies exceeds a preset threshold, it can be determined that the sampling line of the battery system is loosely connected; if the number of anomalies does not exceed the preset threshold, it can be determined that the sampling line of the battery system is properly connected. For example, the preset threshold can be 10.

[0099] S602, if the ratio of the number of abnormal occurrences to the total number of samples is greater than the preset ratio threshold, then the sampling line of the battery system is determined to be disconnected; the total number of samples is the number of samples within the preset time period.

[0100] In this embodiment of the application, the ratio of the number of abnormal samplings to the total number of samplings is determined, that is, the ratio of the abnormal sampling time to the total sampling time. Further, if the ratio is greater than a preset ratio threshold, it can be determined that the sampling line of the battery system is disconnected; if the ratio is not greater than the preset ratio threshold, it can be determined that the sampling line of the battery system is connected normally.

[0101] In the above-mentioned embodiments, the sampling line connection of the battery system is determined to be normal based on the number of abnormalities. The judgment is made by specific values, which improves the accuracy of abnormal identification of sampling line connection.

[0102] In one embodiment, a complete anomaly detection method is also provided, including:

[0103] S1, acquire monitoring data of the target vehicle's battery system during the monitoring period.

[0104] S2, the monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments.

[0105] S3, determine the maximum and minimum voltage values ​​in each data segment.

[0106] S4, determine the average voltage value in each data segment; the average voltage value is the average of the voltage values ​​in the data segment excluding the maximum and minimum voltage values.

[0107] S5. For each data segment, determine whether there is a concentrated anomaly of high voltage cells in the battery system based on the first occurrence frequency and the first frequency threshold corresponding to the maximum voltage value; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0108] S6. For each data segment, determine whether there is a concentrated abnormality of low voltage cells in the battery system based on the second occurrence frequency and the second frequency threshold corresponding to the minimum voltage value; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0109] S7, the difference between the maximum voltage value and the average voltage value is determined as the first difference, and the difference between the average voltage value and the minimum voltage value is determined as the second difference.

[0110] S8, determine whether there is an extreme voltage difference abnormality in the battery system based on the first difference and the first preset difference, and / or determine whether there is an extreme voltage difference abnormality in the battery system based on the second difference and the second preset difference.

[0111] S9, determine the number of times the target vehicle's battery system experiences anomalies within a preset time period, where the length of the preset time period is greater than the length of the monitoring time period.

[0112] S10, if the number of abnormalities is greater than the preset number threshold, the sampling line of the battery system is determined to be loose; if the ratio of the number of abnormalities to the total number of samples is greater than the preset ratio threshold, the sampling line of the battery system is determined to be disconnected; the total number of samples is the number of samples within the preset time period.

[0113] The aforementioned anomaly identification method, device, computer equipment, storage medium, and program product acquire monitoring data of the target vehicle's battery system within a monitoring period. This data is then segmented according to preset State of Charge (SOC) classification intervals and preset temperature intervals, resulting in multiple data segments. Based on these multiple data segments, it is determined whether the battery system is abnormal. Segmenting the monitoring data facilitates the development of more refined anomaly identification strategies for various vehicle types, thereby improving the accuracy of anomaly identification. Furthermore, segmenting the monitoring data based on temperature and SOC improves the efficiency and accuracy of data classification.

[0114] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0115] Based on the same inventive concept, this application also provides an anomaly identification device for implementing the anomaly identification method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more anomaly identification device embodiments provided below can be found in the limitations of the anomaly identification method described above, and will not be repeated here.

[0116] In one embodiment, such as Figure 8 As shown, an anomaly identification device is provided, comprising: an acquisition module 10, a segmentation module 11, and a first determination module 12, wherein:

[0117] The acquisition module 10 is used to acquire monitoring data of the target vehicle's battery system during the monitoring period.

[0118] The segmentation module 11 is used to segment the monitoring data according to the preset state of charge (SOC) classification interval and the preset temperature interval to obtain multiple data segments.

[0119] The first determining module 12 is used to determine whether the battery system is abnormal based on multiple data segments.

[0120] In one embodiment, the first determining module 12 includes: a first determining unit, a second determining unit, and a third determining unit, wherein:

[0121] The first determining unit is used to determine the maximum and minimum voltage values ​​in each data segment.

[0122] The second determining unit is used to determine the average voltage value in each data segment; the average voltage value is the average of the voltage values ​​in the data segment other than the maximum voltage value and the minimum voltage value.

[0123] The third determining unit is used to determine whether the battery system is abnormal based on the maximum voltage value, minimum voltage value, and average voltage value in each data segment.

[0124] In one embodiment, the third determining unit is specifically used to determine, for each data segment, whether there is a concentrated anomaly of high-voltage cells in the battery system based on a first occurrence frequency and a first frequency threshold corresponding to the maximum voltage value; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment; whether there is a concentrated anomaly of low-voltage cells in the battery system based on a second occurrence frequency and a second frequency threshold corresponding to the minimum voltage value; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment; and whether there is an extreme voltage difference anomaly in the battery system based on the maximum voltage value, the minimum voltage value, and the average voltage value.

[0125] In one embodiment, the third determining unit is specifically configured to determine the difference between the maximum voltage value and the average voltage value as a first difference, and the difference between the average voltage value and the minimum voltage value as a second difference; determine whether there is an extreme voltage difference anomaly in the battery system based on the first difference and the first preset difference, and / or determine whether there is an extreme voltage difference anomaly in the battery system based on the second difference and the second preset difference.

[0126] In one embodiment, the above-mentioned anomaly identification device further includes: a second determining module and a third determining module, wherein:

[0127] The second determining module is used to determine the number of times the target vehicle's battery system experiences anomalies within a preset time period, the length of which is greater than the length of the monitoring time period.

[0128] The third determination module is used to determine whether the sampling line connection of the battery system is normal based on the number of anomalies.

[0129] In one embodiment, the third determining module includes: a fourth determining unit and a fifth determining unit, wherein:

[0130] The fourth determining unit is used to determine that the sampling line of the battery system is loose if the number of abnormalities exceeds a preset threshold.

[0131] The fifth determining unit is used to determine that the sampling line of the battery system is disconnected if the ratio of the number of abnormalities to the total number of samples is greater than a preset ratio threshold; the total number of samples is the number of samples within a preset time period.

[0132] Each module in the aforementioned anomaly detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0133] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores anomaly detection data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements an anomaly detection method.

[0134] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0135] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0136] Acquire monitoring data of the target vehicle's battery system during the monitoring period;

[0137] The monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments;

[0138] Based on multiple data segments, determine whether the battery system is malfunctioning.

[0139] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0140] Determine the maximum and minimum voltage values ​​for each data segment;

[0141] Determine the average voltage value in each data segment; the average voltage value is the average of all voltage values ​​in the data segment except for the maximum and minimum voltage values.

[0142] Based on the maximum voltage value, minimum voltage value, and average voltage value in each data segment, determine whether the battery system is abnormal.

[0143] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0144] For each data segment, based on the first occurrence frequency and the first frequency threshold corresponding to the maximum voltage value, it is determined whether there is a concentrated anomaly of high voltage cells in the battery system; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0145] Based on the second occurrence frequency and the second frequency threshold corresponding to the minimum voltage value, it is determined whether there is a concentrated anomaly of low voltage cells in the battery system; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0146] Based on the maximum voltage value, minimum voltage value, and average voltage value, determine whether there is an extreme voltage difference anomaly in the battery system.

[0147] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0148] The difference between the maximum voltage value and the average voltage value is defined as the first difference, and the difference between the average voltage value and the minimum voltage value is defined as the second difference;

[0149] Based on the first difference and the first preset difference, determine whether there is an extreme voltage difference anomaly in the battery system, and / or, based on the second difference and the second preset difference, determine whether there is an extreme voltage difference anomaly in the battery system.

[0150] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0151] Determine the number of times the target vehicle's battery system experiences anomalies within a preset time period, where the preset time period is longer than the monitoring time period.

[0152] Based on the number of anomalies, determine whether the sampling line connection of the battery system is normal.

[0153] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0154] If the number of abnormal occurrences exceeds the preset threshold, the sampling line of the battery system is determined to be loosely connected.

[0155] If the ratio of the number of abnormal occurrences to the total number of samples is greater than a preset ratio threshold, the sampling line of the battery system is determined to be disconnected; the total number of samples is the number of samples within a preset time period.

[0156] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0157] Acquire monitoring data of the target vehicle's battery system during the monitoring period;

[0158] The monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments;

[0159] Based on multiple data segments, determine whether the battery system is malfunctioning.

[0160] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0161] Determine the maximum and minimum voltage values ​​for each data segment;

[0162] Determine the average voltage value in each data segment; the average voltage value is the average of all voltage values ​​in the data segment except for the maximum and minimum voltage values.

[0163] Based on the maximum voltage value, minimum voltage value, and average voltage value in each data segment, determine whether the battery system is abnormal.

[0164] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0165] For each data segment, based on the first occurrence frequency and the first frequency threshold corresponding to the maximum voltage value, it is determined whether there is a concentrated anomaly of high voltage cells in the battery system; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0166] Based on the second occurrence frequency and the second frequency threshold corresponding to the minimum voltage value, it is determined whether there is a concentrated anomaly of low voltage cells in the battery system; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0167] Based on the maximum voltage value, minimum voltage value, and average voltage value, determine whether there is an extreme voltage difference anomaly in the battery system.

[0168] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0169] The difference between the maximum voltage value and the average voltage value is defined as the first difference, and the difference between the average voltage value and the minimum voltage value is defined as the second difference;

[0170] Based on the first difference and the first preset difference, determine whether there is an extreme voltage difference anomaly in the battery system, and / or, based on the second difference and the second preset difference, determine whether there is an extreme voltage difference anomaly in the battery system.

[0171] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0172] Determine the number of times the target vehicle's battery system experiences anomalies within a preset time period, where the preset time period is longer than the monitoring time period.

[0173] Based on the number of anomalies, determine whether the sampling line connection of the battery system is normal.

[0174] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0175] If the number of abnormal occurrences exceeds the preset threshold, the sampling line of the battery system is determined to be loosely connected.

[0176] If the ratio of the number of abnormal occurrences to the total number of samples is greater than a preset ratio threshold, the sampling line of the battery system is determined to be disconnected; the total number of samples is the number of samples within a preset time period.

[0177] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0178] Acquire monitoring data of the target vehicle's battery system during the monitoring period;

[0179] The monitoring data is segmented according to the preset State of Charge (SOC) classification range and the preset temperature range to obtain multiple data segments;

[0180] Based on multiple data segments, determine whether the battery system is malfunctioning.

[0181] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0182] Determine the maximum and minimum voltage values ​​for each data segment;

[0183] Determine the average voltage value in each data segment; the average voltage value is the average of all voltage values ​​in the data segment except for the maximum and minimum voltage values.

[0184] Based on the maximum voltage value, minimum voltage value, and average voltage value in each data segment, determine whether the battery system is abnormal.

[0185] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0186] For each data segment, based on the first occurrence frequency and the first frequency threshold corresponding to the maximum voltage value, it is determined whether there is a concentrated anomaly of high voltage cells in the battery system; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0187] Based on the second occurrence frequency and the second frequency threshold corresponding to the minimum voltage value, it is determined whether there is a concentrated anomaly of low voltage cells in the battery system; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment.

[0188] Based on the maximum voltage value, minimum voltage value, and average voltage value, determine whether there is an extreme voltage difference anomaly in the battery system.

[0189] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0190] The difference between the maximum voltage value and the average voltage value is defined as the first difference, and the difference between the average voltage value and the minimum voltage value is defined as the second difference;

[0191] Based on the first difference and the first preset difference, determine whether there is an extreme voltage difference anomaly in the battery system, and / or, based on the second difference and the second preset difference, determine whether there is an extreme voltage difference anomaly in the battery system.

[0192] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0193] Determine the number of times the target vehicle's battery system experiences anomalies within a preset time period, where the preset time period is longer than the monitoring time period.

[0194] Based on the number of anomalies, determine whether the sampling line connection of the battery system is normal.

[0195] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if the number of exceptions exceeds a preset threshold, it is determined that the sampling line of the battery system is loosely connected;

[0196] If the ratio of the number of abnormal occurrences to the total number of samples is greater than a preset ratio threshold, the sampling line of the battery system is determined to be disconnected; the total number of samples is the number of samples within a preset time period.

[0197] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0198] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0199] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An anomaly identification method characterized by comprising: The method includes: Acquire monitoring data of the target vehicle's battery system during the monitoring period; The monitoring data is sliced ​​according to a preset State of Charge (SOC) classification interval to obtain a first data slice, and the monitoring data is sliced ​​according to a preset temperature interval to obtain a second data slice; multiple data segments are obtained based on the first data slice and the second data slice. Determine the maximum and minimum voltage values ​​in each of the data segments; Determine the average voltage value in each of the data segments; the average voltage value is the average of all voltage values ​​in the data segment except for the maximum voltage value and the minimum voltage value. For each data segment, based on the first occurrence frequency and the first frequency threshold corresponding to the maximum voltage value, it is determined whether the battery system has a concentrated anomaly of high-voltage cells; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total number of voltage values ​​in the data segment; based on the second occurrence frequency and the second frequency threshold corresponding to the minimum voltage value, it is determined whether the battery system has a concentrated anomaly of low-voltage cells; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total number of voltage values ​​in the data segment; based on the maximum voltage value, the minimum voltage value, and the average voltage value, it is determined whether the battery system has an extreme voltage difference anomaly.

2. The method of claim 1, wherein, The step of determining whether the battery system has extreme voltage difference anomalies based on the maximum voltage value, the minimum voltage value, and the average voltage value includes: The difference between the maximum voltage value and the average voltage value is determined as the first difference, and the difference between the average voltage value and the minimum voltage value is determined as the second difference; Based on the first difference and the first preset difference, determine whether the battery system has an extreme voltage difference anomaly, and / or, based on the second difference and the second preset difference, determine whether the battery system has an extreme voltage difference anomaly.

3. The method according to any one of claims 1 or 2, characterized in that, The method further includes: The number of times the target vehicle's battery system experiences anomalies within a preset time period is determined, where the length of the preset time period is greater than the length of the monitoring time period. Based on the number of anomalies, determine whether the sampling line connection of the battery system is normal.

4. The method according to claim 3, characterized in that, The step of determining whether the sampling line connection of the battery system is normal based on the number of anomalies includes: If the number of abnormal occurrences exceeds a preset threshold, then the sampling line of the battery system is determined to be loosely connected. If the ratio of the number of anomalies to the total number of samples is greater than a preset ratio threshold, then the sampling line of the battery system is determined to be disconnected; the total number of samples is the number of samples within the preset time period.

5. An anomaly detection device, characterized in that, The device includes: The acquisition module is used to acquire monitoring data of the target vehicle's battery system during the monitoring period; The segmentation module is used to slice the monitoring data according to a preset state of charge (SOC) classification interval to obtain a first data slice, and to slice the monitoring data according to a preset temperature interval to obtain a second data slice; multiple data segments are obtained based on the first data slice and the second data slice. A first determining module is configured to: determine the maximum and minimum voltage values ​​in each data segment; determine the average voltage value in each data segment; the average voltage value is the average of all voltage values ​​in the data segment except for the maximum and minimum voltage values; for each data segment, determine whether the battery system has a concentrated abnormality of high-voltage cells based on a first occurrence frequency and a first frequency threshold corresponding to the maximum voltage value; the first occurrence frequency is the ratio of the number of maximum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment; determine whether the battery system has a concentrated abnormality of low-voltage cells based on a second occurrence frequency and a second frequency threshold corresponding to the minimum voltage value; the second occurrence frequency is the ratio of the number of minimum voltage values ​​in the data segment to the total amount of voltage values ​​in the data segment; and determine whether the battery system has an extreme voltage difference abnormality based on the maximum voltage value, the minimum voltage value, and the average voltage value.

6. The apparatus according to claim 5, characterized in that, The device further includes: The second determining module is used to determine the number of times the target vehicle's battery system experiences anomalies within a preset time period, wherein the length of the preset time period is greater than the length of the monitoring time period. The third determining module is used to determine whether the sampling line connection of the battery system is normal based on the number of abnormalities.

7. The apparatus according to claim 6, characterized in that, The third determining module includes: The fourth determining unit is used to determine that the sampling line of the battery system is loosely connected when the number of abnormalities exceeds a preset threshold. The fifth determining module is used to determine that the sampling line of the battery system is disconnected when the ratio of the number of abnormalities to the total number of samplings is greater than a preset ratio threshold; the total number of samplings is the number of samplings within the preset time period.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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