Abnormal recognition method and device, computer equipment, storage medium and program product

Abstract

The invention relates to an exception recognition method and device, computer equipment, a storage medium and a program product. The method comprises the steps of obtaining monitoring data of a battery system of a target vehicle in a monitoring time period, performing data segmentation on the monitoring data according to a preset SOC classification interval and a preset temperature interval to obtain a plurality of data segments, and determining whether the battery system is abnormal or not according to the plurality of data segments. According to the method, the monitoring data are segmented, so that a more detailed anomaly determination strategy can be made for various vehicles, the anomaly determination accuracy is improved, the monitoring data are segmented according to the temperature and the SOC, and the data classification efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to an abnormality identification method, device, computer equipment, storage medium and program product. Background Art

[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 the 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 recognition methods suffer from low recognition accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide an abnormality identification method, device, computer equipment, storage medium and program product that can improve the accuracy of abnormality identification of the battery system in order to solve the above technical problems.

[0005] In a first aspect, the present application provides an abnormality identification method, comprising:

[0006] Obtain monitoring data of the battery system of the target vehicle within the monitoring time period;

[0007] Segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments;

[0008] It is determined whether the battery system is abnormal based on the plurality of data segments.

[0009] In one embodiment, determining whether the battery system is abnormal according to the multiple data segments includes:

[0010] Determining the maximum voltage value and the minimum voltage value in each of the data segments;

[0011] Determine a voltage average value in each of the data segments; the voltage average value is an average value of other voltage values ​​in the data segment except the maximum voltage value and the minimum voltage value;

[0012] Whether the battery system is abnormal is determined according to the maximum voltage value, the minimum voltage value and the voltage average value in each of the data segments.

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

[0014] For each data segment, determining whether the battery system has a high-voltage monomer concentration abnormality according to a first occurrence frequency and a first frequency threshold corresponding to the maximum voltage value; the first occurrence frequency is a ratio of the number of the maximum voltage values ​​in the data segment to the total data volume of the voltage values ​​in the data segment;

[0015] Determine whether there is a low-voltage monomer concentration abnormality in the battery system according to a second occurrence frequency and a second frequency threshold corresponding to the minimum voltage value; the second occurrence frequency is a ratio of the number of the minimum voltage values ​​in the data segment to the total data volume of the voltage values ​​in the data segment;

[0016] It is determined whether an extreme voltage difference abnormality exists in the battery system according to the maximum voltage value, the minimum voltage value and the voltage average value.

[0017] In one embodiment, determining whether the battery system has an extreme voltage difference abnormality according to the maximum voltage value, the minimum voltage value and the voltage average value includes:

[0018] Determine a difference between the maximum voltage value and the voltage average value as a first difference value, and determine a difference between the voltage average value and the minimum voltage value as a second difference value;

[0019] Determine whether the battery system has an extreme pressure difference abnormality based on the first difference and the first preset difference, and / or determine whether the battery system has an extreme pressure difference abnormality based on the second difference and the second preset difference.

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

[0021] Determining the number of abnormalities of the battery system of the target vehicle within a preset time period, wherein the length of the preset time period is greater than the length of the monitoring time period;

[0022] According to the number of abnormalities, it is determined 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 according to the number of abnormalities includes:

[0024] If the number of abnormalities is greater than a preset number threshold, determining that the sampling line of the battery system is in a virtual connection;

[0025] If the ratio of the abnormal number of times to the total sampling number of times is greater than a preset ratio threshold, it is determined that the sampling line of the battery system is disconnected; the total sampling number of times is the sampling number within the preset time period.

[0026] In a second aspect, the present application also provides an abnormality identification device, comprising:

[0027] An acquisition module, used to acquire monitoring data of the battery system of the target vehicle within a monitoring period;

[0028] A segmentation module, used to segment the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments;

[0029] The first determination module is used to determine whether the battery system is abnormal according to the multiple data segments.

[0030] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0031] Obtain monitoring data of the battery system of the target vehicle within the monitoring time period;

[0032] Segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments;

[0033] It is determined whether the battery system is abnormal based on the plurality of data segments.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0035] Obtain monitoring data of the battery system of the target vehicle within the monitoring time period;

[0036] Segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments;

[0037] It is determined whether the battery system is abnormal based on the plurality of data segments.

[0038] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0039] Obtain monitoring data of the battery system of the target vehicle within the monitoring time period;

[0040] Segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments;

[0041] It is determined whether the battery system is abnormal based on the plurality of data segments.

[0042] The above-mentioned abnormality identification method, device, computer equipment, storage medium and program product obtain the monitoring data of the battery system of the target vehicle within the monitoring time period, thereby segmenting the monitoring data according to the preset state of charge value SOC classification interval and the preset temperature interval to obtain multiple data segments, and then determine whether the battery system is abnormal based on the multiple data segments. By segmenting the monitoring data, it is convenient to formulate more detailed abnormality determination strategies for various types of vehicles, thereby improving the accuracy of abnormality determination, and segmenting the monitoring data according to temperature and SOC improves the efficiency and accuracy of data classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 An application environment diagram of an abnormality identification method in an embodiment;

[0045] Figure 2 A schematic diagram of a flow chart of an abnormality identification method in one embodiment;

[0046] Figure 3 A schematic diagram of a flow chart of an abnormality identification method in another embodiment;

[0047] Figure 4 A schematic diagram of a flow chart of an abnormality identification method in another embodiment;

[0048] Figure 5 A schematic diagram of a flow chart of an abnormality identification method in another embodiment;

[0049] Figure 6 A schematic diagram of a flow chart of an abnormality identification method in another embodiment;

[0050] Figure 7 A schematic diagram of a flow chart of an abnormality identification method in another embodiment;

[0051] Figure 8 is a structural block diagram of an abnormality identification device in an embodiment;

[0052] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] The abnormality identification method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 sends the monitoring data of the battery system of the target vehicle within the monitoring time period to the server 104, so that the server 104 performs data segmentation on the monitoring data according to the preset state of charge value SOC classification interval and the preset temperature interval. Among them, the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0055] In one embodiment, Figure 2 As shown, a method for abnormal identification is provided, which is applied to Figure 1 The following is an example of a server in the example, including:

[0056] S201, obtaining monitoring data of a battery system of a target vehicle within a monitoring period.

[0057] In the embodiment of the present application, the server may pre-store all monitoring data of the battery system of the target vehicle, thereby determining the corresponding monitoring data from all monitoring data according to the monitoring time period; or, the server may send an acquisition request carrying the monitoring time period to the terminal, and the terminal sends the corresponding monitoring data to the server according to the monitoring time period. The terminal may be an electronic device for monitoring or storage, such as a target vehicle or a data acquisition device.

[0058] S202 , segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments.

[0059] In an embodiment of the present 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 time period can be cleaned, and further, the cleaned monitoring data can be segmented according to the preset state of charge value SOC classification interval and the preset temperature interval to obtain multiple data segments. Exemplarily, for the single cell voltage and probe temperature data, the data within the preset voltage range and the data within the preset temperature range of the probe temperature are retained, and all data outside the voltage and temperature ranges are cleared. For example, the preset voltage range is 2-5V, and the preset temperature range is -40-100°C. Exemplarily, the preset SOC classification interval can be used as 20%, and the monitoring data is divided into 5 first data slices. The monthly temperature interval can be 20°C, and the monitoring data is divided into 7 second data slices, so there are 25 data segments in total.

[0061] S203, determining whether the battery system is abnormal based on the multiple data segments.

[0062] In the embodiment of the present 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 monitoring data may be pre-set, so as to determine whether there is abnormal data in each data segment and the number of data segments with abnormal data according to the normal data range. Further, whether the battery system is abnormal may be determined according to the number of data segments with abnormal data. For example, if the number of data segments with abnormal data is greater than a preset threshold, the battery system is determined to be abnormal, and if the number of data segments with abnormal data is not greater than the preset threshold, the battery system is determined to be normal.

[0064] The above-mentioned abnormality identification method, device, computer equipment, storage medium and program product obtain the monitoring data of the battery system of the target vehicle within the monitoring time period, thereby segmenting the monitoring data according to the preset state of charge value SOC classification interval and the preset temperature interval to obtain multiple data segments, and then determine whether the battery system is abnormal based on the multiple data segments. By segmenting the monitoring data, it is convenient to formulate more detailed abnormality determination strategies for various types of vehicles, thereby improving the accuracy of abnormality determination, and segmenting the monitoring data according to temperature and SOC improves the efficiency and accuracy of data classification.

[0065] In one embodiment, an implementation of the above S203 is provided, such as Figure 3As shown, the above “determining whether the battery system is abnormal according to multiple data segments” includes:

[0066] S301, determining the maximum voltage value and the minimum voltage value in each data segment.

[0067] In the embodiment of the present application, the voltage values ​​in each data segment are numbered and sorted according to the magnitude of each voltage value in the data segment, so as to determine the maximum voltage value and the minimum voltage value in each data segment, as well as the data number corresponding to the maximum voltage value and the data number corresponding to the minimum voltage value. Furthermore, the occurrence frequency of the maximum voltage value and the occurrence frequency of the minimum voltage value can be counted.

[0068] S302, determining an average voltage value in each data segment; the average voltage value is an average value of other voltage values ​​in the data segment except the maximum voltage value and the minimum voltage value.

[0069] In the embodiment of the present application, for each data segment, the maximum voltage value and the minimum voltage value in the data segment are removed, and the average value of the remaining voltage values ​​is determined. The voltage average 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, after removing the maximum voltage value and the minimum voltage value in the data segment, the remaining voltage values ​​are 5V, 6V, 2V, and 1V, and the voltage average value is 3.5V.

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

[0071] Optionally, a first voltage threshold, a second voltage threshold, and a voltage average value range may be pre-set, so as to determine 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 value range.

[0072] Exemplarily, if the maximum voltage value is greater than the first voltage threshold, and / or the minimum voltage value is less than the second voltage threshold, and / or the voltage average value is not within the voltage average value range, it is determined that the battery system is abnormal.

[0073] In the above application embodiment, by analyzing the maximum voltage value, the minimum voltage value and the average voltage value in each data segment, it is determined whether the battery system is abnormal, and the judgment of the abnormal situation is accurate to each data segment, thereby improving the accuracy of abnormal determination.

[0074] In one embodiment, an implementation of the above S303 is provided, such as Figure 4 As shown, for each data segment, the above “determining whether the battery system is abnormal according to the maximum voltage value, the minimum voltage value and the average voltage value in each data segment” includes:

[0075] S401, determining whether there is a high-voltage monomer concentration abnormality 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 a data segment to the total data volume of voltage values ​​in the data segment.

[0076] As an optional implementation, the number of maximum voltage values ​​in a data segment and the total data volume of voltage values ​​in the data segment are determined, so that the ratio of the number of maximum voltage values ​​in the data segment to the total data volume of voltage values ​​in the data segment is determined as a first occurrence frequency. The first occurrence frequency can be expressed as w1. Further, if the first occurrence frequency is greater than a first frequency threshold, it is determined that there is a high-voltage single-cell concentration abnormality in the battery system. If the first occurrence frequency is not greater than the first frequency threshold, it is determined that there is no high-voltage single-cell concentration abnormality 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, expressed as U max , when the first occurrence frequency is greater than the first frequency threshold, determine the U of the data segment max Whether it is greater than a maximum difference threshold, for example, the maximum difference threshold may be 40 mV. If it is greater than, it is determined that a high-voltage single-cell concentration abnormality exists 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, expressed as U max1 , and determine the difference between the maximum voltage value and the average voltage value of the previous data segment of this data segment, expressed as U max2 , further determine whether the maximum voltage difference gradually increases. If it increases, it is determined that there is a high-voltage monomer concentration abnormality in the battery system. max1 For U max2 When the maximum voltage difference is 1.1 times of that, it is determined that the maximum voltage difference gradually increases.

[0079] S402, determining whether there is a low-voltage single-cell concentration abnormality 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 data volume of voltage values ​​in the data segment.

[0080] In an embodiment of the present application, the number of minimum voltage values ​​in a data segment is determined, and the total data volume of voltage values ​​in the data segment is determined, so that the ratio of the number of minimum voltage values ​​in the data segment to the total data volume of voltage values ​​in the data segment is determined as a second occurrence frequency, and the second occurrence frequency can be expressed as w2. Further, if the second occurrence frequency is greater than the second frequency threshold, it is determined that there is a low-voltage single-cell concentration abnormality in the battery system; if the second occurrence frequency is not greater than the second frequency threshold, it is determined that there is no low-voltage single-cell concentration abnormality 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, expressed as U min , when the first occurrence frequency is greater than the first frequency threshold, determine the U min Is it less than the minimum difference threshold? If so, it is determined that there is a concentrated abnormality of high-voltage monomers in the battery system.

[0082] As another optional implementation, determine the difference between the minimum voltage value and the average voltage value of the data segment, expressed as U min1 , and determine the difference between the minimum voltage value of the previous data segment and the voltage average value of the data segment, expressed as U min2 , further determine whether the minimum voltage difference gradually increases. If it increases, it is determined that there is a high-voltage monomer concentration abnormality in the battery system. min1 For U min2 When the minimum voltage difference is 1.1 times of that, it is determined that the minimum voltage difference gradually increases.

[0083] S403, determining whether there is an extreme voltage difference abnormality in the battery system according to the maximum voltage value, the minimum voltage value and the average voltage value.

[0084] In the embodiment of the present application, the difference between the maximum voltage value and the average voltage value is determined, and the difference between the minimum voltage value and the average voltage value is determined. Further, a target difference between the two differences can be determined, so as to determine whether the battery system has an extreme voltage difference abnormality based on the target difference and a preset difference threshold.

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

[0086] S501, determining a difference between a maximum voltage value and an average voltage value as a first difference value, and determining a difference between an average voltage value and a minimum voltage value as a second difference value.

[0087] S502, determining whether the battery system has an extreme pressure difference abnormality based on the first difference and the first preset difference, and / or determining whether the battery system has an extreme pressure difference abnormality based on the second difference and the second preset difference.

[0088] In the embodiment of the present application, the first difference between the maximum voltage value and the average voltage value of the data segment is determined, which is expressed as U max , determine the second difference between the minimum voltage value and the average voltage value of the data segment, expressed as U min Further, 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 pressure 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 pressure difference abnormality.

[0089] In the above application embodiment, whether there is an extreme voltage difference abnormality in the battery system is determined based on the maximum voltage value, the minimum voltage value and the average voltage value, as well as the preset threshold parameters, so that the abnormality determination result is more reliable and the accuracy of the abnormality determination result is improved.

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

[0091] S204, determining the number of abnormalities of the battery system of the target vehicle 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 the embodiment of the present application, the voltage range threshold in each data segment can be determined based on the single battery data of multiple vehicles of the same type as the target vehicle. For example, the highest difference U can be determined by using the box plot method. max-x and the minimum difference U min-x , thereby determining the number of abnormal voltages in each data segment of the target vehicle according to the highest and lowest voltage differences. Exemplarily, an abnormality is when the first voltage difference is greater than the highest difference, or when the second voltage difference is less than the lowest difference.

[0093] S205: Determine whether the sampling line connection of the battery system is normal according to the number of abnormalities.

[0094] In this embodiment, if the number of abnormalities is greater than the preset number threshold, it can be determined that the sampling line connection of the battery system is abnormal; if the number of abnormalities is not greater than the preset number threshold, it can be determined that the sampling line connection of the battery system is normal.

[0095] In the above application embodiment, whether the sampling line connection of the battery system is normal is determined based on the number of abnormalities of the battery system of the target vehicle within a preset time period, which expands the scope of determining the abnormality of the battery system and improves the comprehensiveness of abnormality determination.

[0096] In one embodiment, an implementation of the above S205 is provided, such as Figure 7 As shown, the above “determine whether the sampling line connection of the battery system is normal according to the number of abnormalities” includes:

[0097] S601: If the number of abnormalities is greater than a preset number threshold, it is determined that the sampling line of the battery system is poorly connected.

[0098] In the embodiment of the present application, if the number of abnormalities is greater than the preset number threshold, it can be determined that the sampling line of the battery system is not connected properly; if the number of abnormalities is not greater than the preset number threshold, it can be determined that the sampling line of the battery system is connected normally. For example, the preset number threshold can be 10.

[0099] S602, if the ratio of the number of abnormalities to the total number of samplings is greater than a preset ratio threshold, it is determined that the sampling line of the battery system is disconnected; the total number of samplings is the number of samplings within a preset time period.

[0100] In an embodiment of the present application, the ratio of the number of abnormal times to the total number of sampling times, that is, the ratio of the abnormal sampling time to the total sampling time, is determined. 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 application embodiment, whether the sampling line connection of the battery system is normal is determined based on the number of abnormalities, and the judgment is made through specific values, thereby improving the accuracy of abnormal identification of the sampling line connection.

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

[0103] S1, obtaining monitoring data of the battery system of the target vehicle within a monitoring period.

[0104] S2, segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments.

[0105] S3, determining the maximum voltage value and the minimum voltage value in each data segment.

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

[0107] S5, for each data segment, determine whether there is a high-voltage single-cell concentration abnormality 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 data volume of voltage values ​​in the data segment.

[0108] S6, for each data segment, determine whether there is a low-voltage single-cell concentration abnormality in the battery system according to 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 data volume of voltage values ​​in the data segment.

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

[0110] S8, determining whether the battery system has an extreme pressure difference abnormality based on the first difference and the first preset difference, and / or determining whether the battery system has an extreme pressure difference abnormality based on the second difference and the second preset difference.

[0111] S9, determining the number of abnormalities of the battery system of the target vehicle 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, it is determined that the sampling line of the battery system is virtual connection; if the ratio of the number of abnormalities to the total number of samplings is greater than the preset ratio threshold, it is determined that the sampling line of the battery system is disconnected; the total number of samplings is the number of samplings within a preset time period.

[0113] The above-mentioned abnormality identification method, device, computer equipment, storage medium and program product obtain the monitoring data of the battery system of the target vehicle within the monitoring time period, thereby segmenting the monitoring data according to the preset state of charge value SOC classification interval and the preset temperature interval to obtain multiple data segments, and then determine whether the battery system is abnormal based on the multiple data segments. By segmenting the monitoring data, it is convenient to formulate more detailed abnormality determination strategies for various types of vehicles, thereby improving the accuracy of abnormality determination, and segmenting the monitoring data according to temperature and SOC improves the efficiency and accuracy of data classification.

[0114] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0115] Based on the same inventive concept, the embodiment of the present application also provides an abnormality identification device for implementing the abnormality identification method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more abnormality identification device embodiments provided below can refer to the limitations on the abnormality identification method above, and will not be repeated here.

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

[0117] Acquisition module 10, for acquiring the target vehicle's battery system monitoring data during the monitoring period.

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

[0119] The first determination module 12 is used to determine whether the battery system is abnormal according to the 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 voltage value and the minimum voltage value in each data segment.

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

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

[0124] In one embodiment, the third determination unit is specifically used to determine, for each data segment, whether there is a high-voltage monomer concentration abnormality in the battery system according to 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 data volume of voltage values ​​in the data segment; according to a second occurrence frequency and a second frequency threshold corresponding to the minimum voltage value, determine whether there is a low-voltage monomer concentration abnormality 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 data volume of voltage values ​​in the data segment; according to the maximum voltage value, the minimum voltage value and the average voltage, determine whether there is an extreme voltage difference abnormality in the battery system.

[0125] In one embodiment, the third determination unit is specifically used to determine the difference between the maximum voltage value and the average voltage value as the first difference, and to determine the difference between the average voltage value and the minimum voltage value as the second difference; determine whether the battery system has an extreme voltage difference abnormality based on the first difference and the first preset difference, and / or determine whether the battery system has an extreme voltage difference abnormality based on the second difference and the second preset difference.

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

[0127] The second determination module is used to determine the number of abnormalities of the battery system of the target vehicle within a preset time period, and the length of the preset time period 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 according to the number of abnormalities.

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

[0130] The fourth determination unit is used to determine that the sampling line of the battery system is poorly connected if the number of abnormalities is greater than a preset number threshold.

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

[0132] Each module in the above-mentioned abnormality identification device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0133] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Wherein, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store abnormal identification data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an abnormal identification method is implemented.

[0134] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0135] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0136] Obtain monitoring data of the battery system of the target vehicle within the monitoring time period;

[0137] Segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain multiple data segments;

[0138] Based on the plurality of data segments, it is determined whether the battery system is abnormal.

[0139] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0140] Determine the maximum voltage value and the minimum voltage value in each data segment;

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

[0142] Whether the battery system is abnormal is determined based on the maximum voltage value, the minimum voltage value and the average voltage value in each data segment.

[0143] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0144] For each data segment, determine whether there is a high-voltage monomer concentration abnormality in the battery system according to 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 data volume of voltage values ​​in the data segment;

[0145] Determine whether there is a low-voltage monomer concentration abnormality in the battery system according to 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 data volume of voltage values ​​in the data segment;

[0146] Based on the maximum voltage value, the minimum voltage value and the average voltage value, determine whether the battery system has an extreme voltage difference abnormality.

[0147] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0148] Determine a difference between the maximum voltage value and the voltage average value as a first difference value, and determine a difference between the voltage average value and the minimum voltage value as a second difference value;

[0149] Determine whether the battery system has an extreme pressure difference abnormality based on the first difference and the first preset difference, and / or determine whether the battery system has an extreme pressure difference abnormality based on the second difference and the second preset difference.

[0150] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0151] Determine the number of abnormalities of the battery system of the target vehicle within a preset time period, where the length of the preset time period is greater than the length of the monitoring time period;

[0152] According to the number of abnormalities, determine whether the sampling line connection of the battery system is normal.

[0153] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0154] If the number of abnormalities is greater than the preset number threshold, it is determined that the sampling line of the battery system is not connected properly;

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

[0156] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0157] Obtain monitoring data of the battery system of the target vehicle within the monitoring time period;

[0158] Segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain multiple data segments;

[0159] Based on the plurality of data segments, it is determined whether the battery system is abnormal.

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

[0161] Determine the maximum voltage value and the minimum voltage value in each data segment;

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

[0163] Whether the battery system is abnormal is determined based on the maximum voltage value, the minimum voltage value and the average voltage value in each data segment.

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

[0165] For each data segment, determine whether there is a high-voltage monomer concentration abnormality in the battery system according to 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 data volume of voltage values ​​in the data segment;

[0166] Determine whether there is a low-voltage monomer concentration abnormality in the battery system according to 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 data volume of voltage values ​​in the data segment;

[0167] Based on the maximum voltage value, the minimum voltage value and the average voltage value, determine whether the battery system has an extreme voltage difference abnormality.

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

[0169] Determine a difference between the maximum voltage value and the voltage average value as a first difference value, and determine a difference between the voltage average value and the minimum voltage value as a second difference value;

[0170] Determine whether the battery system has an extreme pressure difference abnormality based on the first difference and the first preset difference, and / or determine whether the battery system has an extreme pressure difference abnormality based on the second difference and the second preset difference.

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

[0172] Determine the number of abnormalities of the battery system of the target vehicle within a preset time period, where the length of the preset time period is greater than the length of the monitoring time period;

[0173] According to the number of abnormalities, 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, the following steps are also implemented:

[0175] If the number of abnormalities is greater than the preset number threshold, it is determined that the sampling line of the battery system is not connected properly;

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

[0177] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0178] Obtain monitoring data of the battery system of the target vehicle within the monitoring time period;

[0179] Segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain multiple data segments;

[0180] Based on the plurality of data segments, it is determined whether the battery system is abnormal.

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

[0182] Determine the maximum voltage value and the minimum voltage value in each data segment;

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

[0184] Whether the battery system is abnormal is determined based on the maximum voltage value, the minimum voltage value and the average voltage value in each data segment.

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

[0186] For each data segment, determine whether there is a high-voltage monomer concentration abnormality in the battery system according to 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 data volume of voltage values ​​in the data segment;

[0187] Determine whether there is a low-voltage monomer concentration abnormality in the battery system according to 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 data volume of voltage values ​​in the data segment;

[0188] Based on the maximum voltage value, the minimum voltage value and the average voltage value, determine whether the battery system has an extreme voltage difference abnormality.

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

[0190] Determine a difference between the maximum voltage value and the voltage average value as a first difference value, and determine a difference between the voltage average value and the minimum voltage value as a second difference value;

[0191] Determine whether the battery system has an extreme pressure difference abnormality based on the first difference and the first preset difference, and / or determine whether the battery system has an extreme pressure difference abnormality based on the second difference and the second preset difference.

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

[0193] Determine the number of abnormalities of the battery system of the target vehicle within a preset time period, where the length of the preset time period is greater than the length of the monitoring time period;

[0194] According to the number of abnormalities, 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, the following steps are further implemented: if the number of abnormalities is greater than a preset number threshold, determining that the sampling line of the battery system is virtually connected;

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

[0197] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0198] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for identifying anomalies, characterized in that: The method comprises: Obtain monitoring data of the battery system of the target vehicle within the monitoring time period; Segmenting the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments; It is determined whether the battery system is abnormal based on the plurality of data segments.

2. The method according to claim 1, characterized in that The determining whether the battery system is abnormal according to the plurality of data segments includes: Determining the maximum voltage value and the minimum voltage value in each of the data segments; Determine a voltage average value in each of the data segments; the voltage average value is an average value of other voltage values ​​in the data segment except the maximum voltage value and the minimum voltage value; Whether the battery system is abnormal is determined according to the maximum voltage value, the minimum voltage value and the voltage average value in each of the data segments.

3. The method according to claim 2, characterized in that The determining whether the battery system is abnormal according to the maximum voltage value, the minimum voltage value and the voltage average value in each of the data segments includes: For each data segment, determining whether the battery system has a high-voltage monomer concentration abnormality according to a first occurrence frequency and a first frequency threshold corresponding to the maximum voltage value; the first occurrence frequency is a ratio of the number of the maximum voltage values ​​in the data segment to the total data volume of the voltage values ​​in the data segment; Determine whether there is a low-voltage monomer concentration abnormality in the battery system according to a second occurrence frequency and a second frequency threshold corresponding to the minimum voltage value; the second occurrence frequency is a ratio of the number of the minimum voltage values ​​in the data segment to the total data volume of the voltage values ​​in the data segment; It is determined whether an extreme voltage difference abnormality exists in the battery system according to the maximum voltage value, the minimum voltage value and the voltage average value.

4. The method according to claim 3, characterized in that The determining, according to the maximum voltage value, the minimum voltage value and the voltage average value, whether the battery system has an extreme voltage difference abnormality includes: Determine a difference between the maximum voltage value and the voltage average value as a first difference value, and determine a difference between the voltage average value and the minimum voltage value as a second difference value; Determine whether the battery system has an extreme pressure difference abnormality based on the first difference and the first preset difference, and / or determine whether the battery system has an extreme pressure difference abnormality based on the second difference and the second preset difference.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Determining the number of abnormalities of the battery system of the target vehicle within a preset time period, wherein the length of the preset time period is greater than the length of the monitoring time period; According to the number of abnormalities, it is determined whether the sampling line connection of the battery system is normal.

6. The method according to claim 5, characterized in that The determining, according to the number of abnormalities, whether the sampling line connection of the battery system is normal includes: If the number of abnormalities is greater than a preset number threshold, determining that the sampling line of the battery system is in a virtual connection; If the ratio of the abnormal number of times to the total sampling number of times is greater than a preset ratio threshold, it is determined that the sampling line of the battery system is disconnected; the total sampling number of times is the sampling number within the preset time period.

7. An abnormality identification device, characterized in that: The device comprises: An acquisition module, used to acquire monitoring data of the battery system of the target vehicle within a monitoring period; A segmentation module, used to segment the monitoring data according to a preset state of charge value SOC classification interval and a preset temperature interval to obtain a plurality of data segments; The first determination module is used to determine whether the battery system is abnormal according to the multiple data segments.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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