Abnormity identification method and device for electric drive axle of vehicle, electronic equipment and medium

Abstract

The invention provides an abnormity identification method and device for an electric drive axle of a vehicle, electronic equipment and a medium, and the method comprises the steps: obtaining the operation data of the electric drive axle of the vehicle under a plurality of operation conditions, the operation data comprising the detection data of each motor of the electric drive axle at each sampling time point under each operation condition; based on the acquired operation data, constructing a test matrix corresponding to the electric drive bridge; extracting operation data under the target operation condition from the test matrix to form a test sub-matrix; for each column in the test sub-matrix, determining whether each detection data of the column falls into a normal cluster, the normal cluster being determined based on sample detection data of the motor corresponding to the column under the target operation condition; and based on the number of the detection data falling into the corresponding normal cluster, identifying whether the electrically driven bridge is abnormal or not. According to the invention, the abnormity identification of the electric drive bridge is realized.

Description

Technical Field

[0001] This application relates to the technical field of vehicle testing. Specifically, it relates to a method, device, electronic device and medium for abnormal identification of an electric drive axle of a vehicle. Background Art

[0002] As the electric drive axle system evolves towards higher integration and high performance, its rotational speed threshold and load-bearing capacity in steady-state operation continue to break through industry limits. This poses more stringent industrial-level requirements for product reliability. Traditional anomaly detection systems built based on manual experience, such as setting rotational speed / torque tolerance intervals through threshold boundary methods or relying on statistical construction of static evaluation indicators using probability density functions, have technical limitations: in the face of scenarios with alternating complex working conditions, the characteristic parameters extracted by traditional methods cannot deeply mine the non-linear correlation features in high-dimensional state data, resulting in low efficiency and insufficient adaptability of anomaly identification. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method, device, electronic device and medium for abnormal identification of an electric drive axle of a vehicle, aiming to achieve abnormal identification of the electric drive axle.

[0004] In a first aspect, this application provides a method for abnormal identification of an electric drive axle of a vehicle, including: obtaining operation data of the electric drive axle of the vehicle under multiple operating conditions, where the operation data includes detection data of each motor of the electric drive axle at each sampling time point under each operating condition; based on the obtained operation data, constructing a test matrix corresponding to the electric drive axle, where each row of the test matrix is used to represent the detection data of all motors of the electric drive axle at a sampling time point, each element in each row is used to represent the detection data of a corresponding motor, and each row of the test matrix is set with a condition identifier for indicating the operating condition in which the electric drive axle is located at the sampling time point corresponding to that row; extracting operation data under a target operating condition from the test matrix to form a test sub-matrix; for each column in the test sub-matrix, determining whether each data in that column falls into a normal cluster, where the normal cluster is determined based on the sample detection data of the corresponding motor under the target operating condition; and identifying whether the electric drive axle is abnormal based on the number of data falling into the corresponding normal cluster.

[0005] In a possible implementation manner, the operation data of the electric drive axle of the vehicle under multiple operating conditions is obtained by the following method: sending an operation instruction to the electric drive axle to drive the electric drive axle to perform periodic operation under the multiple operating conditions; during the operation of the electric drive axle, synchronously collecting the detection data of each motor of the electric drive axle at every preset time interval to generate the operation data under a preset duration, where the detection data includes load data and rotational speed data.

[0006] In a possible implementation manner, the step of identifying whether there is an abnormality in the electric drive bridge based on the number of detection data falling into the corresponding normal clusters includes: for each row in the test sub-matrix, determining the number of normal rows in which each element of the row falls into the corresponding normal cluster; if the number of normal rows is less than half of the total number of rows in the test sub-matrix, it is determined that there is an abnormality in the electric drive bridge.

[0007] In a possible implementation manner, the target operating condition includes at least two target operating conditions, and each target operating condition corresponds to a test sub-matrix. Among them, the method for identifying whether there is an abnormality in the electric drive bridge is as follows: for the test sub-matrix corresponding to each target operating condition, determining the number of normal rows in the test sub-matrix; if the number of normal rows in any test sub-matrix is less than half of the total number of rows in the test sub-matrix, it is determined that there is an abnormality in the electric drive bridge.

[0008] In a possible implementation manner, the normal clusters are determined by the following method: obtaining the sample operation data of each sample electric drive bridge under the multiple operating conditions, and constructing a test sample sub-matrix of the sample electric drive bridge under the target operating condition according to the sample operation data; for each column of each test sample sub-matrix, normalizing the sample data of the column; for each column of each test sample sub-matrix, performing clustering analysis on the sample data of the column based on the clustering algorithm to obtain the normal cluster corresponding to the column.

[0009] In a possible implementation manner, the sample operation data of each sample electric drive bridge under the multiple operating conditions is obtained by the following method: for each sample electric drive bridge, sending an operation instruction to the sample electric drive bridge to drive the sample electric drive bridge to perform periodic operation under the multiple operating conditions; for each sample electric drive bridge, during the operation of the electric drive bridge, synchronously collecting the sample detection data of each motor of the electric drive bridge at every preset time interval to generate the sample operation data of the electric drive bridge under a preset duration, and the sample detection data includes load data and speed data.

[0010] In a possible implementation manner, the electric drive bridge includes a single-motor electric drive bridge and a dual-motor electric drive bridge. The single-motor electric drive bridge includes three motors, and the dual-motor electric drive bridge includes four motors.

[0011] Second aspect, the present application provides an abnormal identification device for an electric drive axle of a vehicle. The device includes: an acquisition module, configured to acquire operation data of the electric drive axle of the vehicle under multiple operating conditions, where the operation data includes detection data of each motor of the electric drive axle at each sampling time point under each operating condition; a first construction module, configured to construct a test matrix corresponding to the electric drive axle based on the acquired operation data, where each row of the test matrix is used to represent the detection data of all motors of the electric drive axle at a sampling time point, and each element in each row is used to represent the detection data of a corresponding motor, and a condition identifier is set for each row of the test matrix to indicate the operating condition in which the electric drive axle is located at the sampling time point corresponding to that row; a second construction module, configured to extract operation data under a target operating condition from the test matrix to form a test sub-matrix; a determination module, configured to determine, for each column in the test sub-matrix, whether each data in that column falls into a normal cluster, where the normal cluster is determined based on sample detection data of the motor corresponding to that column under the target operating condition; and an identification module, configured to identify whether the electric drive axle is abnormal based on the number of data falling into the corresponding normal cluster.

[0012] Third aspect, the present application further provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the above method are executed.

[0013] Fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the above method are executed.

[0014] The present application provides an abnormal identification method, device, electronic device, and medium for an electric drive axle of a vehicle. Among them, the method includes: acquiring operation data of the electric drive axle of the vehicle under multiple operating conditions, where the operation data includes detection data of each motor of the electric drive axle at each sampling time point under each operating condition; constructing a test matrix corresponding to the electric drive axle based on the acquired operation data; extracting operation data under a target operating condition from the test matrix to form a test sub-matrix; determining, for each column in the test sub-matrix, whether each detection data in that column falls into a normal cluster, where the normal cluster is determined based on sample detection data of the motor corresponding to that column under the target operating condition; and identifying whether the electric drive axle is abnormal based on the number of detection data falling into the corresponding normal cluster. The present application aims to realize the abnormal identification of the electric drive axle.

[0015] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specific preferred embodiments are given, and detailed descriptions are made in conjunction with the accompanying drawings as follows. Brief Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following accompanying drawings only show certain embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related accompanying drawings can be obtained based on these accompanying drawings without creative efforts.

[0017] Figure 1 It is a flowchart of a method for identifying abnormal conditions of an electric drive axle of a vehicle provided by an embodiment of the present application;

[0018] Figure 2 It is a schematic diagram of the operating data of an electric drive axle operating under multiple operating conditions provided by an embodiment of the present application;

[0019] Figure 3 It is a flowchart of determining a normal cluster provided by an embodiment of the present application;

[0020] Figure 4 It is a flowchart of identifying abnormal conditions of an electric drive axle provided by an embodiment of the present application;

[0021] Figure 5 It is a schematic diagram of the clustering result provided by an embodiment of the present application;

[0022] Figure 6 It is a schematic structural diagram of an abnormal condition identification device for an electric drive axle of a vehicle provided by an embodiment of the present application;

[0023] Figure 7 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, every other embodiment obtained by those of ordinary skill in the art without creative efforts belongs to the scope of protection of the present application.

[0025] First, the applicable application scenarios of the present application will be introduced. The present application can be applied to vehicle testing.

[0026] It has been found through research that with the improvement of the integration level and efficiency of the electric drive axle, its operating speed and load under stable operating conditions increase, and the product performance requirements are significantly improved. In order to identify the qualification of electric drive products, it is necessary to construct a performance evaluation method based on the monitoring parameters during the normal operation of the electric drive axle to screen out products in abnormal states. Currently, the more mature application for abnormal discrimination is the method based on boundaries and probability density, which establishes thresholds or statistical parameters for monitoring indicators. Such methods are cumbersome in constructing indicators and overly rely on manual experience. At the same time, for a powertrain system with alternating multiple working conditions, unclear and variable fault modes, the indicators extracted to reflect the health status of electric drive products cannot fully reflect the internal information of the status data.

[0027] Based on this, the embodiments of the present application provide a method, device, electronic device and medium for abnormal identification of an electric drive axle of a vehicle, aiming to realize the abnormal identification of the electric drive axle under multi-condition coupling conditions.

[0028] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for abnormal identification of an electric drive axle of a vehicle provided by an embodiment of the present application. As shown in Figure 1 , the method for abnormal identification of an electric drive axle of a vehicle provided by the embodiments of the present application includes:

[0029] S101. Obtain the operation data of the electric drive axle of the vehicle under multiple operating conditions.

[0030] Here, the operation data includes the detection data of each motor of the electric drive axle at each sampling time point under each operating condition.

[0031] As an example, the multiple operating conditions can be: low gear constant speed condition - low gear acceleration condition - high gear acceleration condition - high gear constant speed condition - high gear deceleration condition - low gear deceleration condition - low gear constant speed condition, a total of 7 operating conditions, so that the electric drive axle operates periodically in the above seven operating conditions, where the first three conditions are positive drive, the last three conditions are reverse tow, and the fourth condition includes two processes of positive drive and reverse tow.

[0032] In a preferred example of the present application, the operation data of the electric drive axle of the vehicle under multiple operating conditions can be obtained in the following manner:

[0033] Send an operation instruction to the electric drive axle of the vehicle to drive the electric drive axle to operate periodically in multiple operating conditions; during the operation of the electric drive axle, synchronously collect the detection data of each motor of the electric drive axle at each sampling time point to generate operation data, and the detection data includes load data and speed data.

[0034] As an example, the electric drive axle includes a dual-motor electric drive axle and a single-motor electric drive axle. The dual-motor electric drive axle includes four motors. The collected detection data are the rotational speed data and load data of the front working motor, the rotational speed data and load data of the rear working motor, and the rotational speed data and load data of the two end-loading motors. That is, the detection data of the dual-motor electric drive axle are eight-dimensional data. The single-motor electric drive axle includes three motors. That is, the detection data of the single-motor electric drive axle are six-dimensional data, and each dimension represents a monitoring index.

[0035] S102. Based on the obtained operation data, construct a test matrix corresponding to the electric drive axle.

[0036] Here, each row of the test matrix is used to represent the detection data of all the motors of the electric drive axle at a sampling time point. Each element in each row is used to represent the detection data of a corresponding motor. A working condition identifier is set for each row of the test matrix, which is used to indicate the operating condition of the electric drive axle at the sampling time point corresponding to this row.

[0037] S103. Extract the operation data under the target operating condition from the test matrix to form a test sub-matrix.

[0038] Here, the detection data in each column of the test sub-matrix are normalized, and the value range after processing is (0, 1].

[0039] As an example, the target operating condition can be one or more. In this embodiment, the target operating conditions are selected as the high-gear constant-speed condition and the low-gear constant-speed condition. As Figure 2 shown is a schematic diagram of the operation data of the electric drive axle operating under multiple operating conditions. Figure 2 The abscissa of [] is time, and the ordinate is the unit before normalization of each detection data.

[0040] S104. For each column in the test sub-matrix, determine whether each detection data in this column falls into the normal cluster.

[0041] Here, the normal cluster is determined based on the sample detection data of the motor corresponding to this column under the target operating condition.

[0042] Next, the specific process of determining the normal cluster is introduced through Figure 3 .

[0043] As Figure 3 shown, Figure 3 is a flowchart of determining the normal cluster provided by the embodiment of the present application.

[0044] S201. Obtain the sample operation data of each sample electric drive axle under multiple operating conditions, and construct a test sample sub-matrix of this sample electric drive axle under the target operating condition according to the sample operation data.

[0045] In a preferred example of the present application, the sample operation data of each sample electric drive axle under multiple operating conditions is obtained in the following manner:

[0046] For each sample electric drive axle, an operation instruction is sent to the sample electric drive axle to drive the sample electric drive axle to perform periodic operation under multiple operating conditions; for each sample electric drive axle, during the operation of the electric drive axle, at every preset interval, the detection data of each motor of the electric drive axle is synchronously collected to generate the sample operation data of the operation of the electric drive axle.

[0047] S202. For each column of each test sample sub-matrix, normalize the sample detection data of this column.

[0048] S203. For each column in each test sample sub-matrix, perform clustering analysis on the sample detection data of this column based on a clustering algorithm to obtain the normal cluster corresponding to this column.

[0049] As an example, the clustering algorithm can be the K-means clustering algorithm. The category parameter k in K-means clustering can be set to 2, 3, 4... according to the product status division. Here, the value of the parameter k is selected according to the product status division category. In the embodiment of the present application, K is taken as 2, that is, the normal cluster and the abnormal cluster.

[0050] Return Figure 1 , S105. Based on the number of detection data falling into the corresponding normal cluster, identify whether the electric drive axle is abnormal.

[0051] Next, through Figure 4 introduce the specific process of identifying the abnormality of the electric drive axle.

[0052] As Figure 4 shown, Figure 4 is the flow chart for identifying the abnormality of the electric drive axle provided by the embodiment of the present application.

[0053] S301. For each row in the test sub-matrix, determine the number of normal rows in which each element of this row falls into the corresponding normal cluster.

[0054] S302. If the number of normal rows is less than half of the total number of rows, determine that the electric drive axle is abnormal.

[0055] In a preferred example of the present application, the target operating conditions include at least two target operating conditions, namely high-gear constant-speed operation and low-gear constant-speed operation. Each target operating condition corresponds to a test sub-matrix. Among them, the following method is used to identify whether the electric drive axle is abnormal under at least two target operating conditions:

[0056] For each test sub - matrix corresponding to a target operating condition, determine the number of normal rows in the test sub - matrix; if the number of normal rows in any test sub - matrix is less than half of the total number of rows, it is determined that there is an abnormality in the electric drive axle, such as Figure 5 The figure shows the clustering results of normal products and abnormal products.

[0057] Based on the same inventive concept, an electric drive axle abnormality recognition device for a vehicle corresponding to the electric drive axle abnormality recognition method of the vehicle is also provided in an embodiment of the present application. Since the principle of solving problems by the device in the embodiment of the present application is similar to that of the above - mentioned electric drive axle abnormality recognition method for the vehicle in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0058] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of an electric drive axle abnormality recognition device for a vehicle provided in an embodiment of the present application. The electric drive axle abnormality recognition device 400 for a vehicle includes:

[0059] An acquisition module 401, configured to acquire the operation data of the electric drive axle of the vehicle under multiple operating conditions, where the operation data includes the detection data of each motor of the electric drive axle at each sampling time point under each operating condition.

[0060] A first construction module 402, configured to construct a test matrix corresponding to the electric drive axle based on the acquired operation data. Each row of the test matrix is used to represent the detection data of all motors of the electric drive axle at a sampling time point, each element in each row is used to represent the detection data of a corresponding motor, and a working condition identifier is set for each row of the test matrix to indicate the operating condition of the electric drive axle at the sampling time point corresponding to the row.

[0061] A second construction module 403, configured to extract the operation data under the target operating condition from the test matrix to form a test sub - matrix.

[0062] A determination module 404, configured to determine, for each column in the test sub - matrix, whether each detection data in the column falls into a normal cluster, and the normal cluster is determined based on the sample detection data of the corresponding motor under the target operating condition.

[0063] An identification module 405, configured to identify whether there is an abnormality in the electric drive axle based on the number of detection data falling into the corresponding normal cluster.

[0064] Please refer to Figure 7 , Figure 7 which is a schematic structural diagram of an electronic device provided in an embodiment of the present application. As Figure 7 shown in the figure, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.

[0065] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 runs, the processor 510 communicates with the memory 520 via the bus 530. When the machine-readable instructions are executed by the processor 510, the steps of the method for identifying an abnormality of an electric drive axle of a vehicle in the above method embodiment can be executed. For the specific implementation manner, reference can be made to the method embodiment and will not be elaborated herein.

[0066] An embodiment of the present application further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the method for identifying an abnormality of an electric drive axle of a vehicle in the above method embodiment can be executed. For the specific implementation manner, reference can be made to the method embodiment and will not be elaborated herein.

[0067] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0068] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0069] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0071] When the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0072] Finally, it should be noted that the above-mentioned embodiments are only specific implementation manners of the present application, used to illustrate the technical solutions of the present application, rather than limiting them. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present application can still modify the technical solutions recorded in the foregoing embodiments or easily conceive of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An abnormal recognition method for an electric drive axle of a vehicle, characterized in that, Including: Obtaining the operation data of the electric drive axle of the vehicle under multiple operating conditions, where the operation data includes the detection data of each motor of the electric drive axle at each sampling time point under each operating condition; Based on the obtained operation data, constructing a test matrix corresponding to the electric drive axle, where each row of the test matrix is used to represent the detection data of all motors of the electric drive axle at a sampling time point, each element in each row is used to represent the detection data of a corresponding motor, and a condition identifier is set for each row of the test matrix to indicate the operating condition of the electric drive axle at the sampling time point corresponding to that row; Extracting the operation data under the target operating condition from the test matrix to form a test sub-matrix; For each column in the test sub-matrix, determining whether each data in the column falls into the normal cluster, where the normal cluster is determined based on the sample detection data of the corresponding motor under the target operating condition; Based on the number of data falling into the corresponding normal cluster, identifying whether the electric drive axle is abnormal.

2. The method according to claim 1, characterized in that, Obtaining the operation data of the electric drive axle of the vehicle under multiple operating conditions in the following manner: Sending an operation instruction to the electric drive axle to drive the electric drive axle to perform periodic operation under the multiple operating conditions; During the operation of the electric drive axle, at every preset time interval, synchronously collecting the detection data of each motor of the electric drive axle to generate the operation data under a preset duration, where the detection data includes load data and speed data.

3. The method according to claim 1, wherein The step of identifying whether the electric drive axle is abnormal based on the number of detection data falling into the corresponding normal cluster includes: For each row in the test sub-matrix, determining the number of normal rows where each element in the row falls into the corresponding normal cluster; If the number of normal rows is less than half of the total number of rows in the test sub-matrix, determining that the electric drive axle is abnormal.

4. The method according to claim 3, wherein The target operating condition includes at least two target operating conditions, and each target operating condition corresponds to a test sub-matrix. Among them, identifying whether the electric drive axle is abnormal in the following manner: For the test sub-matrix corresponding to each target operating condition, determining the number of normal rows in the test sub-matrix; If there is any test sub-matrix where the number of normal rows is less than half of the total number of rows in the test sub-matrix, determining that the electric drive axle is abnormal.

5. The method according to claim 1, characterized in that, Determining the normal cluster in the following manner: Obtaining the sample operation data of each sample electric drive axle under the multiple operating conditions, and constructing a test sample sub-matrix of the sample electric drive axle under the target operating condition according to the sample operation data; For each column of each test sample sub-matrix, normalizing the sample data in the column; For each column of each test sample sub-matrix, performing clustering analysis on the sample data in the column based on a clustering algorithm to obtain the normal cluster corresponding to the column.

6. The method according to claim 5, characterized in that, Obtaining the sample operation data of each sample electric drive axle under the multiple operating conditions in the following manner: For each sample electric drive axle, sending an operation instruction to the sample electric drive axle to drive the sample electric drive axle to perform periodic operation under the multiple operating conditions; For each sample electric drive axle, during the operation of the electric drive axle, at every preset time interval, sample detection data of each motor of the electric drive axle is synchronously collected to generate sample operation data of the electric drive axle under a preset duration, and the sample detection data includes load data and speed data.

7. The method according to claim 1, wherein The electric drive axle includes a single-motor electric drive axle and a dual-motor electric drive axle. The single-motor electric drive axle includes three motors, and the dual-motor electric drive axle includes four motors.

8. An abnormal recognition device for an electric drive axle of a vehicle, characterized in that, The device includes: An acquisition module, configured to acquire operation data of the electric drive axle of the vehicle under multiple operating conditions, where the operation data includes detection data of each motor of the electric drive axle at each sampling time point under each operating condition; A first construction module, configured to construct a test matrix corresponding to the electric drive axle based on the acquired operation data. Each row of the test matrix is used to represent the detection data of all motors of the electric drive axle at a sampling time point, each element in each row is used to represent the detection data of a corresponding motor, and a condition identifier is set for each row of the test matrix to indicate the operating condition of the electric drive axle at the sampling time point corresponding to the row; A second construction module, configured to extract operation data under a target operating condition from the test matrix to form a test sub-matrix; A determination module, configured to determine, for each column in the test sub-matrix, whether each data in the column falls into a normal cluster, and the normal cluster is determined based on the sample detection data of the motor corresponding to the column under the target operating condition; An identification module, configured to identify whether there is an abnormality in the electric drive axle based on the number of data falling into the corresponding normal cluster.

9. An electronic device, characterized in that, Includes: A processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus, and the processor executes the machine-readable instructions to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is run by the processor, it performs the steps of the method according to any one of claims 1 to 7.

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