New energy automobile driving motor operation detection bench

Abstract

The invention relates to a new energy automobile driving motor operation detection platform which comprises a test system, a content acquisition module and a diagnostic analysis module. In the teaching mode, the test system detects first motor operation data when a new energy automobile driving motor operates; the content acquisition module acquires first analysis and diagnosis content of the first motor operation data by a tutor; the diagnosis analysis module trains a machine learning model according to the first motor operation data and the explanation content to obtain a motor detection state diagnosis model; in the test mode, the test system detects second motor operation data when the new energy automobile driving motor operates; the content acquisition module acquires second analysis and diagnosis content of the student on the second motor operation data; and the diagnosis analysis module inputs the second motor operation data into the motor detection state diagnosis model to obtain a third analysis and diagnosis content, and obtains a diagnosis score of the second analysis and diagnosis content according to the third analysis and diagnosis content.

Description

Technical Field

[0001] This application relates to the technical field of motor operation detection, and particularly to an operating detection bench for a drive motor of a new energy vehicle. Background Art

[0002] With the development and popularization of new energy vehicles, as an important factor in the quality of new energy vehicles, the production volume of drive motors for new energy vehicles has increased significantly, and the study and research of drive motors for new energy vehicles have also become an important course in the automotive specialty. The existing operating detection bench for drive motors can only simply detect the operating data of the motor, and cannot obtain the diagnostic content of the motor by combining the operating data, nor can it judge whether the diagnostic content proposed by students is correct or not. As a result, when conducting course learning tests, tutors need to analyze and score the diagnostic content proposed by students by combining the operating data of the drive motor detected by the operating detection bench. Therefore, the existing operating detection bench for drive motors has technical defects such as simple functions, lack of functions for analyzing and scoring the diagnostic content proposed by students, and low convenience for application in the learning scenario of drive motors. Summary of the Invention

[0003] Based on this, the purpose of this application is to provide an operating detection bench for a drive motor of a new energy vehicle, which can overcome the deficiencies of the prior art.

[0004] To achieve the above purpose, the technical solution adopted in this application is as follows:

[0005] An operating detection bench for a drive motor of a new energy vehicle, comprising: a test system, a content acquisition module, and a diagnostic analysis module;

[0006] In the teaching mode, the test system detects the first motor operating data when the drive motor of the new energy vehicle is operating; the content acquisition module acquires the first analysis and diagnostic content of the tutor for the first motor operating data; the diagnostic analysis module trains the machine learning model according to the first motor operating data and the explanation content to obtain a motor detection status diagnostic model;

[0007] In the test mode, the test system detects the second motor operating data when the drive motor of the new energy vehicle is operating; the content acquisition module acquires the second analysis and diagnostic content of the student for the second motor operating data; the diagnostic analysis module inputs the second motor operating data into the motor detection status diagnostic model to obtain the third analysis and diagnostic content, and obtains the diagnostic score of the second analysis and diagnostic content according to the third analysis and diagnostic content.

[0008] In one embodiment, the content acquisition module includes a voice acquisition sub-module and a semantic analysis sub-module: the content acquisition module is used to perform the following steps:

[0009] In the teaching mode, the analysis and diagnosis speech of the tutor's explanation and analysis of the first motor operation data is obtained through the voice acquisition sub-module;

[0010] The semantic analysis sub-module performs semantic analysis on the analysis and diagnosis speech to obtain the first analysis and diagnosis content.

[0011] In one embodiment, the semantic analysis sub-module includes a text conversion unit and a text semantic analysis unit; the semantic analysis sub-module is used to perform the following steps:

[0012] The text conversion unit converts the analysis and diagnosis speech to obtain analysis and diagnosis text;

[0013] The text semantic analysis unit performs semantic analysis on the analysis and diagnosis text to obtain the first analysis and diagnosis content.

[0014] In one embodiment, the text conversion unit converts the analysis and diagnosis speech to obtain analysis and diagnosis text, including:

[0015] When the text conversion unit converts the analysis and diagnosis speech, it screens the text content obtained by conversion according to the tutor's voice characteristics to obtain the analysis and diagnosis text corresponding to the tutor's voice characteristics.

[0016] In one embodiment, a communication module is further included;

[0017] The communication module transmits the tutor account logged in by the tutor to the server to obtain the tutor voice characteristics corresponding to the tutor account issued by the server.

[0018] In one embodiment, the tutor voice characteristics are obtained through the following steps:

[0019] The server receives the latest voice content uploaded through the tutor account;

[0020] The server inputs the latest voice content into a voice feature analysis model to obtain the tutor voice characteristics corresponding to the tutor account.

[0021] In one embodiment, the step of the server inputting the latest voice content into a voice feature analysis model to obtain the tutor voice characteristics corresponding to the tutor account includes:

[0022] The server inputs the voice content into a voice feature analysis model to obtain the current tutor voice characteristics;

[0023] The server compares the current tutor voice characteristics with the previous tutor voice characteristics of the tutor account to obtain a feature comparison value;

[0024] If the feature comparison value is greater than or equal to a preset comparison threshold, the server only stores the current tutor voice feature as the tutor voice feature. If the feature comparison value is less than the comparison threshold, the server stores the current tutor voice feature and the previous tutor voice feature as the tutor voice feature.

[0025] In one embodiment, the diagnostic analysis module trains a machine learning model based on the first motor operation data and the explanation content to obtain a motor detection status diagnosis model, and further includes:

[0026] After the content acquisition module acquires the first analysis and diagnosis content of the tutor for the first motor operation data, the communication module uploads the first analysis and diagnosis content to the database of the tutor account;

[0027] The communication module receives the first analysis and diagnosis content after confirmation or modified confirmation by the tutor in the database, and transmits it to the diagnostic analysis module, so that the diagnostic analysis module trains the machine learning model according to the first motor operation data and the explanation content transmitted by the communication module.

[0028] In one embodiment, the second analysis and diagnosis content is text content, and the content acquisition module includes a text semantic analysis unit;

[0029] The content acquisition module performs semantic analysis on the second analysis and diagnosis content through the text semantic analysis unit to obtain the second analysis and diagnosis content.

[0030] In one embodiment, the step of obtaining the diagnosis score of the second analysis and diagnosis content according to the third analysis and diagnosis content includes:

[0031] Compare the third analysis and diagnosis content with the second analysis and diagnosis content to obtain a content similarity ratio;

[0032] Obtain the diagnosis score according to a preset full score value and the content similarity ratio.

[0033] Compared with the traditional technology, the beneficial effects of this application are:

[0034] The running detection bench for the drive motor of a new energy vehicle in this application can, in the teaching mode, obtain the first analysis and diagnosis content of the tutor for the first motor running data according to the first motor running data and the content acquisition module when the test system detects the running of the drive motor of the new energy vehicle, train the machine learning model to obtain the motor detection status diagnosis model, and then in the test mode, input the second motor running data when the test system detects the running of the drive motor of the new energy vehicle into the motor detection status diagnosis model to obtain the third analysis and diagnosis content, and then obtain the diagnosis score of the second analysis and diagnosis content of the student for the second motor running data according to the third analysis and diagnosis content, so that the drive motor running detection bench adds the function of analyzing and scoring the diagnosis content proposed by the student, and improves the convenience of applying the drive motor running detection bench to the drive motor learning scenario.

[0035] For better understanding and implementation, the present application will be described in detail below with reference to the accompanying drawings. Brief Description of the Drawings

[0036] Figure 1 It is a schematic diagram of the system module connection of the running detection bench for the drive motor of a new energy vehicle according to an embodiment of the present application;

[0037] Figure 2 It is a schematic diagram of the appearance of the running detection bench for the drive motor of a new energy vehicle according to an embodiment of the present application;

[0038] 10. Running detection bench for the drive motor of a new energy vehicle; 11. Test system; 12. Content acquisition module; 13. Diagnosis and analysis module. Detailed Embodiment

[0039] To make the purpose, technical solution and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0040] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the embodiments of the present application.

[0041] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances. In the present application and the appended claims, the singular forms of "a", "the", and "said" are also intended to include the plural forms unless the context clearly indicates otherwise. The word "if" used herein can be interpreted as "when" or "while" or "in response to determining".

[0042] In addition, in the description of the present application, unless otherwise specified, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0043] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the system module connection of the operation detection bench for the drive motor of a new energy vehicle in the first embodiment of the present application, including: a test system, a content acquisition module, and a diagnostic analysis module;

[0044] In the teaching mode, the test system detects the first motor operation data when the drive motor of the new energy vehicle is operating; the content acquisition module acquires the first analysis and diagnosis content of the tutor for the first motor operation data; the diagnostic analysis module trains the machine learning model according to the first motor operation data and the explanation content to obtain a motor detection status diagnosis model;

[0045] In the test mode, the test system detects the second motor operation data when the drive motor of the new energy vehicle is operating; the content acquisition module acquires the second analysis and diagnosis content of the student for the second motor operation data; the diagnostic analysis module inputs the second motor operation data into the motor detection status diagnosis model to obtain the third analysis and diagnosis content, and obtains the diagnostic score of the second analysis and diagnosis content according to the third analysis and diagnosis content.

[0046] Among them, the new energy vehicle drive motor operation detection bench can enter the teaching mode or the test mode in response to the user's operation. For example, it can enter the teaching mode or the test mode in response to the mode instruction issued by the tutor account, or it can also enter the teaching mode or the test mode according to the operation of the tutor on the control interface of the new energy vehicle drive motor operation detection bench. Among them, when entering the teaching mode through the operation of the control interface, face recognition of the operator is required to confirm the tutor identity of the operator.

[0047] When obtaining the second analysis and diagnosis content, the content acquisition module simultaneously obtains the identity information of the corresponding student, such as major, class, student number, name, etc. Among them, the identity information of the student can be obtained by reading the student card of the student, or it can also be obtained according to the student account when the student uploads the second analysis and diagnosis content through the student account.

[0048] The diagnosis score can be uploaded to the server or directly displayed on the display interface of the new energy vehicle drive motor operation detection bench.

[0049] In a feasible embodiment, the content acquisition module includes a voice acquisition sub-module and a semantic analysis sub-module: the content acquisition module is used to perform the following steps:

[0050] In the teaching mode, the voice acquisition sub-module acquires the analysis and diagnosis voice of the tutor's explanation and analysis of the first motor operation data.

[0051] The semantic analysis sub-module performs semantic analysis on the analysis and diagnosis voice to obtain the first analysis and diagnosis content.

[0052] In a feasible embodiment, the semantic analysis sub-module includes a text conversion unit and a text semantic analysis unit; the semantic analysis sub-module is used to perform the following steps:

[0053] The text conversion unit converts the analysis and diagnosis voice to obtain analysis and diagnosis text.

[0054] The text semantic analysis unit performs semantic analysis on the analysis and diagnosis text to obtain the first analysis and diagnosis content.

[0055] In a feasible embodiment, the text conversion unit converts the analysis and diagnosis voice to obtain analysis and diagnosis text, including:

[0056] When the text conversion unit converts the analysis and diagnosis voice, it screens the text content obtained by conversion according to the tutor voice feature to obtain the analysis and diagnosis text corresponding to the tutor voice feature.

[0057] In a feasible embodiment, a communication module is further included.

[0058] The communication module transmits the tutor account logged in by the tutor to the server to obtain the tutor voice feature corresponding to the tutor account issued by the server.

[0059] In a feasible embodiment, the tutor voice feature is obtained through the following steps:

[0060] The server receives the latest voice content uploaded through the tutor account;

[0061] The server inputs the latest voice content into the voice feature analysis model to obtain the tutor voice feature corresponding to the tutor account.

[0062] In a feasible embodiment, the step in which the server inputs the latest voice content into the voice feature analysis model to obtain the tutor voice feature corresponding to the tutor account includes:

[0063] The server inputs the voice content into the voice feature analysis model to obtain the current tutor voice feature;

[0064] The server compares the current tutor voice feature with the previous tutor voice feature of the tutor account to obtain a feature comparison value;

[0065] If the feature comparison value is greater than or equal to a preset comparison threshold, the server only stores the current tutor voice feature as the tutor voice feature. If the feature comparison value is less than the comparison threshold, the server stores the current tutor voice feature and the previous tutor voice feature as the tutor voice feature.

[0066] In a feasible embodiment, the diagnostic analysis module trains a machine learning model according to the first motor operation data and the explanation content to obtain a motor detection status diagnosis model, and further includes:

[0067] After the content acquisition module acquires the first analysis and diagnosis content of the tutor for the first motor operation data, the communication module uploads the first analysis and diagnosis content to the database of the tutor account;

[0068] The communication module receives the first analysis and diagnosis content confirmed or modified by the tutor in the database and transmits it to the diagnostic analysis module, so that the diagnostic analysis module trains the machine learning model according to the first motor operation data and the explanation content transmitted by the communication module.

[0069] In a feasible embodiment, the second analysis and diagnosis content is text content, and the content acquisition module includes a text semantic analysis unit;

[0070] The content acquisition module performs semantic analysis on the second analysis and diagnosis content through the text semantic analysis unit to obtain the second analysis and diagnosis content.

[0071] In a feasible embodiment, the step of obtaining the diagnostic score of the second analysis and diagnosis content according to the third analysis and diagnosis content includes:

[0072] Compare the third analysis and diagnosis content with the second analysis and diagnosis content to obtain the content similarity ratio;

[0073] According to the preset full score value and the content similarity ratio, obtain the diagnostic score.

[0074] Compared with the traditional technology, the beneficial effects of this application are:

[0075] The operation detection platform for the drive motor of new energy vehicles in this application can, in the teaching mode, train the machine learning model according to the first motor operation data detected by the test system during the operation of the drive motor of new energy vehicles and the first analysis and diagnosis content obtained by the content acquisition module for the first motor operation data by the tutor, and obtain the motor detection status diagnosis model. Then, in the test mode, input the second motor operation data detected by the test system during the operation of the drive motor of new energy vehicles into the motor detection status diagnosis model to obtain the third analysis and diagnosis content, and then according to the third analysis and diagnosis content, obtain the diagnostic score of the second analysis and diagnosis content of the student for the second motor operation data, which enables the drive motor operation detection platform to add the analysis and scoring function for the diagnostic content proposed by the student and improves the convenience of the drive motor operation detection platform applied to the drive motor learning scenario.

[0076] Please refer to Figure 2 , the operation detection platform for the drive motor of new energy vehicles in this application further includes a power supply module, a communication control module, a display screen (host computer system), a motor controller, a relay, a high-voltage wire harness, a low-voltage wire harness, and a power assembly disassembly module, etc., and the detection platform is configured with the following content:

[0077] Drawers that can store parts and training instruments, with power indicator lights, touch screen computer power buttons, etc. installed on the upper part, which are convenient to operate; the back is designed with open and closed cabinet doors, which is convenient for maintenance and storage of high- and low-voltage cables;

[0078] It is equipped with motor low-voltage control signal input and output plugs, motor three-phase cable sockets, AC220V sockets, and cable storage hooks. The plugging and unplugging operations conform to the ergonomic design and are convenient to use.

[0079] It is equipped with a motor three-phase power input cable socket, and students can complete the high-voltage line assembly and connection between the power drive motor disassembly and assembly training platform and the power drive motor disassembly and assembly test system through the supporting motor three-phase cable.

[0080] Equipped with a low-voltage communication cable socket, students can complete the assembly and connection of the low-voltage circuit between the power drive motor disassembly and assembly training bench and the power drive motor disassembly and assembly test system through the supporting low-voltage communication harness.

[0081] Motor three-phase voltage signal and motor resolver signal detection area. The phase voltage signal between stator windings can be detected with the help of a multimeter; the signal waveform can be diagnosed and analyzed with the help of an oscilloscope device.

[0082] The new energy vehicle drive motor operation detection bench of this application is also equipped with a supporting touch upper computer system, which can control the operation of the motor and is used for the debugging of the motor. The debugging contents include:

[0083] (1) Power on and power off operations can be performed to master the power on and power off control logic of the new energy vehicle drive motor.

[0084] (2) Start, stop, accelerate, decelerate, D gear, and R gear control operations can be performed to simulate the dynamic operation of the new energy power drive assembly.

[0085] (3) The platform is equipped with motor wire interfaces, motor resolver sensor interfaces, and ground wire interfaces, which can facilitate the connection of the power drive motor disassembly and assembly test system to supply power to the motor.

[0086] (4) The test system is equipped with a power supply module, and the supply voltage is DC72V. The system can detect the change of voltage data in real time.

[0087] The new energy vehicle drive motor operation detection bench of this application is also equipped with a drive motor disassembly and assembly bench, which can be used for the motors of BYD models and is configured with a transmission; disassembly and assembly practice can be completed; after the disassembly and assembly practice is completed, it can be connected to the drive motor operation detection bench for drive motor operation testing and debugging.

[0088] The device embodiments described above are only illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0089] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the selected functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the selected functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the selected functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0092] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0093] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0094] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0095] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0096] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A new energy vehicle drive motor operation test bench, characterized in that: include: Testing system, content acquisition module and diagnostic analysis module; In the teaching mode, the test system detects the first motor operation data when the new energy vehicle driving motor is running; the content acquisition module acquires the first analysis and diagnosis content of the first motor operation data by the instructor; The diagnostic analysis module trains a machine learning model according to the first motor operation data and the explanation content to obtain a motor detection state diagnostic model; In the test mode, the test system detects the second motor operation data when the new energy vehicle drive motor is running; the content acquisition module obtains the student's second analysis and diagnosis content of the second motor operation data; the diagnostic analysis module inputs the second motor operation data into the motor detection status diagnostic model to obtain the third analysis and diagnosis content, and obtains the diagnostic score of the second analysis and diagnosis content based on the third analysis and diagnosis content.

2. The new energy vehicle drive motor operation test bench according to claim 1 is characterized in that: The content acquisition module includes a speech acquisition submodule and a semantic analysis submodule: the content acquisition module is used to perform the following steps: In the teaching mode, the voice acquisition submodule acquires the analysis and diagnosis voice of the instructor explaining and analyzing the operation data of the first motor; The semantic analysis submodule performs semantic analysis on the analysis and diagnosis speech to obtain the first analysis and diagnosis content.

3. The new energy vehicle drive motor operation test bench according to claim 2 is characterized in that: The semantic analysis submodule includes a text conversion unit and a text semantic analysis unit; the semantic analysis submodule is used to perform the following steps: The analysis and diagnosis speech is converted by the text conversion unit to obtain an analysis and diagnosis text; The text semantic analysis unit performs semantic analysis on the analysis and diagnosis text to obtain the first analysis and diagnosis content.

4. The new energy vehicle drive motor operation test bench according to claim 3 is characterized in that: The text conversion unit converts the analysis and diagnosis speech to obtain an analysis and diagnosis text, including: When the text conversion unit converts the analysis and diagnosis voice, the converted text content is screened according to the teacher's voice characteristics to obtain the analysis and diagnosis text corresponding to the teacher's voice characteristics.

5. The new energy vehicle drive motor operation test bench according to claim 4 is characterized in that: Also includes a communication module; The communication module transmits the tutor account logged in by the tutor to the server to obtain the tutor voice feature corresponding to the tutor account issued by the server.

6. The new energy vehicle drive motor operation test bench according to claim 5 is characterized in that: The tutor voice feature is obtained by the following steps: The server receives the latest voice content uploaded through the tutor account; The server inputs the latest voice content into a voice feature analysis model to obtain the tutor voice feature corresponding to the tutor account.

7. The new energy vehicle drive motor operation test bench according to claim 6 is characterized in that: The server inputs the latest voice content into a voice feature analysis model to obtain the tutor voice feature corresponding to the tutor account, comprising: The server inputs the speech content into a speech feature analysis model to obtain the current tutor speech feature; The server compares the current tutor voice feature with the previous tutor voice feature of the tutor account to obtain a feature comparison value; If the feature comparison value is greater than or equal to a preset comparison threshold, the server only stores the current tutor voice feature as the tutor voice feature; if the feature comparison value is less than the comparison threshold, the server stores the current tutor voice feature and the previous tutor voice feature as the tutor voice feature.

8. The new energy vehicle drive motor operation test bench according to claim 4 is characterized in that: The diagnostic analysis module trains the machine learning model according to the first motor operation data and the explanation content to obtain a motor detection state diagnostic model, and further includes: After the content acquisition module acquires the first analysis and diagnosis content of the first motor operation data by the tutor, the communication module uploads the first analysis and diagnosis content to the database of the tutor account; The communication module receives the first analysis and diagnosis content after confirmation or modification by the instructor in the database, and transmits it to the diagnosis and analysis module, so that the diagnosis and analysis module trains the machine learning model according to the first motor operation data and the explanation content transmitted by the communication module.

9. The new energy vehicle drive motor operation test bench according to claim 1, characterized in that: The second analysis and diagnosis content is text content, and the content acquisition module includes a text semantic analysis unit; The content acquisition module performs semantic analysis on the second analysis and diagnosis content through the text semantic analysis unit to obtain the second analysis and diagnosis content.

10. The new energy vehicle drive motor operation test bench according to claim 1, characterized in that: The step of obtaining a diagnosis score of the second analysis and diagnosis content according to the third analysis and diagnosis content comprises: Comparing the third analysis and diagnosis content with the second analysis and diagnosis content to obtain a content similarity ratio; The diagnostic score is obtained according to the preset full score value and the content similarity ratio.

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