Experiment working condition generation method and device and computer storage medium

Abstract

The invention provides an experimental working condition generation method and device and a computer storage medium. The method comprises the steps of obtaining user driving data; performing clustering analysis on the user driving data to obtain a plurality of feature regions about a differential mechanism; determining an initial experiment working condition of each feature region based on the feature working condition of each feature region; determining a first loss value based on the user driving data; determining a second loss value based on the initial experiment working condition; according to the equal loss relation between the first loss value and the second loss value, obtaining the number of test cycles; and determining a final experiment working condition of each characteristic region based on the test cycle number of each characteristic region and the initial experiment working condition of each characteristic region. Through the mode, through user data clustering analysis and accelerated life test design, reliability test working conditions conforming to user driving characteristics and life targets can be designed for different vehicle types, and the reliability test practicability and accuracy of the differential mechanism are considered.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a method and device for generating experimental operating conditions and a computer storage medium. Background Art

[0002] In recent years, research on the reliability of electric vehicle differentials has also achieved some results, but these studies are mostly based on the thinking of traditional fuel vehicles. They do not analyze the causes of failures and reliability of electric vehicle powertrains based on their characteristics, nor do they have a unified and complete method for obtaining differential reliability working conditions. In addition, due to the lack of design load spectrum for electric vehicle reducers, in practice, companies mostly use "QC / T 1022-2015 Technical Conditions for Reducer Assemblies for Pure Electric Passenger Vehicles" as the design load spectrum. Since there is a large difference between this load spectrum and the actual load spectrum, it is difficult to truly reflect the actual driving scenario of the vehicle. In practice, it has been found that some working conditions of this standard are unqualified, and a large number of relatively mature differential products cannot pass the working condition test provided by QC / T 1022. Summary of the invention

[0003] The present application provides a method, device and computer storage medium for generating experimental conditions.

[0004] In order to solve the above technical problems, the present application proposes a method for generating experimental operating conditions, which includes: obtaining user driving data; performing cluster analysis on the user driving data to obtain several characteristic areas of the differential; determining the initial experimental operating conditions of each characteristic area based on the characteristic operating conditions of each characteristic area; determining a first loss value based on the user driving data; determining a second loss value based on the initial experimental operating conditions; obtaining the number of test cycles according to the equal-loss relationship between the first loss value and the second loss value; determining the final experimental operating conditions of each characteristic area based on the number of test cycles of each characteristic area and the initial experimental operating conditions of each characteristic area.

[0005] Wherein, the obtaining of user driving data comprises: obtaining original user driving data, wherein the original user driving data comprises left wheel speed, right wheel speed, driving torque, steering wheel angle, and total mileage; in response to the total mileage exceeding a preset experimental mileage, intercepting user driving data from a starting mileage to the preset experimental mileage from the original user driving data; in response to the total mileage not reaching the preset experimental mileage, periodically extending the original user driving data to obtain user driving data reaching the preset experimental mileage.

[0006] The several characteristic regions include at least a low-speed high-torque region and a high-speed low-torque region; the initial experimental conditions of each characteristic region include a test phase, input torque, output speed, test time, differential rate, and number of cycles.

[0007] Among them, the output speed of the initial experimental working condition of the high-speed low-torque zone is the upper limit speed value of the high-speed low-torque zone; the test stages of the initial experimental working condition of the high-speed low-torque zone include at least a first test stage, a second test stage and a third test stage, the input torque of the first test stage is 0 and increases to the boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone, the input torque of the second test stage is the boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone, and the input torque of the third test stage is the boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone and decreases to 0.

[0008] The differential rate of the initial experimental working condition in the high-speed low-torque zone is determined by the differential rate when the car passes through a high-speed ramp in the user driving data.

[0009] Among them, the output speed of the initial experimental working condition in the low-speed and high-torque zone is the maximum speed value corresponding to the upper limit torque value of the low-speed and high-torque zone; the test stages of the initial experimental working condition in the low-speed and high-torque zone include at least the first test stage, the second test stage and the third test stage, the input torque of the first test stage is 0 and increases to the upper limit torque value of the low-speed and high-torque zone, the input torque of the second test stage is the upper limit torque value of the low-speed and high-torque zone, and the input torque of the third test stage is the upper limit torque value of the low-speed and high-torque zone and decreases to 0.

[0010] The differential rate of the initial experimental working condition in the low-speed and high-torque zone is determined by the differential rate when the car turns around in the user's driving data.

[0011] Wherein, obtaining the number of test cycles according to the equal-loss relationship between the first loss value and the second loss value includes: obtaining the user-end driving torque based on the first loss value, and the first cycle number when the driving torque in the initial experimental condition is equal to the user-end driving torque; obtaining the test torque based on the second loss value, and the second cycle number when the driving torque in the initial experimental condition is equal to the test torque; obtaining the number of experimental cycles based on the first cycle number, the second cycle number and the user-end driving torque; wherein the number of experimental cycles includes the number of left-turn experimental cycles and the number of right-turn experimental cycles.

[0012] In order to solve the above technical problems, the present application proposes an experimental condition generating device based on a microstructure, wherein the experimental condition generating device includes a memory and a processor coupled to the memory; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above experimental condition generating method.

[0013] In order to solve the above technical problems, the present application proposes a computer storage medium, which is used to store program data. When the program data is executed by a computer, it is used to implement the above experimental working condition generation method.

[0014] Different from the prior art, the beneficial effects of the present application are as follows: the experimental condition generating device obtains user driving data; cluster analysis is performed on the user driving data to obtain several characteristic areas of the differential; the initial experimental condition of each characteristic area is determined based on the characteristic condition of each characteristic area; the first loss value is determined based on the user driving data; the second loss value is determined based on the initial experimental condition; the number of test cycles is obtained according to the equal loss relationship between the first loss value and the second loss value; the final experimental condition of each characteristic area is determined based on the number of test cycles of each characteristic area and the initial experimental condition of each characteristic area. In the above manner, through user data cluster analysis and accelerated life test design, reliability test conditions that meet the user driving characteristics and life goals of different vehicle models can be designed, taking into account the practicality and accuracy of differential reliability testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments 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 drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 It is a flow chart of the first embodiment of the experimental condition generation method provided by the present application;

[0017] Figure 2 It is a user data clustering diagram provided by this application;

[0018] Figure 3 It is a structural schematic diagram of an embodiment of an experimental working condition generating device provided by the present application;

[0019] Figure 4 It is a structural diagram of an embodiment of a computer storage medium provided by the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The experimental working condition generation method of the present application is applied to an experimental working condition generation device, wherein the experimental working condition generation device of the present application can be a server, or a system composed of a server and a local terminal cooperating with each other. Accordingly, the various parts included in the experimental working condition generation device, such as various units, subunits, modules, and submodules, can all be set in the server, or can be set in the server and the local terminal respectively.

[0022] Furthermore, the above-mentioned server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules, such as software or software modules used to provide distributed servers, or it can be implemented as a single software or software module, which is not specifically limited here. In some possible implementations, the experimental working condition generation method of the embodiment of the present application can be implemented by a processor calling a computer-readable instruction stored in a memory.

[0023] Experimental conditions refer to the specific working conditions under which the object being tested must operate for a period of time before the scientific experiment begins, in order to ensure the accuracy and reliability of the experimental results.

[0024] The differential refers to a structure that can drive the left and right or front and rear drive wheels to rotate at different speeds, thereby ensuring that the drive wheels on both sides perform pure rolling motion. The electric vehicle differential is generally assembled with the reducer, and is mainly composed of planetary gears, half-axle gears, planetary gear shafts and differential cases. Among them, the planetary gears and half-axle gears are collectively called differential gears. The specific structure is shown in the figure below. The load transfer route is roughly: powertrain → drive shaft → driving gear → ring gear → differential case → planetary gear shaft → planetary gear → half-axle gear → half-axle → wheel.

[0025] When a car turns, the two wheels rotate at different speeds. For example, when the car turns left, the center of the arc is on the left, so the left wheel has a shorter trajectory and needs to rotate at a lower speed, while the right wheel has a longer trajectory and needs to rotate at a faster speed. To achieve this, the differential is needed to coordinate the wheel speeds. The two half-axle gears of the differential disconnect the wheel axles on both sides. The load output by the powertrain is distributed to the left and right half-axles after the differential and transmitted to the wheels at different speeds, thereby balancing the trajectory differences of the left and right wheels. If the half-axles are made as a whole, this function will not be achieved, and it will also cause the car to have difficulty turning and skid.

[0026] The working principle of the differential: When the vehicle is driving in a straight line, the resistance on the left and right wheels is equal, and the planetary gears in the differential housing only revolve with the housing and do not rotate. When the vehicle turns, the inner wheel will produce greater resistance, and the different forces on the half shafts on both sides will cause the planetary gear in the middle to rotate, and there will be a speed difference between the half shafts on both sides, and the outer half shaft will rotate faster than the inner half shaft, so that the vehicle can turn smoothly.

[0027] See also Figure 1 , Figure 1 It is a flow chart of the first embodiment of the experimental condition generation method provided in this application.

[0028] like Figure 1 As shown, the specific steps are as follows:

[0029] Step S11: Obtain user driving data.

[0030] The main failure mode of the differential is the breakage / pitting of the differential gear, and the corresponding failure mechanism is fatigue failure caused by repeated meshing of the tooth surface under differential conditions. The differential mainly works when the car is turning, so the data collection object of this application focuses on urban users who have more turning situations.

[0031] Specifically, the present application proposes an embodiment for obtaining user driving data, which is as follows: an experimental condition generating device obtains original user driving data, and in response to the total mileage exceeding a preset experimental mileage, the user driving data from the starting mileage to the preset experimental mileage is intercepted from the original user driving data; in response to the total mileage not reaching the preset experimental mileage, the original user driving data is periodically extended to obtain user driving data that reaches the preset experimental mileage.

[0032] The original user driving data includes left wheel speed, right wheel speed, driving torque, steering wheel angle, and total mileage.

[0033] In the embodiment of the present application, the speed difference is calculated according to the following situations: when the left wheel speed is greater than the right wheel speed, that is, turning right, the speed difference = left wheel speed - right wheel speed; when the left wheel speed is less than the right wheel speed, that is, turning left, the speed difference = right wheel speed - left wheel speed; after pre-processing, the user data of the left turn and the right turn are output respectively, and the newly obtained user data is used to count the number of meshing times of the differential gears under different torques by means of the cyclic rain flow counting method.

[0034] If the design life of the vehicle is w million kilometers, the statistical mileage of each user's data is up to w million kilometers. During this period, the user data that does not meet the w million kilometers needs to be periodically extended to meet the vehicle life target.

[0035] Step S12: Perform cluster analysis on the user's driving data to obtain several characteristic regions related to the differential.

[0036] Clustering is a core unsupervised learning technology that has long played an important role in data mining, machine learning, statistical analysis and many other fields. The basic idea is to organize and classify data based on its internal structure and characteristics, so that the similarities between data points in the same group are as large as possible, while the differences between data points in different groups are as significant as possible. Clustering has a wide range of applications, from market segmentation and social network analysis to bioinformatics, image recognition and other fields.

[0037] The K-means clustering algorithm is simple and efficient. This application uses the K-means algorithm as an example for clustering. In other embodiments of this application, other clustering methods can also be used, and this application does not make specific limitations.

[0038] After the experimental condition generation device performs a preliminary analysis on the data, the number of centroids K is set to 2, and after multiple rounds of iterative calculations, the clustering results of the user data are obtained.

[0039] The clustering results include two characteristic areas of the differential: low-speed high-torque area and high-speed low-torque area.

[0040] In the embodiments of the present application, Figure 2 As shown, Figure 2 The user data clustering diagram provided by the present application is shown in FIG. The characteristic regions include at least a low speed high torque region and a high speed low torque region; the initial experimental conditions of each characteristic region include a test phase, input torque, output speed, test time, differential speed, and number of cycles.

[0041] After classifying driving behaviors through cluster analysis of user data, the upper limit operating point is extracted, which is not only typical and representative, but also conducive to shortening the test cycle. The selected test conditions are more in line with the real driving scene than the standard, and have stronger adaptability and reasonable assessment intensity than the standard.

[0042] The operating characteristics of the low-speed and high-torque zone are: speed ≤2800rpm and torque ≤300Nm; the operating characteristics of the high-speed and low-torque zone are: speed (2800~9000)rpm, and the torque decreases with the increase of speed.

[0043] In an embodiment of the present application, the output speed of the initial experimental working condition in the high-speed low-torque zone is the upper limit speed value of the high-speed low-torque zone; the test stages of the initial experimental working condition in the high-speed low-torque zone include at least a first test stage, a second test stage and a third test stage, the input torque of the first test stage is 0 and increases to the boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone, the input torque of the second test stage is the boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone, and the input torque of the third test stage is the boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone and decreases to 0.

[0044] In the embodiment of the present application, the differential rate of the initial experimental condition in the high-speed low-torque zone is determined by the differential rate when the car passes through the high-speed ramp in the user's driving data.

[0045] In an embodiment of the present application, the output speed of the initial experimental working condition in the low-speed and high-torque zone is the maximum speed value corresponding to the upper limit torque value of the low-speed and high-torque zone; the test stages of the initial experimental working condition in the low-speed and high-torque zone include at least a first test stage, a second test stage and a third test stage, the input torque of the first test stage is 0 and increases to the upper limit torque value of the low-speed and high-torque zone, the input torque of the second test stage is the upper limit torque value of the low-speed and high-torque zone, and the input torque of the third test stage is the upper limit torque value of the low-speed and high-torque zone and decreases to 0.

[0046] In the embodiment of the present application, the differential rate of the initial experimental condition in the low-speed and high-torque zone is determined by the differential rate when the car turns around in the user's driving data.

[0047] Specifically, in order to implement efficient accelerated life tests and shorten laboratory bench test time, the characteristic operating condition in the low-speed and high-torque zone selects the upper limit torque value in this area and its corresponding maximum speed value, and the differential rate is based on the value when the vehicle turns around. Big data statistical results show that this value is approximately 40%; the characteristic operating condition in the high-speed and low-torque zone selects the upper limit speed value in this area and its corresponding boundary torque value, and the differential rate is based on the value when the vehicle passes through the high-speed ramp, which is approximately 20%.

[0048] According to the analysis, the differential has two characteristic areas: high speed and low torque and low speed and high torque. Therefore, the reliability test conditions are also classified accordingly, which is consistent with the classification method of the standard "QC / T 1022-2015 Technical Conditions for Reducer Assemblies for Pure Electric Passenger Vehicles". Among them, the oil temperature and the test time of each condition refer to the standard "QC / T 1022-2015 Technical Conditions for Reducer Assemblies for Pure Electric Passenger Vehicles". The detailed test conditions are shown in the following table:

[0049]

[0050] Since the differential for electric vehicles is integrated into the reducer, for convenience, the differential reliability test should be carried out in the form of a reducer assembly. It should be noted that before the differential reliability test, the differential should be run-in according to factory requirements, and the lubricating oil should be replaced after the running-in is completed.

[0051] Step S13: Determine the initial experimental working condition of each characteristic area based on the characteristic working condition of each characteristic area.

[0052] Step S14: Determine a first loss value based on the user's driving data.

[0053] The test condition generation device has determined the speed, torque, differential speed and test time according to the test condition list. Only the number of cycles is to be determined. The reliability failure of the differential is mainly mechanical fatigue problems such as broken teeth, broken shafts and pitting on the tooth surface. The mechanical fatigue life-stress relationship is usually described by the Inverse Power model. The loss value calculation formula based on the Inverse Power model is as follows:

[0054] D=n*S ^σ (Formula 1)

[0055] The first loss value calculated based on the user-side driving data is: D1 = n1*S1 ^σ .

[0056] Step S15: Determine a second loss value based on the initial experimental conditions.

[0057] The second loss value calculated based on the test condition: D2 = n2*S2 ^σ .

[0058] Wherein, S1 is the user-side driving torque, n1 is the number of loading times when the driving torque is equal to S1, S2 is the test torque, n2 is the number of loading times when the test torque is equal to S2, n1 and n2 are equal to the product of the speed and time at the corresponding torque, and σ is the exponential coefficient.

[0059] Step S16: Obtain the number of test cycles according to the equal loss relationship between the first loss value and the second loss value.

[0060] Specifically, the experimental working condition is generated based on the first loss value to obtain the user-end driving torque and the first cycle number when the driving torque in the initial experimental working condition is equal to the user-end driving torque; based on the second loss value, the test torque is obtained, and the second cycle number when the driving torque in the initial experimental working condition is equal to the test torque; based on the first cycle number, the second cycle number and the user-end driving torque, the experimental cycle number is obtained.

[0061] The number of experimental cycles includes the number of left-turn experimental cycles and the number of right-turn experimental cycles.

[0062] Specifically, based on the equal loss theory, D1=D2, that is, n1*S1^σ=n2*S2^σ, then n2=n1*S1^σ / S2^σ, and the number of test cycles is obtained. According to the user data preprocessing results, the user data is divided into two driving modes: left turn and right turn. Therefore, when calculating n2, n should be calculated based on left turn and right turn respectively. 2左 and n 2右 , the total number of cycles n2 = n 2左 +n 2右 .

[0063] The differential reliability assessment method proposed in this application is based on actual user road data, takes the user data cluster analysis results as the test conditions, and performs equal loss calculation based on the loss model established based on the differential failure mechanism to obtain the test load spectrum.

[0064] Step S17: Based on the number of test cycles for each characteristic area and the initial experimental conditions for each characteristic area, determine the final experimental conditions for each characteristic area.

[0065] In the embodiment of the present application, each test condition in the test process sequentially completes the left turn mode and the right turn mode or the left turn mode and the right turn mode alternately, as shown in the following table:

[0066]

[0067]

[0068] The reliability design concept of this application, which uses the equal loss theory as the basis for test acceleration, solves the problem of insufficient or over-verification of test verification caused by the current use of national recommended standards or enterprise standards, and can take into account both the practicality and accuracy of reliability testing, thereby effectively improving the test effect of the reliability of automobile differentials.

[0069] This application uses the automotive cloud platform to obtain real-time user big data from all regions across the country, identify the weak links in the reliability of the differential system, accurately match the life model, cover the harsh usage scenarios of users, and ensure the accuracy and reliability of the reliability test results; at the same time, the test conditions are closer to the user's actual usage scenarios, and provide more realistic feedback on the reliability of the differential system.

[0070] In order to implement the experimental condition generation method of the above embodiment, the present application also provides an experimental condition generation device, for details, please refer to Figure 3 , Figure 3It is a structural schematic diagram of an embodiment of an experimental condition generating device provided in the present application.

[0071] like Figure 3 As shown, the experimental working condition generating device 600 of this embodiment includes a processor 61 , a memory 62 , an input and output device 63 and a bus 64 .

[0072] The processor 61 , the memory 62 , and the input / output device 63 are respectively connected to the bus 64 . The memory 62 stores a computer program. The processor 61 is used to execute the computer program to implement the experimental condition generation method of the above embodiment.

[0073] In this embodiment, the processor 61 may also be referred to as a CPU (Central Processing Unit). The processor 61 may be an integrated circuit chip having the ability to process signals. The processor 61 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The processor 61 may also be a GPU (Graphics Processing Unit), also known as a display core, a visual processor, or a display chip, which is a microprocessor that is specifically used for image computing on computers, workstations, game consoles, and some mobile devices (such as tablet computers, smart phones, etc.). The purpose of the GPU is to convert and drive the display information required by the computer system, and to provide a line scan signal to the display to control the correct display of the display. It is an important component that connects the display and the computer motherboard. As an important component of the computer host, the graphics card is responsible for outputting display graphics. The general-purpose processor may be a microprocessor or the processor 61 may also be any conventional processor, etc.

[0074] The present application also provides a computer storage medium, such as Figure 4 As shown, the computer storage medium 700 is used to store a computer program 71. When the computer program 71 is executed by a processor, it is used to implement the method described in the embodiment of the experimental working condition generation method of the present application.

[0075] The method involved in the embodiment of the experimental working condition generation method of the present application, when implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a device, such as a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0076] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for generating experimental working conditions based on automobile differential, characterized in that: The experimental condition generation method comprises: Obtain user driving data; Performing cluster analysis on the user driving data to obtain several characteristic regions of the differential; Determining the initial experimental working condition of each characteristic area based on the characteristic working condition of each characteristic area; Determining a first loss value based on the user driving data; Based on the initial experimental condition, determining a second loss value; According to the equal loss relationship between the first loss value and the second loss value, obtaining the number of test cycles; Based on the number of test cycles for each characteristic region and the initial experimental conditions for each characteristic region, the final experimental conditions for each characteristic region are determined.

2. The method for generating experimental conditions according to claim 1, characterized in that: The obtaining of user driving data includes: Acquire original user driving data, wherein the original user driving data includes left wheel speed, right wheel speed, driving torque, steering wheel angle, and total mileage; In response to the total driving mileage exceeding the preset experimental mileage, intercepting the user driving data from the starting mileage to the preset experimental mileage from the original user driving data; In response to the total driving mileage not reaching the preset experimental mileage, the original user driving data is periodically extended to obtain user driving data reaching the preset experimental mileage.

3. The method for generating experimental conditions according to claim 1 or 2, characterized in that: The several characteristic regions include at least a low-speed high-torque region and a high-speed low-torque region; the initial experimental conditions of each characteristic region include a test phase, input torque, output speed, test time, differential speed, and number of cycles.

4. The method for generating experimental conditions according to claim 3, characterized in that: The output speed of the initial experimental working condition in the high-speed low-torque zone is the upper limit speed value of the high-speed low-torque zone; The test stages of the initial experimental working conditions in the high-speed low-torque zone include at least a first test stage, a second test stage and a third test stage. The input torque in the first test stage is 0 and increases gradually to a boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone. The input torque in the second test stage is the boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone. The input torque in the third test stage is the boundary torque value corresponding to the upper limit speed value of the high-speed low-torque zone and decreases gradually to 0.

5. The method for generating experimental conditions according to claim 4, characterized in that: The differential rate of the initial experimental working condition in the high-speed low-torque zone is determined by the differential rate when the car passes through a high-speed ramp in the user driving data.

6. The method for generating experimental conditions according to claim 3, characterized in that: The output speed of the initial experimental working condition in the low-speed high-torque zone is the maximum speed value corresponding to the upper limit torque value of the low-speed high-torque zone; The test stages of the initial experimental working conditions in the low-speed and high-torque zone include at least a first test stage, a second test stage and a third test stage. The input torque in the first test stage is 0 and increases gradually to the upper limit torque value of the low-speed and high-torque zone. The input torque in the second test stage is the upper limit torque value of the low-speed and high-torque zone. The input torque in the third test stage is the upper limit torque value of the low-speed and high-torque zone and decreases gradually to 0.

7. The method for generating experimental conditions according to claim 4, characterized in that: The differential rate of the initial experimental working condition in the low-speed and high-torque zone is determined by the differential rate when the car turns around in the user driving data.

8. The method for generating experimental conditions according to claim 1, characterized in that: The obtaining the number of test cycles according to the equal loss relationship between the first loss value and the second loss value includes: Based on the first loss value, obtaining a user-end driving torque and a first cycle number when the driving torque in the initial experimental condition is equal to the user-end driving torque; Based on the second loss value, obtaining a test torque and a second cycle number when the driving torque in the initial experimental condition is equal to the test torque; Based on the first cycle number, the second cycle number, and the user-end driving torque, obtaining the experimental cycle number; The number of experimental cycles includes the number of left-turn experimental cycles and the number of right-turn experimental cycles.

9. A device for generating experimental conditions based on microstructures, characterized in that: The experimental working condition generating device comprises a memory and a processor coupled to the memory; Wherein, the memory is used to store program data, and the processor is used to execute the program data to implement the experimental working condition generation method as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that: The computer storage medium is used to store program data, and when the program data is executed by a computer, it is used to implement the experimental working condition generation method as described in any one of claims 1 to 8.

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