Torsional vibration testing method and related device

Abstract

The invention discloses a torsional vibration testing method and a related device. The method comprises the following steps: acquiring objective data, score data and physiological data; according to the objective data and the scoring data, mapping relation fitting is carried out through the Stevens law, and a first mapping relation between the objective data and the scoring data is established. According to the objective data and the physiological data, mapping relation fitting is carried out through the data mining technology, and a second mapping relation between the objective data and the physiological data is established. According to the first mapping relation and the second mapping relation, the torsional vibration strength range is determined. By obtaining traditional objective data and the physiological indexes and subjective evaluation of the user, the test data is richer, the comprehensiveness and accuracy of the test are improved, and by establishing the first mapping relation and the second mapping relation, a more sound test evaluation system is obtained, and comparison analysis and product optimization of subsequent torsion test experiments are facilitated.

Description

Technical Field

[0001] The present application relates to the field of vehicle testing, and in particular to a torsional vibration testing method and related devices. Background Art

[0002] In the process of automobile R&D and production, the performance of the transmission system directly affects the driving quality and user experience of the vehicle. Among them, torsional vibration is one of the important factors affecting the smoothness and durability of the transmission system. Its testing and evaluation has always been the focus of attention in the field of automotive engineering. Accurately measuring and evaluating the torsional vibration data of the transmission system not only helps to optimize vehicle design, but also improves the driving performance of the vehicle and the comfort of passengers.

[0003] At present, the traditional torsional vibration test method mainly relies on experimental instruments to obtain test data. However, obtaining test data only through experimental instruments is not comprehensive enough. Summary of the invention

[0004] In view of this, the present application provides a torsional vibration testing method and related devices to solve the problem that torsional vibration test data is not comprehensive enough.

[0005] A first aspect of the present application provides a torsional vibration testing method, the method comprising:

[0006] Obtain objective data, scoring data and physiological data, wherein the objective data is the torsional vibration intensity obtained by testing the test instrument under different torsional vibration conditions, the scoring data is the subjective evaluation score given according to the self-perception under different torsional vibration conditions, and the physiological data is the physiological index corresponding to different torsional vibration conditions;

[0007] According to the objective data and the scoring data, a mapping relationship is fitted by Stevens' law to establish a first mapping relationship between the objective data and the scoring data;

[0008] According to the objective data and the physiological data, a mapping relationship is fitted by using a data mining technology to establish a second mapping relationship between the objective data and the physiological data;

[0009] A torsional vibration intensity range is determined according to the first mapping relationship and the second mapping relationship.

[0010] Optionally, the obtaining of objective data, scoring data and physiological data includes:

[0011] Obtaining objective data of a target driving mode, scoring data of the target driving mode, and physiological data of the target driving mode, wherein the target driving mode is one of the plurality of driving modes;

[0012] The step of fitting a mapping relationship based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data includes:

[0013] According to the objective data of the target driving mode and the scoring data of the target driving mode, a mapping relationship is fitted by Stevens' law to establish a first mapping relationship corresponding to the target driving mode;

[0014] The step of fitting a mapping relationship based on the objective data and the physiological data by using a data mining technique to establish a second mapping relationship between the objective data and the physiological data includes:

[0015] According to the objective data of the target driving mode and the physiological data of the target driving mode, mapping relationship fitting is performed by using data mining technology to establish a second mapping relationship corresponding to the target driving mode;

[0016] The determining the torsional vibration intensity range according to the first mapping relationship and the second mapping relationship includes:

[0017] Acquire a driving mode to be tested, where the driving mode to be tested is one of a plurality of driving modes;

[0018] Acquire, according to the driving mode to be tested, a first mapping relationship corresponding to the driving mode to be tested and a second mapping relationship corresponding to the driving mode to be tested;

[0019] The torsional vibration intensity range corresponding to the driving mode to be tested is determined according to the first mapping relationship corresponding to the driving mode to be tested and the second mapping relationship corresponding to the driving mode to be tested.

[0020] Optionally, obtaining the driving mode to be tested includes:

[0021] According to the driving mode sequence indicated by the standard switching mode, identifying a plurality of driving modes in the driving mode sequence as the driving modes to be tested; or,

[0022] According to the driving mode sequence of the autonomous switching mode setting, a plurality of driving modes are identified by the driving mode sequence as the driving modes to be tested.

[0023] Optionally, performing mapping relationship fitting according to the objective data and the scoring data by Stevens' law to establish a first mapping relationship between the objective data and the scoring data includes:

[0024] According to the objective data and the scoring data, the nonlinear relationship corresponding to the Stevens' law is logarithmically transformed to obtain a linear relationship between the objective data and the scoring data;

[0025] According to the linear relationship, linear fitting is performed on the objective data and the scoring data to establish a first mapping relationship between the objective data and the scoring data;

[0026] The nonlinear relationship corresponding to Stevens' law is as follows:

[0027] n

[0028] S = kI;

[0029] Wherein, S is the scoring data, I is the objective data, n is the growth rate of subjective feeling, and k is the proportional constant;

[0030] The linear relationship between the objective data and the scoring data is as follows:

[0031] log 10 (S) = nlog 10 (I)+log 10 (k);

[0032] Wherein, S is the scoring data, I is the objective data, n is the growth rate of the subjective feeling, and k is the proportional constant.

[0033] Optionally, if the data mining technology is linear regression, multivariate regression, logistic regression and Poisson regression, then the step of fitting a mapping relationship based on the objective data and the physiological data by using the data mining technology to establish a second mapping relationship between the objective data and the physiological data includes:

[0034] According to the objective data and the physiological data, a mapping relationship is fitted by linear regression to establish a second mapping relationship between the objective data and the physiological data; or,

[0035] According to the objective data and the physiological data, a mapping relationship is fitted by multiple regression to establish a second mapping relationship between the objective data and the physiological data; or,

[0036] According to the objective data and the physiological data, a mapping relationship is fitted by logistic regression to establish a second mapping relationship between the objective data and the physiological data; or,

[0037] According to the objective data and the physiological data, a mapping relationship fitting is performed through Poisson regression to establish a second mapping relationship between the objective data and the physiological data.

[0038] Optionally, obtaining the scoring data includes:

[0039] Obtain multiple test users and knowledge reserve thresholds;

[0040] According to the knowledge reserve threshold, screening the plurality of test users to obtain a target user;

[0041] Obtain rating data from the target user.

[0042] Optionally, determining the torsional vibration intensity range according to the first mapping relationship and the second mapping relationship includes:

[0043] Determine, according to the first mapping relationship, a first threshold corresponding to the first mapping relationship;

[0044] Determine, according to the second mapping relationship, a second threshold corresponding to the second mapping relationship;

[0045] If the first threshold is less than or equal to the second threshold, determining the first threshold as the final threshold;

[0046] If the first threshold is greater than the second threshold, determining the second threshold as the final threshold;

[0047] A torsional vibration intensity range is determined according to the final threshold value.

[0048] Optionally, obtaining the objective data includes:

[0049] The torsional vibration test data is obtained based on the non-contact magnetoelectric method through the magnetoelectric sensor and the test gear plate;

[0050] The objective data is obtained, where the objective data is the torsional vibration test data.

[0051] A second aspect of the present application provides a torsional vibration testing device, the device comprising: an acquisition unit, a scoring unit, a physiological unit, and an application unit;

[0052] The acquisition unit is used to acquire objective data, scoring data and physiological data, wherein the objective data is the torsional vibration intensity obtained by testing the test instrument under different torsional vibration conditions, the scoring data is the subjective evaluation score given according to the self-perception under different torsional vibration conditions, and the physiological data is the physiological index corresponding to different torsional vibration conditions;

[0053] The scoring unit is configured to perform mapping relationship fitting based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data;

[0054] The physiological unit is used to perform mapping relationship fitting based on the objective data and the physiological data by using a data mining technology to establish a second mapping relationship between the objective data and the physiological data;

[0055] The application unit is used to determine a torsional vibration intensity range according to the first mapping relationship and the second mapping relationship.

[0056] A third aspect of the present application provides a torsional vibration testing device, the device comprising: a testing instrument, a scoring data acquisition module, a physiological data acquisition module and a processor;

[0057] The testing instrument is used to obtain objective data, wherein the objective data is the torsional vibration intensity obtained by testing under different torsional vibration conditions;

[0058] The scoring data acquisition module is used to obtain scoring data, wherein the scoring data is a subjective evaluation score given according to one's own perception under different torsional vibration conditions;

[0059] The physiological data acquisition module is used to acquire physiological data, wherein the physiological data is a physiological index corresponding to different torsional vibration conditions;

[0060] The processor is configured to perform mapping relationship fitting based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data;

[0061] The processor is further configured to perform mapping relationship fitting based on the objective data and the physiological data by using a data mining technique to establish a second mapping relationship between the objective data and the physiological data;

[0062] The processor is further used to determine a torsional vibration intensity range according to the first mapping relationship and the second mapping relationship.

[0063] It can be seen from the above technical solutions that the present application has the following beneficial effects:

[0064] In order to obtain more comprehensive torsional vibration test results of the vehicle, objective data, scoring data and physiological data are obtained, wherein objective data is the torsional vibration intensity obtained by testing instruments under different torsional vibration conditions, scoring data is the subjective evaluation score given according to one's own perception under different torsional vibration conditions, and physiological data is the physiological index corresponding to different torsional vibration conditions. By obtaining more comprehensive test data, not only the data source of the torsional vibration test method includes traditional objective data, but also the physiological index and subjective evaluation of the user are added, making the test data richer and improving the comprehensiveness and accuracy of the test. According to the objective data and scoring data, the mapping relationship is fitted through Stevens' law to establish the first mapping relationship between the objective data and the scoring data, which can quantify the user's subjective perception and associate it with the objective data, so that the test results are closer to the actual driving experience. According to the objective data and physiological data, the mapping relationship is fitted through data mining technology to establish the second mapping relationship between the objective data and the physiological data, which can associate the user's physiological index with the objective data, so that the test results take into account the user's physiological tolerance, introduce physiological health factors into the test, and the test results are more comprehensive. The torsional vibration intensity range is determined based on the first mapping relationship and the second mapping relationship, and a more complete test evaluation system is provided by combining subjective and objective data, which is convenient for subsequent torsional test experiment comparison analysis and product optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A method flow chart of a torsional vibration testing method provided in an embodiment of the present application;

[0066] Figure 2 A schematic diagram of a non-contact magnetoelectric method provided in an embodiment of the present application;

[0067] Figure 3 A schematic diagram of a torsional vibration testing system provided in an embodiment of the present application;

[0068] Figure 4 A diagram showing the structure of a human-machine operation test platform provided in an embodiment of the present application;

[0069] Figure 5 A schematic structural diagram of a torsional vibration testing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0071] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0072] Torsional vibration refers to a vibration phenomenon caused by unbalanced torque or other excitation sources in a mechanical system, especially in a shaft system or rotating parts. Torsional vibration manifests itself as periodic expansion and contraction or changes in torsion angle of the rotating parts along their axial direction.

[0073] In the automotive transmission system, torsional vibration may be caused by irregular torque generated by the engine, uneven gear meshing, or other nonlinear factors within the system. Torsional vibration will cause noise, reduce component life, and affect the overall performance and ride comfort of the vehicle.

[0074] At present, torsional vibration testing mainly relies on objective data obtained from experimental instrument testing. Although these objective data can provide specific values ​​of vibration intensity, in actual applications, the relationship between the user's perception of vibration and their physiological reactions and subjective feelings is often not fully considered, which leads to the possibility that the test results may be different from the actual driving experience, and cannot fully reflect the user's true feelings, and the test results are not comprehensive enough.

[0075] Based on this, the present application proposes a torsional vibration testing method and related devices. By establishing a first mapping relationship between scoring data and objective data, and a second mapping relationship between physiological data and objective data, an evaluation system combining subjective and objective factors is obtained. The test results not only take into account the objective data measured by the test instrument, but also introduce the user's subjective feelings and physiological indicators, thereby making the test results more comprehensive and facilitating subsequent torsional test experiment comparison analysis and product optimization.

[0076] In order to better understand the technical solution and technical effects of the present application, specific embodiments will be described in detail below with reference to the accompanying drawings.

[0077] See also Figure 1, which is a flow chart of a torsional vibration testing method provided in an embodiment of the present application, and the method specifically comprises the following steps:

[0078] S101: Obtain objective data, scoring data, and physiological data.

[0079] Among them, the objective data is the torsional vibration intensity obtained by testing the test instrument under different torsional vibration conditions, the scoring data is the subjective evaluation score given according to one's own perception under different torsional vibration conditions, and the physiological data is the corresponding physiological indicators under different torsional vibration conditions.

[0080] By installing measuring instruments at different positions on the vehicle or test bench, the torsional angle, period, frequency and other physical quantities of the torsional vibration signal are collected to express the torsional vibration intensity. The torsional vibration intensity is determined by one or more of the multiple physical quantities. The torsional vibration intensity obtained by the measuring instrument under different torsional vibration conditions is obtained as objective data.

[0081] The torsion angle represents the maximum torsion angle change of a rotating part along its axis during torsional vibration. The torsion angle can be used to describe the amplitude of vibration, which reflects the severity of the vibration. For example, in a car transmission system, if the torsion angle is large, it means that a part of the transmission system has undergone a large torsional deformation during the vibration process, which may cause noise, affect the life of the transmission system, and bring an uncomfortable experience to users or passengers.

[0082] The period indicates the time required for a complete cycle of torsional vibration. The length of the period reflects the frequency of vibration. The longer the period, the lower the frequency; vice versa. The period can be used to measure the speed of vibration. A lower frequency (longer period) may give people a sense of stability, while a higher frequency (shorter period) may make people feel uncomfortable.

[0083] Frequency refers to the number of torsional vibrations per unit time, usually expressed in Hertz (Hz). Frequency directly reflects the speed of vibration. High-frequency torsional vibration may cause greater impact on mechanical structures and may also have a greater impact on human comfort.

[0084] The following describes the test scenarios for obtaining objective data, scoring data, and physiological data:

[0085] The scoring data and physiological data come from users, who can be drivers, ordinary passengers, or professionals in the field of torsional vibration.

[0086] Sources of torsional vibration include, but are not limited to:

[0087] (1) Test bench: A facility for conducting torsional vibration tests on vehicle transmission systems that can simulate different torsional vibration intensities or different driving modes.

[0088] (2) Automobiles: Conduct actual vehicle testing in the production workshop or on real road conditions.

[0089] Scoring data collection methods include but are not limited to:

[0090] (1) Collected through a real-time feedback system: During driving, the user can give a real-time rating of the vibration condition through a touch screen or other interactive interface installed in the vehicle.

[0091] (2) Collection through voice recognition system: Using voice recognition technology, users can express their subjective evaluation of vibration conditions through voice commands.

[0092] (3) Collection through mobile applications: Develop a dedicated application that allows users to rate using smartphones or other mobile devices.

[0093] Physiological data collection methods include but are not limited to:

[0094] (1) Collecting physiological data through wearable devices: such as smart watches and heart rate monitors, which can monitor the user's heart rate, blood pressure and other physiological indicators in real time.

[0095] (2) Collecting physiological data through vehicle-mounted sensors: Sensors are installed inside the car, such as seat pressure sensors and steering wheel touch sensors, to monitor the user’s physiological responses. For example, the pressure sensor on the seat can monitor the user’s sitting posture changes and reflect their level of tension.

[0096] (3) Collect physiological data through medical-grade monitoring equipment: such as portable blood pressure monitors and blood glucose meters, which can be used in specific testing sessions to provide more accurate physiological indicator data.

[0097] During the test, users rated the torsional vibration according to their own feelings. For example, when the torsional vibration intensity was low, the user gave a score of 3 points (out of 10 points), indicating that the vibration was light; when the torsional vibration intensity was high, the user gave a score of 5 points, indicating that the vibration was slightly heavy.

[0098] For example, when the torsional vibration intensity is low, the user's heart rate through the collector is 70 beats per minute (bpm), and the blood pressure is 120 / 80 millimeters of mercury (mmHg); when the torsional vibration intensity is high, the user's heart rate is 80bpm, and the blood pressure is 130 / 90mmHg. The user's heart rate and blood pressure corresponding to different torsional vibration conditions are obtained and determined as physiological data.

[0099] By acquiring objective data, scoring data and physiological data, we not only obtained the torsional vibration intensity measured by the measuring instrument, but also the user's physiological indicators that changed with the vibration intensity, and scoring data that quantified the user's inner feelings, thereby obtaining data sources of both subjective and objective dimensions under different torsional vibration conditions.

[0100] S102: According to the objective data and the scoring data, a mapping relationship is fitted by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data.

[0101] In Stevens' law, the scoring data is a power function of the objective data. The size of the scoring data is proportional to the power of the objective data. That is, the scoring data is a power function of the objective data. Stevens' law is expressed by the following formula (1):

[0102] S=kI n (1)

[0103] Among them, S is the scoring data, I is the objective data, n is the growth rate of subjective feeling, k is the proportional constant, and the power function relationship between the scoring data and the objective data is established through Stevens' law.

[0104] When n<1, the growth of scoring data is slower than that of objective data.

[0105] When n=1, Stevens' law is simplified to a linear relationship, that is, S=kI, and the growth rate of the scoring data is equal to the growth rate of the objective data.

[0106] When n>1, the growth of scoring data is faster than that of objective data.

[0107] Through optimization methods such as least squares method, gradient descent method, Newton method, etc., the best fitting parameters k and n are found, thereby establishing the first mapping relationship between the scoring data and the objective data.

[0108] When human beings receive external stimuli of different intensities, the speed of change of perceived intensity is different, so the scoring data does not change linearly with the change of objective data. By fitting the first mapping relationship through Stevens' law, its power function relationship can better capture the complexity of human perception, quantify human subjective perception and associate it with objective data, so that the test results of torsional vibration can be added with the user's psychological feedback, which is more comprehensive.

[0109] S103: According to the objective data and the physiological data, a mapping relationship is fitted by using a data mining technology to establish a second mapping relationship between the objective data and the physiological data.

[0110] Data Mining Technology Data mining is a computer-assisted technique used for analysis to process and explore large data sets. With the help of data mining tools and methods, organizations can discover hidden patterns and relationships in their data.

[0111] The objective data and the physiological data are fitted by using data mining technology to obtain a second mapping relationship between the objective data and the physiological data.

[0112] The following is an example of linear regression, a technology in data mining technology:

[0113] Assume that objective data and physiological data are obtained, including:

[0114] When the torsional vibration frequency is 10Hz, the driver's heart rate is 70bpm.

[0115] When the torsional vibration frequency is 15Hz, the driver's heart rate is 80bpm.

[0116] When the torsional vibration frequency is 20Hz, the driver's heart rate is 90bpm.

[0117] Linear regression technology is used to fit the relationship between heart rate and torsional vibration frequency, and the best fitting function y=ax+b is obtained, where x represents objective data (torsional vibration intensity) and y represents physiological data (heart rate). Through linear fitting, the best fitting parameters a and b are found to establish the second mapping relationship. In the above example, the best fitting parameters are a=2 and b=50, and the second mapping relationship is physiological data=2×objective data+50.

[0118] By combining objective data and physiological data, we can more comprehensively evaluate the physiological impact of torsional vibration on users. By establishing a second mapping relationship between objective data and physiological data, we can more accurately reflect the physiological response of users under different vibration intensities, making the test results closer to the actual experience and making the test results more comprehensive.

[0119] S104: Determine a torsional vibration intensity range according to the first mapping relationship and the second mapping relationship.

[0120] Through S101-S103, a relatively comprehensive torsional vibration test result has been obtained, which includes objective data, scoring data, physiological data, a first mapping relationship (scoring data and objective data) and a second mapping relationship (physiological data and objective data).

[0121] The scoring data quantifies the user's feelings under different torsional vibration conditions. The higher the score, the stronger the vibration for the user. From the perspective of comfort, the scoring threshold is determined according to the intensity of the score. The objective data corresponding to the scoring threshold can be determined according to the first mapping relationship, thereby determining the torsional vibration intensity range corresponding to the scoring data.

[0122] Physiological data is the physiological index corresponding to the user under different torsional vibration conditions. When the heart rate and blood pressure exceed a certain value, it will affect the user's physical health. From the perspective of physiological health, according to medical advice, the threshold of the physiological index is determined, and the objective data corresponding to the threshold of the physiological index can be determined according to the second mapping relationship, thereby determining the torsional vibration intensity range corresponding to the physiological data.

[0123] Therefore, the torsional vibration test method provided by the embodiment of the present application obtains objective data, scoring data and physiological data in order to obtain a more comprehensive torsional vibration test result of the vehicle, wherein the objective data is the torsional vibration intensity obtained by testing the test instrument under different torsional vibration conditions, the scoring data is the subjective evaluation score given according to the self-perception under different torsional vibration conditions, and the physiological data is the physiological index corresponding to different torsional vibration conditions. By obtaining more comprehensive test data, not only the data source of the torsional vibration test method includes traditional objective data, but also the physiological index and subjective evaluation of the user are added, so that the test data is richer and the comprehensiveness and accuracy of the test are improved. According to the objective data and the scoring data, the mapping relationship is fitted by Stevens' law, and the first mapping relationship between the objective data and the scoring data is established, which can quantify the subjective perception of the user and associate it with the objective data, so that the test results are closer to the actual driving experience. According to the objective data and the physiological data, the mapping relationship is fitted by data mining technology, and the second mapping relationship between the objective data and the physiological data is established, which can associate the physiological index of the user with the objective data, so that the test results take into account the physiological tolerance of the user, introduce physiological health factors into the test, and the test results are more comprehensive. According to the first mapping relationship and the second mapping relationship, the torsional vibration intensity range is determined, and by combining subjective and objective data, a more sound test evaluation system is provided to facilitate subsequent torsional test experiment comparison analysis and product optimization. During driving, a car has multiple driving modes due to different models and operations. Testing the torsional vibration of different driving modes is more targeted and can improve the scene coverage. In a possible implementation method, the embodiment of the present application provides a torsional vibration test method for multiple driving modes, see A1-A6:

[0124] A1: Obtain objective data of the target driving mode, scoring data of the target driving mode, and physiological data of the target driving mode.

[0125] The target driving mode is one of the multiple driving modes.

[0126] Driving modes are divided into engine direct drive mode, pure electric (Electric Vehicle, EV) mode and hybrid electric (Hybrid Electric Vehicle, HEV) mode according to different vehicle models.

[0127] In addition to the driving modes corresponding to different models, there are also different driving modes corresponding to different vehicle operations, which are divided into engine start mode, combined drive mode, rapid acceleration mode, rapid deceleration mode, motor dragging engine shutdown mode, etc.

[0128] Different driving modes correspond to different torsional vibration generation scenarios. Objective data, scoring data and physiological data corresponding to the target driving mode are obtained, that is, subjective and objective torsional vibration test data in the scenario corresponding to the target driving mode are obtained.

[0129] A2: According to the objective data of the target driving mode and the scoring data of the target driving mode, a mapping relationship is fitted through Stevens' law to establish a first mapping relationship corresponding to the target driving mode.

[0130] A3: According to the objective data of the target driving mode and the physiological data of the target driving mode, a mapping relationship is fitted by using data mining technology to establish a second mapping relationship corresponding to the target driving mode.

[0131] After A1-A3, the torsional vibration test result corresponding to the target driving mode is obtained, and the torsional vibration test result includes objective data, scoring data, physiological data, a first mapping relationship and a second mapping relationship of the target driving mode.

[0132] Different driving modes are taken as target driving modes in turn, and the torsional vibration test results corresponding to all driving modes are obtained.

[0133] A4: Get the driving mode to be tested.

[0134] The driving mode to be tested is one of multiple driving modes.

[0135] A5: Obtain a first mapping relationship corresponding to the driving mode to be tested and a second mapping relationship corresponding to the driving mode to be tested according to the driving mode to be tested.

[0136] A6: Determine a torsional vibration intensity range corresponding to the driving mode to be tested according to a first mapping relationship corresponding to the driving mode to be tested and a second mapping relationship corresponding to the driving mode to be tested.

[0137] A scoring threshold corresponding to the driving mode to be tested is determined, and a torsional vibration test range corresponding to the scoring data of the driving mode to be tested is determined according to a first mapping relationship of the driving mode to be tested.

[0138] A physiological indicator threshold corresponding to the driving mode to be tested is determined, and a torsional vibration test range corresponding to the driving mode to be tested is determined according to a second mapping relationship of the driving mode to be tested.

[0139] Determine the torsional vibration test range for a specific driving mode, so that the transmission system optimization analysis for this driving mode is more targeted.

[0140] By obtaining the torsional vibration test results of different driving modes and deriving the torsional vibration test range corresponding to the driving mode, the torsional vibration test can cover more driving scenarios and obtain subjective and objective data under more working conditions. This not only makes the test results more comprehensive, but also makes the research, analysis, and iterative optimization of specific driving modes more targeted.

[0141] In the torsional vibration test of multiple driving modes, the embodiment of the present application provides two ways of switching driving modes, which are as follows:

[0142] Method 1: According to the driving mode sequence indicated by the standard switching mode, a plurality of driving modes are identified in the driving mode sequence as the driving modes to be tested.

[0143] In the standard switching mode, the test order of the driving modes is a preset order, which ensures the controllability of the standard test and its suitability for scientific experiments and performance evaluation.

[0144] Method 2: According to the driving mode sequence set by the autonomous switching mode, a plurality of driving modes are marked in the driving mode sequence as driving modes to be tested.

[0145] In the autonomous switching mode, the test order of the driving modes is set by the user according to his or her preferences, which improves the practicality of the torsional vibration test and its closeness to actual driving scenarios, making the test process more interactive and participatory.

[0146] The torsional vibration test can be carried out by using different driving mode switching sequences determined by different methods to meet different testing requirements. When a scientific and standardized performance evaluation is required, the standard switching mode can meet this requirement. When testing is required in combination with user habits, the autonomous switching mode can improve flexibility and better meet user expectations.

[0147] In a possible implementation, the embodiment of the present application further provides a specific implementation of establishing a first mapping relationship, see B1-B2:

[0148] B1: Based on the objective data and scoring data, the nonlinear relationship corresponding to Stevens' law is transformed logarithmically to obtain the linear relationship between the objective data and the scoring data.

[0149] The nonlinear relationship corresponding to Stevens' law is as follows:

[0150] S=kI n (2)

[0151] Among them, S is the scoring data, I is the objective data, n is the growth rate of subjective feeling, and k is the proportional constant.

[0152] The linear relationship between objective data and scoring data is as follows:

[0153] log 10 (S) = n log 10 (I)+log 10 (k) (3)

[0154] Among them, S is the scoring data, I is the objective data, n is the growth rate of subjective feeling, and k is the proportional constant.

[0155] Through logarithmic transformation, the nonlinear relationship corresponding to Stevens' law is converted into a linear relationship. The linear relationship is easier to fit through the standard linear regression method, which improves the fitting accuracy. When the data is widely distributed, the relationship between the data can be better captured by converting the logarithm into a linear relationship.

[0156] B2: According to the linear relationship, linear fitting is performed on the objective data and the scoring data to establish a first mapping relationship between the objective data and the scoring data.

[0157] As an example, the goodness R can be calculated by the following formula during the fitting process: 2

[0158]

[0159]

[0160]

[0161]

[0162] Where m is the number of samples, R 2 The goodness of fit measures the degree of fit of the actual data. The closer it is to 1, the better the model fits the actual data. SST is the total sum of squares, SSR is the residual sum of squares, and SSE is the regression sum of squares. is the mean of the rating data, S i is the actual data of the scoring data, i is the sample number, is the fitted scoring data.

[0163] By calculating the goodness of fit, the fitting effect of the scoring data in the linear fitting process can be evaluated. The first mapping relationship formed by the data with good fitting effect is more accurate.

[0164] By converting the nonlinear relationship logarithm of Stevens' law into a linear relationship and performing linear fitting, the optimal parameters obtained by fitting are more intuitive. While retaining the nonlinear relationship, the complexity of fitting is reduced and the efficiency of establishing the first mapping relationship is improved.

[0165] The mapping relationship is fitted by data mining technology to establish the second mapping relationship. The data mining technology covers a wide range. In a possible implementation, the embodiment of the present application provides four methods for fitting the second mapping relationship by statistical regression, which are as follows:

[0166] Method 1: According to the objective data and the physiological data, a mapping relationship fitting is performed through linear regression to establish a second mapping relationship between the objective data and the physiological data.

[0167] It is applicable when there is a linear relationship between physiological data and objective data. By establishing the linear equation Y=aX+b, we can intuitively understand how objective data affects physiological data.

[0168] Method 2: According to the objective data and the physiological data, a mapping relationship is fitted by multivariate regression to establish a second mapping relationship between the objective data and the physiological data.

[0169] It is suitable for relationship fitting when there are multiple data variables. It can simultaneously consider the influence of multiple objective data on physiological data, and establish a multivariate linear equation Y = a1X1 + a2X2 + a3X3 + ... + a n X n +b, can better understand the second mapping relationship of multiple variables.

[0170] Method three: According to the objective data and the physiological data, a mapping relationship is fitted by logistic regression to establish a second mapping relationship between the objective data and the physiological data.

[0171] It is applicable when the physiological data is a binary variable, such as "normal / abnormal", and the second mapping relationship is fitted by establishing a logistic regression model logit(P)=aX+b.

[0172] Method 4: According to the objective data and the physiological data, a mapping relationship fitting is performed through Poisson regression to establish a second mapping relationship between the objective data and the physiological data.

[0173] It is applicable when the physiological data is count data, such as heart rate, by establishing a Poisson regression model log(λ)=aX+b to fit the second mapping relationship.

[0174] The statistical regression method can use a large amount of existing data to find the quantitative relationship between objective data and physiological data. Compared with other data mining technologies, it has better interpretability, and its parameters are clear and easy to analyze, making the second mapping relationship clearer, which is convenient for subsequent torsion test experiment comparison analysis and product optimization.

[0175] The quality of the scoring data varies. In order to more accurately reflect the subjective feelings of users under different torsional vibration conditions, the present application embodiment provides a specific implementation method for obtaining the scoring data, see C1-C3:

[0176] C1: Obtain multiple test users and knowledge reserve thresholds.

[0177] The test users' perception of torsional vibration intensity varies, including ordinary drivers, ordinary passengers, vehicle torsional vibration research practitioners, etc. The knowledge reserve threshold is the standard for judging whether the test users have the ability to accurately perceive torsional vibration. Test users whose knowledge reserves exceed the knowledge reserve threshold are generally torsional vibration research professionals.

[0178] C2: According to the knowledge reserve threshold, multiple test users are screened to obtain target users.

[0179] For multiple test users, test users whose knowledge reserves are less than the knowledge reserve threshold are screened out to obtain target users with a stronger ability to accurately perceive torsional vibration.

[0180] C3: Obtain rating data from target users.

[0181] By screening out target users with stronger perception of torsional vibration, it can be ensured that the rating data of these users are more accurate and reliable. The professional knowledge and experience of the target users enable them to more accurately perceive and describe the subjective feelings under different torsional vibration conditions, thereby improving the quality of the rating data. Improving the quality of the rating data helps to establish a more accurate first mapping relationship.

[0182] In the process of determining the torsional vibration intensity range, it may happen that the torsional vibration range corresponding to the first mapping relationship is different from the torsional vibration range corresponding to the second mapping relationship. In view of this situation, the embodiment of the present application provides a specific implementation method for determining the torsional vibration intensity range, see D1-D5:

[0183] D1: Determine a first threshold corresponding to the first mapping relationship according to the first mapping relationship.

[0184] From the perspective of comfort, the first threshold is determined according to the intensity of the rating and the first mapping relationship.

[0185] For example, based on comprehensive judgment or statistical analysis, it is determined that when the score data exceeds 5, it belongs to a strong torsional vibration perception level, and the first threshold is determined to be 20 Hz according to the first mapping relationship.

[0186] D2: Determine a second threshold corresponding to the second mapping relationship according to the second mapping relationship.

[0187] From the perspective of physiological health, according to medical advice, the second threshold is determined according to the second mapping relationship.

[0188] For example, according to medical advice, a heart rate exceeding 90 bpm is considered a fast heart rate, and according to the first mapping relationship, the second threshold is 15 Hz.

[0189] D3: If the first threshold is less than or equal to the second threshold, determine the first threshold as the final threshold.

[0190] D4: If the first threshold is greater than the second threshold, determine the second threshold as the final threshold.

[0191] D5: Determine a torsional vibration intensity range according to the final threshold value.

[0192] The smaller one of the first threshold and the second threshold is selected as the final threshold, thereby obtaining a final torsional vibration intensity range.

[0193] For example, the first threshold is 20 Hz, the second threshold is 15 Hz, and the final threshold is determined to be 15 Hz, that is, the torsional vibration intensity range is 0-15 Hz.

[0194] By selecting the smaller one from the first threshold and the second threshold as the final threshold, not only comfort but also physiological tolerance can be considered. The user's subjective perception may often not be completely synchronized with the physiological indicators. For example, the heart rate has reached a very high range, and the score may belong to a more comfortable range. At this time, it is necessary to take both aspects into account and select a smaller range. In this way, when determining the final torsional vibration range, both user comfort and the upper limit of physiological tolerance are satisfied.

[0195] Acquiring accurate torsional vibration signals can not only improve the quality of objective data, but also establish more accurate first mapping relationships and second mapping relationships. The present application embodiment provides a specific implementation method for objective data measurement, see E1-E2:

[0196] E1: Torsional vibration test data is obtained based on the non-contact magnetoelectric method using a magnetoelectric sensor and a test gear plate.

[0197] See also Figure 2 , Figure 2A schematic diagram of a non-contact magnetoelectric method provided in an embodiment of the present application. The magnetoelectric sensor is installed facing the test gear disk. When the shaft system rotates, the tooth edge of the test gear disk cuts the magnetic flux lines, thereby causing the magnetic flux of the magnetic field to change. The amplitude and phase of the torsional vibration are sensed on the sensor. After detection and integration smoothing and removing the DC component, the torsional vibration signal is obtained.

[0198] The calculation method of the torsional vibration signal is as follows:

[0199]

[0200] Among them, t i is the time it takes for the axis of rotation to rotate once, ω i is the average angular velocity, and n is the number of teeth on the test gear plate.

[0201]

[0202] Where ω is the rotation frequency of the shaft system. The sensor outputs n signals when the shaft system rotates one circle. n is the time to capture n signals, θ is the rotation axis at t n The torsion angle in time.

[0203] The t obtained by collecting i and t n Each t can be calculated n The corresponding torsion angle θ is extracted to extract the peak value θ of the positive and negative half axes. n The absolute values ​​are then added to obtain the torsional vibration signal. The period and frequency of the torsional vibration can also be obtained through measurement and calculation.

[0204] E2: Obtain objective data, which is torsional vibration test data.

[0205] Because it is not affected by the errors caused by mechanical contact, the non-contact magnetoelectric method can provide high-precision measurement results, thereby obtaining more accurate and objective data. At the same time, the non-contact characteristics will avoid direct contact with the object being measured and reduce the loss caused by torsional vibration testing.

[0206] The following describes a specific implementation method of applying the above torsional vibration test method. Figure 3 , Figure 3 A schematic diagram of a torsional vibration testing system provided in an embodiment of the present application.

[0207] Figure 3In the figure, each part is described as follows: 1-power source 1, 2-flywheel, 3-torsion vibration damper, 4-magnetoelectric collector, 5-transmission, 6-drive shaft, 7-reducer, 8-load, 9-track seat, 10-power source 2, 11-power source 3, 12-dummy, 13-vibration test system, 14-test analysis system, and 15-result analysis system.

[0208] The power source provides driving torque which is transmitted to the load / driving wheel through the flywheel, torsional vibration damper, transmission shaft and transmission. The vibration signal collector installed on the test bench receives the vibration signal collection units of various parts, the collection unit of the physiological index signal of the dummy / driver sitting on the track seat and the information interaction control unit capable of processing the torsional vibration signal collected by the collection unit, wherein the vibration signal collection units installed at various parts of the test bench can capture the vibration signals of various parts of the transmission system under different driving modes and transmit them to the vibration analysis system, and compare them with the physiological indexes measured on the track seat, and finally output them in the result analysis system.

[0209] The following is an introduction to the equipment structure for applying the above torsional vibration test method. Figure 4 , Figure 4 A diagram showing the structure of a human-machine operation test platform provided in an embodiment of the present application.

[0210] The human-machine interactive torsional vibration tester includes a transmission system simulation system, experimental auxiliary equipment and a data acquisition module.

[0211] With the assistance of experimental auxiliary equipment, the test environment is maintained, and the information of the test environment is used to simulate the torsional vibration signal through the transmission system simulation system. The objective data, scoring data and physiological data are collected through the data acquisition module. The first mapping relationship and the second mapping relationship are obtained through analysis by the data analysis system, thereby obtaining a comprehensive torsional vibration test evaluation system.

[0212] See also Figure 5 , is a schematic diagram of the structure of a torsional vibration testing device provided in an embodiment of the present application, the torsional vibration testing device 200 includes: an acquisition unit 201, a scoring unit 202, a physiological unit 203 and an application unit 204;

[0213] An acquisition unit 201 is used to acquire objective data, scoring data and physiological data, wherein the objective data is the torsional vibration intensity obtained by testing the test instrument under different torsional vibration conditions, the scoring data is the subjective evaluation score given according to the self-perception under different torsional vibration conditions, and the physiological data is the physiological index corresponding to the different torsional vibration conditions;

[0214] A scoring unit 202, configured to perform mapping relationship fitting based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data;

[0215] A physiological unit 203, configured to perform mapping relationship fitting based on the objective data and the physiological data by using a data mining technique to establish a second mapping relationship between the objective data and the physiological data;

[0216] The application unit 204 is configured to determine a torsional vibration intensity range according to the first mapping relationship and the second mapping relationship.

[0217] Therefore, in order to obtain a more comprehensive torsional vibration test result of the vehicle, the torsional vibration test device 200 provided in the embodiment of the present application, the acquisition unit 201 acquires objective data, scoring data and physiological data, wherein the objective data is the torsional vibration intensity obtained by the test instrument under different torsional vibration conditions, the scoring data is the subjective evaluation score given according to the self-perception under different torsional vibration conditions, and the physiological data is the physiological index corresponding to different torsional vibration conditions. By acquiring more comprehensive test data, not only the data source of the torsional vibration test method includes traditional objective data, but also the physiological index and subjective evaluation of the user are added, so that the test data is richer and the comprehensiveness and accuracy of the test are improved. The scoring unit 202 performs mapping relationship fitting based on the objective data and the scoring data through Stevens' law, establishes a first mapping relationship between the objective data and the scoring data, can quantify the user's subjective perception, and associate it with the objective data, so that the test results are closer to the actual driving experience. The physiological unit 203 performs mapping relationship fitting based on the objective data and the physiological data through data mining technology, establishes a second mapping relationship between the objective data and the physiological data, can associate the user's physiological index with the objective data, so that the test results take into account the user's physiological tolerance, introduce physiological health factors into the test, and the test results are more comprehensive. The application unit 204 determines the torsional vibration intensity range according to the first mapping relationship and the second mapping relationship, and provides a more complete test evaluation system by combining subjective and objective data, so as to facilitate subsequent torsional test experiment comparison analysis and product optimization.

[0218] As an implementation manner, the acquisition unit 201 is specifically used for:

[0219] Obtaining objective data of a target driving mode, scoring data of the target driving mode, and physiological data of the target driving mode, wherein the target driving mode is one of the plurality of driving modes;

[0220] The scoring unit 202 is specifically used for:

[0221] According to the objective data of the target driving mode and the scoring data of the target driving mode, a mapping relationship is fitted by Stevens' law to establish a first mapping relationship corresponding to the target driving mode;

[0222] The physiological unit 203 is specifically used for:

[0223] According to the objective data of the target driving mode and the physiological data of the target driving mode, mapping relationship fitting is performed by using data mining technology to establish a second mapping relationship corresponding to the target driving mode;

[0224] The application unit 204 is specifically used for:

[0225] Acquire a driving mode to be tested, where the driving mode to be tested is one of a plurality of driving modes;

[0226] Acquire, according to the driving mode to be tested, a first mapping relationship corresponding to the driving mode to be tested and a second mapping relationship corresponding to the driving mode to be tested;

[0227] The torsional vibration intensity range corresponding to the driving mode to be tested is determined according to the first mapping relationship corresponding to the driving mode to be tested and the second mapping relationship corresponding to the driving mode to be tested.

[0228] As an implementation manner, the application unit 204 is specifically configured to:

[0229] According to the driving mode sequence indicated by the standard switching mode, identifying a plurality of driving modes in the driving mode sequence as the driving modes to be tested; or,

[0230] According to the driving mode sequence of the autonomous switching mode setting, a plurality of driving modes are identified by the driving mode sequence as the driving modes to be tested.

[0231] As an implementation manner, the scoring unit 202 is specifically configured to:

[0232] According to the objective data and the scoring data, the nonlinear relationship corresponding to the Stevens' law is logarithmically transformed to obtain a linear relationship between the objective data and the scoring data;

[0233] According to the linear relationship, linear fitting is performed on the objective data and the scoring data to establish a first mapping relationship between the objective data and the scoring data;

[0234] The nonlinear relationship corresponding to Stevens' law is as follows:

[0235] S=kI n :

[0236] Wherein, S is the scoring data, I is the objective data, n is the growth rate of subjective feeling, and k is the proportional constant:

[0237] The linear relationship between the objective data and the scoring data is as follows:

[0238] log10(S)=n log10(I)+log10(k);

[0239] Wherein, S is the scoring data, I is the objective data, n is the growth rate of the subjective feeling, and k is the proportional constant.

[0240] As an implementation mode, the physiological unit 203 is specifically used for:

[0241] According to the objective data and the physiological data, a mapping relationship is fitted by linear regression to establish a second mapping relationship between the objective data and the physiological data; or,

[0242] According to the objective data and the physiological data, a mapping relationship is fitted by multiple regression to establish a second mapping relationship between the objective data and the physiological data; or,

[0243] According to the objective data and the physiological data, a mapping relationship is fitted by logistic regression to establish a second mapping relationship between the objective data and the physiological data; or,

[0244] According to the objective data and the physiological data, a mapping relationship fitting is performed through Poisson regression to establish a second mapping relationship between the objective data and the physiological data.

[0245] As an implementation manner, the acquisition unit 201 is specifically used for:

[0246] Obtain multiple test users and knowledge reserve thresholds;

[0247] According to the knowledge reserve threshold, screening the plurality of test users to obtain a target user;

[0248] Obtain rating data from the target user.

[0249] As an implementation manner, the application unit 204 is specifically configured to:

[0250] Determine, according to the first mapping relationship, a first threshold corresponding to the first mapping relationship;

[0251] Determine, according to the second mapping relationship, a second threshold corresponding to the second mapping relationship;

[0252] If the first threshold is less than or equal to the second threshold, determining the first threshold as the final threshold;

[0253] If the first threshold is greater than the second threshold, determining the second threshold as the final threshold;

[0254] A torsional vibration intensity range is determined according to the final threshold value.

[0255] As an implementation manner, the acquisition unit 201 is specifically used for:

[0256] The torsional vibration test data is obtained based on the non-contact magnetoelectric method through the magnetoelectric sensor and the test gear plate;

[0257] The objective data is obtained, where the objective data is the torsional vibration test data.

[0258] The embodiment of the present application also provides a torsional vibration testing device, characterized in that the device comprises: a testing instrument, a scoring data acquisition module, a physiological data acquisition module and a processor;

[0259] The testing instrument is used to obtain objective data, wherein the objective data is the torsional vibration intensity obtained by testing under different torsional vibration conditions;

[0260] The scoring data acquisition module is used to obtain scoring data, wherein the scoring data is a subjective evaluation score given according to one's own perception under different torsional vibration conditions;

[0261] The physiological data acquisition module is used to acquire physiological data, wherein the physiological data is a physiological index corresponding to different torsional vibration conditions;

[0262] The processor is configured to perform mapping relationship fitting based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data;

[0263] The processor is further configured to perform mapping relationship fitting based on the objective data and the physiological data by using a data mining technique to establish a second mapping relationship between the objective data and the physiological data;

[0264] The processor is further used to determine a torsional vibration intensity range according to the first mapping relationship and the second mapping relationship.

[0265] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0266] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0267] In the several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0268] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A torsional vibration testing method, characterized in that: The method comprises: Obtain objective data, scoring data and physiological data, wherein the objective data is the torsional vibration intensity obtained by testing the test instrument under different torsional vibration conditions, the scoring data is the subjective evaluation score given according to the self-perception under different torsional vibration conditions, and the physiological data is the physiological index corresponding to different torsional vibration conditions; According to the objective data and the scoring data, a mapping relationship is fitted by Stevens' law to establish a first mapping relationship between the objective data and the scoring data; According to the objective data and the physiological data, a mapping relationship is fitted by using a data mining technology to establish a second mapping relationship between the objective data and the physiological data; A torsional vibration intensity range is determined according to the first mapping relationship and the second mapping relationship.

2. The method according to claim 1, characterized in that The acquisition of objective data, scoring data and physiological data includes: Obtaining objective data of a target driving mode, scoring data of the target driving mode, and physiological data of the target driving mode, wherein the target driving mode is one of the plurality of driving modes; The step of fitting a mapping relationship based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data includes: According to the objective data of the target driving mode and the scoring data of the target driving mode, a mapping relationship is fitted by Stevens' law to establish a first mapping relationship corresponding to the target driving mode; The step of fitting a mapping relationship based on the objective data and the physiological data by using a data mining technique to establish a second mapping relationship between the objective data and the physiological data includes: According to the objective data of the target driving mode and the physiological data of the target driving mode, mapping relationship fitting is performed by using data mining technology to establish a second mapping relationship corresponding to the target driving mode; The determining the torsional vibration intensity range according to the first mapping relationship and the second mapping relationship includes: Acquire a driving mode to be tested, where the driving mode to be tested is one of a plurality of driving modes; Acquire, according to the driving mode to be tested, a first mapping relationship corresponding to the driving mode to be tested and a second mapping relationship corresponding to the driving mode to be tested; The torsional vibration intensity range corresponding to the driving mode to be tested is determined according to the first mapping relationship corresponding to the driving mode to be tested and the second mapping relationship corresponding to the driving mode to be tested.

3. The method according to claim 2, characterized in that The obtaining of the driving mode to be tested comprises: According to the driving mode sequence indicated by the standard switching mode, identifying a plurality of driving modes in the driving mode sequence as the driving modes to be tested; or, According to the driving mode sequence of the autonomous switching mode setting, a plurality of driving modes are identified by the driving mode sequence as the driving modes to be tested.

4. The method according to claim 1, characterized in that The step of fitting a mapping relationship based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data includes: According to the objective data and the scoring data, the nonlinear relationship corresponding to the Stevens' law is logarithmically transformed to obtain a linear relationship between the objective data and the scoring data; According to the linear relationship, linear fitting is performed on the objective data and the scoring data to establish a first mapping relationship between the objective data and the scoring data; The nonlinear relationship corresponding to Stevens' law is as follows: S=kI n , Wherein, S is the scoring data, I is the objective data, n is the growth rate of subjective feeling, and k is the proportional constant; The linear relationship between the objective data and the scoring data is as follows: log 10 (S)=nlog 10 (I)+log 10 (k); Wherein, S is the scoring data, I is the objective data, n is the growth rate of the subjective feeling, and k is the proportional constant.

5. The method according to claim 1, characterized in that If the data mining technology is linear regression, multivariate regression, logistic regression, and Poisson regression, then the step of fitting a mapping relationship based on the objective data and the physiological data by using the data mining technology to establish a second mapping relationship between the objective data and the physiological data includes: According to the objective data and the physiological data, a mapping relationship is fitted by linear regression to establish a second mapping relationship between the objective data and the physiological data; or, According to the objective data and the physiological data, a mapping relationship is fitted by multiple regression to establish a second mapping relationship between the objective data and the physiological data; or, According to the objective data and the physiological data, a mapping relationship is fitted by logistic regression to establish a second mapping relationship between the objective data and the physiological data; or, A mapping relationship is fitted based on the objective data and the physiological data by Poisson regression to establish a second mapping relationship between the objective data and the physiological data.

6. The method according to claim 1, characterized in that Obtaining the rating data includes: Obtain multiple test users and knowledge reserve thresholds; According to the knowledge reserve threshold, screening the plurality of test users to obtain a target user; Obtain rating data from the target user.

7. The method according to claim 1, characterized in that The determining the torsional vibration intensity range according to the first mapping relationship and the second mapping relationship includes: Determine, according to the first mapping relationship, a first threshold corresponding to the first mapping relationship; Determine, according to the second mapping relationship, a second threshold corresponding to the second mapping relationship; If the first threshold is less than or equal to the second threshold, determining the first threshold as the final threshold; If the first threshold is greater than the second threshold, determining the second threshold as the final threshold; A torsional vibration intensity range is determined according to the final threshold value.

8. The method according to claim 1, characterized in that: Obtaining the objective data includes: The torsional vibration test data is obtained based on the non-contact magnetoelectric method through the magnetoelectric sensor and the test gear plate; The objective data is obtained, where the objective data is the torsional vibration test data.

9. A torsional vibration testing device, characterized in that: The device comprises: an acquisition unit, a scoring unit, a physiological unit and an application unit; The acquisition unit is used to acquire objective data, scoring data and physiological data, wherein the objective data is the torsional vibration intensity obtained by testing the test instrument under different torsional vibration conditions, the scoring data is the subjective evaluation score given according to the self-perception under different torsional vibration conditions, and the physiological data is the physiological index corresponding to different torsional vibration conditions; The scoring unit is configured to perform mapping relationship fitting based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data; The physiological unit is used to perform mapping relationship fitting based on the objective data and the physiological data by using a data mining technology to establish a second mapping relationship between the objective data and the physiological data; The application unit is used to determine a torsional vibration intensity range according to the first mapping relationship and the second mapping relationship.

10. A torsional vibration testing device, characterized in that: The device comprises: a test instrument, a scoring data acquisition module, a physiological data acquisition module and a processor; The testing instrument is used to obtain objective data, wherein the objective data is the torsional vibration intensity obtained by testing under different torsional vibration conditions; The scoring data acquisition module is used to obtain scoring data, wherein the scoring data is a subjective evaluation score given according to one's own perception under different torsional vibration conditions; The physiological data acquisition module is used to acquire physiological data, wherein the physiological data is a physiological index corresponding to different torsional vibration conditions; The processor is configured to perform mapping relationship fitting based on the objective data and the scoring data by using Stevens' law to establish a first mapping relationship between the objective data and the scoring data; The processor is further configured to perform mapping relationship fitting based on the objective data and the physiological data by using a data mining technique to establish a second mapping relationship between the objective data and the physiological data; The processor is further used to determine a torsional vibration intensity range according to the first mapping relationship and the second mapping relationship.

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