A method and device for analyzing the vibration isolation performance of a seat, and a storage medium

Abstract

This invention discloses a method, device, and storage medium for analyzing the vibration isolation performance of a seat. The method includes: acquiring seat vibration data and subjective evaluation data from a human-seat vibration test; determining the root mean square (RMS) value of the total weighted acceleration at different measuring points on the seat based on the seat vibration data to obtain the RMS value of the total weighted acceleration for multiple subjects; performing linear regression analysis on the multiple RMS values ​​of the total weighted acceleration and different vibration excitations to determine the benchmark vibration excitation; performing correlation analysis on the multiple RMS values ​​of the total weighted acceleration and subjective evaluation data to determine the target evaluation threshold based on the benchmark vibration excitation; and using the target evaluation threshold as the threshold for objective evaluation indicators to analyze the vibration isolation performance of the seat. In this invention, the target evaluation threshold is used as the comfort evaluation standard, and the objective evaluation indicators have a high degree of consistency with human subjective feelings, thus improving the reliability and accuracy of seat vibration isolation performance analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle performance analysis, and particularly relates to a seat vibration isolation performance analysis method and device and a storage medium. BACKGROUND

[0002] Seat dynamic comfort refers to the comfort of a human body after vibration is transmitted to the human body through a seat framework and a seat cushion under a driving state of a vehicle, and is mainly related to seat vibration characteristics.

[0003] In seat vibration characteristic evaluation, subjective evaluation and objective evaluation are mainly used. In the subjective evaluation, professional evaluators are needed to ensure consistency of evaluation results, and subjective preferences of the evaluators have certain influence on the evaluation results, so that the seat vibration characteristics are not evaluated objectively and accurately. In the objective evaluation, three evaluation indexes are widely used, including weighted acceleration root mean square value, seat effective amplitude transmissibility and seat vibration transmissibility. The weighted acceleration root mean square value is more concerned about the overall characteristics of the human body and the seat, and cannot effectively evaluate the vibration isolation performance of the seat. The seat effective amplitude transmissibility can only reflect the performance of the seat in one direction, and is not consistent with subjective feelings of the human body. The seat vibration transmissibility has many peak values in a transmissibility curve, and cannot be quantified comprehensively. Therefore, the three evaluation indexes are relatively single in evaluating the seat vibration characteristics, and the seat vibration characteristics are not accurately evaluated. SUMMARY

[0004] The present application provides a seat vibration isolation performance analysis method and device and a storage medium to solve the problem that the evaluation indexes are relatively single in evaluating the seat vibration characteristics in the existing seat vibration characteristic evaluation, and the seat vibration characteristics are not accurately evaluated.

[0005] A seat vibration isolation performance analysis method comprises the following steps.

[0006] Obtaining a plurality of test data of a human body and a seat vibration test, the test data including seat vibration data under different vibration excitations and subjective evaluation data of a subject on the seat when the seat vibrates under different vibration excitations;

[0007] Determining a total weighted acceleration root mean square value of different measurement point positions on the seat according to the seat vibration data under the different vibration excitations, to obtain a plurality of total weighted acceleration root mean square values corresponding to the subject;

[0008] Performing linear regression analysis on the plurality of total weighted acceleration root mean square values and the different vibration excitations, to determine a reference vibration excitation;

[0009] Correlate the plurality of total weighted acceleration root mean square values and the subjective evaluation data to determine a target evaluation threshold of the reference vibration excitation as a reference evaluation working condition;

[0010] Analyze the vibration isolation performance of the seat by taking the target evaluation threshold as a threshold of an objective evaluation index.

[0011] Further, the correlation analysis of the plurality of total weighted acceleration root mean square values and the subjective evaluation data to determine a target evaluation threshold of the reference vibration excitation as a reference evaluation working condition comprises:

[0012] Correlate the subjective evaluation data and the total weighted acceleration root mean square value to determine the subjective evaluation score of the seat under the reference evaluation working condition;

[0013] Determine the vibration isolation performance evaluation parameters of the seat vibration data, and determine the target objective evaluation index of the seat vibration data according to each vibration isolation performance evaluation parameter and the subjective evaluation data;

[0014] Determine the target evaluation threshold according to the target objective evaluation index and the subjective evaluation score of the seat under the reference evaluation working condition.

[0015] Further, the correlation analysis of the subjective evaluation data and the total weighted acceleration root mean square value to determine the subjective evaluation score of the seat under the reference evaluation working condition comprises:

[0016] According to Stevens' power law, a linear relationship function between subjective physical quantities and objective physical quantities under different vibration excitations is established;

[0017] Take the median of the plurality of total weighted acceleration root mean square values as the objective physical quantity, take the subjective evaluation corresponding to the median of the plurality of total weighted acceleration root mean square values as the subjective physical quantity, and perform linear regression fitting by using the linear relationship function to obtain a first group of fitting values;

[0018] Input the reference evaluation working condition corresponding weighted acceleration root mean square value and the first group of fitting values into the linear relationship function to obtain the subjective evaluation score of the seat under the reference evaluation working condition.

[0019] Further, the determination of the vibration isolation performance evaluation parameters of the seat vibration data and the target objective evaluation index of the seat vibration data according to each vibration isolation performance evaluation parameter and the subjective evaluation data comprises:

[0020] Determine a preset vibration isolation performance evaluation function, wherein a seat evaluation index parameter in the preset vibration isolation performance evaluation function is represented by a weighted acceleration root mean square value or by a seat effective amplitude transmissibility;

[0021] Determine a plurality of vibration isolation performance evaluation parameters of the seat vibration data according to the preset vibration isolation performance evaluation function and the seat vibration data;

[0022] Perform linear regression fitting on the plurality of vibration isolation performance evaluation parameters and the subjective evaluation data to determine a vibration isolation performance evaluation parameter with the highest fitting accuracy as the target objective evaluation index.

[0023] Further, the determination of the target evaluation threshold according to the target objective evaluation index and the subjective evaluation score of the seat under the reference evaluation condition comprises:

[0024] Use the target objective evaluation index as the objective physical quantity, use the subjective evaluation corresponding to the target objective evaluation index as the subjective physical quantity, and perform linear regression fitting using the linear relationship function to obtain a second set of fitting values;

[0025] Input the subjective evaluation score of the seat under the reference evaluation condition and the second set of fitting values into the linear relationship function to obtain a target objective physical quantity;

[0026] Use the target objective physical quantity as the target evaluation threshold.

[0027] Further, the linear regression analysis on the plurality of total weighted acceleration root mean square values and the different vibration excitations to determine a reference vibration excitation comprises:

[0028] Perform linear regression analysis on the plurality of total weighted acceleration root mean square values and the different vibration excitations to determine a linear regression model;

[0029] Input a pre-designed weighted acceleration root mean square value as a variable into the linear regression model to obtain a vibration excitation corresponding to the pre-designed weighted acceleration root mean square value;

[0030] Use the vibration excitation corresponding to the pre-designed weighted acceleration root mean square value as the reference vibration excitation.

[0031] Further, the analysis of the vibration isolation performance of the seat according to the target evaluation threshold as an objective evaluation index comprises:

[0032] Determine a vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition according to a preset vibration isolation performance evaluation function and test data of each seat under the reference evaluation condition;

[0033] determining whether the vibration isolation performance evaluation parameter value of the seat under the reference evaluation working condition is less than or equal to the target evaluation threshold value;

[0034] if the vibration isolation performance evaluation parameter value of the seat under the reference evaluation working condition is less than or equal to the target evaluation threshold value, determining that the vibration isolation performance of the seat is qualified;

[0035] if the vibration isolation performance evaluation parameter value of the seat under the reference evaluation working condition is greater than the target evaluation threshold value, determining that the vibration isolation performance of the seat is unqualified.

[0036] A seat vibration isolation performance analysis device, comprising:

[0037] an acquisition module, configured to acquire a plurality of test data of a human and a seat vibration test, wherein the test data comprises seat vibration data under different vibration excitations, and subjective evaluation data of a subject on the seat when the seat vibrates under different vibration excitations;

[0038] a first determination module, configured to determine total weighted acceleration root mean square values of different measuring point positions on the seat according to the seat vibration data under the different vibration excitations, to obtain a plurality of total weighted acceleration root mean square values corresponding to the subject;

[0039] a second determination module, configured to perform linear regression analysis on the plurality of total weighted acceleration root mean square values and the different vibration excitations, to determine a reference vibration excitation;

[0040] a third determination module, configured to perform correlation analysis on the plurality of total weighted acceleration root mean square values and the subjective evaluation data, to determine a target evaluation threshold value with the reference vibration excitation as a reference evaluation working condition;

[0041] an analysis module, configured to analyze the vibration isolation performance of the seat by taking the target evaluation threshold value as a threshold value of an objective evaluation index.

[0042] A seat vibration isolation performance analysis device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned seat vibration isolation performance analysis method when executing the computer program.

[0043] A readable storage medium, wherein the readable storage medium stores a computer program, and the computer program implements the steps of the above-mentioned seat vibration isolation performance analysis method when executed by a processor.

[0044] In one scheme provided by the seat vibration isolation performance analysis method, device and storage medium, a plurality of test data of a human and a seat vibration test are acquired, the test data including seat vibration data under different vibration excitations and subjective evaluation data of a subject on the seat when the seat vibrates under different vibration excitations, then the total weight acceleration root mean square values of different measuring point positions on the seat are determined according to the seat vibration data under different vibration excitations, to obtain total weight acceleration root mean square values corresponding to a plurality of subjects, linear regression analysis is performed on the plurality of total weight acceleration root mean square values and different vibration excitations, to determine a reference vibration excitation, and correlation analysis is performed on the plurality of total weight acceleration root mean square values and the subjective evaluation data, to determine a target evaluation threshold of the reference vibration excitation as a reference evaluation condition, and the target evaluation threshold is used as a threshold of an objective evaluation index to analyze the vibration isolation performance of the seat; in the application, linear regression is performed on the total weight acceleration root mean square value and the vibration excitation, to determine the reference evaluation condition, correlation analysis is performed on the subjective evaluation data and the total weight acceleration root mean square value, to determine the target evaluation threshold as a comfort evaluation standard, so that the objective evaluation index and the subjective feeling of the human have high compatibility, and the reliability and accuracy of the seat vibration isolation performance are improved. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0046] Figure 1 is a schematic diagram of the arrangement position of the sensor on the seat in an embodiment of the present application;

[0047] Figure 2 is a flowchart of the seat vibration isolation performance analysis method in an embodiment of the present application;

[0048] Figure 3 is a linear regression curve diagram of the excitation amplitude and the total weight acceleration root mean square value in an embodiment of the present application;

[0049] Figure 4 is a fitting curve diagram of the total weight acceleration root mean square value and the subjective evaluation in an embodiment of the present application;

[0050] Figure 5 is a fitting curve diagram of the vibration isolation performance evaluation parameter and the subjective evaluation in an embodiment of the present application;

[0051] Figure 6 is a structural diagram of the seat vibration isolation performance analysis device in an embodiment of the present application;

[0052] Figure 7 Figure 2 is another structural schematic diagram of the seat vibration isolation performance analysis device in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be clearly and completely described in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0054] The seat vibration isolation performance analysis method provided by the embodiments of the present application can be applied to a seat vibration isolation performance analysis system, which comprises a seat vibration test device and a seat vibration isolation performance analysis device, wherein the seat vibration test device and the seat vibration isolation performance analysis device are communicatively connected. The seat vibration test device comprises a seat, a vibration table, a seat guide rail and a data acquisition device. The data acquisition device comprises a plurality of sensors to acquire seat vibration data under different vibration excitations in a test process and subjective evaluation data of a subject on the seat when the seat vibrates under different vibration excitations. The seat vibration data can be acquired by an acceleration sensor, which can be a three-direction acceleration sensor. The subjective evaluation data can be acquired by a sound sensor or artificially acquired.

[0055] In the embodiments, a plurality of test data of a person and the seat vibration test are acquired, the test data comprising seat vibration data under different vibration excitations and subjective evaluation data of the subject on the seat when the seat vibrates under different vibration excitations. Then, the total weighted acceleration root mean square values of different measurement point positions on the seat are determined according to the seat vibration data under different vibration excitations to obtain total weighted acceleration root mean square values corresponding to a plurality of subjects. Linear regression analysis is performed on the plurality of total weighted acceleration root mean square values and different vibration excitations to determine a reference vibration excitation. Correlation analysis is performed on the plurality of total weighted acceleration root mean square values and the subjective evaluation data to determine a target evaluation threshold of the reference vibration excitation as a reference evaluation condition. The target evaluation threshold is used as a threshold of an objective evaluation index to analyze the vibration isolation performance of the seat. Linear regression is performed on the total weighted acceleration root mean square values and the vibration excitations to determine the reference evaluation condition. Correlation analysis is performed on the subjective evaluation data and the total weighted acceleration root mean square values to determine the target evaluation threshold as a comfort evaluation standard, so that the objective evaluation index and the subjective feeling of the human body have high consistency, and the reliability and accuracy of the seat vibration characteristic evaluation are improved.

[0056] In this embodiment, the seat vibration isolation performance analysis system includes a seat vibration test device and a seat vibration isolation performance analysis device, which are only illustrative. In other embodiments, the seat vibration isolation performance analysis system can also include other devices, which are not described here.

[0057] Before the human-seat vibration test is performed, test preparation needs to be performed, which includes the following contents:

[0058] (1) Sensor arrangement: The sensors need to be calibrated and the number of sensors needs to be ensured. The arrangement positions (measurement points) of the sensors are determined. After the arrangement positions of the sensors are determined, multiple sensors are installed at the corresponding multiple arrangement positions, which can include the vibration table surface, the seat guide rail, the seat backrest, and the seat cushion. The sensor at the seat guide rail is arranged at a preset position of the seat outer guide rail (such as 1 / 5 to 1 / 10 of the seat outer guide rail). At the same time, a sensor is arranged on the vibration table surface for data monitoring to determine whether the test requirements are met. The sensors on the seat are used to collect vibration data of the seat under different vibration excitation amplitudes during the human-seat vibration test.

[0059] The arrangement positions of the sensors on the seat can be as shown in Figure 1 The inclination angle of the seat backrest with the vertical direction is β (β is 24°), and the inclination angle of the seat cushion with the horizontal direction is α. One sensor is arranged at the seat backrest (point A) and one sensor is arranged at the seat cushion (point C), wherein point B is the center point of the intersection line of the seat cushion and the seat backrest. According to the human body sitting posture comfort test, the distance L1 between point A and point B is 320 mm, and the distance L2 between point C and point B is 128 mm. The above distances can ensure that the ischial tuberosity center of most people in a sitting posture coincides with point C, and the center of the waist of the human body coincides with point A, thereby ensuring that the collected seat vibration data is more consistent with the human body's perception, and the seat vibration data is more accurate.

[0060] (2) Determine the vibration excitation of the test: The vibration excitation of the test is single-axis vibration excitation. Multiple vibration acceleration signals with different amplitudes are designed. The excitation sequence of different vibration excitations needs to be random. The excitation sequence of the vibration excitation is applied according to the random number sequence generated in the engineering software, and the signal iteration repetition is completed on the vibration table (such as a six-degree-of-freedom vibration table) in the laboratory. In order to ensure uniform energy distribution, the excitation duration of each vibration excitation should be long enough (such as about 60 s), and the reference signal excitation duration should not be too short.

[0061] (3) Ensure that the fixtures and tooling meet the test requirements. Before collecting data for the formal test, a preliminary test should be conducted to check the data and determine whether the fixtures and tooling of the seat meet the test requirements. If the error between the acceleration signal of the sensor on the vibration table and the acceleration signal of the sensor at the guide rail is within 5%, the subsequent test can be carried out.

[0062] (4) Provide necessary experimental guidance to the subjects: Before the experiment, each subject will be given a short practice session to help them become familiar with the experimental content and process. Subjective evaluation guidance will be provided to the subjects: By letting the subjects experience a vibration reference vibration excitation first, the subjects will be guided to make subjective evaluations using the relative amplitude evaluation method to ensure the accuracy of subsequent subjective scoring data.

[0063] After completing the experimental preparation, instruct the subjects to sit comfortably in the chair, leaning against the backrest and resting their hands on their knees. Throughout the experiment, subjects should maintain a natural posture and relax their upper body to conduct a human-chair vibration test. Vibration data of the chair under different vibration stimuli will be collected using sensors, and subjects will verbally rate the chair vibration under different stimuli. Subjects will also provide a subjective evaluation using the relative amplitude evaluation method, rating the chair comfort relative to a reference vibration stimulus to obtain data for subjective evaluation. The sensor sampling frequency can be 512Hz (or 256Hz), and the frequency resolution can be 0.5Hz (or 0.25Hz).

[0064] During vibration tests between humans and seats, data quality must be checked, and time-domain signals may need to be truncated if necessary. It is necessary to determine the stability of the sensor signal during data acquisition, and whether drift or abnormal spikes have occurred. Since the vibration excitation input during the test is a random signal, it is necessary to pay attention to whether the energy distribution of the sensor signal is uniform throughout the entire sampling period. When abnormalities occur in the sensor signal, it is necessary to truncate the sensor signal to ensure that the truncated sensor signal has a uniform energy distribution, is free from drift, and has no abnormal spikes.

[0065] In one embodiment, such as Figure 2 As shown, a method for analyzing the vibration isolation performance of a seat is provided. Taking the seat vibration isolation performance analysis device in a seat vibration isolation performance analysis system as an example, the method includes the following steps:

[0066] S10: Obtain multiple sets of test data from the human-seat vibration test. The test data includes seat vibration data under different vibration excitations, as well as subjective evaluation data of the subjects on the seat when the seat vibrates under different vibration excitations.

[0067] After conducting the human-seat vibration test, multiple sets of test data were obtained. These data included seat vibration data under different vibration excitations, as well as subjective evaluation data of the subjects on the seat when the seat vibrated under different vibration excitations.

[0068] In this embodiment, different vibration acceleration values ​​are used as the vertical vibration excitation input of the vibration table. The obtained seat vibration data includes the acceleration of the human body in the front-back, left-right, and vertical directions, that is, the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration at each measuring point. Each measuring point includes the seat guide rail, the backrest of the seat, and the seat cushion.

[0069] S20: Based on the seat vibration data under different vibration excitations, determine the total weighted root mean square value of acceleration at different measuring points on the seat, and obtain the total weighted root mean square value of acceleration for multiple subjects.

[0070] After acquiring seat vibration data under different vibration excitations, the root mean square value of total weighted acceleration at different measuring points on the seat is determined based on the seat vibration data under different vibration excitations, and the root mean square value of total weighted acceleration for multiple subjects is obtained.

[0071] In this embodiment, one total weighted root mean square value of acceleration corresponds to one subject, and multiple subjects correspond to multiple total weighted root mean square values ​​of acceleration. The number of subjects can be 10-15 to ensure a sufficient sample size, thereby meeting the requirement of statistically significant differences between the experimental conditions.

[0072] The total weighted root mean square value of acceleration at different measurement points on the seat for each subject was determined as follows:

[0073] First, determine the weighted root mean square (RMS) values ​​of acceleration in different directions at each measuring point. Using the acceleration time-domain value a(t), weight the acceleration time-domain value a(t) to obtain the frequency-weighted acceleration. Then, perform a Fourier transform on the frequency-weighted acceleration to obtain the RMS value 'a' of the weighted acceleration in a specific direction at a given measuring point. w The calculation formula is as follows:

[0074]

[0075] Among them, a w (t) represents the weighted acceleration time history, in meters per second squared (m / s²). 2 T represents the duration of the vibration excitation, in seconds (s), and w represents the different evaluation directions (including X, Y and Z directions) at different measuring points on the seat.

[0076] To determine the weighted root mean square value 'a' of acceleration in a certain direction at a given measuring point location. w Then, based on the weighted root mean square value 'a' of the acceleration at a certain measuring point in a certain direction... w The root mean square (RMS) value of the weighted acceleration at different measuring point locations is determined. The formula for calculating the RMS value of the weighted acceleration at measuring point locations is as follows:

[0077]

[0078] Among them, a vi Let be the weighted root mean square value of the acceleration at a given measurement point location, where i represents different measurement point locations, i = 1, 2, 3…n, and n is the number of measurement point locations; a wx a wy a wz These represent the weighted root mean square values ​​of acceleration in the X, Y, and Z directions at the measurement point location, respectively; k x k y k z These are the axis weighting coefficients for each direction.

[0079] After determining the weighted root mean square (RMS) values ​​of acceleration at different measuring point locations, the total weighted RMS value 'a' of acceleration at different measuring point locations is determined based on these values. v The total weighted root mean square value of acceleration a v The calculation formula is as follows:

[0080]

[0081] When the measuring point is located at the seat rail, the seat back, and the seat cushion, the number of measuring point locations is determined to be 3, i.e., i = 1, 2, 3. This allows us to determine the total weighted root mean square value 'a' of the acceleration at the seat rail, seat back, and seat cushion. v for:

[0082]

[0083] In this embodiment, the measuring points are located at the seat rail, the back of the seat, and the seat cushion, which is only an example. In other embodiments, the measuring points may also include other locations, which will not be elaborated here.

[0084] S30: Perform linear regression analysis on multiple total weighted root mean square acceleration values ​​and different vibration excitations to determine the benchmark vibration excitation.

[0085] After obtaining the total weighted root mean square values ​​of acceleration for multiple subjects, linear regression analysis is performed on the total weighted root mean square values ​​of acceleration and different vibration excitations to establish a linear regression model between the total weighted root mean square values ​​of acceleration and different vibration excitations. Then, the total weighted root mean square value of acceleration corresponding to the subjective evaluation of good comfort is used as the input variable and input into the linear regression model to obtain the corresponding vibration excitation amplitude M, which is used as the benchmark vibration excitation.

[0086] S40: Perform correlation analysis on multiple total weighted root mean square acceleration values ​​and subjective evaluation data to determine the target evaluation threshold for the benchmark vibration excitation as the benchmark evaluation condition.

[0087] After determining the baseline vibration excitation, a correlation analysis is performed on multiple total weighted root mean square acceleration values ​​and subjective evaluation data. Using the baseline vibration excitation as the baseline evaluation condition, the subjective evaluation score of the seat under the baseline evaluation condition is determined. Then, based on the seat vibration data, including the total weighted root mean square acceleration value, multiple seat vibration isolation performance parameters are determined, which are objective evaluation indicators of seat vibration characteristics. Target objective evaluation indicators are then selected, and a target evaluation threshold is determined based on the target objective evaluation indicators and the subjective evaluation score of the seat under the baseline evaluation condition, serving as the evaluation standard for seat vibration isolation performance.

[0088] S50: The vibration isolation performance of the seat is analyzed by using the target evaluation threshold as the threshold of the objective evaluation index.

[0089] After determining the target evaluation threshold, the objective evaluation index of the seat is calculated based on the seat vibration data of each seat under the benchmark evaluation condition. The target evaluation threshold is used as the threshold of the objective evaluation index to analyze the vibration isolation performance of the seat. That is, the benchmark vibration excitation is used as the vertical excitation input of the vibration table to determine the objective evaluation index of the seat under the benchmark vibration excitation. If the objective evaluation index of the seat under the benchmark vibration excitation is less than or equal to the target evaluation threshold, it indicates that the seat has a good vibration isolation effect and the human body feels comfortable.

[0090] In this embodiment, multiple sets of experimental data from a human-seat vibration test are acquired. These data include seat vibration data under different vibration excitations and subjective evaluation data from subjects on the seat regarding the seat's vibration under different excitations. Based on the seat vibration data under different excitations, the root mean square (RMS) value of the total weighted acceleration at different measuring points on the seat is determined, resulting in multiple RMS values ​​for the total weighted acceleration corresponding to multiple subjects. Linear regression analysis is then performed on these multiple RMS values ​​and different vibration excitations to determine the baseline vibration excitation. Furthermore, correlation analysis is conducted on the multiple RMS values ​​and subjective evaluation data to determine the target evaluation threshold, using the baseline vibration excitation as the benchmark evaluation condition. This target evaluation threshold is then used as the threshold for objective evaluation indicators to analyze the seat's vibration isolation performance. By performing linear regression on the RMS values ​​and vibration excitation, the baseline evaluation condition can be determined. Further correlation analysis between subjective evaluation data and the RMS values ​​determines the target evaluation threshold as a comfort evaluation standard, ensuring a high degree of alignment between objective evaluation indicators and human subjective experience, thus improving the reliability and accuracy of seat vibration characteristic evaluation.

[0091] Furthermore, the seat vibration isolation performance analysis method provided in this embodiment does not require multiple tests and manual evaluations, reducing test costs. It also does not require a large amount of data analysis and calculation, resulting in less computation and improving the efficiency of seat vibration characteristic (or vibration isolation performance) evaluation. The objective evaluation indicators have a high degree of consistency with human subjective feelings, which can effectively evaluate and quantify the seat's vibration attenuation performance, providing a theoretical basis and optimization direction for the design of seat vibration comfort.

[0092] In one embodiment, step S30, which involves performing linear regression analysis on multiple total weighted root mean square acceleration values ​​and different vibration excitations to determine the benchmark vibration excitation, specifically includes the following steps:

[0093] S31: Perform linear regression analysis on multiple total weighted root mean square acceleration values ​​and different vibration excitations to determine the linear regression model.

[0094] S32: Input the root mean square value of the pre-designed weighted acceleration as a variable into the linear regression model to obtain the vibration excitation corresponding to the root mean square value of the pre-designed weighted acceleration.

[0095] S33: Use the vibration excitation corresponding to the pre-designed root mean square value of weighted acceleration as the reference vibration excitation.

[0096] After obtaining the total weighted root mean square values ​​of acceleration for multiple subjects, linear regression analysis was performed on the multiple total weighted root mean square values ​​of acceleration and different vibration excitations to establish a linear regression model between the total weighted root mean square values ​​of acceleration and different vibration excitations. Then, the pre-designed weighted root mean square value of acceleration was used as a variable to input into the linear regression model to obtain the vibration excitation M corresponding to the pre-designed weighted root mean square value of acceleration. The vibration excitation M corresponding to the pre-designed weighted root mean square value of acceleration was used as the benchmark vibration excitation.

[0097] The pre-designed weighted root mean square acceleration value can be the weighted root mean square acceleration value corresponding to the subjective human perception of "no discomfort" in the ISO 2631 international standard. Specifically, the six semantic subjective evaluation criteria recommended by the international ISO 2631 are shown in Table 1:

[0098] Table 1

[0099]

[0100] After obtaining the total weighted root mean square (RMS) acceleration values ​​for multiple subjects, linear regression analysis was performed on the multiple total weighted RMS acceleration values ​​and different vibration excitations to establish a linear regression model between the total weighted RMS acceleration values ​​and different vibration excitations. Then, the weighted RMS acceleration value corresponding to the subjective human perception of "no discomfort" in the ISO 2631 international standard, i.e., 0.315 m / s², was calculated. 2 When used as a variable input into a linear regression model, the result is 0.315 m / s. 2 The corresponding vibration excitation amplitude M is 0.315 m / s². 2 The corresponding vibration excitation amplitude M is used as the benchmark vibration excitation, that is, M will be the benchmark evaluation condition for vibration characteristic evaluation.

[0101] For example, performing a linear regression between the total weighted root mean square value of acceleration and the vibration excitation amplitude yields a fitted curve as shown below. Figure 3 As shown, the fitting accuracy R of the fitted curve (linear regression model) is... 2 The accuracy is 99.93%, with a speed of 0.315 m / s. 2 Inputting the linear regression model yields M = 0.43 m / s 2 .

[0102] In this embodiment, the weighted root mean square value of acceleration corresponding to the subjective feeling of "no discomfort" in the ISO2631 international standard is used as the pre-designed weighted root mean square value for illustration only. In other embodiments, the pre-designed weighted root mean square value of acceleration can also be other values, which will not be elaborated here.

[0103] In this embodiment, linear regression analysis is performed on multiple total weighted root mean square acceleration values ​​and different vibration excitations to determine the linear regression model. Then, the pre-designed weighted root mean square acceleration value is used as a variable to input into the linear regression model to obtain the vibration excitation corresponding to the pre-designed weighted root mean square acceleration value. Finally, the vibration excitation corresponding to the pre-designed weighted root mean square acceleration value is used as the benchmark vibration excitation. This clarifies the specific steps for performing linear regression analysis on multiple total weighted root mean square acceleration values ​​and different vibration excitations to determine the benchmark vibration excitation, providing a foundation for the subsequent determination of the target evaluation threshold.

[0104] In one embodiment, step S40, which involves performing a correlation analysis on multiple total weighted root mean square acceleration values ​​and subjective evaluation data to determine the target evaluation threshold based on the benchmark vibration excitation, specifically includes the following steps:

[0105] S41: Perform a correlation analysis on the subjective evaluation data and the root mean square value of the total weighted acceleration to determine the subjective evaluation score of the seat under the benchmark evaluation conditions.

[0106] After obtaining the total weighted root mean square value of acceleration and subjective evaluation data for multiple subjects, a correlation analysis was performed on the subjective evaluation data and the total weighted root mean square value of acceleration to establish a linear relationship function between the total weighted root mean square value of acceleration and the subjective evaluation data. Then, using the benchmark evaluation condition as the input condition, the weighted root mean square value of acceleration corresponding to the benchmark evaluation condition was input into the linear relationship function to determine the subjective evaluation score of the seat under the benchmark evaluation condition.

[0107] S42: Determine the vibration isolation performance evaluation parameters of the seat vibration data, and determine the target objective evaluation index of the seat vibration data based on each vibration isolation performance evaluation parameter and subjective evaluation data.

[0108] After acquiring seat vibration data, the vibration isolation performance evaluation parameters of the seat vibration data are determined. Based on each vibration isolation performance evaluation parameter and subjective evaluation data, the seat vibration data is determined, and a linear relationship between the vibration isolation performance evaluation parameters and subjective evaluation data is established. Then, a fitting is performed, and a vibration isolation performance evaluation parameter value with high fitting accuracy is selected as the target objective evaluation index.

[0109] Among them, the vibration isolation performance evaluation parameters can be one or more of the following: frequency weighted root mean square (RMS) acceleration, seat effective amplitude transmissibility (SEAT), and seat transmissibility. When determining the fit between the vibration isolation performance evaluation parameters and subjective evaluation data, the vibration isolation performance evaluation parameter value with higher fitting accuracy is selected as the target objective evaluation index.

[0110] Seat vibration transmissibility is one of the important indicators for evaluating seat vibration comfort, reflecting the modalities of the "human-seat" system and the main frequency range of vibration energy concentration. The formula for calculating the seat vibration transmissibility H(f) is as follows:

[0111]

[0112] Among them, S xy (f) represents the cross-power spectrum of the input and output accelerations on the seat surface, S xx (f) represents the self-power spectrum of the input acceleration.

[0113] The Seat Effective Amplitude Transmission Rate (SEAT) can be considered a "weighted transmission rate." Generally, the smaller the SEAT value in a certain direction, the better the seat vibration comfort in that direction. The formula for calculating the SEAT value is as follows:

[0114]

[0115] Among them, G ss (f) and G ff (f) represents the acceleration power spectrum at the seat surface and the floor, respectively. i (f) represents the frequency weighting of the human body's response to vibrations of interest. In seat-floor vibration measurements, w i (f) is the weighting function at the seat. When the SEAT value is equal to 100%, it means that the seat has no vibration attenuation; when the SEAT value is greater than 100%, it means that the seat has amplification effect on vibration, and vice versa.

[0116] S43: Determine the target evaluation threshold based on the objective evaluation indicators of the target and the subjective evaluation score of the seat under the benchmark evaluation conditions.

[0117] After determining the objective evaluation indicators, the objective evaluation indicators and their corresponding subjective scores are correlated using Stevens' power law to obtain fitted values. Then, the subjective evaluation scores under the benchmark evaluation conditions are used as input to obtain the target evaluation threshold.

[0118] In this embodiment, by performing correlation analysis on subjective evaluation data and total weighted root mean square acceleration values, the subjective evaluation score of the seat under the benchmark evaluation condition is determined. Then, the vibration isolation performance evaluation parameters of the seat vibration data are determined, and the target objective evaluation index of the seat vibration data is determined based on each vibration isolation performance evaluation parameter and subjective evaluation data. Based on the target objective evaluation index and the subjective evaluation score of the seat under the benchmark evaluation condition, the target evaluation threshold is determined. This refines the steps of performing correlation analysis on multiple total weighted root mean square acceleration values ​​and subjective evaluation data to determine the target evaluation threshold with the benchmark vibration excitation as the benchmark evaluation condition, providing a foundation for subsequent analysis of the seat's vibration isolation performance based on the target evaluation threshold.

[0119] In one embodiment, step S41, which involves performing a correlation analysis on the subjective evaluation data and the root mean square value of the total weighted acceleration to determine the subjective evaluation score of the seat under the benchmark evaluation conditions, specifically includes the following steps:

[0120] S411: Based on Stevens' power law, establish a linear relationship function between subjective and objective physical quantities under different vibration excitations.

[0121] Stevens' power law is used to describe the relationship between subjective human sensations and objective parameters. The formula is as follows:

[0122]

[0123] in, θ represents the subjective physical quantity value under vibration excitation (the subject's subjective evaluation), θ represents the objective physical quantity value (the root mean square value of total weighted acceleration), and K and n are unknown variables.

[0124] Due to the formula The relationship between subjective and objective physical quantities is not linear. By using a logarithmic coordinate system, we can transform it into a linear relationship and obtain the linear relationship function between subjective and objective physical quantities under different vibration excitations.

[0125]

[0126] S412: The median of multiple total weighted root mean square acceleration values ​​is taken as an objective physical quantity, and the subjective evaluation corresponding to the median of multiple total weighted root mean square acceleration values ​​is taken as a subjective physical quantity. Linear regression fitting is performed using a linear relationship function to obtain the first set of fitted values.

[0127] After determining the linear relationship function between subjective and objective physical quantities under different vibration excitations, the median of the total weighted root mean square acceleration values ​​corresponding to multiple subjects is taken as the objective physical quantity, and the subjective evaluation corresponding to this median is taken as the subjective physical quantity. Linear regression fitting is then performed using the linear relationship function to obtain the first set of fitted values: log 10 K1 and n1.

[0128] In the fitting process, R is used. 2 R is used to characterize the fitting accuracy of the curve. 2 The value range is [0, 1], where R 2 A larger R value indicates a higher curve fitting accuracy. Generally, in engineering applications, R should be guaranteed to be within a certain range. 2 A value greater than 0.8 indicates that an R-value greater than 0.8 is required for linear regression fitting using a linear relationship function. 2 Greater than 0.8.

[0129] In this embodiment, the median of the data is used, which is more in line with statistical laws and can reflect the characteristics of most people.

[0130] S413: Input the weighted root mean square value of acceleration and the first set of fitted values ​​corresponding to the benchmark evaluation conditions into the linear relationship function to obtain the subjective evaluation score of the seat under the benchmark evaluation conditions.

[0131] After determining the first set of fitted values, the weighted root mean square value of acceleration corresponding to the benchmark evaluation condition and the first set of fitted values ​​are input into the linear relationship function to obtain the subjective evaluation score of the seat under the benchmark evaluation condition.

[0132] For example, the weighted root mean square acceleration value corresponding to the benchmark evaluation condition is, in the ISO 2631 international standard, the weighted root mean square acceleration value corresponding to the subjective human perception of "no discomfort" (0.315 m / s²). 2 ), then in In the middle, let θ = 0.315, and combine the fitted value log 10 K1 and n1 can be used to obtain the corresponding subjective evaluation score at this time. This represents the subjective evaluation score of the seat under the benchmark evaluation conditions. The median of multiple weighted root mean square acceleration values ​​is used as the objective physical quantity, while the subjective evaluation corresponding to the median of these values ​​is used as the subjective physical quantity. A linear regression is performed using a linear relationship function to obtain the curve shown below. Figure 4 As shown, the fitting accuracy R of the curve is... 2 =99.05%, thus obtaining

[0133] In this embodiment, the weighted root mean square value of acceleration corresponding to the benchmark evaluation condition is the weighted root mean square value of acceleration corresponding to "no discomfort" in the ISO2631 international standard. This is only an example. In other embodiments, the weighted root mean square value of acceleration corresponding to the benchmark evaluation condition can be other values, which will not be elaborated here.

[0134] In this embodiment, based on Stevens' power law, a linear relationship function between subjective and objective physical quantities under different vibration excitations is established. The median of multiple weighted root mean square acceleration values ​​is used as the objective physical quantity, and the subjective evaluation corresponding to the median of multiple weighted root mean square acceleration values ​​is used as the subjective physical quantity. Linear regression fitting is performed using the linear relationship function to obtain the first set of fitted values. The weighted root mean square acceleration values ​​corresponding to the benchmark evaluation condition and the first set of fitted values ​​are input into the linear relationship function to obtain the subjective evaluation score of the seat under the benchmark evaluation condition. This clarifies the specific process of performing correlation analysis between subjective evaluation data and weighted root mean square acceleration values ​​to determine the subjective evaluation score of the seat under the benchmark evaluation condition. Determining the subjective evaluation score based on Stevens' power law is more in line with human subjective feelings, making the subjective evaluation score more accurate and providing a basis for the subsequent determination of the target evaluation threshold.

[0135] In one embodiment, step S42, which involves determining the vibration isolation performance evaluation parameters of the seat vibration data and determining the target objective evaluation index of the seat vibration data based on each vibration isolation performance evaluation parameter and subjective evaluation data, specifically includes the following steps:

[0136] S421: Determine the preset vibration isolation performance evaluation function. The seat evaluation index parameters in the preset vibration isolation performance evaluation function are represented by the weighted root mean square value of acceleration or by the effective amplitude transmissibility of the seat.

[0137] In order to fully reflect the seat's vibration attenuation performance, considering that the vibration excitation is transmitted to the human body through the seat back and cushion, the seat vibration data obtained in the experiment were classified and combined based on the concept of transmissibility, and a parameter Q was designed to evaluate the seat's vibration isolation performance. i Q i This is expressed by a preset vibration isolation performance evaluation function:

[0138]

[0139] Where, Index represents the seat evaluation index parameter, which is generally taken as the weighted root mean square value of acceleration (RMS value), seat effective amplitude transmissibility (SEAT value), or seat vibration transmissibility; W represents different evaluation directions (including X, Y and Z directions) at different seat measurement point positions; cushion is the seat cushion, backrest is the seat back, and floor is the seat rail.

[0140] By pre-setting vibration isolation performance evaluation functions, multi-dimensional evaluation of seat vibration isolation performance can be achieved under single-axis (such as X, Y, Z direction excitation) or multi-axis (mixed excitation, such as road spectrum excitation, etc.) input, increasing the number and diversity of vibration isolation performance evaluation parameters.

[0141] S422: Based on the preset vibration isolation performance evaluation function and seat vibration data, determine multiple vibration isolation performance evaluation parameters of the seat vibration data.

[0142] After determining the preset vibration isolation performance evaluation function, the seat vibration data is classified and combined, and then input into the preset vibration isolation performance evaluation function in sequence to obtain multiple vibration isolation performance evaluation parameters corresponding to the seat vibration data.

[0143] S422: Perform linear regression fitting on multiple vibration isolation performance evaluation parameters and subjective evaluation data to determine the vibration isolation performance evaluation parameter with the highest fitting accuracy as the target objective evaluation index.

[0144] After obtaining multiple vibration isolation performance evaluation parameters from seat vibration data, linear regression fitting is performed on multiple vibration isolation performance evaluation parameters and subjective evaluation data to determine the vibration isolation performance evaluation parameter with the highest fitting accuracy as the target objective evaluation index.

[0145] Among them, the vibration isolation performance evaluation parameter Q corresponding to multiple subjects i The median is taken as the objective physical quantity θ, and the subjective evaluation corresponding to the median is taken as the subjective physical quantity. Using formula Perform linear regression fitting and calculate the fitting accuracy R. 2 Then take the Q with the highest fitting accuracy. i As an objective evaluation index of seat vibration characteristics under vibration excitation in different directions, it is the objective evaluation index of the target.

[0146] For example, according to the formula Taking the seat evaluation index parameter as the weighted root mean square value of acceleration (RMS value) as an example, for Q... i A correlation analysis was performed between the number of subjective evaluations and the fitted curve, as shown in the figure. Figure 5 As shown, the obtained fitting accuracy is R0. 2 The accuracy was 84.20%. Among these, when the index was an RMS or SEAT value, after screening, the RMS value resulted in the highest fitting accuracy. Therefore, the index was set to RMS, and the fitting accuracy R was ultimately selected. 2 The highest vibration isolation performance parameter is Q1, and the expression for Q1 is as follows:

[0147]

[0148] Among them, the W values ​​of cushion and floor are obtained in the Z direction, while the W values ​​of backrest are obtained in the X direction.

[0149] In this embodiment, a preset vibration isolation performance evaluation function is determined. The seat evaluation index parameters in the preset vibration isolation performance evaluation function are represented by the weighted root mean square value of acceleration or by the effective amplitude transmissibility of the seat. Based on the preset vibration isolation performance evaluation function and seat vibration data, multiple vibration isolation performance evaluation parameters of the seat vibration data are determined. Finally, linear regression fitting is performed on multiple vibration isolation performance evaluation parameters and subjective evaluation data to determine the vibration isolation performance evaluation parameter with the highest fitting accuracy as the target objective evaluation index. This clarifies the specific process of determining the vibration isolation performance evaluation parameters of the seat vibration data and determining the target objective evaluation index of the seat vibration data based on each vibration isolation performance evaluation parameter and subjective evaluation data. Using the vibration isolation performance evaluation parameter with the highest fitting accuracy as the target objective evaluation index further improves the accuracy of the target objective evaluation index, thereby ensuring the accuracy of the subsequent target evaluation threshold.

[0150] In one embodiment, step S43, which involves determining the target evaluation threshold based on the objective evaluation index of the target and the subjective evaluation score of the seat under the benchmark evaluation conditions, specifically includes the following steps:

[0151] S431: Take the objective evaluation index of the target as an objective physical quantity, and the subjective evaluation corresponding to the objective evaluation index of the target as a subjective physical quantity. Use the linear relationship function to perform linear regression fitting to obtain the second set of fitted values.

[0152] S432: Input the subjective evaluation score of the seat under the benchmark evaluation condition and the second set of fitted values ​​into the linear relationship function to obtain the target objective physical quantity.

[0153] S433: Use the objective physical quantity of the target as the threshold for target evaluation.

[0154] After determining the objective evaluation indicators and the subjective evaluation scores of the seat under the benchmark evaluation conditions, the objective evaluation indicators are used as objective physical quantities, and the subjective evaluations corresponding to the objective evaluation indicators are used as subjective physical quantities. A linear regression fitting is performed using a linear relationship function to obtain a second set of fitted values. The subjective evaluation scores of the seat under the benchmark evaluation conditions and the second set of fitted values ​​are input into the linear relationship function to obtain the objective physical quantity. The objective physical quantity is then used as the target evaluation threshold.

[0155] That is, using the vibration isolation performance evaluation parameter Q with the highest fitting accuracy. i As an objective physical quantity θ, Q is the vibration isolation performance evaluation parameter with the highest fitting accuracy. i The corresponding subjective evaluation score is used as the subjective physical quantity φ, and the formula is used to... By performing linear regression fitting, we can obtain the second set of fitted values: log 10 K2 and n2; then, the subjective evaluation score of the seat under the benchmark evaluation conditions. As a subjective physical quantity φ, combined with log 10 K2 and n2, input The objective physical quantity θ2 of the target can be obtained, and the objective physical quantity θ2 of the target can be used as the target evaluation threshold.

[0156] In this embodiment, the objective evaluation index is used as an objective physical quantity, and the subjective evaluation corresponding to the objective evaluation index is used as a subjective physical quantity. A linear regression fitting is performed using a linear relationship function to obtain a second set of fitted values. The subjective evaluation score of the seat under the benchmark evaluation condition and the second set of fitted values ​​are then input into the linear relationship function to obtain the objective physical quantity. The objective physical quantity is then used as the target evaluation threshold. This clarifies the specific process of determining the target evaluation threshold based on the objective evaluation index and the subjective evaluation score of the seat under the benchmark evaluation condition, providing a solid foundation for subsequent analysis of the vibration isolation performance of the seat based on the target evaluation threshold.

[0157] In one embodiment, step S50, which uses the target evaluation threshold as an objective evaluation index to analyze the vibration isolation performance of the seat, specifically includes the following steps:

[0158] S51: Based on the preset vibration isolation performance evaluation function and the test data of each seat under the benchmark evaluation condition, determine the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition.

[0159] After obtaining the test data of each seat under the benchmark evaluation condition, the vibration isolation performance evaluation parameter values ​​of the seats under the benchmark evaluation condition are determined based on the preset vibration isolation performance evaluation function and the test data of each seat under the benchmark evaluation condition. The calculation of the preset vibration isolation performance evaluation function and the vibration isolation performance evaluation parameter values ​​is as described above and will not be repeated here.

[0160] S52: Determine whether the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition is less than or equal to the target evaluation threshold.

[0161] After determining the vibration isolation performance evaluation parameter values ​​of the seat under the benchmark evaluation conditions, it is determined whether the vibration isolation performance evaluation parameter values ​​of the seat under the benchmark evaluation conditions are less than or equal to the target evaluation threshold.

[0162] S53: If the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition is less than or equal to the target evaluation threshold, then the vibration isolation performance of the seat is determined to be qualified.

[0163] After determining whether the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is less than or equal to the target evaluation threshold, if the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is less than or equal to the target evaluation threshold, indicating that the vibration isolation effect of the seat is good and the human body feels comfortable, then it is determined that the vibration isolation performance of the seat is qualified.

[0164] S54: If the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is greater than the target evaluation threshold, then it is determined that the vibration isolation performance of the seat is unqualified.

[0165] After determining whether the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is less than or equal to the target evaluation threshold, if the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is less than or equal to the target evaluation threshold, indicating that the vibration isolation effect of the seat is poor and the human body feels uncomfortable, then it is determined that the vibration isolation performance of the seat is unqualified.

[0166] For example, the vibration excitation amplitude M corresponding to the reference evaluation condition is 0.43 m / s 2 , and the target evaluation threshold is 1.13. With the vibration acceleration value of the vibration excitation amplitude of 0.43 m / s 2 as the vertical excitation input of the vibration table, determining that the vibration isolation performance evaluation parameter value of the seat at this time is Q1. When Q1 ≤ 1.13, that is, the vibration isolation effect of the seat is good and the human body feels comfortable, it is determined that the vibration isolation performance of the seat is qualified; when Q1 > 1.13, that is, the vibration isolation effect of the seat is poor and the human body feels uncomfortable, it is determined that the vibration isolation performance of the seat is unqualified.

[0167] In this embodiment, the vibration excitation amplitude M corresponding to the reference evaluation condition is 0.43 m / s 2 , and the target evaluation threshold of 1.13 is only for exemplary illustration. In other embodiments, the vibration excitation amplitude M and the target evaluation threshold corresponding to the reference evaluation condition can also be other values, which will not be elaborated here.

[0168] In this embodiment, according to the preset vibration isolation performance evaluation function and the test data of each seat under the reference evaluation condition, the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is determined, and it is determined whether the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is less than or equal to the target evaluation threshold. If the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is less than or equal to the target evaluation threshold, then it is determined that the vibration isolation performance of the seat is qualified; if the vibration isolation performance evaluation parameter value of the seat under the reference evaluation condition is greater than the target evaluation threshold, then it is determined that the vibration isolation performance of the seat is unqualified, which details the specific steps of analyzing the vibration isolation performance of the seat by using the target evaluation threshold as the threshold of the objective evaluation index, provides a basis for the quantification of the vibration isolation performance of the seat, and improves the efficiency of the vibration isolation performance evaluation of the seat.

[0169] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0170] In one embodiment, a seat vibration isolation performance analysis device is provided, which corresponds one-to-one with the seat vibration isolation performance analysis method described in the above embodiments. For example... Figure 6 As shown, the seat vibration isolation performance analysis device includes an acquisition module 601, a first determination module 602, a second determination module 603, a third determination module 604, and an analysis module 605. Detailed descriptions of each functional module are as follows:

[0171] The acquisition module 601 is used to acquire multiple sets of test data from the human-seat vibration test. The test data includes seat vibration data under different vibration excitations, as well as subjective evaluation data of the subject on the seat when the seat vibrates under different vibration excitations.

[0172] The first determining module 602 is used to determine the total weighted root mean square value of acceleration at different measuring point positions on the seat based on the seat vibration data under different vibration excitations, so as to obtain the total weighted root mean square value of acceleration corresponding to multiple subjects.

[0173] The second determining module 603 is used to perform linear regression analysis on the multiple total weighted root mean square values ​​of acceleration and the different vibration excitations to determine the benchmark vibration excitation.

[0174] The third determining module 604 is used to perform a correlation analysis on the multiple total weighted root mean square values ​​of acceleration and the subjective evaluation data to determine the target evaluation threshold based on the benchmark vibration excitation as the benchmark evaluation condition.

[0175] The analysis module 605 is used to analyze the vibration isolation performance of the seat by using the target evaluation threshold as a threshold for objective evaluation indicators.

[0176] Furthermore, the third determining module 604 is specifically used for:

[0177] A correlation analysis is performed on the subjective evaluation data and the root mean square value of the total weighted acceleration to determine the subjective evaluation score of the seat under the benchmark evaluation condition.

[0178] Determine the vibration isolation performance evaluation parameters of the seat vibration data, and determine the target objective evaluation index of the seat vibration data based on each of the vibration isolation performance evaluation parameters and the subjective evaluation data;

[0179] The target evaluation threshold is determined based on the objective evaluation index of the target and the subjective evaluation score of the seat under the benchmark evaluation conditions.

[0180] Furthermore, the third determining module 604 is specifically used for:

[0181] Based on Stevens' power law, a linear relationship function between subjective and objective physical quantities under different vibration excitations is established.

[0182] The median of the multiple total weighted root mean square acceleration values ​​is taken as the objective physical quantity, and the subjective evaluation corresponding to the median of the multiple total weighted root mean square acceleration values ​​is taken as the subjective physical quantity. Linear regression fitting is performed using the linear relationship function to obtain the first set of fitted values.

[0183] The weighted root mean square value of acceleration corresponding to the benchmark evaluation condition and the first set of fitted values ​​are input into the linear relationship function to obtain the subjective evaluation score of the seat under the benchmark evaluation condition.

[0184] Furthermore, the third determining module 604 is specifically used for:

[0185] A preset vibration isolation performance evaluation function is determined, wherein the seat evaluation index parameters in the preset vibration isolation performance evaluation function are represented by the weighted root mean square value of acceleration or by the effective amplitude transmissibility of the seat;

[0186] Based on the preset vibration isolation performance evaluation function and the seat vibration data, multiple vibration isolation performance evaluation parameters of the seat vibration data are determined;

[0187] Linear regression fitting is performed on multiple vibration isolation performance evaluation parameters and subjective evaluation data to determine the vibration isolation performance evaluation parameter with the highest fitting accuracy as the target objective evaluation index.

[0188] Furthermore, the third determining module 604 is specifically used for:

[0189] The objective evaluation index of the target is used as the objective physical quantity, and the subjective evaluation corresponding to the objective evaluation index of the target is used as the subjective physical quantity. Linear regression fitting is performed using the linear relationship function to obtain a second set of fitted values.

[0190] The subjective evaluation score of the seat under the benchmark evaluation condition and the second set of fitted values ​​are input into the linear relationship function to obtain the target objective physical quantity.

[0191] The objective physical quantity of the target is used as the target evaluation threshold.

[0192] Furthermore, the second determining module 603 is specifically used for:

[0193] Linear regression analysis was performed on the multiple total weighted root mean square values ​​of acceleration and the different vibration excitations to determine the linear regression model;

[0194] The root mean square value of the pre-designed weighted acceleration is input into the linear regression model as a variable to obtain the vibration excitation corresponding to the root mean square value of the pre-designed weighted acceleration.

[0195] The vibration excitation corresponding to the pre-designed root mean square value of weighted acceleration is used as the reference vibration excitation.

[0196] Furthermore, the analysis module 605 is specifically used for:

[0197] Based on the preset vibration isolation performance evaluation function and the test data of each seat under the benchmark evaluation condition, the vibration isolation performance evaluation parameter values ​​of the seat under the benchmark evaluation condition are determined;

[0198] Determine whether the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition is less than or equal to the target evaluation threshold;

[0199] If the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition is less than or equal to the target evaluation threshold, then the vibration isolation performance of the seat is determined to be qualified.

[0200] If the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition is greater than the target evaluation threshold, then the vibration isolation performance of the seat is determined to be unqualified.

[0201] Specific limitations regarding the seat vibration isolation performance analysis device can be found in the limitations of the seat vibration isolation performance analysis method described above, and will not be repeated here. Each module in the aforementioned seat vibration isolation performance analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0202] In one embodiment, a seat vibration isolation performance analysis device is provided, comprising a processor, memory, network interface, display screen, and input device connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements a seat vibration isolation performance analysis method.

[0203] In one embodiment, such as Figure 7 As shown, a seat vibration isolation performance analysis device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned seat vibration isolation performance analysis method.

[0204] In one embodiment, a readable storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described seat vibration isolation performance analysis method.

[0205] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0206] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0207] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for analyzing the vibration isolation performance of a seat, characterized in that, include: Multiple sets of test data were obtained from the human-seat vibration test. The test data included seat vibration data under different vibration excitations, as well as subjective evaluation data of the subjects on the seat when the seat vibrated under different vibration excitations. Based on the seat vibration data under different vibration excitations, the total weighted root mean square value of acceleration at different measuring points on the seat is determined to obtain the total weighted root mean square value of acceleration for multiple subjects. Linear regression analysis is performed on the multiple total weighted root mean square values ​​of acceleration and the different vibration excitations to determine the benchmark vibration excitation. A correlation analysis is performed on the total weighted root mean square acceleration values ​​and the subjective evaluation data to determine the target evaluation threshold for the benchmark vibration excitation as the benchmark evaluation condition. The vibration isolation performance of the seat is analyzed by using the target evaluation threshold as an objective evaluation index.

2. The method for analyzing the vibration isolation performance of a seat as described in claim 1, characterized in that, The step of performing a correlation analysis on multiple total weighted root mean square acceleration values ​​and the subjective evaluation data to determine the target evaluation threshold based on the benchmark vibration excitation includes: A correlation analysis is performed on the subjective evaluation data and the root mean square value of the total weighted acceleration to determine the subjective evaluation score of the seat under the benchmark evaluation condition. Determine the vibration isolation performance evaluation parameters of the seat vibration data, and determine the target objective evaluation index of the seat vibration data based on each of the vibration isolation performance evaluation parameters and the subjective evaluation data; The target evaluation threshold is determined based on the objective evaluation index of the target and the subjective evaluation score of the seat under the benchmark evaluation conditions.

3. The method for analyzing the vibration isolation performance of a seat as described in claim 2, characterized in that, The step of performing a correlation analysis between the subjective evaluation data and the total weighted root mean square value of acceleration to determine the subjective evaluation score of the seat under the benchmark evaluation condition includes: Based on Stevens' power law, a linear relationship function between subjective and objective physical quantities under different vibration excitations is established. The median of the multiple total weighted root mean square acceleration values ​​is taken as the objective physical quantity, and the subjective evaluation corresponding to the median of the multiple total weighted root mean square acceleration values ​​is taken as the subjective physical quantity. Linear regression fitting is performed using the linear relationship function to obtain the first set of fitted values. The weighted root mean square value of acceleration corresponding to the benchmark evaluation condition and the first set of fitted values ​​are input into the linear relationship function to obtain the subjective evaluation score of the seat under the benchmark evaluation condition.

4. The method for analyzing the vibration isolation performance of a seat as described in claim 2, characterized in that, The process of determining the vibration isolation performance evaluation parameters of the seat vibration data, and determining the target objective evaluation index of the seat vibration data based on each of the vibration isolation performance evaluation parameters and the subjective evaluation data, includes: A preset vibration isolation performance evaluation function is determined, wherein the seat evaluation index parameters in the preset vibration isolation performance evaluation function are represented by the weighted root mean square value of acceleration or by the effective amplitude transmissibility of the seat; Based on the preset vibration isolation performance evaluation function and the seat vibration data, multiple vibration isolation performance evaluation parameters of the seat vibration data are determined; Linear regression fitting is performed on multiple vibration isolation performance evaluation parameters and subjective evaluation data to determine the vibration isolation performance evaluation parameter with the highest fitting accuracy as the target objective evaluation index.

5. The method for analyzing the vibration isolation performance of a seat as described in claim 3, characterized in that, The step of determining the target evaluation threshold based on the target objective evaluation index and the subjective evaluation score of the seat under the benchmark evaluation condition includes: The objective evaluation index of the target is used as the objective physical quantity, and the subjective evaluation corresponding to the objective evaluation index of the target is used as the subjective physical quantity. Linear regression fitting is performed using the linear relationship function to obtain a second set of fitted values. The subjective evaluation score of the seat under the benchmark evaluation condition and the second set of fitted values ​​are input into the linear relationship function to obtain the target objective physical quantity. The objective physical quantity of the target is used as the target evaluation threshold.

6. The method for analyzing the vibration isolation performance of a seat as described in claim 1, characterized in that, The step of performing linear regression analysis on multiple total weighted root mean square acceleration values ​​and different vibration excitations to determine the benchmark vibration excitation includes: Linear regression analysis was performed on the multiple total weighted root mean square values ​​of acceleration and the different vibration excitations to determine the linear regression model; The root mean square value of the pre-designed weighted acceleration is input into the linear regression model as a variable to obtain the vibration excitation corresponding to the root mean square value of the pre-designed weighted acceleration. The vibration excitation corresponding to the pre-designed root mean square value of weighted acceleration is used as the reference vibration excitation.

7. The method for analyzing the vibration isolation performance of a seat as described in any one of claims 1-6, characterized in that, The step of analyzing the vibration isolation performance of the seat by using the target evaluation threshold as an objective evaluation index includes: Based on the preset vibration isolation performance evaluation function and the test data of each seat under the benchmark evaluation condition, the vibration isolation performance evaluation parameter values ​​of the seat under the benchmark evaluation condition are determined. Determine whether the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition is less than or equal to the target evaluation threshold; If the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition is less than or equal to the target evaluation threshold, then the vibration isolation performance of the seat is determined to be qualified. If the vibration isolation performance evaluation parameter value of the seat under the benchmark evaluation condition is greater than the target evaluation threshold, then the vibration isolation performance of the seat is determined to be unqualified.

8. A device for analyzing the vibration isolation performance of a seat, characterized in that, include: The acquisition module is used to acquire multiple sets of test data from the human-seat vibration test. The test data includes seat vibration data under different vibration excitations, as well as subjective evaluation data of the subjects on the seat when the seat vibrates under different vibration excitations. The first determining module is used to determine the total weighted root mean square value of acceleration at different measuring point positions on the seat based on the seat vibration data under different vibration excitations, so as to obtain the total weighted root mean square value of acceleration corresponding to multiple subjects. The second determining module is used to perform linear regression analysis on the multiple total weighted root mean square values ​​of acceleration and the different vibration excitations to determine the benchmark vibration excitation. The third determining module is used to perform a correlation analysis on the multiple total weighted root mean square values ​​of acceleration and the subjective evaluation data to determine the target evaluation threshold based on the benchmark vibration excitation as the benchmark evaluation condition. The analysis module is used to analyze the vibration isolation performance of the seat by using the target evaluation threshold as a threshold for objective evaluation indicators.

9. A seat vibration isolation performance analysis device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the seat vibration isolation performance analysis method as described in any one of claims 1 to 7.

10. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the seat vibration isolation performance analysis method as described in any one of claims 1 to 7.

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