Vehicle ride comfort detection method, device and terminal equipment

Abstract

The present invention relates to a vehicle ride comfort detection method, device, and terminal device. The method collects acceleration signals from multiple front axle measurement points and rear axle measurement points of a vehicle passing through a pulse road surface. The acceleration signals from the front axle measurement points and the rear axle measurement points are used to determine the impact strength used to characterize the vehicle's front axle and the impact strength used to characterize the vehicle's rear axle. The maximum value is selected from the impact strengths of the front and rear axles to characterize the impact strength of the entire vehicle. The impact balance coefficient of the vehicle is obtained from the impact strength ratio of the front and rear axles. The acceleration signals from the driver's seat guide rail measurement points and the passenger seat guide rail measurement points are then used to determine the vehicle's impact convergence coefficient used to characterize the vehicle's vibration convergence speed. The vehicle's ride comfort level is obtained by combining the above-mentioned vehicle impact strength, impact balance coefficient, and vehicle impact convergence coefficient with a vehicle ride comfort mathematical model. The vehicle ride comfort detection accuracy is relatively high.

Description

Technical Field

[0001] The present invention is applicable to the field of vehicle testing, and in particular relates to a vehicle ride comfort detection method, device and terminal equipment. Background Art

[0002] In the existing technology, the automobile ride comfort test method published in the GBT 4970 standard is generally used to detect vehicle ride comfort. Specifically, the vehicle's ride comfort performance is determined by calculating the maximum acceleration response, peak coefficient, and convergence time. The disadvantage of this detection method is that these parameters are less reliable for evaluating vehicle ride comfort. For example, the maximum acceleration response cannot effectively represent the impact intensity, the peak coefficient cannot effectively distinguish the convergence characteristics and energy contribution of the front and rear axles, and the convergence time is limited by the characteristics of the vibration signal, which has certain limitations in evaluating vibration convergence. As a result, the existing method has low accuracy in detecting vehicle ride comfort. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a vehicle ride comfort detection method, apparatus, and terminal device to solve the problem of low accuracy in detecting vehicle ride comfort in existing methods.

[0004] In a first aspect, a vehicle ride comfort detection method is provided, the ride comfort detection method comprising:

[0005] Acquire acceleration signals of N1 vehicle front axle measurement points and N2 vehicle rear axle measurement points during vehicle travel within a first set time period, where N1 ≥ 2 and N2 ≥ 2;

[0006] determining the impact strength of the front axle of the vehicle based on the acceleration signal of the front axle measuring point of the vehicle; determining the impact strength of the rear axle of the vehicle based on the acceleration signal of the rear axle measuring point of the vehicle, and taking the maximum value of the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle as the impact strength of the entire vehicle;

[0007] determining the impact balance coefficients of the front and rear axles of the vehicle based on the ratio between the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle;

[0008] acquiring acceleration signals of N3 driver's seat rail measuring points and passenger's seat rail measuring points within a second set time period during vehicle travel, where N3 is greater than or equal to 2; and determining a vehicle impact convergence coefficient for characterizing a vehicle vibration convergence rate based on the acceleration signals of the driver's seat rail measuring points and the passenger's seat rail measuring points;

[0009] The vehicle impact strength, the impact balance coefficient, and the vehicle impact convergence coefficient are substituted into an established vehicle ride comfort mathematical model to determine the vehicle ride comfort level.

[0010] In a second aspect, a vehicle ride comfort detection device is provided, the ride comfort detection device comprising:

[0011] A data acquisition module is used to obtain acceleration signals of N1 vehicle front axle measurement points and N2 vehicle rear axle measurement points during vehicle travel within a first set time period, where N1 ≥ 2 and N2 ≥ 2;

[0012] a vehicle impact strength calculation module, configured to determine the impact strength of the front axle of the vehicle based on the acceleration signal of the front axle measuring point of the vehicle; determine the impact strength of the rear axle of the vehicle based on the acceleration signal of the rear axle measuring point of the vehicle, and take the maximum value of the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle as the impact strength of the vehicle;

[0013] an impact balance coefficient calculation module, configured to determine the impact balance coefficients of the front and rear axles of the vehicle based on a ratio between the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle;

[0014] a vehicle impact convergence coefficient calculation module, configured to obtain acceleration signals of N3 driver seat rail measuring points and passenger seat rail measuring points within a second set time period during vehicle travel, where N3 ≥ 2; and determine a vehicle impact convergence coefficient for characterizing a vehicle vibration convergence rate based on the acceleration signals of the driver seat rail measuring points and the passenger seat rail measuring points;

[0015] The ride comfort level determination module is used to substitute the vehicle impact strength, the impact balance coefficient and the vehicle impact convergence coefficient into an established vehicle ride comfort mathematical model to determine the vehicle ride comfort level.

[0016] In a third aspect, an embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and runnable on the processor, and when the processor executes the computer program, it implements the vehicle smoothness detection method as described in the first aspect.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The present invention collects acceleration signals from a plurality of front axle measuring points and a rear axle measuring point when a vehicle passes through a pulse road surface. The acceleration signals from the front axle measuring point and the rear axle measuring point can determine the impact strength for characterizing the front axle of the vehicle and the impact strength for characterizing the rear axle of the vehicle, screen out the maximum value from the impact strengths of the front axle and the rear axle of the vehicle to characterize the impact strength of the entire vehicle, and obtain the impact balance coefficient of the vehicle through the impact strength ratio of the front axle and the rear axle of the vehicle; then, the acceleration signals from the driver's seat guide rail measuring point and the passenger seat guide rail measuring point are used to determine the impact convergence coefficient of the entire vehicle for characterizing the vibration convergence speed of the vehicle; finally, the above three parameter variables (the impact strength of the entire vehicle, the impact balance coefficient and the impact convergence coefficient of the entire vehicle) are combined with a mathematical model of vehicle smoothness to obtain the smoothness level of the vehicle. The detection accuracy of the vehicle smoothness is high, and the impact vibration level of the vehicle can be accurately judged. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a schematic diagram of an application environment of a vehicle ride comfort detection method provided in the first embodiment of the present invention;

[0021] Figure 2 1 is a flow chart of a vehicle ride comfort detection method provided in the first embodiment of the present invention;

[0022] Figure 3 Schematic diagram of an acceleration signal obtained through a vehicle front axle measurement point according to the first embodiment of the present invention;

[0023] Figure 4 1 is a flow chart of determining the impact strength of a vehicle front axle based on an acceleration signal of a vehicle front axle measuring point, provided in the first embodiment of the present invention;

[0024] Figure 5 1 is a schematic diagram of a flow chart for determining the impact balance coefficients of the front and rear axles of a vehicle provided in the first embodiment of the present invention;

[0025] Figure 6 1 is a schematic diagram of a flow chart for determining an impact convergence coefficient of a front axle of a vehicle provided in the first embodiment of the present invention;

[0026] Figure 7 1 is a schematic diagram of an equivalent impact strength curve provided in Example 1 of the present invention;

[0027] Figure 8Schematic diagrams of two equivalent impact strength curves obtained through acceleration signals of the driver's seat rail measuring point and the passenger seat rail measuring point, respectively, according to the second embodiment of the present invention;

[0028] Figure 9 1 is a schematic structural diagram of a vehicle ride comfort detection device provided in a third embodiment of the present invention;

[0029] Figure 10 This is a structural diagram of a terminal device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0030] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0031] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0032] It will also be understood that the term "and / or" used in the present description and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0033] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0034] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0035] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0036] It should be understood that the order of execution of the steps in the following embodiments does not necessarily mean the order in which they are executed. The order in which each process is executed 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.

[0037] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0038] A vehicle ride comfort detection method provided by the first embodiment of the present invention can be applied in the following situations: Figure 1 In an application environment, a client communicates with a server. Clients include, but are not limited to, PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud-based terminal devices, and personal digital assistants (PDAs). The server can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0039] See also Figure 2 , is a flow chart of a vehicle ride comfort detection method provided by the first embodiment of the present invention. The above ride comfort detection method can be applied to Figure 1 The server in Figure 2 As shown, the ride comfort detection method may include the following steps:

[0040] Step S10, obtaining acceleration signals ai of N1 vehicle front axle measurement points and acceleration signals aj of N2 vehicle rear axle measurement points during vehicle driving within a first set time period, where i=1,2,…,N1,N1≥2; j=1,2,…,N2,N2≥2.

[0041] In one example, the number of the vehicle's front axle measurement points is three, i.e., N1=3, and the vehicle's front axle measurement points include the driver's seat cushion measurement point, the driver's seat back measurement point, and the driver's foot floor measurement point; the number of the vehicle's rear axle measurement points is three, i.e., N2=3, and the three vehicle's rear axle measurement points include the passenger seat cushion measurement point, the passenger seat back measurement point, and the passenger foot floor measurement point.

[0042] In this step, the first set time period includes three consecutive time periods. The first time period is the period before the vehicle passes the pulse road surface during driving, the second time period is the period when the vehicle passes the pulse road surface, and the third time period is the period after the vehicle passes the pulse road surface.

[0043] In one example, if Figure 3 As shown in the figure, taking a certain vehicle front axle measurement point as an example, the first time period of the collected acceleration signal is within 1.5 seconds before the vehicle passes the pulse road surface during driving, the second time period is a period of time [2.3 seconds, 3.1 seconds] when the vehicle passes the pulse road surface, and the third time period is within 1.0 seconds after the vehicle passes the pulse road surface.

[0044] Step S20: Determine the impact strength ΔRMS of the front axle of the vehicle based on the acceleration signals ai of the N1 front axle measurement points of the vehicle. Local-front ; Determine the impact strength ΔRMS of the vehicle's rear axle based on the acceleration signals aj of the N2 vehicle rear axle measurement points Local-rear , take the maximum value of the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle as the impact strength of the entire vehicle.

[0045] Among them, such as Figure 4 As shown, determining the impact strength of the front axle of the vehicle according to the acceleration signal of the front axle measuring point of the vehicle includes:

[0046] Step S201: In the first set time period, a first selected time period in which the vehicle is traveling stably and a second selected time period in which the vehicle is traveling on a pulse road are selected.

[0047] The first selected time period is selected from the first time period recorded in step S10, and the second selected time period is selected from the second time period recorded in step S10. In one example, a period of time can be randomly selected from the first time period as the first selected time period, and a period of time can be randomly selected from the second time period as the second selected time period.

[0048] Preferably, a period of time when the signal is most stable can be selected as the first selected period of time in the first period of time, and a period of time when the signal amplitude fluctuates the most can be selected as the second selected period of time in the second period of time. Figure 3For example, the first selected time period is preferably [0.8s, 1.2s], and the second selected time period is preferably [2.3s, 2.6s].

[0049] Step S202: obtaining a number of sampling points of the acceleration signal of each vehicle front axle measurement point, and obtaining a frequency weighting coefficient of each vehicle front axle measurement point.

[0050] The number of sampling points obtained is specifically the number of sampling points of the acceleration signal of the vehicle front axle measurement point in the first selected time period and the number of sampling points of the acceleration signal of the vehicle front axle measurement point in the second selected time period.

[0051] The frequency weighting coefficient of each vehicle front axle measuring point is determined according to the location of the different measuring points. For the specific determination method, please refer to the relevant records in the GBT 4970 standard.

[0052] Step S203: For each vehicle front axle measuring point, calculate the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the first selected time period to obtain a stable impact strength of the vehicle front axle; calculate the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the second selected time period to obtain a maximum impact strength of the vehicle front axle; and subtract the maximum impact strength from the stable impact strength to obtain the impact strength of each vehicle front axle measuring point.

[0053] The stable impact strength and maximum impact strength of the vehicle's front axle are calculated using the following impact strength calculation formula:

[0054]

[0055] Where ΔRMS Local (p) is the impact value, M Local is the number of sampling points of the acceleration signal of the vehicle front axle measurement point, w j is the frequency weighting coefficient of the vehicle front axle measurement point, j represents the corresponding vehicle front axle measurement point, a p is the acceleration signal of the vehicle's front axle measuring point.

[0056] According to the above formula (1), the stable impact strength of the vehicle front axle and the maximum impact strength of the vehicle front axle can be calculated, and then the impact strength of each vehicle front axle measuring point can be calculated. The calculation formula is as follows:

[0057] ΔRMS Local =ΔRMS Local-max -ΔRMS Local-min (2)

[0058] Where ΔRMS Localis the impact strength of the vehicle front axle measuring point, ΔRMS Local-max is the maximum impact strength, ΔRMS Local-min To stabilize the impact strength.

[0059] Step S204: determining the comprehensive impact strength of the vehicle front axle according to the impact strength of each vehicle front axle measurement point.

[0060] Specifically, determining the comprehensive impact strength of the vehicle front axle based on the impact strength of each vehicle front axle measuring point includes: summing the squares of the impact strengths of each vehicle front axle measuring point to obtain the sum of the squares of the impact strengths of the vehicle front axle measuring points, taking the square root of the sum of the squares, and calculating the comprehensive impact strength of the vehicle front axle.

[0061] The calculation formula of the comprehensive impact strength is as follows:

[0062]

[0063] Where ΔRMS Local-front is the comprehensive impact strength of the vehicle's front axle, ΔRMS i is the impact strength of the vehicle's front axle at measuring point i. It can be understood that when the directionality of the impact strength of the vehicle's front axle at measuring point i in the three-dimensional coordinate system is taken into account, the calculation formula for the comprehensive impact strength of the vehicle's front axle at measuring point i in the X, Y, and Z directions is as follows:

[0064]

[0065] Where ΔRMS front-i is the comprehensive impact strength of the front axle of the vehicle at the measuring point i in the three directions of the coordinate system X&Y&Z, kx, ky, kz are the weight coefficients of the impact strength in the directions of the coordinate system X axis, Y axis, and Z axis, ΔRMS Local-x , ΔRMS Local-y , ΔRMS Local-z is the acceleration signal a p The X-axis, Y-axis, and Z-axis impact strength components are calculated using formula (1) in the X-axis, Y-axis, and Z-axis directions.

[0066] Similarly, determining the impact strength of the rear axle of the vehicle based on the acceleration signal of the rear axle measuring point of the vehicle includes:

[0067] Step S2010: In the first set time period, a first selected time period in which the vehicle is traveling steadily and a second selected time period in which the vehicle is traveling on an impulsive road are selected;

[0068] Step S2020: obtaining a number of sampling points of the acceleration signal of each vehicle rear axle measurement point, and obtaining a frequency weighting coefficient of each vehicle rear axle measurement point;

[0069] Step S2030: For each vehicle rear axle measuring point, calculate the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the first selected time period to obtain a stable impact strength of the vehicle rear axle; calculate the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the second selected time period to obtain a maximum impact strength of the vehicle rear axle; and subtract the maximum impact strength from the stable impact strength to obtain an impact strength of each vehicle rear front axle measuring point;

[0070] Step S2040: determining the comprehensive impact strength of the vehicle's rear axle according to the impact strength of each vehicle's rear axle measuring point.

[0071] The calculation formulas involved in the above steps S2010 to S2040 can refer to the relevant formulas recorded in steps S201 to S204, and the specific implementation process in steps S2010 to S2040 is the same as the process principle recorded in steps S201 to S204, and the specific implementation process in steps S2010 to S2040 will not be repeated.

[0072] After obtaining the impact strength of the front axle and the impact strength of the rear axle of the vehicle, the calculation formula for the impact strength of the entire vehicle is as follows:

[0073] ΔRMS Global =MAX(ΔRMS Local-front , ΔRMS Local-rear ) (5)

[0074] Where ΔRMS Global is the impact strength of the vehicle, MAX represents the maximum value, ΔRMS Local-front is the impact strength of the vehicle's front axle, ΔRMS Local-rear is the impact strength of the vehicle's rear axle.

[0075] Step S30 , determining the impact balance coefficients of the front axle and the rear axle of the vehicle according to the ratio of the impact strength of the front axle of the vehicle to the impact strength of the rear axle of the vehicle.

[0076] Among them, such as Figure 5 As shown, determining the impact balance coefficients of the front and rear axles of the vehicle according to the ratio between the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle includes:

[0077] Step S301: calculating the ratio between the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle;

[0078] Step S302: Calculate the difference between the ratio and 1, and use the absolute value of the difference as the impact balance coefficient of the front axle and the rear axle of the vehicle.

[0079] Specifically, the calculation formula of the impact balance coefficient of the front axle and rear axle of the vehicle is as follows:

[0080]

[0081] In the formula, BI Global is the impact balance coefficient of the front and rear axles of the vehicle, ΔRMS Local-front is the impact strength of the vehicle's front axle, ΔRMS Local-rear is the impact strength of the vehicle's rear axle.

[0082] Step S40, obtaining acceleration signals of N3 driver's seat rail measuring points and N3 passenger seat rail measuring points within a second set time period during vehicle travel, where N3 ≥ 2; and determining a vehicle impact convergence coefficient used to characterize the vehicle vibration convergence speed based on the acceleration signals of the driver's seat rail measuring points and the passenger seat rail measuring points.

[0083] Determining the vehicle impact convergence coefficient for characterizing the vehicle vibration convergence speed based on the acceleration signals of the driver's seat rail measuring point and the passenger seat rail measuring point includes:

[0084] The impact convergence coefficient of the front axle of the vehicle is determined based on the acceleration signal of the driver's seat guide measuring point; the impact convergence coefficient of the rear axle of the vehicle is determined based on the acceleration signal of the passenger seat guide measuring point; and the maximum value of the impact convergence coefficient of the front axle of the vehicle and the impact convergence coefficient of the rear axle of the vehicle is taken as the impact convergence coefficient of the entire vehicle.

[0085] Specifically, such as Figure 6 As shown, determining the impact convergence coefficient of the vehicle front axle according to the acceleration signal of the driver's seat guide measuring point includes:

[0086] Step S401: Performing frequency-weighted processing on the acceleration signal of the driver's seat rail measuring point to obtain a weighted acceleration signal of the driver's seat rail measuring point, and determining a weighted acceleration signal peak value of the weighted acceleration signal within a second set time period; the second set time period is the first oscillation cycle time period of the weighted acceleration signal.

[0087] The method for performing frequency weighting processing includes:

[0088] The acceleration signal of the driver's seat guide rail measuring point is Fourier transformed into a frequency domain signal, and then the frequency domain is processed according to the GBT 4970 standard machine input known evaluation index frequency weighting function, and then Fourier inverse transformation is performed to convert it back to a weighted time domain signal to obtain the weighted acceleration signal a of the driver's seat guide rail measuring point w ().

[0089] In this step, the peak value of the weighted acceleration signal in the first oscillation cycle is the maximum value of the weighted acceleration time domain signal at the measuring point in the first oscillation cycle when the vehicle passes through the pulse path on a single axis. The calculation formula for the peak value of the weighted acceleration signal is as follows:

[0090] P=max(|a w (t)|), t∈[t1,t2] (7)

[0091] Where P is the peak value of the weighted acceleration signal in the first oscillation period (unit: m / s 2 ), a w (t) is the weighted acceleration signal (unit: m / s 2 ), t1 and t2 are the starting and ending times of the first oscillation cycle, that is, the starting and ending times of the first oscillation cycle when the shaft is excited by the road impact.

[0092] Step S402: determining an equivalent impact strength curve of the vehicle front axle within the second set time period based on the acceleration signal of the driver's seat guide rail measuring point, and calculating an envelope area of ​​the equivalent impact strength curve within the second set time period.

[0093] In this step, the method for determining the equivalent impact strength curve is as follows: According to the above formula (1), the equivalent impact strength of the acceleration signal of the driver's seat guide measuring point at each sampling time can be determined, thereby obtaining Figure 7 The equivalent impact strength curve shown is Figure 7 The time period [t1, t2] in is the determined second set time period.

[0094] The envelope area is calculated as follows:

[0095]

[0096] Where ΔRMS Local Area is the envelope area of ​​the equivalent impact strength curve in the second set time period, t1 and t2 are the start and end times of the second set time period, ΔRMS Local It is the equivalent impact strength curve.

[0097] Step S403: determining the impact convergence coefficient of the front axle of the vehicle according to the ratio between the envelope area of ​​the equivalent impact strength curve and the peak value of the weighted acceleration signal.

[0098] Among them, the impact convergence coefficient of the vehicle's front axle is calculated as follows:

[0099] CI Local =ΔRMS Local Area / P (9)

[0100] Where, CI Local is the impact convergence coefficient of the front axle of the vehicle, and P is the peak value of the weighted acceleration signal.

[0101] Similarly, based on the acceleration signal of the passenger seat rail measuring point, determining the impact convergence coefficient of the vehicle rear axle includes:

[0102] Step S4010: performing frequency weighting processing on the acceleration signal of the passenger seat rail measuring point to obtain a weighted acceleration signal of the passenger seat rail measuring point, and determining a weighted acceleration signal peak value of the weighted acceleration signal within a second set time period; the second set time period being the first oscillation cycle time period of the weighted acceleration signal;

[0103] Step S4020: determining an equivalent impact strength curve of the vehicle rear axle within the second set time period based on the acceleration signal of the passenger seat guide measuring point, and calculating an envelope area of ​​the equivalent impact strength curve within the second set time period;

[0104] Step S4030: determining the impact convergence coefficient of the rear axle of the vehicle according to the ratio of the envelope area of ​​the equivalent impact strength curve to the peak value of the weighted acceleration signal.

[0105] The calculation formulas involved in the above steps S4010 to S4030 can refer to the relevant formulas recorded in steps S401 to S403, and the specific implementation process in steps S4010 to S4030 is the same as the process principle recorded in steps S401 to S403, and the specific implementation process in steps S4010 to S4030 will not be repeated.

[0106] After obtaining the impact convergence coefficient of the vehicle's front axle and the impact convergence coefficient of the vehicle's rear axle, the calculation formula for the impact convergence coefficient of the vehicle is as follows:

[0107] CI Global =Max(ΔRMS Local-front-i Area / P front-i , ΔRMS Local-rear-j Area / P rear-j ) (10)

[0108] Where, CI Global is the vehicle impact convergence coefficient, Max is its maximum value function, ΔRMS Local-front-i Area is the envelope area of ​​the equivalent impact strength curve of the vehicle front axle, P front-i is the peak value of the weighted acceleration signal of the front axle of the vehicle, ΔRMS Local-front-i Area / P front-iis the impact convergence coefficient of the vehicle's front axle; ΔRMS Local-rear-j Area is the envelope area of ​​the equivalent impact strength curve of the vehicle rear axle, P rear-j is the peak value of the weighted acceleration signal of the vehicle's rear axle, ΔRMS Local-rear- j Area / P rear-j is the impact convergence coefficient of the vehicle's rear axle, i represents the X, Y, and Z axes of the driver's seat guide measuring point, and j represents the X, Y, and Z axes of the passenger seat guide measuring point.

[0109] In the above formula, ΔRMS Local-front-i Area and ΔRMS Local-rear-j Area is obtained by formula (8) to obtain ΔRMS Local-front-x Area as an example, the ΔRMS in formula (8) Local Take ΔRMS in formula (4) Local-x In the above formula, P front-i and P rear-j By using formula (7), we can get P front-x For example, P = max(|a w (t) x |). a w (t) x is the X-axis component of the weighted acceleration time domain signal.

[0110] Step S50 , substituting the vehicle impact strength, the impact balance coefficient, and the vehicle impact convergence coefficient into an established vehicle ride comfort mathematical model to determine the vehicle ride comfort level.

[0111] Among them, the vehicle smoothness mathematical model includes: the functional mapping relationship between the vehicle's smoothness level and the vehicle impact strength, the impact balance coefficient, and the vehicle impact convergence coefficient. The functional mapping relationship is obtained by performing function fitting on the standard smoothness levels of several vehicles and the corresponding vehicle impact strength, impact balance coefficient, and vehicle impact convergence coefficient.

[0112] The mathematical model of vehicle ride comfort is expressed as follows:

[0113] F(t)=a(ΔRMS Global ×(1+BI Global ))(CI Global )+b (11)

[0114] Where F(t) is the vehicle's ride comfort level, ΔRMS Global is the impact strength of the vehicle, BI Global is the impact balance coefficient, CI Globalis the vehicle impact convergence coefficient, a and b are function fitting coefficients.

[0115] In the above formula, the coefficients a and b will change accordingly as the number of database samples increases. In one example, based on the existing database samples, the fitted coefficients a=0.32 and b=4.54.

[0116] In the second embodiment, a vehicle ride comfort detection method is provided. The ride comfort detection method in this embodiment differs from the ride comfort detection method in the first embodiment in that:

[0117] In the ride comfort detection method of the first embodiment, relevant parameters of the vehicle front axle are obtained based on data from four vehicle front axle measurement points, and relevant parameters of the vehicle rear axle are obtained based on data from four vehicle rear axle measurement points.

[0118] Example 1: Based on the acceleration signals of the driver's seat cushion measurement point, the driver's seat back measurement point, and the driver's footboard measurement point, the impact strength of the vehicle's front axle is obtained; based on the acceleration signals of the driver's seat guide measurement point, the impact convergence coefficient of the vehicle's front axle is obtained.

[0119] Example 2: The impact strength of the vehicle's rear axle is calculated based on the acceleration signals of the passenger seat cushion measurement point, the passenger seat back measurement point, and the passenger footrest measurement point. The impact convergence coefficient of the vehicle's rear axle is calculated based on the acceleration signals of the passenger seat guide measurement point.

[0120] In the ride comfort detection method of this embodiment, the relevant parameters of the front and rear axles of the vehicle are obtained based on the data of the four vehicle front axle measurement points and the data of the four vehicle rear axle measurement points.

[0121] Example 1: Figure 3 The figure shows the acceleration signal obtained through the vehicle's front axle measuring point. As can be seen from the figure, when the vehicle passes through the pulse road surface, the acceleration signal has two large peak fluctuations between [2.3s, 3.1s]. These two large peak fluctuations correspond to the peak fluctuations caused by the vehicle's front and rear axles passing through the pulse road surface respectively. Therefore, the acceleration signal obtained through the vehicle's front axle measuring point can be used to calculate both the impact intensity of the vehicle's front axle and the impact degree of the vehicle's rear axle.

[0122] Example 2: Figure 8Shown are two equivalent impact strength curves obtained through the acceleration signals of the driver's seat guide rail measuring point (also a vehicle front axle measuring point) and the passenger seat guide rail measuring point (also a vehicle rear axle measuring point). Each equivalent impact strength curve has two peaks, and the first peak in each equivalent impact strength curve is higher than the second peak. These two peaks correspond to the fluctuation process of the impact intensity when the front and rear axles of the vehicle pass through the pulse road surface respectively. Therefore, according to each equivalent impact strength curve, the envelope area of ​​the time period corresponding to the two peaks can be calculated. The impact convergence coefficient of the vehicle's front axle is calculated based on one of the envelope areas, and the impact convergence coefficient of the vehicle's rear axle is calculated based on the other envelope area.

[0123] Compared with the method in Example 1, the method in this embodiment can achieve a detection level comparable to that in Example 1. However, relatively speaking, the method in this embodiment has a relatively large amount of calculation. Specifically, since the total number of front axle measurement points and rear axle measurement points is 8, eight groups of front axle-related parameters and rear axle-related parameters need to be calculated separately when calculating the front axle-related parameters and the rear axle-related parameters. This increases the amount of calculation required to ultimately obtain the vehicle ride comfort detection result.

[0124] Corresponding to the method of the above embodiment, Figure 9 The structural block diagram of the vehicle ride comfort detection device provided by the third embodiment of the present invention is shown. The ride comfort detection device is applied to a terminal device. Figure 9 , the ride comfort detection device comprises:

[0125] The data acquisition module 91 is configured to acquire acceleration signals of N1 vehicle front axle measurement points and N2 vehicle rear axle measurement points during vehicle travel within a first set time period, where N1 ≥ 2 and N2 ≥ 2;

[0126] a vehicle impact strength calculation module 92 for determining the impact strength of the vehicle's front axle and the impact strength of the vehicle's rear axle based on the acceleration signal of the vehicle's front axle measurement point and / or the acceleration signal of the vehicle's rear axle measurement point, and taking the maximum value of the impact strength of the vehicle's front axle and the impact strength of the vehicle's rear axle as the vehicle's impact strength;

[0127] an impact balance coefficient calculation module 93 for determining the impact balance coefficients of the front and rear axles of the vehicle based on a ratio between the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle;

[0128] a vehicle impact convergence coefficient calculation module 94 for obtaining acceleration signals from N3 driver's seat rail measuring points and passenger's seat rail measuring points within a second set time period during vehicle travel, where N3 ≥ 2; and determining a vehicle impact convergence coefficient for representing a vehicle vibration convergence rate based on the acceleration signals from the driver's seat rail measuring points and the passenger's seat rail measuring points;

[0129] The ride comfort level determination module 95 is configured to substitute the vehicle impact strength, the impact balance coefficient, and the vehicle impact convergence coefficient into an established vehicle ride comfort mathematical model to determine the vehicle ride comfort level.

[0130] Optionally, in the above-mentioned data acquisition module 91, the vehicle front axle measurement points used to be acquired include the driver's seat cushion measurement point, the driver's seat back measurement point and the driver's foot floor measurement point, and the vehicle rear axle measurement points include the passenger seat cushion measurement point, the passenger seat back measurement point and the passenger foot floor measurement point.

[0131] Optionally, the vehicle impact convergence coefficient calculation module 94 includes:

[0132] a front axle impact convergence coefficient calculation module, configured to determine the impact convergence coefficient of the vehicle's front axle based on the acceleration signal of the driver's seat guide rail measuring point;

[0133] a rear axle impact convergence coefficient calculation module, configured to determine the impact convergence coefficient of the vehicle's rear axle based on the acceleration signal of the passenger seat guide measuring point;

[0134] The comparison and judgment module is used to take the maximum value of the impact convergence coefficient of the front axle of the vehicle and the impact convergence coefficient of the rear axle of the vehicle as the impact convergence coefficient of the entire vehicle.

[0135] Optionally, the front axle impact convergence coefficient calculation module includes:

[0136] a first signal processing module configured to perform frequency-weighted processing on the acceleration signal of the driver's seat rail measuring point to obtain a weighted acceleration signal of the driver's seat rail measuring point, and determine a weighted acceleration signal peak value of the weighted acceleration signal within a second set time period; the second set time period being a first oscillation cycle time period of the weighted acceleration signal;

[0137] a first envelope area calculation module, configured to determine an equivalent impact strength curve of the front axle of the vehicle within the second set time period based on the acceleration signal of the driver's seat guide rail measuring point, and calculate an envelope area of ​​the equivalent impact strength curve within the second set time period;

[0138] The first impact convergence coefficient output module is configured to output the impact convergence coefficient of the front axle of the vehicle according to the ratio between the envelope area of ​​the equivalent impact intensity curve and the peak value of the weighted acceleration signal.

[0139] Optionally, the rear axle impact convergence coefficient calculation module includes:

[0140] a second signal processing module, configured to perform frequency-weighted processing on the acceleration signal of the passenger seat rail measuring point to obtain a weighted acceleration signal of the passenger seat rail measuring point, and determine a weighted acceleration signal peak value of the weighted acceleration signal within a second set time period; the second set time period being a first oscillation cycle time period of the weighted acceleration signal;

[0141] a second envelope area calculation module, configured to determine an equivalent impact strength curve of the vehicle rear axle within a second set time period based on the acceleration signal of the passenger seat guide measuring point, and calculate an envelope area of ​​the equivalent impact strength curve within the second set time period;

[0142] The second impact convergence coefficient output module outputs the impact convergence coefficient of the rear axle of the vehicle according to the ratio between the envelope area of ​​the equivalent impact intensity curve and the peak value of the weighted acceleration signal.

[0143] Optionally, the vehicle impact strength calculation module 92 includes a front axle impact strength calculation module and a rear axle impact strength calculation module, wherein:

[0144] a front axle impact strength calculation module, configured to select, within the first set time period, a first selected time period during which the vehicle is traveling stably and a second selected time period during which the vehicle is traveling on an impulsive road surface;

[0145] Obtaining a number of sampling points of the acceleration signal of each vehicle front axle measurement point, and obtaining a frequency weighting coefficient of each vehicle front axle measurement point;

[0146] For each vehicle front axle measuring point, calculating the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the first selected time period to obtain a stable impact strength of the vehicle front axle; calculating the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the second selected time period to obtain a maximum impact strength of the vehicle front axle; and subtracting the maximum impact strength from the stable impact strength to obtain an impact strength of each vehicle front axle measuring point;

[0147] The comprehensive impact strength of the vehicle's front axle is determined based on the impact strength of each vehicle front axle measuring point.

[0148] a rear axle impact strength calculation module, configured to select, within the first set time period, a first selected time period during which the vehicle is traveling stably and a second selected time period during which the vehicle is traveling on an impulsive road surface;

[0149] Obtaining a number of sampling points of the acceleration signal of each vehicle rear axle measurement point, and obtaining a frequency weighting coefficient of each vehicle rear axle measurement point;

[0150] For each vehicle rear axle measuring point, calculating the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the first selected time period to obtain a stable impact strength of the vehicle rear axle; calculating the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the second selected time period to obtain a maximum impact strength of the vehicle rear axle; subtracting the maximum impact strength from the stable impact strength to obtain an impact strength of each vehicle rear front axle measuring point;

[0151] The comprehensive impact strength of the vehicle's rear axle is determined based on the impact strength of each vehicle rear axle measuring point.

[0152] Optionally, the vehicle smoothness mathematical model includes: a functional mapping relationship between the vehicle's smoothness level and the vehicle's impact strength, the impact balance coefficient, and the vehicle's impact convergence coefficient, and the functional mapping relationship is obtained by performing functional fitting on the standard smoothness levels of several vehicles and the corresponding vehicle's impact strength, impact balance coefficient, and vehicle's impact convergence coefficient.

[0153] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0154] Figure 10 This is a schematic diagram of the structure of a terminal device provided in the fourth embodiment of the present invention. Figure 10 As shown, the terminal device of this embodiment includes: at least one processor ( Figure 10 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned method embodiments are implemented.

[0155] The terminal device may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 10 The terminal device is merely an example and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0156] The processor may be a CPU, or other general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. A general-purpose processor may be a microprocessor, or any conventional processor.

[0157] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory can be the internal memory of the terminal device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium can be the hard disk of the terminal device. In other embodiments, it can also be an external storage device of the terminal device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the terminal device. Furthermore, the memory can also include both the internal storage unit of the terminal device and an external storage device. The memory is used to store the operating system, application programs, boot loaders (BootLoader), data, and other programs, such as the program code of computer programs. The memory can also be used to temporarily store data that has been output or is about to be output.

[0158] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by 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. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned method embodiment. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include at least: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0159] The present invention can implement all or part of the processes in the above-mentioned embodiment method, and can also be completed through a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiment when executing it.

[0160] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0161] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0162] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0163] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0164] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention 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. 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 various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A vehicle ride comfort detection method, characterized in that: The method comprises: Acquire acceleration signals of N1 vehicle front axle measurement points and N2 vehicle rear axle measurement points during vehicle travel within a first set time period, where N1 ≥ 2 and N2 ≥ 2; determining the impact strength of the front axle and the impact strength of the rear axle of the vehicle based on the acceleration signal of the front axle measuring point of the vehicle and the acceleration signal of the rear axle measuring point of the vehicle, and taking the maximum value of the impact strength of the front axle and the impact strength of the rear axle as the impact strength of the entire vehicle; determining the impact balance coefficients of the front and rear axles of the vehicle based on a ratio between the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle; acquiring acceleration signals of N3 driver's seat rail measuring points and passenger's seat rail measuring points within a second set time period during vehicle travel, where N3 is greater than or equal to 2; and determining a vehicle impact convergence coefficient for characterizing a vehicle vibration convergence rate based on the acceleration signals of the driver's seat rail measuring points and the passenger's seat rail measuring points; Substituting the vehicle impact strength, the impact balance coefficient, and the vehicle impact convergence coefficient into an established vehicle ride comfort mathematical model to determine the vehicle ride comfort level; The mathematical model of vehicle ride comfort is expressed as follows: Where, is the ride comfort level of the vehicle, is the impact strength of the vehicle, is the impact balance coefficient, is the vehicle impact convergence coefficient, a and b are function fitting coefficients.

2. The vehicle ride comfort detection method according to claim 1, characterized in that: Determining a vehicle impact convergence coefficient for characterizing a vehicle vibration convergence rate based on acceleration signals of the driver's seat guide rail measuring point and the passenger seat guide rail measuring point includes: The impact convergence coefficient of the front axle of the vehicle is determined based on the acceleration signal of the driver's seat guide measuring point; the impact convergence coefficient of the rear axle of the vehicle is determined based on the acceleration signal of the passenger seat guide measuring point; and the maximum value of the impact convergence coefficient of the front axle of the vehicle and the impact convergence coefficient of the rear axle of the vehicle is taken as the impact convergence coefficient of the entire vehicle.

3. The vehicle ride comfort detection method according to claim 2, characterized in that: Determining the impact convergence coefficient of the front axle of the vehicle according to the acceleration signal of the driver's seat guide rail measuring point includes: performing frequency weighted processing on the acceleration signal of the driver's seat rail measuring point to obtain a weighted acceleration signal of the driver's seat rail measuring point, and determining a weighted acceleration signal peak value of the weighted acceleration signal within a second set time period; the second set time period being a first oscillation cycle time period of the weighted acceleration signal; determining an equivalent impact strength curve of the front axle of the vehicle within the second set time period based on the acceleration signal of the driver's seat guide rail measuring point, and calculating an envelope area of ​​the equivalent impact strength curve within the second set time period; An impact convergence coefficient of the front axle of the vehicle is determined according to a ratio of an envelope area of ​​the equivalent impact strength curve to a peak value of the weighted acceleration signal.

4. The vehicle ride comfort detection method according to claim 2, characterized in that: Determining the impact convergence coefficient of the rear axle of the vehicle according to the acceleration signal of the passenger seat guide measuring point includes: performing frequency weighted processing on the acceleration signal of the passenger seat rail measuring point to obtain a weighted acceleration signal of the passenger seat rail measuring point, and determining a weighted acceleration signal peak value of the weighted acceleration signal within a second set time period; the second set time period being a first oscillation cycle time period of the weighted acceleration signal; determining an equivalent impact strength curve of the rear axle of the vehicle within the second set time period based on the acceleration signal of the passenger seat guide rail measuring point, and calculating an envelope area of ​​the equivalent impact strength curve within the second set time period; An impact convergence coefficient of the rear axle of the vehicle is determined according to a ratio of an envelope area of ​​the equivalent impact strength curve to a peak value of the weighted acceleration signal.

5. The vehicle ride comfort detection method according to claim 1, characterized in that: Determining the impact strength of the front axle of the vehicle includes: In the first set time period, a first selected time period in which the vehicle is traveling steadily and a second selected time period in which the vehicle is traveling on an impulsive road are selected; Obtaining a number of sampling points of the acceleration signal of each vehicle front axle measurement point, and obtaining a frequency weighting coefficient of each vehicle front axle measurement point; For each vehicle front axle measuring point, calculating the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the first selected time period to obtain a stable impact strength of the vehicle front axle; calculating the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the second selected time period to obtain a maximum impact strength of the vehicle front axle; and subtracting the maximum impact strength from the stable impact strength to obtain an impact strength of each vehicle front axle measuring point; The comprehensive impact strength of the vehicle's front axle is determined based on the impact strength of each vehicle front axle measuring point.

6. The vehicle ride comfort detection method according to claim 5, characterized in that: Determining the comprehensive impact strength of the vehicle front axle based on the impact strength of each vehicle front axle measuring point includes: The squares of the impact strengths of each vehicle front axle measuring point are summed to obtain the square sum of the impact strengths of the vehicle front axle measuring points, and the square root of the square sum is taken to calculate the comprehensive impact strength of the vehicle front axle.

7. The vehicle ride comfort detection method according to claim 1, characterized in that: Determining the impact strength of the rear axle of the vehicle includes: In the first set time period, a first selected time period in which the vehicle is traveling steadily and a second selected time period in which the vehicle is traveling on an impulsive road are selected; Obtaining a number of sampling points of the acceleration signal of each vehicle rear axle measurement point, and obtaining a frequency weighting coefficient of each vehicle rear axle measurement point; For each vehicle rear axle measuring point, calculating the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the first selected time period to obtain a stable impact strength of the vehicle rear axle; calculating the root mean square value of the product of the acceleration signals of the plurality of sampling points and the frequency weighting coefficient within the second selected time period to obtain a maximum impact strength of the vehicle rear axle; and subtracting the maximum impact strength from the stable impact strength to obtain an impact strength of each vehicle rear axle measuring point; The comprehensive impact strength of the vehicle's rear axle is determined based on the impact strength of each vehicle rear axle measuring point.

8. The vehicle ride comfort detection method according to any one of claims 1 to 7, characterized in that: The vehicle smoothness mathematical model includes: a functional mapping relationship between the vehicle's smoothness level and the vehicle's impact strength, the impact balance coefficient, and the vehicle's impact convergence coefficient. The functional mapping relationship is obtained by performing functional fitting on the standard smoothness levels of several vehicles and the corresponding vehicle's impact strength, impact balance coefficient, and vehicle's impact convergence coefficient.

9. A vehicle ride comfort detection device, characterized in that: The ride comfort detection device comprises: A data acquisition module is used to obtain acceleration signals of N1 vehicle front axle measurement points and N2 vehicle rear axle measurement points during vehicle travel within a first set time period, where N1 ≥ 2 and N2 ≥ 2; a vehicle impact strength calculation module, configured to determine the impact strength of the front axle and the impact strength of the rear axle of the vehicle based on the acceleration signal of the front axle measuring point of the vehicle and the acceleration signal of the rear axle measuring point of the vehicle, and take the maximum value of the impact strength of the front axle and the impact strength of the rear axle as the impact strength of the vehicle; an impact balance coefficient calculation module, configured to determine the impact balance coefficients of the front and rear axles of the vehicle based on a ratio between the impact strength of the front axle of the vehicle and the impact strength of the rear axle of the vehicle; a vehicle impact convergence coefficient calculation module, configured to obtain acceleration signals of N3 driver seat rail measuring points and passenger seat rail measuring points within a second set time period during vehicle travel, where N3 ≥ 2; and determine a vehicle impact convergence coefficient for characterizing a vehicle vibration convergence rate based on the acceleration signals of the driver seat rail measuring points and the passenger seat rail measuring points; a ride comfort level determination module, configured to substitute the vehicle impact strength, the impact balance coefficient, and the vehicle impact convergence coefficient into an established vehicle ride comfort mathematical model to determine the vehicle ride comfort level; The mathematical model of vehicle ride comfort is expressed as follows: Where, is the ride comfort level of the vehicle, is the impact strength of the vehicle, is the impact balance coefficient, is the vehicle impact convergence coefficient, a and b are function fitting coefficients.

10. A terminal device, characterized in that: The terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the vehicle ride comfort detection method according to any one of claims 1 to 8 is implemented.

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