Method, device, equipment and readable storage medium for evaluating the impact of a vehicle passing over a speed bump

Abstract

A method, device, equipment, and computer-readable storage medium for evaluating the impact of a vehicle passing over a speed bump include: collecting the speed bump dimensions and the vehicle's speed at the speed bump, and calculating the impact vibration dose and attenuation coefficient. These data, along with the speed bump dimensions, speed, impact vibration dose, and attenuation coefficient, are then fed into a trained neural network model to output a subjective evaluation score for the vehicle model tested. During the development of new vehicle models to predict the impact score of a vehicle passing over a speed bump, the speed bump dimensions, speed, impact vibration dose, and attenuation coefficient are fed into the trained neural network model to output a subjective evaluation score. This improves upon the traditional impact evaluation method's reliance solely on subjective evaluations by engineers, which struggles to achieve a universally accepted evaluation method due to individual differences in psychological and physiological qualities and sensitivity to vibration.

Description

Technical Field

[0001] The present application relates to the field of automobile ride comfort testing, and in particular to a method, device, equipment, and computer-readable storage medium for evaluating the impact of a vehicle passing over a speed bump. Background Art

[0002] When a car is driving, road irregularities excite vibrations, which are considered the ride comfort. This ride comfort can be analyzed using the "road-car-driver" system. Road irregularities and vehicle speed form the "input" to the car. This "input" is transmitted through components such as the tires, suspension, and seats, resulting in an acceleration response to the human body. Comfort is evaluated based on the human body's reaction to the vibrations.

[0003] Domestic roads are home to numerous speed bumps of varying shapes and sizes. When a vehicle passes over a speed bump, it can produce a secondary impact, resulting in secondary vibration and insufficient convergence in the suspension. The effects of mechanical vibration on the human body primarily include frequency, intensity, direction, and duration. However, due to individual differences in psychological and physiological qualities, sensitivity to vibration varies greatly, making a universally accepted evaluation method difficult to establish. Summary of the Invention

[0004] The present application provides a method, device, equipment and computer-readable storage medium for evaluating the impact of a car passing over a speed bump, which can solve the problem in related technologies that the evaluation method simply relies on the subjective evaluation of engineers. Due to the differences in psychological and physiological qualities of each person, the sensitivity to vibration also varies greatly, making it difficult to obtain a recognized evaluation method.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating the impact of a vehicle passing over a speed bump, the method comprising:

[0006] Collect the size of the speed bump and the speed of the car when passing over the speed bump, and calculate the impact vibration dose value and attenuation coefficient of the car passing over the speed bump;

[0007] The speed bump size, the speed of the car when passing over the speed bump, the impact vibration dose value and the attenuation coefficient are imported into the trained neural network model to output the subjective evaluation score of the vehicle model in this test.

[0008] In conjunction with the first aspect, in one embodiment, the speed bump size, the speed of the vehicle when passing over the speed bump, the impact vibration dose value, and the attenuation coefficient are introduced into a trained neural network model to output a subjective evaluation score value for the vehicle model in this test, including:

[0009] The speed bump size, the speed of the car when passing over the speed bump, the impact vibration dose value, the attenuation coefficient and the weighted acceleration are imported into the trained neural network model to output the subjective evaluation score of the vehicle model in this test.

[0010] In conjunction with the first aspect, in one embodiment, the speed bump size, the speed of the vehicle when passing over the speed bump, the impact vibration dose value, and the attenuation coefficient are introduced into a trained neural network model to output a subjective evaluation score value for the vehicle model in this test, including:

[0011] The speed bump size, the speed of the car when passing over the speed bump, the impact vibration dose value, the attenuation coefficient, and the acceleration peak value of each vibration cycle during the impact vibration time period of the front and rear wheels passing over the speed bump are imported into the trained neural network model to output the subjective evaluation score of the vehicle model in this test.

[0012] In conjunction with the first aspect, in one embodiment, before collecting the speed bump size and the speed of the vehicle when passing over the speed bump, and calculating the impact vibration dose value and attenuation coefficient of the vehicle passing over the speed bump, the method includes:

[0013] Collect the speed bump dimensions and driving speeds of N vehicles passing through the speed bump, and calculate the impact vibration dose values, attenuation coefficients, and subjective evaluation scores of the N vehicles passing through the speed bump to form N neural network training samples; where N is greater than or equal to 1;

[0014] The neural network model is trained using N neural network training samples to obtain a trained neural network model.

[0015] In conjunction with the first aspect, in one embodiment, the step of calculating the impact vibration dose value and the attenuation coefficient of a vehicle passing over a speed bump includes:

[0016] The impact vibration dose value is calculated based on the weighted acceleration of the front and rear wheels during the impact vibration period when the car passes over the speed bump.

[0017] In conjunction with the first aspect, in one embodiment, before calculating the impact vibration dose value based on the weighted acceleration of the front and rear wheels of the vehicle during the impact vibration period when the vehicle passes over a speed bump, the method includes:

[0018] The weighted acceleration is calculated based on the acceleration time history, frequency weighting function and power spectral density function.

[0019] In conjunction with the first aspect, in one embodiment, the step of calculating the impact vibration dose value and the attenuation coefficient of a vehicle passing over a speed bump includes:

[0020] The attenuation coefficient is calculated based on the peak value of the acceleration wave in each vibration cycle during the impact vibration period of the front and rear wheels when going over the speed bump.

[0021] In a second aspect, an embodiment of the present application provides a device for evaluating the impact of a vehicle passing over a speed bump, the device comprising:

[0022] A data acquisition and calculation module is used to collect the size of the speed bump and the speed of the car passing over the speed bump, and calculate the impact vibration dose value and attenuation coefficient of the car passing over the speed bump;

[0023] The subjective evaluation score output module is used to import the speed bump size, the speed of the car passing over the speed bump, the impact vibration dose value and the attenuation coefficient into the trained neural network model, and output the subjective evaluation score value of the vehicle model in this test.

[0024] In a third aspect, an embodiment of the present application provides a device for evaluating the impact of a vehicle passing over a speed bump, the device comprising a processor, a memory, and a program for evaluating the impact of a vehicle passing over a speed bump stored in the memory and executable by the processor, wherein when the program for evaluating the impact of a vehicle passing over a speed bump is executed by the processor, the steps of the method for evaluating the impact of a vehicle passing over a speed bump as described in some of the above embodiments are implemented.

[0025] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored a program for evaluating the impact of a vehicle passing over a speed bump. When the program for evaluating the impact of a vehicle passing over a speed bump is executed by a processor, the steps of the method for evaluating the impact of a vehicle passing over a speed bump as described in some of the above embodiments are implemented.

[0026] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0027] The system collects data on speed bump dimensions and vehicle speeds, calculates the impact vibration dose and attenuation coefficient, and then feeds these data, including speed bump dimensions, speed, impact vibration dose, and attenuation coefficient, into a trained neural network model to output a subjective evaluation score for the vehicle model tested. During the development of new vehicle models, the speed bump impact score prediction process involves feeding data such as speed bump dimensions, speed, impact vibration dose, and attenuation coefficient into the trained neural network model to generate a subjective evaluation score. This improves upon traditional impact evaluation methods, which rely solely on subjective evaluations by engineers. Due to individual differences in psychological and physiological qualities and sensitivity to vibration, a generally accepted evaluation method is often difficult to establish. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of an embodiment of a method for evaluating the impact of a vehicle passing over a speed bump according to the present application;

[0029] Figure 2 This is a schematic diagram of the vibration acceleration of a car passing over a speed bump in this application;

[0030] Figure 3This is a schematic diagram of the hardware structure of the vehicle speed bump impact assessment device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] It should be noted that the neural network model can learn certain rules through its own training and obtain the result closest to the expected output value when given an input value.

[0033] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0034] In a first aspect, an embodiment of the present application provides a method for evaluating the impact of a vehicle passing over a speed bump.

[0035] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for evaluating the impact of a car passing over a speed bump. Figure 1 As shown in the figure, the impact evaluation method for a car passing over a speed bump includes:

[0036] S100: collecting the size of the speed bump and the speed of the vehicle when passing over the speed bump, and calculating the impact vibration dose value and attenuation coefficient of the vehicle passing over the speed bump;

[0037] S200: Import the speed bump size, the speed of the car when passing over the speed bump, the impact vibration dose value and the attenuation coefficient into the trained neural network model, and output the subjective evaluation score of the vehicle model in this test.

[0038] In this embodiment, the speed bump dimensions and the vehicle's speed over the speed bump are collected, and the impact vibration dose and attenuation coefficient of the vehicle over the speed bump are calculated. These data, including the speed bump dimensions, speed, impact vibration dose, and attenuation coefficient, are then fed into a trained neural network model to output a subjective evaluation score for the vehicle model tested. During the development of new vehicle models to predict the impact score over speed bumps, the data obtained, including speed bump dimensions, speed, impact vibration dose, and attenuation coefficient, are fed into the trained neural network model to output a subjective evaluation score. This improves upon traditional impact evaluation methods, which rely solely on subjective evaluation by engineers. Due to individual differences in psychological and physiological qualities and sensitivity to vibration, a generally accepted evaluation method is difficult to develop.

[0039] Furthermore, in one embodiment, in S200, the following steps are included:

[0040] S201: Import the speed bump size, the speed of the vehicle when passing over the speed bump, the impact vibration dose value, the attenuation coefficient, and the weighted acceleration into the trained neural network model, and output the subjective evaluation score of the vehicle model in this test.

[0041] In this embodiment, the speed bump size, the speed of the car when passing over the speed bump, the impact vibration dose value, the attenuation coefficient and the weighted acceleration are introduced into the trained neural network model to output the subjective evaluation score value of the vehicle model in this test. By adding the variable factor of weighted acceleration on the original basis, the accuracy of the subjective evaluation score value of the vehicle model in this test can be further improved.

[0042] Furthermore, in one embodiment, in S200, the following steps are included:

[0043] S201: The speed bump size, the vehicle's speed when passing over the speed bump, the impact vibration dose value, the attenuation coefficient, and the acceleration peak value of each vibration cycle during the impact vibration period of the front and rear wheels passing over the speed bump are imported into the trained neural network model, and the subjective evaluation score value of the vehicle model in this test is output.

[0044] In this embodiment, the speed bump size, the speed of the car when passing over the speed bump, the impact vibration dose value, the attenuation coefficient and the acceleration peak value of each vibration cycle during the impact vibration time period of the front and rear wheels passing over the speed bump are introduced into the trained neural network model, and the subjective evaluation score value of the vehicle model in this test is output. The variable factor of the acceleration peak value of each vibration cycle during the impact vibration time period of the front and rear wheels passing over the speed bump is added to the original basis, which can further improve the accuracy of the subjective evaluation score value of the vehicle model in this test.

[0045] Furthermore, in one embodiment, before S100, the following steps are included:

[0046] S000-1: Collect the speed bump dimensions and driving speeds of N vehicles passing through the speed bump, and calculate the impact vibration dose values, attenuation coefficients, and subjective evaluation scores of the N vehicles passing through the speed bump to form N neural network training samples; where N is greater than or equal to 1;

[0047] S000-2: Use N neural network training samples to train the neural network model, that is, obtain the trained neural network model.

[0048] In this embodiment, before a new car model is tested over a speed bump, a neural network model needs to be trained. The speed bump size, driving speed, and impact vibration dose value, attenuation coefficient, and subjective evaluation score values ​​corresponding to the N car models passing through the speed bump are used as neural network training samples to train a neural network model. This facilitates the subsequent testing of the new car model by importing the speed bump size, driving speed, impact vibration dose value attenuation coefficient, and subjective evaluation score values ​​of the new car model passing through the speed bump to train a neural network model. This improves the problem of relying solely on the subjective evaluation of engineers. Due to differences in each person's psychological and physiological qualities, the sensitivity to vibration also varies greatly, making it difficult to obtain a recognized evaluation method.

[0049] Furthermore, in one embodiment, in S100, the following steps are included:

[0050] S102-2: Calculate the impact vibration dose value based on the weighted acceleration of the front and rear wheels during the impact vibration period when the car passes over the speed bump.

[0051] In this embodiment, for example, Figure 2 As shown in the figure, the impact vibration dose value is calculated using the weighted acceleration of the front and rear wheels during the impact vibration period when the car passes over the speed bump. This impact vibration dose value is then imported into the trained neural network model to output the subjective evaluation score of the vehicle model in this test. The impact vibration dose value is the cumulative value of the whole-body vibration measurement. It is calculated based on the fourth power of the weighted acceleration during the impact vibration period when the car passes over the speed bump. It is more sensitive to peak values ​​and is expressed as follows:

[0052] VDV=[∫0 T a ω 4 d t ] 0.25

[0053] Where VDV is the shock vibration dose value, a w is the weighted acceleration, T is the impact vibration time period of the front and rear wheels when the car passes the speed bump, and the unit of VDV is ms -1.75 .

[0054] Furthermore, in one embodiment, before S102, the following steps are included:

[0055] S102-1: Calculate weighted acceleration based on the acceleration time history, the frequency weighting function, and the power spectrum density function.

[0056] In this embodiment, for example, the acceleration time history is subjected to a filtering network of a frequency weighted function to obtain weighted acceleration, and the specific calculation method is as follows:

[0057]

[0058] Where a w is the weighted acceleration, G a is the power spectrum density function obtained by spectrum analysis of the vehicle acceleration time history, ω f is the frequency weighting function, and its formula is as follows:

[0059]

[0060] Furthermore, in one embodiment, in S100, the following steps are included:

[0061] S102: Calculating an attenuation coefficient based on the peak value of the acceleration wave in each vibration cycle during the time period of the front and rear wheel impact vibration when going over a speed bump.

[0062] In this embodiment, Figure 2 As shown in the figure, according to the impact vibration time period of the front and rear wheels of the car passing the speed bump, the peak value of each vibration cycle in the impact vibration time period of the front and rear wheels passing the speed bump is extracted and recorded as A0, A1, A2...A i ; Calculate the attenuation coefficient. According to the peak acceleration value, the attenuation coefficient can be calculated. The calculation formula is as follows:

[0063] ε i =A i / A i-1

[0064] Where, ε i is the attenuation coefficient, A i It is the peak value of each vibration cycle during the impact vibration period of the front and rear wheels when passing over the speed bump.

[0065] Furthermore, in one embodiment, in S100, the following steps are included:

[0066] S101: Collect the height and width of the speed bump and the speed of the vehicle when passing the speed bump.

[0067] In this embodiment, the width of the speed bump can be 300 mm, and the height can be 30 mm. The recommended vehicle speed is between 20 km / h and 30 km / h.

[0068] In summary, this application provides the following complete description of the impact evaluation method for a vehicle passing over a speed bump:

[0069] Step 1: Determine basic parameters

[0070] A flat road with rubber speed bumps was selected as the test road. The car was driven at a constant speed of 30km / h through the test road. The rear end of the driver's seat rail was used as the vibration measurement point to test the vibration acceleration data during the speed bump impact process. When the car passes the speed bump, its main impact direction is the Z direction of the vehicle, that is, the vertical direction. Therefore, the Z direction data of the vehicle was selected during the test. When the car passes the speed bump impact, the front and rear wheels pass the speed bump in turn. The vibration acceleration of the test is referenced to Figure 2 As shown, the test is divided into two sections: front wheel impact and rear wheel impact. There are many types of speed bumps, but this time we chose a common rubber speed bump with a width of 300mm and a height of 30mm. The recommended vehicle speed is between 20-30km / h. During testing, the vibration acceleration sampling frequency is recommended to be set to 200Hz.

[0071] Step 2: Build a neural network model

[0072] A neural network prediction model is constructed based on the speed bump width, speed bump height, driving speed, weighted acceleration, impact vibration dose value, acceleration peak value, attenuation coefficient, and subjective evaluation score. For each vehicle model test, the speed bump width, speed bump height, driving speed, weighted acceleration, impact vibration dose value, acceleration peak value, and attenuation coefficient can be obtained. These data form a neural network training sample input data; the corresponding subjective evaluation score value forms a neural network training sample output data (also called a label value). The neural network training sample input data and output data are collectively referred to as the neural network training sample. Collecting data from N vehicles forms N neural network training samples. For example, the number N can be greater than or equal to 30. Use these N neural network training samples to train the neural network model, and a trained neural network model is obtained.

[0073] Among them, for the acceleration time history, the frequency weighting function ω f The filter network obtains the weighted acceleration, and the specific calculation method is:

[0074]

[0075] Among them, a w is the weighted acceleration, G a is the power spectrum density function obtained by spectrum analysis of acceleration time history, ω f is the frequency weighting function, and its formula is as follows:

[0076]

[0077] The impact vibration dose value is the cumulative value of whole-body vibration measurement. It is calculated based on the fourth power of the weighted acceleration of the front and rear wheels during the impact vibration period when the car passes over a speed bump. It is more sensitive to peak values ​​and the formula is:

[0078] VDV=[∫0 T a ω4 d t ] 0.25

[0079] Where VDV is the shock vibration dose value, a w is the weighted acceleration, T is the impact vibration time period of the front and rear wheels when the car passes the speed bump, and the unit of VDV is ms -1.75 .

[0080] Among them, according to the impact vibration time period of the front and rear wheels of the car passing the speed bump, the peak value of each vibration cycle in the impact vibration time period of the front and rear wheels passing the speed bump is extracted and recorded as A0, A1, A2...A i ; Calculate the attenuation coefficient. According to the peak acceleration value, the attenuation coefficient can be calculated. The calculation formula is as follows:

[0081] ε i =A i / A i-1

[0082] Where, ε i is the attenuation coefficient, A i It is the peak value of each vibration cycle during the impact vibration period of the front and rear wheels when passing over the speed bump.

[0083] Step 3: Predict the subjective evaluation score of the new car model when passing over speed bumps

[0084] In the speed bump impact test for new vehicle development, we obtain the speed bump width, height, impact velocity, weighted acceleration, impact vibration dose, peak acceleration, and attenuation coefficient. This data is input into the neural network model trained in step 2 to generate the output value, which is the subjective evaluation score for the test.

[0085] Secondly, embodiments of the present application also provide a device for evaluating the impact of a vehicle passing over a speed bump. The device comprises a data acquisition and calculation module for collecting the speed bump dimensions and the vehicle's speed over the speed bump, and calculating the impact vibration dose and attenuation coefficient of the vehicle passing over the speed bump; and a subjective evaluation score output module for inputting the speed bump dimensions, the vehicle's speed over the speed bump, the impact vibration dose, and the attenuation coefficient into a trained neural network model to output the subjective evaluation score for the vehicle model tested.

[0086] The data acquisition and calculation module collects speed bump dimensions and the vehicle's speed over the speed bump, and calculates the impact vibration dose and attenuation coefficient. The subjective evaluation score output module feeds these data into a trained neural network model to output the subjective evaluation score for the vehicle model tested. During the speed bump impact score prediction process for new vehicle models, the speed bump dimensions, speed, impact vibration dose, and attenuation coefficient are fed into the trained neural network model to generate the subjective evaluation score. This improves upon the traditional impact evaluation method's reliance solely on subjective evaluation by engineers, which struggles to achieve a universally accepted evaluation method due to individual differences in psychological and physiological qualities and sensitivity to vibration.

[0087] In combination with the second aspect, in one embodiment, the subjective evaluation score output module is also used to import the speed bump size, the speed of the car passing the speed bump, the impact vibration dose value, the attenuation coefficient and the weighted acceleration into the trained neural network model, and output the subjective evaluation score value of the vehicle model in this test.

[0088] In combination with the second aspect, in one embodiment, the subjective evaluation score output module is also used to import the speed bump size, the speed of the car when passing the speed bump, the impact vibration dose value, the attenuation coefficient and the acceleration peak value of each vibration cycle during the impact vibration time period of the front and rear wheels when passing the speed bump into the trained neural network model, and output the subjective evaluation score value of the vehicle model in this test.

[0089] In combination with the second aspect, in one embodiment, the data acquisition and calculation module is also used to collect the speed bump size and driving speed of N vehicle models passing through the speed bump, and calculate the impact vibration dose value, attenuation coefficient and subjective evaluation score value of the N vehicle models passing through the speed bump, to form N neural network training samples; where N is greater than or equal to 1; the neural network model is trained using the N neural network training samples to obtain the neural network model after training.

[0090] In conjunction with the second aspect, in one embodiment, the data acquisition and calculation module is further configured to calculate an impact vibration dose value based on weighted accelerations during an impact vibration period of the front and rear wheels of the vehicle when the vehicle passes over a speed bump.

[0091] In combination with the second aspect, in one embodiment, the data acquisition calculation module is further configured to calculate weighted acceleration based on the acceleration time history, the frequency weighting function, and the power spectrum density function.

[0092] In conjunction with the second aspect, in one embodiment, the data acquisition and calculation module is further configured to calculate an attenuation coefficient based on the peak value of the acceleration wave in each vibration cycle during the impact vibration period of the front and rear wheels when going over a speed bump.

[0093] Among them, the functions of each module in the above-mentioned vehicle speed bump impact evaluation device correspond to the steps in the above-mentioned vehicle speed bump impact evaluation method embodiment, and their functions are not repeated here one by one.

[0094] In a third aspect, an embodiment of the present application provides a device for evaluating the impact of a vehicle passing over a speed bump. The device for evaluating the impact of a vehicle passing over a speed bump may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0095] Reference Figure 3 , Figure 3 Schematic diagram of the hardware structure of the vehicle speed bump impact assessment device involved in the embodiment of the present application. In the embodiment of the present application, the vehicle speed bump impact assessment device may include a processor, a memory, a communication interface, and a communication bus.

[0096] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0097] Communication interfaces include input / output (I / O), physical, and logical interfaces, which interconnect components within the speed bump impact assessment device and connect the device to other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber, or ATM interfaces; user devices can include displays and keyboards.

[0098] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0099] The processor may be a general-purpose processor that can call a speed bump impact assessment program stored in a memory and execute the speed bump impact assessment method provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the speed bump impact assessment program is called can be referenced to the various embodiments of the speed bump impact assessment method of the present application and will not be further described here.

[0100] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0101] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0102] The readable storage medium of the present application stores a program for evaluating the impact of a vehicle passing over a speed bump, wherein when the program for evaluating the impact of a vehicle passing over a speed bump is executed by a processor, the steps of the method for evaluating the impact of a vehicle passing over a speed bump as described above are implemented.

[0103] Among them, the method implemented when the vehicle passing over speed bump impact evaluation program is executed can refer to the various embodiments of the vehicle passing over speed bump impact evaluation method of the present application, and will not be repeated here.

[0104] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0105] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0106] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0107] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0108] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0110] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for evaluating the impact of a vehicle passing over a speed bump, characterized in that: The method for evaluating the impact of a vehicle passing over a speed bump comprises: Collect the size of the speed bump and the speed of the car when passing over the speed bump, and calculate the impact vibration dose value and attenuation coefficient of the car passing over the speed bump; The speed bump size, the speed of the car when it passes over the speed bump, the impact vibration dose value and the attenuation coefficient are input into the trained neural network model to output the subjective evaluation score of the vehicle model in this test; The calculation of the impact vibration dose value and attenuation coefficient of a car passing over a speed bump includes: The impact vibration dose value is calculated based on the weighted acceleration of the front and rear wheels during the impact vibration period when the car passes over the speed bump; Before calculating the impact vibration dose value based on the weighted acceleration of the front and rear wheels of the vehicle during the impact vibration period when the vehicle passes over a speed bump, the method includes: The weighted acceleration is calculated based on the acceleration time history, frequency weighting function and power spectrum density function; The calculation of the impact vibration dose value and attenuation coefficient of a car passing over a speed bump includes: The attenuation coefficient is calculated based on the peak value of the acceleration wave in each vibration cycle during the impact vibration period of the front and rear wheels when going over the speed bump.

2. The method for evaluating the impact of a vehicle passing over a speed bump according to claim 1, wherein: The speed bump size, the speed of the car passing over the speed bump, the impact vibration dose value and the attenuation coefficient are introduced into the trained neural network model to output the subjective evaluation score of the vehicle model in this test, including: The speed bump size, the speed of the car when passing over the speed bump, the impact vibration dose value, the attenuation coefficient and the weighted acceleration are imported into the trained neural network model to output the subjective evaluation score of the vehicle model in this test.

3. The method for evaluating the impact of a vehicle passing over a speed bump according to claim 1, wherein: The speed bump size, the speed of the car passing over the speed bump, the impact vibration dose value and the attenuation coefficient are introduced into the trained neural network model to output the subjective evaluation score of the vehicle model in this test, including: The speed bump size, the speed of the car when passing over the speed bump, the impact vibration dose value, the attenuation coefficient, and the acceleration peak value of each vibration cycle during the impact vibration time period of the front and rear wheels passing over the speed bump are imported into the trained neural network model to output the subjective evaluation score of the vehicle model in this test.

4. The method for evaluating the impact of a vehicle passing over a speed bump according to claim 1, wherein: Before collecting the speed bump size and the speed of the vehicle passing over the speed bump, and calculating the impact vibration dose value and attenuation coefficient of the vehicle passing over the speed bump, the following steps are included: Collect the speed bump dimensions and driving speeds of N vehicles passing through the speed bump, and calculate the impact vibration dose values, attenuation coefficients, and subjective evaluation scores of the N vehicles passing through the speed bump to form N neural network training samples; where N is greater than or equal to 1; The neural network model is trained using N neural network training samples to obtain a trained neural network model.

5. A vehicle speed bump impact assessment device, characterized in that: The vehicle speed bump impact assessment device comprises: A data acquisition and calculation module is used to collect the size of the speed bump and the speed of the vehicle when it passes over the speed bump, and calculate the impact vibration dose value and attenuation coefficient of the vehicle passing over the speed bump. The impact vibration dose value is calculated based on the weighted acceleration of the front and rear wheels during the impact vibration period when the vehicle passes over the speed bump; the weighted acceleration is calculated based on the acceleration time history, frequency weighting function, and power spectral density function; and the attenuation coefficient is calculated based on the peak value of the acceleration wave in each vibration cycle during the impact vibration period when the front and rear wheels pass over the speed bump; The subjective evaluation score output module is used to import the speed bump size, the speed of the car passing over the speed bump, the impact vibration dose value and the attenuation coefficient into the trained neural network model, and output the subjective evaluation score value of the vehicle model in this test.

6. An impact assessment device for a vehicle passing over a speed bump, characterized in that: The vehicle speed bump impact evaluation device includes a processor, a memory, and a vehicle speed bump impact evaluation program stored in the memory and executable by the processor. When the vehicle speed bump impact evaluation program is executed by the processor, the steps of the vehicle speed bump impact evaluation method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program for evaluating the impact of a vehicle passing over a speed bump, wherein when the program is executed by a processor, the steps of the method for evaluating the impact of a vehicle passing over a speed bump as claimed in any one of claims 1 to 4 are implemented.

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