Vehicle body fatigue life prediction method, device, equipment, system and storage medium

Abstract

The application discloses a vehicle body fatigue life prediction method, device, equipment, system and storage medium, comprising receiving acceleration excitation load signals of a plurality of test points on a vehicle body; calculating a corresponding self-power spectral density function according to the acceleration excitation load signal of each test point; calculating the mutual power spectral density function between each test point and other test points according to the self-power spectral density function of each test point; inputting the mutual power spectral density function of each test point into a multi-axis random vibration calculation module, obtaining the transfer function of the vehicle body about stress distribution, and then inputting into a fatigue life prediction module to obtain the fatigue life prediction distribution diagram of the vehicle body. The application calculates the multi-axis acceleration load self-power spectral density function and the mutual power spectral density function by using the acceleration load in actual testing to obtain fatigue life prediction, which can better reflect the actual loading condition of the vehicle body and the fatigue damage of the vehicle body structure.

Description

Technical Field

[0001] This invention relates to the technical field of automobile body production, and more specifically, to a method, apparatus, device, system, and storage medium for predicting the fatigue life of an automobile body. Background Technology

[0002] In the early stages of vehicle development, fatigue durability prediction analysis of the body structure is required. In the later stages, any fatigue durability issues that arise need to be analyzed and addressed to ensure that unacceptable failures do not occur during driving. However, the force and displacement loads at the body connection points cannot be measured during vehicle operation or testing. Currently, body fatigue life prediction analysis methods are based on multibody dynamics simulation of the entire vehicle, extracting the loads at the body-in-white connection points and calculating the fatigue life of the body using a quasi-static superposition method. However, the fatigue life calculated by the quasi-static superposition method cannot reflect the actual load conditions of the body and cannot account for the effects of load excitation frequency and body resonance. Summary of the Invention

[0003] To address at least one aspect of the above-mentioned problems, the present invention provides a method, apparatus, device, system, and storage medium for predicting vehicle body fatigue life.

[0004] In a first aspect, this application provides a method for predicting the fatigue life of a vehicle body, comprising the following steps: a receiving step: receiving acceleration excitation load signals in at least one direction from several test points on the vehicle body; a self-power spectral density function calculation step: calculating the corresponding self-power spectral density function based on the acceleration excitation load signal of each test point; a cross-power spectral density function calculation step: calculating the cross-power spectral density function between each test point and other test points based on the self-power spectral density function of each test point; and a stress distribution transfer function acquisition step.

[0005] Preferably, the method further includes the following steps: inputting the cross-power spectral density function of each test point into the multi-axis random vibration calculation module of the Optistrut software, and obtaining the transfer function of the vehicle body with respect to stress distribution from the multi-axis random vibration calculation module of the Optistrut software; obtaining the fatigue life prediction distribution map step: inputting the transfer function with respect to stress distribution into the fatigue life prediction module of the Hyperlife software to obtain the fatigue life prediction distribution map of the vehicle body.

[0006] Preferably, acceleration excitation load signals in the x, y, and z directions are obtained at each test point; in the step of calculating the self-power spectral density function, the self-power spectral density function in the x, y, and z directions of each test point is calculated according to the acceleration excitation load signals in the x, y, and z directions of each test point; in the step of calculating the cross-power spectral density function, the cross-power spectral density function between each test point and the x, y, and z directions of other test points is calculated according to the self-power spectral density function in the x, y, and z directions of each test point; the step of calculating the cross-power spectral density function further includes calculating the cross-power spectral density function between each pair of x, y, and z directions of each test point.

[0007] Preferably, the calculation step of the self-power spectral density function includes: receiving an acceleration excitation load signal in one direction at a certain test point. Then the autocorrelation function in that direction at that test point for:

[0008] ,

[0009] in, Represents the time interval; the autocorrelation function After performing a Fourier transform, the bilateral self-power spectral density function is obtained. for:

[0010] ,

[0011] Then the one-sided spectrum for:

[0012] .

[0013] Preferably, the calculation step of the cross-power spectral density function includes: receiving an acceleration excitation load signal in one direction at a certain test point. The acceleration excitation load signal in another direction of the test point or the acceleration excitation load signal in one direction of other test points is... ,in The single-sided spectrum of the acceleration excitation load signal in that direction at the test point represents the delay of the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point relative to the acceleration excitation load signal in that direction at the test point. The single-sided spectrum of the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point. ,satisfy Then, the cross-power spectral density function between the acceleration excitation load signal in this direction at the test point and the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point is... for:

[0014] .

[0015] Secondly, this application provides a vehicle body fatigue life prediction device, the device comprising: a receiving module configured to receive acceleration excitation load signals in at least one direction from a plurality of test points on the vehicle body; a self-power spectral density function calculation module configured to calculate the corresponding self-power spectral density function based on the acceleration excitation load signal of each test point; and a cross-power spectral density function calculation module configured to calculate the cross-power spectral density function between each test point and other test points based on the self-power spectral density function of each test point.

[0016] Preferably, it also includes a stress distribution transfer function module, configured to input the cross power spectral density function of each test point into the multi-axis random vibration calculation module of the Optistrut software, and obtain the stress distribution transfer function of the vehicle body from the multi-axis random vibration calculation module of the Optistrut software; and a fatigue life prediction distribution map module, configured to input the stress distribution transfer function into the fatigue life prediction module of the Hyperlife software to obtain the fatigue life prediction distribution map of the vehicle body.

[0017] Thirdly, this application provides a vehicle body fatigue life prediction device, the device including a memory and a processor, the memory storing a first computer program, which, when executed by the processor, implements one of the above-described vehicle body fatigue life prediction methods.

[0018] Preferably, the memory also stores computer programs for Optistrut software and Hyperlife software, and when the first computer program, the computer program for Optistrut software, and the computer program for Hyperlife software are executed by the processor, the above-described vehicle body fatigue life prediction method is implemented.

[0019] Fourthly, this application provides a vehicle body fatigue life prediction system, including the aforementioned vehicle body fatigue life prediction device, data acquisition unit, several acceleration sensors, and display device; the several acceleration sensors are respectively installed on several test points on the vehicle body to detect the acceleration excitation load signal at the corresponding test point and send it to the data acquisition unit; the several acceleration sensors and the data acquisition unit are all communicatively connected; the data acquisition unit is used to receive the acceleration excitation load signal detected by the several acceleration sensors and send the acceleration excitation load signal detected by the several acceleration sensors to the vehicle body fatigue life prediction device; the data acquisition unit is communicatively connected to the vehicle body fatigue life prediction device; the display device is used to receive the fatigue life prediction distribution map from the vehicle body fatigue life prediction device and display it.

[0020] Preferably, six acceleration sensors are provided, which are respectively located on the springs on both sides of the front shock absorber, both sides of the rear shock absorber, and both sides of the rear axle.

[0021] Preferably, the accelerometer is a 3-axis accelerometer.

[0022] Fifthly, this application provides a storage medium storing computer-readable instructions that, when executed by a processor, perform the method according to any one of the preceding descriptions.

[0023] The present invention provides a method, apparatus, device, system, and storage medium for predicting vehicle body fatigue life, which has the following beneficial effects:

[0024] This application employs several 3-axis accelerometers to conduct actual measurements on the vehicle body, obtaining acceleration load excitation signals at multiple test points. Based on these signals, the self-power spectral density function (SPDC) of each test point is calculated. Then, the corresponding cross-power spectral density function (CPSDC) is calculated based on the SPDC of each test point. Finally, using the multi-axis random vibration calculation module of Optistrut software and the fatigue life prediction module of Hyperlife software, the calculated CPSDC of several test points can be used to calculate the fatigue life prediction distribution map of the vehicle body, thus achieving the prediction of vehicle body fatigue life. This application, through actual measurement of the vehicle body's acceleration load, better reflects the actual load conditions of the vehicle body compared to the quasi-static superposition method in existing technologies, helping to improve the accuracy of vehicle body fatigue prediction results. Furthermore, by calculating the SPDC and CPSDC, the resonance situation caused when the excitation frequency is close to the natural frequency of the vehicle body is considered, which can more accurately reflect the fatigue damage of the vehicle body structure. In addition, by developing an automatic calculation program for the SPDC and CPSDC of multi-axis acceleration loads, fatigue prediction of the vehicle body becomes more convenient. Attached Figure Description

[0025] To better understand the above and other objects, features, advantages, and functions of the present invention, reference can be made to the embodiments shown in the accompanying drawings. The same reference numerals in the drawings refer to the same parts. Those skilled in the art should understand that the drawings are intended to schematically illustrate preferred embodiments of the invention and do not limit the scope of the invention in any way; the parts in the drawings are not drawn to scale.

[0026] Figure 1 A flowchart of a vehicle body fatigue life prediction method according to an embodiment of the present invention is shown;

[0027] Figure 2 A schematic diagram showing the installation position of the acceleration sensor in a vehicle body fatigue life prediction method according to an embodiment of the present invention is shown.

[0028] Figure 3 A schematic diagram of a multi-axis acceleration excitation load signal in a vehicle body fatigue life prediction method according to an embodiment of the present invention is shown.

[0029] Figure 4 A schematic diagram of the self-power spectral density function of the multi-axis acceleration excitation load in a vehicle body fatigue life prediction method according to an embodiment of the present invention is shown.

[0030] Figure 5 A schematic diagram of the cross power spectral density function of multi-axis acceleration excitation load in a vehicle body fatigue life prediction method according to an embodiment of the present invention is shown.

[0031] Figure 6 The diagram shows the fatigue life prediction distribution in a vehicle body fatigue life prediction method according to an embodiment of the present invention.

[0032] Figure 7 A block diagram of a vehicle body fatigue life prediction device according to an embodiment of the present invention is shown;

[0033] Figure 8 A block diagram of a vehicle body fatigue life prediction system according to an embodiment of the present invention is shown. Detailed Implementation

[0034] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0035] The term "comprising" and its variations as used herein signify open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "at least partially based on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0036] To at least partially address one or more of the aforementioned problems and other potential issues, embodiments of this disclosure propose a method for predicting vehicle body fatigue life, such as... Figures 1 to 6 As shown, it includes the following steps:

[0037] Receiving Steps: Receive acceleration excitation load signals from at least one direction at several test points on the vehicle body; specifically, an acceleration sensor is installed at each test point on the vehicle body to detect the acceleration excitation load signal at the corresponding test point. Preferably, six acceleration sensors are provided, respectively located on the springs on both sides of the front shock absorber, both sides of the rear shock absorber, and both sides of the rear axle. Figure 2 As shown, the accelerometer is a 3-axis accelerometer used to detect the acceleration excitation load signals in the x, y, and z directions at the corresponding test points, respectively. Figure 3 As shown, several accelerometers send acceleration excitation load signals to the data acquisition unit, and then receive acceleration excitation load signals from each test point from the data acquisition unit.

[0038] Steps for calculating the self-power spectral density function: Calculate the corresponding self-power spectral density function based on the acceleration excitation load signal at each test point, such as... Figure 4 As shown; in a preferred embodiment, the accelerometer is a 3-axis accelerometer. In this step, based on the acceleration excitation load signals in the x, y, and z directions of each test point, the self-power spectral density function in the x, y, and z directions of each test point is calculated respectively. Specifically, assuming there are test points 1, 2, 3, 4, 5, and 6 on the vehicle body, the acceleration excitation load signals in the x, y, and z directions of test point 1, test point 2, test point 3, test point 4, test point 5, test point 6, and test point 6 are calculated respectively.

[0039] The steps for calculating the self-power spectral density function include: receiving an acceleration excitation load signal in one direction at a test point. Then the autocorrelation function in that direction at that test point for:

[0040] ,

[0041] in, Represents the time interval; the autocorrelation function After performing a Fourier transform, the bilateral self-power spectral density function is obtained. for:

[0042] ,

[0043] Then the one-sided spectrum for:

[0044] .

[0045] Steps for calculating the cross-power spectral density function: Based on the self-power spectral density function of each test point, calculate the cross-power spectral density function between each test point and other test points, such as... Figure 5 As shown, specifically, when each test point has only one direction of acceleration excitation load signal, assuming there are test points 1, 2, 3, 4, 5, and 6 on the vehicle body, when calculating the cross power spectral density function of test point 1, the cross power spectral density functions between test points 1 and 2, 3, 4, 5, and 6 are calculated respectively, and the cross power spectral density functions between test points 1 and 6 are calculated respectively. Similarly, the cross power spectral density functions of test points 2, 3, 4, 5, and 6 are calculated respectively.

[0046] In a preferred embodiment, the accelerometer is a 3-axis accelerometer. In this step, based on the self-power spectral density functions in the x, y, and z directions of each test point, the cross-power spectral density functions between each test point and other test points in the x, y, and z directions are calculated, respectively. Furthermore, the cross-power spectral density functions between each pair of test points in the x, y, and z directions are also calculated. Specifically, assuming there are test points 1, 2, 3, 4, 5, and 6 on the vehicle body, when calculating the cross-power spectral density function of test point 1, the cross-power spectral density functions between the x and y directions of test point 1 are calculated. The power spectral density function, the cross-power spectral density function between the x and z directions of test point 1, the cross-power spectral density function between the y and z directions of test point 1, the cross-power spectral density function between the x and x directions of test point 1 and test point 2, the cross-power spectral density function between the x and y directions of test point 1 and test point 2, the cross-power spectral density function between the x and z directions of test point 1 and test point 2, the cross-power spectral density function between the y and x directions of test point 1 and test point 2, the cross-power spectral density function between the y and z directions of test point 1 and test point 2, and the cross-power spectral density function between the y and z directions of test point 1 and test point 2. The cross-power spectral density functions between the z-direction and the x-direction of test point 2, the z-direction of test point 1 and the y-direction of test point 2, the z-direction of test point 1 and the z-direction of test point 2, the x-direction of test point 1 and the x-direction of test point 3, the x-direction of test point 1 and the y-direction of test point 3, the x-direction of test point 1 and the z-direction of test point 3, the y-direction of test point 1 and the x-direction of test point 3, the y-direction of test point 1 and the y-direction of test point 3, and the z-direction of test point 1 and the z-direction of test point 3. The cross-power spectral density functions between test points 1 and 3 (z-direction), 3 and 4 (x-direction), 4 and 5 (y-direction), 6 and 7 respectively.The cross-power spectral density functions between the y-direction of test point 1 and the z-direction of test point 4, the cross-power spectral density functions between the z-direction of test point 1 and the x-direction of test point 4, the cross-power spectral density functions between the z-direction of test point 1 and the y-direction of test point 4, the cross-power spectral density functions between the z-direction of test point 1 and the z-direction of test point 4, the cross-power spectral density functions between the x-direction of test point 1 and the x-direction of test point 5, the cross-power spectral density functions between the x-direction of test point 1 and the y-direction of test point 5, the cross-power spectral density functions between the x-direction of test point 1 and the z-direction of test point 5, the cross-power spectral density functions between the y-direction of test point 1 and the x-direction of test point 5, the cross-power spectral density functions between the y-direction of test point 1 and the y-direction of test point 5, the cross-power spectral density functions between the y-direction of test point 1 and the z-direction of test point 5, the cross-power spectral density functions between the z-direction of test point 1 and the x-direction of test point 5, and the cross-power spectral density functions between the z-direction of test point 1 and the y-direction of test point 5. The power spectral density function, the cross-power spectral density function between test point 1 and test point 5 in the z-direction, the cross-power spectral density function between test point 1 and test point 6 in the x-direction, the cross-power spectral density function between test point 1 and test point 6 in the x-direction, the cross-power spectral density function between test point 1 and test point 6 in the y-direction, the cross-power spectral density function between test point 1 and test point 6 in the z-direction, the cross-power spectral density function between test point 1 and test point 6 in the y-direction, the cross-power spectral density function between test point 1 and test point 6 in the y-direction, the cross-power spectral density function between test point 1 and test point 6 in the z-direction, the cross-power spectral density function between test point 1 and test point 6 in the z-direction, the cross-power spectral density function between test point 1 and test point 6 in the z-direction, and so on, are calculated for test points 2, 3, 4, 5, and 6 respectively.

[0047] The steps for calculating the cross-power spectral density function include: receiving an acceleration excitation load signal in one direction at a test point. The acceleration excitation load signal in another direction of the test point or the acceleration excitation load signal in one direction of other test points is... ,in The single-sided spectrum of the acceleration excitation load signal in that direction at the test point represents the delay of the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point relative to the acceleration excitation load signal in that direction at the test point. The single-sided spectrum of the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point. ,satisfy Then, the cross-power spectral density function between the acceleration excitation load signal in this direction at the test point and the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point is... for:

[0048] .

[0049] In a specific embodiment, the method of this application further includes the following steps: Obtaining the transfer function of stress distribution: inputting the cross-power spectral density function of each test point obtained above into the multi-axis random vibration calculation module of the Optistrut software, and obtaining the transfer function of the vehicle body with respect to stress distribution from the multi-axis random vibration calculation module of the Optistrut software; Obtaining the fatigue life prediction distribution map: inputting the transfer function of stress distribution into the fatigue life prediction module of the Hyperlife software to obtain the fatigue life prediction distribution map of the vehicle body, such as... Figure 6 As shown.

[0050] This application also provides a vehicle body fatigue life prediction device, such as Figure 7 As shown, the device includes: a receiving module configured to receive acceleration excitation load signals in at least one direction from a plurality of test points on the vehicle body; a self-power spectral density function calculation module configured to calculate the corresponding self-power spectral density function based on the acceleration excitation load signal of each test point; and a cross-power spectral density function calculation module configured to calculate the cross-power spectral density function between each test point and other test points based on the self-power spectral density function of each test point. Preferably, the device further includes a stress distribution transfer function module configured to input the cross-power spectral density function of each test point into the multi-axis random vibration calculation module of Optistrut software, and obtain the stress distribution transfer function of the vehicle body from the multi-axis random vibration calculation module of Optistrut software; and a fatigue life prediction distribution map module configured to input the stress distribution transfer function into the fatigue life prediction module of Hyperlife software to obtain the fatigue life prediction distribution map of the vehicle body.

[0051] This application also provides a vehicle body fatigue life prediction device, the device including a memory and a processor. The memory stores a first computer program, which, when executed by the processor, implements one of the vehicle body fatigue life prediction methods described above. Preferably, the memory also stores computer programs for Optistrut software and Hyperlife software, which, when executed by the processor, implement one of the vehicle body fatigue life prediction methods described above.

[0052] This application also provides a vehicle body fatigue life prediction system, such as Figure 8 As shown, the device includes the aforementioned vehicle body fatigue life prediction equipment, a data acquisition unit, several acceleration sensors, and a display device. The acceleration sensors are preferably 3-axis acceleration sensors. Several acceleration sensors are installed at several test points on the vehicle body to detect the acceleration excitation load signal at the corresponding test points and send it to the data acquisition unit. Preferably, six acceleration sensors are provided, respectively located on the springs on both sides of the front shock absorber, both sides of the rear shock absorber, and both sides of the rear axle. Figure 2 As shown; several acceleration sensors are all communicatively connected to the data acquisition unit; the data acquisition unit is used to receive the acceleration excitation load signals detected by the several acceleration sensors and send the acceleration excitation load signals detected by the several acceleration sensors to the vehicle body fatigue life prediction device; the data acquisition unit is communicatively connected to the vehicle body fatigue life prediction device; the display device is used to receive the fatigue life prediction distribution map from the vehicle body fatigue life prediction device and display it.

[0053] This application also provides a storage medium storing computer-readable instructions that, when executed by a processor, perform the method according to any one of the preceding descriptions.

[0054] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand this document.

Claims

1. A method for predicting the fatigue life of a vehicle body, characterized in that: Includes the following steps: Receiving steps: Receive acceleration excitation load signals in at least one direction from several test points on the vehicle body; Steps for calculating the self-power spectral density function: Calculate the corresponding self-power spectral density function based on the acceleration excitation load signal at each test point; Steps for calculating the cross-power spectral density function: Based on the self-power spectral density function of each test point, calculate the cross-power spectral density function between each test point and other test points.

2. The method for predicting vehicle body fatigue life according to claim 1, characterized in that: It also includes the following steps: The steps to obtain the stress distribution transfer function are as follows: input the cross power spectral density function of each test point into the multi-axis random vibration calculation module of the Optistrut software, and obtain the stress distribution transfer function of the vehicle body from the multi-axis random vibration calculation module of the Optistrut software. Steps to obtain the fatigue life prediction distribution map: Input the transfer function about the stress distribution into the fatigue life prediction module of the Hyperlife software to obtain the fatigue life prediction distribution map of the vehicle body.

3. The method for predicting vehicle body fatigue life according to claim 1, characterized in that: Acceleration excitation load signals in the x, y, and z directions are obtained at each test point. In the step of calculating the self-power spectral density function, the self-power spectral density function in the x, y, and z directions of each test point is calculated according to the acceleration excitation load signals in the x, y, and z directions of each test point. In the step of calculating the cross-power spectral density function, the cross-power spectral density function between each test point and the x, y, and z directions of other test points is calculated according to the self-power spectral density function in the x, y, and z directions of each test point. The step of calculating the cross-power spectral density function also includes calculating the cross-power spectral density function between each pair of test points in the x, y, and z directions.

4. The method for predicting vehicle body fatigue life according to claim 1, characterized in that: The calculation steps of the self-power spectral density function include: receiving an acceleration excitation load signal in one direction at a certain test point. Then the autocorrelation function in that direction at that test point for: ， in, Represents the time interval; the autocorrelation function After performing a Fourier transform, the bilateral self-power spectral density function is obtained. for: ， Then the one-sided spectrum for: 。 5. The method for predicting vehicle body fatigue life according to claim 4, characterized in that: The calculation steps of the cross-power spectral density function include: receiving an acceleration excitation load signal in one direction at a certain test point. The acceleration excitation load signal in another direction of the test point or the acceleration excitation load signal in one direction of other test points is... ,in The single-sided spectrum of the acceleration excitation load signal in that direction at the test point represents the delay of the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point relative to the acceleration excitation load signal in that direction at the test point. The single-sided spectrum of the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point. ,satisfy Then, the cross-power spectral density function between the acceleration excitation load signal in this direction at the test point and the acceleration excitation load signal in another direction at the test point or the acceleration excitation load signal in one direction at another test point is... for: 。 6. A vehicle body fatigue life prediction device, characterized in that: The device includes: The receiving module is configured to receive acceleration excitation load signals in at least one direction from several test points on the vehicle body; The module for calculating the self-power spectral density function is configured to calculate the corresponding self-power spectral density function based on the acceleration excitation load signal at each test point. The module for calculating the cross-power spectral density function is configured to calculate the cross-power spectral density function between each test point and other test points based on the self-power spectral density function of each test point.

7. A vehicle body fatigue life prediction device, characterized in that: The device includes a memory and a processor, wherein the memory stores a first computer program that, when executed by the processor, implements a vehicle body fatigue life prediction method as described in claim 1, 3, 4, or 5.

8. The vehicle body fatigue life prediction device according to claim 7, characterized in that: The memory also stores computer programs for Optistrut software and Hyperlife software. When the processor executes the first computer program, the computer program for Optistrut software, and the computer program for Hyperlife software, it implements the vehicle body fatigue life prediction method according to any one of claims 1 to 5.

9. A vehicle body fatigue life prediction system, characterized in that: Includes a vehicle body fatigue life prediction device, a data acquisition instrument, several acceleration sensors, and a display device as described in claim 8; Several acceleration sensors are installed at several test points on the vehicle body to detect the acceleration excitation load signal at the corresponding test points and send it to the data acquisition unit; the acceleration sensors are all communicatively connected to the data acquisition unit. The data acquisition unit is used to receive acceleration excitation load signals detected by several acceleration sensors and send these signals to the vehicle body fatigue life prediction device; the data acquisition unit is communicatively connected to the vehicle body fatigue life prediction device. The display device is used to receive and display fatigue life prediction distribution maps from the vehicle body fatigue life prediction equipment.

10. A storage medium, characterized in that: The device stores computer-readable instructions that, when executed by a processor, perform the method according to any one of claims 1, 3, 4, or 5.

Images