Modal-based pavement unevenness reconstruction method and device, electronic equipment and medium

Abstract

The embodiment of the invention relates to the technical field of vehicles, and discloses a modal-based pavement unevenness reconstruction method and device, electronic equipment and a medium. The method comprises the following steps: executing modal test operation based on an actual sample vehicle to obtain a whole vehicle frequency response function; establishing a wheel center virtual test point to expand a whole vehicle frequency response function to obtain a whole vehicle expanded frequency response function; constructing a first relational expression of the road surface unevenness and the whole vehicle acquisition point frequency response function at least based on the whole vehicle frequency response function and the whole vehicle extended frequency response function; collecting vehicle vibration data of the actual operation road condition based on the vehicle collection point; based on the whole vehicle vibration data and the first relational expression, the spatial power spectral density of the road surface unevenness of each road section is obtained; and determining the pavement unevenness of the acquired pavement based on the spatial power spectral density. According to the invention, the accuracy of the road surface unevenness identification result can be improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a modal-based method, apparatus, electronic device, and medium for reconstructing road surface roughness. Background Technology

[0002] Road surface roughness is essential input data for calculating vehicle ride comfort, vehicle vibration response, and other performance parameters. Currently, this data is mainly obtained through scanning with physical equipment such as five-wheel drive systems and laser point cloud systems, or through identification based on vehicle vibration response.

[0003] In the process of vehicle development or solving market problems, road surface unevenness data under actual road conditions is often needed as a basis for analyzing the load of operating vehicles. Obviously, scanning with physical equipment requires purchasing or leasing specialized equipment, which is not only expensive but also takes too long. Therefore, vehicle vibration response is usually used for identification.

[0004] Patent CN202010769710.6, "Road Roughness Identification Method Based on Vehicle Frequency Domain Response," discloses a technical solution: based on the vehicle's measured frequency domain response, combined with a constructed frequency domain response function of "vehicle measured response with respect to vehicle-road contact point displacement," road roughness is identified. This method is mainly based on a 1 / 2 vehicle model and can identify road roughness in the vertical direction. Patent CN202311121512.9, "A Road Roughness Estimation Method Based on Vehicle Vibration Response," discloses another solution: based on a quarter-suspension model of the vehicle, the vehicle model parameters are first identified and solved, and then the road roughness in the vertical direction is obtained through discrete equations.

[0005] Research has found that existing technologies for identifying road surface unevenness through vehicle vibration response are mostly based on simplified vehicle models, and can only identify road surface unevenness in the vertical direction based on the model. However, in real-world scenarios, when a tire contacts the ground, it will generate movement in three directions: front-back, left-right, and vertical. That is, road surface unevenness has three directional components. At the same time, the accuracy of the identification results obtained based on existing models is relatively low. Summary of the Invention

[0006] The purpose of this invention is to provide a modal-based method, apparatus, electronic device, and medium for reconstructing road surface roughness, so as to at least solve the problem of the accuracy of road surface roughness identification results and improve the accuracy of identification results.

[0007] To address the aforementioned technical problems, in a first aspect, the present invention provides a modal-based method for reconstructing road surface roughness, comprising at least:

[0008] Modal testing was performed on an actual prototype vehicle to obtain the vehicle's frequency response function.

[0009] Establish virtual test points at the wheel center to extend the overall vehicle frequency response function and obtain the extended frequency response function of the vehicle;

[0010] At least based on the two-degree-of-freedom model of the vehicle and the extended frequency response function of the whole vehicle, a first relationship between road surface roughness and the frequency response function of the whole vehicle sampling points is established;

[0011] Vehicle vibration data is collected from the vehicle's data collection points based on actual operating road conditions.

[0012] Based on the vehicle vibration data and the first relationship, the spatial power spectral density of road surface unevenness for each road segment is obtained.

[0013] Optionally, the step of performing modal testing based on an actual prototype vehicle to obtain the vehicle's frequency response function specifically includes:

[0014] Modal testing was performed on the actual prototype vehicle to obtain the original frequency response function for at least each measurement point and each excitation point.

[0015] Modal analysis is performed based on each of the original frequency response functions to extract at least the whole vehicle modal parameter set corresponding to the preset frequency range and the set modal order;

[0016] The original frequency response function is reconstructed based on the vehicle modal parameter set to obtain the vehicle frequency response function.

[0017] Optionally, establishing virtual test points at the wheel center to extend the vehicle's frequency response function and obtain the extended vehicle frequency response function specifically includes:

[0018] Establish the virtual test point at the wheel center;

[0019] Within the preset frequency range, determine the velocity relationship between the triaxial acceleration, the center of mass acceleration, and the rigid body rotation angular velocity at any point on the Kth axle;

[0020] Based on the velocity relationship, wheel center acceleration, and the virtual test point at the wheel center, the mode shape vector in the whole vehicle modal parameter group is extended to obtain the extended mode shape vector;

[0021] The extended frequency response function of the whole vehicle is obtained based on the mode shape extension vector.

[0022] Optionally, the establishment of the first relationship between road surface roughness and the frequency response function of the vehicle's sampling points, based at least on the vehicle's two-degree-of-freedom model and the vehicle's extended frequency response function, specifically includes:

[0023] The second relation is determined based on the vertical road surface input model and the bottom fixed model of the vehicle with two degrees of freedom;

[0024] Based on the second relation, a third relation is established for road surface roughness and the extended frequency response function of the whole vehicle;

[0025] Based on the vehicle extended frequency response function, the relevant frequency response function within the vehicle extended frequency response function is obtained according to the actual acquisition point and the virtual wheel center excitation point;

[0026] The first relation is determined based on the third relation and the relevant frequency response function to determine the road surface unevenness and the frequency response function of the whole vehicle acquisition point.

[0027] Optionally, obtaining the spatial power spectral density of road surface unevenness for each road segment based on the vehicle vibration data and the first relational expression specifically includes:

[0028] The triaxial excitation expression for the road surface of the Kth axle is determined based on the first relation.

[0029] Based on the triaxial excitation expression and the vehicle vibration data, the Fourier spectrum of road surface unevenness for each road segment is obtained.

[0030] Based on the Fourier spectrum, the time power spectral density of road surface roughness under preset conditions is obtained;

[0031] The corresponding spatial power spectral density is determined based on the temporal power spectral density of each road segment.

[0032] Secondly, the present invention also provides a road surface roughness reconstruction device, comprising at least:

[0033] The first frequency response module is used to perform modal testing operations based on the actual prototype vehicle in order to obtain the frequency response function of the whole vehicle.

[0034] The second frequency response module is used to establish a virtual test point at the wheel center to extend the overall vehicle frequency response function and obtain the extended frequency response function of the whole vehicle.

[0035] The relationship building module is used to build a first relationship between road surface roughness and the frequency response function of the vehicle acquisition points, based at least on the vehicle frequency response function and the vehicle extended frequency response function;

[0036] The real-scene acquisition module is used to collect vehicle vibration data based on actual operating road conditions from the vehicle's acquisition points;

[0037] The density calculation module is used to obtain the spatial power spectral density of road surface unevenness for each road segment based on the vehicle vibration data and the first relational formula.

[0038] The roughness conversion module is used to determine the road roughness of the collected road surface based on the spatial power spectral density.

[0039] Optionally, the first frequency response module is specifically used for:

[0040] Modal testing is performed on an actual prototype vehicle to obtain the original frequency response function corresponding to at least each measurement point and each excitation point; modal analysis is performed on each of the original frequency response functions to extract the whole vehicle modal parameter set corresponding to a preset frequency range and a set modal order; and the original frequency response function is reconstructed based on the whole vehicle modal parameter set to obtain the whole vehicle frequency response function.

[0041] Optionally, the second frequency response module is specifically used for:

[0042] Establish the virtual test point at the wheel center; and, within the preset frequency range, determine the velocity relationship between the triaxial acceleration, center of mass acceleration, and rigid body rotational angular velocity at any point on the Kth axle; and, based on the velocity relationship, the wheel center acceleration, and the virtual test point at the wheel center, extend the mode shape vector in the whole vehicle modal parameter set to obtain the extended mode shape vector; and, based on the whole vehicle frequency response function and the extended mode shape vector, obtain the whole vehicle extended frequency response function.

[0043] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that the processor, when executing the program, implements the steps in the modal-based road surface roughness reconstruction method according to any one of the first aspects.

[0044] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps in the modal-based road surface roughness reconstruction method according to any one of the first aspects.

[0045] The technical solution provided by this invention firstly involves performing modal testing on an actual prototype vehicle to obtain the vehicle's frequency response function; secondly, establishing virtual test points at the wheel centers to extend the vehicle's frequency response function and obtain the extended vehicle frequency response function; thirdly, establishing a first relationship between road surface roughness and the frequency response function of the vehicle's sampling points, based at least on the vehicle's frequency response function and the extended vehicle frequency response function; and fourthly, collecting vehicle vibration data under actual operating road conditions based on the vehicle's sampling points. Then, based on the vehicle vibration data and the first relationship, obtaining the spatial power spectral density of the road surface roughness for each road segment; finally, determining the road surface roughness of the collected road surface based on the spatial power spectral density.

[0046] Therefore, this invention, on the one hand, obtains the vehicle frequency response function based on modal testing of an actual prototype vehicle, and then expands it to obtain the extended vehicle frequency response function by combining the virtual test points at the wheel centers. This allows for a more comprehensive and accurate understanding of the vehicle's frequency response characteristics under different conditions, laying the foundation for subsequent analysis of the relationship between road surface and vehicle vibration. On the other hand, this invention establishes a first relational formula between road surface roughness and the frequency response function of the vehicle's sampling points, realizing a mathematical correlation between road surface excitation and vehicle vibration response. This enables the deduction of road surface roughness from vehicle vibration data. Furthermore, based on the vehicle vibration data and the first relational formula, the spatial power spectral density of road surface roughness for each road segment can be obtained. This eliminates the need for complex direct road surface measurement equipment and methods, and efficiently and conveniently obtains key indicators reflecting the degree of road surface roughness. Attached Figure Description

[0047] Figure 1 This is a flowchart of a modal-based road surface roughness reconstruction method provided in an embodiment of the present invention;

[0048] Figure 2 This is a flowchart of another modal-based road surface roughness reconstruction method provided in this embodiment of the invention;

[0049] Figure 3 This is a vehicle two-degree vertical road surface input model provided in an embodiment of the present invention;

[0050] Figure 4 This is a two-degree-of-freedom bottom-fixed vehicle model provided in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the structure of a road surface roughness reconstruction device provided in an embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0055] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0056] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0057] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0058] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0059] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0060] Figure 1 This is a flowchart of a modal-based road surface roughness reconstruction method provided by an embodiment of the present invention. This embodiment is applicable to road surface roughness reconstruction scenarios for at least various types of vehicles. The modal-based road surface roughness reconstruction method can be, but is not limited to, executed by the road surface roughness reconstruction device in this embodiment of the present invention as the execution subject. This execution subject can be implemented in software and / or hardware. Figure 1 As shown, the modal-based road surface roughness reconstruction method includes at least the following steps:

[0061] S1. Perform modal testing based on the actual prototype vehicle to obtain the vehicle's frequency response function.

[0062] The prototype vehicle should include dummies in the left and right driver's cab seats, and the configuration should be the same as that of vehicles in actual operation, mainly referring to cargo vehicles using non-disconnectable axles. Modal testing should include at least the axles and frame measurement points.

[0063] S2. Establish a virtual test point at the wheel center to extend the overall vehicle frequency response function and obtain the extended frequency response function of the whole vehicle.

[0064] The purpose of constructing a virtual test point at the wheel center is that the wheel center does not fall within the observable range of the response, so it is necessary to construct a virtual test point at the wheel center.

[0065] S3. At least based on the vehicle's two-degree-of-freedom model and the vehicle's extended frequency response function, establish the first relationship between road surface unevenness and the frequency response function of the vehicle's sampling points.

[0066] S4. Collect vehicle vibration data based on actual operating road conditions from the whole vehicle collection points.

[0067] Each axle should have at least three vehicle-wide data collection points, and each data collection point should be measured using a triaxial accelerometer. The number of data collection points can also be arranged according to the actual needs of the testing personnel.

[0068] During data collection, due to varying road conditions, the collection route needs to be segmented. Vehicles must maintain a constant speed during collection, and when collecting whole-vehicle vibration data, the actual vehicle speed and the total number of collection segments for each segment should be recorded simultaneously. Therefore, vehicles should strive to maintain a straight line as much as possible.

[0069] S5. Based on the whole vehicle vibration data and the first relational formula, obtain the spatial power spectral density of the road surface unevenness of each road segment.

[0070] S6. Determine the road surface roughness based on the spatial power spectral density.

[0071] The technical solution provided in this embodiment firstly involves performing modal testing on an actual prototype vehicle to obtain the overall vehicle frequency response function; secondly, establishing virtual test points at the wheel centers to extend the overall vehicle frequency response function and obtain the extended overall vehicle frequency response function; thirdly, establishing a first relationship between road surface roughness and the frequency response function of the vehicle's sampling points, based at least on the overall vehicle frequency response function and the extended overall vehicle frequency response function; fourthly, collecting vehicle vibration data under actual operating road conditions based on the vehicle's sampling points. Then, based on the vehicle vibration data and the first relationship, obtaining the spatial power spectral density of road surface roughness for each road segment; finally, determining the road surface roughness of the collected road surface based on the spatial power spectral density.

[0072] Therefore, this embodiment, on the one hand, obtains the vehicle frequency response function based on modal testing of the actual prototype vehicle, and then expands it to obtain the extended vehicle frequency response function by combining the virtual test points at the wheel centers. This allows for a more comprehensive and accurate understanding of the vehicle's frequency response characteristics under different conditions, laying the foundation for subsequent analysis of the relationship between road surface and vehicle vibration. On the other hand, this embodiment establishes a first relational formula between road surface roughness and the frequency response function of the vehicle's sampling points, realizing a mathematical correlation between road surface excitation and vehicle vibration response. This allows for the inference of road surface roughness from vehicle vibration data. Furthermore, based on the vehicle vibration data and the first relational formula, the spatial power spectral density of road surface roughness for each road segment can be obtained. This eliminates the need for complex direct road surface measurement equipment and methods, enabling efficient and convenient acquisition of key indicators reflecting the degree of road surface roughness.

[0073] Based on the above embodiments or implementation methods Figure 2 This is a flowchart of another modal-based road surface roughness reconstruction method provided by an embodiment of the present invention. This embodiment is based on the above embodiment with additions. Figure 2 As shown, the modal-based road surface roughness reconstruction method includes at least the following steps:

[0074] S11. Perform modal testing based on the actual prototype vehicle to obtain the original frequency response function corresponding to at least each measurement point and each excitation point.

[0075] In modal testing, the response at the m-th point of all M test points... Incentive points The frequency response function can be expressed as .

[0076] In the formula, H represents the original frequency response function, and mn represents the response at the m-th point caused by the excitation at the n-th point. It can be understood that the number of excitation points is no less than 2.

[0077] S12. Perform modal analysis based on each original frequency response function to extract at least the whole vehicle modal parameter set corresponding to the preset frequency range and the set modal order.

[0078] Within the preset frequency range, the cutoff frequency f can also be understood as... c Within the specified range. The modal order can be set to r-order modes. The vehicle modal parameter set can include the damping ratio ζ. r Undamped natural frequency f r , mode vector φ r Mode A vector a r .

[0079] It is known that the R-order modal elements of the whole vehicle (obtained from modal testing) are rearranged, where the mode shape vector φ r It can be summarized as follows:

[0080] ;

[0081] in: This represents the mode shape vectors of the 1st, 2nd, ..., sth non-disconnected axles. This represents elements not located on the axle.

[0082] Similarly, the response and incentive points can be summarized as follows:

[0083] , ;

[0084] In the formula, Y0, Y1, Y2…Y s Let F0, F1, F2…F represent the response vector of the s-th non-disconnected axle. s Let represent the excitation vector of the s-th non-disconnected axle.

[0085] S13. Reconstruct the original frequency response function based on the vehicle modal parameter set to obtain the vehicle frequency response function.

[0086] (Equation 1);

[0087] Where H represents the vehicle frequency response function, j represents the imaginary unit, ω represents the angular frequency, and T in the upper right corner represents the transpose. It can be seen that the relationship between the response vector Y and the excitation vector F is: (Equation 2).

[0088] S21. Establish a virtual test point at the wheel center.

[0089] S22. Within the preset frequency range, determine the velocity relationship between the triaxial acceleration, the center of mass acceleration, and the rigid body rotation angular velocity at any point on the Kth axle.

[0090] The triaxial acceleration can be the acceleration of any point on the axle along the x, y, and z axes in the inertial coordinate system. In this embodiment, considering that the vehicle is a non-disconnectable axle and the highest frequency of interest on the road surface is less than the first-order mode of the non-disconnectable axle, the non-disconnectable axle can be considered a rigid body within the highest frequency range of interest on the road surface. Furthermore, considering that the wheel center does not fall within the observable range of the response, a virtual test point w is established at the wheel center, and the vector φ of the i-th non-disconnectable axle is considered. ir Therefore, within the preset frequency range, the k-th axle can be approximated as a rigid body, and the triaxial acceleration at any point p on it is... With the acceleration of the center of mass and rigid body rotational angular velocity The relationship is:

[0091] ;

[0092] Where c represents the nth centroid, rcp =r op -r oc o is the inertial coordinate system The origin, For point p to The vector, For the center of mass c to The vector.

[0093] Based on the above formula, the velocity relationship can be deduced as follows:

[0094] (Equation 3);

[0095] In the formula, Here, E is the coefficient matrix; E is the identity matrix.

[0096] ;

[0097] Let x be the rigid body acceleration of the k-th axle. x, y, and z represent the x-axis, y-axis, and z-axis directions in the inertial coordinate system.

[0098] S23. Based on the velocity relationship, wheel center acceleration, and wheel center virtual test point, extend the mode shape vector in the whole vehicle modal parameter group to obtain the mode shape extension vector.

[0099] Furthermore, the relationship between the acceleration of all measuring points on the k-th axle and the rigid body acceleration can be obtained as follows:

[0100] (Equation 4);

[0101] Wheel center acceleration for:

[0102] (Equation 5);

[0103] The acceleration of the wheel center can also be expressed as: It includes the acceleration of the left and right wheel centers, and the acceleration at each point corresponds to acceleration in three directions. The coefficient matrix corresponding to the accelerations of the left and right wheel centers.

[0104] r-th order mode vector middle:

[0105] ;

[0106] The response of the virtual test point w at the wheel center of the k-th axle

[0107] (Formula 6);

[0108] Obtain the extended r-th order mode vector (i.e., mode extension vector):

[0109] (Equation 7);

[0110] S24. Obtain the extended frequency response function of the whole vehicle based on the whole vehicle frequency response function and the mode shape extension vector.

[0111] Among them, the extended frequency response function H of the whole vehicle E The expression is:

[0112] (Equation 8);

[0113] Then the response vector Y E With the extended activation vector F E The relationship is:

[0114] (Equation 9 can be derived from Equation 2);

[0115] S31. Determine the second relation based on the vertical road surface input model and the bottom fixed model of the vehicle with two degrees of freedom.

[0116] in, Figure 3 This is a vehicle two-degree vertical road surface input model provided in an embodiment of the present invention, such as... Figure 3 As shown, consider the vehicle's two-degree vertical road surface input model:

[0117] ;

[0118] (Equation 10);

[0119] In the formula, m1 and m2 represent the masses of two degrees of freedom, C represents the connection damping between m1 and m2, and K represents the connection stiffness between m1 and m2. t Z represents tire stiffness, q represents road surface unevenness, z1 represents displacement of m1, and z2 represents displacement of m2.

[0120] Performing a Fourier transform on Equation 10 and rearranging it, we get:

[0121] (Equation 11);

[0122] in, Represents the frequency response function matrix. express The Fourier transform of q is given by Q, where Q represents the Fourier transform of road surface roughness q.

[0123] Figure 4 This is a two-degree-of-freedom bottom-fixed vehicle model provided in an embodiment of the present invention, such as... Figure 4As shown, considering a two-degree-of-freedom vehicle model with a fixed bottom, we have:

[0124] ;

[0125] (Equation 12);

[0126] Where f1 and f2 represent the excitation forces of m1 and m2, respectively.

[0127] Performing a Fourier transform on Equation 12 and rearranging it, we get:

[0128] (Equation 13)

[0129] Where: F1 and F2 are the Fourier transforms of the excitation forces f1 and f2, respectively. Represents the frequency response function matrix. express Fourier transform.

[0130] Comparing Equations 11 and 13, it can be seen that the frequency response function matrix of the vehicle's two-degree-of-freedom vertical road surface input model is the same as that of the vehicle's two-degree-of-freedom bottom-fixed model. Therefore, the road surface unevenness excitation of the vehicle's two-degree-of-freedom vertical road surface input model can be equivalent to the excitation force F1 at point m1, i.e.:

[0131] (Second relation);

[0132] Similarly, the conclusions of the two-degree-of-self model can be extended to the whole vehicle model.

[0133] S32. Based on the second relation, construct the third relation for road surface unevenness and the extended frequency response function of the whole vehicle.

[0134] Based on the conclusion of step S31, the relationship between the road surface roughness input and the vehicle frequency response function can be established, that is, Equation 9 can be transformed into:

[0135] (Equation 14, i.e., the third relation);

[0136] S33. Obtain the relevant frequency response function within the extended frequency response function of the whole vehicle based on the actual acquisition points and the virtual wheel center excitation points.

[0137] For example, if the actual set of sampling points for the whole vehicle frequency response function is ,but:

[0138] ;

[0139] in: , … This refers to the set of responses from actual data collection points on axles 1, 2, ..., s of the entire vehicle. This refers to the set of responses from all actual data collection points on the vehicle, excluding those on the axles.

[0140] Furthermore, the extended frequency response function of the entire vehicle is derived from the actual sampling points and the virtual wheel center excitation points, based on the overall vehicle frequency response. Filter out relevant frequency response functions .

[0141] S34. Determine the first relational expression for road surface unevenness and the frequency response function of the whole vehicle acquisition point based on the third relational expression and the relevant frequency response function.

[0142] Understandably, Equation 14 can be further simplified to:

[0143] (Equation 15, i.e., the first relation);

[0144] In the formula, Excitation for the k-th axle (Equation 16).

[0145] S4. Collect vehicle vibration data based on actual operating road conditions from the whole vehicle collection points.

[0146] S51. Determine the triaxial excitation expression of the road surface of the Kth axle based on the first relation.

[0147] It can be seen that in Equation 16, , This is a 3x3 diagonal matrix representing the triaxial (x, y, z) stiffness of the tire on the k-th axle. , This represents the triaxial excitation of the road surface at the k-th axle.

[0148] Based on Equations 15 and 16, the three-dimensional excitation expression can be obtained:

[0149] (Three-way excitation expression);

[0150] In the formula, .

[0151] S52. Based on the triaxial excitation expression and vehicle vibration data, obtain the Fourier spectrum of road surface unevenness for each road segment.

[0152] After acquiring the vibration data for each vehicle segment, a Fourier transform needs to be performed on the vibration data of the i-th segment of the road to obtain the data. .

[0153] based on The Fourier spectrum of the road surface roughness of the i-th segment can be obtained from the three-dimensional excitation expression. :

[0154] .

[0155] S53. Based on the Fourier spectrum, obtain the time power spectral density of road surface unevenness under preset conditions.

[0156] Among them, according to Fourier spectrum It can be converted into power spectral density. The specific conversion method is not limited in this embodiment. Power spectral density :

[0157] ;

[0158] In the formula, f represents the frequency in Hz.

[0159] In this embodiment, the vehicle maintains a constant speed and travels in a straight line when collecting vibration data of the entire vehicle, so the power spectral density of each axle is the same. Therefore, the time power spectral density of the road surface unevenness of the i-th segment can be obtained as follows:

[0160] Left side of the vehicle: ;

[0161] Right side of the vehicle: ;

[0162] S54. Determine the corresponding spatial power spectral density based on the temporal power spectral density of each road segment.

[0163] Based on the processing result of step S53, the spatial power spectral density can be obtained:

[0164] Left side of the vehicle: ;

[0165] Right side of the vehicle: ;

[0166] In the formula, n represents the spatial frequency m -1 .

[0167] It can be seen that the spatial power spectral density of road surface roughness for each road segment can be obtained by repeating steps S51-S53.

[0168] S6. Determine the road surface roughness based on the spatial power spectral density.

[0169] The road surface roughness can be determined by analyzing the curve shape, peak position, and attenuation characteristics of the spatial power spectral density. This embodiment limits the specific analysis method.

[0170] The technical solution provided in this embodiment firstly involves performing modal testing on an actual prototype vehicle to obtain the original frequency response function corresponding to at least each measurement point and each excitation point. Further, modal analysis is performed based on each original frequency response function to extract the whole vehicle modal parameter set corresponding to at least a preset frequency range and a set modal order. Further, the original frequency response function is reconstructed based on the whole vehicle modal parameter set to obtain the whole vehicle frequency response function. Further, a virtual test point at the wheel center is established. Further, within a preset frequency range, the velocity relationship between the triaxial acceleration, center-of-mass acceleration, and rigid body rotational angular velocity at any point on the Kth axle is determined. Further, the mode shape vector in the whole vehicle modal parameter set is expanded based on the velocity relationship, wheel center acceleration, and the virtual test point at the wheel center to obtain the mode shape expansion vector. Further, the whole vehicle extended frequency response function is obtained based on the mode shape expansion vector. Further, a second relationship is determined based on the vehicle's two-degree-of-freedom vertical road surface input model and bottom fixed model. Further, a third relationship is established based on the second relationship to establish the relationship between road surface unevenness and the whole vehicle extended frequency response function. Furthermore, the relevant frequency response function within the extended frequency response function of the whole vehicle is obtained based on the actual acquisition points and the virtual wheel center excitation points. Further, the first relationship between road surface roughness and the frequency response function of the whole vehicle acquisition points is determined based on the third relationship and the relevant frequency response function. Further, vehicle vibration data under actual operating road conditions is collected based on the whole vehicle acquisition points. Further, the triaxial excitation expression for the road surface of the Kth axle is determined based on the first relationship. Further, the Fourier spectrum of road surface roughness for each road segment is obtained based on the triaxial excitation expression and the whole vehicle vibration data. Further, the time power spectral density of road surface roughness under preset conditions is obtained based on the Fourier spectrum. Further, the corresponding spatial power spectral density is determined based on the time power spectral density of each road segment. Finally, the road surface roughness of the acquired road surface is determined based on the spatial power spectral density.

[0171] Therefore, this embodiment, on the one hand, obtains the vehicle frequency response function based on modal testing of the actual prototype vehicle, and then expands it to obtain the extended vehicle frequency response function by combining the virtual test points at the wheel centers. This allows for a more comprehensive and accurate understanding of the vehicle's frequency response characteristics under different conditions, laying the foundation for subsequent analysis of the relationship between road surface and vehicle vibration. On the other hand, this embodiment establishes a first relational formula between road surface roughness and the frequency response function of the vehicle's sampling points, realizing a mathematical correlation between road surface excitation and vehicle vibration response. This allows for the inference of road surface roughness from vehicle vibration data. Furthermore, based on the vehicle vibration data and the first relational formula, the spatial power spectral density of road surface roughness for each road segment can be obtained. This eliminates the need for complex direct road surface measurement equipment and methods, enabling efficient and convenient acquisition of key indicators reflecting the degree of road surface roughness.

[0172] Figure 5This is a schematic diagram of a road surface roughness reconstruction device provided in an embodiment of the present invention. This embodiment is applicable to road surface roughness reconstruction scenarios for at least various types of vehicles. The road surface roughness reconstruction device can be implemented using software and / or hardware. Figure 5 As shown, the road surface roughness reconstruction device includes at least:

[0173] The first frequency response module 110 is used to perform modal testing operations based on the actual prototype vehicle to obtain the frequency response function of the whole vehicle.

[0174] The second frequency response module 120 is used to establish a virtual test point at the wheel center to extend the overall vehicle frequency response function and obtain the extended frequency response function of the whole vehicle.

[0175] The relation building module 130 is used to build a first relation between road surface roughness and the frequency response function of the whole vehicle acquisition points, based at least on the vehicle's two-degree-of-freedom model and the vehicle's extended frequency response function.

[0176] The real-scene acquisition module 140 is used to collect vehicle vibration data based on actual operating road conditions from the vehicle acquisition points.

[0177] The density calculation module 150 is used to obtain the spatial power spectral density of road surface unevenness for each road segment based on the whole vehicle vibration data and the first relational formula.

[0178] The roughness conversion module 160 is used to determine the roughness of the collected road surface based on the spatial power spectral density.

[0179] Optionally, the first frequency response module 110 is specifically used for:

[0180] Modal testing is performed on the actual prototype vehicle to obtain the original frequency response function corresponding to at least each measurement point and each excitation point; modal analysis is performed on each original frequency response function to extract the whole vehicle modal parameter set corresponding to the preset frequency range and the set modal order; and the original frequency response function is reconstructed based on the whole vehicle modal parameter set to obtain the whole vehicle frequency response function.

[0181] Optionally, the second frequency response module 120 is specifically used for:

[0182] Establish a virtual test point at the wheel center; and, within a preset frequency range, determine the velocity relationship between the triaxial acceleration, center of mass acceleration, and rigid body rotational angular velocity at any point on the Kth axle; and, based on the velocity relationship, wheel center acceleration, and the virtual test point at the wheel center, expand the mode shape vector in the whole vehicle modal parameter set to obtain the mode shape extension vector; and, based on the whole vehicle frequency response function and the mode shape extension vector, obtain the whole vehicle extended frequency response function.

[0183] Optionally, the relationship building module 130 is specifically used for:

[0184] The second relation is determined based on the vertical road surface input model and the bottom fixed model of the vehicle with two degrees of freedom; and the third relation is established based on the second relation for road surface unevenness and the extended frequency response function of the whole vehicle; the corresponding related frequency response function within the extended frequency response function of the whole vehicle is obtained based on the actual acquisition point and the virtual wheel center excitation point; and the first relation is determined based on the third relation and the related frequency response function for the road surface unevenness and the frequency response function of the whole vehicle acquisition point.

[0185] Optionally, the density calculation module 150 is specifically used for:

[0186] The triaxial excitation expression for the road surface of the Kth axle is determined based on the first relation; and the Fourier spectrum of road surface roughness under each road segment is obtained based on the triaxial excitation expression and vehicle vibration data; the time power spectral density of road surface roughness under preset conditions is obtained based on the Fourier spectrum; and the corresponding spatial power spectral density is determined based on the time power spectral density of each road segment.

[0187] The technical solution provided in this embodiment firstly obtains the vehicle's frequency response function by performing modal testing on an actual prototype vehicle using a first frequency response module. Secondly, it establishes virtual test points at the wheel centers using a second frequency response module to extend the vehicle's frequency response function, resulting in an extended vehicle frequency response function. Thirdly, it establishes a first relational expression between road surface roughness and the frequency response function of the vehicle's sampling points using a relational building module based on the vehicle's frequency response function and the extended vehicle frequency response function. Next, it collects vehicle vibration data under actual operating road conditions using a real-scene acquisition module based on the vehicle's sampling points. Then, it obtains the spatial power spectral density of the road surface roughness for each road segment using a density calculation module based on the vehicle vibration data and the first relational expression. Finally, it determines the road surface roughness of the collected road surface using an roughness conversion module based on the spatial power spectral density.

[0188] Therefore, this embodiment, on the one hand, obtains the vehicle frequency response function based on modal testing of the actual prototype vehicle, and then expands it to obtain the extended vehicle frequency response function by combining the virtual test points at the wheel centers. This allows for a more comprehensive and accurate understanding of the vehicle's frequency response characteristics under different conditions, laying the foundation for subsequent analysis of the relationship between road surface and vehicle vibration. On the other hand, this embodiment establishes a first relational formula between road surface roughness and the frequency response function of the vehicle's sampling points, realizing a mathematical correlation between road surface excitation and vehicle vibration response. This allows for the inference of road surface roughness from vehicle vibration data. Furthermore, based on the vehicle vibration data and the first relational formula, the spatial power spectral density of road surface roughness for each road segment can be obtained. This eliminates the need for complex direct road surface measurement equipment and methods, enabling efficient and convenient acquisition of key indicators reflecting the degree of road surface roughness.

[0189] This embodiment provides an electronic device. Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. See also: Figure 6The electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 1001, the steps in any of the modal-based road surface roughness reconstruction methods described above are performed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other via a communication bus and / or other forms of connection mechanisms (not shown). The memory 1002 stores a processor-executable computer program. When the electronic device 1000 is running, the processor 1001 executes the computer program to perform the modal-based road surface roughness reconstruction method in any optional implementation of the above embodiments, to at least achieve the following functions: performing modal testing operations based on an actual prototype vehicle to obtain the vehicle frequency response function; establishing virtual test points at the wheel center to extend the vehicle frequency response function and obtain the extended vehicle frequency response function; establishing a first relationship between road surface roughness and the frequency response function of the vehicle's acquisition points based at least on the vehicle frequency response function and the extended vehicle frequency response function; and collecting vehicle vibration data under actual operating road conditions based on the vehicle's acquisition points. Based on the whole vehicle vibration data and the first relational formula, the spatial power spectral density of the road surface roughness of each road segment is obtained; the road surface roughness of the collected road surface is determined based on the spatial power spectral density.

[0190] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the modal-based road surface roughness reconstruction method provided in all embodiments of this application: performing modal testing on an actual prototype vehicle to obtain the vehicle's frequency response function; establishing virtual test points at wheel centers to extend the vehicle's frequency response function and obtain the extended vehicle frequency response function; constructing a first relational expression between road surface roughness and the frequency response function of the vehicle's sampling points, based at least on the vehicle's frequency response function and the extended vehicle frequency response function; collecting vehicle vibration data under actual operating road conditions based on the vehicle's sampling points; obtaining the spatial power spectral density of road surface roughness for each road segment based on the vehicle vibration data and the first relational expression; and determining the road surface roughness of the collected road surface based on the spatial power spectral density.

[0191] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0192] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0193] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0194] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0195] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A modal-based method for reconstructing road surface roughness, characterized in that, At least including: Modal testing was performed on an actual prototype vehicle to obtain the vehicle's frequency response function. Establish virtual test points at the wheel center to extend the overall vehicle frequency response function and obtain the extended frequency response function of the vehicle; At least based on the two-degree-of-freedom model of the vehicle and the extended frequency response function of the whole vehicle, a first relationship between road surface roughness and the frequency response function of the whole vehicle sampling points is established; Vehicle vibration data is collected from the vehicle's data collection points based on actual operating road conditions. Based on the vehicle vibration data and the first relational formula, the spatial power spectral density of road surface unevenness for each road segment is obtained. The road surface roughness of the collected road surface is determined based on the spatial power spectral density.

2. The modal-based road surface roughness reconstruction method according to claim 1, characterized in that, The modal testing operation based on the actual prototype vehicle to obtain the vehicle's frequency response function specifically includes: Modal testing was performed on the actual prototype vehicle to obtain the original frequency response function for at least each measurement point and each excitation point. Modal analysis is performed based on each of the original frequency response functions to extract at least the whole vehicle modal parameter set corresponding to the preset frequency range and the set modal order; The original frequency response function is reconstructed based on the vehicle modal parameter set to obtain the vehicle frequency response function.

3. The modal-based road surface roughness reconstruction method according to claim 2, characterized in that, The establishment of virtual test points at the wheel center to extend the vehicle frequency response function and obtain the extended vehicle frequency response function specifically includes: Establish the virtual test point at the wheel center; Within the preset frequency range, determine the velocity relationship between the triaxial acceleration, the center of mass acceleration, and the rigid body rotation angular velocity at any point on the Kth axle; Based on the velocity relationship, wheel center acceleration, and the virtual test point at the wheel center, the mode shape vector in the whole vehicle modal parameter group is extended to obtain the extended mode shape vector; The extended frequency response function of the vehicle is obtained based on the overall vehicle frequency response function and the mode shape extension vector.

4. The modal-based road surface roughness reconstruction method according to claim 2, characterized in that, The first relationship between road surface roughness and the frequency response function of the vehicle's sampling points, established at least based on the vehicle's two-degree-of-freedom model and the vehicle's extended frequency response function, specifically includes: The second relation is determined based on the vertical road surface input model and the bottom fixed model of the vehicle with two degrees of freedom; Based on the second relation, a third relation is established for road surface roughness and the extended frequency response function of the whole vehicle; The relevant frequency response function within the extended frequency response function of the whole vehicle is obtained based on the actual collection points and the virtual wheel center excitation points. The first relation is determined based on the third relation and the relevant frequency response function to determine the road surface unevenness and the frequency response function of the whole vehicle acquisition point.

5. The modal-based road surface roughness reconstruction method according to claim 1, characterized in that, The step of obtaining the spatial power spectral density of road surface unevenness for each road segment based on the vehicle vibration data and the first relational expression specifically includes: The triaxial excitation expression for the road surface of the Kth axle is determined based on the first relation. Based on the triaxial excitation expression and the vehicle vibration data, the Fourier spectrum of road surface unevenness for each road segment is obtained. Based on the Fourier spectrum, the time power spectral density of road surface roughness under preset conditions is obtained; The corresponding spatial power spectral density is determined based on the temporal power spectral density of each road segment.

6. A road surface roughness reconstruction device, characterized in that, At least including: The first frequency response module is used to perform modal testing operations based on the actual prototype vehicle in order to obtain the frequency response function of the whole vehicle. The second frequency response module is used to establish a virtual test point at the wheel center to extend the overall vehicle frequency response function and obtain the extended frequency response function of the whole vehicle. The relation building module is used to build a first relation between road surface roughness and the frequency response function of the vehicle's sampling points, based at least on the vehicle's two-degree-of-freedom model and the vehicle's extended frequency response function. The real-scene acquisition module is used to collect vehicle vibration data based on actual operating road conditions from the vehicle's acquisition points; The density calculation module is used to obtain the spatial power spectral density of road surface unevenness for each road segment based on the vehicle vibration data and the first relational formula. The roughness conversion module is used to determine the road roughness of the collected road surface based on the spatial power spectral density.

7. The road surface roughness reconstruction device according to claim 6, characterized in that, The first frequency response module is specifically used for: Modal testing is performed on an actual prototype vehicle to obtain the original frequency response function corresponding to at least each measurement point and each excitation point; and modal analysis is performed on each of the original frequency response functions to extract the whole vehicle modal parameter set corresponding to at least a preset frequency range and a set modal order. Furthermore, the original frequency response function is reconstructed based on the vehicle modal parameter set to obtain the vehicle frequency response function.

8. The road surface roughness reconstruction device according to claim 6, characterized in that, The second frequency response module is specifically used for: Establish the virtual test point at the wheel center; and, within the preset frequency range, determine the velocity relationship between the triaxial acceleration, center of mass acceleration, and rigid body rotational angular velocity at any point on the Kth axle; and, based on the velocity relationship, the wheel center acceleration, and the virtual test point at the wheel center, extend the mode shape vector in the whole vehicle modal parameter set to obtain the extended mode shape vector; and, based on the whole vehicle frequency response function and the extended mode shape vector, obtain the whole vehicle extended frequency response function.

9. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the modal-based road surface roughness reconstruction method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the modal-based road surface roughness reconstruction method according to any one of claims 1 to 5.

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