Road spectrum load prediction method, device, equipment and medium

Abstract

The invention relates to the technical field of automobiles, in particular to a road spectrum load prediction method and device, equipment and a medium. Correcting the fitting damage value according to the wheel correction coefficient to obtain a corrected fitting damage value, adjusting the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain a target damage correction coefficient, and adjusting the reference road spectrum load according to the target damage correction coefficient to obtain a target road spectrum load. And obtaining the predicted road spectrum load of the to-be-predicted vehicle. According to the method and the device, the fitting damage value corresponding to the to-be-predicted vehicle is determined through the fitting curve between the wheel load and the damage, the fitting damage value is corrected by using the wheel correction coefficient, and the corrected fitting damage value is obtained, so that the accuracy of predicting the road spectrum load is improved, the reference road spectrum load is adjusted according to the target damage correction coefficient, and the accuracy of predicting the road spectrum load is improved. Therefore, the road spectrum acquisition work is reduced, so that the cost of obtaining the road spectrum load is reduced.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a road spectrum load prediction method, device, equipment and medium. Background Art

[0002] When it comes to durability simulation analysis of vehicle models, the road load spectrum is the original input and also an important data for durability evaluation. Although the road spectra collected by various vehicle models can truly reflect the vehicle load information, the collection cycle is long and the cost is high. The road spectrum data generated by the virtual test site may not be able to fully simulate the complex road conditions and traffic conditions in the real world, and the complexity is highly dependent on the simulation model, resulting in certain differences between the virtual road spectrum and the actual road spectrum. Therefore, in the process of obtaining a road spectrum close to the actual road spectrum, how to reduce the cost has become an urgent problem to be solved. Summary of the invention

[0003] In view of this, the embodiments of the present invention provide a road spectrum load prediction method, device, equipment and medium to solve the problem of high cost in the process of obtaining a road spectrum close to the actual road spectrum.

[0004] In a first aspect, an embodiment of the present invention provides a road spectrum load prediction method, comprising: Obtaining a reference road spectrum load and a reference wheel load damage value of a reference vehicle that matches the model of the vehicle to be predicted; Obtaining a fitting curve between wheel load and damage, and translating the fitting curve according to the reference wheel load damage value to obtain a translated fitting curve; Acquire the predicted wheel load and wheel correction coefficient of the vehicle to be predicted, calculate the fitted damage value of the vehicle to be predicted according to the predicted wheel load and the translated fitting curve, and correct the fitted damage value according to the wheel correction coefficient to obtain a corrected fitted damage value; Acquire an initial predicted damage value and an initial damage correction coefficient of the reference vehicle, and adjust the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain a target damage correction coefficient; The reference road spectrum load is adjusted according to the target damage correction coefficient to obtain a predicted road spectrum load of the vehicle to be predicted.

[0005] In a second aspect, an embodiment of the present invention provides a road spectrum load prediction device, including: An acquisition module, used to acquire a reference road spectrum load and a reference wheel load damage value of a reference vehicle matching the vehicle model to be predicted; A translation module, used for obtaining a fitting curve between wheel load and damage, and translating the fitting curve according to the reference wheel load damage value to obtain a translated fitting curve; a calculation module, used to obtain the predicted wheel load and wheel correction coefficient of the vehicle to be predicted, calculate the fitted damage value of the vehicle to be predicted according to the predicted wheel load and the translated fitting curve, and correct the fitted damage value according to the wheel correction coefficient to obtain a corrected fitted damage value; A correction module, used for obtaining an initial predicted damage value and an initial damage correction coefficient of the reference vehicle, and adjusting the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain a target damage correction coefficient; An adjustment module is used to adjust the reference road spectrum load according to the target damage correction coefficient to obtain a predicted road spectrum load of the vehicle to be predicted.

[0006] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the road spectrum load prediction method as described in the first aspect when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the road spectrum load prediction method as described in the first aspect is implemented.

[0008] Compared with the prior art, the present invention has the following beneficial effects: Obtain a baseline road spectrum load and a baseline wheel load damage value of a baseline vehicle that matches the model of the vehicle to be predicted, obtain a fitting curve between wheel load and damage, translate the fitting curve according to the baseline wheel load damage value to obtain the translated fitting curve, obtain the predicted wheel load and wheel correction coefficient of the vehicle to be predicted, calculate the fitted damage value of the vehicle to be predicted according to the predicted wheel load and the translated fitting curve, correct the fitted damage value according to the wheel correction coefficient to obtain the corrected fitting damage value, obtain the initial predicted damage value and the initial damage correction coefficient of the baseline vehicle, adjust the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain the target damage correction coefficient, adjust the baseline road spectrum load according to the target damage correction coefficient to obtain the predicted road spectrum load of the vehicle to be predicted. In the present application, the fitting damage value corresponding to the vehicle to be predicted is determined by the fitting curve between wheel load and damage, and the fitting damage value is corrected using the wheel correction coefficient to obtain the corrected fitting damage value, thereby improving the accuracy of the predicted road spectrum load. The baseline road spectrum load is adjusted according to the target damage correction coefficient to obtain the predicted road spectrum load of the vehicle to be predicted, reducing the road spectrum collection work and thus reducing the cost of obtaining the road spectrum load. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0010] Figure 1 It is a flowchart of a road spectrum load prediction method provided in the first embodiment of the present invention; Figure 2 is a wheel load-damage fitting curve provided in the second embodiment of the present invention; Figure 3 This is a result comparison diagram of a predicted road spectrum load of a vehicle to be predicted provided by the third embodiment of the present invention; Figure 4 It is a structural block diagram of a road spectrum load prediction device provided by Embodiment 4 of the present invention; Figure 5 It is a structural diagram of a computer device provided in Embodiment 5 of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, systems, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] See also Figure 1 , is a flow chart of a road spectrum load prediction method provided by the first embodiment of the present invention, such as Figure 1 As shown, the road spectrum load prediction method may include the following steps.

[0021] S101: Obtain a reference road spectrum load and a reference wheel load damage value of a reference vehicle that matches the vehicle model to be predicted.

[0022] In step S101, the model of the vehicle to be predicted is obtained, and based on the model of the vehicle to be predicted, a benchmark vehicle that matches the model of the vehicle to be predicted is determined, and based on the benchmark vehicle, a benchmark road spectrum load and a benchmark wheel load damage value of the benchmark vehicle are obtained, wherein the benchmark road spectrum load is the road spectrum load corresponding to the benchmark vehicle when it is at the design weight, and the benchmark wheel load damage value is the damage value of the benchmark vehicle at the corresponding wheel load.

[0023] In this embodiment, the model of the vehicle to be predicted is obtained, and based on the model of the vehicle to be predicted, a vehicle that matches the model of the vehicle to be predicted is determined from multiple vehicle models. If there are multiple vehicles, the suspension type and weight of the vehicle to be predicted can be obtained, and the vehicle closest to the vehicle to be predicted is determined as the benchmark vehicle, that is, the suspension type of the vehicle is closest to the vehicle to be predicted, and the weight of the vehicle is closest to the vehicle to be predicted.

[0024] According to the road spectrum load of the benchmark vehicle, the corresponding benchmark road spectrum load and benchmark wheel load damage value are determined.

[0025] Optionally, before obtaining the reference road spectrum load of the reference vehicle, the method further includes: Obtaining a reference vehicle weight and a reference initial road spectrum load of a reference vehicle; According to the reference vehicle weight, the reference initial road spectrum load is vertically translated to obtain the reference road spectrum load.

[0026] In this embodiment, the reference vehicle weight and reference initial road spectrum load of the reference vehicle are obtained, wherein the reference initial road spectrum load is the road spectrum load corresponding to the vehicle weight of the reference vehicle under counterweight, the design vehicle weight of the reference vehicle is obtained, that is, the vehicle weight of the reference vehicle without counterweight, and the proportional relationship between the design vehicle weight and the reference vehicle weight is calculated according to the reference vehicle weight and the design vehicle weight, and the reference initial road spectrum load is vertically translated to the reference road spectrum load according to the corresponding proportional relationship. That is, the proportional relationship between the magnitude of the reference road spectrum load and the magnitude of the reference initial road spectrum load is equal to the proportional relationship between the design vehicle weight and the reference vehicle weight.

[0027] In this embodiment, the benchmark initial road spectrum load is vertically translated according to the benchmark vehicle weight to obtain the benchmark road spectrum load, that is, the road spectrum load of the benchmark vehicle is translated to the equilibrium position, that is, the road spectrum load of the benchmark vehicle without counterweight. Shifting the road spectrum load of the benchmark vehicle to the equilibrium position can effectively prevent the wheel hopping at the initial position from affecting the subsequent scaling process, thereby improving the accuracy of the corresponding road spectrum load of the benchmark vehicle.

[0028] S102: Obtain a fitting curve between wheel load and damage, and translate the fitting curve according to a reference wheel load damage value to obtain a translated fitting curve.

[0029] In step S102, the fitting curve between wheel load and damage, that is, the corresponding relationship curve between wheel load and damage, is translated according to the reference wheel load damage value to obtain a translated fitting curve, wherein the translated fitting curve is a curve after the fitting curve is translated up and down.

[0030] In this embodiment, a fitting curve between wheel load and damage is obtained, wherein the fitting curve is a curve formed by fitting multiple wheel loads and corresponding damage values. Multiple wheel loads and corresponding damage values ​​are obtained from a database, that is, each wheel load corresponds to at least one damage value. A linear fitting is performed based on the multiple wheel loads and corresponding damage values ​​to obtain a fitting curve. Figure 2 , is a wheel load-damage fitting curve provided in the second embodiment of the present invention. Figure 2 In the figure, the horizontal axis is the wheel load and the vertical axis is the damage.

[0031] It should be noted that under different working conditions, the fitting curves between wheel load and damage are different. The wheel load and the corresponding damage value under different working conditions can be obtained, and the corresponding relationship between wheel load and damage under different working conditions can be obtained by fitting.

[0032] According to the reference wheel load damage value, the fitting curve is translated to obtain the translated fitting curve, wherein the reference wheel load damage value is the damage value of the reference vehicle under the corresponding wheel load. According to the reference wheel load damage value, the fitting curve is translated up and down to obtain the translated fitting curve, so that the translated fitting curve is close to the coordinate point corresponding to the reference wheel load damage value, so that the fitting curve can characterize the relationship between the wheel load and damage of the reference vehicle.

[0033] Optionally, the fitting curve is translated according to the reference wheel load damage value to obtain the translated fitting curve, including: According to the reference wheel load damage value, the fitting curve is translated so that the fitting curve passes through the reference wheel load damage value to obtain the translated fitting curve.

[0034] In this embodiment, the fitting curve is translated according to the reference wheel load damage value so that the fitting curve passes through the reference wheel load damage value, i.e., the coordinate point corresponding to the reference wheel load damage value is on the fitting curve, so that the fitting curve can characterize the relationship between the wheel load and damage of the reference vehicle, so as to determine the damage value of the vehicle to be predicted using the translated fitting curve.

[0035] S103: Obtain the predicted wheel load and wheel correction coefficient of the vehicle to be predicted, calculate the fitted damage value of the vehicle to be predicted based on the predicted wheel load and the translated fitting curve, and correct the fitted damage value based on the wheel correction coefficient to obtain the corrected fitted damage value.

[0036] In step S103, the fitting damage value of the vehicle to be predicted is calculated based on the predicted wheel load and the translated fitting curve, that is, the predicted wheel load is brought into the translated fitting curve to obtain the fitting damage value of the vehicle to be predicted. The fitting damage value is corrected according to the wheel correction coefficient to obtain the corrected fitting damage value, wherein the wheel correction coefficient is used to correct the difference in wheel size between the reference vehicle and the vehicle to be predicted.

[0037] In this embodiment, the translated fitting curve is used as the corresponding relationship curve between the wheel load and damage of the vehicle to be predicted, and the predicted wheel load is brought into the translated fitting curve to obtain the fitted damage value of the vehicle to be predicted.

[0038] The wheel correction coefficient is obtained to correct the fitted damage value, that is, to correct the difference in wheel size between the reference vehicle and the vehicle to be predicted, wherein the wheel correction coefficient is related to the wheel size and is represented by the wheel magnification value of the wheel size of the vehicle to be predicted compared with the size of the reference vehicle. The fitted damage value is corrected according to the wheel correction coefficient to obtain a corrected fitted damage value. When making the correction, the difference between the wheels of the reference vehicle and the vehicle to be predicted is taken into account, thereby improving the calculation accuracy of the damage value of the vehicle to be predicted.

[0039] Optionally, the fitting damage value is corrected according to the wheel correction coefficient to obtain a corrected fitting damage value, including: Obtaining a reference wheel size of a reference vehicle and a predicted wheel size of a vehicle to be predicted; The fitted damage value is corrected according to the reference wheel size, the predicted wheel size and the wheel correction coefficient to obtain a corrected fitted damage value.

[0040] In this embodiment, a reference wheel size of the reference vehicle and a predicted wheel size of the vehicle to be predicted are obtained, and different correction methods are used to perform corrections according to the sizes of the reference wheel size of the reference vehicle and the predicted wheel size of the vehicle to be predicted.

[0041] For example, when the predicted wheel size of the vehicle to be predicted is greater than the first preset value, and the reference wheel size of the reference vehicle is also greater than the first preset value, or when the predicted wheel size of the vehicle to be predicted is not greater than the first preset value, and the reference wheel size of the reference vehicle is also not greater than the first preset value, the corrected fitting damage value is the fitting damage value multiplied by the wheel correction coefficient. The calculation formula is as follows: in, is the corrected fitting damage value, is the wheel correction factor, is the fitted damage value.

[0042] When the predicted wheel size of the vehicle to be predicted is not greater than the first preset value, and the reference wheel size of the reference vehicle is greater than the first preset value, the corrected fitting damage value is the fitting damage value multiplied by the wheel correction coefficient and then multiplied by the first preset parameter, and the calculation formula is as follows: in, is the corrected fitting damage value, is the wheel correction factor, To fit the damage value, M is the first preset parameter. The first preset parameter is a pre-calibrated parameter and can be determined according to the size of the first preset value. For example, when the first preset value is 106 mm, the first preset parameter can be 1.5.

[0043] When the predicted wheel size of the vehicle to be predicted is greater than the first preset value, and the reference wheel size of the reference vehicle is not greater than the first preset value, the corrected fitting damage value is the fitting damage value multiplied by the wheel correction coefficient and then divided by the second preset parameter. The calculation formula is as follows: in, is the corrected fitting damage value, is the wheel correction factor, is the fitting damage value, is a second preset parameter, wherein the second preset parameter is a pre-calibrated parameter and can be determined according to the size of the first preset value. For example, when the first preset value is 106 mm, the second preset parameter can be 1.5.

[0044] S104: Obtaining an initial predicted damage value and an initial damage correction coefficient of the reference vehicle, and adjusting the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain a target damage correction coefficient.

[0045] In step S104, the initial predicted damage value is a value calculated based on the actual damage value of the reference vehicle, and the initial damage correction coefficient is a coefficient for correcting the initial predicted damage value so that the corrected initial predicted damage value is close to the corrected fitting damage value. According to the initial predicted damage value and the corrected fitting damage value, the initial damage correction coefficient is adjusted to obtain the target damage correction coefficient, that is, according to the initial predicted damage value and the corrected fitting damage value, the initial damage correction coefficient is iteratively processed to obtain the target damage correction coefficient.

[0046] In this embodiment, when obtaining the initial predicted damage value of the reference vehicle, the initial damage correction coefficient can be calculated based on the product of the damage value collected once by the reference vehicle and the number of cycles, wherein the initial damage correction coefficient can be a pre-set value. The initial damage correction coefficient is adjusted according to the initial predicted damage value and the corrected fitting damage value to obtain the target damage correction coefficient, that is, the initial damage correction coefficient is continuously adjusted so that the product of the initial predicted damage value and the target damage correction coefficient continuously approaches the corrected fitting damage value, so as to use the correspondence between the damage value of the reference vehicle and the damage value of the vehicle to be predicted as the correspondence between the road spectrum load of the reference vehicle and the road spectrum load of the vehicle to be predicted, thereby processing the road spectrum load of the reference vehicle according to the corresponding relationship to obtain the road spectrum load of the vehicle to be predicted.

[0047] According to the initial predicted damage value and the corrected fitting damage value, the initial damage correction coefficient is adjusted to obtain the target damage correction coefficient, including: Calculate the corrected damage value of the reference vehicle based on the initial predicted damage value and the initial damage correction coefficient; Determine whether the corrected damage value and the corrected fitting damage value meet the preset conditions. If the initial corrected loss value and the corrected fitting damage value do not meet the preset conditions, iterate the initial damage correction coefficient to obtain the iterative damage correction coefficient until the corrected damage value and the corrected fitting damage value calculated based on the iterative damage correction coefficient and the initial predicted damage value meet the preset conditions. The iterative damage correction coefficient is determined as the target damage coefficient.

[0048] In this embodiment, the corrected damage value of the reference vehicle is calculated according to the initial predicted damage value and the initial damage correction coefficient, that is, the initial predicted damage value is multiplied by the initial damage correction coefficient to obtain the corrected damage value of the reference vehicle. It is judged whether the corrected damage value and the corrected fitting damage value meet the preset conditions, that is, it is judged whether the error between the initial corrected loss value and the corrected fitting damage value is less than the preset threshold. If the error between the initial corrected loss value and the corrected fitting damage value is less than the preset threshold, it is considered that the corrected damage value and the corrected fitting damage value meet the preset conditions. Otherwise, it is considered that the corrected damage value and the corrected fitting damage value do not meet the preset conditions. If the initial corrected loss value and the corrected fitting damage value do not meet the preset conditions, the initial damage correction coefficient is iteratively processed to obtain the iterated damage correction coefficient, and the iterated damage correction coefficient is used as the initial damage correction coefficient to calculate the corrected damage value of the reference vehicle. The initial damage correction coefficient is iteratively processed to obtain the iterated damage correction coefficient until the calculated corrected damage value and the corrected fitting damage value meet the preset conditions according to the iterated damage correction coefficient and the initial predicted damage value. The iterated damage correction coefficient is determined as the target damage coefficient.

[0049] It should be noted that the preset condition may also be other conditions, such as the error between the corrected damage value and the corrected fitting damage value being less than a preset threshold, etc., which is not limited in this embodiment.

[0050] S105: Adjust the reference road spectrum load according to the target damage correction coefficient to obtain a predicted road spectrum load of the vehicle to be predicted.

[0051] In step S105, the reference road spectrum load is adjusted according to the target damage correction coefficient, that is, the target damage correction coefficient is used as the proportional coefficient between the reference road spectrum load and the predicted road spectrum load, and the reference road spectrum load is proportionally adjusted to obtain the predicted road spectrum load of the vehicle to be predicted.

[0052] In this embodiment, the target damage correction coefficient is used as a proportional coefficient, and the reference road spectrum load is enlarged or reduced according to the proportional coefficient to obtain the predicted road spectrum load of the vehicle to be predicted.

[0053] See also Figure 3 , is a result comparison diagram of a predicted road spectrum load of a vehicle to be predicted provided by the third embodiment of the present invention, Figure 3 In the figure, the horizontal axis is time and the vertical axis is amplitude. The reference item is the actual road spectrum load of the reference vehicle, the linear regression algorithm is the road spectrum load calculated based on the linear regression algorithm, and the linear regression algorithm + vertical translation is the road spectrum load obtained based on the road spectrum load prediction method of this application. Figure 3It can be seen that the road spectrum load obtained by the road spectrum load prediction method based on the present application is close to the actual road spectrum load of the benchmark vehicle, and the accuracy of the road spectrum load obtained by the road spectrum load prediction method based on the present application is higher than the road spectrum load calculated based on the linear regression algorithm.

[0054] Optionally, after adjusting the reference road spectrum load according to the target correction coefficient to obtain the predicted road spectrum load of the vehicle to be predicted, the method further includes: Obtain the predicted vehicle weight and initial vehicle weight of the vehicle to be predicted, and perform vertical translation on the predicted road spectrum load according to the predicted vehicle weight and initial vehicle weight to obtain the predicted road spectrum load after translation. The predicted road spectrum load after translation is the road spectrum load of the vehicle to be predicted at the initial vehicle weight. Verify the predicted road spectrum load according to the wheel jump amount of the predicted road spectrum load after translation and the wheel jump amount of the reference road spectrum, and determine whether the difference between the wheel jump amount of the predicted road spectrum load and the wheel jump amount of the reference road spectrum is less than a preset wheel jump amount threshold; If the difference is less than the preset wheel hop threshold, the predicted road spectrum load is determined as the target road spectrum load corresponding to the vehicle to be predicted.

[0055] In this embodiment, the baseline road spectrum load is adjusted according to the target correction coefficient to obtain the predicted road spectrum load of the vehicle to be predicted, then the predicted vehicle weight and the initial vehicle weight of the vehicle to be predicted are obtained, and the predicted road spectrum load is vertically translated according to the predicted vehicle weight and the initial vehicle weight to obtain the predicted road spectrum load after translation, and the predicted road spectrum load after translation is the road spectrum load of the vehicle to be predicted at the initial vehicle weight, wherein the predicted vehicle weight is the weight of the vehicle to be predicted after counterweighting, and the initial vehicle weight is the weight of the vehicle to be predicted without counterweighting. When the predicted road spectrum load is vertically translated according to the predicted vehicle weight, the translation is performed according to the corresponding relationship between the predicted vehicle weight and the initial vehicle weight, that is, the corresponding relationship between the translated predicted road spectrum load and the predicted road spectrum load is equal to the corresponding relationship between the predicted vehicle weight and the initial vehicle weight.

[0056] The predicted road spectrum load after translation is verified by the wheel jump amount. The wheel jump amount of the predicted road spectrum load after translation and the wheel jump amount of the reference road spectrum are calculated by dynamics. The predicted road spectrum load after translation is verified according to the wheel jump amount of the predicted road spectrum load after translation and the wheel jump amount of the reference road spectrum. It is judged whether the difference between the wheel jump amount of the predicted road spectrum load after translation and the wheel jump amount of the reference road spectrum is less than the preset wheel jump amount threshold, that is, it is judged whether the wheel jump amount of the predicted road spectrum load after translation is close to the wheel jump amount of the reference road spectrum. If the difference is less than the preset wheel jump amount threshold, the predicted road spectrum load is determined as the target road spectrum load corresponding to the vehicle to be predicted. That is, when the wheel jump amount of the predicted road spectrum load after translation is close to the wheel jump amount of the reference road spectrum, the calculated predicted road spectrum load is considered to be correct. Otherwise, the predicted road spectrum load is considered to be incorrect and needs to be recalculated.

[0057] Obtain a baseline road spectrum load and a baseline wheel load damage value of a baseline vehicle that matches the model of the vehicle to be predicted, obtain a fitting curve between wheel load and damage, translate the fitting curve according to the baseline wheel load damage value to obtain the translated fitting curve, obtain the predicted wheel load and wheel correction coefficient of the vehicle to be predicted, calculate the fitted damage value of the vehicle to be predicted according to the predicted wheel load and the translated fitting curve, correct the fitted damage value according to the wheel correction coefficient to obtain the corrected fitting damage value, obtain the initial predicted damage value and the initial damage correction coefficient of the baseline vehicle, adjust the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain the target damage correction coefficient, adjust the baseline road spectrum load according to the target damage correction coefficient to obtain the predicted road spectrum load of the vehicle to be predicted. In the present application, the fitting damage value corresponding to the vehicle to be predicted is determined by the fitting curve between wheel load and damage, and the fitting damage value is corrected using the wheel correction coefficient to obtain the corrected fitting damage value, thereby improving the accuracy of the predicted road spectrum load. The baseline road spectrum load is adjusted according to the target damage correction coefficient to obtain the predicted road spectrum load of the vehicle to be predicted, reducing the road spectrum collection work and thus reducing the cost of obtaining the road spectrum load.

[0058] See also Figure 4 , Figure 4 is a structural block diagram of a road spectrum load prediction device provided by the fourth embodiment of the present invention. For the convenience of explanation, only the part related to the embodiment of the present application is shown. Figure 4 The road spectrum load prediction device 40 includes an acquisition module 41 , a translation module 42 , a calculation module 43 , a correction module 44 , and an adjustment module 45 .

[0059] The acquisition module 41 is used to acquire a reference road spectrum load and a reference wheel load damage value of a reference vehicle that matches the vehicle model to be predicted.

[0060] The translation module 42 is used to obtain a fitting curve between wheel load and damage, and translate the fitting curve according to the reference wheel load damage value to obtain the translated fitting curve.

[0061] The calculation module 43 is used to obtain the predicted wheel load and wheel correction coefficient of the vehicle to be predicted, calculate the fitting damage value of the vehicle to be predicted based on the predicted wheel load and the translated fitting curve, and correct the fitting damage value based on the wheel correction coefficient to obtain the corrected fitting damage value.

[0062] The correction module 44 is used to obtain the initial predicted damage value and the initial damage correction coefficient of the reference vehicle, and adjust the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain the target damage correction coefficient.

[0063] The adjustment module 45 is used to adjust the reference road spectrum load according to the target damage correction coefficient to obtain the predicted road spectrum load of the vehicle to be predicted.

[0064] Optionally, the road spectrum load prediction device 40 further includes: The second acquisition module is used to obtain a reference vehicle weight and a reference initial road spectrum load of a reference vehicle.

[0065] The second translation module is used to vertically translate the reference initial road spectrum load according to the reference vehicle weight to obtain the reference road spectrum load.

[0066] Optionally, the translation module 42 includes: The translation unit is used to translate the fitting curve according to the reference wheel load damage value, so that the fitting curve passes through the reference wheel load damage value to obtain the translated fitting curve.

[0067] Optionally, the calculation module 43 includes: The acquisition unit is used to acquire the reference wheel size of the reference vehicle and the predicted wheel size of the vehicle to be predicted.

[0068] The correction unit is used to correct the fitting damage value according to the reference wheel size, the predicted wheel size and the wheel correction coefficient to obtain the corrected fitting damage value.

[0069] Optionally, the correction module 44 includes: The calculation unit is used to calculate the corrected damage value of the reference vehicle according to the initial predicted damage value and the initial damage correction coefficient.

[0070] The iteration unit is used to determine whether the corrected damage value and the corrected fitting damage value meet the preset conditions. If the initial corrected loss value and the corrected fitting damage value do not meet the preset conditions, the initial damage correction coefficient is iteratively processed to obtain the iterated damage correction coefficient until the corrected damage value and the corrected fitting damage value calculated based on the iterative damage correction coefficient and the initial predicted damage value meet the preset conditions.

[0071] The determination unit is used to determine the iterated damage correction coefficient as the target damage coefficient.

[0072] Optionally, the road spectrum load prediction device 40 further includes: The second translation module is used to obtain the predicted vehicle weight of the vehicle to be predicted, and to perform vertical translation on the predicted road spectrum load according to the predicted vehicle weight to obtain the predicted road spectrum load after translation. The predicted road spectrum load after translation is the road spectrum load of the vehicle to be predicted at the initial vehicle weight; The verification module is used to verify the predicted road spectrum load according to the wheel jump amount of the predicted road spectrum load after translation and the wheel jump amount of the reference road spectrum, and determine whether the difference between the wheel jump amount of the predicted road spectrum load and the wheel jump amount of the reference road spectrum is less than a preset wheel jump threshold.

[0073] The judgment module is used to determine the predicted road spectrum load as the target road spectrum load corresponding to the vehicle to be predicted if the difference is less than a preset wheel jump threshold.

[0074] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0075] Figure 5 Schematic diagram of the structure of a computer device provided by Embodiment 5 of the present invention. Figure 5 As shown, the computer device of this embodiment includes: at least one processor ( Figure 5 Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, and when the processor executes the computer program, the steps in any of the above-mentioned road spectrum load prediction method embodiments are implemented.

[0076] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 5 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0077] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0078] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0079] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0080] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be completed through a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing the computer program product.

[0081] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0083] In the embodiments provided in the present application, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A road spectrum load prediction method, characterized in that: include: Obtaining a reference road spectrum load and a reference wheel load damage value of a reference vehicle that matches the model of the vehicle to be predicted; Obtaining a fitting curve between wheel load and damage, and translating the fitting curve according to the reference wheel load damage value to obtain a translated fitting curve; Acquire the predicted wheel load and wheel correction coefficient of the vehicle to be predicted, calculate the fitted damage value of the vehicle to be predicted according to the predicted wheel load and the translated fitting curve, and correct the fitted damage value according to the wheel correction coefficient to obtain a corrected fitted damage value; Acquire an initial predicted damage value and an initial damage correction coefficient of the reference vehicle, and adjust the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain a target damage correction coefficient; The reference road spectrum load is adjusted according to the target damage correction coefficient to obtain a predicted road spectrum load of the vehicle to be predicted.

2. The road spectrum load prediction method according to claim 1, characterized in that: Before obtaining the reference road spectrum load of the reference vehicle, the method further includes: Obtaining a reference vehicle weight and a reference initial road spectrum load of the reference vehicle; According to the reference vehicle weight, the reference initial road spectrum load is vertically translated to obtain a reference road spectrum load.

3. The road spectrum load prediction method according to claim 1, characterized in that: The step of translating the fitting curve according to the reference wheel load damage value to obtain the translated fitting curve comprises: According to the reference wheel load damage value, the fitting curve is translated so that the fitting curve passes through the reference wheel load damage value to obtain a translated fitting curve.

4. The road spectrum load prediction method according to claim 1, characterized in that: The step of correcting the fitting damage value according to the wheel correction coefficient to obtain a corrected fitting damage value includes: Acquire a reference wheel size of the reference vehicle and a predicted wheel size of the vehicle to be predicted; The fitting damage value is corrected according to the reference wheel size, the predicted wheel size and the wheel correction coefficient to obtain a corrected fitting damage value.

5. The road spectrum load prediction method according to claim 1, characterized in that: The adjusting the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain the target damage correction coefficient includes: Calculating a corrected damage value of the reference vehicle according to the initial predicted damage value and the initial damage correction coefficient; Determine whether the corrected damage value and the corrected fitting damage value meet the preset conditions. If the initial corrected loss value and the corrected fitting damage value do not meet the preset conditions, iteratively process the initial damage correction coefficient to obtain the iterative damage correction coefficient, until the corrected damage value calculated according to the iterative damage correction coefficient and the initial predicted damage value and the corrected fitting damage value meet the preset conditions; The iterated damage correction coefficient is determined as the target damage coefficient.

6. The road spectrum load prediction method according to claim 5, characterized in that: The preset condition is that the error between the initial corrected loss value and the corrected fitting damage value is less than a preset threshold.

7. The road spectrum load prediction method according to claim 1, characterized in that: After the reference road spectrum load is adjusted according to the target correction coefficient to obtain the predicted road spectrum load of the vehicle to be predicted, the method further includes: Obtain the predicted vehicle weight and the initial vehicle weight of the vehicle to be predicted, and perform vertical translation on the predicted road spectrum load according to the predicted vehicle weight and the initial vehicle weight to obtain the predicted road spectrum load after translation, wherein the predicted road spectrum load after translation is the road spectrum load of the vehicle to be predicted at the initial vehicle weight; Verifying the predicted road spectrum load according to the wheel jump amount of the predicted road spectrum load after the translation and the wheel jump amount of the reference road spectrum, and determining whether the difference between the wheel jump amount of the predicted road spectrum load and the wheel jump amount of the reference road spectrum is less than a preset wheel jump amount threshold; If the difference is less than the preset wheel hop threshold, the predicted road spectrum load is determined as the target road spectrum load corresponding to the vehicle to be predicted.

8. A road spectrum load prediction device, characterized in that: include: An acquisition module, used to acquire a reference road spectrum load and a reference wheel load damage value of a reference vehicle matching the vehicle model to be predicted; A translation module, used for obtaining a fitting curve between wheel load and damage, and translating the fitting curve according to the reference wheel load damage value to obtain a translated fitting curve; a calculation module, used to obtain the predicted wheel load and wheel correction coefficient of the vehicle to be predicted, calculate the fitted damage value of the vehicle to be predicted according to the predicted wheel load and the translated fitting curve, and correct the fitted damage value according to the wheel correction coefficient to obtain a corrected fitted damage value; A correction module, used for obtaining an initial predicted damage value and an initial damage correction coefficient of the reference vehicle, and adjusting the initial damage correction coefficient according to the initial predicted damage value and the corrected fitting damage value to obtain a target damage correction coefficient; An adjustment module is used to adjust the reference road spectrum load according to the target damage correction coefficient to obtain a predicted road spectrum load of the vehicle to be predicted.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and the computer device is used to execute the road spectrum load prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the road spectrum load prediction method according to any one of claims 1 to 7 is implemented.

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