Bench load spectrum generation method and device fusing multi-sensor road load data

Abstract

A bench load spectrum generation method, apparatus and device fusing multi-sensor road load data, and a computer readable storage medium, the method comprising: generating a road load spectrum of each acquisition point in each region by acquiring acceleration information according to a preset acceleration sensor at each acquisition point in each region; constructing a load spectrum matrix according to the road load spectrum of each point of each region; and performing optimization operation on the load spectrum matrix according to a preset constraint condition and a preset Lagrange function to generate an optimal rack load spectrum, thereby solving the problem that the rack load spectrum is generated under the condition of road load difference of different fixed points of a single-axis vibration rack and load spectrum compilation without a combination part in the prior art, and improving the reliability of the rack load spectrum. The technical problem that the accuracy of the generated rack load spectrum is low due to the fact that the single-shaft vibration test rack is solved, the verification efficiency of the single-shaft vibration test rack is improved, redundant and under-redundant tests and under-tests can be avoided, and the tests can be considered more accurately.

Description

Technical Field

[0001] The present application relates to the field of vehicle testing technology, and in particular to a method, device, equipment, and computer-readable storage medium for generating a test bench load spectrum by integrating multi-sensor road load data. Background Art

[0002] During the automotive R&D process, testing and verification are divided into components, assemblies, and complete vehicles. The road durability verification of complete vehicles finds a large number of problems and types, but the cycle is long. The cycle of components and assemblies is short, but the verification accuracy is not as good as that of complete vehicles. How to improve the accuracy of test bench durability verification at the component and assembly levels is currently a matter of unremitting efforts by major OEMs. Therefore, it is crucial to match the verification conditions of components and assemblies with the test tracks and user usage conditions at the complete vehicle level. By conducting component-level and assembly-level tests and verifications before installation, the cycle of road durability test verification of complete vehicles and the purpose of saving prototype vehicle resources can be achieved. It is crucial to build sufficient and effective verification capabilities at the component and assembly levels. The existing single-axis vibration test bench does not generate a test bench load spectrum based on the road load conditions of different fixed points. There is an urgent need for a method to solve the problem of low accuracy of the generated test bench load spectrum. Summary of the Invention

[0003] The present application provides a method, device, equipment and computer-readable storage medium for generating a test bench load spectrum by integrating multi-sensor road load data, which can solve the technical problem in the prior art that, for a uniaxial vibration test bench, the load spectrum compilation does not take into account the road load differences of different fixed points of components and assemblies to generate the test bench load spectrum, resulting in low accuracy of the generated test bench load spectrum.

[0004] In a first aspect, the embodiment of the present application provides a method for generating a test bench load spectrum by fusing multi-sensor road load data, comprising:

[0005] Generating a road load spectrum for each collection point in each area according to acceleration information collected by a preset acceleration sensor at each collection point in each area;

[0006] Constructing a load spectrum matrix based on the road load spectrum of each point in each of the areas;

[0007] The load spectrum matrix is ​​optimized according to preset constraints and preset Lagrangian functions to generate an optimal test bench load spectrum, wherein the preset constraints include a first preset constraint and a second preset constraint. The first preset constraint includes that the ultimate response spectrum corresponding to the test bench load spectrum of each of the regions is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the regions, the impact response spectrum corresponding to the test bench load spectrum of each of the regions is less than or equal to the impact response spectrum corresponding to the road load spectrum of each collection point in each of the regions, and the pseudo-damage value corresponding to the test bench load spectrum of each of the regions is equal to the pseudo-damage value corresponding to the road load spectrum of each collection point in each of the regions. The second preset constraint includes that the impact response spectrum corresponding to the road load spectrum of each collection point in each of the regions is less than or equal to zero, the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the regions is less than or equal to zero, and the Lagrangian multiplier is greater than or equal to zero.

[0008] In combination with the first aspect, in one embodiment, the operating the load spectrum matrix according to preset constraints and preset Lagrangian functions to generate an optimal test bench load spectrum includes:

[0009] Obtaining a road load spectrum of each collection point in each of the regions in the load spectrum matrix, extracting a power spectrum density value of each collection point in each of the regions, and generating a load spectrum matrix after multi-source data fusion;

[0010] Constructing a Lagrangian function according to the power spectrum density distribution characteristics of the load spectrum matrix after the multi-source data fusion, wherein the Lagrangian function includes an extreme response spectrum, an impact response spectrum, a pseudo damage value, and a Lagrangian multiplier;

[0011] Based on the second preset constraint, calculating a partial derivative of the Lagrangian function, setting the partial derivative equal to zero, solving the Lagrangian multiplier, and obtaining an initial value of the Lagrangian multiplier, wherein there are multiple initial values ​​of the Lagrangian multiplier;

[0012] Calculating the ultimate response spectrum, the impact response spectrum, and the pseudo damage value of each acquisition point in each of the regions, and the ultimate response spectrum, the impact response spectrum, and the pseudo damage value of each of the regions according to the initial value of the Lagrangian multiplier and the Lagrangian function;

[0013] determining whether the extreme response spectrum, the impact response spectrum, and the pseudo damage value of each acquisition point in each of the regions, and the extreme response spectrum, the impact response spectrum, and the pseudo damage value in each of the regions satisfy the first preset constraint condition;

[0014] If it is determined that the first preset constraint condition is satisfied, obtaining the power spectrum density value of each of the regions satisfying the first preset constraint condition;

[0015] updating the load spectrum matrix after multi-source data fusion according to the power spectrum density value of each of the regions that meets the first preset constraint condition, to obtain an optimized load spectrum matrix;

[0016] Using a matrix operation method, a time series correlation analysis is performed on the optimized load spectrum matrix to obtain the time series correlation characteristics of each acquisition point;

[0017] According to the time series correlation characteristics of each of the acquisition points, the final distribution results of the power spectrum density of each of the regions are generated to generate the optimal test bench load spectrum.

[0018] In conjunction with the first aspect, in one embodiment, after determining the extreme response spectrum, impact response spectrum, and pseudo damage value of each acquisition point in each of the regions, and whether the extreme response spectrum, impact response spectrum, and pseudo damage value in each of the regions meet the first preset constraint condition, the method further includes:

[0019] If it is determined that the first preset constraint condition is not satisfied, adjusting the initial value of the Lagrange multiplier and recalculating the limit response spectrum;

[0020] recalculating the extreme response spectrum, shock response spectrum, and pseudo damage value of each acquisition point in each of the regions, and the extreme response spectrum, shock response spectrum, and pseudo damage value of each of the regions according to the adjusted initial value of the Lagrangian multiplier and the Lagrangian function, and calculating the shock response spectrum corresponding to the power spectral density of each region;

[0021] If it is determined that the first preset constraint condition is satisfied, obtaining the power spectrum density value of each of the regions satisfying the first preset constraint condition;

[0022] updating the load spectrum matrix after multi-source data fusion according to the power spectrum density value of each of the regions that meets the first preset constraint condition, to obtain an optimized load spectrum matrix;

[0023] Using a matrix operation method, a time series correlation analysis is performed on the optimized load spectrum matrix to obtain the time series correlation characteristics of each acquisition point;

[0024] According to the time series correlation characteristics of each of the acquisition points, the final distribution results of the power spectrum density of each of the regions are generated to generate the optimal test bench load spectrum.

[0025] In combination with the first aspect, in one embodiment, the final distribution results of the power spectrum density of each region are generated according to the time series correlation characteristics of the acquisition points to generate the optimal test bench load spectrum.

[0026] According to the time series correlation characteristics of each acquisition point, the final distribution result of the power spectrum density of each region is generated, and the optimized load spectrum matrix is ​​optimized again;

[0027] According to the load spectrum matrix after further optimization, the power spectrum density value of each of the regions is extracted to construct an initial model of the test bench load power spectrum density spectrum;

[0028] Using a Lagrange multiplier optimization method, the initial model of the bench load power spectrum density spectrum is iteratively optimized to obtain an optimized bench load power spectrum density spectrum;

[0029] According to the optimized test bench load power spectrum density spectrum, a final distribution result of the test bench load spectrum is generated to generate an optimal test bench load spectrum.

[0030] In conjunction with the first aspect, in one embodiment, constructing a load spectrum matrix based on the road load spectrum of each point in each area includes:

[0031] Arranging the road load spectra at each point in each of the areas according to the calculated frequency and power spectrum density values;

[0032] Check for missing data during the permutation process;

[0033] If it is determined whether there is missing data during the arrangement process, the missing data is supplemented by a preset interpolation algorithm to construct the load spectrum matrix.

[0034] In conjunction with the first aspect, in one embodiment, generating a road load spectrum for each collection point in each area based on acceleration information collected by a preset acceleration sensor at each collection point in each area includes:

[0035] Acceleration information is collected by a preset acceleration sensor at each collection point in each area, and a time domain signal of the acceleration information of each collection point in each area is extracted to obtain an original vibration data sequence of each collection point in each area;

[0036] Using Fourier transform technology to perform frequency domain analysis on the original vibration data sequence of each collection point in each of the areas to obtain frequency distribution characteristics of each collection point in each of the areas;

[0037] Calculating the power spectrum density value of each collection point in each of the regions according to the frequency distribution characteristics of each collection point in each of the regions, and obtaining a corresponding relationship between the frequency and the power spectrum density of each collection point in each of the regions;

[0038] A road load spectrum of each collection point in each of the areas is generated according to the corresponding relationship between the frequency and the power spectrum density of each collection point in each of the areas.

[0039] In combination with the first aspect, in one embodiment, after obtaining the frequency distribution characteristics of each collection point in each of the areas, the method further includes:

[0040] Determine whether there is an abnormal peak in the frequency distribution characteristics of each acquisition point in each of the regions obtained

[0041] If it is determined that there are abnormal peaks in the obtained frequency distribution characteristics of each collection point in each of the regions, the frequency distribution characteristics of each collection point in each of the regions are corrected by a filtering algorithm;

[0042] Calculating the power spectrum density value of each acquisition point in each of the regions according to the corrected frequency distribution characteristics of each acquisition point in each of the regions, and the corresponding relationship between the frequency and power spectrum density of each acquisition point in each of the regions;

[0043] A road load spectrum of each collection point in each of the areas is generated according to the corresponding relationship between the frequency and the power spectrum density of each collection point in each of the areas.

[0044] In a second aspect, an embodiment of the present application provides a device for generating a test bench load spectrum by fusing multi-sensor road load data, the device comprising:

[0045] A first generating module is configured to generate a road load spectrum for each collection point in each area based on acceleration information collected by a preset acceleration sensor at each collection point in each area;

[0046] A construction module, configured to construct a load spectrum matrix according to the road load spectrum of each point in each of the areas;

[0047] The second generation module is used to perform an optimization operation on the load spectrum matrix according to preset constraints and preset Lagrangian functions to generate an optimal test bench load spectrum, wherein the preset constraints include a first preset constraint and a second preset constraint. The first preset constraint includes that the ultimate response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas, the impact response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the impact response spectrum corresponding to the road load spectrum of each collection point in each of the areas, and the pseudo-damage value corresponding to the test bench load spectrum of each of the areas is equal to the pseudo-damage value corresponding to the road load spectrum of each collection point in each of the areas. The second preset constraint is that the impact response spectrum corresponding to the road load spectrum of each collection point in each of the areas is less than or equal to zero, the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas is less than or equal to zero, and the Lagrangian multiplier is greater than or equal to zero.

[0048] In a third aspect, an embodiment of the present application provides a device for generating a test bench load spectrum by fusing multi-sensor road load data, wherein the device comprises a processor, a memory, and a test bench load spectrum generating program for fusing multi-sensor road load data stored in the memory and executable by the processor, wherein when the test bench load spectrum generating program for fusing multi-sensor road load data is executed by the processor, the steps of the method for generating a test bench load spectrum by fusing multi-sensor road load data as described above are implemented.

[0049] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored a test bench load spectrum generation program for fusing multi-sensor road load data. When the test bench load spectrum generation program for fusing multi-sensor road load data is executed by a processor, the steps of the test bench load spectrum generation method for fusing multi-sensor road load data as described above are implemented.

[0050] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0051] By collecting acceleration information according to preset acceleration sensors at each collection point in each area, a road load spectrum of each collection point in each of the areas is generated; according to the road load spectrum of each point in each of the areas, a load spectrum matrix is ​​constructed; according to preset constraints and preset Lagrangian functions, an optimization operation is performed on the load spectrum matrix to generate an optimal test bench load spectrum, wherein the preset constraints include a first preset constraint and a second preset constraint, the first preset constraint includes that the ultimate response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas, and the impact response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the impact response spectrum corresponding to the road load spectrum of each collection point in each of the areas. The spectrum is obtained by the load spectrum generator, and the pseudo-damage value corresponding to the bench load spectrum of each of the regions is equal to the pseudo-damage value corresponding to the road load spectrum of each collection point in each of the regions. The second preset constraint condition is that the impact response spectrum corresponding to the road load spectrum of each collection point in each of the regions is less than or equal to zero, the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the regions is less than or equal to zero, and the Lagrange multiplier is greater than or equal to zero. This solves the technical problem in the related art that for a uniaxial vibration bench, the load spectrum compilation does not take into account the road load differences of different fixed points of components and assemblies to generate the bench load spectrum, resulting in low accuracy of the generated bench load spectrum. This improves the verification efficiency of the uniaxial vibration test bench, avoids redundant and insufficient tests, and allows for more accurate consideration of tests. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the first embodiment of the method for generating a test bench load spectrum by fusing multi-sensor road load data of the present application;

[0053] Figure 2 For this application Figure 1 Detailed flow chart of step S30;

[0054] Figure 3 This is a functional module diagram of an embodiment of a device for generating a test bench load spectrum by fusing multi-sensor road load data;

[0055] Figure 4 This is a schematic diagram of the hardware structure of a test bench load spectrum generation device that integrates multi-sensor road load data involved in the embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] First, some technical terms in this application are explained to facilitate those skilled in the art to understand this application.

[0058] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0059] In a first aspect, an embodiment of the present application provides a method for generating a test bench load spectrum by fusing multi-sensor road load data.

[0060] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for generating a test bench load spectrum by fusing multi-sensor road load data. Figure 1 As shown in FIG, the method for generating a test bench load spectrum by fusing multi-sensor road load data includes:

[0061] Step S10: generating a road load spectrum for each collection point in each area according to acceleration information collected by the preset acceleration sensor at each collection point in each area;

[0062] Exemplarily, acceleration information is collected by preset acceleration sensors at each collection point in each area, and this acceleration information includes road vibration data. The time domain signal of each measuring point is extracted to obtain the original vibration data sequence. Data processing: If there are abnormal peaks in the characteristics, the noise interference is removed through a filtering algorithm, and the road load data is corrected. Based on the corrected road load data, the load spectrum of each measuring point is constructed to obtain the road load spectrum of each measuring point. The road load data sequence is analyzed in the frequency domain using Fourier transform to obtain the frequency distribution characteristics of each measuring point. Based on the frequency distribution characteristics, the power spectrum density value of each measuring point is obtained, that is, the corresponding relationship between frequency and power spectrum density.

[0063] Specifically, the method of generating a road load spectrum for each collection point in each area based on acceleration information collected by a preset acceleration sensor at each collection point in each area includes: extracting time domain signals of the acceleration information of the measurement points at each collection point in each area based on acceleration information collected by the preset acceleration sensor at each collection point in each area to obtain a raw vibration data sequence for each collection point in each area; performing frequency domain analysis on the raw vibration data sequence for each collection point in each area using Fourier transform technology to obtain frequency distribution characteristics of each collection point in each area; calculating power spectrum density values ​​for each collection point in each area based on the frequency distribution characteristics of each collection point in each area to obtain a corresponding relationship between frequency and power spectrum density of each collection point in each area; and generating a road load spectrum for each collection point in each area based on the corresponding relationship between frequency and power spectrum density of each collection point in each area.

[0064] For example, sensors placed at key points on the vehicle, such as the wheels, suspension, and frame, continuously collect acceleration, strain, or force signals while the vehicle is traveling on a specific road surface at a sampling frequency of 1024 Hz for 10 seconds, generating raw time series data. This data is then preprocessed, including outlier removal and filtering. This removes abnormal data points caused by sensor failure or environmental interference. For example, if a data point exceeds the mean plus or minus three standard deviations, it is considered an outlier and replaced with the average of the adjacent data. The data is then filtered using a low-pass filter with a cutoff frequency of 2048 Hz to remove high-frequency noise. Next, the fast Fourier transform algorithm is used to convert the preprocessed time series signal into a frequency domain signal. For example, a time series signal containing 10,000 data points is fast Fourier transformed to obtain the amplitude and phase information of 5,001 frequency points (due to symmetry, only the positive frequency part needs to be considered), and the power spectrum density value of each frequency point is calculated. The specific calculation method is: the power spectrum density is equal to the square of the amplitude divided by the frequency resolution. For example, if the amplitude of a frequency point is 5 and the frequency resolution is 1 Hz, then the power spectrum density of the frequency point is 5 squared divided by 1, which is 5.

[0065] Step S20: constructing a load spectrum matrix according to the road load spectrum of each point in each area;

[0066] For example, a matrix operation method is used to arrange the road load spectrum data at each measurement point according to frequency and power spectrum density values ​​to generate an initial load spectrum matrix. If there are missing data in the initial load spectrum matrix, the missing values ​​are supplemented through an interpolation algorithm to obtain a complete load spectrum matrix.

[0067] Specifically, constructing a load spectrum matrix based on the road load spectra of each point in each of the areas includes: arranging the road load spectra of each point in each of the areas according to the calculated frequency and power spectrum density values; checking whether there is missing data during the arrangement process; if it is determined that there is missing data during the arrangement process, supplementing the missing data using a preset interpolation algorithm to construct the load spectrum matrix.

[0068] Exemplarily, the frequency of each point and the corresponding power spectrum density value are constructed into a load spectrum matrix. For example, if there are 3 measuring points, named measuring point 1, measuring point 2 and measuring point 3, each measuring point contains 10 frequency points and their corresponding power spectrum density values. The constructed load spectrum matrix is: the first column is the frequency value, from 1Hz to 10Hz, with a step size of 1Hz, the second column is the power spectrum density value of measuring point 1 at each frequency, for example, 1Hz corresponds to 1, 2Hz corresponds to 5, and so on, the third column is the power spectrum density value of measuring point 2, and the fourth column is the power spectrum density value of measuring point 3. Each row represents a frequency point, and each column represents the power spectrum density value of a measuring point at the frequency. The matrix fully describes the load distribution of each measuring point at different frequencies.

[0069] Step S30: performing an optimization operation on the load spectrum matrix according to preset constraints and a preset Lagrangian function to generate an optimal test bench load spectrum, wherein the preset constraints include a first preset constraint and a second preset constraint. The first preset constraint includes that the ultimate response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas, the impact response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the impact response spectrum corresponding to the road load spectrum of each collection point in each of the areas, and the pseudo-damage value corresponding to the test bench load spectrum of each of the areas is equal to the pseudo-damage value corresponding to the road load spectrum of each collection point in each of the areas. The second preset constraint includes that the impact response spectrum corresponding to the road load spectrum of each collection point in each of the areas is less than or equal to zero, the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas is less than or equal to zero, and the Lagrangian multiplier is greater than or equal to zero.

[0070] Exemplarily, the initial value of the Lagrange multiplier is determined by the second preset constraint condition, and the load spectrum matrix is ​​operated using the initial value of the Lagrange multiplier and the preset Lagrange function to calculate the ultimate response spectrum, impact response spectrum, and pseudo-damage value of each sampling point in each of the regions, as well as the ultimate response spectrum, impact response spectrum, and pseudo-damage value of each region. The ultimate response spectrum, impact response spectrum, and pseudo-damage value of each sampling point in each of the regions, as well as the ultimate response spectrum, impact response spectrum, and pseudo-damage value of each region, are constrained by the first preset constraint condition to generate an optimal test bench load spectrum.

[0071] In one embodiment, referring to Figure 2 , Figure 2 For this application Figure 1 Flow chart of step S30 in FIG. Figure 2 As shown, step S30 includes:

[0072] Step S31: obtaining the road load spectrum of each collection point in each area in the load spectrum matrix, extracting the power spectrum density value of each collection point in each area, and generating a load spectrum matrix after multi-source data fusion;

[0073] Step S32: constructing a Lagrangian function according to the power spectrum density distribution characteristics of the load spectrum matrix after the multi-source data fusion, wherein the Lagrangian function includes an extreme response spectrum, an impact response spectrum, a pseudo damage value, and a Lagrangian multiplier;

[0074] Step S33: Based on the second preset constraint, calculate the partial derivative of the Lagrangian function, set the partial derivative equal to zero, solve the Lagrangian multiplier, and obtain the initial value of the Lagrangian multiplier, wherein the initial value of the Lagrangian multiplier is multiple;

[0075] Step S34: calculating the ultimate response spectrum, the impact response spectrum, and the pseudo damage value of each acquisition point in each of the regions, and the ultimate response spectrum, the impact response spectrum, and the pseudo damage value of each of the regions according to the initial value of the Lagrangian multiplier and the Lagrangian function;

[0076] Step S35: determining whether the extreme response spectrum, impact response spectrum, and pseudo damage value of each acquisition point in each of the regions, and the extreme response spectrum, impact response spectrum, and pseudo damage value in each of the regions meet the first preset constraint condition;

[0077] Step S36: If it is determined that the first preset constraint condition is satisfied, obtaining the power spectrum density value of each of the regions satisfying the first preset constraint condition;

[0078] Step S37: updating the load spectrum matrix after the multi-source data fusion according to the power spectrum density value of each of the regions that meets the first preset constraint condition, to obtain an optimized load spectrum matrix;

[0079] Step S38: using a matrix operation method to perform time series correlation analysis on the optimized load spectrum matrix to obtain the time series correlation characteristics of each acquisition point;

[0080] Step S39: generating a final distribution result of the power spectrum density of each of the regions according to the time series correlation characteristics of each of the acquisition points, so as to generate an optimal test bench load spectrum.

[0081] For example, multi-sensor data fusion is used to obtain road load spectrum data for each region, extract power spectral density values, and determine the power spectral density distribution characteristics of the fused multi-source data. Based on the power spectral density distribution characteristics of the fused multi-source data, a Lagrangian function is constructed, introducing Lagrangian multipliers as constraints. The objective function includes the ultimate response spectrum, the impact response spectrum, and the pseudo-damage value. The partial derivatives of the Lagrangian function are calculated and set equal to zero. The Lagrangian multipliers are then solved to obtain the initial values ​​of the Lagrangian multipliers. Based on the initial values ​​of the Lagrangian multipliers, the ultimate response spectrum corresponding to the power spectral density of each region is calculated, and a determination is made as to whether it is less than or equal to the ultimate response spectrum of the road load spectrum for that region. If the constraints are met, the power spectral density value for that region is retained; if not, the Lagrangian multipliers are adjusted and the ultimate response spectrum is recalculated. Based on the adjusted Lagrangian multipliers, the impact response spectrum corresponding to the power spectral density of each region is calculated, and a determination is made as to whether it is less than or equal to the impact response spectrum of the road load spectrum for that region. If the constraints are met, the power spectrum density value of the area is retained; if not, the Lagrange multiplier is further adjusted and the impact response spectrum is recalculated. Based on the final adjusted Lagrange multiplier, the pseudo-damage value of the power spectrum density of each area is calculated to determine whether it is less than or equal to the pseudo-damage value of the road load spectrum in that area. If the constraints are met, the power spectrum density value of the area is determined as the final result; if not, the Lagrange multiplier is readjusted until all constraints are met. Based on the power spectrum density value that meets the constraints, the load spectrum matrix after multi-source data fusion is updated to obtain the optimized load spectrum matrix. Using matrix operation methods, the optimized load spectrum matrix is ​​subjected to time series correlation analysis to obtain the time series correlation characteristics of each fixed point. Based on the time series correlation characteristics, the final distribution results of the power spectrum density of each area are generated, completing the load spectrum optimization under multi-sensor data fusion.

[0082] Based on the obtained Lagrange multipliers, the ultimate response spectrum corresponding to the power spectral density of each region is calculated to determine whether it is less than or equal to the ultimate response spectrum of the road load spectrum in that region. If the constraints are met, the power spectral density value for that region is retained; if not, the Lagrange multipliers are adjusted and the ultimate response spectrum is recalculated. Based on the adjusted Lagrange multipliers, the impact response spectrum corresponding to the power spectral density of each region is calculated to determine whether it is less than or equal to the impact response spectrum of the road load spectrum in that region. If the constraints are met, the power spectral density value for that region is retained; if not, the Lagrange multipliers are further adjusted and the impact response spectrum is recalculated. Based on the final adjusted Lagrange multipliers, the pseudo-damage value of the power spectral density of each region is calculated to determine whether it is less than or equal to the pseudo-damage value of the road load spectrum in that region. If the constraints are met, the power spectral density value for that region is determined as the final result; if not, the Lagrange multipliers are readjusted until all constraints are met. Based on the power spectral density value that meets the constraints, the load spectrum matrix after multi-source data fusion is updated to obtain the optimized load spectrum matrix. Using matrix operations, the optimized load spectrum matrix is ​​subjected to time-series correlation analysis to obtain the time-series correlation characteristics of each fixed point. Based on the time-series correlation characteristics, the final distribution results of the power spectrum density of each region are generated, completing the load spectrum optimization under multi-sensor data fusion. Based on the optimized load spectrum matrix, the power spectrum density values ​​of each region are extracted, and the initial model of the test bench load power spectrum density spectrum is constructed. Using the Lagrange multiplier optimization method, the initial model of the test bench load power spectrum density spectrum is iteratively optimized to obtain the optimized test bench load power spectrum density spectrum. Based on the optimized test bench load power spectrum density spectrum, the final distribution results of the test bench load spectrum are generated, completing the optimization process of the test bench load power spectrum density spectrum.

[0083] Load spectrum optimization using sensor data fusion involves multiple steps, including the calculation of the ultimate response spectrum, shock response spectrum, and pseudo-damage value, as well as constraint evaluation. For example, a vehicle suspension system can collect data from various sensors, including accelerometers, strain gauges, and displacement sensors. Assume that in a certain region, the initial power spectral density (PSD) value obtained by solving the Lagrange multiplier is 0.05 g² / Hz. First, the ultimate response spectrum is calculated and compared with the ultimate response spectrum of the road load spectrum in that region. If the calculated ultimate response spectrum is less than or equal to the ultimate response spectrum of the road load spectrum at all frequencies, the PSD value is retained; otherwise, the Lagrange multiplier is adjusted. For example, if the constraint is exceeded at 100 Hz, the PSD value may need to be adjusted to 0.04 g² / Hz. Next, the shock response spectrum is calculated and similar constraint evaluation is performed. If the adjusted PSD still exceeds the shock response spectrum constraint at 200 Hz, it may need to be further reduced to 0.035 g² / Hz. This step-by-step adjustment ensures that the final result satisfies multiple constraints. Finally, the pseudo-damage value is calculated and evaluated. The pseudo-damage value reflects the cumulative damage effect of the load on the structure. If the calculated pseudo-damage value is less than or equal to the pseudo-damage value of the road load spectrum, the final power spectral density value for that region is determined to be 0.035 g² / Hz. After processing all regions using this method, an optimized load spectrum matrix is ​​obtained. This matrix contains power spectral density information for each frequency point and measurement location. Next, time-series correlation analysis can reveal the dynamic relationships between different measurement points. For example, a 0.2-second time delay may be found between the vibration of the front wheel suspension and a certain point on the vehicle body. Based on the optimized load spectrum matrix, an initial model of the bench load power spectral density spectrum can be constructed. This model attempts to reproduce actual road conditions in a laboratory environment. Iterative optimization using the Lagrange multiplier method yields the optimal bench load power spectral density spectrum. This optimization process aims to make the bench test as close to actual road conditions as possible, while also accounting for the limitations of laboratory equipment. Ultimately, the resulting bench load spectrum distribution can be used to guide fatigue durability testing. The advantage of this method lies in its comprehensive consideration of data from multiple sensors, satisfying multiple constraints, and, through optimization, bringing laboratory simulations closer to actual road conditions. This not only improves the accuracy and reliability of the test, but also has the potential to shorten product development cycles and reduce testing costs.

[0084] In this embodiment, acceleration information is collected by preset acceleration sensors at each collection point in each area, and a road load spectrum of each collection point in each of the areas is generated; a load spectrum matrix is ​​constructed based on the road load spectrum of each point in each of the areas; and an optimization operation is performed on the load spectrum matrix based on preset constraints and preset Lagrangian functions to generate an optimal test bench load spectrum, wherein the preset constraints include a first preset constraint and a second preset constraint, and the first preset constraint includes that the ultimate response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas, and the impact response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas. The impact response spectrum corresponding to the road load spectrum of each point, and the pseudo damage value corresponding to the test bench load spectrum of each of the said areas are equal to the pseudo damage value corresponding to the road load spectrum of each collection point in each of the said areas. The second preset constraint condition is that the impact response spectrum corresponding to the road load spectrum of each collection point in each of the said areas is less than or equal to zero, the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the said areas is less than or equal to zero, and the Lagrange multiplier is greater than or equal to zero. This solves the technical problem in the related art that for a uniaxial vibration test bench, the test bench load spectrum is not generated by combining the road load conditions of different fixed points, resulting in low accuracy of the generated test bench load spectrum. This improves the efficiency of test bench verification, avoids redundant tests and insufficient tests, and allows for more accurate consideration of tests.

[0085] In a second aspect, an embodiment of the present application also provides a device for generating a test bench load spectrum by fusing multi-sensor road load data.

[0086] In one embodiment, referring to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of a device for generating a load spectrum for a test bench that integrates multi-sensor road load data. Figure 3 As shown, the device for generating a bench load spectrum by fusing multi-sensor road load data includes:

[0087] The first generating module 10 is configured to generate a road load spectrum for each collection point in each area based on acceleration information collected by a preset acceleration sensor at each collection point in each area;

[0088] A construction module 20 is configured to construct a load spectrum matrix according to the road load spectrum of each point in each area;

[0089] The second generation module 30 is used to perform an optimization operation on the load spectrum matrix according to preset constraints and a preset Lagrangian function to generate an optimal test bench load spectrum, wherein the preset constraints include a first preset constraint and a second preset constraint. The first preset constraint includes that the ultimate response spectrum corresponding to the test bench load spectrum of each of the regions is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the regions, the impact response spectrum corresponding to the test bench load spectrum of each of the regions is less than or equal to the impact response spectrum corresponding to the road load spectrum of each collection point in each of the regions, and the pseudo-damage value corresponding to the test bench load spectrum of each of the regions is equal to the pseudo-damage value corresponding to the road load spectrum of each collection point in each of the regions. The second preset constraint includes that the impact response spectrum corresponding to the road load spectrum of each collection point in each of the regions is less than or equal to zero, the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the regions is less than or equal to zero, and the Lagrangian multiplier is greater than or equal to zero.

[0090] Furthermore, in one embodiment, the second generating module 30 is configured to:

[0091] Obtaining a road load spectrum of each collection point in each of the regions in the load spectrum matrix, extracting a power spectrum density value of each collection point in each of the regions, and generating a load spectrum matrix after multi-source data fusion;

[0092] Constructing a Lagrangian function according to the power spectrum density distribution characteristics of the load spectrum matrix after the multi-source data fusion, wherein the Lagrangian function includes an extreme response spectrum, an impact response spectrum, a pseudo damage value, and a Lagrangian multiplier;

[0093] Based on the second preset constraint, calculating a partial derivative of the Lagrangian function, setting the partial derivative equal to zero, solving the Lagrangian multiplier, and obtaining an initial value of the Lagrangian multiplier, wherein there are multiple initial values ​​of the Lagrangian multiplier;

[0094] Calculating the ultimate response spectrum, the impact response spectrum, and the pseudo damage value of each acquisition point in each of the regions, and the ultimate response spectrum, the impact response spectrum, and the pseudo damage value of each of the regions according to the initial value of the Lagrangian multiplier and the Lagrangian function;

[0095] determining whether the extreme response spectrum, the impact response spectrum, and the pseudo damage value of each acquisition point in each of the regions, and the extreme response spectrum, the impact response spectrum, and the pseudo damage value in each of the regions satisfy the first preset constraint condition;

[0096] If it is determined that the first preset constraint condition is satisfied, obtaining the power spectrum density value of each of the regions satisfying the first preset constraint condition;

[0097] updating the load spectrum matrix after multi-source data fusion according to the power spectrum density value of each of the regions that meets the first preset constraint condition, to obtain an optimized load spectrum matrix;

[0098] Using a matrix operation method, a time series correlation analysis is performed on the optimized load spectrum matrix to obtain the time series correlation characteristics of each acquisition point;

[0099] According to the time series correlation characteristics of each of the acquisition points, the final distribution results of the power spectrum density of each of the regions are generated to generate the optimal test bench load spectrum.

[0100] Furthermore, in one embodiment, the second generating module 30 is configured to:

[0101] If it is determined that the first preset constraint condition is not satisfied, adjusting the initial value of the Lagrange multiplier and recalculating the limit response spectrum;

[0102] recalculating the extreme response spectrum, shock response spectrum, and pseudo damage value of each acquisition point in each of the regions, and the extreme response spectrum, shock response spectrum, and pseudo damage value of each of the regions according to the adjusted initial value of the Lagrangian multiplier and the Lagrangian function, and calculating the shock response spectrum corresponding to the power spectral density of each region;

[0103] If it is determined that the first preset constraint condition is satisfied, obtaining the power spectrum density value of each of the regions satisfying the first preset constraint condition;

[0104] updating the load spectrum matrix after multi-source data fusion according to the power spectrum density value of each of the regions that meets the first preset constraint condition, to obtain an optimized load spectrum matrix;

[0105] Using a matrix operation method, a time series correlation analysis is performed on the optimized load spectrum matrix to obtain the time series correlation characteristics of each acquisition point;

[0106] According to the time series correlation characteristics of each of the acquisition points, the final distribution results of the power spectrum density of each of the regions are generated to generate the optimal test bench load spectrum.

[0107] Furthermore, in one embodiment, the second generating module 30 is configured to:

[0108] According to the time series correlation characteristics of each acquisition point, the final distribution result of the power spectrum density of each region is generated, and the optimized load spectrum matrix is ​​optimized again;

[0109] According to the load spectrum matrix after further optimization, the power spectrum density value of each of the regions is extracted to construct an initial model of the test bench load power spectrum density spectrum;

[0110] Using a Lagrange multiplier optimization method, the initial model of the bench load power spectrum density spectrum is iteratively optimized to obtain an optimized bench load power spectrum density spectrum;

[0111] According to the optimized test bench load power spectrum density spectrum, a final distribution result of the test bench load spectrum is generated to generate an optimal test bench load spectrum.

[0112] Furthermore, in one embodiment, the construction module 20 is used to:

[0113] Arranging the road load spectra at each point in each of the areas according to the calculated frequency and power spectrum density values;

[0114] Check for missing data during the permutation process;

[0115] If it is determined whether there is missing data during the arrangement process, the missing data is supplemented by a preset interpolation algorithm to construct the load spectrum matrix.

[0116] Furthermore, in one embodiment, the first generating module 10 is configured to:

[0117] Acceleration information is collected by a preset acceleration sensor at each collection point in each area, and a time domain signal of the acceleration information of each collection point in each area is extracted to obtain an original vibration data sequence of each collection point in each area;

[0118] Using Fourier transform technology to perform frequency domain analysis on the original vibration data sequence of each collection point in each of the areas to obtain frequency distribution characteristics of each collection point in each of the areas;

[0119] Calculating the power spectrum density value of each collection point in each of the regions according to the frequency distribution characteristics of each collection point in each of the regions, and obtaining a corresponding relationship between the frequency and the power spectrum density of each collection point in each of the regions;

[0120] A road load spectrum of each collection point in each of the areas is generated according to the corresponding relationship between the frequency and the power spectrum density of each collection point in each of the areas.

[0121] Furthermore, in one embodiment, the first generating module 10 is configured to:

[0122] Determine whether there is an abnormal peak in the frequency distribution characteristics of each acquisition point in each of the regions obtained

[0123] If it is determined that there are abnormal peaks in the obtained frequency distribution characteristics of each collection point in each of the regions, the frequency distribution characteristics of each collection point in each of the regions are corrected by a filtering algorithm;

[0124] Calculating the power spectrum density value of each acquisition point in each of the regions according to the corrected frequency distribution characteristics of each acquisition point in each of the regions, and the corresponding relationship between the frequency and power spectrum density of each acquisition point in each of the regions;

[0125] A road load spectrum of each collection point in each of the areas is generated according to the corresponding relationship between the frequency and the power spectrum density of each collection point in each of the areas.

[0126] Among them, the functional implementation of each module in the above-mentioned test bench load spectrum generation device that integrates multi-sensor road load data corresponds to the various steps in the above-mentioned embodiment of the test bench load spectrum generation method that integrates multi-sensor road load data, and its functions and implementation processes will not be repeated here one by one.

[0127] In a third aspect, an embodiment of the present application provides a test bench load spectrum generation device that integrates multi-sensor road load data. The test bench load spectrum generation device that integrates multi-sensor road load data can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0128] Reference Figure 4 , Figure 4 This is a hardware structure diagram of a device for generating a test bench load spectrum by fusing multi-sensor road load data, as described in an embodiment of the present application. In this embodiment of the present application, the device for generating a test bench load spectrum by fusing multi-sensor road load data may include a processor, a memory, a communication interface, and a communication bus.

[0129] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0130] Communication interfaces include input / output (I / O), physical, and logical interfaces, which interconnect components within the multi-sensor road load data fusion platform load spectrum generator. They also connect the platform load spectrum generator with other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber, or ATM interfaces; user devices can include displays and keyboards.

[0131] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0132] The processor may be a general-purpose processor that can call a test bench load spectrum generation program for fusing multi-sensor road load data stored in a memory and execute the test bench load spectrum generation method for fusing multi-sensor road load data provided in an embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the test bench load spectrum generation program for fusing multi-sensor road load data is called can refer to the various embodiments of the test bench load spectrum generation method for fusing multi-sensor road load data of the present application, and will not be repeated here.

[0133] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0134] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0135] The computer-readable storage medium of the present application stores a test bench load spectrum generation program that fuses multi-sensor road load data. When the test bench load spectrum generation program that fuses multi-sensor road load data is executed by a processor, the steps of the test bench load spectrum generation method that fuses multi-sensor road load data as described above are implemented.

[0136] Among them, the method implemented when the test bench load spectrum generation program that integrates multi-sensor road load data is executed can refer to the various embodiments of the test bench load spectrum generation method that integrates multi-sensor road load data in this application, and will not be repeated here.

[0137] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0138] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0139] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0140] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0141] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0142] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0143] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for generating a test bench load spectrum by fusing multi-sensor road load data, characterized in that: The method for generating a test bench load spectrum by fusing multi-sensor road load data includes: Generating a road load spectrum for each collection point in each area according to acceleration information collected by a preset acceleration sensor at each collection point in each area; Constructing a load spectrum matrix based on the road load spectrum of each point in each of the areas; The load spectrum matrix is ​​optimized according to preset constraints and preset Lagrangian functions to generate an optimal test bench load spectrum, wherein the preset constraints include a first preset constraint and a second preset constraint. The first preset constraint includes that the ultimate response spectrum corresponding to the test bench load spectrum of each of the regions is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the regions, the impact response spectrum corresponding to the test bench load spectrum of each of the regions is less than or equal to the impact response spectrum corresponding to the road load spectrum of each collection point in each of the regions, and the pseudo-damage value corresponding to the test bench load spectrum of each of the regions is equal to the pseudo-damage value corresponding to the road load spectrum of each collection point in each of the regions. The second preset constraint includes that the impact response spectrum corresponding to the road load spectrum of each collection point in each of the regions is less than or equal to zero, the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the regions is less than or equal to zero, and the Lagrangian multiplier is greater than or equal to zero.

2. The method for generating a test bench load spectrum by fusing multi-sensor road load data according to claim 1, characterized in that: The performing of an optimization operation on the load spectrum matrix according to preset constraints and preset Lagrangian functions to generate an optimal test bench load spectrum includes: Obtaining a road load spectrum of each collection point in each of the regions in the load spectrum matrix, extracting a power spectrum density value of each collection point in each of the regions, and generating a load spectrum matrix after multi-source data fusion; Constructing a Lagrangian function according to the power spectrum density distribution characteristics of the load spectrum matrix after the multi-source data fusion, wherein the Lagrangian function includes an extreme response spectrum, an impact response spectrum, a pseudo damage value, and a Lagrangian multiplier; Based on the second preset constraint, calculating a partial derivative of the Lagrangian function, setting the partial derivative equal to zero, solving the Lagrangian multiplier, and obtaining an initial value of the Lagrangian multiplier, wherein there are multiple initial values ​​of the Lagrangian multiplier; Calculating the ultimate response spectrum, the impact response spectrum, and the pseudo damage value of each acquisition point in each of the regions, and the ultimate response spectrum, the impact response spectrum, and the pseudo damage value of each of the regions according to the initial value of the Lagrangian multiplier and the Lagrangian function; determining whether the extreme response spectrum, the impact response spectrum, and the pseudo damage value of each acquisition point in each of the regions, and the extreme response spectrum, the impact response spectrum, and the pseudo damage value in each of the regions satisfy the first preset constraint condition; If it is determined that the first preset constraint condition is satisfied, obtaining the power spectrum density value of each of the regions satisfying the first preset constraint condition; updating the load spectrum matrix after multi-source data fusion according to the power spectrum density value of each of the regions that meets the first preset constraint condition, to obtain an optimized load spectrum matrix; Using a matrix operation method, a time series correlation analysis is performed on the optimized load spectrum matrix to obtain the time series correlation characteristics of each acquisition point; According to the time series correlation characteristics of each of the acquisition points, the final distribution results of the power spectrum density of each of the regions are generated to generate the optimal test bench load spectrum.

3. The method for generating a test bench load spectrum by fusing multi-sensor road load data according to claim 2, wherein: After determining the extreme response spectrum, impact response spectrum, and pseudo damage value of each acquisition point in each of the regions and whether the extreme response spectrum, impact response spectrum, and pseudo damage value in each of the regions meet the first preset constraint condition, the method further includes: If it is determined that the first preset constraint condition is not satisfied, adjusting the initial value of the Lagrange multiplier and recalculating the limit response spectrum; recalculating the extreme response spectrum, shock response spectrum, and pseudo damage value of each acquisition point in each of the regions, and the extreme response spectrum, shock response spectrum, and pseudo damage value of each of the regions according to the adjusted initial value of the Lagrangian multiplier and the Lagrangian function, and calculating the shock response spectrum corresponding to the power spectral density of each region; If it is determined that the first preset constraint condition is satisfied, obtaining the power spectrum density value of each of the regions satisfying the first preset constraint condition; updating the load spectrum matrix after multi-source data fusion according to the power spectrum density value of each of the regions that meets the first preset constraint condition, to obtain an optimized load spectrum matrix; Using a matrix operation method, a time series correlation analysis is performed on the optimized load spectrum matrix to obtain the time series correlation characteristics of each acquisition point; According to the time series correlation characteristics of each of the acquisition points, the final distribution results of the power spectrum density of each of the regions are generated to generate the optimal test bench load spectrum.

4. The method for generating a test bench load spectrum by fusing multi-sensor road load data according to any one of claims 2 or 3, characterized in that: Generating a final distribution result of the power spectrum density of each region according to the time series correlation characteristics of the acquisition points to generate an optimal test bench load spectrum includes: According to the time series correlation characteristics of each acquisition point, the final distribution result of the power spectrum density of each region is generated, and the optimized load spectrum matrix is ​​optimized again; According to the load spectrum matrix after further optimization, the power spectrum density value of each of the regions is extracted to construct an initial model of the test bench load power spectrum density spectrum; Using a Lagrange multiplier optimization method, the initial model of the bench load power spectrum density spectrum is iteratively optimized to obtain an optimized bench load power spectrum density spectrum; According to the optimized test bench load power spectrum density spectrum, a final distribution result of the test bench load spectrum is generated to generate an optimal test bench load spectrum.

5. The method for generating a test bench load spectrum by fusing multi-sensor road load data according to claim 1, wherein: The constructing of a load spectrum matrix according to the road load spectrum of each point in each area includes: Arranging the road load spectra at each point in each of the areas according to the calculated frequency and power spectrum density values; Check for missing data during the permutation process; If it is determined whether there is missing data during the arrangement process, the missing data is supplemented by a preset interpolation algorithm to construct the load spectrum matrix.

6. The method for generating a test bench load spectrum by fusing multi-sensor road load data according to claim 1, characterized in that: The method of collecting acceleration information from preset acceleration sensors at each collection point in each area and generating a road load spectrum for each collection point in each area includes: Acceleration information is collected by a preset acceleration sensor at each collection point in each area, and a time domain signal of the acceleration information of each collection point in each area is extracted to obtain an original vibration data sequence of each collection point in each area; Using Fourier transform technology to perform frequency domain analysis on the original vibration data sequence of each collection point in each of the areas to obtain frequency distribution characteristics of each collection point in each of the areas; Calculating the power spectrum density value of each collection point in each of the regions according to the frequency distribution characteristics of each collection point in each of the regions, and obtaining a corresponding relationship between the frequency and the power spectrum density of each collection point in each of the regions; A road load spectrum of each collection point in each of the areas is generated according to the corresponding relationship between the frequency and the power spectrum density of each collection point in each of the areas.

7. The method for generating a test bench load spectrum by fusing multi-sensor road load data according to claim 6, characterized in that: After obtaining the frequency distribution characteristics of each collection point in each of the areas, the method further includes: Determine whether there is an abnormal peak in the frequency distribution characteristics of each acquisition point in each of the regions obtained If it is determined that there are abnormal peaks in the obtained frequency distribution characteristics of each collection point in each of the regions, the frequency distribution characteristics of each collection point in each of the regions are corrected by a filtering algorithm; Calculating the power spectrum density value of each acquisition point in each of the regions according to the corrected frequency distribution characteristics of each acquisition point in each of the regions, and the corresponding relationship between the frequency and power spectrum density of each acquisition point in each of the regions; A road load spectrum of each collection point in each of the areas is generated according to the corresponding relationship between the frequency and the power spectrum density of each collection point in each of the areas.

8. A device for generating a load spectrum for a test bench by integrating multi-sensor road load data, characterized in that: The device for generating a test bench load spectrum by fusing multi-sensor road load data comprises: A first generating module is configured to generate a road load spectrum for each collection point in each area based on acceleration information collected by a preset acceleration sensor at each collection point in each area; A construction module, configured to construct a load spectrum matrix according to the road load spectrum of each point in each of the areas; The second generation module is used to perform optimization operation on the load spectrum matrix according to preset constraints and preset Lagrangian functions to generate an optimal test bench load spectrum, wherein the preset constraints include a first preset constraint and a second preset constraint. The first preset constraint includes that the ultimate response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas, the impact response spectrum corresponding to the test bench load spectrum of each of the areas is less than or equal to the impact response spectrum corresponding to the road load spectrum of each collection point in each of the areas, and the pseudo-damage value corresponding to the road test bench load spectrum of each of the areas is equal to the pseudo-damage value corresponding to the road load spectrum of each collection point in each of the areas. The second preset constraint is that the impact response spectrum corresponding to the road load spectrum of each collection point in each of the areas is less than or equal to zero, the ultimate response spectrum corresponding to the road load spectrum of each collection point in each of the areas is less than or equal to zero, and the Lagrangian multiplier is greater than or equal to zero.

9. A device for generating a bench load spectrum by fusing multi-sensor road load data, characterized in that: The bench load spectrum generation device for fusing multi-sensor road load data includes a processor, a memory, and a bench load spectrum generation program for fusing multi-sensor road load data stored in the memory and executable by the processor. When the bench load spectrum generation program for fusing multi-sensor road load data is executed by the processor, the steps of the bench load spectrum generation method for fusing multi-sensor road load data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a test bench load spectrum generation program for fusing multi-sensor road load data, wherein when the test bench load spectrum generation program for fusing multi-sensor road load data is executed by a processor, the steps of the test bench load spectrum generation method for fusing multi-sensor road load data as described in any one of claims 1 to 7 are implemented.

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