A method, device, equipment, medium and product for testing strength of an automobile suspension

Abstract

The application discloses a kind of automobile suspension strength test method, device, equipment, medium and product, it is related to vehicle test technical field.The method is first obtained in the process of using drop method to carry out multiple automobile suspension strength test experiments to target automobile suspension by multiple sensors Acquisition Multidimensional Time Series Sensing Data, then Multidimensional Time Series Sensing Data is normalized, obtains two-dimensional normalized matrix data, finally using MC-WNNM algorithm and / or WSNM algorithm Multidimensional Time Series Sensing Data is denoised, obtains new two-dimensional normalized matrix data, and is imported to the automobile suspension strength estimation model based on long short-term memory neural network and is pre-trained, output obtains the strength estimation value of target automobile suspension, so it can be ensured that automobile suspension strength test result accuracy is achieved by the non-removal preprocessing of the obtained sensing data, convenient for practical application and promotion.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of vehicle testing, and particularly relates to a method, device, equipment, medium and product for testing the strength of an automobile suspension. BACKGROUND

[0002] An automobile suspension is a force transmission component connecting a vehicle frame (or body) and an axle (or wheel), and is an important component for ensuring the safety of an automobile. The material used in the structure of an automobile suspension has a crucial impact on the performance and safety of an automobile, and thus it is necessary to specially evaluate the performance of the automobile suspension during the design and manufacturing of the vehicle.

[0003] At present, a commonly used method for evaluating the performance of an automobile suspension is a drop test, that is, the strength and durability of the structure of an automobile suspension are evaluated by simulating the drop of the vehicle under different road conditions to provide important reference data for automobile manufacturers. Specifically, during the strength test of an automobile suspension, a tester freely drops the vehicle from a certain height, observes and records the deformation and damage of the structure of the vehicle suspension, and thus evaluates the performance of the vehicle suspension.

[0004] During the strength test of an automobile suspension, if the sensor used for parameter measurement has insufficient precision, is improperly installed, or the data acquisition system fails, the collected data may not be accurate, which affects the judgment of the response characteristics of the structure of the automobile suspension. Therefore, the obtained sensor data needs to be preprocessed, for example, abnormal data is removed. However, when using the existing LOF algorithm (Local Outlier Factor, which is a density-based unsupervised anomaly detection algorithm that identifies outliers by comparing the local density difference between data points and their neighborhoods), since the automobile suspension will change the data parameters during the test, the use of the anomaly detection algorithm cannot obtain good anomaly detection results, and thus the residual abnormal data will affect the strength test of the automobile suspension.

[0005] To solve the above problems, the existing patent CN118332473A provides a method for testing the strength of an automobile suspension based on artificial intelligence. The method obtains indexes such as related abnormal values, influence difference degrees, and related abnormal indexes according to the correlation between sensor parameters, removes abnormal data based on these indexes, and finally inputs the sensor data after removing the abnormal data into a neural network to complete the strength test of the suspension. However, the amount and position of abnormal data are indefinite, and if too much abnormal data is removed, the integrity of the input items of the suspension strength prediction model will be affected, and the accuracy of the test result of the strength of the automobile suspension will also be affected.

[0006] Therefore, how to provide a new automobile suspension strength test scheme that can ensure the accuracy of automobile suspension strength test results by non-removal preprocessing of the obtained sensing data is a topic that needs to be studied by those skilled in the art. SUMMARY

[0007] The purpose of the present application is to provide an automobile suspension strength test method, device, computer equipment, computer readable storage medium and computer program product, to solve the problem that the existing automobile suspension strength test scheme may affect the accuracy of the automobile suspension strength test results due to the removal of too much abnormal data.

[0008] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0009] In a first aspect, an automobile suspension strength test method is provided, comprising:

[0010] Obtaining multi-dimensional time series sensing data collected by a plurality of sensors in the process of performing a plurality of automobile suspension strength test experiments on a target automobile suspension using a drop method, wherein the multi-dimensional time series sensing data corresponds one-to-one to the plurality of sensors, and each dimension of time series sensing data contains a plurality of sensing values corresponding one-to-one to the plurality of automobile suspension strength test experiments and arranged in order according to the experiment time sequence;

[0011] Performing normalization processing on the multi-dimensional time series sensing data to obtain two-dimensional normalized matrix data, wherein the two-dimensional normalized matrix data contains R×Q elements, R represents the total number of the plurality of sensors, and Q represents the total number of the plurality of automobile suspension strength test experiments;

[0012] Performing denoising processing on the two-dimensional normalized matrix data using an MC-WNNM algorithm and / or a WSNM algorithm to obtain new two-dimensional normalized matrix data;

[0013] Importing the new two-dimensional normalized matrix data into an automobile suspension strength estimation model based on a long short-term memory neural network that has been pre-trained, and outputting to obtain a strength estimation value of the target automobile suspension.

[0014] Based on the above invention content, a new scheme for non-removal preprocessing of obtained sensing data based on image denoising principle and strength estimation of automobile suspension based on processing result is provided, that is, first, multi-dimensional time series sensing data collected by multiple sensors in the process of adopting drop method to perform multiple automobile suspension strength test experiments on target automobile suspension is obtained, then the multi-dimensional time series sensing data is normalized to obtain two-dimensional normalized matrix data, finally, MC-WNNM algorithm and / or WSNM algorithm is adopted to denoise the two-dimensional normalized matrix data to obtain new two-dimensional normalized matrix data, which is imported into the automobile suspension strength estimation model pre-trained based on long short-term memory neural network to output the strength estimation value of the target automobile suspension. In this way, the abnormal sensing data is corrected and restored to real sensing data through image denoising, which can eliminate the adverse effect of abnormal data on the test result of automobile suspension strength to a certain extent, and since the obtained sensing data does not need to be removed, it can also avoid the situation that the accuracy of the test result of automobile suspension strength is affected by removing too much abnormal data, thereby achieving the purpose of ensuring the accuracy of the test result of automobile suspension strength through non-removal preprocessing of the obtained sensing data, which is convenient for practical application and promotion.

[0015] In one possible design, the multiple sensors include a pressure sensor installed in the shock absorber and used to measure the pressure of oil during the shock absorption process, a force sensor installed on the suspension connection point and used to measure the force acting on the suspension, a displacement sensor installed at the spring and used to measure the compression degree of the spring, and / or a rotation angle sensor installed at the steering wheel and used to measure the angle change of the steering wheel.

[0016] In one possible design, the MC-WNNM algorithm and the WSNM algorithm are adopted to denoise the two-dimensional normalized matrix data to obtain new two-dimensional normalized matrix data, including the following steps S31-S38:

[0017] S31. A kth two-dimensional matrix sub-block in the time series dimension is selected from the two-dimensional normalized matrix data in a sliding window manner, and then step S32 is executed, where k represents a positive integer with an initial value of 1, the kth two-dimensional matrix sub-block contains elements, Q represents a positive integer less than Q;

[0018] S32. From the two-dimensional normalized matrix data, M two-dimensional matrix similar sub-blocks located in the predefined neighborhood of the kth two-dimensional matrix sub-block and most similar to the kth two-dimensional matrix sub-block are searched, and then step S33 is executed, where M represents a positive integer;

[0019] S33. The k-th two-dimensional matrix sub-block and the M similar two-dimensional matrix sub-blocks are respectively vectorized and straightened to obtain M+1 one-dimensional vectors. These M+1 one-dimensional vectors are then superimposed to obtain a vector of size [missing value]. The original matrix Y is obtained, and then step S34 is executed;

[0020] S34. Assume the original matrix Y = X + N + S, and use the ADMM algorithm to solve the minimization objective function to obtain the denoised matrix X. Then, execute step S35, wherein the minimization objective function is expressed as follows:

[0021]

[0022] In the formula, N represents Gaussian noise, S represents sparse noise, ||S||1 represents the L1 norm used to constrain the sparse noise S, and ||N|| F This represents the F-norm used to constrain Gaussian noise N. λS represents the Schattenp-norm used to constrain the denoising matrix X, λ1, λ2, and λ3 represent preset regularization coefficients, W represents a multi-channel weighting matrix in diagonal form, r represents a positive integer less than or equal to R, and δ r Let I represent the noise standard deviation of the r-th channel, I represent the identity matrix, min() represent the minimum function, i represent a positive integer, and w represent the minimum value. i Indicates assigning σ i The weights, σ i represents the i-th singular value in the denoising matrix X, p represents the parameter value used to determine the norm type and takes a value in the interval (0,1], and w represents the weighting of the nuclear norm;

[0023] S35. Based on the denoising matrix X, reconstruct a new two-dimensional matrix sub-block corresponding to the k-th two-dimensional matrix sub-block and M new two-dimensional matrix similar sub-blocks corresponding one-to-one with the M two-dimensional matrix similar sub-blocks, and then execute step S36;

[0024] S36. In the two-dimensional normalized matrix data, the k-th two-dimensional matrix sub-block is replaced with the new two-dimensional matrix sub-block, and the M two-dimensional matrix similar sub-blocks are replaced one-to-one with the M new two-dimensional matrix similar sub-blocks to obtain new two-dimensional normalized matrix data, and then step S37 is executed.

[0025] S37. Determine whether the new two-dimensional matrix sub-block is the last two-dimensional matrix sub-block in the time dimension in the new two-dimensional normalized matrix data. If so, end the denoising process; otherwise, proceed to step S38.

[0026] S38. Increment k by 1, and then continue to select the kth two-dimensional matrix sub-block in the time dimension from the new two-dimensional normalized matrix data in the sliding window manner, and return to perform step S32.

[0027] In one possible design, searching for the M two-dimensional matrix similar sub-blocks that are located within the predefined neighborhood of the kth two-dimensional matrix sub-block and most similar to the kth two-dimensional matrix sub-block from the two-dimensional normalized matrix data comprises:

[0028] Calculating the Euler distance between the kth two-dimensional matrix sub-block and each other two-dimensional matrix sub-block in the two-dimensional normalized matrix data that is located within the predefined neighborhood of the kth two-dimensional matrix sub-block and has the same size as the kth two-dimensional matrix sub-block;

[0029] Arranging the each other two-dimensional matrix sub-block in order from near to far according to the Euler distance to obtain a two-dimensional matrix sub-block sequence;

[0030] Selecting the first M two-dimensional matrix sub-blocks from the two-dimensional matrix sub-block sequence as the M two-dimensional matrix similar sub-blocks that are most similar to the kth two-dimensional matrix sub-block, where M represents a positive integer.

[0031] In one possible design, performing vectorization and straightening processing on the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks respectively to obtain M+1 one-dimensional vectors, and performing superposition processing on the M+1 one-dimensional vectors to obtain an original matrix Y with a size of , comprises:

[0032] For each sub-block in the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks, taking an element located at an r'th row and a q'th column in the corresponding sub-block as an h'th element in the corresponding one-dimensional vector to obtain the corresponding one-dimensional vector, where r' represents a positive integer less than or equal to R, q' represents a positive integer less than or equal to , and h' represents a positive integer less than or equal to

[0033] Taking an m'th one-dimensional vector in the M+1 one-dimensional vectors that correspond to the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks one by one as an m'th row element in the original matrix Y with a size of , to obtain the original matrix Y, where m represents a positive integer less than or equal to M+1.

[0034] In one possible design, reconstructing a new two-dimensional matrix sub-block corresponding to the kth two-dimensional matrix sub-block and M new two-dimensional matrix similar sub-blocks corresponding to the M two-dimensional matrix similar sub-blocks according to the de-noised matrix X comprises:

[0035] If the element in the mth row of the original matrix Y is a one-dimensional vector of the kth two-dimensional matrix sub-block, the element located at the mth row and the h' th column in the de-noised matrix X is taken as the element located at the r' th row and the q' th column in the new two-dimensional matrix sub-block corresponding to the kth two-dimensional matrix sub-block;

[0036] If the element in the mth row of the original matrix Y is a one-dimensional vector of the m'th two-dimensional matrix similar sub-block in the M two-dimensional matrix similar sub-blocks, the element located at the mth row and the h' th column in the de-noised matrix X is taken as the element located at the r' th row and the q' th column in the new two-dimensional matrix similar sub-block corresponding to the m'th two-dimensional matrix similar sub-block, wherein m' represents a positive integer less than or equal to M.

[0037] In a second aspect, a device for testing the strength of an automobile suspension is provided, which includes a sensing data acquisition unit, a normalization processing unit, a data de-noising processing unit and a suspension strength estimation unit connected in sequence.

[0038] The sensing data acquisition unit is configured to acquire multi-dimensional time-series sensing data collected by a plurality of sensors in a process of performing a plurality of automobile suspension strength test experiments on a target automobile suspension by using a drop method, wherein the multi-dimensional time-series sensing data correspond one-to-one to the plurality of sensors, and each dimension of time-series sensing data contains a plurality of sensing values corresponding one-to-one to the plurality of automobile suspension strength test experiments and arranged in sequence according to the experiment time sequence.

[0039] The normalization processing unit is configured to perform normalization processing on the multi-dimensional time-series sensing data to obtain two-dimensional normalized matrix data, wherein the two-dimensional normalized matrix data contains R×Q elements, R represents the total number of the plurality of sensors, and Q represents the total number of the plurality of automobile suspension strength test experiments.

[0040] The data de-noising processing unit is configured to perform de-noising processing on the two-dimensional normalized matrix data by using an MC-WNNM algorithm and / or a WSNM algorithm to obtain new two-dimensional normalized matrix data.

[0041] The suspension strength estimation unit is configured to input the new two-dimensional normalized matrix data into an automobile suspension strength estimation model based on a long short-term memory neural network and pre-trained to output a strength estimation value of the target automobile suspension.

[0042] In a third aspect, the present application provides a computer device comprising a storage module, a processing module and a transceiver module connected in sequence, wherein the storage module is configured to store a computer program, the transceiver module is configured to transceive messages, and the processing module is configured to read the computer program and execute the automobile suspension strength test method according to the first aspect or any possible design in the first aspect.

[0043] In a fourth aspect, the present application provides a computer readable storage medium having instructions stored thereon, wherein the instructions, when executed on a computer, perform the automobile suspension strength test method according to the first aspect or any possible design in the first aspect.

[0044] In a fifth aspect, the present application provides a computer program product comprising a computer program or instructions, wherein the computer program or the instructions, when executed on a computer, implement the automobile suspension strength test method according to the first aspect or any possible design in the first aspect.

[0045] Advantages of the above-mentioned scheme:

[0046] (1) The present application creatively provides a new scheme for non-removal preprocessing of obtained sensing data based on the image denoising principle and estimation of automobile suspension strength based on the processing result, that is, first obtaining multi-dimensional time-series sensing data collected by multiple sensors in the process of using the drop method to perform multiple automobile suspension strength test experiments on a target automobile suspension, then performing normalization processing on the multi-dimensional time-series sensing data to obtain two-dimensional normalized matrix data, and finally performing denoising processing on the two-dimensional normalized matrix data using the MC-WNNM algorithm and / or the WSNM algorithm to obtain new two-dimensional normalized matrix data, and importing the new two-dimensional normalized matrix data into an automobile suspension strength estimation model pre-trained based on a long short-term memory neural network to output an estimated value of the strength of the target automobile suspension. In this way, the abnormal sensing data is corrected and restored to the real sensing data through the image denoising method, which can eliminate the adverse effects of abnormal data on the automobile suspension strength test results to a certain extent, and since the obtained sensing data does not need to be removed, it can also avoid the situation that the removal of too much abnormal data affects the accuracy of the automobile suspension strength test results, thereby achieving the purpose of ensuring the accuracy of the automobile suspension strength test results through non-removal preprocessing of the obtained sensing data, which is convenient for practical application and promotion. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0048] Figure 1 A flowchart of a method for testing strength of an automobile suspension according to an embodiment of the present application.

[0049] Figure 2 A structural diagram of a device for testing strength of an automobile suspension according to an embodiment of the present application.

[0050] Figure 3 A structural diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the present application will be briefly introduced below with reference to the drawings and the description of the embodiments or the prior art. Obviously, the following description of the drawings is only some embodiments of the present application, and for those skilled in the art, other embodiments can be obtained without creative labor on the basis of these embodiments. It should be noted that the description of these embodiments is used to help understand the present application, but does not constitute a limitation on the present application.

[0052] It should be understood that although the terms first and second, etc. may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another object. For example, a first object can be referred to as a second object, and similarly a second object can be referred to as a first object, without departing from the scope of the example embodiments of the present application.

[0053] It should be understood that for the term "and / or" which may appear in the present text, it is only a description of the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can mean that A exists alone, B exists alone, or A and B exist together, etc. For example, A, B and / or C, which means that any one of A, B and C exists or any combination thereof; for the term " / and" which may appear in the present text, it is another description of the relationship of the associated objects, which means that there can be two kinds of relationships, for example, A / and B, which means that A exists alone or A and B exist together, etc. In addition, for the character " / " which may appear in the present text, it generally means that the associated objects before and after are an "or" relationship.

[0054] EMBODIMENT

[0055] As Figure 1As shown, the automobile suspension strength test method provided in the first aspect of the present embodiment can be executed by, but is not limited to, a computer device having certain computing resources and being communicatively connected to sensors for measuring parameters related to automobile suspension strength test, such as an upper computer, a cloud server, an edge computer, a personal computer (PC, referring to a multi-purpose computer suitable for personal use in size, price and performance), a smart phone, a personal digital assistant (PDA) or a wearable device, etc. Figure 1 As shown, the automobile suspension strength test method comprises, but is not limited to, the following steps S1-S4.

[0056] S1. Obtain multi-dimensional time-series sensor data collected by a plurality of sensors in the process of performing a plurality of automobile suspension strength test experiments on a target automobile suspension using a drop method, wherein the multi-dimensional time-series sensor data corresponds to the plurality of sensors one by one, and each dimension of time-series sensor data contains, but is not limited to, a plurality of sensor values corresponding to the plurality of automobile suspension strength test experiments one by one and arranged in sequence according to the experimental time sequence.

[0057] In the step S1, since the drop method is an existing method for evaluating the performance of a vehicle suspension, the specific process of the vehicle suspension strength test experiment can be derived based on the existing technology, and will not be described here. Specifically, the plurality of sensors include but are not limited to a pressure sensor installed in a shock absorber and used to measure the pressure of oil during damping, a force sensor installed on a suspension connection point and used to measure the force acting on the suspension, a displacement sensor installed at the spring and used to measure the compression degree of the spring, and / or a rotation angle sensor installed at the steering wheel and used to measure the angle change of the steering wheel, etc.; the foregoing pressure sensor, force sensor, displacement sensor and rotation angle sensor, etc. can be realized by using existing related sensor products; the multi-dimensional time sequence sensing data specifically includes pressure time sequence data collected by the pressure sensor, bearing force time sequence data collected by the force sensor, spring compression degree time sequence data collected by the displacement sensor, and / or angle time sequence data collected by the rotation angle sensor, etc. The target vehicle suspension is the test object, and based on the drop method, hundreds of experiments, for example, 500 times, are generally required, so the pressure time sequence data will include 500 pressure values corresponding to the 500 vehicle suspension strength test experiments in sequence according to the experiment time sequence, the bearing force time sequence data will include 500 bearing force values corresponding to the 500 vehicle suspension strength test experiments in sequence according to the experiment time sequence, the spring compression degree time sequence data will include 500 spring compression degree values corresponding to the 500 vehicle suspension strength test experiments in sequence according to the experiment time sequence, and the angle time sequence data will include 500 angle values corresponding to the 500 vehicle suspension strength test experiments in sequence according to the experiment time sequence. In addition, the multi-dimensional time sequence sensing data can be transmitted to a local device through conventional wireless communication technology to complete data acquisition.

[0058] S2. Normalizing the multi-dimensional time sequence sensing data to obtain two-dimensional normalized matrix data, wherein the two-dimensional normalized matrix data includes R×Q elements, R represents the total number of the plurality of sensors, and Q represents the total number of the plurality of vehicle suspension strength test experiments.

[0059] In the step S2, the specific way of the normalization processing is a prior art means, for example, a Min-Max normalization method, a Z-Score normalization method or a nonlinear normalization method, etc. Specifically, when the plurality of sensors include the pressure sensor, the force sensor, the displacement sensor and the rotation angle sensor, R takes the value of 4, and Q can be exemplified as 500, so the two-dimensional normalized matrix data A can be exemplified as follows:

[0060]

[0061] In the formula, q represents a positive integer less than or equal to Q, A 1,q represents a pressure normalization value corresponding to the qth automobile suspension strength test experiment, A 2,q represents a bearing force normalization value corresponding to the qth automobile suspension strength test experiment, A 3,q represents a spring compression degree normalization value corresponding to the qth automobile suspension strength test experiment, A 4,q represents an angle normalization value corresponding to the qth automobile suspension strength test experiment.

[0062] S3. The MC-WNNM algorithm and / or the WSNM algorithm are used to denoise the two-dimensional normalization matrix data, to obtain new two-dimensional normalization matrix data.

[0063] In the step S3, the MC-WNNM algorithm (Multi-Channel Weighted Nuclear Norm Minimization) is an efficient existing image denoising method, especially suitable for color and spectral images; this algorithm is based on the nuclear norm of the matrix for sparse representation and recovery, and optimizes the nuclear norm of different channels through a weighting factor, to achieve the best denoising effect. The WSNM algorithm (Weighted Singular Value Minimization) is another existing method for image denoising, which is based on the Singular Value Decomposition (SVD) technology, and can effectively remove the noise in the image while preserving important features of the image, such as edges and textures. Since the two-dimensional normalization matrix data is a two-dimensional matrix containing R×Q elements, it can be regarded as a two-dimensional image containing R×Q pixels, and since abnormal sensor data caused by factors such as insufficient accuracy of the multiple sensors, improper installation position, or failure of the data acquisition system can be regarded as original measurement data with added noise, the MC-WNNM algorithm and / or the WSNM algorithm can be applied to denoise the two-dimensional normalization matrix data, to obtain the new two-dimensional normalization matrix data that corrects and recovers the abnormal sensor data (i.e., by denoising the original measurement data to recover the true measurement data), so as to accurately estimate the automobile suspension strength subsequently. The specific process of the denoising can be derived based on the MC-WNNM algorithm and / or the WSNM algorithm, and will not be described here.

[0064] In the step S3, in order to achieve better denoising effect by combining the MC-WNNM algorithm and the WSNM algorithm (i.e. using both the weight matrix W in the MC-WNNM algorithm and the Schatten p-norm in the WSNM algorithm to obtain the new two-dimensional normalized matrix data closer to the real measurement data), preferably, the MC-WNNM algorithm and the WSNM algorithm are used to denoise the two-dimensional normalized matrix data to obtain new two-dimensional normalized matrix data, including but not limited to the following steps S31-S38.

[0065] S31. A k-th two-dimensional matrix sub-block in a time sequence dimension is selected from the two-dimensional normalized matrix data in a sliding window manner, and then the step S32 is performed, wherein k represents a positive integer with an initial value of 1, the k-th two-dimensional matrix sub-block contains elements, wherein Q represents a positive integer less than Q.

[0066] In the step S31, the sliding window manner is a prior manner of dividing time series data into multiple segments, for example, in the case of an overlap rate of 0, the two-dimensional normalized matrix data A can be divided into the following multiple data segments A1, A2, …, A j , …:

[0067]

[0068] wherein j represents a positive integer, A j represents a j-th data segment in a time sequence dimension obtained by dividing the two-dimensional normalized matrix data. Thus, when k is initially 1, the k-th two-dimensional matrix sub-block is the data segment A1, with a value of 4.

[0069] S32. M two-dimensional matrix similar sub-blocks located in a predefined neighborhood of the k-th two-dimensional matrix sub-block and most similar to the k-th two-dimensional matrix sub-block are searched from the two-dimensional normalized matrix data, and then the step S33 is performed, wherein M represents a positive integer.

[0070] In the step S32, the kth two-dimensional matrix sub-block is a two-dimensional matrix, and the two-dimensional matrix similar sub-blocks are other two-dimensional matrices with the same size as the kth two-dimensional matrix sub-block. For example, if A2 is the kth two-dimensional matrix sub-block, the M two-dimensional matrix similar sub-blocks can include, but are not limited to, A1, etc. The size of the range of the predefined neighborhood can be specified by a tester or can be determined regularly according to the results of multiple limited experiments. Specifically, the M two-dimensional matrix similar sub-blocks located in the predefined neighborhood of the kth two-dimensional matrix sub-block and most similar to the kth two-dimensional matrix sub-block are searched from the two-dimensional normalized matrix data, including but not limited to the following steps S321-S323.

[0071] S321. For each other two-dimensional matrix sub-block located in the predefined neighborhood of the kth two-dimensional matrix sub-block and having the same size as the kth two-dimensional matrix sub-block in the two-dimensional normalized matrix data, the Euclidean distance between the kth two-dimensional matrix sub-block and the corresponding sub-block is calculated.

[0072] In the step S321, since the kth two-dimensional matrix sub-block and the other two-dimensional matrix sub-blocks can be regarded as two-dimensional vectors (i.e., each contains elements), the Euclidean distance between the two can be calculated based on the existing Euclidean distance formula.

[0073] S322. The each other two-dimensional matrix sub-block is arranged in order from near to far according to the Euclidean distance to obtain a two-dimensional matrix sub-block sequence.

[0074] S323. The first M two-dimensional matrix sub-blocks are selected from the two-dimensional matrix sub-block sequence as the M two-dimensional matrix similar sub-blocks most similar to the kth two-dimensional matrix sub-block, where M represents a positive integer.

[0075] S33. The kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks are respectively subjected to vectorization and straightening processing to obtain M+1 one-dimensional vectors, and the M+1 one-dimensional vectors are subjected to superposition processing to obtain an original matrix Y with a size of , and then the step S34 is performed.

[0076] In the step S33, the vectorization and straightening processing refers to processing the current two-dimensional vector of the corresponding sub-block into a one-dimensional vector. Specifically, the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks are respectively subjected to vectorization and straightening processing to obtain M+1 one-dimensional vectors, and the M+1 one-dimensional vectors are subjected to superposition processing to obtain an original matrix Y with a size of , including but not limited to the following steps S331-S332.

[0077] S331. For each of the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks, an element located at an r'th row and a q'th column in the corresponding sub-block is taken as an h'th element in a corresponding one-dimensional vector, to obtain the corresponding one-dimensional vector, where r' represents a positive integer less than or equal to R, q' represents a positive integer less than or equal to

[0078] S332. A mth one-dimensional vector in the M+1 one-dimensional vectors corresponding to the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks is taken as a mth row element in a original matrix Y with a size of , to obtain the original matrix Y, where m represents a positive integer less than or equal to M+1.

[0079] S34. Assuming that the original matrix Y = X + N + S, and using an ADMM algorithm to solve a minimization objective function to obtain a denoised matrix X, then step S35 is performed, where the minimization objective function is expressed as follows:

[0080]

[0081] In the formula, N represents Gaussian noise, S represents sparse noise, ||S||1 represents an L1 norm for constraining the sparse noise S, ||N||F represents an F norm for constraining the Gaussian noise N, F represents a Schatten p-norm for constraining the denoised matrix X, λ1, λ2 and λ3 respectively represent preset regularization coefficients, W represents a multi-channel weighting matrix and adopts a diagonal matrix form, r represents a positive integer less than or equal to R, δ r represents a noise standard deviation of an r'th channel, I represents a unit matrix, min() represents a minimum value function, i represents a positive integer, w i represents a weight given to σ i , σ i represents an i'th singular value in the denoised matrix X, p represents a parameter value for determining a norm type and takes a value in an interval (0, 1], and w represents a weighting of a kernel norm.

[0082] ​​In the step S34, the de-noised matrix X represents the potential real measurement data (i.e. the required clean matrix data without noise). The L1 norm, the F norm, and the Schatten p-norm are all existing norms. In addition, the ADMM algorithm (Alternating Direction Method of Multipliers) is an existing core algorithm in the field of mathematical optimization for solving separable block convex optimization problems. A large-scale problem can be decomposed into sub-problems for alternative solution by a decomposition strategy. Therefore, the specific solution process of the minimized objective function can be derived based on the existing ADMM algorithm, and will not be described here.

[0083] S35. According to the de-noised matrix X, a new two-dimensional matrix sub-block corresponding to the kth two-dimensional matrix sub-block and M new two-dimensional matrix similar sub-blocks corresponding to the M two-dimensional matrix similar sub-blocks are reconstructed, and then the step S36 is performed.

[0084] In the step S35, the reconstruction process of the new two-dimensional matrix sub-block and the M new two-dimensional matrix similar sub-blocks is the inverse process of the step S33. Specifically, according to the de-noised matrix X, a new two-dimensional matrix sub-block corresponding to the kth two-dimensional matrix sub-block and M new two-dimensional matrix similar sub-blocks corresponding to the M two-dimensional matrix similar sub-blocks are reconstructed, including but not limited to: if the mth row element in the original matrix Y is a one-dimensional vector of the kth two-dimensional matrix sub-block, then the element located at the mth row and the h' column in the de-noised matrix X is taken as the element located at the r' row and the q' column in the new two-dimensional matrix sub-block corresponding to the kth two-dimensional matrix sub-block; if the mth row element in the original matrix Y is a one-dimensional vector of the m'th two-dimensional matrix similar sub-block in the M two-dimensional matrix similar sub-blocks, then the element located at the mth row and the h' column in the de-noised matrix X is taken as the element located at the r' row and the q' column in the new two-dimensional matrix similar sub-block corresponding to the m'th two-dimensional matrix similar sub-block, where m' represents a positive integer less than or equal to M.

[0085] S36. In the two-dimensional normalized matrix data, the kth two-dimensional matrix sub-block is replaced by the new two-dimensional matrix sub-block, and the M two-dimensional matrix similar sub-blocks are replaced by the M new two-dimensional matrix similar sub-blocks one by one, to obtain new two-dimensional normalized matrix data, and then the step S37 is performed.

[0086] S37. It is judged whether the new two-dimensional matrix sub-block is the last two-dimensional matrix sub-block in the new two-dimensional normalized matrix data in the time sequence dimension. If yes, the de-noising process is ended, otherwise the step S38 is performed.

[0087] S38. Increase k by 1, and then continue to select the k-th two-dimensional matrix sub-block in the time sequence dimension from the new two-dimensional normalized matrix data in the sliding window manner, and return to perform step S32.

[0088] The foregoing steps S31-S36 are the process of local denoising update of the two-dimensional normalized matrix data, and therefore through subsequent steps S37-S38, one denoising update can be iteratively performed on the two-dimensional normalized matrix data in the time sequence dimension, so as to achieve the purpose of global denoising update of the entire two-dimensional normalized matrix data. In addition, after step S38, the existing LOF algorithm can also be used to detect whether there is abnormal data in the new two-dimensional normalized matrix data, and if so, the foregoing steps S31-S38 can also be used to iteratively perform one denoising update on the new two-dimensional normalized matrix data until there is no abnormal data.

[0089] S4. Import the new two-dimensional normalized matrix data into the automobile suspension strength estimation model based on the long short-term memory neural network to complete pre-training, and output to obtain the strength estimation value of the target automobile suspension.

[0090] In step S4, the long short-term memory neural network (LSTM, Long Short-Term Memory) is a kind of time recurrent neural network, which is specially designed to solve the long-term dependence problem existing in the general recurrent neural network, and therefore the automobile suspension strength estimation model can be obtained based on a certain amount of sample data by referring to the conventional training of the prior art (for example, patent “CN118332473A-Automobile suspension strength test method based on artificial intelligence”). In addition, the strength estimation value can be specifically the estimation value of the automobile suspension strength factor, and the estimation value ranges from 0 to 1, and the closer to 1, the better the quality of the target automobile suspension.

[0091] Thus, based on the automobile suspension strength test method described in the foregoing steps S1-S4, a new scheme is provided for non-removal preprocessing of the obtained sensing data based on the image denoising principle and estimating the strength of the automobile suspension based on the processing result, that is, first, multi-dimensional time-series sensing data collected by multiple sensors in the process of using the drop method to perform multiple automobile suspension strength test experiments on the target automobile suspension is obtained, then the multi-dimensional time-series sensing data is normalized to obtain two-dimensional normalized matrix data, and finally, the MC-WNNM algorithm and / or the WSNM algorithm is used to denoise the two-dimensional normalized matrix data to obtain new two-dimensional normalized matrix data, which is imported into the automobile suspension strength estimation model pre-trained based on the long short-term memory neural network to output the strength estimation value of the target automobile suspension. In this way, the abnormal sensing data is corrected and restored to the real sensing data through the image denoising method, which can eliminate the adverse effects of abnormal data on the automobile suspension strength test results to a certain extent, and since the obtained sensing data does not need to be removed, it can also avoid the situation that the removal of too much abnormal data affects the accuracy of the automobile suspension strength test results, thereby achieving the purpose of ensuring the accuracy of the automobile suspension strength test results through non-removal preprocessing of the obtained sensing data, facilitating actual application and promotion.

[0092] As shown in Figure 2 the second aspect of the present embodiment provides a virtual device for implementing the automobile suspension strength test method of the first aspect, comprising a sensing data acquisition unit, a normalization processing unit, a data denoising processing unit and a suspension strength estimation unit connected in sequence;

[0093] The sensing data acquisition unit is configured to acquire multi-dimensional time-series sensing data collected by multiple sensors in the process of using the drop method to perform multiple automobile suspension strength test experiments on the target automobile suspension, wherein the multi-dimensional time-series sensing data corresponds one-to-one to the multiple sensors, and each dimension of time-series sensing data contains multiple sensing values corresponding one-to-one to the multiple automobile suspension strength test experiments and arranged in sequence according to the experimental time sequence;

[0094] The normalization processing unit is configured to normalize the multi-dimensional time-series sensing data to obtain two-dimensional normalized matrix data, wherein the two-dimensional normalized matrix data contains R*Q elements, R represents the total number of the multiple sensors, and Q represents the total number of the multiple automobile suspension strength test experiments;

[0095] The data denoising processing unit is configured to use the MC-WNNM algorithm and / or the WSNM algorithm to denoise the two-dimensional normalized matrix data to obtain new two-dimensional normalized matrix data;

[0096] The suspension strength estimation unit is configured to input the new two-dimensional normalized matrix data into a pre-trained automobile suspension strength estimation model based on a long short-term memory neural network, and output a strength estimation value of the target automobile suspension.

[0097] The working process, working details and technical effects of the foregoing device provided by the second aspect of the embodiment can be referred to the automobile suspension strength test method described in the first aspect, and will not be repeated here.

[0098] As shown in Figure 3 The third aspect of the embodiment provides a computer device for executing the automobile suspension strength test method described in the first aspect, which comprises a storage module, a processing module and a transceiver module connected in sequence, wherein the storage module is configured to store a computer program, the transceiver module is configured to transceive messages, and the processing module is configured to read the computer program and execute the automobile suspension strength test method described in the first aspect. Specifically, the storage module can include, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a flash memory, a first-in first-out memory (FIFO) and / or a first-in last-out memory (FILO), etc.; and the processing module can be, but is not limited to, a microprocessor with a model number of STM32F105 series. In addition, the computer device can further include, but is not limited to, a power module, a display screen and other necessary components.

[0099] The working process, working details and technical effects of the foregoing computer device provided by the third aspect of the embodiment can be referred to the automobile suspension strength test method described in the first aspect, and will not be repeated here.

[0100] The fourth aspect of the embodiment provides a computer readable storage medium storing instructions of the automobile suspension strength test method described in the first aspect, that is, the computer readable storage medium stores instructions, and when the instructions run on a computer, the automobile suspension strength test method described in the first aspect is executed. The computer readable storage medium refers to a carrier for storing data, which can include, but is not limited to, floppy disks, optical disks, hard disks, flash memories, USB flash drives and / or memory sticks, etc. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0101] The working process, working details and technical effects of the aforementioned computer readable storage medium provided by the fourth aspect of the embodiment can be referred to the automobile suspension strength test method as described in the first aspect, and will not be described here.

[0102] The fifth aspect of the embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements the automobile suspension strength test method as described in the first aspect. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0103] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for limiting the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of testing the strength of an automotive suspension, characterized by, The method comprises the following steps: acquiring multi-dimensional time sequence sensor data collected by multiple sensors during multiple automobile suspension strength test experiments on a target automobile suspension by using a drop method, wherein the multi-dimensional time sequence sensor data correspond to the multiple sensors one by one, and each dimension of time sequence sensor data contains multiple sensor values corresponding to the multiple automobile suspension strength test experiments and arranged in time sequence; normalizing the multi-dimensional time sequence sensor data to obtain two-dimensional normalized matrix data, wherein the two-dimensional normalized matrix data contains R×Q elements, R represents the total number of the multiple sensors, and Q represents the total number of the multiple automobile suspension strength test experiments; using MC-WNNM and WSNM algorithms to denoise the two-dimensional normalized matrix data to obtain new two-dimensional normalized matrix data, specifically comprising the following steps S31-S38: S31. A kth two-dimensional matrix sub-block in a time sequence dimension is selected from the two-dimensional normalized matrix data in a sliding window manner, and then step S32 is performed, where k represents a positive integer with an initial value of 1, and the kth two-dimensional matrix sub-block contains elements, where Q represents a positive integer less than Q. S32. searching M two-dimensional matrix similar subblocks located in a predefined neighborhood of the kth two-dimensional matrix subblock and most similar to the kth two-dimensional matrix subblock from the two-dimensional normalized matrix data, and then performing step S33, wherein M represents a positive integer; S33. Vectorizing and straightening the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks respectively, obtaining M+1 one-dimensional vectors, and superimposing the M+1 one-dimensional vectors to obtain an original matrix Y with a size of Then, step S34 is performed. S34. assuming that the original matrix Y = X + N + S, and using an ADMM algorithm to solve a minimization objective function to obtain a denoised matrix X, and then performing step S35, wherein the minimization objective function is represented as follows: where N represents a Gaussian noise, S represents a sparse noise, ||S||1 represents an L1 norm for constraining the sparse noise S, ||N||F represents an F norm for constraining the Gaussian noise N, F represents a Schatten p-norm for constraining the de-noising matrix X, λ1, λ2 and λ3 respectively represent preset regularization coefficients, W represents a multi-channel weighting matrix and adopts a diagonal matrix form, r represents a positive integer less than or equal to R, δ r represents a noise standard deviation of the rth channel, I represents a unit matrix, min() represents a minimum value function, i represents a positive integer, w i represents a weight given to σ i , σ i represents an ith singular value in the de-noising matrix X, p represents a parameter value for determining a norm type and takes a value within an interval (0, 1], and w represents a weighting of a kernel norm, and the ADMM algorithm refers to an alternating direction multiplier algorithm.​ S35. according to the denoised matrix X, reconstructing a new two-dimensional matrix subblock corresponding to the kth two-dimensional matrix subblock and M new two-dimensional matrix similar subblocks corresponding to the M two-dimensional matrix similar subblocks, and then performing step S36; S36. replacing the kth two-dimensional matrix subblock with the new two-dimensional matrix subblock and replacing the M two-dimensional matrix similar subblocks with the M new two-dimensional matrix similar subblocks one by one in the two-dimensional normalized matrix data to obtain new two-dimensional normalized matrix data, and then performing step S37; S37. judging whether the new two-dimensional matrix subblock is the last two-dimensional matrix subblock in the new two-dimensional normalized matrix data in the time sequence dimension, if yes, ending the denoising process, otherwise performing step S38; S38. increasing k by 1, and then continuing to select the kth two-dimensional matrix subblock in the time sequence dimension from the new two-dimensional normalized matrix data by using the sliding window mode, and returning to perform step S32; importing the new two-dimensional normalized matrix data into an automobile suspension strength estimation model based on a long short-term memory neural network and pre-trained to output a strength estimation value of the target automobile suspension.

2. The method of claim 1, wherein, The multiple sensors include a pressure sensor installed in a shock absorber and used for measuring the pressure of oil liquid in a shock absorbing process, a force sensor installed on a suspension connecting point and used for measuring the force acting on the suspension, a displacement sensor installed at a spring and used for measuring the compression degree of the spring, and / or a rotation angle sensor installed at a steering wheel and used for measuring the angle change of the steering wheel.

3. The method of claim 1, wherein, When the MC-WNNM algorithm and the WSNM algorithm are used to denoise the two-dimensional normalized matrix data, M two-dimensional matrix similar subblocks that are located in the predefined neighborhood of the kth two-dimensional matrix subblock and most similar to the kth two-dimensional matrix subblock are searched from the two-dimensional normalized matrix data, including: For each other two-dimensional matrix subblock that is located in the predefined neighborhood of the kth two-dimensional matrix subblock and has the same size as the kth two-dimensional matrix subblock in the two-dimensional normalized matrix data, the Euler distance between the kth two-dimensional matrix subblock and the corresponding subblock is calculated; The each other two-dimensional matrix subblock is arranged in order of the Euler distance from near to far to obtain a two-dimensional matrix subblock sequence; The first M two-dimensional matrix subblocks in the two-dimensional matrix subblock sequence are selected as the M two-dimensional matrix similar subblocks most similar to the kth two-dimensional matrix subblock, wherein M represents a positive integer.

4. The method of claim 1, wherein, When the MC-WNNM algorithm and the WSNM algorithm are used to denoise the two-dimensional normalized matrix data, the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks are respectively subjected to vectorization and straightening processing to obtain M+1 one-dimensional vectors, and the M+1 one-dimensional vectors are subjected to superposition processing to obtain an original matrix Y with a size of . For each of the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks, an element located at an r'th row and a q'th column in the corresponding sub-block is taken as an h'th element in a corresponding one-dimensional vector, to obtain the corresponding one-dimensional vector, where r' represents a positive integer less than or equal to R, q' represents a positive integer less than or equal to Q, and h' represents a positive integer less than or equal to H. The mth one-dimensional vector in the M+1 one-dimensional vectors corresponding to the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks is taken as the mth row element in the original matrix Y with a size of , where m represents a positive integer less than or equal to M+1. The original matrix Y is obtained.

5. The method for testing the strength of an automobile suspension according to claim 4, characterized in that, According to the denoised matrix X, a new two-dimensional matrix subblock corresponding to the kth two-dimensional matrix subblock and M new two-dimensional matrix similar subblocks corresponding to the M two-dimensional matrix similar subblocks are reconstructed, including: If the mth row element in the original matrix Y is a one-dimensional vector of the kth two-dimensional matrix subblock, an element located in the mth row and the h' column in the denoised matrix X is taken as an element located in the r' row and the q' column in the new two-dimensional matrix subblock corresponding to the kth two-dimensional matrix subblock; If the mth row element in the original matrix Y is a one-dimensional vector of the m'th two-dimensional matrix similar subblock in the M two-dimensional matrix similar subblocks, an element located in the mth row and the h' column in the denoised matrix X is taken as an element located in the r' row and the q' column in the new two-dimensional matrix similar subblock corresponding to the m'th two-dimensional matrix similar subblock, wherein m' represents a positive integer less than or equal to M.

6. An automotive suspension strength testing apparatus characterized by comprising: The sensing data acquisition unit, the normalization processing unit, the data denoising processing unit, and the suspension strength estimation unit are sequentially connected in communication. The sensing data acquisition unit is configured to acquire multi-dimensional time-series sensing data collected by a plurality of sensors in a process of performing a plurality of automobile suspension strength test experiments on a target automobile suspension by using a drop method, wherein the multi-dimensional time-series sensing data correspond to the plurality of sensors one by one, and each dimension of time-series sensing data contains a plurality of sensing values corresponding to the plurality of automobile suspension strength test experiments and arranged in sequence according to the experiment time sequence. The normalization processing unit is configured to perform normalization processing on the multi-dimensional time-series sensing data to obtain two-dimensional normalized matrix data, wherein the two-dimensional normalized matrix data contains R×Q elements, R represents the total number of the plurality of sensors, and Q represents the total number of the plurality of automobile suspension strength test experiments. The data denoising processing unit is configured to use the MC-WNNM algorithm and the WSNM algorithm to denoise the two-dimensional normalized matrix data to obtain new two-dimensional normalized matrix data, and specifically includes the following steps S31-S38: S31. A kth two-dimensional matrix sub-block in a time sequence dimension is selected from the two-dimensional normalized matrix data in a sliding window manner, and then step S32 is performed, where k represents a positive integer with an initial value of 1, and the kth two-dimensional matrix sub-block contains elements, where Q represents a positive integer less than k. S32. From the two-dimensional normalized matrix data, search for M two-dimensional matrix similar sub-blocks located within a predefined neighborhood of the kth two-dimensional matrix sub-block and most similar to the kth two-dimensional matrix sub-block, and then perform step S33, wherein M represents a positive integer; S33. Vectorizing and straightening the kth two-dimensional matrix sub-block and the M two-dimensional matrix similar sub-blocks respectively, obtaining M+1 one-dimensional vectors, and superimposing the M+1 one-dimensional vectors to obtain an original matrix Y with a size of Then, step S34 is performed. S34. Assuming that the original matrix Y = X + N + S, and using an ADMM algorithm to solve a minimization objective function to obtain a denoised matrix X, and then perform step S35, wherein the minimization objective function is represented as follows: where N represents a Gaussian noise, S represents a sparse noise, ||S||1 represents an L1 norm for constraining the sparse noise S, ||N||F represents an F norm for constraining the Gaussian noise N, F represents a Schatten p-norm for constraining the de-noising matrix X, λ1, λ2 and λ3 respectively represent preset regularization coefficients, W represents a multi-channel weighting matrix and adopts a diagonal matrix form, r represents a positive integer less than or equal to R, δ r represents a noise standard deviation of the rth channel, I represents a unit matrix, min() represents a minimum value function, i represents a positive integer, w i represents a weight value given to σ i , σ i represents an ith singular value in the de-noising matrix X, p represents a parameter value for determining a norm type and takes a value within an interval (0, 1], and w represents a weighting of a kernel norm, and the ADMM algorithm refers to an alternating direction multiplier algorithm.​ S35. According to the denoised matrix X, reconstruct a new two-dimensional matrix sub-block corresponding to the kth two-dimensional matrix sub-block and M new two-dimensional matrix similar sub-blocks corresponding to the M two-dimensional matrix similar sub-blocks one by one, and then perform step S36; S36. In the two-dimensional normalized matrix data, replace the kth two-dimensional matrix sub-block with the new two-dimensional matrix sub-block, and replace the M two-dimensional matrix similar sub-blocks with the M new two-dimensional matrix similar sub-blocks one by one to obtain new two-dimensional normalized matrix data, and then perform step S37; S37. Determine whether the new two-dimensional matrix sub-block is the last two-dimensional matrix sub-block in the new two-dimensional normalized matrix data and in the time dimension, if so, end the denoising process, otherwise perform step S38; S38. Increase k by 1, and then continue to select the kth two-dimensional matrix sub-block in the time dimension from the new two-dimensional normalized matrix data in the sliding window manner, and return to perform step S32; The suspension strength estimation unit is configured to import the new two-dimensional normalized matrix data into an automobile suspension strength estimation model pre-trained based on a long short-term memory neural network, and output a strength estimation value of the target automobile suspension.

7. A computer device, characterized by The computer readable storage medium has instructions stored thereon, and when the instructions are executed on the computer, the automobile suspension strength test method according to any one of claims 1-5 is executed.

8. A computer-readable storage medium, characterized in that The computer program or the instructions realize the automobile suspension strength test method according to any one of claims 1-5 when executed on the computer.

9. A computer program product comprising computer programs or instructions, characterized in that, The computer program or the instructions realize the automobile suspension strength test method according to any one of claims 1-5 when executed on the computer.

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