Robustness analysis method and device of suspension system, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a robustness analysis method and device of a suspension system, a storage medium and electronic equipment. The method comprises the steps that multiple to-be-tested suspension rigidities of a suspension element in the suspension system are acquired; determining corresponding characteristic parameters of the suspension system under different to-be-measured suspension rigidities to obtain a probability distribution model of the corresponding characteristic parameters of the suspension system; and determining the robustness of the suspension system according to the distribution characteristics of the probability distribution model. According to the technical scheme provided by the embodiment of the invention, the robustness of the suspension system when the suspension rigidity of the suspension element is changed can be determined.

Description

Technical Field

[0001] The present application relates to the technical field of suspension system testing. Specifically, it relates to a method, device, storage medium, and electronic device for analyzing the robustness of a suspension system. Background Art

[0002] With the continuous development of science and technology, using a suspension system to connect and support a powertrain or a fuselage structure to attenuate vibrations generated by the operation of the powertrain or external excitations has become one of the commonly used ways to suppress vibration transmission.

[0003] In related technologies, to determine the robustness of a suspension system when suppressing vibrations, it is usually determined whether the suspension system has robustness in traditional ways such as whether the suspension stiffness of the suspension elements in the suspension system reaches the design reference value, or whether the attenuation amount of the target frequency vibration of the suspension system under the suspension stiffness of the suspension elements reaches a preset threshold. However, during the actual application of the suspension system, the suspension stiffness of the suspension elements will change dynamically under the influence of factors such as material property changes, temperature effects, and aging effects, making it difficult to determine the robustness of the suspension system when the suspension stiffness of the suspension elements changes using traditional methods.

[0004] Therefore, how to determine the robustness of the suspension system when the suspension stiffness of the suspension elements changes has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] To solve the above technical problems, embodiments of the present application provide a method, device, computer-readable storage medium, and electronic device for analyzing the robustness of a suspension system.

[0006] According to one aspect of the embodiments of the present application, a method for analyzing the robustness of a suspension system is provided, including: obtaining a plurality of suspension stiffnesses to be measured of the suspension elements in the suspension system; determining the characteristic parameters corresponding to the suspension system under different suspension stiffnesses to be measured to obtain a probability distribution model of the characteristic parameters of the suspension system; and determining the robustness of the suspension system according to the distribution characteristics of the probability distribution model.

[0007] According to one aspect of the embodiments of the present application, a device for analyzing the robustness of a suspension system is provided, including: an obtaining module configured to obtain a plurality of suspension stiffnesses to be measured of the suspension elements in the suspension system; a calculation module configured to determine the characteristic parameters corresponding to the suspension system under different suspension stiffnesses to be measured to obtain a probability distribution model of the characteristic parameters of the suspension system; and an analysis module configured to determine the robustness of the suspension system according to the distribution characteristics of the probability distribution model.

[0008] In some embodiments of the present application, based on the foregoing solution, when the suspension system includes multiple suspension elements and each of the different suspension elements corresponds to multiple suspension stiffnesses to be measured, the calculation module is further configured to: generate multiple suspension stiffness matrices based on the multiple suspension stiffnesses to be measured corresponding to each of the suspension elements, wherein each suspension stiffness matrix is jointly constituted by one of the suspension stiffnesses to be measured corresponding to each suspension element; determine the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices.

[0009] In some embodiments of the present application, based on the foregoing solution, the calculation module is further configured to: Step S1, determine a target suspension stiffness matrix from the multiple suspension stiffness matrices; Step S2, adjust the suspension stiffness to be measured in the target suspension stiffness matrix based on a preset perturbation interval; Step S3, determine the characteristic parameters corresponding to the suspension system under the target suspension stiffness matrix; Step S4, repeatedly execute Step S1 - Step S3 until an end condition is satisfied.

[0010] In some embodiments of the present application, based on the foregoing solution, when the preset perturbation interval includes multiple sub-perturbation intervals and the multiple sub-perturbation intervals correspond one-to-one to the multiple suspension elements, the calculation module is further configured to: determine the target suspension stiffness to be adjusted in the target suspension stiffness matrix; determine a target sub-perturbation interval from the preset perturbation interval based on the suspension element corresponding to the target suspension stiffness to be measured; adjust the target suspension stiffness to be measured through the target sub-perturbation interval.

[0011] In some embodiments of the present application, based on the foregoing solution, the acquisition module is further configured to: determine the correlation coefficient between each preset suspension stiffness of the suspension element and the characteristic parameter; determine multiple suspension stiffnesses to be measured based on the correlation coefficient corresponding to each preset suspension stiffness.

[0012] In some embodiments of the present application, based on the foregoing solution, the acquisition module is further configured to: respectively determine the correlation sub-coefficient between the preset suspension stiffness and the characteristic parameter under each degree of freedom of the suspension system; determine the correlation coefficient according to the correlation sub-coefficient of the preset suspension stiffness under each degree of freedom.

[0013] In some embodiments of the present application, based on the foregoing solution, the analysis module is further configured to: obtain a preset standard distribution form; determine the goodness of fit between the distribution form reflected by the distribution characteristics of the probability distribution model and the standard distribution form; if the goodness of fit reaches a preset goodness-of-fit threshold, determine that the suspension system has robustness.

[0014] According to one aspect of the embodiments of the present application, there is provided a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method for analyzing the robustness of a suspension system as described in the above embodiments.

[0015] According to one aspect of the embodiments of the present application, there is provided an electronic device, including: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the electronic device to implement the method for analyzing the robustness of a suspension system as described in the above embodiments.

[0016] In the technical solution of the embodiments of the present application, by first obtaining a plurality of to-be-tested suspension stiffnesses of suspension elements in a suspension system, and then determining the characteristic parameters corresponding to the suspension system under different to-be-tested suspension stiffnesses, a probability distribution model of the corresponding characteristic parameters of the suspension system is obtained, so as to determine the performance of the corresponding vibration suppression effect of the suspension system when the suspension stiffness of the suspension elements changes. Then, according to the distribution characteristics of the probability distribution model, the robustness of the suspension system is determined, thereby achieving the purpose of determining the robustness of the suspension system when the suspension stiffness of the suspension elements changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0018] Figure 1 is a flowchart of a method for analyzing the robustness of a suspension system shown in an exemplary embodiment of the present application;

[0019] Figure 2 is Figure 1 a flowchart of step S120 in the shown embodiment in an exemplary embodiment;

[0020] Figure 3 is Figure 2 a flowchart of step S220 in the shown embodiment in an exemplary embodiment;

[0021] Figure 4 is Figure 1 a flowchart of step S110 in the shown embodiment in an exemplary embodiment;

[0022] Figure 5 is a block diagram of a device for analyzing the robustness of a suspension system shown in an exemplary embodiment of the present application;

[0023] Figure 6 It is a schematic structural diagram of an electronic device shown in an exemplary embodiment of the present application. Detailed implementation manners

[0024] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0025] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0026] The block diagrams shown in the accompanying drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0027] The flowcharts shown in the accompanying drawings are only illustrative and do not necessarily include all the content and operations / steps, nor do they necessarily have to be executed in the described order. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0028] It should be noted that: "a plurality of" mentioned herein refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0029] The technical solution of the embodiment of the present application proposes a method for analyzing the robustness of a suspension system. The execution subject of this method can be executed by a data analysis device or equipment, such as a personal computer or a computer cluster, etc., or can also be a terminal with an analysis function, such as a laptop computer, a smart phone, a tablet computer, etc., which is not limited herein. Specifically refer to Figure 1As shown, the method at least includes steps S110 to S130, which are introduced in detail as follows:

[0030] In step S110, multiple suspension stiffnesses to be measured of the suspension elements in the suspension system are obtained.

[0031] It should be noted that the suspension system is a component in mechanical equipment (such as vehicles, ships, machine tools, etc.) used to connect and support the power assembly or the fuselage structure. Through the suspension system, vibrations generated due to the operation of the power assembly or external excitation can be effectively isolated and attenuated, thereby preventing the vibration from being transmitted to other parts of the fuselage.

[0032] Among them, the suspension element is the core component in the suspension system for suppressing vibrations, and the vibration suppression ability of the suspension system changes with the change of the suspension stiffness corresponding to the suspension element.

[0033] The method for obtaining the suspension stiffnesses to be measured of the suspension elements in the suspension system can be flexibly set according to needs. In one example, the suspension stiffness associated with the suspension element can be directly obtained from a preset memory, and the obtained suspension stiffness is used as the suspension stiffness to be measured.

[0034] In another example, the suspension stiffness input by the analyst for the suspension system can be received, and then the input suspension stiffness is used as the suspension stiffness to be measured.

[0035] In step S120, the characteristic parameters corresponding to the suspension system under different suspension stiffnesses to be measured are determined to obtain a probability distribution model of the characteristic parameters corresponding to the suspension system.

[0036] In the implementation manner of the present application, after obtaining multiple suspension stiffnesses to be measured of the suspension elements in the suspension system, the characteristic parameters corresponding to the suspension system under different suspension stiffnesses to be measured can be determined to obtain a probability distribution model of the characteristic parameters corresponding to the suspension system; among them, the probability distribution model is used to reflect the performance of the corresponding vibration suppression effect of the suspension system when the suspension stiffness of the suspension element changes.

[0037] Among them, the characteristic parameters corresponding to the suspension system include but are not limited to modes, decoupling ratios, etc.

[0038] In some embodiments of the present application, in order to determine the characteristic parameters corresponding to the suspension system under different suspension stiffnesses to be measured, the characteristic parameters corresponding to the suspension system can be obtained in sequence when the suspension stiffness of the suspension element is different suspension stiffnesses to be measured.

[0039] For example, when the multiple suspension stiffnesses of the suspension element to be measured are KX1, KX2, and KX3 respectively, the process of obtaining the characteristic parameters corresponding to the suspension system when the suspension stiffness of the suspension element is different suspension stiffnesses to be measured in sequence is to obtain the characteristic parameters corresponding to the suspension system when the suspension stiffnesses of the suspension element are KX1, KX2, and KX3 respectively. When the characteristic parameter is the decoupling rate, if the decoupling rates corresponding to the suspension system obtained when the suspension stiffnesses of the suspension element are KX1, KX2, and KX3 are 95%, 97%, and 96% respectively, then the relative frequencies corresponding to 95%, 97%, and 96% of the decoupling rate in the corresponding probability distribution model are all 1 / 3.

[0040] In some embodiments of the present application, in order to determine the characteristic parameters corresponding to the suspension system under different suspension stiffnesses to be measured, it is also possible to repeatedly determine the current suspension stiffness to be measured from multiple suspension stiffnesses to be measured, then adjust the current suspension stiffness to be measured based on a preset perturbation interval, and then determine the characteristic parameters corresponding to the suspension system under the current suspension stiffness to be measured until the end condition is met, so as to further conform to the randomness when the suspension stiffness of the suspension element changes, thereby improving the authenticity of the probability distribution model determined subsequently.

[0041] In the above embodiments, each suspension stiffness to be measured can be determined as the current suspension stiffness to be measured as the end condition, or alternatively, whether the number of times of determining the current suspension stiffness to be measured from multiple suspension stiffnesses to be measured reaches a preset number can be used as the end condition.

[0042] The preset perturbation interval can be set by an analyst according to experience, or can be set according to the material characteristics of the suspension element, and no limitation is made here.

[0043] The method of adjusting the current suspension stiffness to be measured based on the preset perturbation interval can generate a stiffness matrix through the preset perturbation interval and the current suspension stiffness to be measured, and then use the Monte Carlo simulation method to extract a suspension stiffness from the stiffness matrix as the current suspension stiffness to be measured; or randomly extract a change amount from the preset perturbation interval, and then adjust the current suspension stiffness to be measured by the change amount, so as to perturb the suspension stiffness to be measured through the preset perturbation interval and improve the randomness when simulating the change of the suspension stiffness of the suspension element.

[0044] In step S130, the robustness of the suspension system is determined according to the distribution characteristics of the probability distribution model.

[0045] It should be noted that the suspension stiffness of the suspension element is affected by various factors such as manufacturing tolerances during the production process, or by changes in material properties, temperature effects, aging effects, etc. during actual application. Its value often fluctuates around the design reference value, resulting in the suspension stiffness of the suspension element being approximately normally distributed after the change. Correspondingly, the characteristic parameters corresponding to the robust suspension system will change regularly based on the change in the suspension stiffness of the suspension element, that is, the distribution form corresponding to the characteristic parameters of the suspension system will also be approximately normally distributed with the change in the suspension stiffness.

[0046] In the implementation manner of the present application, after obtaining the probability distribution model of the characteristic parameters corresponding to the suspension system, the robustness of the suspension system can be determined according to the distribution characteristics of the probability distribution model, where the distribution characteristics include maximum value, minimum value, average value, standard deviation, skewness, and kurtosis.

[0047] The method for determining the robustness of the suspension system according to the distribution characteristics of the probability distribution model can be flexibly set as needed. In one example, the stability of the suspension system can be determined by the distribution characteristics of the probability distribution model and the preset distribution characteristics, where the preset distribution characteristics are the distribution characteristics associated with the stable suspension system. That is, when the distribution characteristics of the probability distribution model do not reach the preset distribution characteristics, it is determined that the suspension system does not have stability. On the contrary, when the distribution characteristics of the probability distribution model reach the preset distribution characteristics, it is determined that the suspension system has stability.

[0048] In another example, a preset standard distribution form can be obtained first, and then the goodness of fit between the distribution form reflected by the distribution characteristics of the probability distribution model and the standard distribution form can be determined. Then, by judging whether the goodness of fit reaches the preset goodness-of-fit threshold, it can be determined whether the suspension system has robustness. That is, if the goodness of fit reaches the preset goodness-of-fit threshold, it indicates that the distribution form of the characteristic parameters corresponding to the suspension system when the suspension stiffness of the suspension element changes approaches the standard distribution form with robustness, and it can be determined that the suspension system has robustness. On the contrary, if the goodness of fit does not reach the preset goodness-of-fit threshold, it indicates that the distribution form of the characteristic parameters corresponding to the suspension system when the suspension stiffness of the suspension element changes is different from the standard distribution form with robustness, and it is determined that the suspension system does not have robustness.

[0049] Secondly, when obtaining the standard distribution form, the corresponding target distribution form can be determined according to the test requirements of the suspension system, and the target distribution form can be used as the standard distribution form to improve the flexibility in subsequent determination of whether the suspension system has robustness. The test requirements include, but are not limited to, testing the robustness of the suspension system under different operating conditions of the mechanical equipment and testing the robustness of the suspension system under different operating environments of the mechanical equipment.

[0050] Through the above embodiments, multiple suspension stiffnesses of the suspension elements in the suspension system can be obtained first, and then the characteristic parameters corresponding to the suspension system under different measured suspension stiffnesses can be determined, so as to obtain the probability distribution model of the corresponding characteristic parameters of the suspension system, thereby determining the performance of the vibration suppression effect of the suspension system when the suspension stiffness of the suspension element changes. Then, the robustness of the suspension system can be determined according to the distribution characteristics of the probability distribution model, and further the purpose of determining the robustness of the suspension system when the suspension stiffness of the suspension element changes can be achieved.

[0051] See Figure 2 , Figure 2 is a flowchart of step S120 in the exemplary embodiment shown in Figure 1 As shown in Figure 2 When the suspension system includes multiple suspension elements and each suspension element corresponds to multiple measured suspension stiffnesses, the process of determining the characteristic parameters corresponding to the suspension system under different measured suspension stiffnesses may include steps S210 to S220, which are introduced in detail as follows:

[0052] In step S210, multiple suspension stiffness matrices are generated based on the multiple measured suspension stiffnesses corresponding to each suspension element.

[0053] In the embodiments of the present application, when the suspension system includes multiple suspension elements and each suspension element corresponds to multiple measured suspension stiffnesses, in order to determine the characteristic parameters corresponding to the suspension system under different measured suspension stiffnesses, multiple suspension stiffness matrices can be generated first based on the multiple measured suspension stiffnesses corresponding to each suspension element, where the suspension stiffness matrix is composed of one measured suspension stiffness corresponding to each suspension element.

[0054] The method of generating multiple suspension stiffness matrices based on the multiple measured suspension stiffnesses corresponding to each suspension element can be flexibly set according to needs. In one example, the suspension stiffness matrix can be generated by extracting the multiple measured suspension stiffnesses corresponding to each suspension element through the Latin hypercube sampling method.

[0055] In another example, the suspension stiffness matrix can be generated by extracting the multiple measured suspension stiffnesses corresponding to each suspension element through the Monte Carlo simulation method.

[0056] In step S220, the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices are determined.

[0057] In an embodiment of the present application, after generating multiple suspension stiffness matrices, the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices can be determined, so that the subsequent probability distribution model can reflect the performance of the vibration suppression effect of the suspension system when the suspension stiffness of different suspension elements changes. Furthermore, under the condition that the suspension system has multiple suspension elements, the purpose of determining the robustness of the suspension system when the suspension stiffness of different suspension elements changes is achieved.

[0058] In some embodiments of the present application, in order to determine the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices, the characteristic parameters corresponding to the suspension system can be obtained in sequence when the suspension stiffness of different suspension elements constitutes different suspension stiffness matrices.

[0059] For example, when the multiple suspension stiffnesses to be measured of the first suspension element are KX1, KX2, and KX3 respectively, the multiple suspension stiffnesses to be measured of the second suspension element are KY1, KY2, and KY3 respectively, and the multiple suspension stiffnesses to be measured of the third suspension element are KZ1, KZ2, and KZ3 respectively, if the suspension stiffness matrix includes [KX1, KY1, KZ1], [KX2, KY2, KZ2], [KX3, KY3, KZ3], then the process of obtaining the characteristic parameters corresponding to the suspension system when the suspension stiffness of different suspension elements constitutes different suspension stiffness matrices, that is, when the suspension stiffness of different suspension elements constitutes [KX1, KY1, KZ1], [KX2, KY2, KZ2], or [KX3, KY3, KZ3], the characteristic parameters corresponding to the suspension system are obtained.

[0060] In some embodiments of the present application, the process of determining the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices can be referred to Figure 3 as shown below and is introduced in detail as follows:

[0061] Step S1, determine the target suspension stiffness matrix from multiple suspension stiffness matrices;

[0062] Step S2, adjust the suspension stiffness to be measured in the target suspension stiffness matrix based on a preset perturbation interval;

[0063] Step S3, determine the characteristic parameters corresponding to the suspension system under the target suspension stiffness matrix;

[0064] Step S4, loop through steps S1 - S3 until the end condition is met.

[0065] In the above process, in order to determine the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices, steps S1 to S3 can be cyclically executed. That is, first determine the target suspension stiffness matrix from multiple suspension stiffness matrices, then adjust the suspension stiffness to be measured in the target suspension stiffness matrix based on a preset perturbation interval, and then determine the characteristic parameters corresponding to the suspension system under the target suspension stiffness matrix until the end condition is met, so as to conform to the randomness when the suspension stiffness of each suspension element changes.

[0066] Among them, the setting method of the end condition can refer to the setting method described in step S120 above, that is, determining each suspension stiffness matrix as the target suspension stiffness matrix as the end condition, or alternatively, using whether the number of times of determining the target suspension stiffness matrix from multiple suspension stiffness matrices reaches a preset number as the end condition.

[0067] In some embodiments of the present application, the method of determining the target suspension stiffness matrix from multiple suspension stiffness matrices can be flexibly set according to needs. For example, a suspension stiffness matrix can be extracted from multiple suspension stiffness matrices as the target suspension stiffness matrix by the Latin hypercube sampling method; or, a suspension stiffness matrix can be extracted from multiple suspension stiffness matrices as the target suspension stiffness matrix by the Monte Carlo simulation method, which is not limited here.

[0068] In some embodiments of the present application, the method of adjusting the suspension stiffness to be measured in the target suspension stiffness matrix based on a preset perturbation interval can refer to the method described in step S120 above, that is, adjusting the suspension stiffness to be measured in the target suspension stiffness matrix through a preset perturbation interval to expand the coverage range of the target suspension stiffness matrix, and then using the Monte Carlo simulation method to extract the suspension stiffness to be measured of each suspension element from the expanded target suspension stiffness matrix as the adjusted target suspension stiffness matrix; or, randomly extracting a change amount from the preset perturbation interval, and then adjusting the suspension stiffness to be measured of each suspension element in the target suspension stiffness matrix through the change amount to obtain the adjusted target suspension stiffness matrix, so as to perturb the suspension stiffness matrix through the preset perturbation interval and improve the randomness when simulating the change of the suspension stiffness of different suspension elements.

[0069] Taking into account that the material properties of different suspension elements in the suspension system may differ, based on this, in an embodiment of the present application, the preset disturbance interval may include multiple sub-disturbance intervals, and the multiple sub-disturbance intervals correspond one-to-one to multiple suspension elements. Correspondingly, in the process of adjusting the target suspension stiffness to be measured in the target suspension stiffness matrix based on the preset disturbance interval, the target suspension stiffness to be adjusted in the target suspension stiffness matrix can be first determined, and then the target sub-disturbance interval is determined from the preset disturbance interval based on the suspension element corresponding to the target suspension stiffness to be measured. Thereafter, the target suspension stiffness to be measured is adjusted through the target sub-disturbance interval, so that the suspension stiffness to be measured of each suspension element in the suspension stiffness matrix can be adjusted based on its own material properties, thereby improving the flexibility in simulating the suspension stiffness changes of different suspension elements.

[0070] See also Figure 4 , Figure 4 is Figure 1 The flowchart of step S110 in the embodiment shown is in an exemplary embodiment. Figure 4 As shown, the process of obtaining multiple suspension stiffnesses to be measured of the suspension elements in the suspension system may include steps S310 to S320, which are described in detail as follows:

[0071] In step S310 , a correlation coefficient between each predetermined suspension stiffness of the suspension element and a characteristic parameter is determined.

[0072] The preset suspension stiffness may be a suspension stiffness predetermined based on material properties of the suspension element, or may be a suspension stiffness manually input by an analyst.

[0073] In an embodiment of the present application, in order to obtain multiple suspension stiffnesses to be measured of suspension elements in a suspension system, the correlation coefficient between each preset suspension stiffness of the suspension element and the characteristic parameter can be first determined, wherein the correlation coefficient represents the degree of linear correlation between the preset suspension stiffness and the characteristic parameter.

[0074] In some embodiments of the present application, the correlation coefficient may include a Pearson linear correlation coefficient. To determine the correlation coefficient between each preset suspension stiffness of the suspension element and the characteristic parameter, the following formula may be used:

[0075] ρ X,Y = Cov(X,Y) / σ X σ Y

[0076] Among them, ρ X,Y is the correlation coefficient between the preset mount stiffness and characteristic parameters, Cov(X,Y) is the covariance between the preset mount stiffness and characteristic parameters, σ X is the standard deviation of the preset suspension stiffness when it fluctuates, σ Yis the standard deviation corresponding to the characteristic parameter under the fluctuation of the preset mounting stiffness.

[0077] Considering that in the actual application process of the mounting system, the mounting system will receive vibrations transmitted from different directions, so that the mounting system needs to suppress the vibrations of different degrees of freedom respectively. Based on this, in some embodiments of the present application, in order to determine the correlation coefficient between each preset mounting stiffness of the mounting element and the characteristic parameter, the correlation sub-coefficient between the preset mounting stiffness and the characteristic parameter under each degree of freedom of the mounting system can be determined first, and then the correlation coefficient can be determined according to the correlation sub-coefficient of the preset mounting stiffness under each degree of freedom, so as to improve the accuracy of the correlation coefficient between the preset mounting stiffness and the characteristic parameter determined.

[0078] Among them, the method of determining the correlation coefficient according to the correlation sub-coefficient of the preset mounting stiffness under each degree of freedom can be flexibly set as needed. In one example, the largest correlation sub-coefficient among the correlation sub-coefficients of the preset mounting stiffness under each degree of freedom can be used as the correlation coefficient of the preset mounting stiffness. In another example, the sum of the correlation sub-coefficients of the preset mounting stiffness under each degree of freedom can be determined and used as the correlation coefficient.

[0079] In step S320, a plurality of to-be-tested mounting stiffnesses are determined based on the correlation coefficient corresponding to each preset mounting stiffness.

[0080] In the implementation manner of the present application, after determining the correlation coefficient between each preset mounting stiffness and the characteristic parameter, a plurality of to-be-tested mounting stiffnesses can be determined based on the correlation coefficient corresponding to each preset mounting stiffness, that is, the to-be-tested mounting stiffnesses can be screened from a plurality of preset mounting stiffnesses according to requirements, so as to reduce the amount of data to be analyzed in a way that improves the quality of the to-be-tested mounting stiffnesses on the premise of ensuring that the accuracy of determining the robustness of the mounting system is not easily reduced, and improve the efficiency when determining the robustness of the mounting system.

[0081] Among them, the method of determining a plurality of to-be-tested mounting stiffnesses based on the correlation coefficient corresponding to each preset mounting stiffness can be flexibly set as needed. In one example, the preset mounting stiffness with the foremost position can be repeatedly selected in sequence according to the size order from the correlation coefficients corresponding to each preset mounting stiffness as the to-be-tested mounting stiffness until the number of to-be-tested mounting stiffnesses reaches the preset number.

[0082] In another example, the median of the correlation coefficients corresponding to each preset mounting stiffness can be determined first, and then the target correlation coefficient with the smallest difference from the median is repeatedly determined from the correlation coefficients corresponding to each preset mounting stiffness, and the preset mounting stiffness corresponding to the target correlation coefficient is used as the to-be-tested mounting stiffness until the number of to-be-tested mounting stiffnesses reaches the preset number.

[0083] The following introduces the device embodiments of the present application, which can be used to execute the robustness analysis method of the suspension system in the above embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the embodiments of the robustness analysis method of the suspension system in the above of the present application.

[0084] Figure 5 FIG. shows a block diagram of a robustness analysis device 100 for a suspension system according to an embodiment of the present application.

[0085] Referring to Figure 5 As shown, a robustness analysis device 100 for a suspension system according to an embodiment of the present application includes: an acquisition module 110 configured to acquire a plurality of to-be-tested suspension stiffnesses of suspension elements in the suspension system; a calculation module 120 configured to determine characteristic parameters corresponding to the suspension system under different to-be-tested suspension stiffnesses to obtain a probability distribution model of the characteristic parameters of the suspension system; and an analysis module 130 configured to determine the robustness of the suspension system according to the distribution characteristics of the probability distribution model.

[0086] In some embodiments of the present application, based on the foregoing solution, under the condition that the suspension system includes a plurality of suspension elements and each suspension element corresponds to a plurality of to-be-tested suspension stiffnesses, the calculation module 120 is further configured to: generate a plurality of suspension stiffness matrices based on the plurality of to-be-tested suspension stiffnesses corresponding to each suspension element, where the suspension stiffness matrix is jointly constituted by one to-be-tested suspension stiffness corresponding to each suspension element; and determine the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices.

[0087] In some embodiments of the present application, based on the foregoing solution, the calculation module 120 is further configured to: Step S1, determine a target suspension stiffness matrix from the plurality of suspension stiffness matrices; Step S2, adjust the to-be-tested suspension stiffness in the target suspension stiffness matrix based on a preset perturbation interval; Step S3, determine the characteristic parameters corresponding to the suspension system under the target suspension stiffness matrix; Step S4, repeatedly execute Step S1 - Step S3 until an end condition is satisfied.

[0088] In some embodiments of the present application, based on the foregoing solution, under the condition that the preset perturbation interval includes a plurality of sub-perturbation intervals and the plurality of sub-perturbation intervals correspond to the plurality of suspension elements one by one, the calculation module 120 is further configured to: determine the target to-be-tested suspension stiffness to be adjusted in the target suspension stiffness matrix; determine a target sub-perturbation interval from the preset perturbation interval based on the suspension element corresponding to the target to-be-tested suspension stiffness; and adjust the target to-be-tested suspension stiffness through the target sub-perturbation interval.

[0089] In some embodiments of the present application, based on the foregoing solution, the acquisition module 110 is further configured to: determine the correlation coefficient between each preset suspension stiffness of the suspension element and the characteristic parameter; determine a plurality of suspension stiffnesses to be measured based on the correlation coefficient corresponding to each preset suspension stiffness.

[0090] In some embodiments of the present application, based on the foregoing solution, the acquisition module 110 is further configured to: respectively determine the correlation sub-coefficient between the preset suspension stiffness and the characteristic parameter under each degree of freedom of the suspension system; determine the correlation coefficient according to the correlation sub-coefficient of the preset suspension stiffness under each degree of freedom.

[0091] In some embodiments of the present application, based on the foregoing solution, the analysis module 130 is further configured to: obtain a preset standard distribution form; determine the degree of fit between the distribution form reflected by the distribution characteristics of the probability distribution model and the standard distribution form; if the degree of fit reaches a preset fit threshold, determine that the suspension system has robustness.

[0092] It should be noted that the suspension system robustness analysis device 100 provided in the above embodiments and the suspension system robustness analysis method provided in the above embodiments belong to the same concept. The specific manners in which each module and unit perform operations have been described in detail in the method embodiments and will not be elaborated here.

[0093] An embodiment of the present application further provides an electronic device, including a processor and a memory. The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the suspension system robustness analysis method as described above is implemented.

[0094] Figure 6 The structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown.

[0095] It should be noted that Figure 6 The computer system 200 of the electronic device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0096] As Figure 6As shown, the computer system 200 includes a Central Processing Unit (CPU) 201, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 202 or the program loaded from the storage section 208 into the Random Access Memory (RAM) 203, such as executing the methods described in the above embodiments. In the RAM 203, various programs and data required for system operation are also stored. The CPU 201, ROM 202, and RAM 203 are connected to each other via a bus 204. An Input / Output (I / O) interface 205 is also connected to the bus 204.

[0097] The following components are connected to the I / O interface 205: an input section 206 including a keyboard, a mouse, etc.; an output section 207 including, for example, a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 209 performs communication processing via a network such as the Internet. A drive 210 is also connected to the I / O interface 205 as needed. A removable medium 211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 210 as needed so that a computer program read from it can be installed into the storage section 208 as needed.

[0098] Specifically, according to the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 209, and / or installed from the removable medium 211. When the computer program is executed by the Central Processing Unit (CPU) 201, various functions defined in the system of the present application are executed.

[0099] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0101] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the unit itself.

[0102] As another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiments.

[0103] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0104] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented in software or in the form of software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of this application.

[0105] After considering the specification and practicing the embodiments disclosed herein, those skilled in the art will readily conceive of other embodiments of this application. This application is intended to cover any variations, uses, or adaptations of this application, which follow the general principles of this application and include well-known knowledge or conventional technical means in the technical field not disclosed in this application.

[0106] It should be understood that this application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is only limited by the appended claims.

Claims

1. A robustness analysis method for a suspension system, characterized in that The method includes: Obtaining a plurality of to-be-tested suspension stiffnesses of suspension elements in a suspension system; Determining characteristic parameters corresponding to the suspension system under different to-be-tested suspension stiffnesses to obtain a probability distribution model of the characteristic parameters corresponding to the suspension system; Determining the robustness of the suspension system according to the distribution characteristics of the probability distribution model.

2. The method according to claim 1, characterized in that The suspension system includes a plurality of suspension elements, and each of the different suspension elements corresponds to a plurality of to-be-tested suspension stiffnesses. The determining of the characteristic parameters corresponding to the suspension system under different to-be-tested suspension stiffnesses includes: Generating a plurality of suspension stiffness matrices based on the plurality of to-be-tested suspension stiffnesses corresponding to each suspension element, wherein the suspension stiffness matrix is jointly constituted by one to-be-tested suspension stiffness corresponding to each suspension element; Determining the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices.

3. The method according to claim 2, wherein The determining of the characteristic parameters corresponding to the suspension system under different suspension stiffness matrices includes the following steps: Step S1, determining a target suspension stiffness matrix from a plurality of suspension stiffness matrices; Step S2, adjusting the to-be-tested suspension stiffness in the target suspension stiffness matrix based on a preset perturbation interval; Step S3, determining the characteristic parameters corresponding to the suspension system under the target suspension stiffness matrix; Step S4, repeatedly executing Step S1 - Step S3 until an end condition is met.

4. The method according to claim 3, wherein The preset perturbation interval includes a plurality of sub-perturbation intervals, and the plurality of sub-perturbation intervals correspond to the plurality of suspension elements one by one. The adjusting of the to-be-tested suspension stiffness in the target suspension stiffness matrix based on the preset perturbation interval includes: Determining the target to-be-tested suspension stiffness to be adjusted in the target suspension stiffness matrix; Determining a target sub-perturbation interval from the preset perturbation interval based on the suspension element corresponding to the target to-be-tested suspension stiffness; Adjusting the target to-be-tested suspension stiffness through the target sub-perturbation interval.

5. The method according to claim 1, characterized in that The obtaining of the plurality of to-be-tested suspension stiffnesses of the suspension elements in the suspension system includes: Determining the correlation coefficient between each preset suspension stiffness of the suspension element and the characteristic parameter; Determining a plurality of to-be-tested suspension stiffnesses based on the correlation coefficient corresponding to each preset suspension stiffness.

6. The method according to claim 5, wherein The determining of the correlation coefficient between each preset suspension stiffness of the suspension element and the characteristic parameter includes: Respectively determining the correlation sub-coefficients between the preset suspension stiffness and the characteristic parameter under each degree of freedom of the suspension system; Determining the correlation coefficient according to the correlation sub-coefficients of the preset suspension stiffness under each degree of freedom.

7. The method according to claim 1, characterized in that The determining of the robustness of the suspension system according to the distribution characteristics of the probability distribution model includes: Obtaining a preset standard distribution form; Determining the fitting degree between the distribution form reflected by the distribution characteristics of the probability distribution model and the standard distribution form; If the fitting degree reaches a preset fitting degree threshold, determining that the suspension system has robustness.

8. A robustness analysis device for a suspension system, characterized in that, Includes: An obtaining module configured to obtain a plurality of to-be-tested suspension stiffnesses of suspension elements in a suspension system; A calculation module, configured to determine characteristic parameters corresponding to the suspension system under different suspension stiffnesses to be measured, so as to obtain a probability distribution model of the characteristic parameters of the suspension system; An analysis module, configured to determine the robustness of the suspension system according to the distribution characteristics of the probability distribution model.

9. A computer-readable storage medium, characterized in that, It stores computer-readable instructions, which, when executed by a processor of a computer, cause the computer to execute the method for analyzing the robustness of the suspension system according to any one of claims 1-7.

10. An electronic device, characterized in that, Comprising: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the method for analyzing the robustness of the suspension system according to any one of claims 1 to 7.

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