A Suspension Optimization Method, System, Device, Equipment and Medium

Abstract

The present invention discloses a suspension optimization method, system, device, equipment and medium. The method includes: before the target vehicle travels to the current position point, obtaining the road surface roughness level of the current position point from a set list according to the position coordinates of the target vehicle; obtaining the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard; combining the sprung mass classification of the target vehicle and the road surface roughness level of the current position point to obtain a target group; obtaining suspension optimization parameters corresponding to the target group from a pre-set optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters. Through the technical solution of the present invention, it is possible to efficiently and accurately optimize the suspension of the vehicle, improving the comfort of the vehicle and the possibility of passing through different road conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive suspension optimization, and particularly to a suspension optimization method, system, device, equipment and medium. Background Art

[0002] With the intelligent development in the automotive field, the chassis system of vehicles has gradually become more intelligent and networked. Moreover, the requirements for the safety, economy and comfort of vehicles are also getting higher and higher. As an important system affecting vehicle comfort, the suspension system has gradually evolved from traditional passive suspensions to active suspensions, intelligent suspensions and networked suspensions, enabling the suspension characteristic parameters to be changed under different working conditions through intelligent control means to improve vehicle comfort and the possibility of passing through different road conditions.

[0003] In the prior art, road surface information is usually collected by cameras to control the suspension. However, using cameras to collect road surface information increases the cost of the whole vehicle, the algorithm complexity in the cameras increases the estimation difficulty of the suspension, and the lag time is also relatively long. Moreover, the limited data acquisition volume of the cameras leads to a reduction in the estimation accuracy of the suspension. Therefore, how to efficiently and accurately optimize the vehicle suspension and improve the vehicle comfort and the possibility of passing through different road conditions is an urgent problem to be solved currently. Summary of the Invention

[0004] The present invention provides a suspension optimization method, system, device, equipment and medium, which can solve the problem of low optimization accuracy of vehicle suspensions.

[0005] According to one aspect of the present invention, a suspension optimization method is provided, which includes:

[0006] Before the target vehicle travels to the current position point, obtain the road surface unevenness level of the current position point from a set list according to the position coordinates of the target vehicle;

[0007] According to the sprung mass of the target vehicle and the sprung mass grading standard, obtain the sprung mass grading of the target vehicle;

[0008] Combine the sprung mass grading of the target vehicle and the road surface unevenness level of the current position point to obtain a target group;

[0009] Obtain the suspension optimization parameters corresponding to the target group from a pre-set optimization template, and control the target vehicle to optimize the suspension parameters using the suspension optimization parameters.

[0010] According to another aspect of the present invention, a suspension optimization system is provided, which is characterized by including: a cloud end and a vehicle end, wherein the vehicle end includes at least one vehicle;

[0011] The cloud is used to obtain the road surface unevenness level of the current position point from a set list according to the position coordinates of the target vehicle before the target vehicle travels to the current position point; obtain the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard; combine the sprung mass classification of the target vehicle and the road surface unevenness level of the current position point to obtain a target group; obtain suspension optimization parameters corresponding to the target group from a pre-set optimization template, and control the target vehicle to optimize the suspension parameters using the suspension optimization parameters;

[0012] The vehicle end is used to obtain the position coordinates of the target vehicle and transmit them to the cloud; collect the sprung mass of the target vehicle and transmit it to the cloud; receive the suspension optimization parameters transmitted by the cloud and optimize the suspension parameters.

[0013] According to another aspect of the present invention, there is provided a suspension optimization device, characterized in that it includes:

[0014] A level acquisition module, which is used to obtain the road surface unevenness level of the current position point from a set list according to the position coordinates of the target vehicle before the target vehicle travels to the current position point;

[0015] A mass classification module, which is used to obtain the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard;

[0016] A grouping generation module, which is used to combine the sprung mass classification of the target vehicle and the road surface unevenness level of the current position point to obtain a target group;

[0017] A parameter acquisition module, which is used to obtain suspension optimization parameters corresponding to the target group from a pre-set optimization template, and control the target vehicle to optimize the suspension parameters using the suspension optimization parameters.

[0018] According to another aspect of the present invention, there is provided an electronic device, and the electronic device includes:

[0019] At least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the suspension optimization method according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the suspension optimization method according to any embodiment of the present invention when executed.

[0023] In the technical solution of the embodiment of the present invention, the road surface unevenness level of the current position point of the target vehicle obtained by querying in the set list is combined with the sprung mass classification of the target vehicle obtained by calculation to obtain a target group, and then the corresponding suspension optimization parameters are obtained from a pre-set optimization template according to the target group to optimize the suspension parameters, solving the problem of efficiently and accurately optimizing the suspension of the vehicle and improving the comfort of the vehicle and the possibility of passing through different road conditions.

[0024] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0026] Figure 1 is a flowchart of a suspension optimization method according to Embodiment 1 of the present invention;

[0027] Figure 2a is a flowchart of a suspension optimization method according to Embodiment 2 of the present invention;

[0028] Figure 2b is a schematic diagram of a quarter-vehicle model applicable to Embodiment 2 of the present invention;

[0029] Figure 2c is a schematic flow diagram of a suspension optimization method according to Embodiment 2 of the present invention;

[0030] Figure 3 is a schematic structural diagram of a suspension optimization system according to Embodiment 3 of the present invention;

[0031] Figure 4 is a schematic structural diagram of a suspension optimization device according to Embodiment 4 of the present invention;

[0032] Figure 5 is a schematic structural diagram of an electronic device for implementing the suspension optimization method of the embodiment of the present invention. Detailed implementation manners

[0033] In order to enable those skilled in the art of the present technology to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that the terms "target", "original", etc. in the description and claims of the present invention and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0035] Embodiment 1

[0036] Figure 1 FIG. 1 is a flowchart of a suspension optimization method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of optimizing a vehicle suspension. This method can be executed by a suspension optimization device, which can be implemented in the form of hardware and / or software, and the suspension optimization device can be configured in an electronic device. As Figure 1 shown, the method includes:

[0037] S110. Before the target vehicle travels to the current position point, obtain the road surface unevenness level of the current position point from a set list according to the position coordinates of the target vehicle.

[0038] Among them, the position point can refer to a pre-set position identification point of the current road surface. Exemplarily, a position point can be set every five meters in the current road surface by using the Global Position System (GPS). The target vehicle can refer to the vehicle to which the suspension to be optimized belongs. The current position point can refer to the position point that the target vehicle is about to pass through. Exemplarily, it can be determined whether the target vehicle has traveled to the current position point according to the difference between the coordinates of the absolute position of the target vehicle and the coordinates of the current position point. The road surface unevenness level can refer to the level obtained after evaluating the road surface unevenness of this position point. The setting list can refer to a list containing information such as the coordinates of the position point and the road surface unevenness level of the position point.

[0039] Specifically, during the driving process of the target vehicle, the position coordinates of the target vehicle are obtained in real time and compared with the coordinates of each position point. When the distance between the target vehicle and the current position point is less than the five-meter travel range, that is, the road surface unevenness level of the current position point is obtained according to the coordinates of the current position point in the setting list. Thus, by using the position coordinates of the target vehicle collected at the vehicle end to obtain the road surface unevenness level of the current position point, the credibility of the information can be greatly improved, providing an effective basis for subsequent operations.

[0040] S120. Obtain the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard.

[0041] Among them, the sprung mass can refer to the mass borne by the chassis skeleton of the target vehicle and all other elastic components. The sprung mass classification standard of the target vehicle can refer to the standard for classifying the sprung mass of the target vehicle according to the sprung mass of the target vehicle under different loads. The sprung mass classification of the target vehicle can refer to the level of the sprung mass obtained according to the sprung mass classification standard. Exemplarily, if the sprung mass classification standard of the target vehicle classifies the sprung mass in the range of twenty kilograms to forty kilograms as the second level, then when the sprung mass of the target vehicle is thirty kilograms, the sprung mass classification of the target vehicle is the second level.

[0042] Optionally, obtaining the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard includes: obtaining the standard load of the target vehicle and the full load of the target vehicle, and evenly dividing the sprung mass between the standard load and the full load of the target vehicle into a set number of levels to obtain the sprung mass classification standard corresponding to the target vehicle; comparing the sprung mass of the target vehicle with the sprung mass classification standard corresponding to the target vehicle to obtain the sprung mass classification of the target vehicle. Among them, the standard load may refer to the load standard value marked when the vehicle leaves the factory. The full load may refer to the load value when the vehicle is fully loaded. The set number of levels may refer to the number of levels into which the sprung mass between the standard load and the full load of the target vehicle is divided. In the embodiments of the present invention, it is preferably to evenly divide the sprung mass between the standard load and the full load of the target vehicle into 5 levels, and the sprung mass classification standard corresponding to the target vehicle may be respectively marked as m 1a 、m 1b 、m 1c 、m 1d 、m 1e . Thus, by evenly dividing the sprung mass between the standard load and the full load of the target vehicle into a set number of levels to obtain the sprung mass classification standard corresponding to the target vehicle, and then comparing the sprung mass of the target vehicle with the sprung mass classification standard corresponding to the target vehicle, the current sprung mass classification of the target vehicle can be obtained.

[0043] S130. Combine the sprung mass classification of the target vehicle and the road surface unevenness level of the current position point to obtain a target group.

[0044] Among them, the target group may refer to a set composed of the sprung mass classification of the target vehicle and the road surface unevenness level of the current position point.

[0045] S140. Obtain the suspension optimization parameters corresponding to the target group in a pre-set optimization template, and control the target vehicle to optimize the suspension parameters using the suspension optimization parameters.

[0046] Among them, the optimization template may refer to a pre-set template for optimizing suspension parameters. Exemplarily, it may include a set composed of the sprung mass classification and the road surface unevenness level of the current position point, and the suspension optimization parameters corresponding to this set. The suspension optimization parameters may refer to the expected values after optimizing and adjusting the suspension parameters. In the embodiments of the present invention, the suspension parameters may include the suspension damping coefficient (C) and the suspension stiffness coefficient (K). Thus, the set corresponding to the target group can be calculated in the pre-set optimization template according to the target group, and then the suspension optimization parameters corresponding to this set are used as the suspension optimization parameters corresponding to the target group, and the suspension parameters of the target vehicle are optimized based on the suspension optimization parameters.

[0047] In the technical solution of the embodiment of the present invention, by combining the road surface unevenness level of the current position point of the target vehicle obtained from the set list with the sprung mass classification of the target vehicle obtained by calculation to obtain a target group, and then obtaining the corresponding suspension optimization parameters from a pre-set optimization template according to the target group to optimize the suspension parameters, the problem of efficiently and accurately optimizing the suspension of the vehicle is solved, and the comfort of the vehicle and the possibility of passing through different road conditions are improved.

[0048] Embodiment 2

[0049] Figure 2a FIG. is a flowchart of a suspension optimization method provided by Embodiment 2 of the present invention. This embodiment is an addition based on the above embodiment. Specifically, in this embodiment, an addition is made to how to construct a pre-set optimization template, which may specifically include: dividing the road surface to be tested into levels according to the road surface bumpiness to obtain the road surface level; obtaining the target vehicle information and target suspension parameters of each test vehicle when running in the test field of each road surface level, and inputting the road surface level, target vehicle information, and target suspension parameters into a quarter-vehicle model to obtain the corresponding road surface unevenness level; dividing groups according to the road surface unevenness level and the sprung mass classification of the test vehicle, and clustering the unsprung mass acceleration and sprung mass acceleration in the target vehicle information corresponding to each group to obtain the clustering center points of each group; wherein, the clustering center points include the sprung mass classification and road surface unevenness level corresponding to each group; obtaining the corresponding suspension optimization parameters according to the characteristics of the suspension in each group, and storing the corresponding suspension optimization parameters and clustering center points of each group to obtain a pre-set optimization template; correspondingly, obtaining the suspension optimization parameters corresponding to the target group in the pre-set optimization template includes: locating the clustering center point corresponding to the target group in the pre-set optimization template, and obtaining the corresponding suspension optimization parameters according to the clustering center point.

[0050] As Figure 2a shown, the method includes:

[0051] S210. Divide the road surface to be tested into levels according to the road surface bumpiness to obtain the road surface level.

[0052] Among them, the road surface level may refer to the level obtained by dividing the unevenness of the road surface to be tested according to the road grade division standard. Exemplarily, the unevenness of the road surface to be tested may be divided into a total of eight levels: A, B, C, D, E, F, G, and H. Specifically, road surfaces including highways, national roads, provincial roads, etc., which are relatively flat and have few continuous curves, may be divided into level A; continuous mountain roads, roads with many curves and no visible front angle when turning, flat dirt roads, and gravel roads may be divided into level B; and so on. The higher the level, the greater the road surface bumpiness.

[0053] S220, obtaining the test vehicle information and test vehicle suspension parameters of each test vehicle when it is running in a test field of each road surface grade, and inputting the road surface grade, test vehicle information and test vehicle suspension parameters into a vehicle quarter model to obtain a corresponding road surface roughness grade.

[0054] The test vehicle may refer to a vehicle used in a pre-test. The test vehicle information may refer to vehicle information of the test vehicle during driving on each road surface level, which may include sprung mass, unsprung mass, unsprung mass acceleration, sprung mass acceleration, and vehicle speed, for example.

[0055] Among them, the vehicle quarter model can refer to a model used to analyze the most basic frequency and vibration characteristics of the vehicle, such as Figure 2b The figure shows a schematic diagram of the model of a quarter vehicle model. Specifically, m1 is the sprung mass, m2 is the unsprung mass, z1 is the vertical displacement of the sprung mass, z2 is the vertical displacement of the unsprung mass, K is the suspension stiffness, and C is the suspension damping. The suspension system dynamics model can be modeled by using a quarter vehicle model, and the state space analysis method can be used to analyze the effects of different suspension stiffness and suspension damping, different road surface grades, and different sprung masses on the vehicle's ride smoothness, that is, the road surface roughness grade. It is worth noting that since the road surface roughness grade corresponds to the road surface grade one by one, the road surface roughness grades for the eight road surface grades can be labeled as q in turn. A ,q B ,q C ,q D ,q E ,q F ,q G and q H .

[0056] S230, dividing the test vehicles into groups according to the road surface roughness level and the sprung mass level of the test vehicles, and clustering the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group to obtain the cluster center point of each group; wherein the cluster center point includes the sprung mass level and the road surface roughness level corresponding to each group.

[0057] Clustering may refer to a technique of comparing the similarity of unsprung mass acceleration or sprung mass acceleration and grouping similar unsprung mass acceleration or sprung mass acceleration into the same group, such as a Kmeans clustering algorithm. It is worth noting that the unsprung mass acceleration and sprung mass acceleration need to be filtered by a filtering algorithm, such as a moving average filter, before clustering.

[0058] In an optional embodiment, the grouping is performed according to the road surface unevenness level and the sprung mass classification of the test vehicle, and the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group are clustered to obtain the clustering center points of each group, including: corresponding the road surface unevenness level with the sprung mass classification of the test vehicle one by one to perform full-grouping; in each group, using the clustering algorithm to cluster the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information to obtain the clustering center points of each group. Specifically, a large amount of data of the collected sprung mass acceleration (a z1 ) and the unsprung mass acceleration (a z2 ) are grouped according to the road surface unevenness level (q A -q H ) and the sprung mass classification (m 1a -m 1e ), and a total of 8 * 5 = 40 groups are divided. Exemplarily, under the road surface of grade A, it is divided into 5 groups according to 5 grades of the sprung mass. Then, the Kmeans clustering algorithm is used to cluster a z1 and a z2 in each group to obtain the clustering center points of 40 groups, which are respectively marked as N qAm1a , N qAm1b , ……, N qHm1e .

[0059] S240. According to the characteristics of the suspension in each group, obtain the corresponding suspension optimization parameters, and store the corresponding suspension optimization parameters and the clustering center points of each group to obtain a preset optimization template.

[0060] Among them, the characteristics of the suspension can refer to the softness and hardness of the suspension. If the suspension is too soft, there will be a serious problem of vehicle body shaking. Therefore, according to the characteristics of the suspension in 40 groups of data, the suspension parameters can be optimized to obtain 40 groups of suspension optimization parameters including damping optimization values and stiffness optimization values. Exemplarily, each suspension optimization parameter can be marked as [K qAm1a, C qAm1a , [K qAm1b, C qAm1b , ……, [K qHm1e, C qHm1e .

[0061] S250. Before the target vehicle travels to the current position point, obtain the road surface unevenness level of the current position point according to the position coordinates of the target vehicle in the set list.

[0062] Specifically, the method further includes: obtaining the unsprung mass acceleration and sprung mass acceleration of the original vehicle at the current position point, clustering the unsprung mass acceleration and sprung mass acceleration according to the sprung mass classification of the original vehicle to obtain a clustering result; taking the group corresponding to the clustering center point with the smallest distance from the clustering result as the group of the original vehicle at the current position point; obtaining the original road surface unevenness level at the current position point according to the sprung mass classification of the original vehicle and the group of the original vehicle at the current position point; storing the original road surface unevenness level and the position coordinates at the current position point to obtain a set list. Wherein, the original vehicle may refer to a vehicle that previously traveled on the current road surface. The original road surface unevenness level may refer to the road surface unevenness level calculated when the original vehicle travels to this position point. Exemplarily, when the original vehicle travels to a preset position point, obtain the unsprung mass acceleration and sprung mass acceleration of the original vehicle, cluster the unsprung mass acceleration and sprung mass acceleration to obtain a clustering result, and take the group corresponding to the clustering center point with the smallest distance from the eight clustering center points pre-matched with the sprung mass classification of the original vehicle as the group of the original vehicle at the current position point. Then, according to the sprung mass classification of the original vehicle, the original road surface unevenness level at the current position point can be known. Specifically, if the sprung mass classification of the original vehicle is m 1b then the eight road surface unevenness clustering center points corresponding to the m 1b level are N qAm1b 、N qBm1b 、N qCm1b 、N qDm1b 、N qEm1b 、N qFm1b 、N qGm1b 、N qHm1b Calculate the distances from the unsprung mass acceleration and sprung mass acceleration of the original vehicle at the current position point to the above eight clustering center points, find the clustering center point with the smallest distance, determine the group at the current position point, and then obtain the original road surface unevenness level at the current position point according to the sprung mass classification of the original vehicle. Thus, by storing the original road surface unevenness level and the position coordinates at the current position point to generate a set list, subsequent vehicles can know the road surface unevenness level at the current position point before traveling to the current position point, which is convenient for timely optimizing the suspension parameters of the vehicle and improving the efficiency of suspension optimization.

[0063] It should be noted that, in this embodiment, vehicle information can be obtained and clustered every 50 ms, and all center point results calculated within a 5 m travel range corresponding to the current position point are recorded, and the one with the highest frequency of occurrence is selected as the clustering result.

[0064] S260. Obtain the standard load and full load of the target vehicle, and evenly divide the sprung mass between the standard load and full load of the target vehicle into set levels to obtain the sprung mass classification standard corresponding to the target vehicle.

[0065] S270. Compare the sprung mass of the target vehicle with the sprung mass classification standard corresponding to the target vehicle to obtain the sprung mass classification of the target vehicle.

[0066] S280. Combine the sprung mass classification of the target vehicle and the road surface unevenness level at the current position point to obtain the target group.

[0067] S290. Locate the clustering center point corresponding to the target group in the pre-set optimization template, and obtain the corresponding suspension optimization parameters based on the clustering center point.

[0068] S2100. Control the target vehicle to optimize the suspension parameters using the suspension optimization parameters.

[0069] In an alternative embodiment, the above embodiment further includes: obtaining the unsprung mass acceleration and sprung mass acceleration of the target vehicle at the current position point, and clustering the unsprung mass acceleration and sprung mass acceleration according to the sprung mass classification of the target vehicle to obtain a clustering result; taking the group corresponding to the clustering center point with the smallest distance from the clustering result as the group of the target vehicle at the current position point; obtaining the road surface unevenness level at the current position point according to the sprung mass classification of the target vehicle and the group of the target vehicle at the current position point, and storing it; if the road surface unevenness level at the current position point is different from the original road surface unevenness level of the current position point, mark the road surface unevenness level at the current position point and the original road surface unevenness level of the current position point; if there are a set number of current position points where the road surface unevenness level is different from the original road surface unevenness level of the current position point, update the original road surface unevenness level of the current position point to the road surface unevenness level with the highest frequency of occurrence among the set number. Specifically, when the target vehicle travels to the current position point, in addition to optimizing the suspension parameters of the target vehicle according to the pre-set optimization template, it is also necessary to calculate the road surface unevenness level of the current position point according to the vehicle information of the target vehicle. If the road surface unevenness level at the current position point is different from the original road surface unevenness level of the current position point, then mark the road surface unevenness level at the current position point and the original road surface unevenness level. When the calculation results of the road surface unevenness levels of 10 consecutive groups of current position points are all different from the original road surface unevenness level, update the original road surface unevenness level to the road surface unevenness level with the highest proportion among the 10 groups of data, so as to improve the suspension optimization method and further improve the accuracy of suspension optimization.

[0070] In the technical solution of the embodiment of the present invention, by obtaining the test vehicle information and test vehicle suspension parameters obtained in a test site with a set road surface grade, and inputting the road surface grade, test vehicle information, and test vehicle suspension parameters into a quarter-vehicle model, the corresponding road surface unevenness grade is obtained; and by using the road surface unevenness grade and the sprung mass classification of the test vehicle to cluster the unsprung mass acceleration and sprung mass acceleration in the test vehicle information to obtain the clustering center points of each group; then storing the corresponding suspension optimization parameters and clustering center points of each group to obtain a preset optimization template; when the target vehicle travels to a position before the current position point, the road surface unevenness grade of the current position point can be obtained from a set list according to the position coordinates of the target vehicle, and combined with the calculated sprung mass classification of the target vehicle to obtain the target group; finally, locating the clustering center point corresponding to the target group in the preset optimization template, and obtaining the corresponding suspension optimization parameters according to the clustering center point to optimize the suspension parameters of the target vehicle, which solves the problem of efficiently and accurately optimizing the suspension of the vehicle, and improves the comfort of the vehicle and the possibility of passing through different road conditions.

[0071] Figure 2c The figure shows a schematic flow chart of a suspension optimization method provided by an embodiment of the present invention. Specifically, the sprung mass of the test vehicle from the standard load to the full load is divided into 5 levels, and the test vehicles corresponding to the 5 sprung mass classifications are driven on the road surfaces of 8 road surface grades to collect the test vehicle information and test vehicle suspension parameters of the test vehicle; then, the road surface grade, test vehicle information, and test vehicle suspension parameters are input into a quarter-vehicle model to obtain the corresponding road surface unevenness grade; and groupings are made according to the road surface unevenness grade and the sprung mass classification of the test vehicle, and the unsprung mass acceleration and sprung mass acceleration in the test vehicle information corresponding to each group are clustered to obtain the clustering center points of each group; further, according to the characteristics of the suspension in each group, the corresponding suspension optimization parameters are obtained. When the original vehicle travels to the current position point, the original road surface unevenness grade of the current position point is calculated according to the original vehicle information and stored for subsequent vehicles to use. When the target vehicle travels to a position before the current position point, 100 ms in advance, the road surface unevenness grade of the current position point is obtained from a set list according to the position coordinates of the target vehicle, and the suspension parameters are optimized according to the stored suspension optimization parameters. At the same time, the road surface unevenness grade of the current position point is calculated according to the vehicle information of the target vehicle at the current position point, and the original road surface unevenness grade of the current position point is verified to improve the suspension optimization method.

[0072] Embodiment III

[0073] Figure 3The structural schematic diagram of a suspension optimization system provided in Embodiment 3 of the present invention is as follows. As Figure 3 shown, the system includes: a cloud end 310 and a vehicle end 320, wherein the vehicle end 310 includes at least one vehicle;

[0074] The cloud end 310 is configured to, before the target vehicle travels to the current position point, obtain the road surface unevenness level of the current position point from a set list according to the position coordinates of the target vehicle; obtain the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard; combine the sprung mass classification of the target vehicle and the road surface unevenness level of the current position point to obtain a target group; obtain the suspension optimization parameters corresponding to the target group from a preset optimization template, and control the target vehicle to optimize the suspension parameters by using the suspension optimization parameters;

[0075] The vehicle end 320 is configured to obtain the position coordinates of the target vehicle and transmit them to the cloud end; collect the sprung mass of the target vehicle and transmit it to the cloud end; receive the suspension optimization parameters transmitted by the cloud end and optimize the suspension parameters.

[0076] Optionally, the cloud end 310 may specifically be configured to divide the to-be-tested road surface into levels according to the road surface bumpiness degree to obtain the road surface levels; obtain the test vehicle information and the test vehicle suspension parameters when each test vehicle runs in the test fields of each road surface level, and input the road surface levels, the test vehicle information, and the test vehicle suspension parameters into a quarter-vehicle model to obtain the corresponding road surface unevenness levels; perform group division according to the road surface unevenness levels and the sprung mass classifications of the test vehicles, and cluster the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group to obtain the clustering center points of each group; wherein, the clustering center points include the sprung mass classifications and the road surface unevenness levels corresponding to each group; obtain the corresponding suspension optimization parameters according to the characteristics of the suspensions in each group, store the corresponding suspension optimization parameters of each group and the clustering center points to obtain a preset optimization template; and, locate the clustering center point corresponding to the target group in the preset optimization template, and obtain the corresponding suspension optimization parameters according to the clustering center point;

[0077] Correspondingly, the vehicle end 320 may specifically be configured to obtain the test vehicle information and the test vehicle suspension parameters and transmit them to the cloud end.

[0078] Optionally, the cloud 310 can be specifically configured to obtain the unsprung mass acceleration and sprung mass acceleration of the original vehicle at the current position point, and cluster the unsprung mass acceleration and sprung mass acceleration according to the sprung mass classification of the original vehicle to obtain a clustering result; use the group corresponding to the clustering center point with the smallest distance from the clustering result as the group of the original vehicle at the current position point; obtain the original road surface unevenness level at the current position point according to the sprung mass classification of the original vehicle and the group of the original vehicle at the current position point; store the original road surface unevenness level and the position coordinates at the current position point to obtain a set list.

[0079] Optionally, the cloud 310 can be specifically configured to obtain the standard load of the target vehicle and the full load of the target vehicle, and evenly divide the sprung mass between the standard load and the full load of the target vehicle into a set number of levels to obtain the sprung mass classification standard corresponding to the target vehicle; compare the sprung mass of the target vehicle with the sprung mass classification standard corresponding to the target vehicle to obtain the sprung mass classification of the target vehicle.

[0080] Optionally, the cloud 310 can be specifically configured to correspond the road surface unevenness level one by one with the sprung mass classification of the test vehicle for full group division; in each group, use the clustering algorithm to cluster the unsprung mass acceleration and sprung mass acceleration in the test vehicle information to obtain the clustering center point of each group.

[0081] Optionally, the cloud 310 can be specifically configured to obtain the unsprung mass acceleration and sprung mass acceleration of the target vehicle at the current position point, and cluster the unsprung mass acceleration and sprung mass acceleration according to the sprung mass classification of the target vehicle to obtain a clustering result; use the group corresponding to the clustering center point with the smallest distance from the clustering result as the group of the target vehicle at the current position point; obtain the road surface unevenness level at the current position point according to the sprung mass classification of the target vehicle and the group of the target vehicle at the current position point, and store it; if the road surface unevenness level at the current position point is different from the original road surface unevenness level at the current position point, mark the road surface unevenness level at the current position point and the original road surface unevenness level at the current position point; if there are a set number of current position points where the road surface unevenness level is different from the original road surface unevenness level at the current position point, update the original road surface unevenness level at the current position point to the road surface unevenness level with the highest frequency of occurrence among the set number.

[0082] Embodiment 4

[0083] Figure 4 FIG. is a schematic structural diagram of a suspension optimization device provided in Embodiment 4 of the present invention. As Figure 4As shown in the figure, the device includes: a level acquisition module 410, a quality classification module 420, a grouping generation module 430, and a parameter acquisition module 440;

[0084] Among them, the level acquisition module 410 is used to obtain the road surface unevenness level of the current position point in the set list according to the position coordinates of the target vehicle before the target vehicle travels to the current position point;

[0085] The quality classification module 420 is used to obtain the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard;

[0086] The grouping generation module 430 is used to combine the sprung mass classification of the target vehicle and the road surface unevenness level of the current position point to obtain a target group;

[0087] The parameter acquisition module 440 is used to obtain the suspension optimization parameters corresponding to the target group in the pre-set optimization template, and control the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.

[0088] The technical solution of the embodiment of the present invention combines the road surface unevenness level of the current position point of the target vehicle queried from the set list with the calculated sprung mass classification of the target vehicle to obtain a target group, and then obtains the corresponding suspension optimization parameters from the pre-set optimization template according to the target group to optimize the suspension parameters, solving the problem of efficiently and accurately optimizing the suspension of the vehicle, and improving the comfort of the vehicle and the possibility of passing different road conditions.

[0089] Optionally, the suspension optimization device may further include a template generation module, which is used to divide the road surface to be tested into levels according to the road surface bumpiness degree to obtain the road surface level; obtain the test vehicle information and test vehicle suspension parameters when each test vehicle runs in the test site of each road surface level, and input the road surface level, test vehicle information, and test vehicle suspension parameters into the quarter vehicle model to obtain the corresponding road surface unevenness level; perform group division according to the road surface unevenness level and the sprung mass classification of the test vehicle, and cluster the unsprung mass acceleration and sprung mass acceleration in the test vehicle information corresponding to each group to obtain the clustering center points of each group; among them, the clustering center points include the sprung mass classification and road surface unevenness level corresponding to each group; obtain the corresponding suspension optimization parameters according to the characteristics of the suspension in each group, and store the corresponding suspension optimization parameters and clustering center points of each group to obtain the pre-set optimization template;

[0090] Correspondingly, the parameter acquisition module 440 can be specifically configured to locate the clustering center point corresponding to the target group in a preset optimization template, and obtain the corresponding suspension optimization parameters according to the clustering center point.

[0091] Optionally, the suspension optimization device may further include a list generation module, configured to obtain the unsprung mass acceleration and sprung mass acceleration of the original vehicle at the current position point, and cluster the unsprung mass acceleration and sprung mass acceleration according to the sprung mass classification of the original vehicle to obtain a clustering result; take the group corresponding to the clustering center point with the smallest distance from the clustering result as the group of the original vehicle at the current position point; obtain the original road surface unevenness level of the current position point according to the sprung mass classification of the original vehicle and the group of the original vehicle at the current position point; store the original road surface unevenness level and the position coordinates of the current position point to obtain a set list.

[0092] Optionally, the mass classification module 420 can be specifically configured to obtain the standard load of the target vehicle and the full load of the target vehicle, and evenly divide the sprung mass between the standard load and the full load of the target vehicle into a set number of levels to obtain the sprung mass classification standard corresponding to the target vehicle; compare the sprung mass of the target vehicle with the sprung mass classification standard corresponding to the target vehicle to obtain the sprung mass classification of the target vehicle.

[0093] Optionally, the template generation module can be specifically configured to correspond the road surface unevenness level with the sprung mass classification of the test vehicle one by one for full group division; in each group, use the clustering algorithm to cluster the unsprung mass acceleration and sprung mass acceleration in the test vehicle information respectively to obtain the clustering center points of each group.

[0094] Optionally, the suspension optimization device may further include a post-processing module, specifically configured to obtain the unsprung mass acceleration and sprung mass acceleration of the target vehicle at the current position point, and cluster the unsprung mass acceleration and sprung mass acceleration according to the sprung mass classification of the target vehicle to obtain a clustering result; take the group corresponding to the clustering center point with the smallest distance from the clustering result as the group of the target vehicle at the current position point; obtain the road surface unevenness level of the current position point according to the sprung mass classification of the target vehicle and the group of the target vehicle at the current position point, and store it; if the road surface unevenness level of the current position point is different from the original road surface unevenness level of the current position point, mark the road surface unevenness level of the current position point and the original road surface unevenness level of the current position point; if there are a set number of current position points where the road surface unevenness level is different from the original road surface unevenness level of the current position point, update the original road surface unevenness level of the current position point to the road surface unevenness level with the highest frequency of occurrence among the set number.

[0095] The suspension optimization device provided by the embodiment of the present invention can execute the suspension optimization method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0096] Embodiment Five

[0097] Figure 5 FIG. shows a schematic structural diagram of an electronic device 510 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0098] As Figure 5 shown, the electronic device 510 includes at least one processor 520, and a memory communicatively connected to the at least one processor 520, such as a read-only memory (ROM) 530, a random access memory (RAM) 540, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 520 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 530 or the computer program loaded from the storage unit 590 into the random access memory (RAM) 540. In the RAM 540, various programs and data required for the operation of the electronic device 510 can also be stored. The processor 520, the ROM 530, and the RAM 540 are connected to each other through a bus 550. The input / output (I / O) interface 560 is also connected to the bus 550.

[0099] A plurality of components in the electronic device 510 are connected to the I / O interface 560, including: an input unit 570, such as a keyboard, a mouse, etc.; an output unit 580, such as various types of displays, speakers, etc.; a storage unit 590, such as a magnetic disk, an optical disc, etc.; and a communication unit 5100, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 5100 allows the electronic device 510 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0100] The processor 520 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 520 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 520 executes the various methods and processes described above, such as the suspension optimization method.

[0101] The method includes:

[0102] Before the target vehicle travels to the current position point, obtain the road surface roughness level of the current position point from the set list according to the position coordinates of the target vehicle;

[0103] Obtain the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard;

[0104] Combine the sprung mass classification of the target vehicle and the road surface roughness level of the current position point to obtain a target group;

[0105] Obtain the suspension optimization parameters corresponding to the target group from the pre-set optimization template, and control the target vehicle to optimize the suspension parameters using the suspension optimization parameters.

[0106] In some embodiments, the suspension optimization method can be implemented as a computer program, which is tangibly included in a computer-readable storage medium, such as the storage unit 590. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 510 via the ROM 530 and / or the communication unit 5100. When the computer program is loaded into the RAM 540 and executed by the processor 520, one or more steps of the suspension optimization method described above can be executed. Alternatively, in other embodiments, the processor 520 can be configured to execute the suspension optimization method in any other suitable way (e.g., by means of firmware).

[0107] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0108] The computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0109] In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or 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 foregoing.

[0110] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input received from the user can be in any form (including acoustic input, voice input, or tactile input).

[0111] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0112] A computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0113] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0114] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A suspension optimization method, characterized in that, it includes: Before the target vehicle travels to the current position point, obtain the road surface unevenness level of the current position point from the set list according to the position coordinates of the target vehicle; According to the sprung mass of the target vehicle and the sprung mass grading standard, obtain the sprung mass grading of the target vehicle; Combine the sprung mass grading of the target vehicle and the road surface unevenness level of the current position point to obtain a target group; Obtain the suspension optimization parameters corresponding to the target group from the pre-set optimization template, and control the target vehicle to optimize the suspension parameters by using the suspension optimization parameters; Wherein, the method further includes: Obtain the unsprung mass acceleration and sprung mass acceleration of the original vehicle at the current position point, and cluster the unsprung mass acceleration and sprung mass acceleration according to the sprung mass grading of the original vehicle to obtain a clustering result; wherein, the clustering result is the center point result with the highest occurrence frequency selected from all the center point results calculated within the 5m travel range corresponding to the current position point by obtaining vehicle information every 50ms and performing clustering; Take the group corresponding to the clustering center point with the smallest distance from the clustering result as the group of the original vehicle at the current position point; Obtain the original road surface unevenness level of the current position point according to the sprung mass grading of the original vehicle and the group of the original vehicle at the current position point; Store the original road surface unevenness level and position coordinates of the current position point to obtain a set list.

2. The method according to claim 1, characterized in that, it further includes: Classify the road surface to be tested according to the degree of road surface bumps to obtain a road surface grade; Obtain the test vehicle information and test vehicle suspension parameters when each test vehicle runs in the test field of each road surface grade, and input the road surface grade, test vehicle information and test vehicle suspension parameters into the quarter-vehicle model to obtain the corresponding road surface unevenness level; Conduct group division according to the road surface unevenness level and the sprung mass grading of the test vehicle, and cluster the unsprung mass acceleration and sprung mass acceleration in the test vehicle information corresponding to each group to obtain the clustering center points of each group; wherein, the clustering center points include the sprung mass grading and road surface unevenness level corresponding to each group; Obtain the corresponding suspension optimization parameters according to the characteristics of the suspension in each group, and store the corresponding suspension optimization parameters and clustering center points of each group to obtain a pre-set optimization template; Correspondingly, obtaining the suspension optimization parameters corresponding to the target group from the pre-set optimization template includes: Locate the clustering center point corresponding to the target group in the pre-set optimization template, and obtain the corresponding suspension optimization parameters according to the clustering center point.

3. The method according to claim 1, characterized in that, The obtaining the sprung mass grading of the target vehicle according to the sprung mass of the target vehicle and the sprung mass grading standard includes: Obtain the standard load of the target vehicle and the full load of the target vehicle, and evenly divide the sprung mass between the standard load and the full load of the target vehicle into set levels to obtain the sprung mass classification standard corresponding to the target vehicle; Compare the sprung mass of the target vehicle with the sprung mass classification standard corresponding to the target vehicle to obtain the sprung mass classification of the target vehicle.

4. The method according to claim 2, wherein, the method of dividing groups according to the road surface unevenness level and the sprung mass classification of the test vehicle, and clustering the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group to obtain the clustering center points of each group includes: Make a one-to-one correspondence between the road surface unevenness level and the sprung mass classification of the test vehicle to perform full group division; In each group, use the clustering algorithm to cluster the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information respectively to obtain the clustering center points of each group.

5. The method according to claim 1, wherein, further includes: Obtain the unsprung mass acceleration and the sprung mass acceleration of the target vehicle at the current position point, and cluster the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass classification of the target vehicle to obtain a clustering result; Take the group corresponding to the clustering center point with the smallest distance from the clustering result as the group of the target vehicle at the current position point; Obtain the road surface unevenness level at the current position point according to the sprung mass classification of the target vehicle and the group of the target vehicle at the current position point, and store it; If the road surface unevenness level at the current position point is different from the original road surface unevenness level at the current position point, mark the road surface unevenness level at the current position point and the original road surface unevenness level at the current position point; If there are a set number of current position points where the road surface unevenness level is different from the original road surface unevenness level at the current position point, update the original road surface unevenness level at the current position point to the road surface unevenness level with the highest frequency of occurrence among the set number.

6. A suspension optimization system, wherein, includes: The cloud and the vehicle end, wherein the vehicle end includes at least one vehicle; The cloud is used to obtain the road surface unevenness level at the current position point from a set list according to the position coordinates of the target vehicle before the target vehicle travels to the current position point; obtain the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard; combine the sprung mass classification of the target vehicle and the road surface unevenness level at the current position point to obtain a target group; obtain the suspension optimization parameters corresponding to the target group from a pre-set optimization template, and control the target vehicle to optimize the suspension parameters using the suspension optimization parameters; The vehicle end is used to obtain the position coordinates of the target vehicle and transmit them to the cloud; collect the sprung mass of the target vehicle and transmit it to the cloud; receive the suspension optimization parameters transmitted by the cloud and optimize the suspension parameters. Wherein, the cloud is specifically configured to obtain the unsprung mass acceleration and sprung mass acceleration of the original vehicle at the current position point, and cluster the unsprung mass acceleration and sprung mass acceleration according to the sprung mass classification of the original vehicle to obtain a clustering result; wherein, the clustering result is the center point result with the highest occurrence frequency selected from all the center point results calculated within the 5m travel range corresponding to the current position point by obtaining vehicle information every 50ms and performing clustering; the group corresponding to the clustering center point with the smallest distance from the clustering result is used as the group of the original vehicle at the current position point; according to the sprung mass classification of the original vehicle and the group of the original vehicle at the current position point, the original road surface unevenness level at the current position point is obtained; the original road surface unevenness level and the position coordinates at the current position point are stored to obtain a set list.

7. A suspension optimization device, Characterized in that, Comprising: A level acquisition module, configured to acquire the road surface unevenness level at the current position point from the set list according to the position coordinates of the target vehicle before the target vehicle travels to the current position point; A mass classification module, configured to obtain the sprung mass classification of the target vehicle according to the sprung mass of the target vehicle and the sprung mass classification standard; A grouping generation module, configured to combine the sprung mass classification of the target vehicle and the road surface unevenness level at the current position point to obtain a target grouping; A parameter acquisition module, configured to acquire suspension optimization parameters corresponding to the target grouping from a pre-set optimization template, and control the target vehicle to optimize the suspension parameters using the suspension optimization parameters; Wherein, the device further comprises: A list generation module, configured to obtain the unsprung mass acceleration and sprung mass acceleration of the original vehicle at the current position point, and cluster the unsprung mass acceleration and sprung mass acceleration according to the sprung mass classification of the original vehicle to obtain a clustering result; wherein, the clustering result is the center point result with the highest occurrence frequency selected from all the center point results calculated within the 5m travel range corresponding to the current position point by obtaining vehicle information every 50ms and performing clustering; the group corresponding to the clustering center point with the smallest distance from the clustering result is used as the group of the original vehicle at the current position point; according to the sprung mass classification of the original vehicle and the group of the original vehicle at the current position point, the original road surface unevenness level at the current position point is obtained; the original road surface unevenness level and the position coordinates at the current position point are stored to obtain a set list.

8. An electronic device, Characterized in that, The electronic device comprises: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the suspension optimization method according to any one of claims 1-5.

9. A computer-readable storage medium, Characterized in that, The computer-readable storage medium stores computer instructions for implementing the suspension optimization method according to any one of claims 1-5 when executed by a processor.

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