Control method and system for adjusting air suspension of tractor and medium

Abstract

The invention relates to a control method and system for adjusting an air suspension of a tractor and a medium, and the method comprises the steps: Q1, enabling a vehicle to run on a road, obtaining the image data information of the road in real time based on a vehicle-mounted camera, obtaining the point cloud data information of the road in real time based on a vehicle-mounted laser radar, acquiring data information of the suspension height of a front axle of the vehicle in real time based on a vehicle-mounted first height sensor, and acquiring data information of the suspension height of a rear axle of the vehicle in real time based on a vehicle-mounted second height sensor; and Q2, on the basis of the image data information of the road and the point cloud data information of the road, constructing an ETA depth model of the road based on image convolution road condition prediction, and predicting the road condition of the road to obtain predicted road condition data information of the road. The height of the air suspension of the front and rear axles of the vehicle is accurately adjusted and controlled according to the real-time road condition of the road, manual adjustment of a user is not needed, adjustment of the vehicle suspension is more intelligent, and user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tractor air suspension, and in particular to a control method, system and medium for adjusting tractor air suspension. Background Art

[0002] Vehicle suspension plays an important role in the stability, comfort and safety of vehicle driving. According to different road conditions, adjusting the suspension gears of different heights can bring users a more comfortable driving and riding experience. In related technologies, the adjustment of vehicle suspension mainly depends on the user's active judgment of road conditions, and then makes adjustments. For roads that users often drive on, if they want to maintain the same experience, they need to make adjustments every time, which is cumbersome to operate, poorly intelligent, and poor user experience. Summary of the invention

[0003] In view of the above problems, the present invention provides a control method, system and medium for adjusting the air suspension of a tractor, which not only accurately adjusts and controls the height of the air suspension of the front and rear axles of the vehicle according to the real-time road conditions, but also does not require manual adjustment by the user. The vehicle suspension adjustment is more intelligent, thereby improving the user experience.

[0004] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0005] A control method for adjusting an air suspension of a tractor, the method comprising:

[0006] Q1. When a vehicle is driving on a road, the vehicle camera obtains image data information of the road in real time, the vehicle laser radar obtains point cloud data information of the road in real time, the vehicle first height sensor obtains data information of the vehicle front axle suspension height in real time, and the vehicle second height sensor obtains data information of the vehicle rear axle suspension height in real time;

[0007] Q2. Based on the image data information of the road and the point cloud data information of the road, construct an ETA deep model of the road based on graph convolution road condition prediction, predict the road condition, and obtain the predicted road condition data information;

[0008] Q3. Based on the predicted road condition data information, the vehicle front axle suspension height data information and the vehicle rear axle suspension height data information, a Markov model for predicting the front and rear axle suspension heights is constructed to predict the front and rear axle suspension heights to obtain the predicted front and rear axle suspension height data information;

[0009] Q4. Based on the predicted data information of the front and rear axle suspension heights, the front and rear axle suspension heights are optimized using a salp swarm algorithm that integrates random inertia weights and differential mutation operations to obtain optimized data information of the front and rear axle suspension heights.

[0010] Furthermore, in step Q2, the construction of the ETA deep model based on graph convolution road condition prediction of the road and the prediction of the road condition include:

[0011] Q21. Based on the image data information of the road and the point cloud data information of the road, establish a fusion function W of the image and the point cloud of the road,

[0012]

[0013] Among them, x is the image data information of the road, y is the point cloud data information of the road, α1, α2 and α3 are the fusion factors of the image and point cloud of the road, and the image and point cloud of the road are fused to obtain the fused state data information of the road;

[0014] Q22. Based on the fused road state data information, construct a spatiotemporal feature extraction function R of the road.

[0015]

[0016] Among them, z is the state data information of the fused road, β1, β2 and β3 are the extraction factors of the spatiotemporal characteristics of the road, and the spatiotemporal characteristics of the state of the road are extracted to obtain the spatiotemporal characteristic data information of the state of the road;

[0017] Q23. Based on the spatiotemporal characteristic data information of the road state, establish a graph convolution road condition prediction function P of the road,

[0018]

[0019] Among them, r is the spatiotemporal characteristic data information of the road state, δ1, δ2 and δ3 are the dependency parameters of the spatiotemporal characteristics of the road state, and the road condition is predicted to obtain the predicted road condition data information.

[0020] Furthermore, the dependency parameters δ1, δ2 and δ3 of the spatiotemporal characteristics of the road state are:

[0021]

[0022] Among them, r is the spatiotemporal characteristic data information of the road status.

[0023] Furthermore, the constraint conditions of the dependency parameters δ1, δ2 and δ3 of the spatiotemporal characteristics of the road state are:

[0024]

[0025] The constraint function f of the fusion factors α1, α2 and α3 of the image and point cloud of the road is,

[0026]

[0027] Among them, the value range of the constraint function f is (3,5).

[0028] Furthermore, in step Q3, the Markov model for predicting the front and rear axle suspension heights is constructed to predict the front and rear axle suspension heights, including:

[0029] Q31. Based on the predicted road condition data information, the vehicle front axle suspension height data information and the vehicle rear axle suspension height data information, establish a state transfer function S of the vehicle front and rear axle suspension heights,

[0030]

[0031] Among them, a1 is the road condition data information of the predicted road, a2 is the data information of the front axle suspension height of the vehicle, a3 is the data information of the rear axle suspension height of the vehicle, γ1, γ2 and γ3 are the state difference factors of the front and rear axle suspension heights of the vehicle, and the state transition probability matrix of the front and rear axle suspension heights of the vehicle is characterized to obtain the state transition probability matrix data information of the front and rear axle suspension heights of the vehicle;

[0032] Q32. Based on the state transition probability matrix data information of the front and rear axle suspension height of the vehicle, establish the joint probability distribution function G of the n observation sequences of the front and rear axle suspension height of the vehicle,

[0033] Where n is the sample size, b is i is the i-th state transition probability matrix data information of the vehicle front and rear axle suspension height, η i is the weight coefficient of the i-th state transition probability matrix of the vehicle front and rear axle suspension height, and p is the observation sequence function of the vehicle front and rear axle suspension height;

[0034] Q33. Based on the joint probability distribution function G of the n observation sequences of the front and rear axle suspension heights of the vehicle, the heights of the front and rear axle suspensions are predicted to obtain data information of the predicted front and rear axle suspension heights.

[0035] Furthermore, the observation sequence function p of the vehicle front and rear axle suspension height is,

[0036]

[0037] Where n is the sample size, b isi is the i-th state transition probability matrix data information of the vehicle front and rear axle suspension height, η i is the weight coefficient of the i-th state transition probability matrix of the vehicle's front and rear axle suspension height.

[0038] Further, in step Q4, the optimization of the height of the front and rear axle suspensions by using the salp swarm algorithm integrating random inertia weight and differential mutation operation includes:

[0039] Q41. Based on the predicted data information of the front and rear axle suspension heights, the salp population is initialized, population parameters are determined, and data information of the initialized salp population is obtained;

[0040] Q42. Based on the data information of the initialized salp population, establish the fitness function H of the salp population individuals,

[0041]

[0042] Among them, c is the data information of the initialized salp population, λ1, λ2 and λ3 are the random inertia weight factors of the individuals in the salp population, and the fitness values ​​of the individuals in the salp population are calculated to obtain the fitness value data information of the individuals in the salp population;

[0043] Q43. Based on the fitness value data information of the individual salps population, establish a target optimization function L,

[0044]

[0045] Among them, g is the fitness value data information of the individual salps population, μ1, μ2 and μ3 are the differential variation parameters of the individual salps population, and the height of the front and rear axle suspension is optimized to obtain the optimized front and rear axle suspension height data information.

[0046] Furthermore, the differential variation parameters μ1, μ2 and μ3 of the individuals of the salp population are,

[0047]

[0048] Among them, g is the fitness value data information of the individual salps population.

[0049] In order to achieve the above-mentioned object and other related objects, the present invention also provides a control system for adjusting the air suspension of a tractor, including a computer device, which is programmed or configured to execute the steps of any one of the control methods for adjusting the air suspension of a tractor.

[0050] In order to achieve the above-mentioned object and other related objects, the present invention also provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the control methods for adjusting the air suspension of a tractor.

[0051] The present invention has the following positive effects:

[0052] 1. The present invention predicts the road condition by constructing an ETA deep model based on graph convolution road condition prediction, and predicts the height of the front and rear axle suspension by combining a Markov model for predicting the front and rear axle suspension height to obtain data information of the predicted front and rear axle suspension height. This not only can accurately adjust and control the height of the air suspension of the front and rear axles of the vehicle according to the real-time road condition, but also does not require manual adjustment by the user, making the vehicle suspension adjustment more intelligent and improving the user experience.

[0053] 2. The present invention optimizes the height of the front and rear axle suspensions by adopting a salp swarm algorithm that integrates random inertia weights and differential mutation operations. It can not only optimize and adjust the height of the front and rear axle suspensions of the vehicle, but also the adjustment process has strong adaptability and can be adjusted according to different road conditions, thereby improving the user's riding experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0055] Figure 2 A schematic diagram of the process of constructing an ETA deep model for road condition prediction based on graph convolution of the present invention;

[0056] Figure 3 A schematic diagram of a process for constructing a Markov model for predicting the front and rear axle suspension height of the present invention;

[0057] Figure 4 It is a flow chart of the salp swarm algorithm integrating random inertia weight and differential mutation operation of the present invention;

[0058] Figure 5 It is a schematic structural diagram of the tractor of the present invention.

[0059] Explanation of the numbers in the figure: 1-camera, 2-laser radar, 3-first height sensor, 4-second height sensor. DETAILED DESCRIPTION

[0060] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0061] Example 1: Figure 1 or Figure 5 As shown, a control method for adjusting the air suspension of a tractor vehicle comprises:

[0062] Q1. The vehicle is driving on the road, and the vehicle camera 1 obtains the image data information of the road in real time, the vehicle laser radar 2 obtains the point cloud data information of the road in real time, the vehicle first height sensor 3 obtains the data information of the vehicle front axle suspension height in real time, and the vehicle second height sensor 4 obtains the data information of the vehicle rear axle suspension height in real time;

[0063] Q2. Based on the image data information of the road and the point cloud data information of the road, construct an ETA deep model of the road based on graph convolution road condition prediction, predict the road condition, and obtain the predicted road condition data information;

[0064] Q3. Based on the predicted road condition data information, the vehicle front axle suspension height data information and the vehicle rear axle suspension height data information, a Markov model for predicting the front and rear axle suspension heights is constructed to predict the front and rear axle suspension heights to obtain the predicted front and rear axle suspension height data information;

[0065] Q4. Based on the predicted data information of the front and rear axle suspension heights, the front and rear axle suspension heights are optimized using a salp swarm algorithm that integrates random inertia weights and differential mutation operations to obtain optimized data information of the front and rear axle suspension heights.

[0066] In this embodiment, if Figure 2 As shown, in step Q2, the construction of the ETA deep model based on graph convolution road condition prediction of the road and the prediction of the road condition include:

[0067] Q21. Based on the image data information of the road and the point cloud data information of the road, establish a fusion function W of the image and the point cloud of the road,

[0068]

[0069] Among them, x is the image data information of the road, y is the point cloud data information of the road, α1, α2 and α3 are the fusion factors of the image and point cloud of the road, and the image and point cloud of the road are fused to obtain the fused state data information of the road;

[0070] Q22. Based on the fused road state data information, construct a spatiotemporal feature extraction function R of the road.

[0071]

[0072] Among them, z is the state data information of the fused road, β1, β2 and β3 are the extraction factors of the spatiotemporal characteristics of the road, and the spatiotemporal characteristics of the state of the road are extracted to obtain the spatiotemporal characteristic data information of the state of the road;

[0073] Q23. Based on the spatiotemporal characteristic data information of the road state, establish a graph convolution road condition prediction function P of the road,

[0074]

[0075] Among them, r is the spatiotemporal characteristic data information of the road state, δ1, δ2 and δ3 are the dependency parameters of the spatiotemporal characteristics of the road state, and the road condition is predicted to obtain the predicted road condition data information.

[0076] In this embodiment, the dependency parameters δ1, δ2 and δ3 of the spatiotemporal characteristics of the road state are:

[0077]

[0078] Among them, r is the spatiotemporal characteristic data information of the road status.

[0079] In this embodiment, the constraint conditions of the dependency parameters δ1, δ2 and δ3 of the spatiotemporal characteristics of the road state are:

[0080]

[0081] The constraint function f of the fusion factors α1, α2 and α3 of the image and point cloud of the road is,

[0082]

[0083] Among them, the value range of the constraint function f is (3,5).

[0084] Embodiment 2: Based on the control method for adjusting the air suspension of a tractor vehicle in Embodiment 1, the present invention is further illustrated and described below.

[0085] like Figure 1 or Figure 5 As shown, a control method for adjusting the air suspension of a tractor vehicle comprises:

[0086] Q1. The vehicle is driving on the road, and the vehicle camera 1 obtains the image data information of the road in real time, the vehicle laser radar 2 obtains the point cloud data information of the road in real time, the vehicle first height sensor 3 obtains the data information of the vehicle front axle suspension height in real time, and the vehicle second height sensor 4 obtains the data information of the vehicle rear axle suspension height in real time;

[0087] Q2. Based on the image data information of the road and the point cloud data information of the road, construct an ETA deep model of the road based on graph convolution road condition prediction, predict the road condition, and obtain the predicted road condition data information;

[0088] Q3. Based on the predicted road condition data information, the vehicle front axle suspension height data information and the vehicle rear axle suspension height data information, a Markov model for predicting the front and rear axle suspension heights is constructed to predict the front and rear axle suspension heights to obtain the predicted front and rear axle suspension height data information;

[0089] Q4. Based on the predicted data information of the front and rear axle suspension heights, the front and rear axle suspension heights are optimized using a salp swarm algorithm that integrates random inertia weights and differential mutation operations to obtain optimized data information of the front and rear axle suspension heights.

[0090] In this embodiment, if Figure 3 As shown, in step Q3, the Markov model for predicting the front and rear axle suspension height is constructed to predict the height of the front and rear axle suspensions, including:

[0091] Q31. Based on the predicted road condition data information, the vehicle front axle suspension height data information and the vehicle rear axle suspension height data information, establish a state transfer function S of the vehicle front and rear axle suspension heights,

[0092]

[0093] Among them, a1 is the road condition data information of the predicted road, a2 is the data information of the front axle suspension height of the vehicle, a3 is the data information of the rear axle suspension height of the vehicle, γ1, γ2 and γ3 are the state difference factors of the front and rear axle suspension heights of the vehicle, and the state transition probability matrix of the front and rear axle suspension heights of the vehicle is characterized to obtain the state transition probability matrix data information of the front and rear axle suspension heights of the vehicle;

[0094] Q32. Based on the state transition probability matrix data information of the front and rear axle suspension height of the vehicle, establish the joint probability distribution function G of the n observation sequences of the front and rear axle suspension height of the vehicle,

[0095] Where n is the sample size, b is i is the i-th state transition probability matrix data information of the vehicle front and rear axle suspension height, η i is the weight coefficient of the i-th state transition probability matrix of the vehicle front and rear axle suspension height, and p is the observation sequence function of the vehicle front and rear axle suspension height;

[0096] Q33. Based on the joint probability distribution function G of the n observation sequences of the front and rear axle suspension heights of the vehicle, the heights of the front and rear axle suspensions are predicted to obtain data information of the predicted front and rear axle suspension heights.

[0097] In this embodiment, the observation sequence function p of the vehicle front and rear axle suspension height is:

[0098] Where n is the sample size, b is i is the i-th state transition probability matrix data information of the vehicle front and rear axle suspension height, η i is the weight coefficient of the i-th state transition probability matrix of the vehicle's front and rear axle suspension height.

[0099] In this embodiment, if Figure 4 As shown, in step Q4, the optimization of the height of the front and rear axle suspensions by using the salp swarm algorithm integrating random inertia weight and differential mutation operation includes:

[0100] Q41. Based on the predicted data information of the front and rear axle suspension heights, the salp population is initialized, population parameters are determined, and data information of the initialized salp population is obtained;

[0101] Q42. Based on the data information of the initialized salp population, establish the fitness function H of the salp population individuals,

[0102]

[0103] Among them, c is the data information of the initialized salp population, λ1, λ2 and λ3 are the random inertia weight factors of the individuals in the salp population, and the fitness values ​​of the individuals in the salp population are calculated to obtain the fitness value data information of the individuals in the salp population;

[0104] Q43. Based on the fitness value data information of the individual salps population, establish a target optimization function L,

[0105]

[0106] Among them, g is the fitness value data information of the individual salps population, μ1, μ2 and μ3 are the differential variation parameters of the individual salps population, and the height of the front and rear axle suspension is optimized to obtain the optimized front and rear axle suspension height data information.

[0107] In this embodiment, the differential variation parameters μ1, μ2 and μ3 of the individuals of the salp population are,

[0108]

[0109] Among them, g is the fitness value data information of the individual salps population.

[0110] In this embodiment, the present invention provides a control system for adjusting air suspension of a tractor, including a computer device, wherein the computer device is programmed or configured to execute the steps of any one of the control methods for adjusting air suspension of a tractor.

[0111] In this embodiment, the present invention provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the control methods for adjusting the air suspension of a tractor.

[0112] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0113] In summary, the present invention not only accurately adjusts and controls the height of the air suspension of the front and rear axles of the vehicle according to the real-time road conditions, but also does not require manual adjustment by the user. The vehicle suspension adjustment is more intelligent, thereby improving the user experience.

[0114] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A control method for adjusting the air suspension of a tractor, characterized in that: The method comprises: Q1. When a vehicle is driving on a road, the vehicle camera obtains image data information of the road in real time, the vehicle laser radar obtains point cloud data information of the road in real time, the vehicle first height sensor obtains data information of the vehicle front axle suspension height in real time, and the vehicle second height sensor obtains data information of the vehicle rear axle suspension height in real time; Q2. Based on the image data information of the road and the point cloud data information of the road, construct an ETA deep model of the road based on graph convolution road condition prediction, predict the road condition, and obtain the predicted road condition data information; Q3. Based on the predicted road condition data information, the vehicle front axle suspension height data information and the vehicle rear axle suspension height data information, a Markov model for predicting the front and rear axle suspension heights is constructed to predict the front and rear axle suspension heights to obtain the predicted front and rear axle suspension height data information; Q4. Based on the predicted data information of the front and rear axle suspension heights, the front and rear axle suspension heights are optimized using a salp swarm algorithm that integrates random inertia weights and differential mutation operations to obtain optimized data information of the front and rear axle suspension heights.

2. The control method for adjusting the air suspension of a tractor according to claim 1, characterized in that: In step Q2, the construction of the ETA deep model based on graph convolutional road condition prediction for the road and the prediction of the road condition include: Q21. Based on the image data information of the road and the point cloud data information of the road, establish a fusion function W of the image and the point cloud of the road, Among them, x is the image data information of the road, y is the point cloud data information of the road, α1, α2 and α3 are the fusion factors of the image and point cloud of the road, and the image and point cloud of the road are fused to obtain the fused state data information of the road; Q22. Based on the fused road state data information, construct a spatiotemporal feature extraction function R of the road. Among them, z is the state data information of the fused road, β1, β2 and β3 are the extraction factors of the spatiotemporal characteristics of the road, and the spatiotemporal characteristics of the state of the road are extracted to obtain the spatiotemporal characteristic data information of the state of the road; Q23. Based on the spatiotemporal characteristic data information of the road state, establish a graph convolution road condition prediction function P of the road, Among them, r is the spatiotemporal characteristic data information of the road state, δ1, δ2 and δ3 are the dependency parameters of the spatiotemporal characteristics of the road state, and the road condition is predicted to obtain the predicted road condition data information.

3. The control method for adjusting the air suspension of a tractor according to claim 2, characterized in that: The dependency parameters δ1, δ2 and δ3 of the spatiotemporal characteristics of the road state are: Among them, r is the spatiotemporal characteristic data information of the road status.

4. The control method for adjusting the air suspension of a tractor according to claim 2, characterized in that: The constraints of the dependency parameters δ1, δ2 and δ3 of the spatiotemporal characteristics of the road state are: The constraint function f of the fusion factors α1, α2 and α3 of the image and point cloud of the road is, Among them, the value range of the constraint function f is (3,5).

5. The control method for adjusting the air suspension of a tractor according to claim 1, characterized in that: In step Q3, the Markov model for predicting the front and rear axle suspension height is constructed to predict the front and rear axle suspension heights, including: Q31. Based on the predicted road condition data information, the vehicle front axle suspension height data information and the vehicle rear axle suspension height data information, establish a state transfer function S of the vehicle front and rear axle suspension heights, Among them, a1 is the road condition data information of the predicted road, a2 is the data information of the front axle suspension height of the vehicle, a3 is the data information of the rear axle suspension height of the vehicle, γ1, γ2 and γ3 are the state difference factors of the front and rear axle suspension heights of the vehicle, and the state transition probability matrix of the front and rear axle suspension heights of the vehicle is characterized to obtain the state transition probability matrix data information of the front and rear axle suspension heights of the vehicle; Q32. Based on the state transition probability matrix data information of the front and rear axle suspension height of the vehicle, establish the joint probability distribution function G of the n observation sequences of the front and rear axle suspension height of the vehicle, Where n is the sample size, b is i is the i-th state transition probability matrix data information of the vehicle front and rear axle suspension height, η i is the weight coefficient of the i-th state transition probability matrix of the front and rear axle suspension height of the vehicle, and p is the observation sequence function of the front and rear axle suspension height of the vehicle; Q33. Based on the joint probability distribution function G of the n observation sequences of the front and rear axle suspension heights of the vehicle, the heights of the front and rear axle suspensions are predicted to obtain data information of the predicted front and rear axle suspension heights.

6. The control method for adjusting the air suspension of a tractor according to claim 5, characterized in that: The observation sequence function p of the vehicle front and rear axle suspension height is: Where n is the sample size, b is i is the i-th state transition probability matrix data information of the vehicle front and rear axle suspension height, η i is the weight coefficient of the i-th state transition probability matrix of the vehicle's front and rear axle suspension height.

7. The control method for adjusting the air suspension of a tractor according to claim 1, characterized in that: In step Q4, the optimization of the height of the front and rear axle suspensions by using the salp swarm algorithm integrating random inertia weight and differential mutation operation includes: Q41. Based on the predicted data information of the front and rear axle suspension heights, the salp population is initialized, population parameters are determined, and data information of the initialized salp population is obtained; Q42. Based on the data information of the initialized salp population, establish the fitness function H of the salp population individuals, Among them, c is the data information of the initialized salp population, λ1, λ2 and λ3 are the random inertia weight factors of the individuals in the salp population, and the fitness values ​​of the individuals in the salp population are calculated to obtain the fitness value data information of the individuals in the salp population; Q43. Based on the fitness value data information of the individual salps population, establish a target optimization function L, Among them, g is the fitness value data information of the individual salps population, μ1, μ2 and μ3 are the differential variation parameters of the individual salps population, and the height of the front and rear axle suspension is optimized to obtain the optimized front and rear axle suspension height data information.

8. The control method for adjusting the air suspension of a tractor according to claim 7, characterized in that: The differential variation parameters μ1, μ2 and μ3 of the individuals in the salp population are: Among them, g is the fitness value data information of the individual salps population.

9. A control system for adjusting the air suspension of a tractor, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the control method for adjusting the air suspension of a tractor vehicle as claimed in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program programmed or configured to execute the control method for adjusting the tractor air suspension according to any one of claims 1 to 8.

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