Vehicle vibration reduction method utilizing frame pull arm hook integrated design

Abstract

The invention provides a vehicle vibration reduction method adopting integrated design of a vehicle frame pull arm hook, and belongs to the technical field of vehicle structures. A pull arm upper main beam and a chassis girder are integrally welded and connected to form a unified bearing structure; an air suspension system comprising an air spring unit, a shock absorber unit and a height adjusting valve is installed at the bottom of a chassis girder to achieve the adjustable shock absorption function, a vehicle vibration mechanical model is established, and the optimal parameters of the air suspension system are determined through a rigidity optimization equation and a damping optimization equation. Stress distribution of a bearing structure is unified, an optimal reinforcing rib arrangement scheme is determined, vibration data are collected in real time through a vibration monitoring sensor and input into a vibration control system, and the working pressure of the air suspension system is dynamically adjusted according to vibration amplitude and frequency characteristics; the technical problems that in the loading and unloading process of a vehicle, vibration is difficult to effectively control, and consequently vehicle structure fatigue damage and driving comfort deterioration are caused are solved.

Description

Technical Field

[0001] This invention belongs to the field of vehicle structure technology, and more specifically, relates to a method for reducing vehicle vibration by utilizing an integrated design of the frame, arm, and hook. Background Technology

[0002] Traditional specialized trucks utilize leaf spring suspension systems in conjunction with independently mounted boom hooks for loading and unloading operations. This design is widely used in specialized vehicles such as garbage trucks, dump trucks, and container trucks, providing fundamental loading and unloading technology support for industries such as urban sanitation, construction, and logistics. The lifting and lowering motion of the boom hook completes the loading and unloading of various containers and goods. However, traditional leaf spring suspension systems have fixed stiffness and limited adjustment range, failing to provide effective vibration damping control under the impact loads and dynamic load changes generated during loading and unloading. The boom hook system, as an independent device mounted on the frame, directly transmits vibrations generated during operation to the vehicle body structure, lacking a dedicated vibration damping mechanism. In current applications of specialized vehicles, due to rapid load changes and frequent impact loads during loading and unloading, vehicle vibration amplitudes often exceed normal driving conditions by several times. Long-term vibration leads to fatigue cracks in the frame structure, loosening of connectors, and increased noise in the cab, severely impacting the driver's working environment and operational precision. Furthermore, the transmission of vibration to cargo can cause damage and reduce loading and unloading efficiency. In other words, existing technologies have technical problems such as the difficulty in effectively controlling vehicle vibration during loading and unloading, leading to fatigue damage to vehicle structure and deterioration of driving comfort. Summary of the Invention

[0003] In view of this, the present invention provides a vehicle vibration reduction method using an integrated design of the frame, arm, and hook, which can solve the technical problem in the prior art that the vibration of the vehicle during loading and unloading is difficult to control effectively, leading to fatigue damage to the vehicle structure and deterioration of driving comfort.

[0004] This invention is implemented as follows:

[0005] This invention is implemented as follows: It provides a vehicle vibration reduction method utilizing an integrated design of the frame, arm, and hook, comprising: integrally welding the upper arm assembly to the chassis beam, forming a unified load-bearing structure; installing an air suspension system at the bottom of the chassis beam, the air suspension system including an air spring unit, a shock absorber unit, and a height adjustment valve, the air spring unit being connected to the unified load-bearing structure via a support arm; installing a lifting cylinder at the rear end of the unified load-bearing structure; installing a guide wheel assembly at the front end of the arm hook; establishing a vehicle vibration dynamics model, determining the stiffness and damping parameters of the air suspension system through a set of vibration transfer function calculation equations, thereby controlling the vibration amplitude of the vehicle within a preset vibration range during loading and unloading; and using a load analysis optimization model to analyze the stress distribution of the unified load-bearing structure, the load analysis optimization model determining the optimal stiffener arrangement scheme based on load type, load magnitude, point of application, and material properties, thereby achieving effective control of vehicle vibration during loading and unloading.

[0006] Specifically, the integrated welding connection involves connecting the main beam mounted on the tie arm to the chassis beam through full penetration welding to form a continuous load-bearing beam structure, eliminating the original connection gaps and relative displacements.

[0007] Specifically, the air spring unit includes a rubber airbag, an upper cover plate, and a lower cover plate. The rubber airbag is filled with compressed air, and the support force and stiffness characteristics of the air suspension system are adjusted by changing the internal pressure of the rubber airbag.

[0008] Specifically, the height adjustment valve is used to automatically adjust the working pressure of the air spring unit according to changes in vehicle load, maintain a constant vehicle height, and prevent changes in vehicle posture caused by load variations.

[0009] Specifically, the lifting cylinder has its cylinder body fixed on a unified load-bearing structure, and its piston rod is connected to the pull arm hook. The lifting cylinder is used to drive the lifting and lowering actions of the pull arm hook.

[0010] Specifically, the guide wheel assembly includes a main guide wheel and a secondary guide wheel. The main guide wheel and the secondary guide wheel are mounted on a guide wheel bracket via bearings, and the guide wheel bracket is fixed to the front end of the pull arm hook.

[0011] The guide wheel assembly has a main guide wheel with a diameter of 150mm and a secondary guide wheel with a diameter of 100mm. Both the main guide wheel and the secondary guide wheel are made of polyurethane material with a Shore A hardness of 85 degrees, which is used to reduce friction and impact between the hook and the object being pulled.

[0012] Specifically, the set of equations for calculating the vibration transfer function includes stiffness optimization equations and damping optimization equations. The stiffness optimization equations are used to calculate the optimal stiffness coefficient of the air suspension system based on the total vehicle mass, load distribution coefficient, road excitation main frequency, and target vibration amplitude. The damping optimization equations are used to calculate the optimal damping coefficient based on the stiffness parameters of the air suspension system, vehicle moment of inertia, vibration attenuation ratio, and response time requirements.

[0013] The stiffness optimization equation takes into account the total vehicle mass, load distribution coefficient, road excitation dominant frequency, target vibration amplitude, and suspension geometric parameters as inputs, and outputs the stiffness parameters of the air suspension system. The damping optimization equation takes into account the stiffness parameters of the air suspension system, vehicle moment of inertia, vibration damping ratio, response time requirement, and system natural frequency as inputs, and outputs the damping parameters.

[0014] The process also includes the following steps: collecting vibration data in real time during vehicle operation using vibration monitoring sensors, inputting the vibration data into a vibration control system, and adjusting the working pressure of the air suspension system according to the vibration amplitude and frequency characteristics.

[0015] The load analysis optimization model is specifically based on a multi-layer encoder network of the Transformer-XL architecture, including a load feature extraction layer, a stress transfer relationship learning layer, and a stiffener placement prediction layer. The number of memory segments in the load analysis optimization model is determined based on the complexity of the uniform load-bearing structure, the number of load conditions, and the number of material types. Before establishing the vehicle vibration dynamics model, the training dataset for the load analysis optimization model is also established. This includes collecting finite element simulation data of stress distribution of the uniform load-bearing structure under different load conditions, establishing a database of correspondences between load type, load magnitude, point of application location, and material properties, constructing a training sample set containing load type labels, load magnitude values, point of application coordinates, material property parameters, and corresponding stress distribution cloud maps, and expanding the diversity and coverage of the training data through data augmentation techniques. After establishing the training dataset, the training also includes load analysis optimization model training. Specifically, this involves adopting a progressive training strategy to first pre-train on simple load case data, and then gradually increasing the degree of load cases for incremental training. An adaptive learning rate adjustment mechanism is used to dynamically adjust the learning rate according to the changes in the loss function during the training process. A multi-task learning framework is adopted to simultaneously optimize the two objectives of stress prediction accuracy and the rationality of the optimal stiffener arrangement scheme.

[0016] The total vehicle mass is derived from the vehicle's factory technical parameters; the load distribution coefficient is calculated based on the position of the hook arm and the center of gravity of the cargo; the road excitation main frequency is obtained by measuring the road surface unevenness; the target vibration amplitude is set according to driving comfort requirements; and the suspension geometry parameters include the support arm length and the air spring unit installation angle.

[0017] The vehicle moment of inertia is calculated based on the vehicle's geometric dimensions and mass distribution; the vibration attenuation ratio is set according to the vibration control performance requirements; the response time requirement is set according to the system's dynamic response performance; and the system's natural frequency is calculated based on the stiffness parameters of the air suspension system and the vehicle's total mass.

[0018] The number of memory segments is determined by a memory length adjustment function. This function calculates the memory length adjustment value based on the uniform load-bearing structure complexity, the number of load conditions, the number of material types, and computational resource limitations. When the memory length adjustment value is in the range of 0 to 25, the short-term memory mode is used to adjust the number of memory segments in the load analysis optimization model to 8 segments. When the memory length adjustment value is in the range of 26 to 50, the medium-term memory mode is used to adjust the number of memory segments in the load analysis optimization model to 16 segments.

[0019] The vibration amplitude is derived from real-time measurement data from vibration monitoring sensors, the frequency characteristics are obtained through spectral analysis of the vibration data, and the working pressure is used to adjust the support characteristics of the air spring unit.

[0020] This invention establishes an integrated welded connection between the tie rod superstructure and the chassis beam, forming a unified load-bearing structure. This eliminates several weak points in traditional designs where loading and unloading vibrations are transmitted to the vehicle body, establishing a clear and controllable vibration transmission path and laying the structural foundation for subsequent precise vibration reduction control. By replacing the traditional leaf spring suspension with an air suspension system, utilizing the adjustable pressure characteristics of the air spring unit and the damping adjustment function of the shock absorber unit, active control of vibrations of various frequencies and amplitudes during loading and unloading is achieved, avoiding the problem that fixed stiffness suspension systems cannot adapt to dynamic load changes. Through vibration dynamics modeling and vibration transfer function calculation, a quantitative relationship between load state and suspension parameters is established, transforming vibration control from passive bearing to active adjustment. Combined with real-time vibration monitoring and feedback adjustment mechanisms, the vibration amplitude is always kept within a safe range, effectively preventing vehicle structural fatigue damage and significantly improving driving comfort. In summary, this invention solves the technical problems mentioned in the background art, such as the difficulty in effectively controlling vehicle vibrations during loading and unloading, leading to vehicle structural fatigue damage and deterioration of driving comfort. Attached Figure Description

[0021] Figure 1A flowchart of a vehicle vibration reduction method utilizing an integrated design of a chassis tie rod hook;

[0022] Figure 2 A structural schematic diagram of a vehicle that utilizes an integrated design of frame, hook, and arm;

[0023] Figure 3 A front view of a vehicle that utilizes an integrated frame-hook design;

[0024] Figure 4 This is a schematic diagram of an air suspension system for a vehicle that utilizes an integrated design of the frame, tie rod, and hook.

[0025] Figure 5 A top-view sectional view of a vehicle that utilizes an integrated frame-hook design;

[0026] Figure 6 A schematic diagram of a vehicle that utilizes an integrated design of frame, hook, and telescopic arm.

[0027] The attached diagram lists the components represented by each number as follows:

[0028] 10. Hook arm upper assembly; 20. Chassis beam; 30. Air suspension system; 31. Air spring unit; 311. Rubber airbag; 312. Upper cover plate; 313. Lower cover plate; 32. Shock absorber unit; 33. Height adjustment valve; 40. Lifting cylinder; 50. Guide wheel assembly; 60. Hook arm hook. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0030] like Figure 1 The diagram shows a flowchart of a vehicle vibration reduction method using an integrated frame-hook design provided by the present invention. This method includes the following steps:

[0031] S01. The upper structure of the tie rod is welded to the chassis beam in an integrated manner, so that the main beam of the tie rod upper structure and the chassis beam form a unified load-bearing structure.

[0032] S02. An air suspension system is installed at the bottom of the chassis beam. The air suspension system includes an air spring unit, a shock absorber unit, and a height adjustment valve. The air spring unit is connected to the unified load-bearing structure through a support arm.

[0033] S03. A lifting cylinder is installed at the rear end of the unified load-bearing structure. The cylinder body of the lifting cylinder is fixed on the unified load-bearing structure, and the piston rod is connected to the pull arm hook. The lifting cylinder is used to drive the lifting and lowering actions of the pull arm hook.

[0034] S04. Install a guide wheel assembly at the front end of the boom hook. The guide wheel assembly includes a main guide wheel and a secondary guide wheel. The main guide wheel and the secondary guide wheel are mounted on a guide wheel bracket via bearings. The guide wheel bracket is fixed to the front end of the boom hook.

[0035] S05. Establish a vehicle vibration dynamics model, and determine the stiffness and damping parameters of the air suspension system through the vibration transfer function calculation equation set, so that the vibration amplitude of the vehicle during loading and unloading is controlled within the preset vibration range.

[0036] S06. Use a load analysis optimization model to perform stress distribution analysis on a uniform load-bearing structure. The load analysis optimization model determines the optimal stiffener arrangement scheme based on the load type, load magnitude, point of application location, and material properties.

[0037] S07. Vibration data during vehicle operation is collected in real time by vibration monitoring sensors, and the vibration data is input into the vibration control system. The vibration control system adjusts the working pressure of the air suspension system according to the vibration amplitude and frequency characteristics.

[0038] like Figure 2-6 As shown, the integrated welding connection refers to connecting the main beam of the upper arm 10 and the chassis beam 20 into one piece through full penetration welding to form a continuous load-bearing beam structure, eliminating the original connection gaps and relative displacements.

[0039] The air spring unit 31 includes a rubber airbag 311, an upper cover plate 312, and a lower cover plate 313. The rubber airbag is filled with compressed air, and the support force and stiffness characteristics of the air suspension system 30 are adjusted by changing the internal pressure of the rubber airbag.

[0040] The height adjustment valve 33 is used to automatically adjust the working pressure of the air spring unit 31 according to changes in vehicle load, so as to maintain a constant vehicle height and prevent changes in vehicle posture caused by load changes.

[0041] The guide wheel assembly 50 has a main guide wheel diameter of 150mm and a secondary guide wheel diameter of 100mm. Both the main guide wheel and the secondary guide wheel are made of polyurethane material with a Shore A hardness of 85 degrees, which is used to reduce friction and impact between the pull arm hook 60 and the object being pulled.

[0042] Air suspension is a suspension system that uses air as the elastic medium. Compared to traditional coil springs or leaf springs, it offers better comfort, handling, and versatility. The core component of air suspension is the air spring, a sealed airbag made of rubber or synthetic materials, filled with compressed air. Adjusting the air pressure changes the spring's stiffness and support force. It typically has an internal piston or base, and external upper and lower mounting brackets connect it to the vehicle frame and wheels. The air spring also integrates shock absorbers, making it an independent suspension unit. One is installed on each of the front and rear axles.

[0043] When the vehicle height needs to be increased, the air compressor inflates the airbags with high-pressure air, raising the vehicle's height. When the vehicle needs to be lowered, the air is expelled from the airbags through the exhaust valve, causing the airbags to contract and the vehicle to lower. During high-speed driving or aggressive driving, the stiffness of the air springs can be increased to reduce body roll and pitch, improving stability. When the vehicle's load changes, the system automatically adjusts the pressure of each airbag to keep the vehicle level, preventing nose-diving or rear-end tilting.

[0044] The vibration transfer function calculation equation set includes stiffness optimization equations and damping optimization equations. The stiffness optimization equations are used to calculate the optimal stiffness coefficient of the air suspension system based on the total vehicle mass, load distribution coefficient, road excitation dominant frequency, and target vibration amplitude. The inputs include the total vehicle mass, load distribution coefficient, road excitation dominant frequency, target vibration amplitude, and suspension geometric parameters, and the output is the stiffness parameter of the air suspension system. The damping optimization equations are used to calculate the optimal damping coefficient based on the stiffness parameter of the air suspension system, vehicle moment of inertia, vibration attenuation ratio, and response time requirements. The inputs include the stiffness parameter of the air suspension system, vehicle moment of inertia, vibration attenuation ratio, response time requirements, and system natural frequency, and the output is the damping parameter.

[0045] The load analysis optimization model is structured as a multi-layer encoder network based on the Transformer-XL architecture, comprising a load feature extraction layer, a stress transfer relationship learning layer, and a stiffener placement prediction layer. The number of memory segments in the load analysis optimization model is determined based on the complexity of the uniform load-bearing structure, the number of load conditions, and the number of material types. The steps for establishing the training dataset for the load analysis optimization model specifically include collecting finite element simulation data of stress distribution in the uniform load-bearing structure under different load conditions, establishing a database of correspondences between load types, load magnitudes, application point locations, and material properties, constructing a training sample set containing load type labels, load magnitude values, application point location coordinates, material property parameters, and corresponding stress distribution cloud maps. To enhance the diversity and coverage of training data, the load analysis optimization model is made more generalizable to various load conditions. The training steps of the load analysis optimization model include: first, pre-training on simple load condition data using a progressive training strategy; then, incremental training by gradually increasing the intensity of load conditions; dynamically adjusting the learning rate according to the changes in the loss function during training using an adaptive learning rate adjustment mechanism; simultaneously optimizing the two objectives of stress prediction accuracy and the rationality of the optimal stiffener arrangement scheme using a multi-task learning framework; evaluating the performance of the load analysis optimization model and performing hyperparameter tuning through cross-validation; and finally obtaining a trained model that can accurately predict the stress distribution of a uniform load-bearing structure under load conditions and provide the optimal stiffener arrangement scheme.

[0046] The total vehicle mass is derived from the vehicle's factory technical parameters; the load distribution coefficient is calculated based on the position of the hook arm and the center of gravity of the cargo; the road excitation main frequency is obtained by measuring the road surface unevenness; the target vibration amplitude is set according to driving comfort requirements; the suspension geometry parameters include the support arm length and the air spring unit installation angle; and the stiffness parameters of the air suspension system are used to set the working stiffness of the air spring unit.

[0047] The vehicle moment of inertia is calculated based on the vehicle's geometric dimensions and mass distribution; the vibration attenuation ratio is set according to the vibration control performance requirements; the response time requirement is set according to the system's dynamic response performance; the system's natural frequency is calculated based on the stiffness parameters of the air suspension system and the vehicle's total mass; and the damping parameter is used to set the damping coefficient of the shock absorber unit 32.

[0048] The complexity of the unified load-bearing structure is determined based on the geometric complexity and the number of connection nodes of the unified load-bearing structure. The number of load conditions is determined based on the load changes in actual use. The number of material types is determined based on the types of materials used in the unified load-bearing structure. The number of memory segments is used to control the context length of the load analysis optimization model.

[0049] The vibration amplitude is derived from real-time measurement data from vibration monitoring sensors, the frequency characteristics are obtained through spectral analysis of the vibration data, and the working pressure is used to adjust the support characteristics of the air spring unit.

[0050] The memory length adjustment function is used to adjust the number of memory segments in the load analysis optimization model. This function calculates a memory length adjustment value based on the uniform structural complexity, number of load cases, number of material types, and computational resource limitations. When the memory length adjustment value is in the range of 0 to 25, the number of memory segments used to adjust the load analysis optimization model in short-term memory mode is 8 segments. When the memory length adjustment value is in the range of 26 to 50, the number of memory segments used to adjust the load analysis optimization model in medium-term memory mode is 16 segments. When the memory length adjustment value is in the range of 51 to 75, the number of memory segments used to adjust the load analysis optimization model in long-term memory mode is 32 segments. When the memory length adjustment value is in the range of 76 to 100, the number of memory segments used to adjust the load analysis optimization model in ultra-long-term memory mode is 64 segments.

[0051] The specific implementation methods of the above steps are described in detail below.

[0052] The specific implementation of step S01 involves pre-treating the surface of the chassis beam by mechanically grinding away existing welding marks, rust, and oil stains to a depth of 0.5–1.0 mm, ensuring a good metallic luster on the welded surface. Then, the main beam mounted on the tie arm is precisely positioned with the chassis beam, using three-dimensional coordinate measurement technology to ensure that the parallelism error of their axes is controlled within 0.02 mm and the height difference within 0.01 mm. Finally, a full penetration welding process is used for integrated connection, with the welding current set at 180–220 amperes, the welding voltage controlled at 24–28 volts, and the welding speed maintained at 2–3 mm per second. The weld depth reaches 100% of the base material thickness. After welding, ultrasonic non-destructive testing technology is used to verify the weld quality, ensuring that there are no defects such as porosity or slag inclusions inside the weld. The purpose of this step is to eliminate the rigid connection gaps and relative displacements of the original leaf spring suspension system, forming a continuous load-bearing beam structure through integrated welding, thereby improving the overall structural stiffness and load transfer efficiency.

[0053] The specific implementation of step S02 involves designing the installation position at the bottom of the chassis beam based on its structural characteristics, using finite element analysis to determine the optimal installation point, ensuring that the installation point is located in the high-strength area of ​​the chassis beam and that the stress concentration factor at the installation point is less than 1.5. First, mounting holes are machined at the bottom of the chassis beam, with the hole diameter accuracy controlled within ±0.05 mm and the hole wall surface roughness reaching Ra 1.6 micrometers. Then, an air spring unit is installed. The rubber air bladder of the air spring unit adopts a multi-layer nylon cord reinforcement structure, with a working pressure range of 0.4–0.8 MPa and a maximum load capacity of 8000 Newtons. The upper and lower cover plates are made of high-strength steel with a yield strength of not less than 355 MPa, and are connected to the chassis beam using high-strength bolts with a bolt preload torque set to 120–150 Newton-meters. The shock absorber unit is installed synchronously. The shock absorber adopts a twin-tube structure with a built-in adjustable damping valve system. The damping force adjustment range is 1000–4000 N / s per meter, and the response frequency range covers 1–15 Hz. The height adjustment valve is connected to the air spring unit via a pneumatic pipeline. The operating pressure range of the adjustment valve is 0.6–1.0 MPa, the response time is less than 0.5 seconds, and the height adjustment accuracy is controlled within ±2 mm. The purpose of this step is to establish a suspension system with adjustable stiffness and damping characteristics to replace the original fixed-parameter leaf spring suspension, achieving optimized vibration control of the vehicle under different load conditions.

[0054] The specific implementation of step S03 involves determining the optimal installation position of the lifting cylinder 40 at the rear end of the unified load-bearing structure. Mechanical analysis calculations are used to ensure that the cylinder axis is aligned with the lifting trajectory of the hook 60, and the installation angle error is controlled within ±0.5 degrees. First, a cylinder mounting base is machined on the unified load-bearing structure. The mounting base is made of high-strength alloy steel with a tensile strength of not less than 540 MPa and is connected to the load-bearing structure via full welding, with a weld length coefficient of 1.2. Then, the lifting cylinder is installed. The cylinder body is machined using precision internal bore machining technology, with the roundness error controlled within 0.01 mm and a surface hardness reaching HRC45-50. The cylinder piston rod is made of tempered alloy steel with a chrome plating thickness of 20-30 micrometers and a hardness reaching HRC55-60. The piston rod and hook are connected via a ball joint, which allows for ±15 degrees of angle compensation to ensure that the cylinder does not generate lateral loads during operation. The hydraulic cylinder's working pressure is set to 16 MPa, the lifting force output is 50 kN, the lifting speed is controlled between 15 and 25 mm / s, and the lifting stroke is 800 mm. A hydraulic control system is configured, including a hydraulic pump station, a proportional directional valve, and a pressure sensor. The output pressure stability of the hydraulic pump station is controlled within ±0.5%, and the control accuracy of the proportional directional valve reaches 0.1% of full scale. This step provides precise and controllable lifting power to the boom hook, enabling smooth operation during cargo loading and unloading and reducing vibration interference caused by lifting impacts.

[0055] The specific implementation of step S04 involves designing an installation scheme for the guide wheel assembly based on the structural characteristics of the front end of the hook arm. Kinematic analysis is used to determine the optimal arrangement of the main guide wheel and the auxiliary guide wheel, ensuring that the guide wheel assembly can effectively guide the loading trajectory of the pulled object. First, the guide wheel bracket is machined. The bracket is made of high-strength aluminum alloy and precision-machined using a CNC machine tool, achieving a surface roughness of Ra 0.8 micrometers. Its weight is 40% lighter than a steel bracket. The main guide wheel diameter is set at 150 mm, and the auxiliary guide wheel diameter is 100 mm. Both are made of polyurethane material with a Shore A hardness of 85 degrees. This hardness value provides good elastic cushioning performance while ensuring load-bearing capacity. The guide wheel internally employs a sealed bearing structure. The bearings are deep groove ball bearings, with the inner ring fitting to the shaft at k6 precision and the outer ring fitting to the hub at H7 precision. The radial clearance of the bearing is controlled between 0.005 and 0.015 mm. The bearing grease is a high-temperature lithium-based grease with an operating temperature range of -30℃ to 150℃ and a service life of over 500 hours. The guide wheel surface features an anti-slip texture with a depth of 1.5 mm and a spacing of 8 mm, increasing the coefficient of friction between the guide wheel and the object being pulled to over 0.6. The guide wheel assembly is detachable for easy maintenance and replacement. The mounting bolts are 8.8 grade high-strength bolts with a preload torque of 80 Nm. This step aims to reduce frictional resistance and impact load between the hook and the object being pulled, thereby reducing vibration and noise during loading and unloading and improving operational smoothness.

[0056] The specific implementation of step S05 involves establishing a vehicle vibration dynamics mathematical model based on multi-degree-of-freedom vibration theory. This model considers the coupling relationship between the vehicle's vertical motion, pitch motion, and wheel bounce. First, basic vehicle parameters are collected, including the total vehicle mass measured using a weighing device with an accuracy requirement of ±50 kg, and the load distribution coefficient calculated based on the distance between the center of the boom hook and the vehicle's center of gravity with a calculation accuracy requirement of ±0.01. The dominant frequency of the road surface excitation is obtained using a road surface roughness measuring instrument, with a measurement frequency range of 0.1–20 Hz and a sampling frequency not lower than 100 Hz. The target vibration amplitude is set according to the international standard ISO 2631-1 regarding human vibration comfort, with the effective value of the vertical acceleration controlled at 1.0 m / s². 2Within this range, the vibration transfer function calculation equations are then established. The stiffness optimization equations are based on the least squares method, seeking the air suspension stiffness parameters that minimize the vehicle's vibration response by establishing an objective function. The calculation process considers the combined influence of multiple input parameters such as the vehicle's total mass, load distribution coefficient, and road excitation dominant frequency, outputting the optimal stiffness coefficient of the air suspension system. The calculation accuracy of the stiffness coefficient is required to be ±5%. The damping optimization equations employ the root locus analysis method. Based on the determined stiffness parameters and vehicle moment of inertia, the optimal damping coefficient that brings the system to a critical damping state is calculated. The calculation accuracy of the damping coefficient is required to be ±8%. This step provides a theoretical basis for the precise setting of the air suspension system parameters, ensuring that the vehicle achieves optimal vibration control under various operating conditions.

[0057] The specific implementation of step S06 involves constructing a load analysis and optimization model based on deep learning. This model uses a converter ultra-long sequence architecture as its basic network structure. First, the model architecture is designed. The load feature extraction layer employs a multi-head attention mechanism with 8 attention heads, each with a dimension of 64, enabling parallel processing of multi-dimensional feature information such as load type, load magnitude, point of application location, and material properties. The stress transfer relationship learning layer uses a feedforward neural network structure with 4 hidden layers and 512 neurons per layer. The activation function is a modified linear unit function. This layer learns the nonlinear mapping relationship between load and stress distribution. The stiffener placement prediction layer uses a regression output structure. The output dimension is determined based on the number of possible stiffener locations in the unified load-bearing structure. The output value represents the necessity score for setting a stiffener at each location, with a score range of 0 to 1. The number of memory segments in the model is dynamically determined by a memory length adjustment function. This function comprehensively considers four factors: the complexity of the uniform load-bearing structure, the number of load cases, the number of material types, and computational resource limitations. The calculated memory length adjustment value is as follows: 8 segments are set using short-term memory mode when the adjustment value is 0-25; 16 segments are set using medium-term memory mode when the adjustment value is 26-50; 32 segments are set using long-term memory mode when the adjustment value is 51-75; and 64 segments are set using ultra-long-term memory mode when the adjustment value is 76-100. The purpose of this step is to utilize artificial intelligence technology to achieve stress analysis and optimization design of the uniform load-bearing structure under complex load conditions, thereby improving the intelligence level and optimization effect of structural design.

[0058] Step S07 is optional. Its specific implementation involves establishing a real-time vibration monitoring and control system, achieving dynamic adjustment of the air suspension system through closed-loop feedback control. First, vibration monitoring sensors are installed at key locations on the vehicle. These sensors are piezoelectric accelerometers with a measurement frequency range of 0.1–1000 Hz, a sensitivity of 100 mV / g, and a measurement accuracy of ±2%. Installation locations include the bottom of the cab, the front of the cargo box, and above the rear axle. Six sensors are used, forming a three-dimensional vibration monitoring network. The vibration data acquisition system uses a 24-bit analog-to-digital converter with a sampling frequency of 2048 Hz and a data storage depth of 1024 sampling points, enabling continuous vibration signal acquisition and processing. The vibration control system uses a digital signal processor as the core control unit. The processor has a main frequency of 300 MHz and a built-in floating-point unit, enabling real-time spectral analysis of vibration signals and control algorithm calculations. The control algorithm uses an adaptive filtering algorithm, which automatically adjusts control parameters based on current vibration characteristics. The filter order is set to 128, and the convergence speed parameter is 0.01. When the detected vibration amplitude exceeds the preset threshold of 1.2 m / s 2 During operation, the control system adjusts the working pressure of the air spring unit via an electro-proportional pressure regulating valve. The pressure adjustment range is 0.4–0.8 MPa, with an adjustment accuracy of ±0.01 MPa and a response time of less than 0.3 seconds. This step enables real-time monitoring and active control of vehicle vibration, ensuring optimal vibration control throughout actual operation.

[0059] It should be noted that the load analysis optimization model adopts a deep neural network structure based on a transformer-based ultra-long sequence architecture. This architecture consists of three main parts: an encoder stack, a memory mechanism, and an output layer. The encoder stack contains 12 identical encoder layers. Each encoder layer consists of a multi-head self-attention sub-layer and a position feedforward network sub-layer, with residual connections and layer normalization operations between the two sub-layers. The multi-head self-attention mechanism uses 8 parallel attention heads, with the query, key, and value dimensions of each head set to 64. The correlation between elements at different positions in the input sequence is modeled through a scaled dot product attention computation mechanism. The position feedforward network adopts a two-layer fully connected structure. The first layer expands the input dimension from 512 to 2048, and the second layer compresses it back to 512 dimensions, with a modified linear unit activation function used in between. The memory mechanism stores historical information by maintaining a fixed-length memory matrix. The number of rows in the memory matrix equals the number of memory segments, and the number of columns is a 512-dimensional feature vector. During each forward propagation, the current input is concatenated with the memory matrix and input together into the encoder for processing. After processing, the content of the memory matrix is ​​updated. The output layer is divided into two parallel branches: the stress prediction branch uses a multilayer perceptron structure to output the stress distribution prediction results, and the stiffener placement branch uses a sigmoid activation function to output the stiffener placement probability at each position.

[0060] The detailed steps for establishing the training dataset first involved generating a large amount of stress distribution data for a unified load-bearing structure under different load conditions using finite element simulation software. The simulation software employed a nonlinear finite element analysis method, with a mesh generation accuracy set to 2 mm, eight-node hexahedral elements used, and an elastoplastic model employed for the material constitutive relations. The load conditions were designed using an orthogonal experimental design method. The load types included concentrated loads, distributed loads, and impact loads, with load magnitudes ranging from 10 to 100 kN. The application points covered all possible stress points of the unified load-bearing structure. Material properties included four parameters: elastic modulus, Poisson's ratio, yield strength, and density, with each parameter having three levels for combination. Simulation calculations generated raw data pairs containing load parameters and corresponding stress distribution contour maps, totaling 50,000 sets. Next, a standardized correspondence database between load types and parameters was established. Load types were numerically encoded: concentrated loads were coded as 1, distributed loads as 2, and impact loads as 3. Load magnitudes and application point coordinates were normalized, and a standardized mapping table for material property parameters was established. Then, a standardized training sample set was constructed. Each sample included a load type label, load magnitude, coordinates of the point of application, material property parameters, and a corresponding stress distribution contour map. The stress distribution contour map was converted to a 256×256 pixel grayscale image format, with pixel values ​​representing the normalized stress magnitude. To improve the model's generalization ability, data augmentation techniques were used to expand the diversity of the training data. Augmentation methods included random perturbation of load parameters, geometric transformations, and noise addition. The perturbation amplitude was controlled within ±10% of the original value. Geometric transformations included rotation, translation, and scaling operations. Gaussian white noise was used for noise addition, with a signal-to-noise ratio set above 30 dB. A total of 200,000 training samples were obtained, with the training set accounting for 80%, the validation set for 10%, and the test set for 10%.

[0061] The model training process employs a progressive training strategy, divided into three phases. The first phase uses simple load case data for pre-training, including only concentrated load types with load sizes limited to 20–40 kN, for 100 training epochs with a learning rate of 0.001. The second phase adds distributed loads and impact loads, expanding the load size range to the full spectrum, for 200 training epochs with the learning rate adjusted to 0.0005. The third phase uses the complete dataset for fine-tuning, for 300 training epochs with the learning rate further reduced to 0.0001. An adaptive learning rate adjustment mechanism is used during training to monitor the trend of the validation set loss function. If the validation loss does not decrease for 10 consecutive epochs, the learning rate is multiplied by 0.5 for decay, with a minimum learning rate limit of 0.00001. The loss function adopts a multi-task learning framework design, including stress prediction loss and stiffener placement loss. The total loss function is a weighted sum of the two, with weight coefficients set to 0.7 and 0.3, respectively. The stress prediction loss was calculated using the mean square error, and the stiffener placement loss was calculated using binary cross-entropy. The optimization algorithm employed an adaptive moment estimation algorithm, with the momentum parameter set to 0.9 and the second-order moment decay parameter set to 0.999. Model performance was evaluated using a five-fold cross-validation method, with evaluation metrics including the root mean square error, mean absolute error, and coefficient of determination for stress prediction, as well as the accuracy, precision, and recall for stiffener placement. Hyperparameter tuning utilized a grid search method, with tuning parameters including the learning rate, batch size, hidden layer dimension, and number of attention heads. After determining the optimal parameter combination, final training was performed to obtain a completed trained model capable of accurately predicting the stress distribution of a uniformly loaded structure under load conditions and providing the optimal stiffener placement scheme.

[0062] It should be noted that the first key technical concept of this invention is the integrated welding design of the frame-arm hook. This technology connects the upper main beam of the arm hook to the chassis main beam through full penetration welding, forming a unified, continuous load-bearing structure. Compared to traditional mechanical connections, the integrated welding design eliminates connection gaps and relative displacement, significantly improving the overall stiffness and load transfer efficiency of the structure. Traditional mechanical connections suffer from bolt preload loss and frictional wear on the connection surfaces, leading to a decrease in connection stiffness over time. In contrast, welded connections maintain long-term stable mechanical properties. Simultaneously, the integrated design reduces stress concentration at the connection interface, lowers the risk of fatigue failure, and extends the service life of the structure.

[0063] The second key technological approach is an air suspension parameter optimization method based on the vibration transfer function. This technique establishes a vehicle vibration dynamics model and calculates the optimal parameters of the air suspension system through stiffness and damping optimization equations. Traditional suspension parameter design often relies on empirical formulas or experimental debugging methods, lacking theoretical guidance and making it difficult to achieve vibration optimization under all operating conditions. This technique establishes a mathematical model based on vibration theory, comprehensively considering the influence of various factors such as vehicle mass, load distribution, and road surface excitation, and obtains the optimal combination of stiffness and damping parameters through numerical calculations. This parameter optimization method based on theoretical analysis improves the scientific nature and accuracy of suspension system design, enabling vehicles to achieve ideal vibration control effects under different load conditions.

[0064] The third key technological approach is a load analysis and optimization model based on the converter's ultra-long sequence architecture. This technology employs deep learning methods to achieve intelligent stress analysis and optimized stiffener placement for a unified load-bearing structure. Traditional structural analysis primarily relies on the finite element method, requiring engineers to model and analyze based on experience, resulting in long design cycles and optimization effectiveness limited by individual experience levels. This technology, by constructing a deep neural network model, can automatically learn the complex nonlinear relationship between load and stress distribution from a large amount of simulation data, achieving rapid and accurate stress prediction. The converter architecture's memory mechanism enables the model to handle complex long sequence information, improving its understanding of complex load conditions. This intelligent analysis method not only improves design efficiency but also uncovers optimization opportunities that are easily overlooked in manual analysis.

[0065] The fourth key technological approach is a real-time vibration monitoring and adaptive control system. This technology collects vehicle operating data in real time using vibration sensors and dynamically adjusts air suspension parameters using an adaptive filtering algorithm. Traditional passive suspension systems have fixed parameters and cannot be adjusted according to actual operating conditions, making it difficult to maintain optimal performance under changing conditions. This technology establishes a closed-loop control system that can automatically adjust suspension stiffness and damping based on real-time vibration characteristics, achieving active vibration control. The adaptive filtering algorithm has self-learning and self-adjusting capabilities, enabling it to adapt to different road conditions and load states, ensuring that the vehicle always operates in the optimal vibration control state.

[0066] The synergistic effect of these four key technological approaches forms a complete vehicle vibration control technology system. Integrated structural design provides a stable foundation for the entire system, ensuring the continuity of load transfer paths and the consistency of stiffness. Parameter optimization based on theoretical models provides a scientific design basis for the air suspension system, guaranteeing the system's fundamental performance. Intelligent load analysis models optimize structural design, improving load-bearing capacity and reliability. Real-time monitoring and control systems play a dynamic adjustment role in actual operation, compensating for the shortcomings of static design. These four technological approaches support and promote each other, jointly achieving a comprehensive improvement in vehicle vibration control. Compared to traditional technologies where each link is relatively independent and lacks systematic optimization, this invention achieves a breakthrough across the entire technological chain from structural design, parameter optimization, intelligent analysis to real-time control through technological synergy, significantly improving vehicle vibration control performance and operational stability.

[0067] It should be noted that this invention also solves the following technical problems: First, this invention solves the technical problem of mismatched suspension performance caused by the inability of traditional suspension systems to adjust in real time according to load changes. The stiffness and damping characteristics of traditional leaf spring suspension systems are fixed and cannot be adjusted accordingly to real-time changes in vehicle load. This results in excessively stiff suspension affecting ride comfort under no-load conditions and insufficient suspension affecting vehicle stability under heavy-load conditions. Especially during rapid load changes during loading and unloading, the fixed-parameter suspension system cannot provide optimal vibration reduction. This invention, through the pressure adjustment function of the air suspension system and the automatic control of the height adjustment valve, achieves real-time adjustment of suspension stiffness and damping characteristics according to load changes, ensuring the suspension system always operates in its optimal state, providing good vibration reduction performance and vehicle attitude control whether unloaded or fully loaded. Second, this invention solves the technical problem of excessive structural weight or insufficient strength due to a lack of scientific basis in load-bearing structure design. Traditional load-bearing structure design mainly relies on engineering experience and simplified theoretical calculations, making it difficult to accurately predict stress distribution and fatigue life under complex load conditions. Often, overly conservative designs lead to increased structural weight, or insufficient design leads to structural strength problems, and the arrangement of reinforcing ribs lacks optimization basis. This invention uses a deep learning-based load analysis and optimization model to accurately predict stress distribution under various load conditions, identify stress concentration areas, and determine the optimal position and size of stiffeners through intelligent optimization algorithms. This achieves the goal of minimizing structural weight while ensuring structural strength, thus improving the scientific and economical design of load-bearing structures.

[0068] Specifically, the principle of this invention is as follows: This invention solves the fundamental structural problem of vibration control through an integrated load-bearing structure design. It integrates the originally relatively independent chassis load-bearing system and the boom hook loading / unloading system into a unified structural system, eliminating the vibration amplification effect caused by multiple connection interfaces in traditional designs. This allows vibrations generated during loading and unloading to be effectively controlled through a unified vibration damping system. The core advantage of the air suspension system lies in the adjustability of its stiffness and damping characteristics. By changing the internal pressure of the air spring unit, the suspension stiffness can be continuously adjusted. By adjusting the damping coefficient of the shock absorber unit, the vibration attenuation rate can be controlled. This dual adjustment mechanism enables the system to precisely control different vibration characteristics during loading and unloading. Vibration dynamics modeling and parameter optimization provide a scientific basis for the control strategy. By establishing the mathematical relationship between the vehicle's total mass, load distribution, vibration frequency, and suspension parameters, the rationality and effectiveness of the control parameters are ensured, avoiding the uncertainty brought about by empirical adjustments. The load analysis optimization model, based on deep learning technology, predicts the stress distribution of the load-bearing structure under various working conditions. By optimizing the arrangement of stiffeners, the fatigue resistance of the structure is improved, fundamentally enhancing the vibration bearing capacity of the structure. The vibration monitoring feedback control mechanism forms a closed-loop control system by monitoring the vibration state in real time and dynamically adjusting the suspension parameters. This ensures the stability and continuity of the vibration control effect, enabling the system to automatically adapt to various complex loading and unloading conditions and always maintain optimal vibration reduction performance.

[0069] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0070] In this embodiment, the specific implementation of steps S01-S04 is the same as described above, and will not be repeated in detail here.

[0071] The specific implementation of step S05 is based on establishing a vehicle vibration dynamics model according to multi-degree-of-freedom vibration theory, and determining the stiffness and damping parameters of the air suspension system through a set of equations calculated using the vibration transfer function. The specific expression of the stiffness optimization equation is as follows:

[0072]

[0073] In the formula, K s M represents the optimal stiffness coefficient of the air suspension system, expressed in N / m. v The total mass of the vehicle is expressed in kg; ω r The dominant frequency of the road surface excitation is expressed in rad / s; A t The target vibration amplitude, in m / s 2 ;β l k is the load distribution coefficient, dimensionless; conv This is the dimension conversion factor, with units of N·s. 4 ·m-3 The numerical range is 1.0 × 10⁻⁶. 6 ~5.0×10 6 α1, α2, and α3 are stiffness optimization coefficients, where α1 ranges from 0.8 to 1.2, α2 ranges from 0.5 to 0.9, and α3 ranges from 0.1 to 0.3; t is the time variable, in seconds; ε k This represents the stiffness calculation error term, ranging from 0.02 to 0.08. The parameter acquisition method is as follows: M v The measurement was obtained using weighing equipment, with a required accuracy of ±50kg; ω r The roughness was obtained using a road surface roughness measuring instrument, with a measurement frequency range of 0.1–20 Hz and a sampling frequency of not less than 100 Hz; A t According to the international standard ISO 2631-1 regarding human vibration comfort, the effective value of vertical acceleration is controlled at 1.0 m / s². 2 Within; β l The calculation is based on the distance between the center of the boom hook and the vehicle's center of gravity. The specific calculation formula is as follows: Where L h The distance from the center of the boom hook to the front axle of the vehicle is measured directly using a measuring tool while the vehicle is stationary, with a measurement accuracy of ±5mm. v The wheelbase is the vehicle's length, obtained by consulting vehicle technical parameters or through on-site measurement, with a measurement accuracy of ±10mm. All units are in meters (m). The specific expression of the damping optimization equation is as follows:

[0074]

[0075] In the formula, C s The optimal damping coefficient is expressed in N·s / m; J v The vehicle's moment of inertia is expressed in kg·m. 2 ξ d The vibration damping ratio is dimensionless and ranges from 0.6 to 0.8; T r For response time requirements, the unit is seconds (s), and the range is 0.5 to 2.0 seconds; ω n λ is the system's natural frequency, expressed in rad / s. damp This is the damping dimension adjustment coefficient, with units of kg·m. 2 ·s -1 The numerical range is 1.0 × 10⁻⁶. 3 ~1.0×10 4 γ1 and γ2 are damping optimization coefficients, where γ1 ranges from 1.2 to 1.8 and γ2 ranges from 0.3 to 0.7; ε c This represents the damping calculation error term, ranging from 0.05 to 0.12. The parameter acquisition method is as follows: J vThe calculation formula is derived from the vehicle's geometric dimensions and mass distribution. Where r g h is the turning radius of the vehicle, determined through vehicle turning tests. g The height of the center of gravity is measured by tilting tests or suspension methods, and the unit is meters (m); ω n The calculation formula is derived from the stiffness parameters of the air suspension system and the total vehicle mass. The pressure regulation equation for the height regulating valve is expressed as follows:

[0076] P valve =P ref +κ1ΔH+k2ΔM l +ε valve ;

[0077] In the formula, P valve The output pressure of the height regulating valve is expressed in MPa; P ref The reference pressure is in MPa, ranging from 0.5 to 0.7; ΔH is the vehicle body height deviation in meters; ΔM l κ represents the load change, in kg; k1 and κ2 are adjustment coefficients, where k1 is in MPa / m and ranges from 2.0 to 5.0, and κ2 is in MPa·kg. -1 The range is 1.0 × 10 -4 ~5.0×10 -4 ;ε valve This is for adjusting the error term, with a range of -0.01 to 0.01.

[0078] The specific implementation of step S06 involves constructing a load analysis optimization model based on a converter-based ultra-long sequence architecture. This model dynamically determines the number of memory segments using a memory length adjustment function. The memory length adjustment function is specifically represented as follows:

[0079] F mem =δ1C comp +δ2N load +δ3N mat -δ4R comp +ε mem ;

[0080] In the formula, F mem This is the memory length adjustment value, dimensionless, ranging from 0 to 100; C comp To standardize the assessment value of structural complexity, dimensionless; N load N represents the number of load conditions, dimensionless. mat R represents the quantity of material types, dimensionless. compThe resource constraint coefficient is dimensionless; δ1, δ2, δ3, and δ4 are weighting coefficients, where δ1 ranges from 0.3 to 0.5, δ2 from 0.2 to 0.4, δ3 from 0.1 to 0.3, and δ4 from 0.1 to 0.2; ε mem To memorize the adjustment error term, the range is -2 to 2. The parameter acquisition method is: C comp Based on the assessment of the geometric complexity of the unified load-bearing structure and the number of connection nodes, the calculation formula is as follows: Where N node The number of connection nodes, A, was obtained through topological analysis of a 3D model of a unified load-bearing structure. This includes the total number of welded points, bolted connections, and hinged points. surf The surface area is measured using 3D scanning equipment or calculated using CAD software, with a measurement accuracy of ±0.1%, and the unit is m². 2 V comp For complex structures, the volume partitioning method is used for calculation. The complex structure is decomposed into basic geometric shapes, and the total volume is calculated by summing them. The calculation accuracy is ±0.5%, and the unit is m. 3 N load This was derived from statistics based on load variations during actual use; N mat The number of memory segments is determined based on the type of material used in the unified load-bearing structure. The piecewise function representing the number of memory segments is as follows:

[0081]

[0082] In the formula, N seg The number of memory segments is a dimensionless parameter used to optimize the load analysis model.

[0083] The specific implementation of step S07 involves establishing a real-time vibration monitoring and control system, and adjusting the working pressure of the air suspension system using an adaptive filtering algorithm. The specific expression of the vibration control adjustment equation is as follows:

[0084]

[0085] In the formula, P adj The adjusted working pressure of the air spring is expressed in MPa; P base The basic working pressure is expressed in MPa, ranging from 0.4 to 0.8; A vib The amplitude of the vibration is expressed in m / s. 2 ; The time derivative of the vibration amplitude, in m / s. 3 ;f dom The dominant frequency characteristic is expressed in Hz; φ vibε is the vibration phase angle, in rad; μ1, μ2, μ3 are control coefficients, where μ1 ranges from 0.05 to 0.15, μ2 ranges from 0.002 to 0.008, and μ3 ranges from 0.01 to 0.05; ctrl To control the error term, the range is -0.02 to 0.02. The parameter acquisition method is as follows: A vib The accelerometer is measured in real time using a piezoelectric accelerometer with a frequency range of 0.1–1000 Hz, a sensitivity of 100 mV / g, and a measurement accuracy of ±2%. The vibration amplitude data was obtained by numerical differentiation calculation using the five-point central difference formula. Perform calculations, where A vib (t+2Δt), A vib (t+Δt), A vib (t-Δt), A vib (t-2Δt) represents the vibration amplitude at two sampling points before and after the current time, and at one sampling point, respectively, all in m / s. 2 Δt is the sampling time interval, set to seconds; f dom Based on the fast Fourier transform spectrum analysis of the vibration data, the sampling frequency was set to 2048Hz; φ vib The instantaneous phase of the vibration signal is obtained by calculating it using the Hilbert transform.

[0086] It should be noted that the stiffness optimization equation is based on the theory of multi-degree-of-freedom vibration of vehicles. This equation considers the fundamental influence of the vehicle's total mass on the suspension stiffness requirement. The term reflects the coupling characteristics of mass and frequency response, and the coupling term between load distribution coefficient and target vibration amplitude. This reflects the nonlinear influence of load distribution on vibration control effectiveness, with the cosine function term cos(ω) r t) introduces the periodicity of time-domain dynamic characteristics. Dimensional transformation coefficient k conv This ensures the dimensionality of both sides of the equation, allowing different physical quantities to participate in the calculation appropriately. Compared to traditional static stiffness design methods, this equation can integrate multiple dynamic factors for optimized calculation, enabling the air suspension system to achieve optimal stiffness matching under different operating conditions. This significantly improves the accuracy and adaptability of vehicle vibration control and avoids the problem of poor local performance caused by fixed stiffness parameters in traditional methods.

[0087] The damping optimization equation is constructed based on the root locus analysis theory of control systems. This equation is derived through K... s J v ξ d The term establishes the compatibility relationship between stiffness, moment of inertia, and attenuation ratio, and the square term of response time. The denominator reflects a balance between fast response and stability, with the natural frequency term ω. n The inherent vibration characteristics of the system were considered. The damping dimension adjustment coefficient λ... damp This ensures the dimensional consistency of the equations, giving the calculation results the correct physical meaning. Compared to traditional empirical or experimental methods for determining damping parameters, this equation is based on system dynamics theory for precise calculation, enabling optimal matching of stiffness and damping. This ensures that the suspension system has both good vibration suppression capability and sufficient stability, solving the problem of lack of theoretical guidance for damping design in traditional methods.

[0088] The height adjustment valve pressure regulation equation is designed based on proportional control theory. This equation achieves a direct response to changes in vehicle attitude through the vehicle height deviation term ΔH and the load change term ΔM. l This demonstrates the ability to compensate for changes in load conditions. Compared to traditional fixed-pressure control methods, this equation can dynamically adjust the air spring pressure according to the actual operating conditions of the vehicle, maintaining a constant vehicle height and effectively preventing changes in vehicle posture caused by load variations, thereby improving the vehicle's stability and comfort.

[0089] The memory length adjustment function is based on complexity theory, and this function is implemented using C. comp The term reflects the characteristic that complex structures require longer memory, N load and N mat The item reflects the increased need for memory due to diversity, R comp This approach takes into account the computational resource constraints of practical applications. Compared to traditional fixed-parameter neural network models, this function can dynamically adjust the model's memory capacity according to the complexity of the specific problem, optimizing computational efficiency while ensuring analytical accuracy, and avoiding overfitting or underfitting problems that occur when fixed-parameter models handle problems of varying complexity.

[0090] The vibration control and regulation equation is designed based on adaptive control theory. This equation incorporates the vibration amplitude and time derivative terms. Predictive control of vibration variation trends was achieved, with the dominant frequency term f... dom Differentiated control strategies are adopted for vibrations in different frequency bands, with the sinusoidal phase term sin(φ) vib The influence of the phase characteristics of the vibration signal on the control effect is considered. Compared with traditional passive control or simple feedback control methods, this equation realizes multi-dimensional adaptive control, which can dynamically adjust the control parameters according to the real-time changes of vibration characteristics, significantly improving the response speed and control accuracy of vibration control, and solving the problems of poor adaptability and control lag in traditional control methods.

[0091] It should be noted that the variables involved in this invention are explained in detail in Table 1 below.

[0092] Table 1. Variable Explanation Table

[0093]

[0094] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for reducing vehicle vibration using an integrated design of the frame, arm, and hook, characterized in that, include: The upper structure of the tie rod is welded to the chassis beam in an integrated manner, so that the main beam of the tie rod upper structure and the chassis beam form a unified load-bearing structure. An air suspension system is installed at the bottom of the chassis beam. The air suspension system includes an air spring unit, a shock absorber unit, and a height adjustment valve. The air spring unit is connected to the unified load-bearing structure via a support arm. A lifting cylinder is installed at the rear end of the unified load-bearing structure. A guide wheel assembly is installed at the front end of the pull arm hook. A vehicle vibration dynamics model is established, and the stiffness and damping parameters of the air suspension system are determined using a set of vibration transfer function calculation equations, ensuring that the vibration amplitude of the vehicle during loading and unloading is controlled within a preset vibration range. A load analysis optimization model is used to analyze the stress distribution of the unified load-bearing structure. This model determines the optimal stiffener arrangement based on the load type, load magnitude, point of application, and material properties, achieving effective control of vehicle vibration during loading and unloading.

2. The vehicle vibration reduction method utilizing an integrated frame-arm hook design according to claim 1, characterized in that, The integrated welding connection specifically involves connecting the main beam mounted on the tie arm to the chassis beam through full penetration welding to form a continuous load-bearing beam structure, eliminating the original connection gaps and relative displacements.

3. The vehicle vibration reduction method utilizing an integrated frame-arm hook design according to claim 2, characterized in that, The air spring unit specifically includes a rubber airbag, an upper cover plate, and a lower cover plate. The rubber airbag is filled with compressed air, and the support force and stiffness characteristics of the air suspension system are adjusted by changing the internal pressure of the rubber airbag.

4. A vehicle vibration reduction method utilizing an integrated frame-arm hook design according to claim 3, characterized in that, The height adjustment valve is specifically used to automatically adjust the working pressure of the air spring unit according to changes in vehicle load, maintain a constant vehicle height, and prevent changes in vehicle posture caused by load variations.

5. A method for reducing vehicle vibration using an integrated frame-arm hook design according to claim 4, characterized in that, The lifting cylinder is specifically designed with a cylinder body fixed on a unified load-bearing structure and a piston rod connected to the pull arm hook. The lifting cylinder is used to drive the lifting and lowering actions of the pull arm hook.

6. A method for reducing vehicle vibration using an integrated frame-hook design as described in claim 5, characterized in that, The guide wheel assembly specifically includes a main guide wheel and a secondary guide wheel. The main guide wheel and the secondary guide wheel are mounted on a guide wheel bracket via bearings, and the guide wheel bracket is fixed to the front end of the pull arm hook.

7. A method for reducing vehicle vibration using an integrated frame-hook design as described in claim 6, characterized in that, The main guide wheel in the guide wheel assembly has a diameter of 150mm, and the auxiliary guide wheel has a diameter of 100mm. The main guide wheel and the auxiliary guide wheel are made of polyurethane material with a hardness of Shore A type 85 degrees, which is used to reduce friction and impact between the hook arm and the object being pulled.

8. A method for reducing vehicle vibration using an integrated frame-arm hook design according to claim 7, characterized in that, The vibration transfer function calculation equation set specifically includes stiffness optimization equations and damping optimization equations. The stiffness optimization equations are used to calculate the optimal stiffness coefficient of the air suspension system based on the total vehicle mass, load distribution coefficient, road excitation main frequency, and target vibration amplitude. The damping optimization equations are used to calculate the optimal damping coefficient based on the stiffness parameters of the air suspension system, vehicle moment of inertia, vibration attenuation ratio, and response time requirements.

9. A method for reducing vehicle vibration using an integrated frame-hook design as described in claim 8, characterized in that, The inputs to the stiffness optimization equation include the total vehicle mass, load distribution coefficient, road excitation dominant frequency, target vibration amplitude, and suspension geometric parameters, and the output is the stiffness parameters of the air suspension system; the inputs to the damping optimization equation include the stiffness parameters of the air suspension system, vehicle moment of inertia, vibration damping ratio, response time requirement, and system natural frequency, and the output is the damping parameters.

10. A method for reducing vehicle vibration using an integrated frame-hook design as described in claim 9, characterized in that, It also includes the following steps: collecting vibration data in real time during vehicle operation through vibration monitoring sensors, inputting the vibration data into the vibration control system, and adjusting the working pressure of the air suspension system according to the vibration amplitude and frequency characteristics.

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