A soil particle mesoscopic parameter calibration method and device and unmanned vehicle simulation method

Abstract

The application discloses a kind of soil granule mesoscopic parameter calibration methods, it is characterized in that, comprising the following steps: step 1, predetermined load is applied to soil sample, and the actual soil settlement amplitude under the predetermined load is obtained;Step 2, obtain soil sample image and extract soil granule geometric parameter;Step 3, input soil granule mesoscopic parameter;Step 4, according to the actual soil settlement amplitude in step 1, the soil granule geometric parameter in step 2, the soil granule mesoscopic parameter input in step 3, the soil granule mesoscopic parameter is calculated by iterative optimization algorithm;Step 5, output calculated soil granule mesoscopic parameter as calibrated soil mesoscopic parameter, the accuracy of soil granule mesoscopic parameter calibration is improved, and calibration precision is improved by high-resolution image and iterative optimization algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned vehicle mobility simulation evaluation, and particularly relates to a soil particle mesoscopic parameter calibration method and device and an unmanned vehicle simulation method. BACKGROUND

[0002] In the field of unmanned vehicles, especially when they are applied to soft ground (such as deserts, marshes, snow-covered areas, etc.), it is crucial to ensure that the vehicle has good passability and stability. Traditionally, the mobility evaluation of vehicles in these complex terrains mainly relies on macroscopic mechanical models, which are usually based on the overall properties of the soil, such as hardness, compression modulus, and friction coefficient. However, this method has obvious shortcomings in predicting the specific response of the soil under stress, especially the settlement deformation of fine-grained soil, resulting in inaccurate evaluation results and difficulty in meeting actual needs. SUMMARY

[0003] The purpose of the present application is to provide a soil particle mesoscopic parameter calibration method and device and an unmanned vehicle simulation method. By integrating a wheel loading instrument, a soil sample platform, an environmental sensor, a machine vision system, a settlement measurement device, and a numerical calculation control unit, a highly automated experimental platform is constructed. This platform can apply different loads to the soil under controlled conditions and monitor the settlement deformation of soil particles under stress in real time. More importantly, the present application uses an innovative inversion algorithm to automatically adjust and optimize the mesoscopic parameters of soil particles by comparing the differences between simulation results and experimental measurement results until they reach the best match. This not only accurately determines the key parameters that affect the mechanical properties of the soil, but also provides reliable data support for subsequent soil-wheel interaction simulation.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] A soil particle mesoscopic parameter calibration method, comprising the following steps:

[0006] Step 1: Apply a predetermined load to the soil sample and obtain the actual soil settlement amplitude under the predetermined load;

[0007] Step 2: Obtain the soil sample image and extract the soil particle geometric parameters;

[0008] Step 3: Input the soil particle mesoscopic parameters;

[0009] Step 4: Calculate the soil particle mesoscopic parameters through an iterative optimization algorithm based on the actual soil settlement amplitude in step 1, the soil particle geometric parameters in step 2, and the input soil particle mesoscopic parameters in step 3;

[0010] Step 5, output the calculated soil particle mesoscopic parameters as the calibrated soil mesoscopic parameters.

[0011] As a further scheme of the present application: in step 1, a predetermined load is applied to the soil sample by a wheel loading instrument, the settlement amplitude of the soil in the loading process is detected by a laser displacement sensor, and the ambient temperature and ambient humidity information are obtained.

[0012] As a further scheme of the present application: in step 2, the soil image is obtained by a high-resolution camera, and the geometric parameters of the soil particles are obtained by image processing.

[0013] As a further scheme of the present application: the iterative optimization algorithm in step 4 adopts a back propagation algorithm based on a neural network; the iterative optimization algorithm in step 4 includes the following steps:

[0014] Step 4.1, calculate the theoretical soil settlement amplitude by the soil particle mesoscopic parameters input by step 3, the soil particle geometric parameters extracted by step 2, and the applied predetermined load;

[0015] Step 4.2, compare the actual soil settlement amplitude obtained in step 1 with the theoretical soil settlement amplitude obtained in step 4.1, if the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is outside the error threshold, perform iterative calculation based on the back propagation algorithm of the neural network; if the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is within the error threshold, end the iteration, execute step 5, and record and output the soil mesoscopic particle parameters.

[0016] As a further scheme of the present application: in step 4.2, adjusting the soil particle mesoscopic parameters is performed according to a set step size.

[0017] As a further scheme of the present application: the input of the iterative algorithm is , the output of the iterative algorithm is , and the loss function L of the iterative algorithm is as follows:

[0018]

[0019] The loss function L is to calculate the error between the output value of n samples and the predicted value of the simulation calculation, when the loss function L converges to less than the error threshold L0, the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is within the error threshold, and the soil particle mesoscopic parameters at this time are output as the calibrated parameters;

[0020] wherein;

[0021] k1 represents the normal contact stiffness parameter of the particle itself, k2 represents the tangential contact stiffness parameter of the particle itself, k3 represents the particle friction coefficient, k4 represents the normal cohesion parameter of the particle itself, k5 represents the tangential cohesion parameter of the particle itself, k6 represents the average diameter of the particle, k7 represents the load applied by the wheel type load instrument, k8 represents the ambient temperature, and k9 represents the ambient humidity;

[0022] h1 represents the actual measured soil settlement height value of the first sensor position, h2 represents the actual measured soil settlement height value of the second sensor position, h3 represents the actual measured soil settlement height value of the third sensor position, and h4 represents the actual measured soil settlement height value of the fourth sensor position.

[0023] A soil particle mesoscopic parameter calibration device, comprising:

[0024] A soil sample table for placing a soil sample;

[0025] A wheel type loading instrument arranged above the soil sample table for applying a load to the soil sample on the soil sample table;

[0026] A settlement measuring device for measuring the settlement amplitude of the soil sample;

[0027] A machine vision system for acquiring the geometric parameters of the soil sample;

[0028] A numerical calculation unit in signal communication with the machine vision system, the wheel type loading instrument, and the settlement measuring device, for receiving signals and processing.

[0029] As a further scheme of the present application, the soil sample cover comprises a fixed support and a sample arranged on the fixed support, the wheel type loading instrument comprises a loading wheel, a load control system, a pressure sensor, and a moving mechanism, the moving mechanism is arranged on the upper end of the fixed support, the load control system is located below the moving mechanism and connected between the loading wheel and the moving mechanism, and the pressure sensor is arranged on the loading wheel.

[0030] As a further scheme of the present application, the machine vision system comprises a high-resolution camera, a light source system, and image processing software, and the settlement measuring device comprises a plurality of laser displacement sensors fixedly connected on the soil sample table through a sensor mounting plate.

[0031] Further comprising an environmental sensor, the environmental sensor comprising a temperature sensor and a humidity sensor.

[0032] An unmanned vehicle simulation method, the soil micro parameter output in step 5 is input into the unmanned vehicle simulation system, and the unmanned vehicle simulation is carried out through the unmanned vehicle simulation system and the soil micro parameter.

[0033] Compared with the prior art, the beneficial effects of the present application are:

[0034] 1、The present application improves the accuracy of soil particle micro parameter calibration and improves the calibration precision through high-resolution images and iterative optimization algorithms;

[0035] 2、The present application has high automation level, and the entire calibration process from data acquisition to parameter optimization can be automatically completed, reducing manual intervention, improving work efficiency, reducing experimental cost and time, and being suitable for large-scale soil sample analysis;

[0036] 3、The present application can not only be applied to unmanned vehicle design and testing, but also can be extended to the fields of agricultural machinery and military equipment. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The present embodiment is a calibration device composition block diagram;

[0038] Figure 2 The present embodiment is a calibration method flow chart;

[0039] Figure 3 The present embodiment is a calibration device structure schematic diagram;

[0040] In the figure:

[0041] 100-wheeled loading instrument, 101-loading wheel, 102-load control system, 103-moving mechanism, 104-pressure sensor;

[0042] 110-machine vision system, 111-high-resolution camera, 112-light source system, 113-image processing software;

[0043] 120-numerical calculation control unit;

[0044] 130-soil sample table, 131-sample box, 132-fixing support;

[0045] 140-settlement measuring device, 141-laser displacement sensor, 142-sensor mounting plate;

[0046] 150-environmental sensor, 151-humidity sensor, 152-temperature sensor. DETAILED DESCRIPTION

[0047] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0048] Please refer to Figure 1 In the embodiments of the present application, a soil particle mesoscopic parameter calibration device comprises:

[0049] Wheel loading instrument 100: Wheel loading instrument 100 is a device used to simulate the force generated when the unmanned vehicle wheel contacts the ground. It is composed of loading wheel 101, load control system 102, pressure sensor 104 and moving mechanism 103. Loading wheel 101 is a part simulating the vehicle tire, which is a wheel that can bear and transfer the preset load. Load control system 102 is responsible for adjusting and controlling the force exerted by loading wheel 101 on the soil. This system usually includes electric or hydraulic drive devices, which can set different load levels according to experimental requirements, thereby simulating different vehicle weights or load conditions. Pressure sensor 104 can record the force generated when loading wheel 101 contacts the soil in real time, providing basic data for subsequent data analysis. Moving mechanism 103 enables loading wheel 101 to move up and down on the soil surface to simulate the situation when the vehicle is driving. Moving mechanism 103 drives the loading wheel to move by motor.

[0050] Soil sample table 130: Soil sample table 130 is an important part of the soil particle mesoscopic parameter calibration method and device for unmanned vehicle mobility simulation evaluation. Soil sample table 130 is composed of sample box 131 and fixing support 132. Sample box 131 is a square box made of metal material, which is designed to provide a stable and controllable bearing platform for soil samples. Above sample box 131 is fixing support 132, which is used to install wheel loading instrument 100 and sedimentation measuring device.

[0051] Machine vision system 110: The machine vision system 110 is mainly used in this system to capture and analyze the meso-features of soil particles to obtain detailed information about soil particles. The machine vision system 110 mainly consists of a high-resolution camera 111, a light source system 112 and an image processing software 113. The high-resolution camera 111 is used to take high-definition images of soil particles to ensure that the details of the soil particles can be clearly captured. The light source system 112 provides appropriate lighting conditions to ensure the quality of the images. The light source system 112 is a ring-shaped LED lighting device to reduce the effects of shadows and reflections. The image processing software 113 includes image enhancement, segmentation, feature extraction and other functional modules for analyzing the captured image data and extracting useful information such as the size of soil particles.

[0052] Settlement measurement device: The settlement measurement device is used to accurately measure the settlement amplitude of soil during loading and is an important tool for verifying the calibration results of soil particle meso-parameters. The device consists of four high-precision laser displacement detection sensors 141 and a sensor mounting plate 142. The four high-precision laser displacement detection sensors 141 are arranged in a rectangular array on the sensor mounting plate 142 for non-contact measurement of soil displacement changes. The settlement measurement device is installed on the fixed bracket 132 above the soil sample table 130.

[0053] Environmental sensor 150: Including temperature sensor 152 and humidity sensor 151, used for real-time monitoring and recording the temperature and humidity of the test environment.

[0054] Numerical calculation control unit 120 is the brain of the whole system, responsible for the motion and loading control of the wheel loading instrument 100, processing data collected from various sensors, and calculating the meso-parameters of soil particles through iterative optimization algorithm. The calculated parameters are used for unmanned vehicle mobility simulation evaluation to improve the accuracy and efficiency of the evaluation. The numerical calculation control unit 120 is connected to the wheel loading instrument 100 load control system 102 through the CAN port and sends motion control instructions. The numerical calculation control unit 120 is connected to the machine vision system 110 through the network port to obtain the size and information of soil particles. The numerical calculation control unit 120 is connected to the environmental sensor 150 through the serial port to obtain the temperature and humidity of the experimental environment. The numerical calculation control unit 120 is connected to the settlement measurement device through the USB port to obtain the soil settlement displacement change data.

[0055] A soil particle meso-parameter calibration method, comprising the following steps:

[0056] Step 1, applying a predetermined load to the soil sample and obtaining the actual soil settlement amplitude under the predetermined load, applying a predetermined load to the soil sample by a wheel loading instrument, detecting the settlement amplitude of the soil in the loading process by a laser displacement sensor, and simultaneously obtaining the environmental temperature and humidity information;

[0057] Step 2, obtaining soil sample images and extracting soil particle geometric parameters, obtaining soil images by a high-resolution camera, and obtaining soil particle geometric parameters by image processing;

[0058] Step 3, inputting soil particle mesoscopic parameters;

[0059] Step 4, according to the actual soil settlement amplitude in step 1, the soil particle geometric parameters in step 2, and the input soil particle mesoscopic parameters in step 3, the soil particle mesoscopic parameters are calculated by an iterative optimization algorithm;

[0060] The iterative optimization algorithm in step 4 adopts a back propagation algorithm based on a neural network; the iterative optimization algorithm in step 4 includes the following steps:

[0061] Step 4.1, calculating the theoretical soil settlement amplitude by the soil particle mesoscopic parameters input in step 3, the soil particle geometric parameters extracted in step 2, and the predetermined load applied;

[0062] Step 4.2, comparing the actual soil settlement amplitude obtained in step 1 with the theoretical soil settlement amplitude obtained in step 4.1, if the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is outside the error threshold, the back propagation algorithm based on the neural network is executed for iterative calculation; if the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is within the error threshold, the iteration is ended, step 5 is executed, and the soil mesoscopic particle parameters are recorded and output.

[0063] As a further scheme of the application: the adjustment of the soil particle mesoscopic parameters in step 4.2 is performed according to a set step size.

[0064] As a further scheme of the application: the iterative algorithm input , the output of the iterative algorithm , and the loss function L of the iterative algorithm is as follows:

[0065]

[0066] The loss function L is to calculate the error between the output value of n samples and the predicted value of the simulation calculation, when the loss function L converges to less than the error threshold L0, the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is within the error threshold, and the soil particle mesoscopic parameters at this time are output as the calibration parameters;

[0067] wherein;

[0068] k1 represents the normal contact stiffness parameter of the particle itself, k2 represents the tangential contact stiffness parameter of the particle itself, k3 represents the particle friction coefficient, k4 represents the normal cohesion parameter of the particle itself, k5 represents the tangential cohesion parameter of the particle itself, k6 represents the average particle diameter, k7 represents the load applied by the wheel loading instrument, k8 represents the ambient temperature, and k9 represents the ambient humidity;

[0069] h1 represents the actual measured soil settlement height value at the position of the first sensor, h2 represents the actual measured soil settlement height value at the position of the second sensor, h3 represents the actual measured soil settlement height value at the position of the third sensor, and h4 represents the actual measured soil settlement height value at the position of the fourth sensor;

[0070] Step 5: Output the calculated soil particle mesoscopic parameters as the calibrated soil mesoscopic parameters.

[0071] An unmanned vehicle simulation method, the soil mesoscopic parameters output in step 5 are input into the unmanned vehicle simulation system, and the unmanned vehicle simulation is carried out through the unmanned vehicle simulation system and the soil mesoscopic parameters. Specific embodiments

[0073] A soil particle mesoscopic parameter calibration device

[0074] Wheel loading instrument

[0075] Loading wheel: a hard wheel with a diameter of 300mm and a width of 150mm, simulating the tire of an unmanned vehicle.

[0076] Load control system: equipped with a hydraulic drive device, which can set and maintain a load range of 5kN to 20kN to simulate vehicles of different weight grades.

[0077] Pressure sensor: installed below the loading wheel, capable of monitoring the pressure distribution and displacement changes of the contact surface between the loading wheel and the soil in real time.

[0078] Moving mechanism: driven by a servo motor, moving along the guide rail to control the up-and-down movement of the loading wheel.

[0079] Soil sample platform

[0080] Size: square with a length and width of 1m, and a depth of 0.5m, made of stainless steel.

[0081] Function: provides a stable testing platform for soil samples, ensuring that there is no deviation during the loading process.

[0082] Machine vision system

[0083] High-resolution camera: Equipped with a 10 million-pixel industrial-grade CCD camera to capture high-definition images of soil particles.

[0084] Light source system: Uses ring-shaped LED lights to provide uniform lighting, reducing shadows and ensuring image quality.

[0085] Image processing software: With functions such as image enhancement, segmentation, and feature extraction, it can identify the size of soil particles.

[0086] Settlement measurement device

[0087] High-precision laser displacement detection sensor: Installed above the sample table in a rectangular array with a spacing of 20 cm, used for non-contact measurement of soil settlement displacement.

[0088] Data acquisition module: Converts sensor signals into digital data and transmits them to the numerical control unit through USB or RS232 interface.

[0089] Environmental sensors

[0090] Including temperature and humidity sensors to monitor and record experimental environmental conditions.

[0091] Numerical control unit

[0092] Hardware configuration: High-performance workstation with powerful data processing capabilities.

[0093] Software system: Integrates load control, data acquisition, image processing, and simulation calculation functions.

[0094] A method for calibrating the mesoscopic parameters of soil particles

[0095] Step 1: Apply a predetermined load to the soil sample, set the load amplitude of the wheel loading instrument to 3kN to simulate the load conditions of a single wheel of a medium-weight vehicle, and obtain the actual soil settlement amplitude under the predetermined load.

[0096] Step 2: Obtain soil sample images and extract soil particle geometric parameters;

[0097] Step 3: Input soil particle mesoscopic parameters; set the initial parameters of soil particles, the normal contact stiffness parameter k1 is 1e7 N / m, the tangential contact stiffness parameter k2 is 1e6 N / m, the particle friction coefficient k3 is 0.5, the normal adhesion force parameter k4 is 100 N / m², and the tangential adhesion force parameter k5 is 50 N / m².

[0098] Step 4, according to the actual soil settlement amplitude in step 1, the soil particle geometric parameters in step 2, and the input soil particle mesoscopic parameters in step 3, the soil particle mesoscopic parameters are calculated through an iterative optimization algorithm;

[0099] Step 4.1, the load is set to 3KN, the average diameter of the soil particles is 0.3mm, the experimentally measured settlement amplitude is 5mm, and the error threshold is set to 0.1mm. In the initial iteration, the soil settlement amplitude is calculated using simulation software. The predicted settlement amplitude of the model is compared with the experimentally measured settlement amplitude, and if the error exceeds the set threshold, the mesoscopic parameters are adjusted and re-run.

[0100] Step 4.2, first iteration: the calculated settlement amplitude is 5.5mm. The absolute error is 0.5mm, which exceeds the set error threshold. Reduce the normal contact stiffness k1 to 8e6 N / m.

[0101] Second iteration: the calculated settlement amplitude is 4.8mm. The absolute error is 0.2mm, which still exceeds the threshold. Increase the tangential contact stiffness k2 to 1.2e6 N / m.

[0102] Third iteration: the calculated settlement amplitude is 5.1mm. The absolute error is 0.1mm, which meets the error threshold. Stop iteration and record the final mesoscopic parameters as the calibration result.

[0103] Step 5, output the calculated soil particle mesoscopic parameters as the calibrated soil mesoscopic parameters.

[0104] Through the operation of the above specific embodiments, high-precision calibration of soil particle mesoscopic parameters is achieved, providing reliable data support for the mobility simulation of unmanned vehicles.

[0105] An unmanned vehicle simulation method

[0106] To evaluate the passability of unmanned vehicles in different soil conditions, a discrete element method (DEM) based on soil meso-particle parameters is used for simulation calculation. First, the geometric parameters and physical properties of soil particles, such as particle size distribution, are obtained through laboratory tests and image analysis. Then, these data are used to calibrate the meso-parameters in the DEM model, including normal contact stiffness, tangential contact stiffness, particle friction coefficient, normal cohesive force, and tangential cohesive force. Next, a dynamic model of the unmanned vehicle is established, taking into account factors such as vehicle mass, center of mass position, tire characteristics, and others. By coupling the vehicle model with the soil DEM model, the interaction between the tires and the soil during vehicle travel is simulated, and the soil deformation, settlement, and shear stress responses are calculated. Finally, the simulation results are analyzed to evaluate the vehicle's traction performance, sink depth, and driving stability, thereby optimizing the vehicle's design and control strategies and improving its passability in complex terrain.

[0107] During the design process of a new unmanned vehicle, the mechanical performance parameters of different soil types are calibrated using this embodiment. The calibrated parameters are input into the simulation platform to simulate the vehicle's driving conditions on different soft road surfaces. Key performance indicators such as vehicle passability, stability, and energy consumption are evaluated. Based on the simulation results, the vehicle's design parameters, such as tire type and suspension system, are adjusted to effectively improve the vehicle's mobility. Through multiple tests and verifications, the effectiveness and reliability of this embodiment are proven.

[0108] It is apparent to those skilled in the art that the present application is not limited to the details of the foregoing exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, and the scope of the present application should be defined by the appended claims rather than the above description, and it is intended to include all changes falling within the meaning and range of equivalents of the claims. Any reference signs in the claims should not be construed as limiting the claims to which they belong.

[0109] In addition, it should be understood that, although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that those skilled in the art can understand.

Claims

1. A method for calibrating microscopic parameters of soil particles, characterized in that, Includes the following steps: Step 1: Apply a predetermined load to the soil sample and obtain the actual soil settlement amplitude under the predetermined load; Step 2: Acquire soil sample images and extract soil particle geometry parameters; Step 3: Input soil particle microstructure parameters; Step 4: Based on the actual soil settlement amplitude in Step 1, the soil particle geometric parameters in Step 2, and the soil particle micro-parameters input in Step 3, calculate the soil particle micro-parameters using an iterative optimization algorithm. The iterative optimization algorithm in step 4 employs a backpropagation algorithm based on neural networks; the iterative optimization algorithm in step 4 includes the following steps: Step 4.1: Calculate the theoretical soil settlement amplitude using the soil particle micro-parameters input in Step 3, the soil particle geometric parameters extracted in Step 2, and the applied predetermined load. Step 4.2: Compare the actual soil settlement amplitude obtained in Step 1 with the theoretical soil settlement amplitude obtained in Step 4.

1. If the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is outside the error threshold, then perform iterative calculation and adjust the soil micro-parameters based on the backpropagation algorithm of the neural network. If the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is within the error threshold, then the iteration ends, and Step 5 is executed to record and output the soil micro-parameters. Step 5: Output the calculated soil particle micro-parameters as the calibrated soil micro-parameters.

2. The method and apparatus for calibrating soil particle micro-parameters according to claim 1, characterized in that, In step 1, a predetermined load is applied to the soil sample using a wheel-type loader, and the settlement amplitude of the soil during the loading process is detected by a laser displacement sensor to obtain information on ambient temperature and humidity.

3. The method and apparatus for calibrating soil particle micro-parameters according to claim 1, characterized in that, In step 2, soil images are acquired using a high-resolution camera, and the geometric parameters of soil particles are obtained through image processing.

4. The method and apparatus for calibrating soil particle micro-parameters according to claim 1, characterized in that, In step 4.2, the soil particle micro-parameters are adjusted according to the set step size.

5. The method and apparatus for calibrating soil particle micro-parameters according to claim 4, characterized in that, Iterative algorithm input The output of the iterative algorithm The loss function L of the iterative algorithm is as follows: The loss function L is the error between the output value of n samples and the predicted value calculated by simulation. When the loss function L converges to less than the error threshold L0, the difference between the theoretical soil settlement amplitude and the actual soil settlement amplitude is within the error threshold. The soil particle micro-parameters at this time are output as calibration parameters. in; k1 represents the normal contact stiffness parameter of the particle itself, k2 represents the tangential contact stiffness parameter of the particle itself, k3 represents the particle friction coefficient, k4 represents the normal cohesion parameter of the particle itself, k5 represents the tangential cohesion parameter of the particle itself, k6 represents the average diameter of the particle, k7 represents the load applied by the wheel load tester, k8 represents the ambient temperature, and k9 represents the ambient humidity. h1 represents the actual measured soil settlement height at sensor location 1, h2 represents the actual measured soil settlement height at sensor location 2, h3 represents the actual measured soil settlement height at sensor location 3, and h4 represents the actual measured soil settlement height at sensor location 4.

6. An apparatus for calibrating soil particle micro-parameters according to any one of claims 1-5, characterized in that, include: Soil sample stage (130) for placing soil samples; A wheel-type loader (100) is disposed above the soil sample stage (130) and is used to apply load to the soil sample on the soil sample stage (130); A settlement measuring device (140) is used to measure the settlement amplitude of a soil sample; A machine vision system (110) is used to acquire the geometric parameters of a soil sample; The numerical calculation unit (120) is connected to the machine vision system (110), the wheel loader (100), and the settlement measurement device (140) for receiving and processing signals.

7. The soil particle microstructure parameter calibration device according to claim 6, characterized in that, The soil sample stage (130) includes a fixed support (132) and a sample (131) disposed on the fixed support. The wheel-type loading device (100) includes a loading wheel (101), a load control system (102), a pressure sensor (104), and a moving mechanism (103). The moving mechanism (103) is disposed on the upper end of the fixed support (132). The load control system (102) is located below the moving mechanism (103) and connected between the loading wheel (101) and the moving mechanism (103). The pressure sensor (104) is disposed on the loading wheel (101).

8. A soil particle microstructure parameter calibration device according to claim 6, characterized in that, The machine vision system (110) includes a high-resolution camera (111), a light source system (112), and image processing software (113). The sedimentation measurement device includes multiple laser displacement sensors (141), which are fixedly connected to the soil sample stage (130) via a sensor mounting plate (142). It also includes an environmental sensor (150), which includes a temperature sensor (152) and a humidity sensor (151).

9. An unmanned vehicle simulation method using a soil particle micro-parameter calibration method according to any one of claims 1-5, characterized in that, Input the soil microstructure parameters output in step 5 into the unmanned vehicle simulation system, and perform unmanned vehicle simulation using the unmanned vehicle simulation system and the soil microstructure parameters.

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