Method for testing the depth of soil compaction

Abstract

The application discloses a kind of test methods of soil compaction depth, comprising the following steps: soil particle parameter calibration;According to parameter calibration result, establish simulation soil tank model;The influence of different operation factors on soil compaction depth is simulated and analyzed by using EDEM and Recurdyn software coupling, single factor test is carried out, and the operation factor significantly affecting compaction depth is screened out for further simulation test;Simulation is carried out with the screened operation factor, the corresponding soil compaction depth of the operation factor under different parameter ranges is obtained, nonlinear power function regression analysis is carried out on the simulation test results using SPSS 27 software, and the mathematical model between the screened factor and soil compaction depth is established;Using the application can accurately establish the mathematical model of the operation factor and compaction depth, facilitate the real-time acquisition of soil compaction depth during operation, and provide a basis for improving the accuracy during later depth detection.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery operation parameter detection technology, and in particular to a method for testing soil compaction depth. Background Technology

[0002] In traditional techniques, the measurement of tillage depth in China mainly relies on manual sampling, which results in poor measurement accuracy, low efficiency, and the inability to obtain accurate tillage depth data in real time, leading to delayed adjustments and compromised post-tillage quality. Existing technologies analyze changes in the posture of agricultural implements and establish a tillage depth detection model based on the angle of the tractor's pull rod. While this solves the problem of low detection efficiency in traditional techniques, it does not consider the influence of the tractor's tires on the ground compaction depth, thus affecting the accuracy of tillage depth detection.

[0003] However, current research on soil compaction mainly focuses on its impact on soil stress, soil properties, and crops, with very little research on soil compaction depth. Therefore, there is an urgent need for a testing method that can reliably detect soil compaction depth. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the problems existing in the current soil compaction depth test, this invention is proposed. This invention uses the soil compression ratio as the evaluation index to determine the various parameters of the soil EEPA contact model, establishes a soil trough model based on the parameter calibration results, and uses coupled simulation to screen out the operational factors that have a significant impact on compaction depth through single-factor experiments. Finally, a mathematical model of the operational factors and compaction depth is established, which provides a basis for improving the accuracy of subsequent tillage depth detection.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for testing soil compaction depth, comprising the following steps:

[0007] The S1 soil particle parameters were calibrated to determine the coefficient of restitution, static friction coefficient, rolling friction coefficient, surface energy, plastic deformation ratio, bond branching index, and tangential stiffness factor.

[0008] S2 establishes a simulation soil trench model based on the parameter calibration results;

[0009] S3 used EDEM and Recurdyn software to perform coupled simulation analysis on the effects of different operational factors on soil compaction depth, conducted single-factor experiments, and screened out operational factors that significantly affected compaction depth for further simulation experiments;

[0010] S4 simulates the selected operational factors to obtain the soil compaction depth corresponding to the operational factors under different parameter ranges. SPSS27 software is used to perform nonlinear power function regression analysis on the simulation test results to establish a mathematical model between the selected factors and the soil compaction depth.

[0011] As a preferred embodiment of the soil compaction depth testing method in this invention, the specific calibration steps are as follows: S101 The compression ratio is calculated based on the height of the consolidated sample after the load is completely unloaded to evaluate the compressibility of the soil.

[0012] S102 is based on uniaxial compression tests of soil to calibrate soil particle parameters. The coefficient of restitution X1, static friction coefficient X2, rolling friction coefficient X3, surface energy X4, plastic deformation ratio X5, bond branching index X6, and tangential stiffness factor X7 are used as test factors. Soil compressibility performance index α is used as the evaluation index. First, the Plackett-Burman test is used to screen out the model parameters that are significantly related to soil compressibility performance. Then, the Box-Behnken Design test is used to obtain the regression model between the compressibility performance index and the significant factors, and the factor parameters corresponding to the actual compressibility performance index are solved.

[0013] S103 determines the relevant parameters of the straw discrete element model.

[0014] As a preferred embodiment of the soil compaction depth testing method in this invention, in step S101, the formula for calculating the compression ratio is...

[0015]

[0016] Where h6 is the soil height of the consolidated specimen under a load of 100 kPa, in mm; h7 is the soil height of the consolidated specimen under a load of 200 kPa after compression, in mm; h8 is the initial height of the consolidated specimen under a load of 100 kPa, in mm; and h9 is the initial height of the consolidated specimen under a load of 200 kPa.

[0017] As a preferred embodiment of the soil compaction depth testing method in this invention, the following steps are taken: In the uniaxial closed compression simulation test conducted in an EDEM, a cylinder with a height of 250 mm and a bottom diameter of 50 mm is used to simulate an acrylic cylinder. The particle-cylinder simulation parameters are set, and the Hertze-Mindlin model is selected for the particle-contact component model. A particle factory is set up inside the cylinder to generate 300 g of soil particles. The initial height of the model is measured using a loading speed of 30 mm / s for the compression simulation test. When the axial pressure reaches 196.3 N, the model moves in the opposite direction at the same speed until the pressure is completely unloaded. The height h6 of the model at this point is measured. The simulation test is repeated. When the axial pressure reaches 392.7 N, the model returns at the same speed. After complete unloading, the consolidation height h7 of the sample is measured, and the compression ratio α is calculated.

[0018] As a preferred embodiment of the soil compaction depth testing method in this invention, in step S102, the surface soil is modeled using particles with a radius of 5 mm. Based on the contribution analysis of seven experimental factors to soil compressibility and bearing capacity in the Plackett-Burman compression test, the contribution of 5 mm soil particles to compressibility, ranked from largest to smallest, is X5, X2, X7, X6, X1, X4, and X3. A Box-Behnken Design experiment is designed for analysis and to find the optimal solution. Multiple regression analysis is performed on the experimental results using Design Expert, and insignificant terms in the variance analysis are removed. The regression model for the 5 mm particle compression ratio Y1 is obtained as follows:

[0019] Y1=a1+a2*X2+a3*X5+a4*X2*X5+a5*X5*X7-a6X7 2 ;

[0020] Where a1 to a6 are constants.

[0021] As a preferred embodiment of the soil compaction depth testing method in this invention, the bottom soil is modeled using particles with a radius of 10 mm. The contributions of the particles affecting the compressibility of 10 mm soil particles, ranked from largest to smallest, are X3, X5, X2, X7, X1, X4, and X6. Based on the parameter sensitivity analysis results, a Box-Behnken Design experiment is designed for analysis and to find the optimal solution. Multiple regression analysis is performed on the experimental results using Design Expert, and insignificant terms in the analysis of variance are removed. The regression model for the compressibility ratio Y2 of 10 mm particles is obtained as follows:

[0022] Y2=b1+b2*X2+b3*X3+b4*X5-b5*X2*X5-b6*X2 2 -b7*X3 2 -b8*X5 2 ;

[0023] Where b1 to b8 are constants.

[0024] As a preferred embodiment of the soil compaction depth testing method in this invention, step S3 specifically includes the following steps:

[0025] S301 imports three cones into the EDEM, gives a vertical downward speed of 0.1 m / s, and measures the soil compaction at a depth of 0-20 cm every 5 cm. The peak pressure on the cones is extracted and the average value is calculated. Different soil compaction is obtained by adjusting the degree of compaction of the soil trough model, and soil trough models with different compaction are established.

[0026] S302 uses CATIA to create a three-dimensional structural model of the tractor's herringbone tire, and then imports the created three-dimensional tire structural model into Recurdyn software;

[0027] The S303 uses EDEM-Recurdyn software for coupled simulation. The step function is used to allow the tire to fall freely onto the soil trough model within 0 to 0.5 seconds. From 0.5 to 0.7 seconds, the tire is accelerated uniformly to the working speed. After 0.7 seconds, it moves forward at a constant speed to carry out compaction simulation test. The compaction depth of the tire on the soil trough model under different working factors is obtained. The different working factors include working speed, soil firmness and straw coverage.

[0028] S304 screened out operational factors that significantly affect compaction depth.

[0029] As a preferred embodiment of the soil compaction depth testing method in this invention, the selected operational factor that significantly affects compaction depth is soil firmness.

[0030] As a preferred embodiment of the soil compaction depth testing method in this invention, the mathematical model between soil compaction depth and soil firmness is as follows:

[0031] L6 = a * Y3 -b ;

[0032] In the formula, L6 is the soil compaction depth, with the mathematical unit being mm; Y3 is the soil firmness, with the mathematical unit being MPa; and a and b are positive constants.

[0033] The beneficial effects of this invention are as follows: By using the soil compression ratio as the evaluation index for determining various parameters of the soil EEPA contact model in the simulation experiment, three experimental factors that significantly affect the compression ratio are selected from seven experimental factors. Insignificant terms in the analysis of variance are eliminated, and multiple regression analysis is performed on the experimental results using Design Expert software to obtain regression models for the compression ratio of 5mm and 10mm particles, respectively. This yields parameters corresponding to the simulation experiment that are consistent with the field test results. Finally, operational factors that significantly affect compaction depth are selected, and a mathematical model of these operational factors and compaction depth is established, facilitating the acquisition of compaction depth and providing a foundation for improving the accuracy of subsequent tillage depth detection. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0035] Figure 1 This is a schematic diagram of soil compression in this invention.

[0036] Figure 2 This is a scene diagram of a soil uniaxial compression test conducted according to the present invention. In the diagram, 1 is a universal testing machine, 2 is an acrylic cylinder, and 3 is a clamp.

[0037] Figure 3 These are four commonly used soil contact models in EDEM.

[0038] Figure 4 This is a simulation diagram of soil uniaxial compression in this invention.

[0039] Figure 5 This is a measurement diagram of the straw-rubber static friction coefficient measured using an inclined plane test in this invention.

[0040] Figure 6 This is a test diagram for measuring the collision recovery coefficient of straw-rubber.

[0041] Figure 7 This is a schematic diagram for measuring the length of straw.

[0042] Figure 8 This is the discrete element model of straw established in this invention.

[0043] Figure 9 This is a diagram showing the working posture of the tractor with its lower lever.

[0044] Figure 10 This is a 3D model of a tire created using CATIA in this invention.

[0045] Figure 11 This is a diagram showing the measurement of the solidity of the soil trough model in this invention.

[0046] Figure 12 This is a simulation diagram of the soil compaction process in this invention.

[0047] Figure 13 This refers to the soil-straw discrete element soil trough model established in this invention.

[0048] Figure 14 This is a diagram of tire compaction marks during the simulation test in this invention.

[0049] Figure 15 This is a cross-sectional view of the compaction marks during the simulation test in this invention.

[0050] Figure 16 Figure for tire compaction field test

[0051] Figure 17 This is a simulation result diagram of soil compaction.

[0052] Figure 18 This is a diagram showing the results of a field experiment on soil compaction. Detailed Implementation

[0053] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0056] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0057] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0058] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0059] In this invention, EDEM is a multi-purpose discrete element method modeling software, Recurdyn is a multibody system dynamics simulation software, EEPA (Edinburgh Elasto-Plastic Adhesion Model) is the Edinburgh Elasto-Plastic Adhesion Model, Hertze-Mindlin (no slip) is the Hertze-Mindlin (no slip) model, Design Expert is a response surface methodology software, and SPSS (Statistical Package for the Social Sciences) is a statistical software package for the social sciences. The software used in this application is prior art.

[0060] Example 1

[0061] Reference Figures 1-4 , Figure 8 and Figures 10-15 This is the first embodiment of the present invention, which provides a method for testing soil compaction depth, including the following steps:

[0062] S1: Calibration of soil particle parameters:

[0063] S101 uses the height of the consolidated specimen after complete unloading to calculate the compression ratio to evaluate the compressibility of soil. The formula for calculating the compression ratio is as follows:

[0064]

[0065] Where h6 is the soil height of the consolidated specimen with a load of 100 kPa, in mm; h7 is the soil height of the consolidated specimen after compression with a load of 200 kPa, in mm; h8 is the initial height of the consolidated specimen with a load of 100 kPa, in mm; and h9 is the initial height of the consolidated specimen with a load of 200 kPa.

[0066] S102 calibrates soil particle parameters by first conducting a uniaxial compression test to calculate the compression ratio. Using the coefficient of restitution (X1), static friction coefficient (X2), rolling friction coefficient (X3), surface energy (X4), plastic deformation ratio (X5), bond branching index (X6), and tangential stiffness factor (X7) as experimental factors, a uniaxial closed-circuit compression simulation test is performed in an EDEM (Electronic Design Model). A cylinder with a height of 250 mm and a bottom diameter of 50 mm is used to simulate an acrylic cylinder. Particle-cylinder simulation parameters are set, and the Hertze-Mindlin model is selected for the particle-contact component model. A particle factory is set up inside the cylinder to generate 300g of soil. For the particles, the initial height of the model was measured using a loading speed of 30 mm / s for compression simulation tests. When the axial pressure reached 196.3 N, the model was moved in the opposite direction at the same speed until the pressure was completely unloaded. The height h6 of the model at this time was measured. The simulation test was repeated. When the axial pressure reached 392.7 N, the model was moved back at the same speed. After complete unloading, the consolidation height h7 of the sample was measured, and the compression ratio α was calculated. Using the soil compression performance index α as the evaluation index, the Plackett-Burman test was first used to screen out the model parameters that were significantly related to the soil compression performance. Then, the Box-Behnken Design test was used to obtain the regression model of the compression performance index and significant factors, and the factor parameters corresponding to the actual compression performance index were solved.

[0067] S103 determines the relevant parameters of the straw discrete element model;

[0068] S2 establishes a simulation soil trench model based on the parameter calibration results;

[0069] S3 used coupled EDEM and Recurdyn software for simulation analysis of soil compaction, and screened out operational factors that significantly affected compaction depth for further simulation experiments. Specifically,

[0070] S301 imports three cones into the EDEM, gives a vertical downward speed of 0.1 m / s, and measures the soil compaction at a depth of 0-20 cm every 5 cm. The peak pressure on the cones is extracted and the average value is calculated. Different soil compaction is obtained by adjusting the degree of compaction of the soil trough model, and soil trough models with different compaction are established.

[0071] S302 uses CATIA to create a three-dimensional structural model of the tractor's herringbone tire, and then imports the created three-dimensional tire structural model into Recurdyn software;

[0072] The S303 was simulated using EDEM-Recurdyn software. The step function allowed the tire to fall freely onto the soil trough model within 0–0.5 seconds, then accelerated uniformly to the operating speed within 0.5–0.7 seconds, and finally moved forward at a constant speed after 0.7 seconds to conduct a compaction simulation test. Figure 12 (a) shows the interface display in the EDEM during simulation. Figure 12 (b) is the interface display in Recurdyn during simulation, which obtains the compaction depth of the tire on the soil trough model under different operating factors, including operating speed, soil firmness and straw coverage.

[0073] S304 screened out the operational factors that significantly affect compaction depth, and the selected operational factor that significantly affects compaction depth is soil firmness.

[0074] S4 uses the selected operational factors for simulation to obtain the corresponding soil compaction depth under different parameters. SPSS 27 software is then used to perform nonlinear power function regression analysis on the simulation results to establish a mathematical model between the selected factors and the soil compaction depth.

[0075] L6 = a * Y3 -b ;

[0076] In the formula, L6 is the soil compaction depth, with the mathematical unit being mm; Y3 is the soil firmness, with the mathematical unit being MPa; and a and b are positive constants.

[0077] In step S102, the specific steps of the uniaxial compression test of soil are as follows:

[0078] Apply a suitable amount of lubricating oil to the inner wall of an acrylic cylinder (cut along the axis) with an inner diameter of 50mm, an outer diameter of 70mm, and a height of 150mm to reduce the friction between soil particles and the inner wall during the test. Fix the cylinder with a clamp and place a 300g soil sample inside the cylinder. Measure the initial height h8 of the soil sample. Set the universal testing machine to push the pressure plate downwards at a constant speed of 8mm / s until the axial pressure on the pressure plate reaches 196.3N (load 100kPa). Then move the pressure plate upwards at the same speed until the pressure is completely unloaded. Measure the soil height h6 at this point. Repeat the above test, setting the axial pressure on the pressure plate to 392.7N (load 200kPa) and moving the pressure plate upwards at the same speed. Measure the consolidation height h7 and the initial height h9 of the sample, and calculate the compression ratio α. Repeat the test three times and take the average value to obtain the compression ratio of the surface soil and the compression ratio of the subsurface soil.

[0079] The specific steps of the soil uniaxial compression simulation test are as follows:

[0080] To ensure sufficient particle production in the pellet mill, a 250mm high, 50mm bottom diameter cylinder was used to simulate an acrylic cylinder. The Hertze-Mindlin (no slip) model was selected for the particle-contact component model. A pellet mill was set up inside the cylinder to produce 300g of soil particles, and the initial height of the model was measured. Simulated compression tests revealed that applying loading rates of 8mm / s and 30mm / s to the same sample resulted in the same compression ratio. To improve simulation efficiency, a loading rate of 30mm / s was used for the compression simulation test. Figure 4 The compression simulation test process was described. During the test, the pressure plate moved downwards at 30 mm / s. When the axial pressure reached 196.3 N (load 100 kPa), it moved in the opposite direction at the same rate until the pressure was completely unloaded. The height h6 of the model was measured at this point. The simulation test was repeated. When the axial pressure was 392.7 N (load 200 kPa), the engine was turned back at the same speed. After complete unloading, the consolidation height h7 of the sample was measured, and the compression ratio α was calculated. Due to the large amount of soil particle modeling, to balance computational efficiency and simulation quality, two particle sizes were selected for modeling: 5 mm radius particles for the surface soil and 10 mm radius particles for the subsurface soil. Based on the contribution analysis of seven experimental factors to soil compressibility and bearing capacity in the Plackett-Burman compression test, the contribution of 5 mm soil particles to compressibility, ranked from largest to smallest, was X5, X2, X7, X6, X1, X4, X3. The contribution of 10 mm soil particles to compressibility, ranked from largest to smallest, was X3, X5, X2, X7, X1, X4, X6. A Box-Behnken Design test was designed for analysis and to find the optimal solution. The expert conducted a multiple regression analysis on the experimental results, removed insignificant terms from the analysis of variance, and obtained the regression model for the compression ratio Y1 of 5mm particles as follows:

[0081] Y1=a1+a2*X2+a3*X5+a4*X2*X5+a5*X5*X7-a6X7 2 a1 to a6 are constants.

[0082] The regression model for obtaining the compression ratio Y2 of 10mm particles is as follows:

[0083] Y2=b1+b2*X2+b3*X3+b4*X5-b5*X2*X5-b6*X2 2 -b7*X3 2 -b8*X5 2 b1 to b8 are constants.

[0084] This invention uses soil compression ratio as the evaluation index for various parameters in simulation experiments. From seven experimental factors, three factors significantly affecting the compression ratio are selected. Insignificant terms in the analysis of variance are eliminated. Design Expert software is used to perform multiple regression analysis on the experimental results, obtaining regression models for the compression ratios of 5mm and 10mm particles, respectively. This yields soil EEPA model parameters for the simulation experiment that closely match the field test results. Finally, operational factors significantly affecting compaction depth are selected, and a mathematical model of these operational factors and compaction depth is established, facilitating the acquisition of compaction depth and providing a foundation for improving the accuracy of subsequent tillage depth detection.

[0085] Example 2

[0086] Reference Figures 5-8 This is the first embodiment of the present invention, which provides a method for testing soil compaction depth, which can further determine the relevant parameters of the straw discrete element model in conjunction with actual experiments.

[0087] Specifically, three straw collection points were randomly selected in the experimental field, and 10 stalks of straw were collected from each point. The length of the straw was measured using a steel ruler, and the diameter was measured using a vernier caliper. The average value was calculated, and the average straw length was found to be 85.4 mm, the average diameter was 4.96 mm, and the straw density was measured to be 153 kg / m³. 3 The Poisson's ratio is set to 0.4 and the shear modulus to 1 MPa.

[0088] In EDEM, a rigid element consisting of five spherical particles with a diameter of 5 mm, a center-to-center distance of 3 mm, and a length of 17 mm is created. These five rigid elements are then joined together using metaparticles to form a straw model with a total length of 85 mm. Adjacent rigid elements are connected via bonding bonds in the BondingV2 model, with a normal bonding stiffness of 1.2 × 10⁻⁶. 7 N / m 3 Tangential bond stiffness 1×10 7 N / m 3 The critical normal stress is 4.3 × 10⁻⁶. 7 N / m 2 The critical tangential stress is 1.26 × 10⁻⁶. 7 N / m 2 ;

[0089] The EEPA contact model was selected to reconstruct the elastoplastic and viscous contact characteristics inside the straw pile and between the straw and the soil when straw is compressed by a tire. The EEPA contact model parameters inside the straw were set as follows: constant pull-out force of 0 and surface energy of 5.194 J / m². 2The contact plastic deformation ratio was 0.5, the loading branching index was 1.5, the bond branching index was 1.662, and the tangential stiffness factor was 0.6. The parameters of the straw-soil EEPA contact model were set as follows: constant pull-out force 0, surface energy 10 J / m². 2 The contact plastic deformation ratio is 0.4, the loading branching index is 1.5, the bond branching index is 1.75, and the tangential stiffness factor is 0.667.

[0090] In this application, the selected tire is an 18.4-38 type tractor herringbone tire with an R2 tread pattern, a standard rim size of W16L, a rim diameter of 970mm, a rated air pressure of 230kPa, an inflation outer diameter of 1807mm, a section width of 467mm, and a measured tire density of 1208kg / m³. 3 Rubber is an incompressible material, with a Poisson's ratio of 0.5 and a shear modulus of 2900 MPa. The final contact parameters between soil, straw, and rubber are shown in Table 1.

[0091] Table 1. Contact parameters for soil, straw, and rubber.

[0092]

[0093] The specific steps for determining the coefficient of recovery between straw and tires are as follows:

[0094] After the rice was harvested in the experimental field, the straw was naturally dried for 10 days. The static friction coefficient between the straw and rubber was measured using an inclined plane test. Figure 5 (a) is the static friction coefficient measurement scenario. During the test, a straw is placed longitudinally on a rubber plate, and the rubber plate is slowly raised. The raising is stopped the instant the straw begins to slide on the rubber plate. The inclination angle of the rubber plate is measured at this moment. The test is repeated 10 times and the average value is taken to obtain the straw-rubber static friction coefficient of 0.82.

[0095] The method for measuring the rolling friction coefficient is the same as that for the static friction coefficient. Figure 5 (b) For the rolling friction coefficient measurement scenario, a straw is placed horizontally on a rubber plate, and the rubber plate is slowly raised. The raising is stopped the instant the straw begins to roll on the rubber plate. The inclination angle of the rubber plate is measured at this moment. The test is repeated 10 times and the average value is taken to obtain the rolling friction coefficient of straw-rubber as 0.16.

[0096] The collision restitution coefficient is the ratio of the relative velocity of two objects after a collision to their relative velocity before the collision. It depends only on the materials of the colliding objects, and its calculation formula is: H1 is the initial height of the straw when it falls, in mm; H2 is the maximum height of the straw after it bounces back after impact, in mm.

[0097] Figure 6For the straw-rubber collision recovery coefficient measurement test scenario, the straw was dropped freely from a height of 300mm, and bounced after colliding with the rubber plate. The maximum height after bounce was recorded using a high-speed camera. The test was repeated 10 times, and the average value was taken to calculate the straw-rubber recovery coefficient as 0.45.

[0098] Example 3

[0099] This is the third embodiment of the present invention, which is based on embodiment 1. This embodiment further derives the formula for calculating the compression ratio.

[0100] In soil mechanics, the standard compression coefficient α is usually used. 1-2 To evaluate the compressibility of soil, the higher the compressibility coefficient, the higher the compressibility. 1-2 Defined as the decrease in void ratio caused by a unit increase in vertical pressure when the load on the soil increases from p1 (100 kPa) to p2 (200 kPa) under uniaxial compression (fully confined):

[0101]

[0102] In the formula, e1 is the porosity at a load of 100 kPa, and e2 is the porosity at a load of 200 kPa.

[0103] Void ratio is the ratio of the volume of pores in soil to the volume of soil particles. Figure 1 This diagram illustrates the volume change during soil compression. P3 represents the initial load on the soil before compression, and P4 represents the load on the soil after compression. Soil compression is primarily caused by the compression of pore volume; the compression of soil particles is minimal, and the particle height can be considered constant before and after compression.

[0104]

[0105] In the formula, h s h1 is the height of soil particles and gaps before compression, in mm; e3 is the void ratio of the soil when the initial load is P3, which is obtained from the initial moisture content, initial density, water density, and soil particle density before compression and can be regarded as a constant; h2 is the height of soil particles and gaps after compression, in mm; e4 is the void ratio of the soil when the load is P4; s is the compression distance, in mm.

[0106] From equation (2), we can obtain e4 as:

[0107]

[0108] From equation (3), it can be seen that when h1 is constant, e4 is linearly correlated with s. To simplify the analysis, the sum of the heights of pores, particles, and water in the soil is considered as the total soil height. The soil compressibility is evaluated by the soil height compression reduction ratio α when the load on the soil increases from 100 kPa to 200 kPa.

[0109]

[0110] In the formula, h3 is the soil height when the load is 100 kPa, in mm; h4 is the soil height when the load is 200 kPa, in mm; and h5 is the initial soil height, in mm.

[0111] Soil compression deformation is primarily plastic deformation, accompanied by a small degree of elastic deformation. In uniaxial closed-circuit compression simulation tests of soil using EDEM software, when the load exceeds 100 kPa, the uncompacted soil model exhibits significantly greater elastic deformation during compression compared to the actual compression test. This means that the compression reduction ratio in the simulation test is greater than that in the actual test under the same conditions, which contradicts the actual experiment. Therefore, the compression ratio is calculated based on the height of the consolidated sample after complete unloading to evaluate the soil's compressibility, and α is corrected as follows:

[0112] Example 4

[0113] Reference Figures 1 to 18 This is the fourth embodiment of the present invention. This embodiment is based on embodiment 2. This embodiment combines specific field experiments to further verify the reliability of this application.

[0114] Soil sampling was conducted in a rice stubble experimental field in Jiangdu District, Yangzhou City. The soil type was clay. Soil samples were taken from the top layer (0–225 mm) and the bottom layer (225–450 mm) at depths of 0–225 mm. Soil particle density was measured using the hydrostatic bottle method. The measurements were repeated three times and the average value was taken. The density of the top layer soil was found to be 2009 kg / m³. 3 The density of the bottom soil is 2173 kg / m³. 3 During the uniaxial compression test of the soil, the compression ratio of the surface soil was 6.57%, and the compression ratio of the subsurface soil was 5.63%.

[0115] In the uniaxial compression simulation test of soil, the EEPA (Edinburgh Elasto-Plastic Adhesion) model was selected as the soil contact model. The parameters that need to be determined include constant pull-out force (f0), surface energy (Δγ), contact plastic deformation ratio (λp), loading branching index (n), cohesion branching index (X), and tangential stiffness factor (ktm). f0 is defined as 0. In EDEM, the loading branching index n can only be 1 or 1.5. Since the stress-strain characteristics of actual soil are nonlinear, n is set to 1.5. The discrete element model of soil also needs to determine the contact parameters including the collision restitution coefficient (e), static friction coefficient (μs), and rolling friction coefficient (μr) between soil particles. The Poisson's ratio of the soil is set to 0.38, and the soil shear modulus is set to 1 MPa. In the uniaxial closed compression simulation test, to ensure sufficient particle production by the particle factory, a cylinder with a height of 250 mm and a bottom diameter of 50 mm was used to simulate an acrylic cylinder. The particle-cylinder simulation parameters are shown in Table 2. The Hertze-Mindlin (no slip) model was selected for the particle-contact component model. A particle factory was set up inside the cylinder to produce 300 g of soil particles, and the initial height of the model was measured. The simulated compression test revealed that applying loading speeds of 8 mm / s and 30 mm / s to the same sample resulted in the same compression ratio. To improve simulation efficiency, a loading speed of 30 mm / s was used for the compression simulation test. Figure 4 The compression simulation test process was described. During the test, the pressure plate moved downwards at 30 mm / s. When the axial pressure reached 196.3 N (load 100 kPa), it moved in the opposite direction at the same speed until the pressure was completely unloaded. The height h6 of the model at this point was measured. The simulation test was repeated. When the axial pressure reached 392.7 N (load 200 kPa), the plate moved back at the same speed. After complete unloading, the consolidation height h7 of the specimen was measured, and the compression ratio α was calculated.

[0116] Table 2. Simulation parameters for particles-cylinder

[0117]

[0118] Using soil compressibility performance as the response value, the Plackett-Burman experiment was first used to screen out model parameters significantly related to compressibility performance to reduce parameter calibration factors. Then, the Box-Behnken Design experiment was used to obtain a regression model between the compressibility performance index and significant factors, and the factor parameters corresponding to the actual compressibility performance index were solved. The soil model parameter levels determined based on existing technology and actual simulation results are shown in Table 3, with one high level and one low level selected for each, for a total of 7 parameters.

[0119] Table 3. Simulation Experiment Factors and Levels

[0120]

[0121] The amount of soil particle modeling is enormous. To balance computational efficiency and simulation quality, two particle sizes were selected for modeling: 5mm radius particles for the surface soil and 10mm radius particles for the subsurface soil. Since the contact parameters between soil particles of different sizes differ, Plackett-Burman experiments were used to study the sensitivity of the model parameters for both particle sizes. The simulation experiment scheme was designed using Design Expert software. The simulation experiment design and results are shown in Table 3. X8~X 11 Four blank columns are used for error analysis.

[0122] Table 4. Plackett-Burman Simulation Compression Test Design and Results

[0123]

[0124]

[0125] Considering that the height of the soil discrete element model decreases after compaction, and that the depth of tire-compacted soil generally does not exceed the surface soil depth, the average soil firmness at a depth of 0–20 cm was used as the soil firmness level in the experiment. Soil firmness at depths of 0–20 cm was measured every 5 cm in different experimental plots using a TJS-100 soil firmness meter, and the average value was calculated to determine the soil firmness level range. The factor level ranges determined based on tractor operating speed, experimental plot soil firmness, and straw coverage are shown in Table 5.

[0126] Table 5 Factor Levels in Soil Compaction Test

[0127]

[0128] Single-factor simulation experiments were designed to analyze the effects of operating speed, soil firmness, and straw coverage on the soil compaction depth caused by tires. Based on the experimental results, a uniform test was designed to establish a soil compaction depth model. Each factor was set to five levels, and the single-factor soil compaction simulation experiment is shown in Table 6. Since the soil firmness in the experimental field is difficult to adjust manually, it is not possible to select different soil firmness levels equally. Based on the actual soil firmness of different experimental plots in Jiangdu District, Yangzhou City, a single-factor soil compaction field experiment was designed, as shown in Table 7.

[0129] Table 6. Single-factor simulation test design for soil compaction.

[0130]

[0131]

[0132] Table 7 Single-factor field experiment design for soil compaction

[0133]

[0134] Table 8 shows the contribution of seven factors to soil compressibility and bearing capacity in the Plackett-Burman compression test. The contribution of factors affecting the compressibility of 5mm soil particles, ranked from largest to smallest, is X5, X2, X7, X6, X1, X4, X3. The contribution of factors affecting the compressibility of 10mm soil particles, ranked from largest to smallest, is X3, X5, X2, X7, X1, X4, X6. Based on the parameter sensitivity analysis results, a Box-Behnken Design test was designed to analyze and find the optimal solution. For the 5mm particle model, the plastic deformation ratio X5, static friction coefficient X2, and tangential stiffness factor X7 were selected, and the levels of each factor are shown in Table 9. The Box-Behnken Design test is shown in Table 10. For the 10mm particle model, the rolling friction coefficient X3, plastic deformation ratio X5, and static friction coefficient X2 were selected, and the levels of each factor are shown in Table 11. The Box-Behnken Design test is shown in Table 12. The parameters of other factors were set to intermediate values.

[0135] Table 8. Sensitivity analysis of Plackett-Burman compression test

[0136]

[0137] Table 9. Levels of various factors in the Box-Behnken test for 5mm particles.

[0138]

[0139] Table 10 Box-Behnken test scheme and results for particles with a radius of 5 mm

[0140]

[0141] Table 11. Factor levels in the Box-Behnken test for 10mm radius particles.

[0142]

[0143] Table 12 Box-Behnken test scheme and results for 10mm radius particles

[0144]

[0145]

[0146] The analysis of variance for the quadratic regression model is shown in Table 13. X2 and X5 significantly affect the compression ratio in the uniaxial compression test of particles with a radius of 5 mm, with the significance of X5 > X2. X2X5 and X5X7 significantly affect the compression ratio, while the remaining interaction terms have no significant effect on the compression ratio. (Quadratic term X7) 2 It has a significant impact on the compression ratio, X2 2 X5 2 The effect on compression ratio was not significant. Using Design Expert, a multiple regression analysis was performed on the experimental results. After removing insignificant terms from the ANOVA, the regression model for the compression ratio of 5mm particles was obtained as follows:

[0147] Y1=5.69+1.16X2+3.12X5+2.19X2X5+0.88X5X7-1.37X7 2 ;

[0148] The model's coefficient of determination R 2 R is 0.97. adj 2 The value was 0.93, indicating a good fit and high reliability of the regression equation.

[0149] Using Design Expert constraint solver with a compression ratio of 6.57% as the target value, soil compression simulation tests were conducted to verify the results. The solution with the smallest relative error between the simulated and experimental compression ratios was selected. The optimal solution for the 5mm radius particle model was: static friction coefficient X2 = 0.45, plastic deformation ratio X5 = 0.64, and tangential stiffness factor X7 = 0.63. Five soil compression simulation tests were conducted using the above parameters, with other parameters taken at intermediate levels. The average soil compression ratio was 6.47%, with a relative error of -1.52% compared to the actual experimental measurement. Analysis shows that the error between the simulation results and the actual experimental compression ratio is small.

[0150] Table 13. Analysis of variance of the quadratic regression model for the 5mm particle Box-Behnken experiment.

[0151]

[0152]

[0153] For a 10mm radius particle model, Design Expert software was used to solve for a compression ratio of 5.63%. Soil compression simulation tests were conducted using the solved parameters, and the solution with the smallest difference between the simulated and experimental compression ratios was selected. The optimal solution obtained was: static friction coefficient X2 = 0.36, rolling friction coefficient X3 = 0.25, and plastic deformation ratio X5 = 0.65. Five soil compression simulation tests were conducted using the optimal solution, and the average value of the five tests was 5.81%, which is close to the actual value with a relative error of 3.20%. The error between the simulation result and the actual experimental compression ratio is small.

[0154] Table 14. Analysis of variance of quadratic regression model for 10mm particle Box-Behnken test

[0155]

[0156] Note: P-value < 0.05 indicates significance, and P-value < 0.01 indicates extremely significant.

[0157] Table 14 of the analysis of variance shows that in the simulated compression test of particles with a radius of 10 mm, X2, X3, and X5 have a significant impact on the compression ratio, with the significance level being X3 > X5 > X2. X2 and X5 have a significant impact on the compression ratio, while X2 and X3, and X3 and X5 have no significant impact. (The quadratic term X2...) 2 X5 2 X7 2 The compression ratio is significantly affected. Using Design Expert, a multiple regression analysis was performed on the experimental results. After removing insignificant terms from the analysis of variance, the regression model for the compression ratio of 10mm particles was obtained as follows:

[0158] Y2=6.88+0.76X2+1.25X3+0.92X5-0.72X2X5-1.19X2 2 -1.31X3 2 -1.87X5 2 (twenty two)

[0159] The model's coefficient of determination R 2 R is 0.99. adj 2 The value of 0.97 indicates that the regression equation has a good fit and high reliability.

[0160] The coefficient of restitution between the topsoil and subsoil was set to 0.3, the static friction coefficient to 0.5, and the rolling friction coefficient to 0.3. Since the calibration results of the EEPA contact model parameters for the topsoil and subsoil particles are similar, the average of the two calibration values ​​was used for the EEPA model of the contact between the topsoil and subsoil particles, i.e., the surface energy is 12.5 J / m². 2The contact plastic deformation ratio is 0.645, the bond branching index is 2.25, and the tangential stiffness factor is 0.665.

[0161] Figure 13 The discrete element model diagram of soil-straw is shown below. Based on the parameter calibration results, a 225mm high subsoil layer was constructed using 10mm radius particles, generating a total of 97,590 particles. A 225mm high topsoil layer was constructed using 5mm radius particles, generating a total of 772,085 particles. Straw piles were then generated on the topsoil, producing a total of 91,950 straws (1.2kg / m³). 2 (Coverage). Figure 14 Images showing tire compaction marks from EDEM and Recurdyn. Figure 15 This is a cross-sectional view of the compaction marks. As can be seen from the figure, the straw broke and bent under the compression of the tires and mixed with soil particles, which is consistent with the actual experiment, indicating that the established model can simulate the real experimental scenario well. Figure 16 (a) is a field test scene of tire compaction. Figure 16 (b) shows tire compaction marks. Figure 16 (c) Measurement of soil compaction depth.

[0162] Figure 17 The results of soil compaction simulation are as follows. Figure 18 The results are from a field test of soil compaction. When soil firmness and straw cover remained constant, the tire compaction depth in both the simulation and field tests decreased with increasing operating speed. This is because higher tire speeds result in shorter compaction times, meaning less pressure acting on the soil, leading to a decrease in compaction depth. In the simulation test, the compaction depth at 7.5 km / h was 94.81% of that at 1.5 km / h, a reduction of 5.2 mm; in the field test, the compaction depth at 7.5 km / h was 93.88% of that at 1.5 km / h, a reduction of 6 mm. Overall, the effect of operating speed on compaction depth was not significant; the decrease in soil compaction depth was small with increasing operating speed. In subsequent simulation tests, the operating speed was taken as the median value of the experimental factor range, i.e., 4.5 km / h.

[0163] When the working speed and straw coverage remain constant, the compaction depth in both simulation and field experiments decreases with increasing soil compaction. In the simulation experiment, the compaction depth at a compaction density of 3 MPa is 64.36% of that at 1 MPa, a decrease of 43.8 mm. In the field experiment, the compaction depth at a compaction density of 2.87 MPa is 66.11% of that at 1.12 MPa, a decrease of 41 mm. This demonstrates that changes in soil compaction density have a significant impact on compaction depth. For every 0.5 MPa increase in compaction density, the soil compaction depth decreases successively by 12.5 mm, 11.9 mm, 10.2 mm, and 9.2 mm. This is because as soil compaction increases, the soil's resistance to external compaction increases, making it increasingly difficult to compress.

[0164] When the operating speed and soil firmness remain constant, the compaction depth decreases with increasing straw coverage. The straw acts as a buffer during tire compaction, reducing the degree of soil compaction. In the simulation experiment, the straw coverage was 1.2 kg / m². 2 The compaction depth at that time was 96.12% of the compaction depth without straw cover, a reduction of 3.9 mm; the straw cover amount in the field trial was 1.2 kg / m². 2 The compaction depth at that time was 94.90% of the compaction depth without straw cover, a decrease of 5 mm. It can be seen that the change in straw cover amount has no significant effect on the compaction depth. In subsequent simulation experiments, the straw cover amount was taken as the median value of the experimental factor range, i.e., 0.6 kg / m². 2 .

[0165] Based on the results of the single-factor experiment, the operating speed was set at 4.5 km / h and the straw mulch amount at 0.6 kg / m². 2 With soil firmness as the experimental factor and soil compaction depth as the response quantity, a single-factor nine-level iso-level simulation experiment was designed and the results are shown in Table 15.

[0166] Table 15. Design and Results of Simulation Experiments at Different Levels

[0167]

[0168] Theoretically, assuming infinite soil depth, as soil compaction approaches infinity, the compaction depth approaches zero. When soil compaction approaches zero, the tire will sink completely into the soil, and the compaction depth will approach infinity. Therefore, the compaction depth model should be a power function. To reduce error, the actual soil compaction in the discrete element model is used as the independent variable, and SPSS 27 software is used to perform nonlinear power function regression analysis on the simulation results. The fitting results are as follows:

[0169] L6 = 127.339 * Y3 -0.401

[0170] In the formula, the correlation coefficient R of the model 2 The value of 0.992 indicates that the model fits well.

[0171] To verify the soil compaction depth model, five experimental fields with different compaction conditions were randomly selected for tire compaction tests. The tractor speed was 4.5 km / h and the straw mulch was 0.6 kg / m². 2 The experimental results are shown in Table 16. It can be seen that the relative error is within ±3.44%, and the consistency between the model predictions and experimental values ​​is good.

[0172] Table 16 Validation Results of Soil Compaction Depth Model Test

[0173]

[0174] As can be seen from this embodiment, the present invention can be used to establish a mathematical model of soil compaction depth when agricultural machinery is operating in the field, providing a research basis for improving the accuracy of subsequent tillage depth detection.

[0175] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium. The method can be implemented using standard programming techniques—including a non-transitory computer-readable storage medium configured with a computer program, wherein such a storage medium causes the computer to operate in a specific and predefined manner—according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).

[0176] Furthermore, the procedures described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by the context. The procedures described herein (or variations and / or combinations thereof) may be executed under the control of one or more computer systems configured with executable instructions, and may be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. A computer program comprises multiple instructions executable by one or more processors.

[0177] Furthermore, the method can be implemented in any suitable type of computing platform, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices, etc. Aspects of the invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. Furthermore, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. The invention includes these and other different types of non-transitory computer-readable storage media when such media comprises instructions or programs that implement the steps above in conjunction with a microprocessor or other data processor. When programmed according to the method and techniques of the invention, the invention also includes the computer itself. The computer program can be applied to input data to perform the functions described herein, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the invention, the converted data represents physical and tangible objects, including specific visual depictions of physical and tangible objects generated on a display.

[0178] As used herein, the terms “component,” “module,” “system,” etc., are intended to refer to a computer-related entity, which may be hardware, firmware, a combination of hardware and software, software, or running software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, a running thread, a program, and / or a computer. As an example, an application running on a computing device and the computing device itself can both be components. One or more components may reside in a running process and / or thread, and components may be located in a single computer and / or distributed among two or more computers. Furthermore, these components are capable of execution from various computer-readable media having various data structures thereon. These components may communicate locally and / or remotely via signals, such as based on one or more data packets (e.g., data from a component that interacts with a local system, another component in a distributed system, and / or signals that interact with other systems via a network such as the Internet).

Claims

1. A method for testing soil compaction depth, characterized in that: Includes the following steps: The calibration of S1 soil particle parameters, including the coefficient of restitution, static friction coefficient, rolling friction coefficient, surface energy, plastic deformation ratio, bond branching index, and tangential stiffness factor, involves the following specific steps: S101 uses the height of the consolidated specimen after complete unloading to calculate the compression ratio to evaluate the compressibility of soil. The formula for calculating the compression ratio is as follows: ； in, h 6 The soil height (in mm) of the consolidated specimen subjected to a load of 100 kPa; h 7 The height of the soil mass after compression of a consolidated specimen subjected to a load of 200 kPa is shown in mm. h 8 The initial height of the consolidated specimen under a load of 100 kPa is shown in mm. h 9 The initial height of the consolidated specimen under a load of 200 kPa; S102 is based on uniaxial compression tests of soil to calibrate soil particle parameters. The coefficient of restitution X1, static friction coefficient X2, rolling friction coefficient X3, surface energy X4, plastic deformation ratio X5, bond branching index X6, and tangential stiffness factor X7 are used as test factors. Soil compressibility performance index α is used as the evaluation index. First, the Plackett-Burman test is used to screen out the model parameters that are significantly related to soil compressibility performance. Then, the Box-Behnken Design test is used to obtain the regression model between the compressibility performance index and the significant factors, and the factor parameters corresponding to the actual compressibility performance index are solved. S103 determines the relevant parameters of the straw discrete element model; S2 establishes a simulation soil trench model based on the parameter calibration results; S3 used EDEM and Recurdyn software to perform coupled simulation analysis on the effects of different operational factors on soil compaction depth, conducted single-factor experiments, and screened out operational factors that significantly affected compaction depth for further simulation experiments; S4 simulates the selected operational factors to obtain the soil compaction depth corresponding to the operational factors under different parameter ranges. SPSS 27 software is used to perform nonlinear power function regression analysis on the simulation test results to establish a mathematical model between the selected factors and the soil compaction depth.

2. The method for testing soil compaction depth as described in claim 1, characterized in that: In the uniaxial closed compression simulation test in EDEM, an acrylic cylinder with a height of 250 mm and a bottom diameter of 50 mm was used to simulate the acrylic cylinder. The particle-cylinder simulation parameters were set, and the Hertze-Mindlin model was selected for the particle-contact component model. A particle factory was set up in the cylinder to generate 300g of soil particles. The initial height of the model was measured. The compression simulation test was carried out using a loading speed of 30 mm / s. When the axial pressure reached 196.3 N, the model was moved in the opposite direction at the same speed until the pressure was completely unloaded. The height h6 of the model at this time was measured. The simulation test was repeated. When the axial pressure reached 392.7 N, the model was moved back at the same speed. After complete unloading, the consolidation height h7 of the sample was measured, and the compression ratio α was calculated.

3. The method for testing soil compaction depth as described in claim 2, characterized in that: In step S102, 5 mm radius particles were used to model the surface soil. Based on the contribution analysis of seven experimental factors to soil compressibility and bearing capacity in the Plackett-Burman compression test, the contribution of 5 mm soil particles to compressibility, ranked from largest to smallest, was X5, X2, X7, X6, X1, X4, and X3. A Box-Behnken Design experiment was designed for analysis and to find the optimal solution. Multiple regression analysis was performed on the experimental results using Design Expert, and insignificant terms in the analysis of variance were removed to obtain the compressibility ratio of 5 mm particles. Y 1 The regression model is as follows: Y 1 = a1 + a2* X 2+ a3* X 5+ a4* X 2* X 5+ a5*X5*X7- a6 X 7 2 ; Where a1 to a6 are constants.

4. The method for testing soil compaction depth as described in claim 2, characterized in that: The subsoil was modeled using particles with a radius of 10 mm. The contributions of the factors influencing the compressibility of 10 mm soil particles, ranked from largest to smallest, were X3, X5, X2, X7, X1, X4, and X6. Based on the parameter sensitivity analysis results, a Box-Behnken Design experiment was designed for analysis and to find the optimal solution. Multiple regression analysis was performed on the experimental results using Design Expert, and insignificant terms in the analysis of variance were removed to obtain the compressibility ratio of 10 mm particles. Y 2 The regression model is as follows: Y 2 = b1 + b2* X 2+ b3* X 3+ b4* X 5- b5*X2*X5- b6*X2 2 - b7*X3 2 - b8*X5 2 ; Where b1 to b8 are constants.

5. The method for testing soil compaction depth as described in claim 2, characterized in that: Step S3 details Includes the following steps, S301 imports three cones into the EDEM, gives a vertical downward velocity of 0.1 m / s, and measures the soil compaction at a depth of 0~20 cm every 5 cm. The peak pressure on the cones is extracted to calculate the average value. Different soil compaction is obtained by adjusting the degree of compaction of the soil trough model, and soil trough models with different compaction are established. S302 uses CATIA to create a three-dimensional structural model of the tractor's herringbone tire, and then imports the created three-dimensional tire structural model into Recurdyn software; S303 uses EDEM-Recurdyn software for coupled simulation. The step function is used to allow the tire to fall freely onto the soil trough model within 0~0.5 s. The tire is then accelerated uniformly to the working speed from 0.5 s to 0.7 s. After 0.7 s, the tire moves forward at a constant speed to carry out compaction simulation test. The compaction depth of the tire on the soil trough model under different working factors is obtained. The different working factors include working speed, soil firmness and straw coverage. S304 identifies operational factors that significantly affect compaction depth.

6. The method for testing soil compaction depth as described in claim 5, characterized in that: Soil firmness was identified as a significant operational factor affecting compaction depth.

7. The method for testing soil compaction depth as described in claim 6, characterized in that: The mathematical model relating soil compaction depth and soil firmness is as follows: L6=a*Y3 -b ; In the formula, L6 is the soil compaction depth, with the mathematical unit being mm; Y3 is the soil firmness, with the mathematical unit being MPa; and a and b are positive constants.

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