A method for constructing a soil compaction risk early warning system based on in-situ detection technology

Abstract

The application discloses a method for constructing a soil compaction risk early warning system based on in-situ detection technology, and belongs to the technical field of intelligent agriculture and soil health management. The method obtains the soil stress distribution characteristics under different agricultural machine operations by laying multiple layers of stress sensors, combines the initial bulk density, clay content and depth of the soil to construct a preliminary consolidation pressure prediction model, realizes regional compressive strength spatial differentiation evaluation, uses a thermal pulse-TDR probe to continuously monitor the soil thermal conductivity and moisture content in-situ, inverts the porosity change and calculates the volume strain, finally fuses the stress-strain response and soil layer characteristics to establish a layered soil bulk density evolution model, and divides the compaction risk grades into three levels of low, medium and high according to the suitable bulk density threshold of crop root growth. The application realizes pre-operation risk prediction and operation strategy optimization, has the advantages of foresight, non-destructiveness and high spatio-temporal resolution, and is suitable for the mechanized operation management of black soil areas and other easily compacted farmlands.

Description

Technical Field

[0001] This invention belongs to the field of smart agriculture and soil health management technology, specifically relating to a method for constructing a soil compaction risk early warning system based on in-situ detection technology. Background Technology

[0002] Soil compaction refers to the process by which soil particles are rearranged due to agricultural machinery operations, resulting in reduced soil porosity and increased soil bulk density. Soil compaction damages soil structure, restricts soil function, and affects crop growth and soil ecological functions. For example, compaction reduces soil infiltration, aeration, purification capacity, and biodiversity. Therefore, soil compaction is considered one of the important factors restricting sustainable agricultural development.

[0003] Surveys in the Guanzhong Plain and North China Plain of my country have revealed soil compaction problems, with the Northeast Black Soil Region exhibiting a relatively higher risk of compaction due to its lower bulk density. There is an urgent need to develop intelligent and proactive soil health management tools.

[0004] Existing technologies for preventing soil compaction mainly include: reducing the number of times agricultural machinery travels, reducing the contact area of ​​agricultural machinery by fixing the machinery's travel lanes, and prevention methods based on the soil compaction process. Reducing the number of times agricultural machinery travels can effectively reduce the area and degree of soil compaction.

[0005] The number of times agricultural machinery travels directly determines the size of the area where it compacts the soil (excluding stationary travel), and the degree of soil compaction within the same area is positively correlated with the number of times the machinery travels. This is because, on the one hand, soil compaction involves the compression of the solid, liquid, and gaseous phases of the soil. While the solid and gaseous phase changes can be completed relatively quickly, the liquid phase change depends on the soil's hydraulic conductivity and is often difficult to complete in a single compaction operation. On the other hand, soil elastic deformation can increase soil suction, thereby increasing the absorption of moisture from the lower soil layers by the compacted soil, thus increasing the moisture content of the compacted layer and further increasing the risk of compaction. Currently, the application of non-mechanical surface operations, represented by drones, in agriculture can effectively reduce the number of times agricultural machinery travels. Similarly, conservation tillage measures such as no-till and reduced tillage also reduce the number of times agricultural machinery travels, thereby reducing soil compaction. However, non-mechanical surface operations are limited to fertilization and pesticide spraying in reducing the number of travels, and cannot replace machinery that poses the greatest risk of soil compaction (such as harvesters), even in conservation tillage measures.

[0006] Fixed machinery driving lanes can effectively reduce the area of ​​soil compaction in the field. However, fixed machinery driving lanes are often limited by the size of different agricultural machinery and crop types, making them difficult to promote, and their current global application is relatively small.

[0007] Soil working conditions and type of agricultural machinery are the main factors affecting soil compaction risk during the compaction process. For example, soil moisture is a highly sensitive factor affecting compaction and is easily controlled in agricultural management; therefore, choosing to work in the field when the soil is dry can effectively reduce soil compaction risk. Given the limitations of the first two preventive measures in actual production, their promotion is difficult. Therefore, optimizing field working conditions and type of agricultural machinery based on the soil compaction process to reduce soil compaction risk is particularly important.

[0008] However, our understanding of the stress distribution and strain characteristics caused by the compaction process under different types of agricultural machinery (such as seeders and harvesters) and soil conditions (such as different moisture content, bulk density, and clay content) is still limited. At the same time, there are few studies on the spatiotemporal differentiation of regional soil compressibility, and there is a lack of technical solutions to directly link in-situ detection data with risk warning models, making it difficult to achieve accurate and real-time prediction of soil compaction risk.

[0009] Based on this, the present invention provides a method for constructing a soil compaction risk early warning system based on in-situ detection technology. Summary of the Invention

[0010] To address the aforementioned technical problems in the prior art, this invention provides a method for constructing a soil compaction risk early warning system based on in-situ detection technology.

[0011] To achieve the above objectives, the technical solution of the present invention is as follows: A method for constructing a soil compaction risk early warning system based on in-situ detection technology includes: S1. Obtain soil stress distribution characteristics for different types of agricultural machinery: The types of agricultural machinery include seeders and harvesters. The harvester tires are set to two working conditions: high pressure (i.e., the manufacturer's recommended tire pressure) and low pressure (60% of the recommended tire pressure). The seeder is set to two working conditions: 1 compaction and 3 compaction cycles. Disc-shaped stress sensors with a diameter of 5cm and a height of 3cm are arranged in 5 layers (10cm, 20cm, 30cm, 50cm, and 70cm) below the ground surface. Five sensors are evenly distributed in each layer along the direction perpendicular to the direction of the agricultural machinery's movement (one located below the center of the tire, one located below the edge of the tire, and three located between the two). The agricultural machinery is controlled to pass through the sensor arrangement area at a constant speed of 4-5km / h to collect vertical stress and obtain soil stress distribution characteristics. S2. Based on the soil stress distribution characteristics and combined with a transformation function, the spatial differentiation of regional soil compressive strength is obtained, wherein the compressive strength is defined by the pre-consolidation pressure. express; S3. Based on in-situ detection technology, the change in porosity is inverted through a pre-calibrated physical model to obtain the soil volumetric strain and establish the stress-strain relationship: The in-situ detection technology uses thermal pulse-time domain reflectance (TDR) probes, which are arranged in the same layer as the stress sensor (2 probes per layer, spaced 5-10cm apart) to simultaneously monitor the soil thermal conductivity and volumetric water content. S4. Based on the stress distribution, volumetric strain and preconsolidation pressure, calculate the soil layer specific modulus and predict the soil bulk density after operation. Combine the appropriate bulk density threshold of crops to establish a graded risk prediction model to achieve dynamic early warning of soil compaction risk.

[0012] Furthermore, step S1 specifically includes: S11. Construct a stress distribution measurement model. Based on soil profile characteristics (such as soil layer thickness and texture differences), arrange disc-shaped stress sensors (5cm in diameter and 3cm in height) at 10cm, 20cm, 30cm, 50cm, and 70cm below the ground surface. Distribute 5 sensors evenly along the direction perpendicular to the agricultural machinery's forward movement in each layer (1 sensor located below the center of the tire, 1 sensor located below the edge of the tire, and 3 sensors located between the two). S12. The wheel passes through the soil where stress sensors are arranged at a constant speed. Based on the vertical stress collected by the stress sensors, the wheel load and the surface stress of the wheel-soil contact surface are obtained. ; S13. Measure the stress transfer coefficient of each soil layer using a uniaxial compression test. ; S14, Based on stress transfer coefficient Calculate the concentration factor of each soil layer. ; S15, Concentration coefficient based on each soil layer and surface stress Thus, the stress distribution model of the i-th soil layer was obtained.

[0013] Furthermore, the characteristics of soil stress distribution are represented using a soil stress distribution model:

[0014] in, The equivalent radius of the stress distribution area in the surface soil, in meters (m). Indicates the depth of the first soil layer, in meters (m). This represents the concentration factor of the first soil layer; The equivalent radius (m) represents the area of ​​soil stress distribution on the upper surface of the second soil layer. Indicates the depth of the second soil layer, in meters (m). This represents the concentration factor of the second soil layer; The equivalent radius of the soil stress distribution area on the upper surface of the i-th soil layer, in meters; Let represent the depth of the i-th soil layer, in meters. This represents the concentration factor of the i-th soil layer.

[0015] Furthermore, the spatial differentiation of soil compressive strength is represented by the preconsolidation pressure:

[0016] Indicates the initial consolidation pressure; indicates the soil compressive strength; , , , These are the fitting coefficients; Indicates the first The depth of the layer from the soil surface; Indicates the initial bulk density of the soil; Indicates the clay content of the soil; If the actual stress is less than This indicates that the soil has undergone elastic compression; If the actual pressure is greater than or equal to This indicates that the soil has undergone elastoplastic compression.

[0017] Furthermore, step S3 specifically includes: S31. Monitor soil thermal conductivity and volumetric water content, and invert the corresponding porosity; S32. Establish a model to show the relationship between porosity and soil thermal conductivity and volumetric water content; S33. Using the relational model, calculate the porosity after the new operation; S34. Calculate the soil volumetric strain based on the porosity after the new operation.

[0018] Furthermore, the soil volumetric strain is:

[0019] In the formula, Indicates soil volumetric strain; Indicates initial porosity. This indicates the porosity after the operation.

[0020] Furthermore, step S4 specifically includes: S41. Calculate the soil layer specific modulus; S42. Predict real-time soil bulk density; S43. Establish a risk prediction model to determine the soil compaction risk based on the predicted soil bulk density after compaction and the suitable bulk density threshold for crops.

[0021] Furthermore, the soil-specific modulus is calculated, specifically including: During the elastic deformation stage, then:

[0022] During the elastoplastic deformation stage, then:

[0023] in, Indicates the vertical compression amount; ; Indicates the thickness of the soil layer; Indicates the first Soil stress in the layer; Indicates the initial consolidation pressure of the soil; Indicates soil volumetric strain; , These are soil-specific moduli at different deformation stages.

[0024] Furthermore, the real-time soil bulk density is: During the elastic deformation stage:

[0025] Indicates the prediction was suppressed after the first The bulk density of the soil layer; During the elastic-plastic deformation stage:

[0026] Indicates the prediction was suppressed after the first The soil bulk density of the soil layer, Indicates the first Initial bulk density of the soil layer. Low risk: ≤1.3g / cm 3 (Applicable to dryland crops such as corn and soybeans), indicating that the soil pore structure is not significantly damaged, and aeration and infiltration rates remain at normal levels; Medium risk: 1.3g / cm³ 3 < ≤1.6g / cm 3This indicates that soil aeration has decreased by 15%-30% and infiltration rate by 20%-40% compared to the initial state, requiring adjustment of agricultural machinery operation frequency; High risk: >1.6g / cm 3 This indicates that the soil porosity has decreased by more than 30% compared to the initial state, which restricts crop root growth and requires soil improvement measures such as deep plowing and loosening.

[0027] Compared with the prior art, the present invention has the following beneficial effects: This invention predicts soil compaction risk by using easily obtainable indicators such as machinery model, number of compaction cycles, and soil moisture content. Based on the judgment results, it can determine or improve soil operation methods in advance, thereby avoiding soil compaction. Compared with the existing technology of remedial measures after soil compaction, this invention is more practical and forward-looking, and can effectively prevent soil compaction. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the planar arrangement of the stress sensor of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0030] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.

[0031] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0032] This embodiment provides a method for constructing a soil compaction risk early warning system based on in-situ detection technology, such as... Figure 1 As shown, it includes: S1. Obtain soil stress distribution characteristics for different types of agricultural machinery; Two typical agricultural machinery types and four typical black soil types were selected to obtain soil stress distribution characteristics. The agricultural machinery types included a seeder and a harvester. The harvester used was a Lovol CD03, with both high tire pressure (the manufacturer's recommended pressure) and low tire pressure (60% of the high pressure) conditions. The seeder used was a Lovol M3004-7N tractor, with only different compaction times (1 and 3 times). The operating speed was controlled at 4-5 km / h to ensure consistent experimental conditions. The seeder treatment was conducted during the sowing season, and the harvester treatment during the harvesting season.

[0033] Black soil types include black soil, black calcareous soil, meadow soil, and dark brown soil; soil moisture status is selected under two conditions: field capacity and 60% field capacity. Soil moisture status is controlled at different time intervals after a full irrigation.

[0034] The tire-soil contact area and stress distribution were measured under different configuration conditions. A disc-type stress sensor (sensor 1) with a diameter of 5 cm and a height of 3 cm was used. Sensor 1 was arranged in five layers, with five sensors in each layer. The depths of the five layers were 10 cm, 20 cm, 30 cm, 50 cm, and 70 cm below the ground surface, respectively; all sensors were placed in the soil beneath the tire. The five sensors 1 in each layer were evenly distributed horizontally perpendicular to the tire's direction of travel. Figure 2 As shown, a sensor 1 is positioned below the center of the tire. Figure 2 (At the center of the ellipse), one is positioned below the tire edge ( Figure 2 The five sensors are positioned as follows: one on the far right and the other three on the far right. The remaining three stress sensors are positioned between the first two, meaning that all five sensors are located below the half of the tire's contact surface with the soil. This reduces the number of stress sensors required and allows for a symmetrical view of the entire stress distribution.

[0035] After sensor 1 is set up, the agricultural machinery is made to travel along the set track, with the tires passing over sensor 1. The time it takes for the front and rear tires to pass over sensor 1 and the distance between the front and rear axles are measured to calculate the agricultural machinery's speed. (1) in, Indicates the speed of agricultural machinery. This is the distance between the front and rear axles. This indicates the time interval between the front and rear tires passing the stress sensor.

[0036] The tire is a wide rubber tire, and the contact surface between the tire and the soil is approximately a regular ellipse. The major and minor axes of the tire-soil elliptical contact area can be calculated by the driving speed and contact time. (2) in, Indicates the major axis, The time it takes for a single tire to pass the stress sensor, i.e., the duration from when the stress signal rises to when it disappears; (3) in, Indicates the length of the minor axis. For tire width, This is a correction factor, typically taken as 0.6 to 0.8.

[0037] The tire-soil contact area was calculated based on the major and minor axes: (4) in, Indicates the contact area.

[0038] By using the rotating units of the front and rear wheels of a given vehicle to determine the distance between the front and rear axles, and calculating the driving speed, the contact area can be obtained. Based on the stress measured by stress sensors and the contact area, the wheel load can be calculated. (5) in, Indicates wheel load; This indicates the recorder frequency, measured in Hz. Indicates time, in seconds (s); Indicates the first The monitoring time period of each stress sensor (the duration from when the stress signal rises to when it disappears). This indicates the monitoring time period of the first stress sensor; Right now This indicates the monitoring time period of the 5th stress sensor; This indicates the spacing between adjacent stress sensors, in meters (m). This indicates the total number of stress sensors, with a value of 5. This represents the vertical stress measured by the first sensor in the top layer at time t, in kPa. This indicates the first sensor in the top layer. Vertical stress measured by a sensor at time t, in kPa; This indicates the lateral position coordinates of the discretely arranged stress sensors, where lateral is the horizontal direction perpendicular to the direction of travel. This represents the vertical stress measured by the fifth sensor in the top layer at time t, in kPa.

[0039] Assuming the load from the wheel is uniformly distributed over an elliptical contact surface with an equivalent radius of R, the stress at the vertical point Z directly below the contact surface can be expressed as: (6) in, This represents the surface stress at the tire-soil contact surface, expressed in kPa. This represents the total load within the tire-soil contact area, calculated using formula (5). , kN.

[0040] At this moment, the vertical stress at point z directly below the center of the contact surface is... It can be represented as: (7) in, Indicates the depth from the soil surface; denoted by ; k represents the concentration factor, used to control the stress attenuation rate, determine the attenuation characteristics of vertical stress with depth, and quantify the concentration / diffusion characteristics of soil stress transmission.

[0041] equivalent radius Defined as: (8) This equivalent radius maintains the same contact area: .

[0042] The concentration factor was converted into a function of the stress transfer factor, the equivalent radius of the soil-tire contact surface, and the vertical distance between the tire-soil contact surface and the stress prediction point directly below, thereby quantifying the variation of the concentration factor with soil environment: (9) in, , This represents the soil stress transfer coefficient.

[0043] In a fixed soil environment, the soil profile is divided, and the thickness of each soil layer is as follows: Assuming that the soil stress is uniformly distributed vertically downwards in each soil layer during the downward stress transmission process, and using the soil stress on the lower surface of any soil layer as model input to calculate the soil stress of the next soil layer, while also assuming that the tire-soil contact surface is ideally elliptical. Therefore, under a load of... Major axis is minor axis is Under the action of the elliptical pressure plate, the stress at the pressure plate-soil contact surface is The stresses in the vertical direction of the lower surface of each soil layer are respectively , kPa), and the corresponding stress transfer coefficients are respectively , .

[0044] Therefore, the soil stress on the lower surface of each soil layer can be expressed as: (10) The recursive formula for the equivalent radius of each layer is: (11) In the formula: —Soil stress diffusion angle; —The depth to the upper surface of the i-th layer, m; ——No. Stress on the upper surface of the soil layer; ——No. Compaction stress on the upper surface of the soil layer Equivalent radius of the distribution area, in meters; ——No. The thickness of each soil layer; ——No. Concentration factor corresponding to each soil layer; No. The soil stress in the layer can be expressed as: (12) No. The soil stress in the layer can be expressed as: (13) The simplified derivation yields: (14) In the formula —Stress transfer coefficient of the entire soil layer, It is about , and The function of stress transmission in soil, except It is a measurable constant. and As the soil depth changes, the stress transfer coefficient of each soil layer can be determined as follows: (15) In the formula Indicates the first Stress transfer coefficient of each soil layer, assuming stress transfer coefficient of each soil layer , , …, The values ​​can be obtained by measuring the stress transfer coefficient of each layer through stratified field sampling combined with indoor uniaxial compression tests. Then, based on the stress transfer coefficient of each layer The concentration factor of each layer is calculated. , (16) Then the calculation yields... This yields the stress distribution in each soil layer.

[0045] S2. Based on the characteristics of soil stress distribution, combined with the transformation function, the spatial differentiation of regional soil compressive strength is obtained; Based on the physical properties and stress distribution characteristics of the soil, a suitable transformation function is selected or established. Preferably, the transformation function is based on stratified soil samples (0-70 cm) from 10 sampling points in a typical black soil region of Northeast China. The pre-consolidation pressure was determined through indoor uniaxial compression tests, and the results were obtained through regression analysis with basic physical properties such as density and clay content. The coefficient of determination is... It exhibits good regional applicability. The parameters in the transformation function are estimated using experimental data or known compressive strength data, and then calibrated to ensure the accuracy of the transformation function.

[0046] The construction process of the transformation function is as follows: With stress Soil physical properties (initial bulk density) Clay content ) is the independent variable. As the dependent variable, the fit yields: (17) , , , The fitting coefficients are calibrated from uniaxial compression experimental data sampled in the field. Indicates the initial consolidation pressure; followed by the soil compressive strength. Indicates the first The depth of the layer from the soil surface.

[0047] If the actual stress is less than This indicates that the soil has undergone elastic compression; If the actual pressure is greater than or equal to This indicates that the soil has undergone elastoplastic compression. The actual stress is the value calculated above. .

[0048] S3. Determine the soil stress-strain relationship based on in-situ detection technology; By installing thermal pulse-time domain reflectance probes in situ in the field, soil strain can be inferred by measuring soil thermal conductivity and moisture content, thus achieving continuous in-situ observation of soil strain. Synchronous, continuous, in-situ measurement of soil thermal conductivity ( ) and volumetric water content ( Establish and rate a reliable " λ - - "Physical or empirical conversion model. Using this model, real-time measurements (..." , The data is inverted to the current porosity. The volumetric strain is calculated by comparing the current porosity with the initial porosity. ).

[0049] Specifically, the current porosity is inverted using a pulse-time domain reflection probe. : (18) in, , , The coefficient is determined by collecting a large number of soil samples and measuring them in the laboratory. , , The values ​​are then used to determine the coefficients and thus the model.

[0050] Calculate volumetric strain : (19) Indicates initial porosity. This indicates the porosity after the operation.

[0051] S4. Based on the stress-strain relationship and soil compressive strength, a risk prediction model is established.

[0052] S41. Calculate the soil layer specific modulus; During the elastic deformation stage, i.e., when the actual stress is less than... ,but: (20) During the elastoplastic deformation stage, i.e., the actual pressure is greater than or equal to... ,but: (twenty one) in, This represents the vertical compression; its calculation method is as follows: (twenty two) Indicates the thickness of the soil layer; Indicates the first Soil stress in the soil layer.

[0053] S42. Predict real-time soil bulk density; During the elastic deformation stage: (twenty three) Indicates the prediction was suppressed after the first The soil bulk density of the soil layer, Indicates the first Initial bulk density of the soil layer; During the elastic-plastic deformation stage: (twenty four) Indicates the prediction was suppressed after the first The soil bulk density of the soil layer, Indicates the first The initial bulk density of the soil layer.

[0054] S43. Establish a risk prediction model and judge the soil compaction risk based on the predicted soil bulk density after compaction and the suitable bulk density threshold for crops. For example: Low risk: This indicates that the soil structure has not been significantly damaged; Medium risk: This indicates a decrease in ventilation / infiltration rate; High risk: This indicates a severe reduction in porosity, which limits crop growth.

[0055] By using the above methods, the compaction risk of each soil layer after it is compacted can be predicted, and reasonable remediation and defense measures can be taken.

[0056] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for constructing a soil compaction risk early warning system based on in-situ detection technology, characterized in that, include: S1. Obtain soil stress distribution characteristics for different types of agricultural machinery; S2. Based on the soil stress distribution characteristics and combined with a transformation function, the spatial differentiation of regional soil compressive strength is obtained, wherein the compressive strength is defined by the pre-consolidation pressure. express; S3. Based on in-situ detection technology, the porosity change is inverted through a pre-calibrated physical model to obtain the soil volumetric strain and establish the stress-strain relationship; S4. Based on the stress distribution, volumetric strain and preconsolidation pressure, calculate the soil layer specific modulus and predict the soil bulk density after operation. Combine the suitable bulk density threshold of crops to establish a graded risk prediction model to realize dynamic early warning of soil compaction risk. Step S1 specifically includes: S11. Construct a stress distribution measurement model and deploy stress sensors; S12. The wheel passes through the soil where stress sensors are arranged at a constant speed. Based on the vertical stress collected by the stress sensors, the wheel load and the surface stress of the wheel-soil contact surface are obtained. ; S13. Measure the stress transfer coefficient of each soil layer using a uniaxial compression test. ; S14, Based on stress transfer coefficient Calculate the concentration factor of each soil layer. ; S15, Concentration coefficient based on each soil layer and surface stress , thus arriving at the first Soil stress distribution model; Spatial differentiation of soil compressive strength is represented by preconsolidation pressure: Indicates the initial consolidation pressure; indicates the soil compressive strength; , , , These are the fitting coefficients; Indicates the first The depth of the layer from the soil surface; Indicates the initial bulk density of the soil; Indicates the clay content of the soil; Indicates the first Soil stress in the layer; If the actual stress is less than This indicates that the soil has undergone elastic compression; If the actual pressure is greater than or equal to This indicates that the soil has undergone elastoplastic compression; Step S3 specifically includes: S31. Monitor soil thermal conductivity and volumetric water content, and invert the corresponding porosity; S32. Establish a model to show the relationship between porosity and soil thermal conductivity and volumetric water content; S33. Using the relational model, calculate the porosity after the new operation; S34. Calculate the soil volumetric strain based on the porosity after the new operation; Soil volumetric strain is: In the formula, Indicates soil volumetric strain; Indicates initial porosity. Indicates porosity after the operation; Step S4 specifically includes: S41. Calculate the soil layer specific modulus; S42. Predict real-time soil bulk density; S43. Establish a risk prediction model to determine the soil compaction risk based on the predicted soil bulk density after compaction and the suitable bulk density threshold for crops.

2. The method according to claim 1, characterized in that, The calculation of soil-specific modulus includes: During the elastic deformation stage, then: During the elastoplastic deformation stage, then: in, Indicates the vertical compression amount; ; Indicates the thickness of the soil layer; Indicates the first Soil stress in the layer; Indicates the initial consolidation pressure of the soil; Indicates soil volumetric strain; , These are soil-specific moduli at different deformation stages.

3. The method according to claim 2, characterized in that, Real-time soil bulk density is: During the elastic deformation stage: Indicates the prediction was suppressed after the first The bulk density of the soil layer; During the elastic-plastic deformation stage: Indicates the prediction was suppressed after the first The soil bulk density of the soil layer, Indicates the first The initial bulk density of the soil layer.

4. The method according to any one of claims 1 to 3, characterized in that, The types of agricultural machinery include seeding machinery and harvesting machinery; the tires of the agricultural machinery are set to a low pressure state of recommended air pressure or 60% of the recommended air pressure, in order to compare and analyze the differences in stress distribution under different ground pressures.

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