Quantitative analysis method for passability of no-tillage seeding process machine based on discrete element method

Abstract

This invention discloses a quantitative analysis method for the passability of no-till seeding implements based on discrete element method (DEM) analysis. The method includes: establishing a whole soil sensor A based on soil layer location, establishing a whole straw sensor C based on straw layer location, analyzing and calculating the overall soil and straw loss mass in a virtual soil trough, calculating the overall loss mass Φ, establishing a soil blockage sensor B based on the location of the anti-blockage unit, establishing a straw blockage sensor D based on the location of the anti-blockage unit, real-time statistical analysis of soil and straw blockage around the anti-blockage unit, calculating the maximum blockage mass Y, calculating the implement blockage rate Δ, and analyzing and classifying the experimental data. This invention visualizes the interaction process between no-till anti-blockage units and soil and straw, extracts real-time soil and straw quality information within the test area, and uses a quantitative analysis model for implement passability to calculate the blockage rate of no-till anti-blockage units, thereby accurately and efficiently determining the blockage situation.

Description

Technical Field

[0001] This invention relates to the technical field of agricultural machinery, and in particular to a quantitative analysis method for the passability of machinery in the no-till seeding process based on discrete element method. Background Technology

[0002] Conservation tillage, by not using moldboard plows and covering the land with over 30% crop straw and stubble, effectively mitigates soil erosion by wind and water compared to traditional farming methods, thus reducing negative impacts on crop growth. No-till and reduced-till seeding significantly increase soil moisture content, promotes topsoil root growth, and long-term conservation tillage can increase crop yields.

[0003] In traditional farming methods, the seeder delivers seeds along a pre-set trajectory through the interaction between its soil-contacting components and the soil. However, because straw mulch is a key feature of conservation tillage, the traditional two-factor interaction between the machine and soil transforms into a three-factor interaction between the machine, straw, and soil. The large amount of pulverized straw covering the ground can easily cause blockages in the no-till seeder's implements during operation, resulting in poor field maneuverability.

[0004] Currently, the method for assessing the mobility of no-till seeding machinery is still qualitative, relying on observation to determine whether blockages occur during field operations. However, this method introduces a degree of subjectivity into the observer's assessment of the machinery's field mobility, leading to inaccurate results. Summary of the Invention

[0005] The purpose of this invention is to provide a quantitative analysis method for the passability of machinery in the no-till seeding process based on discrete element method, so as to solve the problems mentioned in the background art.

[0006] A quantitative analysis method for the passability of no-till seeding machinery based on discrete element method is applied to a passability quantitative analysis system, which includes: a no-till anti-blockage unit, a virtual soil trough, and a machinery passability quantitative analysis model;

[0007] The method includes:

[0008] A comprehensive soil sensor A is established based on the location of soil layers;

[0009] A comprehensive straw sensor C is established based on the location of the straw layer;

[0010] Analyze and calculate the overall soil and straw loss quality in the virtual soil trough;

[0011] Calculate the total mass loss Φ;

[0012] Establish soil blocking sensor B based on the location of the blocking unit;

[0013] Establish a straw blocking sensor D based on the location of the blocking unit;

[0014] Real-time statistics on the soil and straw blockage around the blockage unit;

[0015] Calculate the maximum congestion mass Υ;

[0016] The computer has a congestion rate Δ;

[0017] Analyze and classify the experimental data.

[0018] The no-till anti-clogging unit is used to conduct machine passability tests in a virtual soil trough, and the machine passability quantitative analysis model is used to calculate the clogging rate of the no-till anti-clogging unit and classify the clogging situation.

[0019] The virtual soil trough serves as the testing environment for the no-till anti-blocking unit, allowing the no-till anti-blocking unit to simulate the no-till operation process and providing a material carrier and data source for the quantitative analysis model of machine passability;

[0020] The quantitative analysis model for machine passability is used to quantitatively analyze the passability of no-till anti-clogging units, calculate the clogging rate, and determine the clogging situation.

[0021] The overall soil sensor A established based on soil layer location includes:

[0022] The overall soil sensor A is designated as an overall mass sensor, which only reads the real-time mass of soil particles within the sensor; wherein, the parameters of the overall soil sensor A include length l. A Width w A Height h A And relative position;

[0023] The overall straw sensor C established based on the location of the straw layer includes:

[0024] The overall straw sensor C is defined as an overall mass sensor, which only reads the real-time mass of straw particles within the sensor; wherein, the parameters of the overall straw sensor C include length l C Width w C Height h C And relative position;

[0025] The establishment of the soil blocking sensor B based on the location of the blocking unit includes:

[0026] The soil clogging sensor B is designated as a bulk mass sensor, which only reads the real-time mass of soil particles within the sensor; wherein, the parameters of the soil clogging sensor B include length l. BWidth w B Height h B Relative position and movement route;

[0027] The establishment of the straw blocking sensor D based on the location of the anti-blocking unit includes:

[0028] The type of straw blockage sensor D is defined as a whole-body mass sensor, which only reads the real-time mass of straw particles within the sensor; wherein, the parameters of the straw blockage sensor D include length l D Width w D Height h D The relative position and the path of movement.

[0029] Optionally, the establishment of the overall soil sensor A based on soil layer location includes:

[0030] When conducting machine passability tests in the field, based on the stated length l A Width w A Height h A The overall soil sensor A is determined by its relative position.

[0031] Optionally, the establishment of the overall straw sensor C based on the location of the straw layer includes:

[0032] When conducting machine passability tests in the field, it is necessary to determine the required length l. C Width w C Height h C The overall straw sensor C is determined by its relative position.

[0033] Optionally, the analysis and calculation of the overall soil and straw loss mass in the virtual soil trough includes:

[0034] Run and observe the overall simulation process, and observe and analyze the start and end times of the simulation; wherein, the parameters of the overall simulation process include the total simulation running time t, the soil particle mass m at the start time of the overall soil sensor A. A | t＝0 The mass of soil particles (m) at the termination time of the overall soil sensor A A | t＝3 The mass m of straw particles at the initial moment of the overall straw sensor C. C | t＝0 The mass m of straw particles at the termination time of the overall straw sensor C. C | t＝3 Calculate the total soil loss mass (m³) within the virtual soil trench. A | t＝3 -m A | t＝0 ), calculate the total mass of straw loss within the virtual soil trough (m)C | t＝3 -m C | t＝0 ).

[0035] Optionally, the calculation of the overall loss of mass Φ includes:

[0036] Establish the calculation equation for the overall loss mass Φ of soil particles and straw particles:

[0037] Φ=μ(m A | t＝3 -m A | t＝0 )+ν(m C | t＝3 -m C | t＝0 )

[0038] The overall loss mass Φ is calculated using the aforementioned calculation equation, where μ is the soil clogging coefficient, ranging from 0 to 5; and ν is the straw clogging coefficient, ranging from 0 to 20.

[0039] Optionally, the step of establishing the soil blocking sensor B based on the location of the blocking unit includes:

[0040] When conducting machine passability tests in the field, based on the stated length l B Width w B Height h B The relative position and movement path of the soil blockage sensor B are used to determine the soil blockage.

[0041] Optionally, the step of establishing the straw blockage sensor D based on the location of the anti-blockage unit includes:

[0042] When conducting machine passability tests in the field, based on the stated length l D Width w D Height h D The relative position and movement path determine the obstruction of straw sensor D.

[0043] Optionally, the real-time statistical analysis of soil and straw blockage around the anti-blocking unit includes:

[0044] Run and observe the overall simulation process, and record each interval point saved after the simulation ends;

[0045] Provide storage intervals to determine the soil and straw blockage around the blockage unit;

[0046] The parameters of the overall simulation process include the total simulation run time t and the target saving interval.

[0047] Optionally, the calculation of the maximum congestion mass Y includes:

[0048] The calculation equation for the maximum clogging mass Y of soil particles and straw particles is as follows:

[0049] Υ=max{μ·m B | t＝i +ν·m D | t＝i},{i|0≤i≤3}

[0050] The maximum congestion mass Y is calculated using the equation for Y.

[0051] Where μ is the soil clogging coefficient, ranging from 0 to 5; ν is the straw clogging coefficient, ranging from 0 to 20; and i is the μ·m value during the overall simulation motion. B +ν·m D The moment when the maximum value is reached.

[0052] Optionally, the computer has a congestion rate Δ, including:

[0053] The formula for calculating the equipment blockage rate Δ is as follows:

[0054]

[0055] The calculation equation for Δ is used to calculate the soil clogging rate Δ, where μ is the soil clogging coefficient, ranging from 0 to 5; ν is the straw clogging coefficient, ranging from 0 to 20; and i is the μ·m value during the overall simulation process. B +ν·m D The moment when the maximum value is reached.

[0056] Optionally, the analysis and classification of the experimental data includes:

[0057] The calculation results of the clogging rate Δ and the clogging situation that occurs in the actual operation of no-till seeders are classified and graded; among them, when μ·m B +ν·m D When the maximum value is reached, the machine is most likely to become clogged; when Δ≤8%, the machine will not become clogged; when 8%<Δ≤12%, the machine will be slightly clogged; when 12%<Δ≤18%, the machine will be moderately clogged; when Δ>18%, the machine will become severely clogged.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The discrete element simulation method can be used to preliminarily determine the passability of the no-till anti-blockage equipment designed by researchers before processing the equipment, thus saving costs and increasing efficiency;

[0060] 2. It can quantitatively analyze the field mobility of machinery to a certain extent, filling a gap in this field and improving the objectivity of judging the field mobility of no-till seeding equipment. It provides theoretical reference and technical support for researchers in the field of no-till seeding to calculate the mobility of machinery. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0062] Figure 1 This is a system framework diagram of a quantitative analysis method for the passability of machinery in the no-till seeding process based on discrete element method.

[0063] Figure 2a This is a top view of a virtual soil trench;

[0064] Figure 2b This is a side view of a virtual soil trench;

[0065] Figure 2c This is a slanted view of a virtual soil trench;

[0066] Figure 3 This is a flowchart illustrating a quantitative analysis method for the passability of machinery in the no-till seeding process based on discrete element method.

[0067] Figure 4a This is a side view of the soil quality monitoring sensor in the quantitative analysis model of equipment passability;

[0068] Figure 4b This is a front view of the soil quality monitoring sensor in the quantitative analysis model of equipment passability.

[0069] Figure 4c This is a front view of the soil quality monitoring sensor in the quantitative analysis model of equipment passability.

[0070] Figure 5a This is a side view of the straw quality monitoring sensor in the quantitative analysis model of machine passability;

[0071] Figure 5b This is a front view of the straw quality monitoring sensor in the quantitative analysis model of machine passability;

[0072] Figure 5c This is a front view of the straw quality monitoring sensor in the quantitative analysis model of machine passability;

[0073] Figure 6This is a flowchart illustrating a quantitative analysis method for the passability of machinery in the no-till seeding process based on discrete element method. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] The discrete element method-based quantitative analysis method for the passability of no-till seeding machinery provided in this invention can be applied to, for example... Figure 1 The system shown is a quantitative analysis system for passability, which includes a no-till anti-blockage unit 1, a virtual soil trough 2, and a quantitative analysis model for machine passability 3.

[0076] Specifically, the no-till anti-clogging unit 1 is used to conduct machinery passability tests in the virtual soil trough 2. The clogging rate of the no-till anti-clogging unit 1 is calculated based on the machinery passability quantitative analysis model 3, and the clogging situation is classified.

[0077] Virtual trench 2 serves as the testing environment for no-till anti-clogging unit 1, providing conditions for simulating no-till operations and offering a material carrier and data source for the quantitative analysis model 3 of machine passability. Virtual trench 2, as... Figures 2a to 2c As shown.

[0078] Preferably, in this embodiment, the virtual soil trough 2 is set with a length, width, and height of L×W×H, and dimensions of 3000mm×1000mm×280mm, and the average straw covering thickness is H. s =47mm. Furthermore, to best replicate and simulate the actual operation of the no-till anti-blocking unit 1, a seeding baffle is constructed on the virtual soil trough to block splashed soil and straw particles. The distance between the baffle and the surface of the soil particle layer is H. D =475mm.

[0079] The quantitative analysis model 3 for machine passability is used to quantitatively analyze the machine passability of the no-till anti-blockage unit 1, and can calculate the blockage rate and determine the blockage situation.

[0080] It should be noted that, Figure 1 This is merely an example of the system architecture of an embodiment of the present invention, and the present invention does not impose any specific limitations on it.

[0081] Based on the system architecture shown above Figure 3 This is a flowchart illustrating a method for quantitative analysis of the passability of machinery in no-till seeding based on discrete element method, as provided in an embodiment of the present invention. The method includes:

[0082] Step 301: Establish an overall soil sensor A based on the soil layer location.

[0083] Optionally, step 301 may include: during field testing of implement passability, based on the length l A Width w A Height h A The overall soil sensor A is determined by its relative position.

[0084] Specifically, the type of the overall soil sensor A is set as an overall mass sensor, which only reads the real-time mass of soil particles within the sensor. The length l of the overall soil sensor A is set. A The range is 0.1 to 1 L, and preferably, in this embodiment, it is 1 L. A =2 / 3L = 2000mm. The width w of the overall soil sensor A. A Equal to the distance between two adjacent no-till anti-blocking units 1, in this embodiment, w is taken as A =340mm. The height h of the overall soil sensor A. A The range is 0.1 to 1H, and preferably, in this embodiment, h is taken as h. A =200mm. The center of the integral soil sensor A is the center of the virtual soil trough 2. The top of the integral soil sensor A is positioned so that it just covers the soil particles in the virtual soil trough 2 and fits against the bottom of the blocking soil sensor B. The position of the integral soil sensor A remains fixed. Figures 4a to 4c As shown. It should be noted that when conducting machine passability tests in the field, the overall soil sensor A needs to be set to only read the real-time soil particle mass within the sensor, and it should be installed around the soil layer being tested for more accurate testing.

[0085] Step 302: Establish an overall straw sensor C based on the location of the straw layer.

[0086] Optionally, step 302 may include: when conducting machine passability tests in the field, it is necessary to determine the appropriate parameters based on the length l. C Width w C Height h C The overall straw sensor C is determined by its relative position.

[0087] Specifically, the overall straw sensor C is set to an overall mass sensor, which only reads the real-time mass of straw particles within the sensor. The length l of the overall straw sensor C is set. C The range is 0.1 to 1 L, and preferably, in this embodiment, it is 1 L. C =l A =2000mm. Width w of the overall straw sensor C. CEqual to the distance between two adjacent no-till anti-blocking units 1, in this embodiment, w is taken as C =w A =340mm. The height h of the overall straw sensor C. C The range is 0.1 to 1H, and preferably, in this embodiment, h is taken as h. C =h A =200mm. The top of the integral straw sensor C exactly covers the centroid of the straw particles. The top of the integral straw sensor C is in contact with the bottom of the obstructing straw sensor D, and the position of the integral straw sensor C remains fixed. Figures 5a to 5c As shown. It should be noted that when conducting machine passability tests in the field, the overall straw sensor C needs to be set to only read the real-time straw particle mass within the sensor, and it should be installed around the straw layer being tested for more accurate testing.

[0088] Step 303: Analyze and calculate the overall soil and straw loss quality of the virtual soil trough.

[0089] Specifically, the overall simulation process is run and observed, and the start and end times of the simulation are observed and analyzed. Preferably, the total simulation time in this embodiment is t = 3s. The soil particle mass at the start time of the overall soil sensor A is read and recorded as m. A|t＝0 The soil particle mass at the termination time of the overall soil sensor A is recorded as m. A|t＝3 The mass of straw particles at the initial moment of the overall straw sensor C is read and denoted as m. C|t＝0 The mass of straw particles at the termination time of the overall straw sensor C is read and denoted as m. C|t＝3 Calculate the total soil loss mass within the virtual soil trough (m). A | t＝3 -m A | t＝0 ), calculate the total mass of straw lost within the virtual soil trough (m C | t＝3 -m C | t＝0 ).

[0090] Step 304: Calculate the total loss mass Φ.

[0091] Step 304 specifically includes:

[0092] Establish the calculation equation for the overall loss mass Φ of soil particles and straw particles:

[0093] Φ=μ(m A | t＝3 -m A | t＝0 )+ν(m C | t＝3 -m C| t＝0 )

[0094] The overall loss mass Φ was calculated using this formula, where μ is the soil clogging coefficient, ranging from 0 to 5; and ν is the straw clogging coefficient, ranging from 0 to 20.

[0095] Step 305: Establish the soil blocking sensor B based on the location of the blocking unit.

[0096] Optionally, step 305 may include: during field implement passability testing, based on the length l B Width w B Height h B The relative position and movement path of the soil blockage sensor B are used to determine the soil blockage.

[0097] Specifically, the type of soil clogging sensor B is set as a bulk mass sensor, which only reads the real-time mass of soil particles within the sensor. The length l of soil clogging sensor B is set. B Slightly larger than the length of the no-till anti-blocking unit 1, and preferably an integer; in this embodiment, it is preferably l. B =1100mm. Width w of soil clogging sensor B B Equal to the distance between two adjacent no-till anti-blocking units 1, in this embodiment, w is taken as B =340mm. The height h of the soil clogging sensor B. B Slightly larger than the height of the no-till anti-blocking unit 1, and preferably rounded to an integer; in this embodiment, h is preferred. B =500mm. The center of the soil clogging sensor B coincides with the centroid of the no-till anti-clogging unit 1, and the top plane of the soil clogging sensor B is set slightly higher than the sowing baffle. The soil clogging sensor B is fixed in the vertical direction and moves with the no-till anti-clogging unit 1 in the horizontal direction. That is, the soil clogging sensor B and the no-till anti-clogging unit 1 remain relatively stationary when moving in the horizontal direction. Figures 4a to 4b As shown. It should be noted that when conducting machine passability tests in the field, the soil blockage sensor B needs to be set to only read the real-time soil particle mass within the sensor, and it should be installed around the no-till anti-blockage unit 1 for more accurate testing.

[0098] Step 306: Establish the straw blockage sensor D based on the location of the anti-blockage unit.

[0099] Optionally, step 306 may include: during field implement passability testing, based on the length l D Width w D Height h D The relative position and movement path determine the obstruction of straw sensor D.

[0100] Specifically, the type of the straw blockage sensor D is set as a whole-mass sensor, which only reads the real-time mass of straw particles within the sensor. The length l of the straw blockage sensor D is set. D Slightly larger than the length of the no-till anti-blocking unit 1, and preferably an integer; in this embodiment, it is preferably l. D =l B =1100mm. Width w of the straw blockage sensor D. B Equal to the distance between two adjacent no-till anti-blocking units 1, in this embodiment, w is taken as D =w B =340mm. The height h of the straw blockage sensor D. D Approximately equal to the height h of the soil-clogging sensor B B With an average straw cover thickness of H s The difference, i.e. h D ≈h B -H s And it should be an integer if possible; preferably, in this embodiment, h is taken as h. D =455mm. The top plane of the straw clogging sensor D is slightly higher than the sowing baffle. The straw clogging sensor D is fixed in the vertical direction and moves horizontally with the no-till anti-clogging unit 1. That is, the straw clogging sensor D and the no-till anti-clogging unit 1 remain relatively stationary when moving horizontally. Figures 5a to 5c As shown. It should be noted that when conducting machine passability tests in the field, the straw blockage sensor D needs to be set to only read the real-time straw particle mass inside the sensor, and it should be installed around the no-till anti-blockage unit 1 for more accurate testing.

[0101] Step 307: Real-time statistics on the soil and straw blockage around the anti-blocking unit.

[0102] Step 307 may specifically include:

[0103] Run and observe the overall simulation process, and record each interval point saved after the simulation ends;

[0104] The storage interval is set to determine the soil and straw blockage around the blockage unit; wherein, the parameters of the overall simulation process include the total simulation run time t and the target storage interval.

[0105] The target saving interval during the simulation process should be kept as small as possible to obtain more and more accurate data distribution points. Preferably, in this embodiment, the total simulation running time is t = 3s, and the target saving interval is set to 0.01s, which can acquire 300 sets of data. The data on the mass of soil particles in the soil blockage sensor B and the mass of straw particles in the straw blockage sensor D are exported in the form of an Excel spreadsheet, and the sum of the mass of soil particles and the mass of straw particles is observed at which moment, and all the maximum values ​​appearing in the data are recorded.

[0106] Step 308: Calculate the maximum blockage mass Y.

[0107] Specifically, step 308 includes: establishing the calculation equation for the maximum clogging mass Y of soil particles and straw particles as follows:

[0108] Υ=max{μ·m B | t＝i +ν·m D | t＝i},{i|0≤i≤3}

[0109] The maximum clogging mass Y is calculated using the equation described above, where μ is the soil clogging coefficient, ranging from 0 to 5; ν is the straw clogging coefficient, ranging from 0 to 20; and i is the mass of the entire simulated motion process (μ·m). B +ν·m D The moment when the maximum value is reached.

[0110] Step 309, computer has congestion rate Δ.

[0111] Specifically, step 309 includes:

[0112] The formula for calculating the equipment blockage rate Δ is as follows:

[0113]

[0114] The calculation equation for Δ is used to calculate the soil clogging rate Δ, where μ is the soil clogging coefficient, ranging from 0 to 5; ν is the straw clogging coefficient, ranging from 0 to 20; and i is the μ·m value during the overall simulation process. B +ν·m D The moment when the maximum value is reached.

[0115] Step 310: Analyze and classify the experimental data.

[0116] Specifically, step S310 includes:

[0117] The calculation results of the clogging rate Δ and the clogging situation that occurs in the actual operation of no-till seeders are classified and graded; among them, when μ·m B +ν·mD When the maximum value is reached, the machine is most likely to become clogged; when Δ≤8%, the machine will not become clogged; when 8%<Δ≤12%, the machine will be slightly clogged; when 12%<Δ≤18%, the machine will be moderately clogged; when Δ>18%, the machine will become severely clogged.

[0118] To better explain the present invention, the following will be used... Figures 2a to 2c Taking the virtual soil tank 2 as an example, the steps and flow of the embodiment of the present invention are described as follows: Figure 6 As shown, the details are as follows:

[0119] Step 601: Set up the geometric model of soil and straw and input their physical parameters.

[0120] Specifically, the material parameters of the no-till anti-blocking unit 1, as well as the contact parameters between soil and material, soil and straw, soil and soil, straw and material, and straw and straw are set.

[0121] It should be noted that Poisson's ratio refers to the ratio of the absolute values ​​of the transverse normal strain to the axial normal strain when a material is under uniaxial tension or compression; it is also called the transverse deformation coefficient and is an elastic constant reflecting the transverse deformation of a material. Shear modulus, also known as shear stress to strain or rigidity modulus, is one of the mechanical property indicators of a material. It is the ratio of shear stress to shear strain under shear stress within the elastic deformation proportional limit. It characterizes the material's ability to resist shear strain. A large modulus indicates high rigidity of the material.

[0122] For example, the Poisson's ratio of soil particles is set to 0.38 and the shear modulus of soil particles is set to 1.0 × 10⁻⁶. 6 MPa, soil particle density is 1850 kg / m³ 3 The Poisson's ratio of the straw pellets is 0.40, and the shear modulus of the straw pellets is 1.0 × 10⁻⁶. 6 MPa, straw particle density is 240 kg / m³ 3 The soil particle model is designed with spherical particles, each with a radius of 5 mm. The straw particle model uses a long linear model, with its baseline model consisting of 22 spherical particles, each with a radius and a center-to-center spacing of 6 mm, forming a straw particle model with a length of 138 mm. The material properties of the no-till anti-clogging unit 1 are set as 45# steel with a density of 7800 kg / m³. 3 The Poisson's ratio is 0.31, and the shear modulus is 7.0 × 10⁻⁶. 10 Pa.

[0123] Step 602: Establish a virtual soil trough model based on the actual field environment.

[0124] Specifically, soil particles are first generated and allowed to settle naturally under gravity. By appropriately extending the simulation time, stable bonds are formed between the soil particles, and they are kept as still as possible. After the soil has settled, straw particles are added on top of the soil particles to make the model as close as possible to the actual situation of soil and straw. Then, the simulation time is extended to allow the soil and straw particles to settle fully, forming the virtual soil trough 2 as shown in Figure 2.

[0125] Step 603: Import the no-till anti-blocking unit model into the virtual soil trough and set the operation parameters to simulate the field operation process.

[0126] Specifically, after the virtual soil trench is established, preferably in this embodiment, the SolidWorks 3D model of the no-till anti-blocking unit 1 at a scale of 1:1 is imported into the discrete element simulation software EDEM as a .STEP format file.

[0127] The no-till anti-clogging unit 1 is positioned at one end of the virtual soil trough 2 to begin operation. To ensure the continuity of the operation process of the no-till anti-clogging unit 1 and the movement of soil and straw particles, the minimum calculation step size is set to 0.0001s, the total simulation time is 3.0s, and the grid size is set to 2.5 times the minimum soil particle size, i.e., 17.5mm. The simulation operation process of the no-till anti-clogging unit 1 is shown in Figures 4 and 5.

[0128] Step 604: Compare the simulation results with the actual results.

[0129] Specifically, after the simulation is completed, the simulation process needs to be observed multiple times, and the interaction between the no-till anti-blocking monomer 1 and the soil and straw particles needs to be analyzed to see if it is consistent with the field operation process. If the simulation effect is not ideal, it is necessary to return to step 601 to readjust the parameters until the ideal simulation effect is achieved before proceeding to the next step.

[0130] Step 605: Establish a quantitative analysis model for the machine's passability.

[0131] Specifically, the quantitative analysis model for the machine's passability is the sum of steps 301 to 308, as detailed above.

[0132] Step 309, the computer has a congestion rate Δ;

[0133] Step 310: Analyze and classify the experimental data.

[0134] It should be noted that these two steps are exactly the same as the steps with the same numbers mentioned above, so no further explanation is needed here.

[0135] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] In a typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input / output interfaces, network interfaces, etc.

[0137] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM, and memory includes at least one memory chip. Memory is an example of computer-readable media.

[0138] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0139] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0141] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0142] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A quantitative analysis method for the passability of machinery in no-till seeding process based on discrete element method, characterized in that, The system is applied to a quantitative analysis system for passability, which includes: a no-till anti-blockage unit (1), a virtual soil trough (2), and a quantitative analysis model for the passability of machinery (3); The method includes: A comprehensive soil sensor A is established based on the location of soil layers; A comprehensive straw sensor C is established based on the location of the straw layer; Analyze and calculate the overall soil and straw loss quality in the virtual soil trough; Calculate the total mass loss Φ; Establish soil blocking sensor B based on the location of the blocking unit; Establish a straw blocking sensor D based on the location of the blocking unit; Real-time statistics on the soil and straw blockage around the blockage unit; Calculate the maximum congestion mass Υ; The computer has a congestion rate Δ; Analyze and classify the experimental data. The no-tillage anti-blockage unit (1) is used to conduct machinery passability tests in a virtual soil trough (2), and the machinery passability quantitative analysis model (3) is used to calculate the blockage rate of the no-tillage anti-blockage unit (1) and classify the blockage situation. The virtual soil trough (2) serves as the test environment for the no-till anti-blocking unit (1), allowing the no-till operation process to be simulated by the no-till anti-blocking unit (1), and providing a material carrier and data source for the quantitative analysis model (3) of machine passability. The quantitative analysis model (3) for the passability of the machinery is used to quantitatively analyze the passability of the no-till anti-blockage unit (1), calculate the blockage rate and determine the blockage situation; The overall soil sensor A established based on soil layer location includes: The overall soil sensor A is designated as an overall mass sensor, which only reads the real-time mass of soil particles within the sensor; wherein, the parameters of the overall soil sensor A include length l. A Width w A Height h A And relative position; The overall straw sensor C established based on the location of the straw layer includes: The overall straw sensor C is defined as an overall mass sensor, which only reads the real-time mass of straw particles within the sensor; wherein, the parameters of the overall straw sensor C include length l C Width w C Height h C And relative position; The establishment of the soil blocking sensor B based on the location of the blocking unit includes: The soil clogging sensor B is designated as a bulk mass sensor, which only reads the real-time mass of soil particles within the sensor; wherein, the parameters of the soil clogging sensor B include length l. B Width w B Height h B Relative position and movement route; The establishment of the straw blocking sensor D based on the location of the anti-blocking unit includes: The type of straw blockage sensor D is defined as a whole-body mass sensor, which only reads the real-time mass of straw particles within the sensor; wherein, the parameters of the straw blockage sensor D include length l D Width w D Height h D The relative position and the path of movement.

2. The method for quantitative analysis of the passability of machinery in no-till seeding process based on discrete element method according to claim 1, characterized in that, The overall soil sensor A established based on soil layer location includes: When conducting machine passability tests in the field, based on the stated length l A Width w A Height h A The overall soil sensor A is determined by its relative position.

3. The method for quantitative analysis of the passability of machinery in no-till seeding process based on discrete element method according to claim 1, characterized in that, The overall straw sensor C established based on the location of the straw layer includes: When conducting machine passability tests in the field, it is necessary to determine the required length l. C Width w C Height h C The overall straw sensor C is determined by its relative position.

4. The method for quantitative analysis of the passability of machinery in the no-till seeding process based on discrete element method according to claim 1, characterized in that, The analysis and calculation of the overall soil and straw loss mass in the virtual soil trough includes: Run and observe the overall simulation process, and observe and analyze the start and end times of the simulation; wherein, the parameters of the overall simulation process include the total simulation running time t, the soil particle mass m at the start time of the overall soil sensor A. A | t＝0 The mass of soil particles (m) at the termination time of the overall soil sensor A A | t＝3 The mass m of straw particles at the initial moment of the overall straw sensor C. C | t＝0 The mass m of straw particles at the termination time of the overall straw sensor C. C | t＝3 Calculate the total soil loss mass (m³) within the virtual soil trench. A | t＝3 -m A | t＝0 ), calculate the total mass of straw loss within the virtual soil trough (m) C | t＝3 -m C | t＝0 ).

5. The method for quantitative analysis of the passability of machinery in the no-till seeding process based on discrete element method according to claim 1, characterized in that, The calculation of the overall loss mass Φ includes: Establish the calculation equation for the overall loss mass Φ of soil particles and straw particles: Φ=μ(m A | t＝3 -m A | t＝0 )+ν(m C | t＝3 -m C | t＝0 ) The overall loss mass Φ is calculated using the aforementioned calculation equation, where μ is the soil blockage coefficient, ranging from 0 to 5; and ν is the straw blockage coefficient, ranging from 0 to 20.

6. The method for quantitative analysis of the passability of machinery in no-till seeding process based on discrete element method according to claim 1, characterized in that, The establishment of the soil blocking sensor B based on the location of the blocking unit includes: When conducting machine passability tests in the field, based on the stated length l B Width w B Height h B The relative position and movement path of the soil blockage sensor B are used to determine the soil blockage.

7. The method for quantitative analysis of the passability of machinery in no-till seeding process based on discrete element method according to claim 1, characterized in that, The establishment of the straw blocking sensor D based on the location of the anti-blocking unit includes: When conducting machine passability tests in the field, based on the stated length l D Width w D Height h D The relative position and movement path determine the obstruction of straw sensor D.

8. The method for quantitative analysis of the passability of machinery in no-till seeding process based on discrete element method according to claim 1, characterized in that, The real-time statistical analysis of soil and straw blockage around the anti-blocking unit includes: Run and observe the overall simulation process, and record each interval point saved after the simulation ends; Provide storage intervals to determine the soil and straw blockage around the blockage unit; The parameters of the overall simulation process include the total simulation run time t and the target saving interval.

9. The method for quantitative analysis of the passability of machinery in no-till seeding process based on discrete element method according to claim 1, characterized in that, The calculation of the maximum congestion mass Y includes: The calculation equation for the maximum clogging mass Y of soil particles and straw particles is as follows: Y=max{μ·m B | t＝i +n·m D | t＝i },{i|0≤i≤3} The maximum congestion mass Y is calculated using the equation for Y. Where μ is the soil clogging coefficient, ranging from 0 to 5; ν is the straw clogging coefficient, ranging from 0 to 20; and i is the μ·m value during the overall simulation motion. B +ν·m D The moment when the maximum value is reached.

10. The method for quantitative analysis of the passability of machinery in no-till seeding process based on discrete element method according to claim 1, characterized in that, The computer has a congestion rate Δ, including: The formula for calculating the equipment blockage rate Δ is as follows: The calculation equation for Δ is used to calculate the soil clogging rate Δ, where μ is the soil clogging coefficient, ranging from 0 to 5; ν is the straw clogging coefficient, ranging from 0 to 20; and i is the μ·m value during the overall simulation process. B +ν·m D The moment when the maximum value is reached.

11. The method for quantitative analysis of the passability of machinery in no-till seeding process based on discrete element method according to claim 1, characterized in that, The analysis and classification of experimental data includes: The calculation results of the clogging rate Δ and the clogging situation of no-till seeders during actual operation are classified and graded. Among them, when μ·mB+ν·mD reaches its maximum value, the machine is most likely to be clogged; when Δ≤8%, the machine will not be clogged; when 8%<Δ≤12%, the machine is slightly clogged; when 12%<Δ≤18%, the machine is generally clogged; when Δ>18%, the machine will be severely clogged.

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