A quantitative analysis method of weed control effect based on discrete element flexible weed model

Abstract

The present invention discloses a method for quantitatively analyzing weeding effects based on a discrete element flexible weed model, and belongs to the field of agricultural machinery. The method includes importing a weeding mechanism model into a virtual soil trough, setting motion parameters and simulating a weeding operation process, wherein the virtual soil trough includes a flexible weed model and a soil model established based on a discrete element method; comparing the simulated weeding operation effect with the actual weeding operation effect, and if the first preset threshold is met, proceeding to the next step, otherwise, re-establishing the virtual soil trough; constructing sensors required for quantitative analysis of the weeding mechanism operation effect, including a weed quality sensor and a soil loss sensor; establishing a quantitative analysis model for the weeding mechanism operation effect, wherein the analysis model obtains the force condition of the weeding knife, the movement speed of the soil during the weeding process, the weed removal rate, and the operation soil movement rate based on the constructed sensors. The method can quantitatively analyze the operation performance of the weeding knife, and promote the development of weeding mechanization.

Description

Technical Field

[0001] The invention belongs to the field of agricultural machinery, and in particular relates to a weed control effect quantitative analysis method based on a discrete element flexible weed model. Background Art

[0002] For a long time, weeds have had a significant impact on agricultural production. They compete with crops for sunlight, water and nutrients, severely restricting crop growth, directly affecting yield and quality, and thus reducing farmers' income and agricultural production efficiency. Take corn as an example. As an important food crop in the world, corn is often disturbed by weeds during the 3-5 leaf stage, especially weeds such as field bindweed. If not removed in time, it may lead to slow growth and reduced yield. Traditional weeding methods require huge manpower investment and are inefficient, making it difficult to meet the needs of modern agricultural refined management. Therefore, it is particularly important to develop corn weeding robots that can accurately weed and reduce seedling injury rates. Such robots should be suitable for automated operations in narrow fields, reduce labor intensity, improve weeding efficiency, and promote the development of intelligent and precise agriculture.

[0003] In recent years, the discrete element method (DEM) has been widely used in the field of agricultural mechanization as an effective tool for analyzing and simulating the behavior of granular objects. This method can be used to study the interaction between weeding machinery, crops and soil, and provide important theoretical support for optimizing weeding mechanisms. The discrete element method can simulate the mechanical properties of weeds at different growth stages and their interaction with soil, helping researchers to deeply understand the impact of weeds on mechanical operations, thereby optimizing the design of weeding tools and actuators. By quantitatively analyzing the operation process, researchers can clarify the impact of different mechanical parameters on the weeding effect and improve the design accuracy and efficiency of weeding robots.

[0004] Combined with discrete element simulation technology, researchers were able to build a flexible weed model to simulate its growth, distribution, and dynamic behavior during mechanical operation. This simulation not only accurately reflects the interaction between weeds and soil, but also provides data support for the optimization of weeding mechanisms. For example, through simulation analysis, the optimal operating speed, tool angle, and force application method can be determined, thereby improving the working effect of the weeding robot. In addition, the application of simulation technology helps to reduce resource waste in actual experiments and speed up the research and development process. Therefore, the flexible weed model based on discrete element simulation and its quantitative analysis of the operating effect of the weeding mechanism have important research value and practical application prospects. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a quantitative analysis method of weeding effect based on a discrete element flexible weed model, which can quantitatively analyze the operating performance of a weeding knife, promote the development of weeding mechanization, improve operating efficiency and reduce production costs.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] A method for quantitatively analyzing weed control effects based on a discrete element flexible weed model, the method comprising:

[0008] The weeding mechanism model is imported into a virtual soil trough, motion parameters are set and a weeding operation process is simulated, wherein the virtual soil trough includes a flexible weed model and a soil model established based on a discrete element method;

[0009] Compare the simulated weeding operation effect with the actual weeding operation effect, if the first preset threshold is met, proceed to the next step, otherwise, re-establish the virtual soil trough;

[0010] Construct sensors required for quantitative analysis of the effect of weed control mechanisms, including weed quality sensors and soil loss sensors;

[0011] A quantitative analysis model for the operation effect of a weeding mechanism is established. The analysis model obtains the force condition of the weeding blade, the movement speed of the soil during the weeding process, the weed removal rate, and the operation soil movement rate based on the constructed sensor.

[0012] The beneficial effects of the present invention are:

[0013] (1) A flexible discrete element simulation model of cornfield weeds was constructed, which can quantitatively analyze the operating performance of the weeding knife and provide an important basis for optimizing the weeding mechanism;

[0014] (2) This method provides reference and technical support for researchers in the field of field weed control technology, promotes the development of mechanized weed control, improves operational efficiency and reduces production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is an implementation framework diagram of a method for quantitatively analyzing weed control effects based on a discrete element flexible weed model of the present invention;

[0016] Figure 2 Schematic diagram of the process for establishing a flexible weed model;

[0017] Figure 3 (a) is a schematic diagram of determining weed species and measuring weed size and parameter ranges on site;

[0018] FIG3 (b) is a schematic diagram of using 3D modeling software to build a 3D model of weeds;

[0019] Figure 3 (c) is a schematic diagram of mesh division of the three-dimensional weed model;

[0020] FIG3( d ) is a schematic diagram of determining and outputting the centroid coordinate data of each grid;

[0021] Figure 3 (e) is a partial display of the data of the output centroid coordinate parameters of each grid;

[0022] Figure 3 (f) is a schematic diagram of the rapid particle filling of the weed model using the discrete element method;

[0023] Figure 3 (g) is a schematic diagram of the structure of the output discrete element simulation flexible weed model;

[0024] Figure 4 Schematic diagram of the process of establishing soil model;

[0025] Figure 5 It is a schematic diagram of the structure and parameters of the virtual soil trough;

[0026] Figure 6 It is a schematic flow chart of a method for quantitatively analyzing weed control effects based on a discrete element flexible weed model of the present invention;

[0027] FIG7 (a) is a schematic diagram of establishing a weed quality sensor and testing weed quality before operation;

[0028] FIG7( b ) is a schematic diagram of establishing a weed quality sensor and testing the weed quality after operation;

[0029] FIG8 (a) is a schematic diagram for analyzing the weeding mechanism starting to contact the weeds;

[0030] FIG8 (b) is a schematic diagram for analyzing the moment when the weeding mechanism pushes the weeds to the elastic deformation limit position;

[0031] Figure 8 (c) is a schematic diagram of analyzing the plastic tearing of weeds;

[0032] FIG8( d ) is a schematic diagram for analyzing the weeding mechanism cutting off weeds. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described in more detail below in conjunction with the drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The embodiments of the present invention are described in detail below in conjunction with the drawings.

[0034] The embodiment of the present invention provides a method for quantitatively analyzing weed control effects based on a discrete element flexible weed model, which can be applied to Figure 1In the system architecture shown, the system architecture includes a weeding mechanism model 1, a virtual soil trough 2, and an operation effect quantitative analysis model 3.

[0035] Specifically, the weeding mechanism model 1 is used to test the weeding operation effect in the virtual soil trough 2, so as to calculate the values ​​of the force and weed removal rate of the weeding mechanism model 1 through the operation effect quantitative analysis model 3, and analyze the operation effect.

[0036] Preferably, the weeding mechanism can be any form of in-ground or non-in-ground weeding tool or mechanism, such as a telescopic knife, a single-stage knife, a multi-stage knife, a curved / surface knife, a telescopic spring tooth, etc., which can be imported into this model to test the weeding effect.

[0037] The virtual soil trough 2 serves as a test environment for the weeding mechanism model 1, so that the weeding mechanism model 1 can simulate the weeding operation process and provide a material carrier and data source for the operation effect quantitative analysis model 3. The construction process of the virtual soil trough 2 includes establishing a flexible weed model and establishing a soil model. Figure 5 shown.

[0038] The size range of the virtual soil trough, that is, the range of length, width and height is: L s Take 50~4000 mm, W s Take 50~4000mm, H s Take 10~200 mm. Preferably, in this embodiment, the length, width and height of the virtual soil trough 2 are set to L×W×H respectively, the size parameters are 80 mm×80 mm×24 mm, and the depth of the flexible weed model into the soil is 20 mm. In addition, during the simulation process, only one weed can be operated, or multiple weeds can be operated at the same time. The number of weeds that can be operated at the same time in the present invention is 1-50. Preferably, in this embodiment, the number of weeds in the virtual soil trough 2 is set to 1.

[0039] The operation effect quantitative analysis model 3 is used to quantitatively calculate the values ​​of the force and weed removal rate of the weeding mechanism model 1, and analyze the operation process and results of the weeding mechanism model 1 in the virtual soil trough 2.

[0040] It should be noted that Figure 1 This is only an example of the system architecture of the embodiment of the present invention, and the present invention does not make any specific limitation to this.

[0041] Based on the system architecture shown above, Figure 2 A schematic diagram of a process for establishing a flexible weed model provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the method includes:

[0042] Step 202, determine the weed species and measure the weed size and parameter range in the field.

[0043] Specifically, in Northeast China, when corn grows to the 3-5 leaf stage, field bindweed is one of the most threatening weeds in the field. In order to construct a flexible simulation model of field bindweed, its physical dimensions during the growth period need to be accurately obtained. Figure 3 (a) shows the growth morphology of field bindweed at the 3-5 leaf stage of corn. Preferably, the weed height range measured on the spot in the present invention is 2-5 cm, the weed width is 5-10 cm, the stem length is 1-8 cm, the stem diameter is 0.1-0.3 cm, the number of leaves is 3-10, and the leaf area is 1-3 cm 2 The number of roots is 1-5, the length of the root system is 3-12 cm, and the rooting depth is 1-3 cm. These data provide an important basis for the subsequent construction of flexible three-dimensional weed models.

[0044] Step 203: Use 3D modeling software to create a 3D model of weeds.

[0045] Specifically, based on the main size parameters of the weeds listed above, the three-dimensional model was established using SolidWorks software, and the model was saved as a general ".STEP" format file. The model is shown in Figure 3 (b), and the dimensions of the three-dimensional model are all within the size range listed above.

[0046] The step 204 is to divide the three-dimensional weed model into meshes.

[0047] Specifically, the constructed three-dimensional weed model was imported into the Ansys Workbench software for meshing. The filling size of the particles was controlled by adjusting the mesh size. The size range of the mesh unit was set to 0.2~1 mm, and the maximum mesh size range was 1~5 mm. Finally, 15409 mesh units were generated. After the meshing was completed, the file was saved as a ".mesh" file in the Fluent input format. The mesh model of the weed after meshing is shown in Figure 3 (c).

[0048] Step 205, determining and outputting the centroid coordinate data of each grid.

[0049] Specifically, after the divided weed mesh model is imported into the Fluent software, the particle factory is compiled. First, open the Fluent software and read the ".mesh" file of the weed mesh model. Select the Compiled option in the Functions module of the User Defined interface, load and compile the CalcRadius.c file, as shown in Figure 3 (d). Then, select the Execute on Demand module in the User Defined interface, run the CalcRadius Volume program, and the system will generate a "point.txt" file in the specified folder. Copy the grid coordinate information in the file to the "Block_Factory_Data.txt" file, and the compilation result is shown in Figure 3 (e). The 0.001 in the first line indicates the start time of particle filling, and the second line shows that the total number of particles generated is 15409, followed by the grid coordinate data.

[0050] Step 206: Use the discrete element method to quickly fill the weed model with particles.

[0051] Specifically, EDEM software is used to quickly fill the weed 3D model with particles. Import the weed 3D model in ".STEP" format in the Geometry module of the Creator interface, and load the "BlockFactory_x64.dll" file in the same directory as the "Block_Factory_Data.txt" file in the particle plug-in factory (Plugin Factory). After running the simulation, the discrete element weed model that is quickly filled is shown in Figure 3 (f). At present, no mechanical relationship has been established between the particles, and relevant properties and physical interactions need to be added to the particles in the future.

[0052] Step 207, adding particle attributes and physical interactions to the discrete element weed model.

[0053] Specifically, first, in the Analyst interface of the EDEM software, use the Export Results Data option to export the X, Y, and Z coordinate parameters of each particle in the quickly filled weed model. Then, create a new weed particle model template, add a Meta-Particle model and import these coordinates to generate a discrete element weed model that can add particle properties and physical interactions.

[0054] Secondly, the particle properties and physical interactions were set, single spherical particles were selected as templates, the particle radius was set to 0.1-0.5 mm, the contact radius was 0.15-0.6 mm, and Bonding V2 was used as the mechanical relationship model between particles.

[0055] Then, set the particle parameters of the weed particles. For example, set the Poisson's ratio of the weed particles to 0.447 and the shear modulus of the weed particles to 5.214×10 7 MPa, weed particle density is 870 kg / m 3 The coefficient of restitution between weed particles is 0.40, the coefficient of static friction between weed particles is 0.40, and the coefficient of dynamic friction between weed particles is 0.15; the coefficient of restitution between soil and weed particles is 0.50, the coefficient of static friction between soil and weed particles is 0.70, and the coefficient of dynamic friction between soil and weed particles is 0.30; the coefficient of restitution between weed removal mechanism model 1 and weed particles is 0.424, the coefficient of static friction between weed removal mechanism model 1 and weed particles is 0.76, and the coefficient of dynamic friction between weed removal mechanism model 1 and weed particles is 0.131. The normal stiffness per unit area of ​​weeds is 3.12×10 9 N / m 3 , the tangential stiffness per unit area of ​​weeds is 3×10 8 N / m 3 , the critical normal stress of weeds is 9×10 6 Pa, the critical tangential stress of weeds is 400000 Pa, and the weed bonding radius is 5.

[0056] It should be noted that Poisson's ratio refers to the ratio of the absolute value of the transverse normal strain to the axial normal strain when the material is subjected to unidirectional tension or compression, also called the transverse deformation coefficient. It is an elastic constant that reflects the transverse deformation of the material. Shear modulus is the ratio of shear stress to strain. It is also called shear modulus or rigidity modulus. It is one of the mechanical properties of the material. It is the ratio of shear stress to shear strain within the proportional limit of elastic deformation under the action of shear stress. It characterizes the material's ability to resist shear strain. A large modulus means a strong rigidity of the material.

[0057] Finally, add a particle factory in the Geometry option to generate a particle model. To prevent particle explosion, set a smaller time step. The simulation generates 15403 particles and forms a total of 217724 bonding keys. The discrete element weed model after the simulation is shown in Figure 3 (g).

[0058] Step 208 : Compare and verify whether the physical properties and interactions of the discrete element weed model and actual weeds are similar or consistent.

[0059] Specifically, through methods such as three-point bending test and shear simulation test, it is tested whether the weeds have good flexibility characteristics, and whether their flexibility characteristics meet the second preset threshold, or are similar or consistent with the characteristics of the real root system. If the test result is not good, and the characteristics of the simulated flexible weeds are inconsistent with those of the real weeds, then the process returns to step 207 to recalibrate the particle properties and physical interactions for the discrete element weed model; until the characteristics of the simulated flexible weeds are similar or consistent with those of the real weeds, the next step is continued.

[0060] Step 209: outputting the discrete element simulation flexible weed model and its parameters.

[0061] Specifically, at this point, a discrete element simulation flexible weed model was successfully constructed, and the corresponding particle properties and physical interactions were recorded.

[0062] Further, the schematic diagram of the process of establishing the soil model of the present invention is as follows: Figure 4 As shown, the specific methods include:

[0063] Step 402, determine the properties and physical interactions of soil particles.

[0064] Specifically, the particle parameters of the soil particles are set. The Poisson's ratio of the soil particles is 0.38, and the shear modulus of the soil particles is 1.0×10 6 MPa, soil particle density is 1850 kg / m 3 The coefficient of restitution between soil particles is 0.60, the static friction coefficient between soil particles is 0.60, the dynamic friction coefficient between soil particles is 0.40, and the normal stiffness per unit area of ​​soil is 2.5×10 7 N / m 3 , the tangential stiffness per unit area of ​​soil is 1.5×10 7 N / m 3 , the critical normal stress of the soil is 250000 Pa, the critical tangential stress of the soil is 150000 Pa, and the soil bonding radius is 5. The coefficient of restitution between the weeding mechanism model 1 and the soil particles is 0.30, the static friction coefficient between the weeding mechanism model 1 and the soil particles is 0.50, and the dynamic friction coefficient between the weeding mechanism model 1 and the soil particles is 0.04. Set the material properties of the weeding mechanism model 1 to 45# steel with a density of 7865 kg / m 3 , Poisson's ratio is 0.3, and the shear modulus is 7.9×10 10 Pa.

[0065] Step 403: Use discrete element method to settle and fill soil particles.

[0066] Specifically, after generating the weed model, the soil particle model was further constructed. A single spherical particle was selected as the soil model. In order to balance the computational performance and simulation efficiency, the physical radius of the soil particle was set to 1-5 mm, and the contact radius was 1.5-7.5 mm. The particle size was randomly distributed, with a ratio ranging from 0.9 to 1.1. The soil particles were allowed to settle naturally under the action of gravity. By appropriately extending the simulation time, stable bonding bonds were generated between the soil particles and between the soil particles and the flexible weed particles, and the particles were kept as still as possible, so as to complete the settlement and filling of the soil particles.

[0067] Step 404 , comparing and verifying whether the physical properties and interactions of the discrete element soil model and the actual soil are similar or consistent.

[0068] Specifically, the soil particles are tested to see whether they have good surface soil properties through simulation and verification tests such as static friction test and soil slope test. If the test result is not good, the simulated soil particles and the real surface soil do not meet the third preset threshold or are inconsistent, then the process returns to step 402 to recalibrate the particle properties and physical interactions for the discrete element soil model; until the simulated soil particles and the real surface soil properties are similar or consistent, proceed to the next step.

[0069] Step 405: output the soil model and its parameters of discrete element simulation.

[0070] Specifically, at this point, a discrete element soil particle model was successfully constructed, and the corresponding particle properties and physical interactions were recorded.

[0071] Further, the schematic diagram of the process of the quantitative analysis method of the weeding mechanism operation effect of the present invention is as follows: Figure 6 As shown, the specific methods include:

[0072] Specifically, establishing the flexible weed model is the sum of steps 202 to 209, and establishing the soil model is the sum of steps 402 to 405. The established flexible weed model and the established soil model constitute the virtual soil trough 2, which has been described in detail above.

[0073] Step 601, import the weeding mechanism model into the virtual soil trough to set the motion parameters and simulate the weeding operation process.

[0074] Specifically, after the virtual soil trough 2 is established, preferably, in this embodiment, the SolidWorks three-dimensional model of the weeding mechanism model 1 with a ratio of 1:1 is imported into the discrete element simulation software EDEM in the form of a .STEP format file.

[0075] The weeding mechanism model 1 is set to start working at one end of the virtual soil trough 2. To ensure the continuity of cutting and the movement of weeds and soil particles during the operation of the weeding mechanism model 1, the minimum operation step is set to 0.00001 s, the total simulation time is 0.1 s, and the grid size is set to 2.5 times the minimum soil particle size, that is, 0.25 mm. The simulation operation process of the weeding mechanism model 1 is shown in Figure 8 (a) to Figure 8 (d).

[0076] The step 602 is to compare the simulated weeding effect with the actual weeding effect.

[0077] Specifically, observe the weeding process of the weeding mechanism model 1 cutting the flexible weed model to determine whether the weeding process of the weeding mechanism model 1 meets the first preset threshold, or is close to or consistent with the weeding process of the weeding mechanism model 1. If the weeding operation effect is not good, and the simulation operation process is inconsistent with the real operation process, then re-establish the virtual soil trough 2, including re-calibrating the particle properties and physical interactions of the soil and the flexible weeds; until the simulation weeding operation process is similar or consistent with the real operation process, proceed to the next step.

[0078] The step 603 is to construct sensors required for quantitative analysis of the working effect of the weeding mechanism.

[0079] Specifically, a weed mass sensor A is constructed as shown in FIG7 (a)-FIG7 (b). The type of the weed mass sensor A is set to an integral mass sensor, which only reads the mass of the flexible weed particles of the real-time discrete element weed model in the sensor. The length L of the weed mass sensor A is set to m Range: 0.6~1L s , set the width W of weed quality sensor A m Range: 0.6~1W s Preferably, in the present invention, L s =W s =80 mm, take L m =W m =65 mm, L s is the length of the virtual soil trench, W s is the width of the virtual soil trough. The height H of the weed quality sensor A m Range: 0.8~1.2H s Preferably, in the present invention, H m =25 mm, H s =24 mm, H s is the height of the virtual soil trough. The top of the weed quality sensor A just covers the top of the flexible weed model. The bottom of the weed quality sensor A is buried deep in the soil. m =5 mm, the position of weed quality sensor A is always fixed.

[0080] Specifically, a soil loss sensor B is constructed. The type of soil loss sensor B is set to an integral mass sensor, which only reads the mass of soil particles in the real-time discrete element soil model in the sensor. The length L of soil loss sensor B is set to t Range is L t =0.5~2L s , set the width W of soil loss sensor B t Range is W t =0.5~2W s , set the height H of soil loss sensor B t The range is 0.5~12H s Preferably, in the present invention, L t =L s =80 mm, take W t =W s =80 mm, take H t =H s =80 mm. Soil loss sensor B completely overlaps with virtual soil trough 2. The position of soil loss sensor B is always fixed.

[0081] The step 604 is to establish a quantitative analysis model for the operation effect of the weeding mechanism.

[0082] Specifically, the quantitative analysis model of the weeding mechanism operation effect includes the following analysis indicators: w , the soil movement speed v during weeding s , the movement speed of weeds v w , weed removal rate η, and earth-moving rate Д, etc.

[0083] Specifically, the force F of the weeding knife w According to Newton's third law, the force is mutual. By measuring the force on the weeding knife during weed cutting, the minimum cutting force required can be determined. In the Analyst interface of the EDEM software, the force data of the weeding mechanism model 1 of each saved node is exported through the Export Results Data option, and the maximum stress on the weeding knife during the cutting process is screened out, that is, F w =F max(t＝0→t＝0.1) , F w As the final force result. The total simulation running time in this embodiment is t=0.1s, and the target saving interval is set to 0.0001s to obtain 1000 sets of data. The Total Force magnitude data of the weeding knife is exported in the form of an Excel table, and it is observed at which moment the weeding knife is subjected to the maximum force. All the maximum value points that appear in the data are recorded and combined with the actual weeding process, so that some interference data at impossible moments can be screened out.

[0084] Specifically, the soil movement speed v during weeding s In the Analyst interface of EDEM software, export the soil particle velocity data of each saved node through the Export Results Data option, and export the Velocity data of soil particles in the form of an Excel table to obtain the soil movement velocity v at any time. s The same method can also be used to obtain the movement speed v of the flexible weed model w .

[0085] Specifically, the weed removal rate η is: Through the weed mass sensor A constructed as above. At t = 0 s, the total mass of weed particles m1 in the test area is recorded; at t = 0.1 s, the total mass of weed particles m2 is recorded. By calculating the change in weed mass before and after the operation, the weed removal rate η is obtained, and the formula is as follows:

[0086] ,

[0087] Specifically, the calculation results of the weed removal rate η and whether the weeding requirements are met during the weeding operation are classified. Through a large number of simulation experiments, the researchers concluded that: when η≥95%, the weeding operation is effective; when 85%≤η<95%, the weeding operation is qualified, and the weeding mechanism model 1 can meet the weeding agronomic requirements; when η<85%, the weeding operation is poor, and the weeding mechanism model 1 cannot meet the weeding agronomic requirements.

[0088] Specifically, the soil movement rate Д is: through the soil loss sensor B constructed above. At t = 0 s, the total mass of soil particles in the test area is recorded m4; at t = 0.1 s, the total mass of soil particles is recorded m3. By calculating the change in soil mass before and after the operation, the soil movement rate Д is obtained, and the formula is as follows:

[0089] ,

[0090] Specifically, the calculation results of the soil movement rate Д and whether the soil movement rate requirements are met during the weeding operation are classified. Through a large number of simulation experiments, the researchers concluded that: when Д≤3%, the soil movement is small and almost no soil movement; when 10%≥Д>3%, the soil movement rate is qualified, and the weeding mechanism model 1 can meet the weeding soil movement rate requirements; when Д>10%, the weeding operation soil movement rate is large, and the weeding mechanism model 1 cannot meet the weeding operation soil movement rate requirements.

[0091] The step 605 is to analyze the test data and the operation results.

[0092] By observing the discrete element simulation operation process, the weeding process can be divided into four stages: the weeding knife contacts the weeds, the weeds move with the weeding knife to the elastic deformation limit, the weeds are plastically torn, and the weeding knife cuts off the weeds, as shown in Figure 8 for details.

[0093] As shown in Figure 8 (a), the weeding knife has not yet touched the weed stems in the dotted frame at the beginning, and the stems are not subjected to impact force and are almost not subjected to force. As the weeding knife rotates and contacts the weeds, due to its strong swinging property, the stems first bounce in the direction of the weeding knife after being subjected to force. However, although the acceleration of the stem is large at this time, the initial speed is low, and the linear speed is lower than that of the weeding knife rotating at a constant speed, so the weeding knife pushes the stem forward. At the same time, the force on the stem in the dotted frame gradually increases.

[0094] As the weeding blade continues to rotate, the stem is stretched to the limit of elastic deformation, as shown in Figure 8 (b). At this time, the force in the dotted box gradually increases. At the same time, the stem can no longer continue to swing or stretch with the weeding blade, reaching the limit of elastic deformation at the microscopic level, and the total force also reaches the maximum value of linear growth during the elastic deformation process.

[0095] Subsequently, as shown in Figure 8 (c), the rotational cutting force of the weeding knife exceeds the strength limit of the stem, and the stem is torn. The sum of the tearing force of the stem and the cutting force of the weeding knife exceeds the limit force of elastic deformation. Finally, as shown in Figure 8 (d), the stem in the dotted frame breaks under the cutting action, the cotyledon separates from the stem, and the force at the crack gradually decreases.

[0096] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantitative analysis of weed control effects based on a discrete element flexible weed model, characterized in that: The method comprises: The weeding mechanism model is imported into a virtual soil trough, motion parameters are set and a weeding operation process is simulated, wherein the virtual soil trough includes a flexible weed model and a soil model established based on a discrete element method; Compare the simulated weeding operation effect with the actual weeding operation effect, if the first preset threshold is met, proceed to the next step, otherwise, re-establish the virtual soil trough; Constructing sensors required for quantitative analysis of the working effect of the weed removal mechanism, including a weed quality sensor and a soil loss sensor; the weed quality sensor is used to read the quality of the flexible weed particles of the flexible weed model in real time, and the position is always fixed; Establishing a quantitative analysis model of the operation effect of the weeding mechanism, wherein the analysis model obtains the force of the weeding blade, the movement speed of the soil during the weeding process, the weed removal rate, and the operation soil movement rate based on the constructed sensor; Wherein, the establishment of a flexible weed model based on discrete element method comprises: Identify weed species and conduct field measurements of weed size and parameter ranges; Use 3D modeling software to build a 3D model of weeds; Mesh the three-dimensional weed model and control the filling size of particles by adjusting the mesh size; Determine and output the centroid coordinate data of each grid; The discrete element method is used to quickly fill particles in the three-dimensional weed model to obtain a discrete element weed model; Add particle properties and physical interactions to the discrete element weed model; Comparatively verify whether the physical properties and interactions of the discrete element weed model and actual weeds meet the second preset threshold; Output discrete element simulation flexible weed model and its parameters.

2. The method for quantitative analysis of weed control effect based on discrete element flexible weed model according to claim 1, characterized in that: The comparative demonstration of whether the physical properties and interactions of the discrete element weed model and the actual weeds meet the second preset threshold includes testing whether the flexibility characteristics of the discrete element weed model and the characteristics of the actual root system meet the second preset threshold through a three-point bending test and a shear simulation test method. If not, recalibrating the particle properties and physical interactions of the discrete element weed model.

3. The method for quantitative analysis of weed control effect based on discrete element flexible weed model according to claim 1, characterized in that: The soil model established based on the discrete element method includes: determine the properties and physical interactions of soil particles; Use discrete element method to settle and fill soil particles; Comparatively verify whether the physical properties and interactions between the discrete element soil model and the actual soil meet the third preset threshold; Output the soil model and its parameters of discrete element simulation.

4. The method for quantitative analysis of weed control effect based on discrete element flexible weed model according to claim 3, characterized in that: The comparative demonstration of whether the physical properties and interactions between the discrete element soil model and the actual soil meet the third preset threshold includes testing whether the surface soil characteristics of the soil particles meet the third preset threshold through static friction tests and soil slope tests. If not, recalibrating the particle properties and physical interactions for the discrete element soil model.

5. The method for quantitative analysis of weed control effect based on discrete element flexible weed model according to claim 1, characterized in that: The soil loss sensor is used to read the soil particle mass of the discrete element soil model in real time.

6. The method for quantitative analysis of weed control effect based on discrete element flexible weed model according to claim 1, characterized in that: The analysis model obtains the force of the weeding blade based on the constructed sensor, the movement speed of the soil during weeding, the weed removal rate, and the soil movement rate of the operation, including: The stress condition of the weeding knife is the maximum stress on the weeding knife during the cutting process of the weeding mechanism model; The movement speed of the soil during the weeding process is the movement speed of soil particles in the discrete element soil model; The weed removal rate is the mass change rate of weed particles at different times measured by a weed mass sensor; The soil movement rate is the mass change rate of soil particles at different times measured by a soil loss sensor.

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