Method and system for optimizing parameters of spiral and shifting finger sugarcane supporting mechanism

Abstract

The invention provides a parameter optimization method and system for a spiral and shifting finger sugarcane supporting mechanism. The optimization method comprises the steps that parameters influencing the sugarcane supporting success rate are determined, and a dynamic simulation model is constructed; designing a virtual orthogonal test to simulate the influence of different parameter combinations on the action of the sugarcane supporting mechanism; constructing a prediction model of the sugarcane mass center lifting height according to the data of the orthogonal test; according to the prediction model, constructing a multi-target optimization model with the highest sugarcane centroid lifting height and the highest harvesting efficiency as targets; solving and evaluating the multi-objective optimization model by using a grey wolf optimization algorithm and a TOPSIS algorithm, and selecting an optimal parameter combination; and redesigning and optimizing the sugarcane supporting mechanism according to the optimal parameter combination. The optimization system is constructed based on an optimization method. After the design parameters of the sugarcane supporting mechanism are optimized through the optimization method and the optimization system, the sugarcane supporting mechanism can better adapt to sugarcanes under different lodging conditions, and therefore the sugarcane supporting success rate and the harvesting efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural machinery and equipment, and specifically to a parameter optimization method and optimization system for a "helix + finger" sugarcane supporting mechanism. Background Art

[0002] Due to the slender rod of sugarcane, after being planted, it is prone to lodging, that is, the sugarcane rod tilts to the side under the influence of bad weather such as typhoons and heavy rains. When using a sugarcane harvester to harvest sugarcane, if the lodged sugarcane cannot be righted to the upright state before cutting, a large number of missed cuts and incorrect cuts will occur, affecting the sugarcane harvesting efficiency. Therefore, generally, sugarcane harvesters on the market need to be equipped with a sugarcane supporting mechanism to right the lodged sugarcane and then send it to the sugarcane cutting disc to cut the roots of the righted sugarcane. Currently, most of the existing sugarcane supporting mechanisms are "helix + finger" sugarcane supporting mechanisms, that is, a sugarcane supporting mechanism constructed by combining a helical rotating roller and a chain finger. It uses the inclined helical rotating roller and the chain finger beside the helical rotating roller in cooperation to jointly right the lodged sugarcane and send the righted sugarcane to the sugarcane cutting disc for root cutting.

[0003] According to the mechanism setting and mechanical principle of the sugarcane supporting mechanism, the design parameters of its main components have a significant impact on the working performance indicators such as the sugarcane supporting success rate, working condition adaptability, and sugarcane harvesting efficiency of the product. However, currently, for the design of such "helix + finger" sugarcane supporting mechanisms, the parameter setting and adjustment are usually carried out by empirical design or simple trial test methods. This method is inefficient, costly, and it is difficult to find the optimal design parameters. Therefore, most of the existing "helix + finger" sugarcane supporting mechanisms have problems such as low sugarcane supporting success rate and poor adaptability, resulting in low sugarcane harvesting efficiency. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a parameter optimization method and optimization system for a "helix + finger" sugarcane supporting mechanism to optimize the design parameters of the sugarcane supporting mechanism, thereby improving the sugarcane supporting success rate and sugarcane harvesting efficiency of the sugarcane supporting mechanism.

[0005] In the first aspect, the present invention provides a parameter optimization method for a "helix + finger" sugarcane supporting mechanism, which includes the following steps: S1: Determine the parameters that affect the sugarcane supporting success rate of the "helix + finger" sugarcane supporting mechanism, and construct a dynamic simulation model of the "helix + finger" sugarcane supporting mechanism to simulate the kinematic characteristics and dynamic response of the sugarcane supporting mechanism under different parameters; the parameters include the helical rotation speed n1, the finger rotation speed n2, the forward speed v, the forward installation angle β of the helix, and the lateral installation angle α of the helix.

[0006] S2: Use the dynamic simulation model to perform dynamic simulation analysis of the sugarcane supporting mechanism, design a virtual orthogonal experiment, simulate the influence of different parameter combinations on the action of the sugarcane supporting mechanism, and obtain the sugarcane center of mass lifting height and corresponding harvesting efficiency under different parameters; the harvesting efficiency is considered based on the forward speed of the mechanism, that is, the harvesting efficiency is equal to the forward speed v.

[0007] S3: Based on the data obtained from the pseudo-orthogonal experiment, the relationship between different parameters of the sugarcane supporting mechanism and the lifting height of the sugarcane center of mass is constructed to obtain a prediction model for the lifting height of the sugarcane center of mass.

[0008] S4: Based on the prediction model of sugarcane mass center lifting height, the optimization objective function and constraints are determined, and a multi-objective optimization model with the highest sugarcane mass center lifting height and the highest harvesting efficiency as the goals is constructed.

[0009] S5: Use the Grey Wolf optimization algorithm to solve the multi-objective optimization model to obtain the optimal parameter solution set, then use the TOPSIS algorithm to evaluate the optimal parameter solution set and select the optimal parameter combination that meets the requirements.

[0010] S6: Redesign and optimize the “spiral + finger-push” sugarcane supporting mechanism based on the selected optimal parameter combination, and simulate the redesigned sugarcane supporting mechanism to verify the optimization effect.

[0011] In a second aspect, the present invention provides a parameter optimization system for a "spiral + finger" sugarcane supporting mechanism, in which a program module for implementing the above-mentioned parameter optimization method for the "spiral + finger" sugarcane supporting mechanism is deployed.

[0012] The “screw + finger” sugarcane supporting mechanism parameter optimization system includes the following modules: Dynamic simulation model construction module: used to construct the dynamic simulation model of the "spiral + finger-push" sugarcane supporting mechanism.

[0013] Virtual orthogonal test module: used to carry out sugarcane supporting dynamics simulation using the dynamics simulation model of the sugarcane supporting mechanism according to the designed virtual orthogonal test parameter table, and obtain the sugarcane center of mass lifting height and corresponding harvesting efficiency under different parameters.

[0014] Minitab regression fitting module: used to construct the relationship between different parameters of the sugarcane supporting mechanism and the lifting height of the sugarcane center of mass based on the data obtained from the pseudo-orthogonal experiment using Minitab software, and obtain a prediction model for the lifting height of the sugarcane center of mass.

[0015] Multi-objective optimization model construction module: used to construct a multi-objective optimization model with the highest sugarcane mass center lifting height and the highest harvesting efficiency as the goals based on the prediction model of the sugarcane mass center lifting height.

[0016] Grey Wolf Algorithm Solving and TOPSIS Evaluation Module: It is used to solve the multi-objective optimization model to obtain the optimal parameter solution set, and then evaluate the optimal parameter solution set to obtain the optimal parameter combination that meets the requirements.

[0017] Design Optimization Module: It is used to redesign and optimize the "spiral + finger-pulling" sugarcane supporting mechanism according to the selected optimal parameter combination.

[0018] After optimizing the design parameters of the "spiral + finger-pulling" sugarcane supporting mechanism using the parameter optimization method and optimization system of the present invention, the sugarcane supporting mechanism can better adapt to sugarcane in different lodging situations, improve its adaptability under different working conditions, thereby improving the success rate of sugarcane support and the sugarcane harvesting efficiency of the sugarcane supporting mechanism. In addition, compared with the traditional method of determining the design parameters of the sugarcane supporting mechanism through repeated trial tests, the present invention adopts virtual orthogonal tests and computer simulation technology, which can greatly reduce the test cost and time cost. Brief Description of the Drawings

[0019] Figure 1 It is the flowchart of the steps of the parameter optimization method for the "spiral + finger-pulling" sugarcane supporting mechanism of the present invention.

[0020] Figure 2 It is the schematic diagram of the spiral installation.

[0021] Figure 3 It is the model of the "spiral + finger-pulling" sugarcane supporting mechanism designed using SolidWorks software.

[0022] Figure 4 It is a state diagram of the simplified dynamic simulation model using Adams software.

[0023] Figure 5 It is another state diagram of the simplified dynamic simulation model using Adams software.

[0024] Figure 6 It is the flowchart of the Grey Wolf Algorithm for solving the multi-objective model.

[0025] Figure 7 It is the Pareto solution set of the multi-objective Grey Wolf Algorithm for solving.

[0026] Figure 8 It is the flowchart of the TOPSIS algorithm. Detailed Implementation Manner

[0027] The present invention will be further described below with reference to the drawings and embodiments. Embodiment 1

[0028] Embodiment 1 is Figure 1 The specific embodiment carried out according to the "spiral + finger-pulling" sugarcane supporting mechanism parameter optimization method shown, including the following steps: S1: Determine the parameters affecting the success rate of the sugarcane lifting mechanism with "helix + finger dial", and construct a dynamic simulation model of the sugarcane lifting mechanism with "helix + finger dial" to simulate the kinematic characteristics and dynamic responses of the sugarcane lifting mechanism under different parameters; the parameters include the screw rotation speed n1, the finger dial rotation speed n2, the forward speed v, the forward installation angle β of the helix, and the lateral installation angle α of the helix.

[0029] Figure 2 It is a schematic diagram of the helix installation, where the forward installation angle β of the helix is the angle between the helix axis and the forward direction; the lateral installation angle α of the helix is the angle between the helix axis and the lateral vertical plane.

[0030] Use SolidWorks software to design the model of the sugarcane lifting mechanism with "helix + finger dial", as Figure 3 shown, Figure 3 in which 45 is the helix and 153 is the finger dial. Import the model into Adams software. To reduce the complexity of the simulation process, the simulation model is simplified, only the helix and the finger dial mechanism are retained, and corresponding connections and drives are set according to the operating principle of the sugarcane lifting mechanism with "helix + finger dial"; set the diameter of the sugarcane to 29 mm, the length of the sugarcane to 3000 mm, and the density to 4.38E-7 kg / mm 3 , the sugarcane is modeled as a cylindrical flexible body with a uniform cross-section, and the constraint effect of the ground on the sugarcane is simulated according to the spherical hinge and rubber bushing constraints. The stiffness coefficient is set to 2510 N·m / °, the moment for lifting the sugarcane is set to 5000 N·mm, and the lodging angle and lateral deviation angle of the sugarcane are both selected as 30º. The helix and the finger dial are set as rigid bodies with the material of iron. Finally, the simplified dynamic simulation model is obtained as shown in Figure 4 and Figure 5 shown.

[0031] S2: Use the dynamic simulation model to conduct dynamic simulation analysis of sugarcane lifting, design a virtual orthogonal experiment, simulate the influence of different parameter combinations on the actions of the sugarcane lifting mechanism, and obtain the height of the sugarcane centroid lift and the corresponding harvesting efficiency under different parameters.

[0032] The designed orthogonal experiment table is shown in Table 1.

[0033]

[0034] Conduct dynamic simulation experiments using the dynamic simulation model according to the data in the table, and record the height of the sugarcane centroid lift VHC and the harvesting efficiency under each group of parameters. The harvesting efficiency is considered based on the forward speed of the mechanism, that is, the harvesting efficiency is equal to the forward speed v. Change the parameters and repeat the experiment until all the data in the table are simulated. The experimental data records are shown in Table 2.

[0035]

[0036] S3: Based on the data obtained from the virtual orthogonal experiment, establish the relationship between different parameters of the cane supporting mechanism and the lifting height of the cane centroid, and obtain the prediction model of the cane centroid lifting height.

[0037] Use Minitab software to perform regression fitting on the data obtained from the virtual orthogonal experiment, and obtain the regression model equation:

[0038] Perform an analysis of variance on the regression model equation, and the results are shown in Table 3.

[0039]

[0040] The p-value of this model is less than 0.01, and R 2 = 0.9841. This indicates that it has strong significance. The spiral lateral installation angle α, the finger rotation speed n2, the interaction term n2*n2 of the finger rotation speed n2, the interaction term α*v of the spiral lateral installation angle α and the forward speed v, and the interaction term n1*v of the spiral rotation speed n1 and the forward speed v have no significant effect (p > 0.1), that is, they are eliminated from the regression model. Other factors have a significant impact on cane support (p < 0.05). After eliminating the insignificant factors, the new regression model equation is:

[0041] S4: According to the prediction model of the cane centroid lifting height, determine the optimization objective function and constraint conditions, and construct a multi-objective optimization model with the highest cane centroid lifting height and the highest harvesting efficiency as the objectives.

[0042] According to the limiting conditions of the corresponding parameters of the cane supporting mechanism, the constraint conditions can be set as:

[0043] According to the prediction model and constraint conditions, the multi-objective optimization model with the highest cane centroid lifting height and the highest harvesting efficiency as the objectives can be expressed as:

[0044] In the formula, f(x) -1 represents the reciprocal of the cane centroid lifting height (1 / VHC), and g(x) -1 represents the reciprocal of the forward speed (1 / v).

[0045] S5: Use the grey wolf optimization algorithm to solve the multi-objective optimization model to obtain the optimal parameter solution set, and then use the TOPSIS algorithm to evaluate the optimal parameter solution set to select the optimal parameter combination that meets the requirements.

[0046] The grey wolf algorithm is used to solve the above multi-objective optimization model. The flow chart of the grey wolf algorithm is as Figure 6 shown. The population size of the grey wolf algorithm is set to 50, the maximum number of iterations is set to 100, and the search range is set according to the range of parameters. The grey wolf algorithm is programmed using MATLAB for iterative calculation, and the optimal solution of each iteration is recorded. The Pareto optimal solution set obtained by solving is as Figure 7 shown.

[0047] The TOPSIS algorithm is used to evaluate the Pareto optimal solution set obtained by the grey wolf algorithm. The flow chart of the TOPSIS algorithm is as Figure 8 shown. The weight of the vertical height of cane centroid VHC is set to 0.6; the weight of the harvesting efficiency (v) is set to 0.4. The scoring table obtained by solving with the TOPSIS algorithm is shown in Table 4.

[0048]

[0049] The parameters corresponding to the optimal solution with the highest score are: the forward installation angle of the helix β = 65°, the lateral installation angle of the helix α = 20°, the rotational speed of the helix n1 = 150 r / min, the rotational speed of the finger dial n2 = 150 r / min, and the forward speed v = 0.4347 m / s.

[0050] S6: According to the selected optimal parameter combination, the "helix + finger dial" cane supporting mechanism is redesigned and optimized, and the simulation is carried out on the redesigned cane supporting mechanism to verify the optimization effect.

[0051] The dynamic simulation model is reconstructed in SolidWorks software and Adams software according to the optimal parameter combination, and the dynamic simulation is carried out to verify the optimization effect. Example 2

[0052] Example 2 is to construct a parameter optimization system for the "helix + finger dial" cane supporting mechanism. The system is deployed with program modules for implementing the above-mentioned parameter optimization method for the "helix + finger dial" cane supporting mechanism. The system includes the following modules: Dynamic simulation model construction module: used to construct the dynamic simulation model of the "helix + finger dial" cane supporting mechanism. This module is constructed using SolidWorks software and Adams software. The "helix + finger dial" cane supporting mechanism model is designed by SolidWorks software and imported into Adams software. The physical property parameters of the cane, such as elastic modulus and yield limit, are set. At the same time, the material properties of the helix and finger dial are set to obtain the dynamic simulation model.

[0053] Virtual Orthogonal Experiment Module: It is used to carry out the dynamics simulation of sugarcane lifting according to the designed virtual orthogonal experiment parameter table by using the dynamics simulation model of the sugarcane lifting mechanism, and obtain the sugarcane centroid lifting height and the corresponding harvesting efficiency under different parameters. The virtual orthogonal experiment parameter table designed by this module includes different level combinations of factors such as the screw rotation speed n1 (100 r / min, 125 r / min, 150 r / min), the finger dial rotation speed n2 (100 r / min, 125 r / min, 150 r / min), the forward speed v (0.4 m / s, 0.5 m / s, 0.6 m / s), the forward installation angle β of the screw (55°, 60°, 65°), and the lateral installation angle α of the screw (10°, 20°, 30°).

[0054] Minitab Regression Fitting Module: It is used to construct the relationship between different parameters of the sugarcane lifting mechanism and the sugarcane centroid lifting height by using Minitab software according to the data obtained from the virtual orthogonal experiment, and obtain the prediction model of the sugarcane centroid lifting height. This module is constructed by using Minitab software, and Minitab software is used to perform regression fitting on the experimental data to establish the relationship model between the parameters and the sugarcane centroid lifting height.

[0055] Multi-objective Optimization Model Construction Module: It is used to construct a multi-objective optimization model with the highest sugarcane centroid lifting height and the highest harvesting efficiency as the objectives according to the prediction model of the sugarcane centroid lifting height. When constructing the multi-objective optimization model in this module, the constraint conditions of the corresponding parameters of the sugarcane lifting mechanism need to be set, and the constraint conditions are set as: 55° ≤ the forward installation angle β of the screw ≤ 65°; 10° ≤ the lateral installation angle α of the screw ≤ 30°; 100 r / min ≤ the screw rotation speed n1 ≤ 150 r / min; 100 r / min ≤ the finger dial rotation speed n2 ≤ 150 r / min; 0.4 m / s ≤ the forward speed v ≤ 0.6 m / s.

[0056] Grey Wolf Algorithm Solving and TOPSIS Evaluation Module: It is used to solve the multi-objective optimization model to obtain the optimal parameter solution set, and then evaluate the optimal parameter solution set to obtain the optimal parameter combination that meets the requirements. In this module, the population size of the grey wolf algorithm is set to 50, the maximum number of iterations is set to 100, and the search range is set according to the range of the parameters.

[0057] Design Optimization Module: It is used to redesign and optimize the "screw + finger dial" sugarcane lifting mechanism according to the selected optimal parameter combination. In this module, the "screw + finger dial" sugarcane lifting mechanism is imported into SolidWorks software and Adams software according to the optimal parameter combination to readjust the design parameters of the dynamics simulation model, and then the dynamics simulation is performed to verify the optimization effect.

[0058] After the corresponding design parameters of the cane-holding mechanism are optimized by the parameter optimization method and optimization system of the present invention, the finally manufactured cane-holding mechanism can better adapt to sugarcane in different lodging situations, thereby improving the success rate of cane holding and the sugarcane harvesting efficiency.

[0059] The above illustrations are only typical embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A parameter optimization method for a sugarcane supporting mechanism of "helix + finger flicking", characterized in that It includes the following steps: S1: Determine the parameters affecting the success rate of the sugarcane lifting mechanism of the "helix + finger dial" type, and construct a dynamic simulation model of the "helix + finger dial" sugarcane lifting mechanism to simulate the kinematic characteristics and dynamic responses of the sugarcane lifting mechanism under different parameters; S2: Use the dynamic simulation model to conduct sugarcane lifting dynamics simulation analysis, design a virtual orthogonal experiment, simulate the influence of different parameter combinations on the actions of the sugarcane lifting mechanism, and obtain the lifting height of the sugarcane centroid and the corresponding harvesting efficiency under different parameters; the harvesting efficiency is considered based on the forward speed of the mechanism, that is, the harvesting efficiency is equal to the forward speed v; S3: According to the data obtained from the virtual orthogonal experiment, establish the relationship between different parameters of the sugarcane lifting mechanism and the lifting height of the sugarcane centroid, and obtain a prediction model for the lifting height of the sugarcane centroid; S4: According to the prediction model of the sugarcane centroid lifting height, determine the optimization objective function and constraint conditions, and construct a multi-objective optimization model with the highest sugarcane centroid lifting height and the highest harvesting efficiency as the objectives; S5: Use the Grey Wolf Optimization algorithm to solve the multi-objective optimization model to obtain the optimal parameter solution set, and then use the TOPSIS algorithm to evaluate the optimal parameter solution set, and select the optimal parameter combination that meets the requirements; S6: Redesign and optimize the "helix + finger dial" sugarcane lifting mechanism according to the selected optimal parameter combination, and conduct simulation verification on the optimization effect of the redesigned sugarcane lifting mechanism.

2. The parameter optimization method of the "helix + finger-poking" sugarcane supporting mechanism according to claim 1, characterized in that, In the step S1, the parameters include the screw rotation speed n1, the finger dial rotation speed n2, the forward speed v, the forward installation angle β of the screw, and the lateral installation angle α of the screw.

3. The parameter optimization method of the "helix + finger-poking" sugarcane supporting mechanism according to claim 2, characterized in that In the step S1, use SolidWorks software to design the model of the "helix + finger dial" sugarcane lifting mechanism, and then import the model into Adams software to construct a dynamic simulation model. When constructing the dynamic simulation model, only the screw and finger dial mechanisms are retained.

4. The parameter optimization method of the "helix + finger-poking" sugarcane supporting mechanism according to claim 2, characterized in that, In the step S2, three levels are selected for each parameter factor in the orthogonal experiment. Among them, the screw rotation speed n1 is selected as 100 r / min, 125 r / min, 150 r / min, the finger dial rotation speed n2 is selected as 100 r / min, 125 r / min, 150 r / min, the forward speed v is selected as 0.4 m / s, 0.5 m / s, 0.6 m / s, the forward installation angle β of the screw is selected as 55°, 60°, 65°, and the lateral installation angle α of the screw is selected as 10°, 20°, 30°.

5. The parameter optimization method of the "spiral + finger-poking" sugarcane supporting mechanism according to claim 2, characterized in that In the step S4, the constraint conditions can be set as: 55° ≤ the forward installation angle β of the screw ≤ 65°; 10° ≤ the lateral installation angle α of the screw ≤ 30°; 100 r / min ≤ the screw rotation speed n1 ≤ 150 r / min; 100 r / min ≤ the finger dial rotation speed n2 ≤ 150 r / min; 0.4 m / s ≤ the forward speed v ≤ 0.6 m / s.

6. The parameter optimization method of the "helix + finger-poking" sugarcane supporting mechanism according to claim 2, characterized in that, In the step S5, the population size of the Grey Wolf algorithm is set to 50, the maximum number of iterations is set to 100, and the search range is set according to the range of the parameters.

7. A parameter optimization system for a sugarcane supporting mechanism of "helix + finger pushing", characterized in that, A program module for implementing the sugarcane lifting mechanism parameter optimization method according to any one of claims 1-6 is deployed in the system.

8. The "helix + finger-poking" sugarcane supporting mechanism parameter optimization system according to claim 7, characterized in that, The system includes the following modules: Dynamic simulation model construction module: used to construct the dynamic simulation model of the "helix + finger dial" sugarcane supporting mechanism; Virtual orthogonal test module: used to carry out the sugarcane supporting dynamics simulation by using the dynamic simulation model of the sugarcane supporting mechanism according to the designed virtual orthogonal test parameter table, and obtain the sugarcane centroid lifting height and the corresponding harvesting efficiency under different parameters; Minitab regression fitting module: used to construct the relationship between different parameters of the sugarcane supporting mechanism and the sugarcane centroid lifting height by using Minitab software according to the data obtained from the virtual orthogonal test, and obtain the prediction model of the sugarcane centroid lifting height; Multi-objective optimization model construction module: used to construct a multi-objective optimization model with the highest sugarcane centroid lifting height and the highest harvesting efficiency as the objectives according to the prediction model of the sugarcane centroid lifting height; Grey wolf algorithm solution and TOPSIS evaluation module: used to solve the multi-objective optimization model to obtain the optimal parameter solution set, and then evaluate the optimal parameter solution set to obtain the optimal parameter combination that meets the requirements; Design optimization module: used to re-design and optimize the "helix + finger dial" sugarcane supporting mechanism according to the selected optimal parameter combination.

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