Design method for front blade of anti-drag bionic subsoiler based on ellipse fitting and model regression

Abstract

The invention relates to agricultural machinery, and aims to provide a method for designing a front blade of a resistance-reducing bionic subsoiler based on ellipse fitting and model regression so as to reduce tillage resistance borne by the subsoiler in subsoiling operation. According to the technical scheme, the method comprises the following steps: 1, selecting animal claws and toes skilled in mining as bionic objects, and processing biological information according to claw and toe high-definition pictures to obtain point cloud data of inner and outer contours of the animal claws and toes; 2, constructing an ellipse fitting equation, and fitting the point cloud data of the inner and outer contours of the animal claw toes by using an elliptic curve; 3, according to the ellipse fitting equation, applying the n elliptic curves to the cutting edge design of the front blade of the subsoiler; 4, performing a soil simulation test on the designed bionic subsoiler corresponding to different fitting curves to obtain the tillage resistance of the correspondingly designed subsoiler; and 5, designing the cutting edge of the front blade of the subsoiler according to the optimal drag reduction elliptic curve fitting range, and verifying the accuracy and drag reduction performance of the regression model by using a soil simulation test.

Description

Technical Field

[0001] This invention relates to an agricultural machine, specifically a drag-reducing biomimetic design method for the front edge of a deep tillage shovel based on ellipse fitting and model regression, in order to reduce the tillage resistance experienced by the deep tillage shovel during deep tillage operations. Background Technology

[0002] Subtillage, as a core technology of conservation tillage, plays a vital role in breaking up the plow pan, improving soil structure, and enhancing water retention capacity. The subtillage shovel is a key component in subtillage operations, and its working resistance directly affects energy consumption and efficiency. Currently, mainstream subtillage shovels suffer from high resistance and high fuel consumption. Existing methods for reducing the working resistance of the shovel handle are mostly limited to biomimetic design of the handle's guideline. While this method can reduce the working resistance to some extent, the drag reduction effect still needs improvement. Furthermore, this method involves significant modifications compared to the arc-shaped subtillage shovel handle specified in national machinery industry standards, leading to increased manufacturing difficulty and costs. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings in the above-mentioned background technology and provide a drag-reducing biomimetic deep loosening shovel front edge design method based on ellipse fitting and model regression; this method can utilize the biological information of animals to provide an effective approach for drag-reducing deep loosening shovel design.

[0004] The technical solution provided by this invention is:

[0005] A design method for the front cutting edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression includes the following steps:

[0006] Step 1: Select the claws and toes of animals that are good at digging as biomimetic objects, obtain biological information from high-definition images of claws and toes, and process the biological information to obtain point cloud data of the inner and outer contours of the animal claws and toes.

[0007] Step 2: Construct an ellipse fitting equation and use an ellipse curve to fit the point cloud data of the inner and outer contours of the animal's claws and toes. Simultaneously, evaluate the fit based on ellipse fitting evaluation metrics.

[0008] Step 3: Based on the ellipse fitting equation, select n different fitting curves within the fitting range, and apply the obtained n ellipse curves to the front cutting edge design of the deep loosening shovel.

[0009] Step 4: Conduct soil simulation experiments on the biomimetic deep tillage shovels designed with different fitting curves to obtain the tillage resistance of the corresponding deep tillage shovel designs. Construct a regression model between the selected range of the fitting curve and the tillage resistance to obtain the mapping relationship between the selected range of the fitting curve and the tillage resistance. Based on this regression model, obtain the optimal elliptic curve fitting range for drag reduction.

[0010] Step 5: Design the cutting edge of the deep loosening shovel based on the optimal drag-reducing elliptic curve fitting range, and verify the accuracy and drag-reducing properties of the regression model using soil simulation tests.

[0011] In step 1, the processing of the biological information includes: firstly, extracting the inner and outer contours of the animal's claws and toes, and then using tools (Matlab, Python) to obtain the inner and outer contour point cloud data through grayscale conversion, binarization, and filtering.

[0012] In step 2, the ellipse fitting equation is:

[0013] x t = (x-x0)cosα0+(y-y0)sinα0

[0014] y t = -(x-x0)sinα0+(y-y0)cosα0

[0015]

[0016] In the formula, (x0, y0) are the coordinates of the geometric center point of the ellipse fitting equation in a Cartesian coordinate system; α0 is the angle between the major semi-axis of the ellipse and the x-axis; a and b are the lengths of the minor and major semi-axis of the ellipse, respectively; x t and y t These are the algebraic expressions for the x and y coordinates of the standard ellipse equation, respectively.

[0017] The optimal ellipse fitting equation is found by minimizing the absolute value of the geometric algebraic error between the contour point cloud and the ellipse curve; specifically, this is achieved through the following optimization model:

[0018]

[0019] In the formula, (x pi ,y pi ) represents the horizontal and vertical coordinates of the outline point cloud.

[0020] The constraints on the control parameters of the ellipse fitting equation are as follows:

[0021]

[0022] The evaluation index for the ellipse fitting equation is:

[0023]

[0024] In the formula, ρ i The distance between the point cloud and the center of the ellipse; This represents the mean distance between the point cloud and the center of the ellipse; ρ is the distance between the intersection point and the center of the ellipse; p is the number of independent variables (explanatory variables) in the regression model. SST, SSE, RMSE, R 2 and These are the sum of squares, squared residuals, root mean square error, coefficient of determination, and corrected coefficient of determination, respectively, for the fitted ellipse corresponding to the inner and outer contours; the smaller the RMSE, the higher the R² value. 2 , The closer the value is to 1, the better the fit.

[0025] The formula for calculating the polar radius of an ellipse with its center as the pole is:

[0026]

[0027] In the formula, θ is the polar angle.

[0028] In step 3, the design range of the fitting curve is as follows: An ellipse is constructed with its center as the origin and its major axis as the polar axis. The lower bound β0 of the fitting curve design range is the angle between the line connecting the animal claw / toe excavation tip point cloud coordinates to the geometric center of the ellipse (the pole (x0, y0)) and the polar axis. The upper bound β0+β is the angle between the polar radius (which rotates freely around the pole) and the polar axis. The entire fitting angle region ranges from [β0, β0+β], the angle region corresponding to the bionic fitting segment curve is β, and the angle region value of the entire claw / toe bionic fitting segment curve is β. max .

[0029] This invention applies to the national standard light-duty subsoiler shovel with standard number JB / T 9788-2020. Applying the fitted curve to the design of the subsoiler cutting edge requires scaling it to the same size as the national standard subsoiler shovel. The straight-line distance between the two endpoints of the transition arc curve between the national standard shovel cutting edge and the shovel side is used as the reference for scaling the fitted curve, denoted as L. c The straight-line distance between the two endpoints of the fitted curve is B. i The method for selecting n fitting curves is as follows: in β max Within the range, β is selected There are n fitted curves in total, arranged in proportion. Scaling is applied to the cutting edge design of the deep loosening shovel using the obtained fitted curves.

[0030] In step 4, the subsoiler handle designed in step 3 is paired with a national standard subsoiler tip for soil simulation testing to obtain the tillage resistance of the subsoiler within the corresponding design range; the steps are as follows:

[0031] (1) Establish a regression equation between tillage resistance and the design range β of the fitted curve, where the design range β is the independent variable and the tillage resistance F is the response value. The regression equation is as follows:

[0032] F=a0+a1β+a2β2

[0033] In the formula, a0, a1, and a2 are coefficients to be determined.

[0034] (2) The simulated tillage resistance values ​​F1, F2, ... F of the deep tillage shovel designed with corresponding β values ​​were obtained through discrete element simulation. n (where n is a positive integer), written in matrix form as follows:

[0035]

[0036] (3) The above equation regarding the resistance of biomimetic shovel tillage can be expressed as F i If the number of equations in the system equal to Xa exceeds the number of unknowns, the system may not have an exact solution. In this case, define the residual vector ε = F - Xa and minimize the sum of squares of the residuals. The optimization problem is then transformed into:

[0037] min a ||F i -Xa|| 2 =min a (F i -Xa) T (F i -Xa)

[0038] (4) Based on tillage resistance and β max The coefficients a0, a1, and a2 are calculated to obtain the regression model of the relationship between tillage resistance and β; the objective function is to minimize tillage resistance: min|F|

[0039] (5) The β value corresponding to the minimum tillage resistance is used for the optimal design of the final deep tillage shovel blade, and the model is designed using 3D software.

[0040] In step 5, the deep tillage shovel designed by the preferred scheme in step 4 and the ordinary national standard light deep tillage shovel (JB / T9788-2020) are subjected to discrete element soil simulation under the same conditions (tillage speed v, working depth h); the simulation results are compared to verify the accuracy and drag reduction of the regression model in step 4.

[0041] The beneficial effects of this invention are:

[0042] This invention employs elliptic equations to fit point cloud data of animal claw and toe contours. Compared to traditional methods of utilizing claw and toe information, this approach divides the contour information to be fitted into intervals, enabling more effective processing of biological information. By using the discretized fitting curve to design a biomimetic deep tillage shovel, a regression equation is established based on the simulated tillage resistance and the selected range of the fitting curve, ultimately determining the optimal design range for drag reduction. This improves the utilization of biological information and provides a more effective solution for drag reduction design of deep tillage shovels. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the high-resolution image processing of the giant anteater's claws and toes in an embodiment of this application.

[0044] Figure 2 This is a linear extraction map of the point cloud of the claw and toe contours of a giant anteater in this embodiment of the application.

[0045] Figure 3 This is a schematic diagram of elliptic curve fitting in an embodiment of this application.

[0046] Figure 4 This is a schematic diagram of the application object and reference benchmark of the ordinary national standard light-duty subsoil shovel in the embodiments of this application.

[0047] Figure 5 This is a schematic diagram of the fitting curve applied to the deep loosening shovel in the embodiments of this application.

[0048] Figure 6 This is a comparison diagram of the fitting curves of different intervals of the biomimetic model in the embodiments of this application and the cutting edge curve of the handle of a conventional deep tillage shovel.

[0049] Figure 7 This is a schematic diagram of the discrete element simulation environment setup for soil tillage in this embodiment of the application.

[0050] Figure 8 This is a comparison diagram of discrete element simulation of tillage resistance between a conventional deep tillage shovel and a bionic shovel in the embodiments of this application.

[0051] In the figure, 1 represents the biomimetic curve B. O1 ;2 is the biomimetic curve B O2 ;3 is the biomimetic curve B O3 ;4 is the biomimetic curve B O4 ;5 is the biomimetic curve B O5 ;6. Common deep tillage shovel cutting edge curve;

[0052] 10 is application B O1 Curved design for deep loosening shovel; 20 for application B O2 Curved design for deep loosening shovel; 30 for application B O3 Curved design for deep loosening shovel; 40 for application B O4 Curved design for deep loosening shovel; 50 for application B O5 Deep loosening shovel with curved design;

[0053] 100 represents the application area of ​​the fitted curve (the front cutting edge of the subsoiler); 200 represents the handle of the subsoiler; and 300 represents the tip of the national standard subsoiler.

[0054] Figure 2The English meanings are: Inner Contour Point Cloud; Outer Contour Point Cloud; X coordinate; Y coordinate. Detailed Implementation

[0055] The present invention will be further described below with reference to the embodiments shown in the accompanying drawings.

[0056] This invention includes the following steps:

[0057] Step 1: Select the claws and toes of a giant anteater, which is good at digging, as a biomimetic object. Obtain biological information based on high-definition images of its forefoot claws and toes, and process the biological information to obtain point cloud data of the inner and outer contours of the animal claws and toes.

[0058] Image processing workflow as follows: Figure 1 As shown: First, a high-resolution image of the giant anteater's foreleg claws is converted to grayscale, then binarized, and finally filtered to obtain the image shown. Figure 2 The shown is the point cloud data of the claw and toe contours.

[0059] Step 2: Taking the processing of claw and toe outer contour point cloud information as an example, the optimal ellipse fitting equation is found by minimizing the absolute value of the geometric algebraic error between the contour point cloud and the ellipse equation curve; specifically, this is done through the following optimization model:

[0060]

[0061] In the formula, (x pi ,y pi ) represents the horizontal and vertical coordinates of the outline point cloud.

[0062] The constraints on the control parameters of the ellipse equation are as follows:

[0063]

[0064] Based on the above formula and combination Figure 3 The diagram shown is a schematic of elliptic curve fitting. (x0, y0) are the coordinates of the geometric center of the ellipse equation in a Cartesian coordinate system; α0 is the angle between the major semi-axis of the ellipse and the x-axis; a and b are the lengths of the minor and major semi-axis of the ellipse, respectively.

[0065] Solving the optimization model yields x0, y0, a, b, and α0 values ​​of -126.94, 123.4, 161.58, 228.25, and 56.19, respectively. Substituting these parameters into the ellipse equation, we obtain the ellipse fitting equation:

[0066] x t=(x-126.94)cos56.19+(y+123.4)sin56.19

[0067] y t =-(x-126.94)sin56.19+(y+123.4)cos56.19

[0068]

[0069] According to the evaluation index of the ellipse fitting equation, the root mean square error (RMSE) of this evaluation index is 0.5592, and the coefficient of determination (R²) is... 2 The coefficient of determination (COP) is 0.9991. The value of 0.9982 indicates that the ellipse fitting effect is good and can be used for the design of the cutting edge of the deep loosening shovel.

[0070] Step 3: Reference Figure 3 An ellipse is constructed with its center as the origin of the polar coordinate system and its major axis as the polar axis. The lower bound of the fitting curve design range is β0 = 16.35°, defined by the angle between the line connecting the animal claw / toe excavation tip's point cloud coordinates, the ellipse's geometric center (the pole (x0, y0)), and the polar axis. The upper bound of the fitting curve design range is β0 + β, defined by the angle between the polar radius (rotating freely around the pole) and the polar axis. The entire fitting angle range is [β0, β0 + β]. The fitting angle range corresponding to the bionic segment is β, and the fitting angle range for the entire claw / toe is β. max =126.93°.

[0071] The application object of this invention is the national standard light-duty subsoiler with standard number JB / T 9788-2020; to apply the fitted curve to the design of the subsoiler cutting edge, the fitted curve needs to be scaled to the same size as the national standard subsoiler. For example... Figure 4 As shown, the straight-line distance between the two endpoints of the transition arc curve between the blade edge and the side surface of the national standard shovel is used as the reference for scaling the fitted curve, denoted as L. c =249.19mm. For example... Figure 3 As shown, the straight-line distance between the two endpoints of the fitted curve is B. i In this embodiment, five fitting curves are selected in step 3; the method for selecting the five fitting curves is as follows: in β max Within the range, β is selected β max There are five fitted curves in total. The actual distances between the endpoints of the five fitted curves are B1 93.1307 mm, B2 147.2232 mm, B3 236.1767 mm, B4 297.8960 mm, and B5 361.8266 mm. These five fitted curves are then scaled according to... The values ​​2.6757, 1.6926, 1.0551, 0.8365, and 0.6887 were magnified by 2.6757 times, 1.6926 times, and 1.0551 times, and reduced by 0.8365 times and 0.6887 times, respectively. These five newly obtained fitted curves were applied to the design of the front cutting edge of the deep loosening shovel, resulting in the following... Figure 5 The biomimetic deep tillage shovel handle is shown. To more intuitively compare the differences in biomimetic curves, such as... Figure 6 As shown, the five biomimetic fitting curves obtained are placed in the same deep loosening shovel handle as the curve at the ordinary cutting edge.

[0072] Step 4: Conduct soil simulation tests by combining the subsoiler handle designed in Step 3 with the national standard subsoiler tip to obtain the tillage resistance of the subsoiler within the corresponding design range. The simulation environment is illustrated below. Figure 7 As shown, the discrete element tillage conditions are set as follows: the subsoiler's forward speed is v = 5 km / h, and the tillage depth is h = 260 mm. The tillage resistance of the ordinary and biomimetic subsoilers is plotted on the same graph during the time the subsoiler is fully embedded in the soil. Different symbols are used to represent the resistance lines of different subsoilers (e.g., ...). Figure 8 As shown in the figure, the horizontal line represents the average tillage resistance of the deep loosening shovel.

[0073] A regression equation was established between tillage resistance and the design range β of the fitted curve, where the design range β is the independent variable and the tillage resistance F is the response value. The regression equation is as follows:

[0074] F=a0+a1β+a2β 2

[0075] In the formula, a0, a1, and a2 are coefficients to be determined.

[0076] The value of β was obtained through discrete element simulation. β max At that time, the simulated tillage resistance values ​​of the designed deep tillage shovel were F1, F2, F3, F4, and F5(N), which can be written in matrix form as follows:

[0077]

[0078] In the formula, the values ​​of F1, F2, F3, F4, and F5 obtained from soil simulation experiments are 714.2831, 708.8247, 715.2990, 714.2940, and 719.8174, respectively; β max The value is 126.93°.

[0079] The above equation regarding the resistance of biomimetic shovel tillage can be expressed as F. i If the number of equations in the system equal to Xa exceeds the number of unknowns, the system may not have an exact solution. In this case, define the residual vector ε = F - Xa, and minimize the sum of squares of the residuals. The optimization problem is then transformed into:

[0080] min a ||F i -Xa|| 2 =min a (F i -Xa) T (F i -Xa)

[0081] The coefficients a0, a1, and a2, calculated from the sum of tillage resistance values, are 716.7844, -0.1805, and 0.0016, respectively.

[0082] The final regression model for the relationship between tillage resistance and the following is obtained:

[0083] F=716.7844-0.1805β+0.0016β 2

[0084] The objective function is to minimize tillage resistance:

[0085] min|F|

[0086] When the theoretical minimum tillage resistance is found to be 711.7736 N, the corresponding β value is 55.153°. This value, corresponding to the minimum tillage resistance, is used in the final optimal design of the subsoiler blade, and modeled using 3D software.

[0087] In step 5, the preferred design of the deep tillage shovel handle from step 4 and a standard lightweight deep tillage shovel handle (JB / T9788-2020), paired with the same standard deep tillage shovel tip, were subjected to discrete element soil simulation under the same conditions (tillage speed of 5 km / h and working depth of 280 mm). The soil simulation test tillage resistance of the preferred deep tillage shovel was 701.8423 N, with an error of 1.3952 compared to the theoretical value of 711.7736 N. The soil simulation test tillage resistance of the standard lightweight deep tillage shovel was 725.1478 N. Compared with the designed bionic shovel handle, the bionic shovel handle can reduce the tillage resistance by 3.2139%, proving the effectiveness of the method and the drag reduction performance of the designed bionic shovel.

[0088] The biomimetic design method proposed in this invention, based on ellipse fitting and model regression, breaks through the empirical limitations of traditional biomimetic design by quantifying the morphological characteristics of the biological prototype blade and establishing a response model with tillage parameters, thus realizing efficient and low-resistance operation of the deep tillage shovel front blade structure under multiple working conditions.

[0089] It should be noted that although the present invention has been described through the above embodiments, the present invention may have many other embodiments. Without departing from the spirit and scope of the present invention, those skilled in the art can obviously make various corresponding changes and modifications to the present invention, but all such changes and modifications should fall within the scope of protection of the appended claims and their equivalents.

Claims

1. A method for designing the front cutting edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression, comprising the following steps: Step 1: Select the claws and toes of animals that are good at digging as biomimetic objects, obtain biological information from high-definition images of claws and toes, and process the biological information to obtain point cloud data of the inner and outer contours of the animal claws and toes. Step 2: Construct an ellipse fitting equation, use ellipse curves to fit the point cloud data of the inner and outer contours of the animal's claws and toes, and evaluate the fitting according to the ellipse fitting evaluation index. Step 3: Based on the ellipse fitting equation, select n different fitting curves within the fitting range and apply them to the front cutting edge design of the deep loosening shovel; Step 4: Conduct soil simulation tests on the biomimetic deep loosening shovels designed with different fitting curves to obtain the tillage resistance of the corresponding deep loosening shovels; A regression model is constructed to determine the range of the fitted curve and the tillage resistance, thus obtaining the mapping relationship between the range of the fitted curve and the tillage resistance. Based on this regression model, the optimal elliptic curve fitting range for drag reduction is obtained. Step 5: Design the cutting edge of the deep loosening shovel based on the optimal drag-reducing elliptic curve fitting range, and verify the accuracy and drag-reducing properties of the regression model using soil simulation tests.

2. The design method for the front edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression according to claim 1, characterized in that: In step 1, the biological information is processed by first extracting the inner and outer contours of the animal's claws and toes, and then using tools to obtain the inner and outer contour point cloud data through grayscale conversion, binarization, and filtering.

3. The design method for the front cutting edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression according to claim 2, characterized in that: In the two steps, The ellipse fitting equation is: x t =(x-x0)cosα0+(y-y0)sinα0 and t =-(x-x0)sinα0+(y-y0)cosα0 In the formula: (x0, y0) are the coordinates of the geometric center point of the ellipse fitting equation in a Cartesian coordinate system; α0 is the angle between the major semi-axis of the ellipse and the x-axis; a and b are the lengths of the minor and major semi-axis of the ellipse, respectively; x t and y t These are the algebraic expressions for the x and y coordinates of the standard ellipse equation, respectively.

4. The design method for the front edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression according to claim 3, characterized in that: In the two steps, the ellipse fitting equation is obtained through the following optimization model: In the formula: (x pi ,y pi ) represents the horizontal and vertical coordinates of the outline point cloud.

5. The design method for the front edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression according to claim 4, characterized in that: The constraints on the control parameters of the ellipse fitting equation are as follows: The evaluation index for the ellipse fitting equation is: In the formula: ρ i The distance between the point cloud and the center of the ellipse; This represents the mean distance between the point cloud and the center of the ellipse; ρ is the distance between the intersection point and the center of the ellipse; p is the number of independent variables in the regression model; SST, SSE, RMSE, R 2 ,and These are the total sum of squares, residual squares, root mean square error, coefficient of determination, and corrected coefficient of determination for the fitted ellipse corresponding to the inner and outer contours, respectively.

6. The design method for the front edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression according to claim 5, characterized in that: In the two steps described above, the formula for calculating the polar radius of the ellipse with the center of the ellipse as the pole is as follows: In the formula, θ is the polar angle.

7. The design method for the front cutting edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression according to claim 6, characterized in that: In the three steps described, the design range of the fitting curve is as follows: An ellipse is constructed with its center as the origin and its major axis as the polar axis; the lower bound β0 of the fitting curve design range is the angle between the line connecting the animal claw / toe excavation tip point cloud coordinates and the geometric center of the ellipse (the pole (x0, y0)) and the polar axis; the upper bound β0+β is the angle between the polar radius (which rotates freely around the pole) and the polar axis; the entire fitting angle region ranges from [β0, β0+β]; the angle region range corresponding to the bionic segment fitting curve is β; and the angle region value of the entire claw / toe fitting curve is β. max ; The method for selecting the n fitting curves is as follows: in β max Within the range, β is selected There are a total of n fitted curves in proportion. Scaling is applied to the cutting edge design of the deep loosening shovel, and the resulting fitted curves are then used; where: L c为 The straight-line distance between the two endpoints of the transition arc curve between the blade edge and the side of the shovel, according to national standards, is B. i This represents the straight-line distance between the two endpoints of the fitted curve.

8. The design method for the front cutting edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression according to claim 7, characterized in that: In the four steps, the deep tillage shovel designed in step 3 is subjected to a soil simulation test to obtain the tillage resistance of the deep tillage shovel within the corresponding design range; the steps are as follows: (1) Establish a regression equation between tillage resistance and the design range β of the fitted curve, where the design range β is the independent variable and the tillage resistance F is the response value. The regression equation is as follows: F＝α0+α1β+α2β 2 In the formula, α0, α1, and α2 are coefficients to be determined; (2) The simulated tillage resistance values ​​F1, F2, ... F of the deep tillage shovel designed with corresponding β values ​​were obtained through discrete element simulation. n (where n is a positive integer), written in matrix form as follows: (3) The above equation regarding the resistance of biomimetic shovel tillage can be expressed as F i If the number of equations in the system equal to Xa exceeds the number of unknowns, the system may not have an exact solution. In this case, define the residual vector ε = F - Xa and minimize the sum of squares of the residuals. The optimization problem is then transformed into: min a ||F i -If|| 2 =min a (F i -Xa) T (F i -Xa) (4) Based on tillage resistance and β max The coefficients α0, α1, and α2 were calculated, and the regression model of the relationship between tillage resistance and β was finally obtained. The objective function is to minimize tillage resistance: min F (5) The β value corresponding to the minimum tillage resistance is used for the optimal design of the final deep tillage shovel blade, and the model is designed using 3D software.

9. The design method for the front cutting edge of a drag-reducing biomimetic deep loosening shovel based on ellipse fitting and model regression according to claim 8, characterized in that: In the five steps, the deep tillage shovel designed by the preferred scheme in step 4 and the ordinary national standard light deep tillage shovel are subjected to discrete element soil simulation with the same tillage speed and working depth. The simulation results are compared to verify the accuracy and drag reduction of the regression model in step 4.

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