Suspension type agricultural machine tilling depth online detection method

Abstract

The invention discloses a suspension type agricultural machinery tilling depth on-line detection method, which comprises the following steps that S1, at least one laser sensor is installed at the top of a rack of a walking device, and the transmitting end of the laser sensor is aligned with a cover plate; s2, a first attitude sensor, a second attitude sensor and a third attitude sensor are installed at the horizontal positions of the rack, the cover plate and the lower pull rod correspondingly; s3, the laser sensor detects the distance L1 between a transmitting point of the laser sensor and the cover plate in real time, the first attitude sensor detects the pitch angle phi 1 of the walking device in real time, the second attitude sensor detects the pitch angle phi 2 of the rotary cultivator in real time, and the third attitude sensor detects the included angle phi 3 between the lower pull rod and the horizontal plane in real time; s4, calculating the tilling depth G according to the signals detected by the sensors; when the tilling depth is measured, the operation factor of the tire compaction depth is added, and the tilling depth detection precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent agricultural machinery planting technology, and in particular to an online detection method for the tillage depth of a suspended agricultural machinery. Background Art

[0002] Soil tillage is the first step in rice and wheat planting, and tillage depth and its stability are important technical indicators for evaluating tillage quality. Most of the rice and wheat planting in my country requires straw return to the field, and tillage depth plays a major role in the effect of straw return to the field. If the tillage layer is too shallow, it will affect the return of straw to the field, fail to meet agronomic requirements, be unfavorable for seed implantation, and affect crop growth and development; if the tillage layer is too deep, the tractor power consumption will increase, the soil bulk density will decrease, and the crop yield will be affected. In traditional technology, the measurement method of tillage depth in China is mainly based on manual sampling, which has poor measurement accuracy and low efficiency, and it is impossible to accurately obtain tillage depth data in real time, resulting in adjustment lag and the quality after tillage cannot be guaranteed. In the existing technology, indirect detection is generally used to automatically detect the tillage depth. This method mainly analyzes the changes in the working posture of agricultural tools and establishes a tillage depth detection model based on the angle of the tractor's lower pull rod. However, this method does not consider the influence of the compaction depth of the tractor tire on the measurement results, and the tillage depth detection accuracy is not high. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above-mentioned existing problems, the present invention is proposed, which solves the technical problem that the influence of the tractor tire compaction depth on the measurement result is not considered during indirect detection in the prior art. The present invention adds the operating factor of the tire compaction depth when measuring the plowing depth, thereby improving the detection accuracy of the plowing depth.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for online detection of tillage depth of a suspended agricultural machinery, wherein the suspended agricultural machinery comprises a traveling device, a rotary tiller is suspended at the rear of the traveling device, the rear end of the traveling device is connected to the rotary tiller via a lower pull rod, the rotary tiller comprises a cover plate, the left and right ends of the lower side of the cover plate are fixedly connected with vertical plates, a rotary tiller shaft arranged at intervals in the front and rear directions is rotatably connected between the two vertical plates, and when detecting the tillage depth of the rotary tiller, the following steps are included:

[0006] S1 installs at least one laser sensor on the top of the frame of the walking device, and the transmitting end of the laser sensor is aimed at the cover plate;

[0007] S2 installs a first attitude sensor, a second attitude sensor and a third attitude sensor at the horizontal positions of the frame, the cover plate and the lower pull rod respectively;

[0008] The S3 laser sensor detects the distance L1 between the laser sensor's emission point and the cover in real time, and the first attitude sensor detects the pitch angle of the walking device in real time. The second attitude sensor detects the pitch angle of the rotary tiller in real time The third attitude sensor detects the angle between the lower pull rod and the horizontal plane in real time

[0009] S4 calculates the tillage depth G according to the signals detected by each sensor.

[0010] As a preferred solution of the online detection method of tillage depth of suspended agricultural machinery described in the present invention, the calculation formula of tillage depth G is:

[0011] G = L1 sinα + L6-L7-L8;

[0012] Among them, L1 is the distance measured by the laser sensor to the cover of the rotary tiller, α is the angle between the light emitted by the laser sensor and the horizontal plane, L6 is the compaction depth of the soil by the walking device, L7 is the vertical distance between the laser emission point and the cover on the rotary tiller when the walking device and the bottom of the rotary tiller are in the same horizontal plane when not in operation, L8 is the distance between the axis of the rear rotary tiller shaft and point B in the vertical direction due to the change in distance caused by the pitch of the rotary tiller, and point B is the irradiation point of the laser sensor.

[0013] As a preferred solution of the online detection method of tillage depth of suspended agricultural machinery described in the present invention, wherein:

[0014]

[0015] Among them, L2 is the vertical distance from the axis of the rear rotary blade shaft to the cover plate, L3 is the distance from the axis of the rear rotary blade group to the hinge point of the lower pull rod, L4 is the projection length of the lower pull rod on the plane, and L5 is the horizontal distance between the emission point of the laser sensor and the hinge point of the lower pull rod on the walking device.

[0016] As a preferred solution of the online detection method of tillage depth of suspended agricultural machinery described in the present invention, the specific calculation formula of compaction depth L6 is:

[0017] L6=a*Y1 -b ;

[0018] Among them, Y1 is the soil firmness, the unit is MPa; a and b are constants.

[0019] As a preferred solution of the online detection method of tillage depth of suspended agricultural machinery described in the present invention, two laser sensors are installed on the top of the frame of the walking device, and the two laser sensors are symmetrically arranged about the center of the frame in the left and right directions, and L1 is the average value of the distance between the emission points of the two laser sensors and the cover plate.

[0020] Beneficial effects of the present invention: The present invention considers the influence of tire compaction depth on tillage depth detection during tillage depth detection, establishes a mathematical model of tire compaction depth, and integrates the mathematical model of tire compaction depth into the mathematical model of tillage depth, thereby improving the accuracy of tillage depth detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0022] Figure 1 This is a horizontal posture diagram of the suspended agricultural machinery when not in operation.

[0023] Figure 2 This is a horizontal posture diagram of a suspended agricultural machinery in operation.

[0024] Figure 3 This is a diagram of the bumpy posture of suspended agricultural machinery.

[0025] Among them, 100 is a walking device, 101 is a frame, 200 is a rotary tiller, 201 is a cover plate, 202 is a vertical plate, 203 is a rotary tiller shaft, and 300 is a laser sensor.

[0026] Figure 4 It is a schematic diagram of soil compression in the present invention.

[0027] Figure 5 This is a scene diagram when a uniaxial compression test of soil is performed in the present invention, in which 1 is a universal testing machine, 2 is an acrylic cylinder, and 3 is a hoop.

[0028] Figure 6 This is a simulation test diagram of soil uniaxial compression in the present invention.

[0029] Figure 7 This is a measurement diagram of measuring the static friction coefficient of straw-rubber using an inclined plane test in the present invention.

[0030] Figure 8 This is the test diagram for measuring the coefficient of recovery of straw-rubber collision.

[0031] Fig. 9This is the straw discrete element model established in the present invention.

[0032] Fig.10 This is a three-dimensional image of the tire created using CATIA in the present invention.

[0033] Fig.11 This is a solidity measurement diagram of the soil trench model in the present invention.

[0034] Fig.12 It is a simulation process diagram of soil compaction in the present invention.

[0035] Fig.13 This is the soil-straw discrete element soil trough model established in the present invention.

[0036] Fig.14 This is a diagram of tire compaction traces during the simulation test in the present invention.

[0037] Fig.15 It is a cross-sectional diagram of the compaction traces during the simulation test in the present invention.

[0038] Fig.16 This is the soil compaction simulation result diagram.

[0039] Fig.17 This is a graph showing the results of a soil compaction field test.

[0040] Fig.18 This is a picture of the suspended agricultural machinery operation site.

[0041] Fig.19 This is a picture of the operation scene of suspended agricultural machinery. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0045] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0046] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0047] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] Example 1

[0049] Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, and provides an online detection method for the tillage depth of a suspended agricultural machinery. The suspended agricultural machinery includes a traveling device, and a rotary tiller is suspended at the rear of the traveling device. The rear end of the traveling device is connected to the rotary tiller through a lower pull rod. The rotary tiller includes a cover plate, and vertical plates are fixedly connected to the left and right ends of the lower side of the cover plate. A rotary tiller shaft arranged at intervals in the front-to-back direction is rotatably connected between the two vertical plates. When detecting the tillage depth of the rotary tiller, the following steps are included:

[0050] S1 installs two laser sensors on the top of the frame of the walking device, and the transmitting end of the laser sensor is aimed at the cover plate;

[0051] S2 installs a first attitude sensor, a second attitude sensor and a third attitude sensor at the horizontal positions of the frame, the cover plate and the lower pull rod respectively;

[0052] The S3 laser sensor detects the distance L1 between the laser sensor's emission point and the cover in real time, and the first attitude sensor detects the pitch angle of the walking device in real time. The second attitude sensor detects the pitch angle of the rotary tiller in real time The third attitude sensor detects the angle between the lower pull rod and the horizontal plane in real time

[0053] S4 calculates the tillage depth G according to the signals detected by each sensor. The calculation formula of the tillage depth G is:

[0054] G = L1 sinα + L6-L7-L8;

[0055]

[0056] L6=a*Y1 -b ;

[0057] Among them, the two laser sensors are symmetrically arranged about the center of the frame in the left and right directions, L1 is the average value of the distance between the emission points of the two laser sensors and the cover plate, α is the angle between the light emitted by the laser sensor and the horizontal plane, L6 is the compaction depth of the soil by the walking device, L7 is the vertical distance between the laser emission point and the cover plate on the rotary tiller when the walking device and the bottom of the rotary tiller are in the same horizontal plane when not in operation, L8 is the distance between the axis of the rear rotary tiller shaft and point B in the vertical direction due to the change distance caused by the pitch of the rotary tiller, point B is the irradiation point of the laser sensor, L2 is the vertical distance from the axis of the rear rotary tiller shaft to the cover plate, L3 is the distance from the axis of the rear rotary tiller group to the hinge point of the lower pull rod, L4 is the projection length of the lower pull rod on the plane, L5 is the horizontal distance between the emission point of the laser sensor and the hinge point of the lower pull rod on the walking device; Y1 is the soil firmness, in MPa.

[0058] In this embodiment, the walking device is preferably a tractor; the present invention considers the influence of tire compaction depth on tillage depth detection during tillage depth detection, establishes a mathematical model of tire compaction depth, and integrates the mathematical model of tire compaction depth into the mathematical model of tillage depth to improve the accuracy of tillage depth detection.

[0059] Example 2

[0060] Reference Figures 4 to 17 , which is the second embodiment of the present invention, is based on embodiment 1, and further illustrates the derivation process of the mathematical model of the tire compaction depth, and verifies the accuracy of the mathematical model of the compaction depth by means of experimental verification.

[0061] The model process of establishing the compaction depth L6 of the soil by the walking device specifically includes the following steps:

[0062] 1: Calibration of soil particle parameters:

[0063] 101 The compression ratio is calculated by the height of the consolidated sample after the load is completely unloaded to evaluate the compression performance of the soil. In soil mechanics, the standard compression coefficient a is usually used. 1-2 To evaluate the compressibility of soil, the larger the compression coefficient, the higher the compressibility. 1-2 It is defined as the decrease in porosity caused by the unit vertical pressure increment when the load increases from p1 (100 kPa) to p2 (200 kPa) when the soil is under uniaxial compression (fully confined):

[0064]

[0065] Where e1 is the porosity at a load of 100 kPa, and e2 is the porosity at a load of 200 kPa.

[0066] The porosity ratio is the ratio of the pore volume in the soil to the volume of soil particles. Figure 4 The following is a schematic diagram of the volume change of soil during compression. P3 is the initial load on the soil before compression, and P4 is the load on the soil after compression. Soil compression is mainly caused by the compression of pore volume. The compression of soil particles is very small, and the particle height before and after compression can be considered to remain unchanged.

[0067]

[0068] In the formula, h s is the height of soil particles, mm; h1 is the sum of the heights of soil particles and gaps before compression, mm; e3 is the porosity when the initial load on the soil is P3, which is calculated from the initial moisture content, initial density, water density and soil particle density before soil compression and can be regarded as a constant; h2 is the sum of the heights of soil particles and gaps after compression, mm; e4 is the porosity when the load on the soil is P4; s is the compression distance, mm.

[0069] From formula (2), we can get e4 as:

[0070]

[0071] It can be seen from formula (3) that when h1 is constant, e4 is linearly correlated with s. To simplify the analysis, the sum of the heights of the voids, particles, and water in the soil is regarded as the total height of the soil, and the compression reduction ratio α of the soil height when the load on the soil increases from 100 kPa to 200 kPa is used to evaluate the compression performance of the soil:

[0072]

[0073] Where, h3 is the height of the soil when the load is 100 kPa, mm; h4 is the height of the soil when the load is 200 kPa, mm; h5 is the initial height of the soil, mm;

[0074] The soil compression deformation is mainly plastic deformation, accompanied by a small degree of elastic deformation. When the load is greater than 100kPa in the uniaxial closed compression simulation test of soil in EDEM software, the uncompacted soil model will produce a larger elastic deformation during compression than the actual compression test, that is, the compression reduction ratio of the simulation test under the same conditions will be greater than the actual compression test, which is inconsistent with the actual test. Therefore, the compression ratio is calculated based on the height of the consolidated sample after the load is completely unloaded to evaluate the compression performance of the soil, and α is corrected to:

[0075]

[0076] Among them, h6 is the soil height of the consolidated sample with a load of 100 kPa, mm; h7 is the soil height of the consolidated sample with a load of 200 kPa after compression, mm; h8 is the initial height of the consolidated sample with a load of 100 kPa, mm; h9 is the initial height of the consolidated sample with a load of 200 kPa;

[0077] 102 The soil particle parameters were calibrated. A uniaxial compression test of the soil was first performed to calculate the compression ratio. The coefficient of recovery X1, static friction coefficient X2, rolling friction coefficient X3, surface energy X4, plastic deformation ratio X5, bonding branch index X6 and tangential stiffness factor X7 were used as test factors. A uniaxial closed compression simulation test was performed in EDEM. A cylinder with a height of 250 mm and a bottom diameter of 50 mm was used to simulate an acrylic cylinder. The particle-cylinder simulation parameters were set. The particle-contact component model selected the Hertze-Mindlin model. A particle factory was set in the cylinder to generate 300 g of soil particles. The initial height of the model is measured. A compression simulation test is performed using a loading speed of 30 mm / s. When the axial pressure reaches 196.3 N, the model moves in the opposite direction at the same rate until the pressure is completely unloaded. The height h6 of the model is measured at this time. The simulation test is repeated. When the axial pressure is 392.7 N, the model is returned at the same rate. After complete unloading, the consolidation height h7 of the sample is measured. The compression ratio α is calculated. The soil compression performance index α is used as the evaluation index. The model parameters significantly related to the soil compression performance are first screened out using the Plackett-Burman test. Then, the regression model of the compression performance index and the significant factors is obtained through the Box-Behnken Design test. The factor parameters corresponding to the actual compression performance index are solved.

[0078] 103 Determine the relevant parameters of the straw discrete element model;

[0079] 2. Establish a simulation soil trough model based on the parameter calibration results;

[0080] 3. EDEM and Recurdyn software were used to couple the simulation to analyze soil firmness, and the operating factors with significant impact on compaction depth were selected for further simulation tests. Specifically,

[0081] 301 Three cones were introduced into EDEM, and a vertical downward speed of 0.1 m / s was given. The firmness of the soil model at a depth of 0 to 20 cm was measured every 5 cm. The peak pressure on the cones was extracted and the average value was calculated. Different soil firmness was obtained by adjusting the compaction degree of the soil trough model, and soil trough models with different firmness were established.

[0082] 302 Use CATIA to build a three-dimensional structural model of the tractor herringbone tire, and import the built tire three-dimensional structural model into Recurdyn software;

[0083] 303 Using EDEM-Recurdyn software coupled simulation, the step function was used to let the tire fall freely onto the soil trough model within 0 to 0.5 seconds, and the tire was evenly accelerated to the operating speed within 0.5 to 0.7 seconds. After 0.7 seconds, the tire moved forward at a uniform speed for compaction simulation test ( Fig.12 (a) is the interface display in EDEM during simulation. Fig.12 (b) is the interface display in Recurdyn during simulation), and the compaction depth of the tire on the soil trough model under different operating factors is obtained. The different operating factors include operating speed, soil firmness and straw coverage;

[0084] 304 The operating factors that are significant to the compaction depth are screened out, and the operating factors that are significant to the compaction depth are screened out as soil firmness.

[0085] 4 The selected operating factors were simulated to obtain the soil compaction depth corresponding to the operating factors under different parameters. SPSS27 software was used to perform nonlinear power function regression analysis on the simulation test results to establish a mathematical model between the selected factors and the soil compaction depth.

[0086] L6=a*Y1 -b .

[0087] In step 102, the specific steps of the soil uniaxial compression test are:

[0088] An appropriate amount of lubricating oil was applied to the inner wall of an acrylic cylinder (cut along the axis) with an inner diameter of 50 mm, an outer diameter of 70 mm, and a height of 150 mm to reduce the friction between the soil particles and the inner wall during the test. The cylinder was fixed with a hoop, and a 300 g soil sample was placed in the cylinder. The initial height h8 of the soil sample was measured, and the universal testing machine was set to push the platen downward at a constant speed of 8 mm / s until the axial pressure on the platen was 196.3 N (load 100 kPa). The platen was moved upward at the same rate until the pressure was completely unloaded. The soil height h6 at this time was measured. The above test was repeated, and the axial pressure on the platen was set to 392.7 N (load 200 kPa). The platen was moved upward at the same rate, and the consolidation height h7 and initial height h9 of the sample were measured, and the compression ratio α was calculated. The test was repeated three times, and the average value was taken to obtain the compression ratio of the surface soil and the compression ratio of the bottom soil.

[0089] The specific steps of the soil uniaxial compression simulation test are as follows:

[0090] To ensure that the particle factory generates enough particles, a cylinder with a height of 250mm and a bottom diameter of 50mm is used to simulate an acrylic cylinder. The particle-contact component model selects the Hertze-Mindlin (no slip) model. A particle factory is set up in the cylinder to generate 300g of soil particles and measure the initial height of the model. In the simulation compression test, it was found that the compression ratio obtained by applying a loading speed of 8mm / s and 30mm / s to the same sample was the same. In order to improve the simulation efficiency, a compression simulation test was performed using a loading speed of 30mm / s. Figure 5This is the compression simulation test process. During the test, the pressure plate moves downward at 30 mm / s, and when the axial pressure reaches 196.3 N (load 100 kPa), it moves in the opposite direction at the same rate until the pressure is completely unloaded, and the height h6 of the model is measured at this time. The simulation test was repeated. When the axial pressure was 392.7N (load 200kPa), the carriage was returned at the same speed. After complete unloading, the consolidation height h7 of the sample was measured, and the compression ratio α was calculated. The amount of soil particle modeling was huge. In order to balance the calculation efficiency and simulation quality, two sizes of particles were selected for modeling. The surface soil was modeled with particles with a radius of 5mm, and the bottom soil was modeled with particles with a radius of 10mm. According to the contribution analysis of the seven experimental factors in the Plackett-Burman compression test to the soil compression performance and bearing capacity, the contribution of the 5mm soil particle compression performance was ranked from large to small as X5, X2, X7, X6, X1, X4, and X3, and the contribution of the 10mm soil particle compression performance was ranked from large to small as X3, X5, X2, X7, X1, X4, and X6. A Box-Behnken Design experiment was designed to analyze and find the optimal solution. The Design The expert conducted a multivariate regression analysis on the test results, eliminated the insignificant items in the variance analysis, and obtained the regression model of the 5mm particle compression ratio Y1 as follows:

[0091] Y1=a1+a2*X2+a3*X5+a4*X2*X5+a5*X5*X7-a6X7 2 ; a1~a6 are constants.

[0092] The regression model for the compression ratio Y2 of 10mm particles is:

[0093] Y2=b1+b2*X2+b3*X3+b4*X5-b5*X2*X5-b6*X2 2 -b7*X3 2 -b8*X5 2 ; b1~b8 are constants.

[0094] The following actual experiments are used to determine the relevant parameters of the straw discrete model.

[0095] Three straw collection points were randomly selected in the experimental field. Ten pieces of broken straw were collected at each collection point. The straw length was measured with a steel ruler and the straw diameter was measured with a vernier caliper. Finally, the average value was calculated. The average straw length was 85.4 mm, the average diameter was 4.96 mm, and the straw density was 153 kg / m 3 , set the Poisson's ratio to 0.4 and the shear modulus to 1MPa;

[0096] In EDEM, a rigid unit consisting of five spherical particles with a diameter of 5 mm, a spherical center distance of 3 mm, and a length of 17 mm was established. The five rigid units were spliced ​​into a straw model through the element particles, with a total length of 85 mm. The adjacent rigid units were connected by bonding bonds in the BondingV2 model, and the normal bonding stiffness was set to 1.2×10 7 N / m 3 , tangential bond stiffness 1×10 7 N / m 3 , normal critical stress 4.3×10 7 N / m 2 , tangential critical stress 1.26×10 7 N / m 2 ;

[0097] The EEPA contact model was selected to restore the elastic-plastic and viscous contact characteristics inside the straw pile and between the straw and the soil when the straw was squeezed by the tire. The parameters of the EEPA contact model inside the straw were set as follows: the constant pull-off force was 0, the surface energy was 5.194 J / m 2 , contact plastic deformation ratio is 0.5, loading branch index is 1.5, bonding branch index is 1.662, and tangential stiffness factor is 0.6; the parameters of the straw-soil EEPA contact model are: constant pull-off force 0, surface energy 10 J / m 2 , contact plastic deformation ratio 0.4, loading branch index 1.5, bond branch index 1.75 and tangential stiffness factor 0.667.

[0098] In this application, the tire selected is a 18.4-38 tractor herringbone tire, with a pattern type of R2, a standard rim W16L, a rim diameter of 970mm, a rated air pressure of 230kPa, an inflated outer diameter of 1807mm, a section width of 467mm, and a tire density of 1208kg / m 3 , rubber is an incompressible material, Poisson's ratio is set to 0.5, and the shear modulus is set to 2900 MPa. The final contact parameters between soil, straw, and rubber are shown in Table 1.

[0099] Table 1 Contact parameters of soil, straw and rubber

[0100]

[0101] The specific steps for determining the coefficient of restitution between straw and tire are:

[0102] After the rice was harvested, the rice straw was naturally dried for 10 days. The static friction coefficient of the rice straw-rubber was measured by an inclined plane test. Figure 7(a) is the static friction coefficient measurement scenario. During the test, a straw is placed longitudinally on a rubber plate, and the rubber plate is slowly lifted. The lifting is stopped at the moment when the straw starts to slide on the rubber plate. The inclination angle of the rubber plate is measured at this time. The test is repeated 10 times and the average value is taken to obtain a straw-rubber static friction coefficient of 0.82.

[0103] The measurement method of rolling friction coefficient is the same as that of static friction coefficient. Figure 7 (b) is the rolling friction coefficient measurement scenario. A straw is placed horizontally on a rubber plate, and the rubber plate is slowly lifted. The lifting is stopped when the straw starts to roll on the rubber plate. The inclination angle of the rubber plate is measured at this time. The test is repeated 10 times and the average value is taken to obtain a straw-rubber rolling friction coefficient of 0.16.

[0104] The collision restitution coefficient is the ratio of the relative velocity of two objects after the collision to the relative velocity before the collision. It is only related to the materials of the colliding objects. Its calculation formula is: H1 is the initial height of the straw when it falls, mm; H2 is the maximum height of the straw that bounces after collision, mm.

[0105] Figure 8 This is the test scenario for measuring the recovery coefficient of straw-rubber collision. During the test, the straw falls freely from a height of 300 mm and bounces back after colliding with the rubber plate. A high-speed camera is used to record the maximum height after the bounce. The test is repeated 10 times, and the average value is calculated to obtain a recovery coefficient of 0.45 between the straw and rubber.

[0106] The soil sampling site was the rice stubble experimental field in Jiangdu District, Yangzhou City. The soil type was clay. Soil sampling was carried out at the surface layer with a depth of 0-225 mm and the bottom layer with a depth of 225-450 mm. The density of soil particles was measured using the pycnometer method. The measurement was repeated three times and the average value was taken. The surface soil density was measured to be 2009 kg / m 3 The density of the bottom soil is 2173kg / m 3 ,During the uniaxial compression test of soil, the compression ratio of the surface soil is 6.57%, and the compression ratio of the bottom soil is 5.63%.

[0107] In the soil uniaxial compression simulation test, the soil contact model uses the EEPA (Edinburgh Elasto-Plastic Adhesion) model. The parameters that need to be determined include constant pull-off force (f0), surface energy (△γ), contact plastic deformation ratio (λp), loading branch index (n), bonding branch index (X) and tangential stiffness factor (ktm). Among them, f0 is defined as 0. In EDEM, the loading branch index n can only be selected as 1 or 1.5. Since the stress-strain characteristics of actual soil are nonlinear, n is set to 1.5. The contact parameters that need to be determined in the soil discrete element model include the collision recovery coefficient (e) between soil particles, the static friction coefficient (μs), and the rolling friction coefficient (μr). The soil Poisson's ratio is set to 0.38 and the soil shear modulus is set to 1MPa. When conducting a uniaxial closed compression simulation test, in order to ensure that the particle factory generates sufficient particles, a cylinder with a height of 250 mm and a bottom diameter of 50 mm is used to simulate the acrylic cylinder. The particle-cylinder simulation parameter settings are shown in Table 2. The particle-contact component model selects the Hertze-Mindlin (no slip) model. A particle factory is set in the cylinder to generate 300 g of soil particles and measure the initial height of the model. In the simulation compression test, it was found that the compression ratio obtained by applying a loading speed of 8 mm / s and 30 mm / s to the same sample was the same. In order to improve the simulation efficiency, a compression simulation test was performed using a loading speed of 30 mm / s. Figure 6 This is the compression simulation test process. During the test, the pressure plate moves downward at 30mm / s. When the axial pressure reaches 196.3N (load 100kPa), it moves in the opposite direction at the same rate until the pressure is completely unloaded. The height h6 of the model is measured at this time. Repeat the simulation test. When the axial pressure is 392.7N (load 200kPa), return at the same rate. After complete unloading, the consolidation height h7 of the sample is measured, and the compression ratio α is calculated.

[0108] Table 2 Particle-cylinder simulation parameters

[0109]

[0110] Taking the soil compression performance index as the response value, the Plackett-Burman test is first used to screen out the model parameters significantly related to the compression performance to reduce the parameter calibration factors, and then the regression model of the compression performance index and the significant factors is obtained through the Box-Behnken Design test to solve the factor parameters corresponding to the actual compression performance index. The soil model parameter levels determined according to the existing technology and combined with the actual simulation situation are shown in Table 3, with one high level and one low level for each, for a total of 7 parameters.

[0111] Table 3 Simulation test factors and levels

[0112]

[0113] The amount of soil particle modeling is huge. In order to balance the calculation efficiency and simulation quality, two sizes of particles are selected for modeling. The surface soil is modeled with particles with a radius of 5 mm, and the bottom soil is modeled with particles with a radius of 10 mm. Due to the different contact parameters between soil particles of different sizes, the Plackett-Burman test is used to study the sensitivity of the model parameters of the two sizes of particles. The simulation test plan is designed using the software Design Expert. The simulation test design plan and results are shown in Table 3. Set X8~X 11 A total of 4 blank columns are used for error analysis.

[0114] Table 4 Plackett-Burman simulation compression test design and results

[0115]

[0116]

[0117] Considering that the height of the soil discrete element model will decrease after compaction, and the depth of the soil compacted by the tire generally does not exceed the depth of the surface soil, the test uses the average value of the soil firmness at a depth of 0 to 20 cm as the level of soil firmness. The soil firmness at a depth of 0 to 20 cm was measured every 5 cm in different test plots by using the TJS-100 soil firmness meter, and then the average value was calculated to determine the range of soil firmness levels. The range of factor levels was determined according to the tractor operating speed, soil firmness of the test field, and straw coverage, as shown in Table 5.

[0118] Table 5 Factor levels of soil compaction test

[0119]

[0120] A single factor simulation test was designed to analyze the influence of operating speed, soil firmness, and straw coverage on the soil compaction depth of tires, and then a uniform test was designed based on the test results to establish a soil compaction depth model. Each factor was taken at 5 levels, and a single factor soil compaction simulation test was designed as shown in Table 6. Since the soil firmness of the test field is difficult to adjust manually, it is impossible to select different soil firmnesses at the same level. According to the actual situation of the firmness of different test plots in Jiangdu District, Yangzhou City, a single factor soil compaction field test was designed as shown in Table 7.

[0121] Table 6 Soil compaction single factor simulation test design

[0122]

[0123] Table 7 Single factor field test design for soil compaction

[0124]

[0125] The contribution of seven factors to soil compression performance and bearing capacity in the Plackett-Burman compression test is shown in Table 8. The contribution of 5mm soil particles to the compression performance is ranked from large to small as X5, X2, X7, X6, X1, X4, and X3, and the contribution of 10mm soil particles to the compression performance is ranked from large to small as X3, X5, X2, X7, X1, X4, and X6. According to the results of parameter sensitivity analysis, a Box-Behnken Design experiment is designed to analyze and find the optimal solution. For the 5mm particle model, the plastic deformation ratio X5, static friction coefficient X2, and tangential stiffness factor X7 are selected to design the levels of each factor as shown in Table 9, and the design of the Box-Behnken Design experiment is shown in Table 10. For the 10mm particle model, the rolling friction coefficient X3, plastic deformation ratio X5, and static friction coefficient X2 are selected to design the levels of each factor as shown in Table 11, and the design of the Box-Behnken Design experiment is shown in Table 12. The parameters of other factors are set to intermediate values.

[0126] Table 8 Plackett-Burman compression test sensitivity analysis

[0127]

[0128] Table 9 Factor levels of Box-Behnken test for 5 mm particles

[0129]

[0130] Table 10 Box-Behnken test scheme and results for particles with a radius of 5 mm

[0131]

[0132] Table 11 Factor levels of Box-Behnken test for particles with a radius of 10 mm

[0133]

[0134] Table 12 Box-Behnken test plan and results for particles with a radius of 10 mm

[0135]

[0136] The variance analysis of the quadratic regression model is shown in Table 13. X2 and X5 have significant effects on the compression ratio in the uniaxial compression test of particles with a radius of 5 mm, and the significance of the effect is X5>X2. X2X5 and X5X7 have significant effects on the compression ratio, and the other interaction terms have no significant effect on the compression ratio. The quadratic term X7 2The compression ratio is significantly affected, X2 2 、X5 2 The effect on the compression ratio is not significant. Using Design Expert to perform multiple regression analysis on the test results, eliminating the insignificant items in the variance analysis, the regression model of the compression ratio of 5mm particles is obtained as follows:

[0137] Y1=5.69+1.16X2+3.12X5+2.19X2X5+0.88X5X7-1.37X7 2 ;

[0138] The coefficient of determination R of the model 2 is 0.97, R adj 2 It is 0.93, and the regression equation has a good fitting degree and high reliability.

[0139] The Design expert constraint solving tool was used to solve the compression ratio of 6.57% as the target value. The soil compression simulation test was carried out to verify the obtained several groups of solutions, and the group of solutions with the smallest relative error between the simulation compression ratio and the test compression ratio was selected. The optimal solution of the 5mm radius particle model is: static friction coefficient X2 is 0.45, plastic deformation ratio X5 is 0.64, and tangential stiffness factor X7 is 0.63. Five soil compression simulation tests were carried out using the above parameters, and the remaining parameters were taken at the middle level. The average soil compression ratio was 6.47%, and the relative error with the actual test measurement value was -1.52%. Analysis shows that the error between the simulation result and the actual test compression ratio is small.

[0140] Table 13 Analysis of variance of quadratic regression model for Box-Behnken test of 5 mm particles

[0141]

[0142] For the particle model with a radius of 10mm, the Design expert software was used to solve the problem with a compression ratio of 5.63% as the target value. The solved parameters were used for soil compression simulation test, and a set of solutions with the smallest difference between the simulation compression ratio and the test compression ratio was selected. The optimal solution obtained was: static friction coefficient X2 was 0.36, rolling friction coefficient X3 was 0.25, and plastic deformation ratio X5 was 0.65. Five soil compression simulation tests were carried out using the optimal solution, and the average value of the five tests was 5.81%, which was close to the actual value, with a relative error of 3.20%. The error between the simulation result and the actual test compression ratio was small.

[0143] Table 14 Analysis of variance of quadratic regression model for Box-Behnken test of 10 mm particles

[0144]

[0145] Note: P value < 0.05 indicates significant, and < 0.01 indicates extremely significant.

[0146] From the variance analysis table 14, it can be seen that in the simulation compression test of particles with a radius of 10 mm, X2, X3, and X5 have significant effects on the compression ratio, and the significance of the influence is X3>X5>X2. X2X5 has a significant effect on the compression ratio, while X2X3 and X3X5 have no significant effect on the compression ratio. The quadratic term X2 2 、X5 2 、X7 2 The compression ratio is significantly affected. Using Design Expert to perform multiple regression analysis on the test results, eliminating the insignificant items in the variance analysis, the regression model of the compression ratio of 10mm particles is obtained as follows:

[0147] Y2=6.88+0.76X2+1.25X3+0.92X5-0.72X2X5-1.19X2 2 -1.31X3 2 -1.87X5 2 (twenty two)

[0148] The coefficient of determination R of the model 2 is 0.99, R adj 2 It is 0.97, indicating that the regression equation has a good fit and high reliability.

[0149] For the particle model with a radius of 10mm, the Design expert software was used to solve the problem with a compression ratio of 5.63% as the target value. The solved parameters were used for soil compression simulation test, and a set of solutions with the smallest difference between the simulation compression ratio and the test compression ratio was selected. The optimal solution obtained was: static friction coefficient X2 was 0.36, rolling friction coefficient X3 was 0.25, and plastic deformation ratio X5 was 0.65. Five soil compression simulation tests were carried out using the optimal solution, and the average value of the five tests was 5.81%, which was close to the actual value, with a relative error of 3.20%. The error between the simulation result and the actual test compression ratio was small.

[0150] The coefficient of restitution between the surface soil and the bottom soil is set to 0.3, the coefficient of static friction is set to 0.5, and the coefficient of rolling friction is set to 0.3. Since the calibration results of the EEPA contact model parameters of the surface and bottom soil particles are similar, the EEPA model of the contact between the surface and bottom soil particles takes the average of the calibration values ​​of the two, that is, the surface energy is 12.5 J / m 2 , contact plastic deformation ratio 0.645, bond branching index 2.25, and tangential stiffness factor 0.665.

[0151] Fig.13This is the soil-straw discrete element model diagram. According to the parameter calibration results, a 225 mm high bottom soil layer is established using 10 mm radius particles, with a total of 97,590 particles generated. A 225 mm high surface soil layer is established using 5 mm radius particles, with a total of 772,085 particles generated. A straw pile is generated on the surface soil, with a total of 91,950 straws generated (1.2 kg / m 2 coverage amount). Fig.14 Figure 2. The tire compaction traces in EDEM and Recurdyn. Fig.15 It is a cross-sectional view of the compaction trace. It can be seen from the figure that the straw broke and bent under the compression of the tire and mixed with the soil particles, which is consistent with the actual test, indicating that the constructed model can simulate the real test scene well.

[0152] Fig.16 is the soil compaction simulation result. Fig.17 The results of the soil compaction field test. When the soil firmness and straw coverage remain unchanged, the tire compaction depth in both the simulation test and the field test decreases with the increase of the operating speed. This is because when the tire compacts the soil, the higher the speed, the shorter the time it takes to compact the soil, that is, the shorter the time the pressure acts on the soil, resulting in a decrease in the compaction depth. In the simulation test, the compaction depth at a speed of 7.5 km / h is 94.81% of the compaction depth at a speed of 1.5 km / h, a decrease of 5.2 mm; in the field test, the compaction depth at a speed of 7.5 km / h is 93.88% of the compaction depth at a speed of 1.5 km / h, a decrease of 6 mm. In general, the effect of the change in operating speed on the compaction depth is not significant. As the operating speed increases, the degree of reduction in the soil compaction depth is small. In subsequent simulation tests, the operating speed is taken as the middle value of the test factor range, that is, 4.5 km / h.

[0153] When the operating speed and the amount of straw coverage remain unchanged, the compaction depth of both the simulation test and the field test decreases with the increase of firmness. In the simulation test, the compaction depth of the compaction depth of 3MPa is 64.36% of the compaction depth of 1MPa, which is 43.8mm lower; in the field test, the compaction depth of 2.87MPa is 66.11% of the compaction depth of 1.12MPa, which is 41mm lower. It can be seen that the change of firmness has a significant impact on the compaction depth. For every 0.5MPa increase in firmness, the soil compaction depth decreases by 12.5mm, 11.9mm, 10.2mm, and 9.2mm, respectively, and gradually decreases. This is because as the soil firmness increases, the soil's ability to resist external compaction becomes stronger and becomes more and more difficult to compress.

[0154] When the operating speed and soil firmness remain unchanged, the compaction depth decreases with the increase of straw coverage. The straw plays a certain buffering role when the tire compacts the soil, reducing the degree of soil compaction by the tire. In the simulation test, the straw coverage is 1.2kg / m 2 The compaction depth was 96.12% of that without straw covering, which was 3.9 mm lower. The straw covering amount in the field test was 1.2 kg / m 2 The compaction depth is 94.90% of that without straw covering, which is 5mm lower. It can be seen that the change of straw covering amount has no significant effect on the compaction depth. In the subsequent simulation test, the straw covering amount is taken as the middle value of the test factor range, that is, 0.6kg / m 2 .

[0155] According to the single factor test results, the operating speed was set to 4.5 km / h and the straw coverage was set to 0.6 kg / m 2 , taking soil firmness as the test factor and soil compaction depth as the response quantity, a single factor nine-level equal-level simulation test was designed and the results are shown in Table 15.

[0156] Table 15 Design and results of equal-level simulation tests

[0157]

[0158]

[0159] From a theoretical analysis, assuming that the soil depth is infinite, when the soil firmness tends to infinity, the compaction depth tends to 0. When the soil firmness tends to 0, the tire will sink into the soil and the compaction depth tends to infinity. Therefore, the compaction depth model should be a power function. In order to reduce the error, the actual firmness of the discrete element model is used as the independent variable, and the simulation test results are analyzed by nonlinear power function regression using SPSS27 software. The fitting results are as follows:

[0160] L6=127.339*Y1 -0.401

[0161] In the formula, the correlation coefficient R of the model is 2 It is 0.992, indicating that the model has a good fit.

[0162] To verify the soil compaction depth model, five test plots with different compactness were randomly selected for tire compaction tests. The tractor speed was 4.5 km / h and the straw coverage was 0.6 kg / m 2 ,The test results are shown in Table 16. It can be seen that the relative error is within the range of ±3.44%, and the consistency between the model prediction value and the test value is good.

[0163] Table 16 Verification results of soil compaction depth model test

[0164]

[0165] It can be seen from this embodiment that the mathematical model of the compaction depth of the tractor tire on the soil established in the present invention has high accuracy.

[0166] Example 3

[0167] Reference Fig.18 and Fig.19 , which is the second embodiment of the present invention, is based on embodiment 1 and proves the high precision of tillage depth detection through experimental verification.

[0168] The hardware part uses a laser sensor with a measurement accuracy of ±1mm and a posture sensor with a measurement accuracy of ±0.05°. The single-chip microcomputer STM32 F429 is used as the controller. Two laser sensors and three posture sensors are used to detect the posture information of the agricultural machinery in real time during operation and transmit it to the single-chip microcomputer. There is a touch screen in the cab of the tractor. The relevant parameters of the machine and the test field are input on the touch screen. The single-chip microcomputer displays the tillage depth data calculated during the operation of the agricultural machinery on the touch screen according to the set time interval, and stores the data in the data storage module.

[0169] In order to test the detection accuracy of the tillage depth detection device and analyze the influence of soil compaction depth on the detection accuracy, a tillage depth detection test was carried out in the experimental field in Jiangdu District, Yangzhou City. The average soil firmness at a depth of 0 to 20 cm in the experimental field was 2.38 MPa, and the straw coverage was 0.79 kg / m 2 The sensors were fixed on tractors and rotary tillers, and tillage depth detection tests were carried out at speeds of 1.5km / h, 3km / h, 4.5km / h, 6km / h, and 7.5km / h.

[0170] In the measurement area, taking the initial point of the machine operation as the starting point and the machine's forward distance of 1s as the interval, one point is measured on both sides of the center line along the forward direction of the unit, and a total of 11 measurements are made. The average tillage depth a is:

[0171]

[0172] c i is the tillage depth value of the ith measuring point, mm; n is the number of measuring points.

[0173] The tillage quality can be evaluated by the tillage depth stability coefficient u, which is calculated as follows:

[0174]

[0175] u=1-v (9)

[0176] s is the standard deviation of tillage depth, mm; v is the coefficient of variation of tillage depth.

[0177] The detection accuracy ε of the i-th measuring point i for:

[0178] ε i =c i -A i (10)

[0179] c i is the tillage depth value of the ith measuring point, in mm; Ai is the tillage depth value of the ith measuring point measured by the detection device, in mm.

[0180] The system detection accuracy ε is:

[0181] ε=±max|ε i | (11)

[0182] max|ε i | is the detection accuracy ε i The maximum value in ;

[0183] The system detection error E is:

[0184]

[0185] for The maximum value in .

[0186] The soil firmness of the experimental field was 2.38MPa, the straw coverage was 0.79kg / m2, and the compaction depth predicted by the soil compaction model was 89.9mm. Fig.18 This is a machine operation scene. Fig.19 (a) is the effect diagram of the machine operation. After the soil is double-pressed by the seeder, the elevation of the land after rotary tillage is basically the same as that of the uncultivated land. The loose soil on the soil surface is slowly removed, and the tillage depth is measured based on the surface after pressing. Fig.19 (b) is the tillage depth measurement scenario. Table 17 shows the results of the field test for tillage depth detection. It can be seen that the compaction depth model can greatly reduce the tillage depth detection error. As the machine operating speed increases, the tillage depth and the tillage depth stability coefficient decrease, and the detection error increases. This is because as the machine operating speed increases, the tillage resistance increases, the machine bumps more severely, while the tractor PTO output speed remains unchanged, the rotary tiller speed remains unchanged, the rotary tiller cutting soil time decreases, the soil cutting pitch increases, the rotary tiller's ability to cut the soil decreases, it cannot penetrate deep into the soil, the soil is not turned over thoroughly, resulting in a decrease in operation quality and a decrease in tillage depth.

[0187] Too high an operating speed will reduce the work quality, while too low an operating speed will reduce the work efficiency. Taking into account the work efficiency and work quality, 4.5km / h is the optimal operating speed for the machine. The average tillage depth tested at an operating speed of 4.5km / h is about 199mm. The detection accuracy of the detection device is ±8.2mm, and the detection error is 4.07%. The error is small and can meet the tillage depth detection needs.

[0188] Table 17 Ploughing depth test results

[0189]

[0190] It should be appreciated that embodiments of the present invention may be implemented or enforced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method may be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and drawings described in the specific embodiments. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program may be implemented in assembly or machine language. In any case, the language may be a compiled or interpreted language. In addition, the program may be run on a programmed dedicated integrated circuit for this purpose.

[0191] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. A computer program includes a plurality of instructions that may be executed by one or more processors.

[0192] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or communicates with a charged particle tool or other imaging device, etc. Aspects of the present invention can be implemented in a machine-readable code stored on a non-transitory storage medium or device, whether it is removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by a computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or part thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the above steps in conjunction with a microprocessor or other data processor, the invention herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself. The computer program can be applied to input data to perform the functions of this article, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on a display.

[0193] As used in this application, the terms "component", "module", "system", etc. are intended to refer to a computer-related entity, which can be hardware, firmware, a combination of hardware and software, software, or software in operation. For example, a component can be, but is not limited to: a process running on a processor, a processor, an object, an executable file, a thread in execution, a program, and / or a computer. As an example, an application running on a computing device and the computing device can both be components. One or more components can exist in a process and / or thread in execution, and a component can be located in a computer and / or distributed between two or more computers. In addition, these components can be executed from various computer-readable media having various data structures thereon. These components can communicate in a local and / or remote process manner, such as according to a signal having one or more data packets (e.g., data from a component that interacts with another component in a local system, a distributed system, and / or interacts with other systems in a signal manner through a network such as the Internet).

[0194] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for online detection of tillage depth of suspended agricultural machinery, characterized in that: The suspended agricultural machinery includes a traveling device, the rear of which is connected to a rotary tiller by suspension. The rear end of the traveling device is connected to the rotary tiller via a lower pull rod. The rotary tiller includes a cover plate, and vertical plates are fixedly connected to the left and right ends of the lower side of the cover plate. A rotary tiller shaft arranged at intervals in the front and rear directions is rotatably connected between the two vertical plates. When detecting the tillage depth of the rotary tiller, the following steps are included: S1 installs at least one laser sensor on the top of the frame of the walking device, and the transmitting end of the laser sensor is aimed at the cover plate; S2 installs a first attitude sensor, a second attitude sensor and a third attitude sensor at the horizontal positions of the frame, the cover plate and the lower pull rod respectively; The S3 laser sensor detects the distance L1 between the emission point of the laser sensor and the cover plate in real time, the first attitude sensor detects the pitch angle φ1 of the walking device in real time, the second attitude sensor detects the pitch angle φ2 of the rotary tiller in real time, and the third attitude sensor detects the angle φ3 between the lower pull rod and the horizontal plane in real time; S4 calculates the tillage depth G according to the signals detected by each sensor.

2. The online detection method for tillage depth of a suspended agricultural machinery according to claim 1, characterized in that: The calculation formula of tillage depth G is: ； Among them, L1 is the distance measured by the laser sensor to the cover of the rotary tiller, α is the angle between the light emitted by the laser sensor and the horizontal plane, L6 is the compaction depth of the soil by the walking device, L7 is the vertical distance between the laser emission point and the cover on the rotary tiller when the walking device and the bottom of the rotary tiller are on the same horizontal plane when not in operation, L8 is the distance between the axis of the rear rotary tiller shaft and point B in the vertical direction due to the change in distance caused by the pitch of the rotary tiller, and point B is the irradiation point of the laser sensor.

3. The online detection method for tillage depth of a suspended agricultural machinery according to claim 2, characterized in that: ； Among them, L2 is the vertical distance from the axis of the rear rotary blade shaft to the cover plate, L3 is the distance from the axis of the rear rotary blade group to the hinge point of the lower pull rod, L4 is the projection length of the lower pull rod on the plane, and L5 is the horizontal distance between the emission point of the laser sensor and the hinge point of the lower pull rod on the walking device.

4. The online detection method for tillage depth of a suspended agricultural machinery according to claim 2 or 3, characterized in that: The specific calculation formula for compaction depth L6 is: L6=a*Y1 -b ; Among them, Y1 is the soil firmness, the unit is MPa; a and b are constants.

5. The online detection method for tillage depth of a suspended agricultural machinery according to claim 2 or 3, characterized in that: Two laser sensors are installed on the top of the frame of the walking device. The two laser sensors are symmetrically arranged about the center of the frame in the left and right directions. L1 is the average value of the distance between the emission points of the two laser sensors and the cover plate.

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