Method for controlling arc knife of arc knife type tree mover to meet hard objects and arc knife type tree mover

Abstract

The invention relates to a control method for an arc cutter of an arc cutter type tree mover meeting a hard object, and the method comprises the steps: obtaining the cutter tree mover data of a plurality of tree movers, and combining the obtained tree mover data with the depth of mining to create a fusion curve; then screening the cutter tree digging data based on the rotation data of the cutter and the stress data of the cutter during tree digging of the cutter to obtain screened cutter tree digging data, and taking the screened cutter tree digging data as a reference; calculating a prediction curve of the screened cutter tree digging data by using a fusion curve of the screened cutter tree digging data corresponding to the cutter operation environment data in the screened cutter tree digging data, creating a cutter operation prediction model, and training the cutter operation prediction model by using the data as training data; and a tool operation prediction model with a tool operation data prediction function is obtained, so that when the tool operation data exceeds the predicted data in the actual operation process of the tool, alarm information is sent.

Description

Technical Field

[0001] This application relates to the field of tree digging machine technology, and in particular to a method for controlling the arc blade of a circular arc blade tree digger when it encounters hard objects, and the circular arc blade tree digger itself. Background Technology

[0002] With the rapid development of urban construction and roads, the demand for supporting greening work is increasing, and the work of transplanting trees and nurseries is also increasing. In order to improve work efficiency and the survival rate of trees, mechanized transplanting has become an inevitable trend to replace manual transplanting.

[0003] Currently, existing tree diggers used for transplanting trees and nurseries are prone to damage due to the uncertainties in the soil environment. When large and hard foreign objects are present in the soil, the blades of the digger will come into direct contact with the objects, causing damage to the blades. The traditional solution is for the operator to control the blade rotation and replace it with a new size blade when it comes into contact with a hard object. If the blade does not rotate in time, it will be damaged. The traditional solution lacks real-time prediction and early warning of foreign objects by the blades during the operation of the tree digger, which makes it too dependent on the operator's experience, resulting in high technical costs. Only highly experienced operators can be competent to dig trees, and the easy damage of the blades also leads to excessive costs. Summary of the Invention

[0004] Therefore, it is necessary to address the many uncertainties in the soil environment where traditional tree diggers operate. When there are large and hard foreign objects in the soil, the blades of the arc-blade tree digger will directly come into hard contact with the foreign objects, requiring the operator to rely on experience to judge the timing of the blade rotation, which places excessive demands on the operator's skills. Therefore, it is necessary to provide a method for controlling the arc blade of the arc-blade tree digger when it encounters hard objects.

[0005] This application provides a method for controlling the arc blade of a circular arc-blade tree digger when it encounters hard objects, including: Acquire sample data from multiple samples, including tool rotation data, tool force data, and tool operating environment data; Based on the tool rotation data and tool force data in each sample data, a fusion curve is created for each sample data. Based on the tool rotation data and tool force data in each sample data, the fusion curve of each sample data is filtered to obtain the filtered sample data; Based on the fusion curve of each filtered sample data corresponding to the tool operating environment data in each filtered sample data, the prediction curve of each filtered sample data is calculated. A tool operation prediction model is created and trained based on tool rotation data, tool force data, each filtered sample data, operating environment data, fusion curve of each filtered sample data, and prediction curve of each filtered sample data. Obtain a sample of data to be tested; Input the tool running environment data in the sample data to be tested into the tool running prediction model, start the tool running prediction model, and obtain the prediction curve of the sample data to be tested output by the tool running prediction model; Acquire the rotation data and force data of the test tool in the test sample corresponding to the current soil depth, as well as the range of predicted tool rotation data and predicted tool force data in the prediction curve; Determine whether the following conditions are met simultaneously: the rotation data of the test tool is within the range of the predicted rotation data of the test tool and the force data of the test tool is within the range of the predicted force data of the test tool. If the measured tool rotation data and the measured tool force data are not simultaneously within the predicted tool rotation data range, then the first alarm message is sent.

[0006] Furthermore, the creation of a fusion curve for each sample data point based on the tool rotation data and tool force data in each sample data point includes: Select a sample data; Analyze the sample data to obtain the tool rotation data and tool force data. Soil depth-rotation data curves were created based on the soil depth dimension to generate tool rotation data. Soil depth-stress data curves are created based on soil depth dimension to generate tool stress data; By fusing the soil depth-rotation data curve and the soil depth-force data curve into the same coordinate system, a fused curve of the sample data is obtained. Return to the previous step of selecting a sample data point, and continue until each sample data point has been selected once.

[0007] Furthermore, the fusion curve of each sample data is filtered based on the tool rotation data and tool force data in each sample data to obtain the filtered sample data, including: Select a fusion curve from a sample of data; The cut blending curves corresponding to tool rotation angles from 0 degrees to 90 degrees are defined as the first blending curve. The cut curves are defined as the blending curves corresponding to the rotation angles of the cutter from 90 degrees to 180 degrees. The resulting cut blending curves are defined as the second blending curves.

[0008] Furthermore, the step of filtering the fusion curve of each sample data based on the tool rotation data and tool force data in each sample data to obtain the filtered sample data also includes: Select a fusion curve; Analyze the fusion curve to obtain the first fusion curve and the second fusion curve. Select a soil depth value from the first fusion curve; The soil depth value is analyzed to obtain the tool rotation data for the soil depth value, and the tool rotation data includes the tool rotation angle. Obtain the preset first tool rotation angular velocity value; Determine whether the tool rotation angular velocity at the soil depth value is less than or equal to the preset first tool rotation angular velocity value; If the tool rotation angular velocity of the soil depth value is less than or equal to the preset first tool rotation angular velocity value, then the fusion curve is deleted; If the angular velocity of the tool rotation at the soil depth value is greater than the preset first tool rotation angular velocity value, then it is determined whether each soil depth value in the first fusion curve has been selected once. If each soil depth value in the first fusion curve has not been selected at least once, then return to the step of selecting a soil depth value from the first fusion curve. If each soil depth value in the first fusion curve has been selected once, then one soil depth value in the second fusion curve is selected. The soil depth value is analyzed to obtain the tool rotation data for the soil depth value, and the tool rotation data includes the tool rotation angle. Obtain the preset angular velocity value of the second tool rotation; Determine whether the angular velocity of the tool rotation at the soil depth value is less than or equal to the preset second tool rotation angular velocity value; If the tool rotation angular velocity of the soil depth value is less than or equal to the preset second tool rotation angular velocity value, then the fusion curve is deleted; If the tool rotation angular velocity of the soil depth value is greater than the preset tool rotation angular velocity value, then determine whether each soil depth value in the second fusion curve has been selected once. If each soil depth value in the second fusion curve has not been selected at least once, then return to the step of selecting a soil depth value in the second fusion curve. If each soil depth value in the second fusion curve has been selected once, then the fusion curve is retained. Return to the process of selecting a fusion curve, until each of the fusion curves has been selected once.

[0009] Furthermore, before calculating the prediction curve for each selected sample data based on the fusion curve of the tool operating environment data corresponding to each selected sample data in each selected sample data, the following steps are also included: Select one of the filtered sample data; The filtered sample data is analyzed to obtain the tool operating environment data in the filtered sample data, which includes soil hardness data; Based on soil depth and soil hardness data, a soil depth-soil hardness curve is created. Return to the previous step and select one of the filtered sample data, until each filtered sample data has been selected once.

[0010] Furthermore, the step of calculating the prediction curve for each selected sample data based on the fusion curve of the tool operating environment data corresponding to each selected sample data in each selected sample data includes: Select one of the filtered sample data; The filtered sample data was analyzed to obtain the soil depth-soil hardness curve, the first fusion curve, and the second fusion curve in the filtered sample data. Select a value from a soil depth-soil hardness curve; Analyze a value in the soil depth-soil hardness curve to obtain the first fusion curve data and the second fusion curve data corresponding to the value in the soil depth-soil hardness curve. Return to the selected value in the soil depth-soil hardness curve, until every value in the soil depth-soil hardness curve has been selected once; Return to the previous step and select one of the filtered sample data, until each filtered sample data has been selected once.

[0011] Furthermore, the step of calculating the prediction curve for each selected sample data based on the fusion curve of the tool operating environment data corresponding to each selected sample data in each selected sample data also includes: Select a soil hardness value at a specific soil depth; Analyze a soil hardness value at a given soil depth to obtain all first fusion curve data and all second fusion curve data corresponding to a soil hardness value at that soil depth. The minimum boundary value among all the obtained first fusion curve data is used as the first predicted value; The maximum boundary value among all the obtained first fusion curve data is used as the second predicted value; The minimum boundary value among all the obtained second fusion curve data is used as the third prediction value; The maximum boundary value among all the obtained second fusion curve data is used as the fourth prediction value; Return to the selected soil hardness value for a given soil depth, until every soil hardness value for every soil depth has been selected once.

[0012] Furthermore, the step of calculating the prediction curve for each selected sample data based on the fusion curve of the tool operating environment data corresponding to each selected sample data in each selected sample data also includes: Based on the rotation direction of the tool, all the first and third predicted values ​​are incorporated into the same coordinate system to obtain the first predicted curve; Based on the rotation direction of the tool, all the second and fourth predicted values ​​are incorporated into the coordinate system of the first predicted curve to obtain the second predicted curve.

[0013] Furthermore, it also includes: Obtain the preset range of the first tool rotation angle and the maximum force on the first tool; Determine whether the tool has moved within the preset first tool rotation angle range; If the tool moves within the preset first tool rotation angle range, then determine whether the tool force data is greater than or equal to the first tool force maximum value; If the force data of the cutting tool is greater than or equal to the maximum force value of the first cutting tool, a second alarm message will be sent.

[0014] This application also provides an arc-shaped blade tree digger, comprising: Support frame; The power component is fixedly mounted on the support frame; The cutting tool is rotatably mounted on the support frame, and the cutting tool is fixedly connected to the power component. An angular velocity sensor is fixedly mounted on the power component and is used to detect the rotational angular velocity of the output shaft of the power component. A force sensor is fixedly mounted on the cutting tool and is used to detect the resistance experienced by the cutting tool. A soil data detection device is fixedly mounted on the support frame. The soil data detection device is used to detect the depth and hardness of the soil. A processing device is mounted on the support frame. The angular velocity sensor is communicatively connected to the processing device, the force sensor is communicatively connected to the processing device, and the soil data detection device is communicatively connected to the processing device. The processing device is used to execute the control method for the arc blade of the arc-shaped tree digger encountering hard objects as described above.

[0015] This application relates to a control method for the arc blade of a tree digger when it encounters hard objects. The method involves acquiring tree-digging data from multiple tree diggers and creating a fusion curve based on the depth of the joint digging data. Then, the tree-digging data is filtered based on the blade's rotation and force data during digging, resulting in filtered tree-digging data. Using this filtered data as a benchmark, a prediction curve is calculated from the fusion curve corresponding to the blade's operating environment data within the filtered tree-digging data. This creates a blade operation prediction model, which is then trained using the aforementioned data. This results in a blade operation prediction model capable of predicting blade operation data, and consequently, an alarm is triggered when the actual blade operation data exceeds the predicted data. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for controlling the arc blade of a tree digger when it encounters a hard object, as provided in an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the structure of a circular arc blade tree digger provided in an embodiment of this application.

[0018] Figure label: 11. Support frame; 12. Power component; 13. Cutting tool; 14. Angular velocity sensor; 15. Force sensor; 16. Soil data detection component; 17. Processing device. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] like Figure 1 As shown, in one embodiment of this application, the method for controlling the arc blade of the arc-shaped tree digger when it encounters a hard object includes the following steps S001 to S012: S001, acquire sample data from multiple samples, including tool rotation data, tool force data, and tool operating environment data.

[0021] Specifically, when a tree digger is digging up trees, it needs to use its blades to cut off the roots of the trees, while also preserving some of the roots and soil so that the trees can be easily transplanted to other locations after the digging.

[0022] More specifically, the tool rotation data refers to the rotation data of the tool throughout the entire process of digging a tree, from when it moves above the tree root to when it begins to rotate and dig, until the digging is completed. This includes the rotational angular velocity and the angle of rotation. The tool force data refers to the resistance data experienced by the tool throughout the entire process of digging a tree, from when it moves above the tree root to when it begins to rotate and dig, until the digging is completed. The tool operating environment data refers to the soil hardness data throughout the entire process of digging a tree, from when it moves above the tree root to when it begins to rotate and dig, until the digging is completed.

[0023] S002, based on the tool rotation data and tool force data in each sample data, create a fusion curve for each sample data.

[0024] S003, based on the tool rotation data and tool force data in each sample data, the fusion curve of each sample data is filtered to obtain the filtered sample data.

[0025] S004, based on the fusion curve of each filtered sample data corresponding to the tool operating environment data in each filtered sample data, calculate the prediction curve of each filtered sample data.

[0026] S005, Create a tool operation prediction model, and train the tool operation prediction model based on the tool rotation data, tool force data, each filtered sample data, the operating environment data in each filtered sample data, the fusion curve of each filtered sample data, and the prediction curve in each filtered sample data.

[0027] S006, Obtain a sample data to be tested.

[0028] S007, input the tool running environment data in the sample data to be tested into the tool running prediction model, start the tool running prediction model, and obtain the prediction curve of the sample data to be tested output by the tool running prediction model.

[0029] S008, obtain the rotation data and force data of the test tool in the test sample corresponding to the current soil depth, as well as the range of predicted tool rotation data and the range of predicted tool force data in the prediction curve.

[0030] S009, determine whether the rotation data of the tool under test is within the range of the predicted tool rotation data and the force data of the tool under test is within the range of the predicted tool force data simultaneously.

[0031] S010, if the rotation data of the tool under test is within the range of the predicted tool rotation data and the force data of the tool under test is within the range of the predicted tool force data, but these conditions are not met simultaneously, an alarm message is sent.

[0032] In this embodiment, tree-digging data from multiple tree-digging machines is acquired, and a fusion curve is created based on the depth of the joint digging of the acquired tree-digging data. Then, the tree-digging data is filtered based on the rotation data and force data of the blades during tree digging, resulting in filtered tree-digging data. Using the filtered tree-digging data as a benchmark, a prediction curve for the filtered tree-digging data is calculated from the fusion curve of the filtered tree-digging data corresponding to the blade operating environment data in the filtered tree-digging data. A blade operation prediction model is created, and the above data is used as training data to train the blade operation prediction model, resulting in a blade operation prediction model with the function of predicting blade operation data. This allows an alarm message to be sent when the blade operation data exceeds the predicted data during actual operation.

[0033] In one embodiment of this application, the step of creating a fusion curve for each sample data based on the tool rotation data and tool force data in each sample data includes the following S002 to S002f: S002a, Select a sample data.

[0034] S002b, parse the sample data to obtain the tool rotation data and tool force data in the sample data.

[0035] S002c, a soil depth-rotation data curve for creating tool rotation data based on the soil depth dimension.

[0036] S002d, a soil depth-force data curve is created based on the soil depth dimension to generate tool stress data.

[0037] S002e, the soil depth-rotation data curve and the soil depth-force data curve are fused into the same coordinate system to obtain the fused curve of the sample data.

[0038] S002f, return to the selected sample data, until each sample data has been selected once.

[0039] Specifically, the soil depth-rotation data curve in S002c refers to the curve formed by sequentially connecting the rotation data corresponding to different depths of the cutter; the soil depth-force data curve in S002d refers to the curve formed by sequentially connecting the force data of the cutter when it digs to different depths.

[0040] In this embodiment, by fusing the soil depth-rotation data curve and the soil depth-force data curve of the same sample data into a single coordinate system, a fused curve of the sample data is obtained, thereby establishing the correlation between soil depth, rotation data, and force data, which facilitates further analysis of the influence relationship between different rotation data and different force data at the same soil depth.

[0041] In one embodiment of this application, the step of filtering the fusion curve of each sample data based on the tool rotation data and tool force data in each sample data to obtain the filtered sample data includes the following S003a to S003c: S003a, Select the fusion curve of a sample data.

[0042] S003b, cut the blending curve corresponding to the tool rotation angle from 0 degrees to 90 degrees, and define the resulting cut blending curve as the first blending curve.

[0043] S003c, cut the blending curve corresponding to the tool rotation angle from 90 degrees to 180 degrees, and define the resulting cut blending curve as the second blending curve.

[0044] Specifically, based on the complete tree excavation, the angle of the cutter rotation is mostly between 0 and 180 degrees. However, in the actual tree excavation process, the data changes when the cutter encounters obstacles between 0 and 90 degrees are asymmetrical with the data changes when the cutter encounters obstacles between 90 and 180 degrees.

[0045] In this embodiment, since the data changes when the cutter encounters obstacles between 0 and 90 degrees are asymmetrical with those when the cutter encounters obstacles between 90 and 180 degrees, the entire tree excavation process is divided into two parts to facilitate accurate and efficient data processing and reduce interference between data obtained at the same soil depth.

[0046] In one embodiment of this application, the step of filtering the fusion curve of each sample data based on the tool rotation data and tool force data in each sample data to obtain the filtered sample data further includes the following S003d to S003u: S003d, select a fusion curve.

[0047] S003e, analyze the fusion curve to obtain the first fusion curve and the second fusion curve in the fusion curve.

[0048] S003f, select a soil depth value from the first fusion curve.

[0049] S003g, analyze the soil depth value to obtain the tool rotation data for the soil depth value, the tool rotation data includes the tool rotation angle.

[0050] S003h, obtain the preset first tool rotation angular velocity value.

[0051] S003i, determine whether the tool rotation angular velocity of the soil depth value is less than or equal to the preset first tool rotation angular velocity value.

[0052] S003j, if the tool rotation angular velocity of the soil depth value is less than or equal to the preset first tool rotation angular velocity value, then delete the fusion curve.

[0053] S003k, if the tool rotation angular velocity of the soil depth value is greater than the preset first tool rotation angular velocity value, then determine whether each soil depth value in the first fusion curve has been selected once. S003l, if each soil depth value in the first fusion curve has not been selected once, then return to the step of selecting a soil depth value in the first fusion curve.

[0054] S003m, if each soil depth value in the first fusion curve has been selected once, then select a soil depth value from the second fusion curve.

[0055] S003n, analyze the soil depth value to obtain the tool rotation data for the soil depth value, the tool rotation data including the tool rotation angle.

[0056] S003o, obtain the preset second tool rotation angular velocity value.

[0057] S003p, determine whether the angular velocity of the tool rotation at the soil depth value is less than or equal to the preset second tool rotation angular velocity value.

[0058] S003q, if the tool rotation angular velocity of the soil depth value is less than or equal to the preset second tool rotation angular velocity value, then delete the fusion curve.

[0059] S003r, if the tool rotation angular velocity of the soil depth value is greater than the preset tool rotation angular velocity value, then determine whether each soil depth value in the second fusion curve has been selected once. S003s, if each soil depth value in the second fusion curve has not been selected once, then return to the step of selecting a soil depth value in the second fusion curve.

[0060] S003t, if each soil depth value in the second fusion curve has been selected once, then the fusion curve is retained.

[0061] S003u, return to the step of selecting a fusion curve, until each of the fusion curves has been selected once.

[0062] In this embodiment, multiple first and second fusion curves contained in the same fusion curve are screened, and screening criteria are set for the first and second fusion curves respectively. Only when both the first and second fusion curves pass the screening criteria is the fusion curve considered qualified. More specifically, the fusion curve is considered qualified when the fusion curve indicates that the tool does not encounter abnormal resistance during the digging of the tree. The abnormal resistance can refer to the resistance provided by hard objects in the soil to the tool.

[0063] In one embodiment of this application, before calculating the prediction curve of each filtered sample data based on the fusion curve of each filtered sample data corresponding to the tool operating environment data in each filtered sample data, the following steps are further included: S014 to S044: S014, Select one of the filtered sample data.

[0064] S024, parse the filtered sample data to obtain the tool operating environment data in the filtered sample data, the tool operating environment data including soil hardness data.

[0065] S034, based on soil depth and soil hardness data, creates a soil depth-soil hardness curve.

[0066] S044, return to the selected filtered sample data, until each filtered sample data has been selected once.

[0067] Specifically, the soil depth-soil hardness curve in S034 refers to the soil hardness data obtained at different soil depths, and the curve formed by connecting the obtained soil hardness data sequentially in the coordinate system based on the order of soil depth from shallow to deep.

[0068] In one embodiment of this application, the step of calculating the prediction curve for each selected sample data based on the fusion curve of each selected sample data corresponding to the tool operating environment data in each selected sample data includes the following S004a to S004f: S004a, Select one of the filtered sample data.

[0069] S004b, parse the filtered sample data to obtain the soil depth-soil hardness curve, the first fusion curve, and the second fusion curve in the filtered sample data.

[0070] S004c, select a value from a soil depth-soil hardness curve.

[0071] S004d, analyze a value in the soil depth-soil hardness curve to obtain the first fusion curve data and the second fusion curve data corresponding to the value in the soil depth-soil hardness curve.

[0072] S004e, return to the value selected in the soil depth-soil hardness curve, until each value in the soil depth-soil hardness curve has been selected once.

[0073] S004f, return to the selected filtered sample data, until each filtered sample data has been selected once.

[0074] Specifically, a value in a soil depth-soil hardness curve refers to a point on the curve, or more specifically, a soil hardness value corresponding to a soil depth.

[0075] In this embodiment, by establishing associations between the first fusion curve data and the second fusion curve data corresponding to each value in the soil depth-soil hardness curve of each screened sample data, multiple corresponding first fusion curve data and second fusion curve data are obtained for different soil depths and different soil hardness.

[0076] In one embodiment of this application, the step of calculating the prediction curve of each filtered sample data based on the fusion curve of each filtered sample data corresponding to the tool operating environment data in each filtered sample data further includes the following S004g to S004o: S004g is a soil hardness value selected at a soil depth.

[0077] S004h, analyze a soil hardness value at this soil depth, and obtain all first fusion curve data and all second fusion curve data corresponding to a soil hardness value at this soil depth.

[0078] S004i, the minimum boundary value among all the obtained first fusion curve data is taken as the first predicted value.

[0079] S004j, the maximum boundary value among all the first fusion curve data obtained is used as the second predicted value.

[0080] S004k uses the minimum boundary value among all the obtained second fusion curve data as the third prediction value.

[0081] S004l, the maximum boundary value among all the obtained second fusion curve data is used as the fourth prediction value.

[0082] S004m, return to the selected soil hardness value for a soil depth, until every soil hardness value for every soil depth has been selected once.

[0083] S004n, based on the rotation direction of the tool, incorporates all the first predicted values ​​and all the third predicted values ​​into the same coordinate system to obtain the first predicted curve.

[0084] S004o, based on the rotation direction of the tool, all the second predicted values ​​and all the fourth predicted values ​​are incorporated into the coordinate system where the first predicted curve is located to obtain the second predicted curve.

[0085] In this embodiment, by statistically analyzing all first fusion curve data and all second fusion curve data corresponding to the same soil depth and soil hardness conditions, the minimum value among all first fusion curve data is taken as the first predicted value; the maximum boundary value among all first fusion curve data is taken as the second predicted value; the minimum boundary value among all second fusion curve data is taken as the third predicted value; and the maximum boundary value among all second fusion curve data is taken as the fourth predicted value, further statistically analyzing all first predicted values, all second predicted values, all third predicted values, and all fourth predicted values ​​corresponding to different soil depths and different soil hardness conditions.

[0086] Furthermore, all the first and third predicted values ​​are incorporated into the same coordinate system to obtain the first predicted curve, and all the second and fourth predicted values ​​are incorporated into the coordinate system of the first predicted curve to obtain the second predicted curve.

[0087] In one embodiment of this application, the method for controlling the arc blade of the arc-shaped tree digger when it encounters a hard object further includes the following steps S011 to S0041: S011, obtain the preset first tool rotation angle range and the maximum force on the first tool.

[0088] S021, determine whether the tool has moved within the preset first tool rotation angle range.

[0089] S031, if the tool moves to the preset first tool rotation angle range, then determine whether the tool force data is greater than or equal to the first tool force maximum value.

[0090] S041, if the tool force data is greater than or equal to the first maximum tool force value, then send the second alarm information.

[0091] Specifically, the first tool rotation angle range in S0011 is within the range of 90 degrees to 180 degrees, and the maximum value of the first tool rotation angle range is equal to the tool rotation angle of 180 degrees, while the minimum value of the first tool rotation angle range is close to the tool rotation angle of 180 degrees.

[0092] The maximum force on the first cutting tool in S0011 refers to the maximum resistance that the cutting tool can withstand during the process of digging trees or the cutting tool itself.

[0093] In this embodiment, the technical solution and the technical solution for predicting the cutting tool's operating data are two different alarm schemes executed in parallel. S0011 to S0041 are the steps performed by the cutting tool as it moves from the bottom of the tree root to one side of the tree. More specifically, when the cutting tool is digging from the soil depth from deep to shallow, if it encounters obstacles such as stones or iron blocks near the soil surface, the resistance provided by the obstacle can be overcome by the cutting tool due to the direction of the cutting tool's movement and the relatively small amount of soil covering the top of the obstacle. This further enhances the protection of the cutting tool and improves the efficiency of the cutting tool in digging trees.

[0094] like Figure 2 As shown, in one embodiment of this application, the arc-shaped tree digger includes a support frame 11, a power component 12, a cutting tool 13, an angular velocity sensor 14, a force sensor 15, a soil data detection component 16, and a processing device 17.

[0095] The power component 12 is fixedly mounted on the support frame 11.

[0096] The cutting tool 13 is rotatably mounted on the support frame 11, and the cutting tool 13 is fixedly connected to the power component 12.

[0097] The angular velocity sensor 14 is fixedly mounted on the power component 12, and the angular velocity sensor 14 is used to detect the rotational angular velocity of the output shaft of the power component 12.

[0098] The force sensor 15 is fixedly mounted on the cutting tool 13, and the force sensor 15 is used to detect the resistance experienced by the cutting tool 13.

[0099] The soil data detection device 16 is fixedly mounted on the support frame 11. The soil data detection device 16 is used to detect the depth and hardness of the soil.

[0100] The processing device 17 is mounted on the support frame 11. The angular velocity sensor 14 is communicatively connected to the processing device 17. The force sensor 15 is communicatively connected to the processing device 17. The soil data detection device 16 is communicatively connected to the processing device 17. The processing device 17 is used to execute the control method for the arc blade of the arc-shaped tree digger encountering hard objects as described above.

[0101] Specifically, the cutter 13 includes a blade-shaped blade with multiple cutter heads fixedly connected to both sides along the length of the cutter 13. The multiple cutter heads located on one side along the length of the cutter 13 are equidistantly distributed. The multiple cutter heads on both sides of the cutter 13 facilitate the cutter 13 to dig trees from either side and facilitate the cutter 13 to return to the bottom of the tree roots.

[0102] In this embodiment, the support frame 11 is moved to one side of the tree to be excavated, and the support frame 11 drives the cutter 13 to approach the soil surface. During this process, the soil data detection device 16 is inserted into the soil on one side of the tree to detect the data in the soil and send it to the processing device 17. The processing device 17 generates a predicted running curve of the cutter 13 based on the data in the soil, and then controls the cutter 13 to rotate to excavate the tree, and obtains the real-time rotation data, real-time force data and digging depth of the cutter 13. The real-time rotation data, real-time force data and digging depth of the cutter 13 are compared with the predicted running curve of the cutter 13 to determine whether the cutter 13 encounters abnormal resistance.

[0103] The technical features of the above embodiments can be combined arbitrarily, and the execution order of the method steps is not restricted. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for controlling the arc blade of a circular arc-blade tree digger when it encounters hard objects, characterized in that, The method for controlling the arc-shaped blade of the arc-shaped tree digger when it encounters hard objects includes: Acquire sample data from multiple samples, including tool rotation data, tool force data, and tool operating environment data; Based on the tool rotation data and tool force data in each sample data, a fusion curve is created for each sample data. Based on the tool rotation data and tool force data in each sample data, the fusion curve of each sample data is filtered to obtain the filtered sample data; Based on the fusion curve of each filtered sample data corresponding to the tool operating environment data in each filtered sample data, the prediction curve of each filtered sample data is calculated. A tool operation prediction model is created and trained based on tool rotation data, tool force data, each filtered sample data, operating environment data, fusion curve of each filtered sample data, and prediction curve of each filtered sample data. Obtain a sample of data to be tested; Input the tool running environment data in the sample data to be tested into the tool running prediction model, start the tool running prediction model, and obtain the prediction curve of the sample data to be tested output by the tool running prediction model; Acquire the rotation data and force data of the test tool in the test sample corresponding to the current soil depth, as well as the range of predicted tool rotation data and predicted tool force data in the prediction curve; Determine whether the following conditions are met simultaneously: the rotation data of the test tool is within the range of the predicted rotation data of the test tool and the force data of the test tool is within the range of the predicted force data of the test tool. If the measured tool rotation data and the measured tool force data are not simultaneously within the predicted tool rotation data range, then the first alarm message is sent.

2. The method for controlling the arc blade of a tree digger encountering hard objects according to claim 1, characterized in that, The process of creating a fusion curve for each sample data point based on the tool rotation data and tool force data in each sample data point includes: Select a sample data; Analyze the sample data to obtain the tool rotation data and tool force data. Soil depth-rotation data curves were created based on the soil depth dimension to generate tool rotation data. Soil depth-stress data curves are created based on soil depth dimension to generate tool stress data; By fusing the soil depth-tool rotation data curve and the soil depth-tool force data curve into the same coordinate system, a fused curve of the sample data is obtained. Return to the previous step of selecting a sample data point, and continue until each sample data point has been selected once.

3. The method for controlling the arc blade of a tree digger encountering hard objects according to claim 2, characterized in that, The fusion curve of each sample data is filtered based on the tool rotation data and tool force data in each sample data to obtain the filtered sample data, including: Select a fusion curve from a sample of data; The cut blending curves corresponding to tool rotation angles from 0 degrees to 90 degrees are defined as the first blending curve. The cut curves are defined as the blending curves corresponding to the rotation angles of the cutter from 90 degrees to 180 degrees. The resulting cut blending curves are defined as the second blending curves.

4. The method for controlling the arc blade of a circular arc-blade tree digger when encountering hard objects, as described in claim 3, is characterized in that... The process of filtering the fusion curve of each sample data based on the tool rotation data and tool force data in each sample data to obtain the filtered sample data also includes: Select a fusion curve; Analyze the fusion curve to obtain the first fusion curve and the second fusion curve. Select a soil depth value from the first fusion curve; The soil depth value is analyzed to obtain the tool rotation data for the soil depth value, and the tool rotation data includes the tool rotation angle. Obtain the preset first tool rotation angular velocity value; Determine whether the tool rotation angular velocity at the soil depth value is less than or equal to the preset first tool rotation angular velocity value; If the tool rotation angular velocity of the soil depth value is less than or equal to the preset first tool rotation angular velocity value, then the fusion curve is deleted; If the angular velocity of the tool rotation at the soil depth value is greater than the preset first tool rotation angular velocity value, then it is determined whether each soil depth value in the first fusion curve has been selected once. If each soil depth value in the first fusion curve has not been selected at least once, then return to the step of selecting a soil depth value from the first fusion curve. If each soil depth value in the first fusion curve has been selected once, then one soil depth value in the second fusion curve is selected. The soil depth value is analyzed to obtain the tool rotation data for the soil depth value, and the tool rotation data includes the tool rotation angle. Obtain the preset angular velocity value of the second tool rotation; Determine whether the angular velocity of the tool rotation at the soil depth value is less than or equal to the preset second tool rotation angular velocity value; If the tool rotation angular velocity of the soil depth value is less than or equal to the preset second tool rotation angular velocity value, then the fusion curve is deleted; If the tool rotation angular velocity of the soil depth value is greater than the preset tool rotation angular velocity value, then determine whether each soil depth value in the second fusion curve has been selected once. If each soil depth value in the second fusion curve has not been selected at least once, then return to the step of selecting a soil depth value in the second fusion curve. If each soil depth value in the second fusion curve has been selected once, then the fusion curve is retained. Return to the process of selecting a fusion curve, until each of the fusion curves has been selected once.

5. The method for controlling the arc blade of a tree digger encountering hard objects according to claim 4, characterized in that, Before calculating the prediction curve for each selected sample data based on the fusion curve of the tool operating environment data corresponding to each selected sample data, the following steps are also included: Select one of the filtered sample data; The filtered sample data is analyzed to obtain the tool operating environment data in the filtered sample data, which includes soil hardness data; Based on soil depth and soil hardness data, a soil depth-soil hardness curve is created. Return to the previous step and select one of the filtered sample data, until each filtered sample data has been selected once.

6. The method for controlling the arc blade of a circular arc-blade tree digger when encountering hard objects according to claim 5, characterized in that, The calculation of the prediction curve for each filtered sample data based on the fusion curve of the tool operating environment data corresponding to each filtered sample data includes: Select one of the filtered sample data; The filtered sample data was analyzed to obtain the soil depth-soil hardness curve, the first fusion curve, and the second fusion curve in the filtered sample data. Select a value from a soil depth-soil hardness curve; Analyze a value in the soil depth-soil hardness curve to obtain the first fusion curve data and the second fusion curve data corresponding to the value in the soil depth-soil hardness curve. Return to the selected value in the soil depth-soil hardness curve, until every value in the soil depth-soil hardness curve has been selected once; Return to the previous step and select one of the filtered sample data, until each filtered sample data has been selected once.

7. The method for controlling the arc blade of a circular arc-blade tree digger when encountering hard objects, as described in claim 6, is characterized in that... The step of calculating the prediction curve for each selected sample data based on the fusion curve of each selected sample data corresponding to the tool operating environment data in each selected sample data further includes: Select a soil hardness value at a specific soil depth; Analyze a soil hardness value at a given soil depth to obtain all first fusion curve data and all second fusion curve data corresponding to a soil hardness value at that soil depth. The minimum boundary value among all the obtained first fusion curve data is used as the first predicted value; The maximum boundary value among all the obtained first fusion curve data is used as the second predicted value; The minimum boundary value among all the obtained second fusion curve data is used as the third prediction value; The maximum boundary value among all the obtained second fusion curve data is used as the fourth prediction value; Return to the selected soil hardness value for a given soil depth, until every soil hardness value for every soil depth has been selected once.

8. The method for controlling the arc blade of a circular arc-blade tree digger when encountering hard objects according to claim 7, characterized in that, The step of calculating the prediction curve for each selected sample data based on the fusion curve of each selected sample data corresponding to the tool operating environment data in each selected sample data further includes: Based on the rotation direction of the tool, all the first and third predicted values ​​are incorporated into the same coordinate system to obtain the first predicted curve; Based on the rotation direction of the tool, all the second and fourth predicted values ​​are incorporated into the coordinate system of the first predicted curve to obtain the second predicted curve.

9. The method for controlling the arc blade of a tree digger encountering hard objects according to claim 8, characterized in that, Also includes: Obtain the preset range of the first tool rotation angle and the maximum force on the first tool; Determine whether the tool has moved within the preset first tool rotation angle range; If the tool moves within the preset first tool rotation angle range, then determine whether the tool force data is greater than or equal to the first tool force maximum value; If the force data of the cutting tool is greater than or equal to the maximum force value of the first cutting tool, a second alarm message will be sent.

10. A circular arc blade tree digger, characterized in that, The arc-shaped blade tree digger includes: Support frame; The power component is fixedly mounted on the support frame; The cutting tool is rotatably mounted on the support frame, and the cutting tool is fixedly connected to the power component. An angular velocity sensor is fixedly mounted on the power component and is used to detect the rotational angular velocity of the output shaft of the power component. A force sensor is fixedly mounted on the cutting tool and is used to detect the resistance experienced by the cutting tool. A soil data detection device is fixedly mounted on the support frame. The soil data detection device is used to detect the depth and hardness of the soil. A processing device is mounted on the support frame. The angular velocity sensor is communicatively connected to the processing device, the force sensor is communicatively connected to the processing device, and the soil data detection device is communicatively connected to the processing device. The processing device is used to execute the control method for the arc blade of the arc-shaped tree digger encountering hard objects as described in any one of claims 1 to 9.

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