A method and system for generating an intelligent trajectory for adhesive liquid collection operation and a medium
By obtaining a three-dimensional envelope model of the rubber tree using X-ray images, the problem of accurately fitting the tapping equipment to the bark and sapwood was solved, enabling efficient and precise intelligent tapping operations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SICHUAN SANZEQI ROBOT CO LTD
- Filing Date
- 2026-03-27
- Publication Date
- 2026-06-26
Smart Images
Figure CN122289531A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of adhesive sampling technology, specifically relating to a method, system, and medium for generating intelligent adhesive sampling operation trajectories. Background Technology
[0002] In the rubber harvesting industry, tapping is a crucial step in rubber plantation. Traditional tapping often involves manually tapping the bark of rubber trees, a method that relies excessively on manual experience to control the tapping depth. Rubber farmers' reliance on accumulated experience makes precise tapping difficult, resulting in low efficiency, extreme labor intensity, and long hours of repetitive work that can damage their health and negatively impact rubber yield stability.
[0003] With the development of technology, basic mechanized rubber tapping equipment has emerged. Some of these devices have introduced simple sensors, such as lasers or mechanical touch sensors, to control the blades, which can standardize the tapping operation to some extent. However, these devices lack high-precision imaging technology, making it difficult to dynamically adapt to the complex and varied shapes of rubber tree bark and accurately conform to the bark contours, thus limiting their effectiveness in practical applications. Summary of the Invention
[0004] The purpose of this invention is to provide a method, system, and medium for generating intelligent rubber sap collection operation trajectories, in order to solve the problem that existing rubber tapping equipment lacks high-precision imaging technology support, making it difficult to dynamically adapt to the complex and varied morphology of rubber tree bark, and unable to accurately fit the contours of the bark and sapwood for operation.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for generating a trajectory for intelligent adhesive collection operations, the method comprising: Acquire X-ray images of living trees within a preset area; The X-ray image is subjected to grayscale conversion and filtering to obtain the processed image; Based on the processed image, a three-dimensional envelope model of a living tree is constructed. The three-dimensional envelope model of the living tree includes: a bark envelope model and a core-edge envelope model. The rubber tapping trajectory is generated based on the bark envelope model and the septum-core envelope model. The rubber tapping trajectory includes the bark cutting length, cutting direction, and cutting depth.
[0006] Preferably, the X-ray image undergoes grayscale conversion and filtering to obtain a processed image, including: The X-ray image is converted to grayscale to obtain a grayscale image; The grayscale image is filtered once to obtain the grayscale image after filtering. The grayscale image after the first filtering is subjected to a second filtering to obtain a grayscale image after the second filtering. The grayscale image after the second filtering is used as the processed image.
[0007] Preferably, the X-ray image is obtained by scanning the living tree within a preset area according to a preset motion trajectory using a detection device. Based on the processed image, a three-dimensional envelope model of the living tree is constructed, including: Edge detection is performed on the processed image to obtain the bark edges and core edges of the living trees; Boundary fitting is performed on the bark edge and the core edge to obtain boundary curves, which include: bark boundary curve and core boundary curve; Construct a three-dimensional cylindrical coordinate system and determine the boundary coordinates of the boundary curve in the three-dimensional cylindrical coordinate system; The boundary coordinates of the boundary curve in the three-dimensional cylindrical coordinate system are interpolated in three dimensions to obtain a three-dimensional envelope surface model of the living tree.
[0008] Preferably, edge detection is performed on the processed image to obtain the bark edge and core edge of the living tree, including: Pixel detection is performed on the processed image based on the Sobel operator to obtain the gradient value of each pixel in the processed image; Traverse all pixels in the processed image, extract pixels that meet the first preset condition, and obtain the first set of pixels; wherein, the first preset condition is that the gradient value of the pixel exceeds the preset gradient threshold, and the pixels in the first set of pixels are taken as the bark edge of the living tree. Traverse all pixels in the processed image, extract pixels that satisfy the second preset condition, and obtain the second set of pixels. The second preset condition is whether the gradient value of a pixel is a local maximum in the gradient direction of the pixel. Based on a pre-constructed dual threshold, valid pixels are extracted from the second set of pixels. Based on the effective pixels, determine the edge of the core of the living tree.
[0009] Preferably, the dual thresholds include: a first threshold and a second threshold, wherein the first threshold is greater than the second threshold; based on the pre-constructed dual thresholds, valid pixels are extracted from the second pixel set, including: Traverse all pixels in the second pixel set, extract pixels with gradient values greater than the first threshold, and designate these pixels as strong edges; and extract pixels with gradient values between the first threshold and the second threshold, and designate these pixels as candidate edges. Based on the edge connection method, strong edges are connected with candidate edges to obtain effective edges, and the pixels corresponding to the effective edges are used as effective pixels.
[0010] Preferably, the functional expression of the three-dimensional envelope surface model of the living tree is: ; In the formula, This is a three-dimensional envelope model of a living tree. Let be the weight coefficient corresponding to the j-th boundary point on the boundary curve, J be the total number of boundary points on the boundary curve, and r be the radius of the trajectory. Let be the distance between the center of the trajectory and the j-th boundary point. For radial basis functions, is the polar angle in a three-dimensional cylindrical coordinate system.
[0011] Preferably, boundary fitting is performed on the bark edge and the core edge to obtain a boundary curve, including: Select a predetermined number of sample points from the bark edge and the core edge; Construct an interpolation function based on sample points; Based on the interpolation function, cubic spline interpolation of bark edge and core edge is generated; Boundary fitting is performed based on the bark edge and the corresponding cubic spline interpolation to obtain the bark boundary curve; Boundary fitting is performed based on the edge of the core and the corresponding cubic spline interpolation to obtain the edge-core boundary curve.
[0012] Secondly, the present invention provides a system for generating intelligent adhesive collection operation trajectories, for implementing the above-mentioned method for generating intelligent adhesive collection operation trajectories, the system comprising: The image acquisition module is used to acquire X-ray images of living trees in a preset area; The image processing module is used to perform grayscale conversion and filtering on X-ray images to obtain the processed image; The model building module is used to construct a three-dimensional envelope model of a living tree based on the processed image. The three-dimensional envelope model of the living tree includes: a bark envelope model and a core-edge envelope model. The trajectory generation module is used to generate a rubber tapping trajectory based on the bark envelope model and the edge-core envelope model. The rubber tapping trajectory includes the bark cutting length, cutting direction, and cutting depth.
[0013] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for generating intelligent adhesive collection operation trajectories.
[0014] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for generating intelligent adhesive collection operation trajectories.
[0015] Beneficial effects: This invention involves acquiring X-ray images of each rubber tree and establishing a three-dimensional envelope model of the tree using these images. This three-dimensional envelope model can reflect changes in the bark morphology in real time and accurately identify the boundaries between the latex layer and the cambium layer of the rubber tree. Furthermore, the three-dimensional envelope model of each rubber tree is used to plan its own tapping trajectory, thus achieving efficient and precise intelligent tapping operations. Attached Figure Description
[0016] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for generating intelligent adhesive collection operation trajectory according to one embodiment of the present invention; Figure 2 This is a schematic diagram of the installation of an X-ray detector provided in one embodiment of the present invention; Figure 3 This is a block diagram of a system for generating intelligent adhesive collection operation trajectories according to one embodiment of the present invention.
[0017] Explanation of reference numerals in the attached figures: 1. Scanner; 2. Detector plate; 3. Mounting bracket; 4. Slide rail; 5. Rack; 6. Circumferential travel assembly; 7. Lifting travel assembly; 8. U-shaped frame. Detailed Implementation
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.
[0019] Example 1 Figure 1 This is a flowchart of a method for generating intelligent adhesive collection operation trajectories according to one embodiment of the present invention. Figure 1 As shown, this embodiment provides a method for generating a trajectory for intelligent adhesive collection operations. This method runs on a host computer and includes: Step S10: Obtain X-ray images of living trees in a preset area; In this embodiment, an X-ray detection device is used to scan the preset area of the living tree according to a preset motion trajectory to obtain X-ray images; Since the tapping area of a rubber tree is an arc-shaped area, this arc-shaped area can be used as the preset area, and only the core (which includes the core and sapwood) of the arc-shaped area is scanned, which can greatly reduce the damage caused by X-rays to living trees.
[0020] In this embodiment, the detection device includes: a scanner 1 and a detection plate 2, which can communicate with a host computer. A walking mechanism is installed on the living tree, and the detection device is mounted on the walking mechanism; Figure 2 As shown, the walking mechanism includes: a mounting frame 3 installed on the tree trunk, a circumferential walking component 6 on the mounting frame 3, a lifting walking component 7 mounted on the circumferential walking component 6, the lifting walking component 7 being used to carry the scanner 1 and the detection plate 2, the lifting walking component 7 being used to drive the scanner 1 and the detection plate 2 to rise or fall synchronously along the axial direction of the tree trunk, and the circumferential walking component being used to drive the scanner 1 and the detection plate 2 to rotate synchronously around the circumference of the tree trunk.
[0021] In this embodiment, the mounting frame 3 is directly installed on the tree trunk, and the walking mechanism can be directly installed on the tree trunk without disassembly. When performing detection work on the tree trunk, only the scanner 1, the detection plate 2, and the host computer need to be carried. These devices are small in size, making them easier to carry and install, thus improving the convenience and efficiency of the detection work.
[0022] The top and bottom of the mounting frame 3 are both arc-shaped, and slide rails 4 and racks 5 are deployed on the top and bottom of the mounting frame 3. The circumferential walking component 6 is slidably connected to the slide rails 4; and both the circumferential walking component 6 and the lifting walking component 7 are driven by motors.
[0023] With the cooperation of the circumferential walking component 6 and the lifting walking component 7, the scanner 1 and the detector plate 2 can move in an arc-shaped upward or downward motion on the outside of the tree trunk.
[0024] In this embodiment, a U-shaped frame 8 is installed on the lifting and walking assembly 7, the scanner 1 is installed on one end of the U-shaped frame 8, and the detection plate 2 is installed on the other end of the U-shaped frame 8. At this time, the U-shaped frame 8, the detection plate 2 and the scanner 1 rise or fall in an arc shape, so as to realize local scanning of the rubber tapping area of the tree trunk, greatly reduce the scanning range, and reduce damage to living trees.
[0025] Step S20: Perform grayscale conversion and filtering on the X-ray image to obtain the processed image.
[0026] Specifically, the X-ray image undergoes grayscale conversion and filtering to obtain the processed image, including: Step S201: Perform grayscale conversion on the X-ray image to obtain a grayscale image; wherein, the formula for grayscale conversion is: (1); In equation (1), Let (x, y) be the grayscale value of a pixel in the X-ray image. x The x-coordinate of the pixel is y The ordinate of the pixel is k 1 is the first coefficient. k 2 is the second coefficient. k 3 is the third coefficient. R , G , B These are the values of the red, green, and blue color components of the pixel; among them, k The value of 1 ranges from 250 to 350. k The value of 2 ranges from 560 to 630. k The value of 3 ranges from 100 to 125.
[0027] Step S202: Perform a first-pass filtering on the grayscale image to obtain a first-pass filtered grayscale image; in this embodiment, the first-pass filtering uses a filter for smoothing to reduce the impact of noise.
[0028] The function expression for a single filtering process is: (2); In equation (2), Let be the grayscale value of a pixel (x, y) in the grayscale image after one filtering. Let e be the standard deviation of the control filter, and e be the natural constant.
[0029] Step S203: Perform a second filter on the grayscale image after the first filter to obtain a grayscale image after the second filter, and use the grayscale image after the second filter as the processed image.
[0030] In this embodiment, the secondary filtering preferably employs adaptive median filtering, which calculates the median within the local window and replaces abnormal pixels. The filtering process is as follows: Calculate the minimum value within the window I min and maximum value I max and median I m If the current pixel grayscale value exist[ I min , I max In addition to ], use I m Replacement. Traverse the entire image pixel by pixel to ensure noise removal and edge features are preserved.
[0031] Step S30: Based on the processed image, construct a three-dimensional envelope model of the living tree. The three-dimensional envelope model of the living tree includes: a bark envelope model and a core-sapwood (including the core and sapwood) envelope model.
[0032] Specifically, based on the processed image, a three-dimensional envelope model of the living tree is constructed, including: Step S301: Perform edge detection on the processed image to obtain the bark edge and core edge of the living tree.
[0033] In this embodiment, edge detection is performed on the processed image to obtain the bark edge and core edge of the living tree, including: Step a10: Perform pixel detection on the processed image based on the Sobel operator to obtain the gradient values of each pixel in the processed image; the Sobel operator is a set of convolution kernels used in image processing, mainly used for edge detection tasks. It highlights the edges in the image by calculating the spatial gradient of the image brightness; the Sobel operator contains two 3x3 convolution kernels, which are used to detect edges in the horizontal and vertical directions, respectively.
[0034] Therefore, the gradient value includes the horizontal (x-axis) gradient and the vertical (y-axis) gradient, and the expressions for the horizontal and vertical gradients are: (3); (4); In equations (3) and (4), , The images are respectively in shaft and The gradient on the axis determines the final edge strength. The calculation is as follows: (5).
[0035] Step a20: Traverse all pixels in the processed image, extract pixels that meet the first preset condition, and obtain the first set of pixels; wherein, the first preset condition is that the gradient value of the pixel exceeds the preset gradient threshold, and the pixels in the first set of pixels are used as the bark edge of the living tree.
[0036] Step a30: Traverse all pixels in the processed image, extract pixels that satisfy the second preset condition, and obtain the second set of pixels, wherein the second preset condition is whether the gradient value of a pixel is a local maximum in the gradient direction of the pixel.
[0037] In this embodiment, the gradient direction is typically rounded to one of the four main directions (0°, 45°, 90°, 135°).
[0038] For each pixel, check if it is a local maximum along its gradient direction. If not, set the value of that pixel to 0. This step ensures that each edge is only one pixel wide.
[0039] Step a50: Extract valid pixels from the second set of pixels based on the pre-constructed dual thresholds.
[0040] In this embodiment, the dual thresholds include: a first threshold and a second threshold, wherein the first threshold is greater than the second threshold; based on the pre-constructed dual thresholds, valid pixels are extracted from the second pixel set, including: Step a501: Traverse all pixels in the second pixel set, extract pixels with gradient values greater than the first threshold, and designate these pixels as strong edges; and extract pixels with gradient values between the first threshold and the second threshold, and designate these pixels as candidate edges. Step a502: Based on the edge connection method, connect the strong edge with the candidate edge to obtain the effective edge, and use the pixel point corresponding to the effective edge as the effective pixel point.
[0041] In this embodiment, after obtaining strong edges and candidate edges, the pixels of weak edges are checked. If the pixels of weak edges are connected to the pixels of strong edges, then the pixels of weak edges are considered as valid edges, and strong edges are also considered as valid edges.
[0042] Step a60: Determine the core edge of the living tree based on the effective pixels.
[0043] Step S302: Perform boundary fitting on the bark edge and the core edge to obtain boundary curves, which include: bark boundary curve and core boundary curve.
[0044] Specifically, boundary fitting is performed on the bark edge and the core edge to obtain boundary curves, including: Step b10: Select a predetermined number of sample points from the bark edge and the core edge. .
[0045] Step b20: Construct an interpolation function based on the sample points; the expression for the interpolation function is: (6); In the formula, , , , All of these are interpolation coefficients, and all were calculated using boundary conditions.
[0046] Step b30: Generate cubic spline interpolation of bark edge and core edge based on the interpolation function.
[0047] Step b40: Perform boundary fitting based on the bark edge and the corresponding cubic spline interpolation to obtain the bark boundary curve.
[0048] Step b50: Perform boundary fitting based on the edge of the core and the corresponding cubic spline interpolation to obtain the edge-core boundary curve.
[0049] Step S303: Construct a three-dimensional cylindrical coordinate system and determine the boundary coordinates of the boundary curve in the three-dimensional cylindrical coordinate system; wherein, the boundary coordinates of each boundary point on the boundary curve in the three-dimensional cylindrical coordinate system are: ,in, Let be the distance between the center of the detection device's trajectory and the j-th boundary point. Let the polar angle be the j-th boundary point. Let be the height of the j-th boundary point, and let be the motion trajectory of the detector during the scanning process.
[0050] Step S304: Perform three-dimensional interpolation on the boundary coordinates of the boundary curve in the three-dimensional cylindrical coordinate system to obtain the three-dimensional envelope surface model of the living tree.
[0051] The function expression for the three-dimensional envelope surface model of the living tree in this embodiment is: (7); In the formula, This is a three-dimensional envelope model of a living tree. Let be the weighting coefficient corresponding to the j-th boundary point on the boundary curve, J be the total number of boundary points on the boundary curve, and r be the radius of the trajectory of the detection device. Let be the distance between the center of the detection device's trajectory and the j-th boundary point. For radial basis functions, is the polar angle in a three-dimensional cylindrical coordinate system.
[0052] Step S40: Generate a tapping trajectory based on the bark envelope model and the sapwood (including sapwood) envelope model. The tapping trajectory includes the bark cutting length, cutting direction, and cutting depth.
[0053] This invention involves acquiring X-ray images of each rubber tree (and other trees from which latex can be collected), and establishing a three-dimensional envelope model of the rubber tree using the X-ray images. This three-dimensional envelope model can reflect changes in the bark morphology in real time and accurately identify the boundaries between the latex layer and the cambium layer of the rubber tree. Furthermore, the three-dimensional envelope model of each rubber tree is used to plan its own tapping trajectory, thus achieving efficient and precise intelligent tapping operations.
[0054] Example 2 Figure 3 This is a block diagram of a system for generating intelligent adhesive collection operation trajectories according to one embodiment of the present invention. Figure 3 As shown, this embodiment provides a system for generating intelligent adhesive collection operation trajectories, used to implement the method for generating intelligent adhesive collection operation trajectories in Embodiment 1. The system includes: The image acquisition module is used to acquire X-ray images of living trees in a preset area; The image processing module is used to perform grayscale conversion and filtering on X-ray images to obtain the processed image; The model building module is used to construct a three-dimensional envelope model of a living tree based on the processed image. The three-dimensional envelope model of the living tree includes: a bark envelope model and a core-edge envelope model. The trajectory generation module is used to generate a rubber tapping trajectory based on the bark envelope model and the edge-core envelope model. The rubber tapping trajectory includes the bark cutting length, cutting direction, and cutting depth.
[0055] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for generating intelligent adhesive collection operation trajectory in Embodiment 1.
[0056] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for generating intelligent adhesive collection operation trajectories in Embodiment 1.
[0057] This invention involves acquiring X-ray images of each rubber tree and establishing a three-dimensional envelope model of the tree using these images. This three-dimensional envelope model can reflect changes in the bark morphology in real time and accurately identify the boundaries between the latex layer and the cambium layer of the rubber tree. Furthermore, the three-dimensional envelope model of each rubber tree is used to plan its own tapping trajectory, thus achieving efficient and precise intelligent tapping operations.
[0058] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0059] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.
[0060] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for generating a trajectory for intelligent adhesive collection operations, characterized in that, The method includes: Acquire X-ray images of living trees within a preset area; The X-ray image is subjected to grayscale conversion and filtering to obtain the processed image; Based on the processed image, a three-dimensional envelope model of a living tree is constructed. The three-dimensional envelope model of the living tree includes: a bark envelope model and a core-edge envelope model. The rubber tapping trajectory is generated based on the bark envelope model and the septum-core envelope model. The rubber tapping trajectory includes the bark cutting length, cutting direction, and cutting depth.
2. The method for generating intelligent adhesive collection operation trajectory according to claim 1, characterized in that, The X-ray image undergoes grayscale conversion and filtering to obtain the processed image, including: The X-ray image is converted to grayscale to obtain a grayscale image; The grayscale image is filtered once to obtain the grayscale image after filtering. The grayscale image after the first filtering is subjected to a second filtering to obtain a grayscale image after the second filtering. The grayscale image after the second filtering is used as the processed image.
3. The method for generating a trajectory for intelligent adhesive collection operations according to claim 1 or 2, characterized in that, The X-ray image is obtained by scanning the living tree within a preset area according to a preset motion trajectory using a detection device. Based on the processed image, a three-dimensional envelope model of the living tree is constructed, including: Edge detection is performed on the processed image to obtain the bark edges and core edges of the living trees; Boundary fitting is performed on the bark edge and the core edge to obtain boundary curves, which include: bark boundary curve and core boundary curve; Construct a three-dimensional cylindrical coordinate system and determine the boundary coordinates of the boundary curve in the three-dimensional cylindrical coordinate system based on the motion trajectory; The boundary coordinates of the boundary curve in the three-dimensional cylindrical coordinate system are interpolated in three dimensions to obtain a three-dimensional envelope surface model of the living tree.
4. The method for generating intelligent adhesive collection operation trajectory according to claim 3, characterized in that, Edge detection is performed on the processed image to obtain the bark edges and core edges of the living tree, including: Pixel detection is performed on the processed image based on the Sobel operator to obtain the gradient value of each pixel in the processed image; Traverse all pixels in the processed image, extract pixels that meet the first preset condition, and obtain the first set of pixels; wherein, the first preset condition is that the gradient value of the pixel exceeds the preset gradient threshold, and the pixels in the first set of pixels are taken as the bark edge of the living tree. Traverse all pixels in the processed image, extract pixels that satisfy the second preset condition, and obtain the second set of pixels. The second preset condition is whether the gradient value of a pixel is a local maximum in the gradient direction of the pixel. Based on a pre-constructed dual threshold, valid pixels are extracted from the second set of pixels. Based on the effective pixels, determine the edge of the core of the living tree.
5. The method for generating intelligent adhesive collection operation trajectory according to claim 4, characterized in that, The dual thresholds include: a first threshold and a second threshold, wherein the first threshold is greater than the second threshold; based on the pre-constructed dual thresholds, valid pixels are extracted from the second pixel set, including: Traverse all pixels in the second pixel set, extract pixels with gradient values greater than the first threshold, and designate these pixels as strong edges; and extract pixels with gradient values between the first threshold and the second threshold, and designate these pixels as candidate edges. Based on the edge connection method, strong edges are connected with candidate edges to obtain effective edges, and the pixels corresponding to the effective edges are used as effective pixels.
6. The method for generating intelligent adhesive collection operation trajectory according to claim 3, characterized in that, The functional expression of the three-dimensional envelope surface model of the living tree is: ； In the formula, This is a three-dimensional envelope model of a living tree. Let be the weight coefficient corresponding to the j-th boundary point on the boundary curve, J be the total number of boundary points on the boundary curve, and r be the radius of the trajectory. Let be the distance between the center of the trajectory and the j-th boundary point. For radial basis functions, is the polar angle in a three-dimensional cylindrical coordinate system.
7. The method for generating a trajectory for intelligent adhesive collection operations according to claim 3, characterized in that, Boundary fitting is performed on the bark edge and sapwood edge to obtain boundary curves, including: Select a predetermined number of sample points from the bark edge and the core edge; Construct an interpolation function based on sample points; Based on the interpolation function, cubic spline interpolation of bark edge and core edge is generated; Boundary fitting is performed based on the bark edge and the corresponding cubic spline interpolation to obtain the bark boundary curve; Boundary fitting is performed based on the edge of the core and the corresponding cubic spline interpolation to obtain the edge-core boundary curve.
8. A system for generating intelligent adhesive collection operation trajectories, used to implement the method for generating intelligent adhesive collection operation trajectories as described in any one of claims 1-7, characterized in that, The system includes: The image acquisition module is used to acquire X-ray images of living trees in a preset area; The image processing module is used to perform grayscale conversion and filtering on X-ray images to obtain the processed image; The model building module is used to construct a three-dimensional envelope model of a living tree based on the processed image. The three-dimensional envelope model of the living tree includes: a bark envelope model and a core-edge envelope model. The trajectory generation module is used to generate a rubber tapping trajectory based on the bark envelope model and the edge-core envelope model. The rubber tapping trajectory includes the bark cutting length, cutting direction, and cutting depth.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for generating intelligent adhesive collection operation trajectory as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for generating intelligent adhesive collection operation trajectory as described in any one of claims 1-7.