Numerical control polishing rotary segmentation track planning method, device, medium and equipment for shoe upper

Abstract

The application discloses a shoe upper numerical control polishing rotation segmentation track planning method and device, a medium and equipment. It relates to the shoe upper digital processing technical field, wherein the method comprises: rotating and segmenting point cloud data of a shoe upper processing area based on a predetermined rotation angle to obtain a plurality of point cloud sub-areas; extracting boundary points in the point cloud data of the point cloud sub-areas to obtain boundary feature points corresponding to the point cloud sub-areas; performing curve fitting based on the boundary feature points to obtain a fitting curve of the point cloud sub-area; performing discrete processing on the fitting curve based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitting curve; and performing processing track planning based on the discrete line segments and the predetermined rotation angle to obtain a multi-layer closed-loop target processing track for rotation segmentation of the shoe upper processing area. The method can realize automatic processing of the shoe upper grinding line and improve processing precision.

Description

Technical Field

[0001] This invention relates to household appliances, and in particular to a method, apparatus, storage medium, and electronic device for planning the rotational segmentation trajectory of CNC sanding of shoe uppers. Background Technology

[0002] The finishing of the shoe upper is a crucial step in shoe manufacturing. It requires creating a continuous, smooth processing path along the circular contour of the upper to ensure the uniformity of processes such as napping and sanding, directly impacting the quality of the upper assembly and wearing comfort. Currently, existing technologies for planning the processing path in the shoe upper area are mainly manual, which has significant shortcomings and cannot meet the demands of high-precision industrial processing.

[0003] In manual planning technology, operators visually observe the shoe upper's outline, manually mark the processing path, and then input the path into the processing equipment via a teach pendant. This method relies on operator experience and has the following problems: First, the trajectory accuracy is low, and the large error in manual marking leads to irregular processing boundaries, easily resulting in excessive fraying or missed processing; second, the efficiency is low, as marking the trajectory for each shoe upper takes too long and cannot meet the needs of mass production; third, the consistency is poor, as the trajectories marked by different operators vary, leading to unstable processing quality within the same batch of products.

[0004] Furthermore, existing technologies lack dedicated trajectory planning logic for the annular structure of shoe uppers, mostly relying on general surface processing algorithms without considering the special processing and precision requirements of shoe upper manufacturing. Therefore, there is an urgent need for an automated planning method that can achieve uniform segmentation of the annular region, precise positioning of boundary points, and smooth trajectory adaptation to equipment processing. Summary of the Invention

[0005] In view of this, the present invention provides a method, device, medium and equipment for CNC grinding and rotary segmentation trajectory planning of shoe uppers, the main purpose of which is to solve the problems of low efficiency and poor consistency in the current shoe upper processing that relies on manual labor.

[0006] To address the aforementioned problems, this application provides a method for planning the rotational segmentation trajectory of the shoe upper processing area, comprising: Based on a predetermined rotation angle, the point cloud data of the shoe upper processing area is rotated and segmented to obtain several point cloud sub-regions; Boundary points are extracted from the point cloud data in the point cloud sub-region to obtain boundary feature points corresponding to the point cloud sub-region. Based on the boundary feature points, curve fitting is performed to obtain the fitted curve of the point cloud sub-region; The fitted curve is discretized based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve. Based on the discrete line segments and the predetermined rotation angle, a multi-layer closed-loop target processing trajectory for rotational segmentation of the shoe upper processing area is obtained.

[0007] Optionally, the point cloud data of the shoe upper processing area is rotated and segmented based on a predetermined rotation angle to obtain several point cloud sub-regions, specifically including: A three-dimensional Cartesian coordinate system is constructed with the geometric center of the shoe upper processing area as the origin. The y-axis of the three-dimensional Cartesian coordinate system points towards the shoe toe, the z-axis is perpendicular to the shoe upper processing area and points upward, and the x-axis points towards the side of the shoe upper using the right-hand rule. Several half-planes are generated based on a predetermined rotation angle, with the z-axis as the rotation axis. The shoe upper processing area is segmented based on each of the half-planes to obtain several point cloud sub-regions.

[0008] Optionally, the step of extracting boundary points from the point cloud data in the point cloud sub-region to obtain boundary feature points corresponding to the point cloud sub-region specifically includes: Based on the point cloud data in the point cloud sub-region, boundary point projection optimization processing is performed to obtain candidate feature points; Boundary point features are extracted based on the candidate feature points to obtain the boundary feature points corresponding to the point cloud sub-region.

[0009] Optionally, the step of performing boundary point projection optimization processing based on the point cloud data in the point cloud sub-region to obtain candidate feature points specifically includes: The first position coordinates of the point cloud data are projected onto a first target plane to obtain the second position coordinates of the projected position corresponding to the point cloud data. The position vector of the projected position is obtained by calculating based on the second position coordinates and the origin coordinates corresponding to the geometric center point of the shoe upper processing area; The dot product value is obtained by calculating based on the position vector and the unit normal vector of the half-plane used to segment the point cloud sub-region; When the dot product value meets the preset conditions, the point cloud data is determined as a candidate feature point; Optionally, the step of extracting boundary point features based on the candidate feature points to obtain boundary feature points corresponding to the point cloud sub-region specifically includes: Projecting the candidate feature point along the direction of the unit normal vector onto the half-plane yields the third position coordinates of the candidate feature point on the half-plane. The radial distance corresponding to the candidate feature point is obtained by performing calculations based on the third position coordinates. The candidate feature points are filtered based on the radial distance and the predetermined Z-axis height interval to obtain the boundary feature points.

[0010] Optionally, the step of performing curve fitting based on the boundary feature points to obtain the fitted curve of the point cloud sub-region specifically includes: Construct a cubic polynomial for fitting the fitted curve; The coefficients of the cubic polynomial are solved based on the boundary feature points to obtain the target cubic polynomial; Based on the target cubic polynomial, a bilinear interpolation algorithm is used to perform curve fitting to obtain the fitted curve.

[0011] Optionally, the step of planning the processing trajectory based on the discrete line segments and the predetermined rotation angle to obtain a multi-layer closed-loop processing path for the rotational segmentation of the shoe upper processing area specifically includes: Convert the discrete line segments into three-dimensional Cartesian coordinates; According to the rotation direction of the predetermined rotation angle, the target points corresponding to adjacent point cloud sub-regions are connected in layers according to the three-dimensional Cartesian coordinates to obtain a multi-layer closed-loop processing path for rotating segmentation of the shoe upper processing area. The tangential unit vector, normal unit vector, and radial unit vector of the multi-layer closed-loop machining path are calculated to obtain the machining posture information; A multi-layer closed-loop target machining trajectory is generated based on the machining posture information and the multi-layer closed-loop machining path.

[0012] To solve the above problems, this application provides a CNC grinding rotary segmentation trajectory planning device for shoe uppers, comprising: The segmentation module is used to rotate and segment the point cloud data of the shoe upper processing area based on a predetermined rotation angle to obtain several point cloud sub-regions. The boundary point extraction module is used to extract boundary points from the point cloud data in the point cloud sub-region to obtain boundary feature points corresponding to the point cloud sub-region. The curve fitting module is used to perform curve fitting based on the boundary feature points to obtain the fitted curve of the point cloud sub-region. The discrete processing module is used to perform discrete processing on the fitted curve based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve. The processing trajectory planning module is used to plan the processing trajectory based on the discrete line segments and the predetermined rotation angle to obtain a multi-layer closed-loop processing path for the rotational segmentation of the shoe upper processing area.

[0013] To solve the above problems, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described CNC grinding and rotary segmentation trajectory planning method for shoe uppers.

[0014] To solve the above problems, this application provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor executes the computer program in the memory to implement the steps of the above-described CNC grinding and rotary segmentation trajectory planning method for shoe uppers.

[0015] The beneficial effects of this application are as follows: This application achieves high segmentation accuracy. Through rotational segmentation at a predetermined rotation angle and projection optimization, it realizes uniform sub-region division, unique boundary point assignment, and a first-to-last connection deviation of ≤0.02mm, far superior to the 0.5mm error level of existing technologies, meeting the millimeter-level precision requirements of shoe upper processing. It employs cubic polynomial fitting combined with chordal error constraints, resulting in no sharp corners after approximation of straight line segments, adapting to five-axis equipment processing, avoiding vibration and surface scratches, and improving the flatness of the shoe upper boundary by 40% after processing, with superior trajectory smoothness. The generated trajectory carries processing posture information, directly adapting to the processing coordinate system of five-axis napping machines, grinding machines, and other equipment without secondary adjustments, thus enhancing compatibility. Through multiple denoising strategies such as projection optimization and interval filtering, it exhibits strong resistance to scanning noise and point cloud redundancy, improving processing quality consistency, with a processing error of ≤0.05mm for products in the same batch, resulting in better stability.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a method for planning the rotational segmentation trajectory of CNC grinding of shoe uppers according to an embodiment of this application is shown. Figure 2 A flowchart illustrating a method for planning the rotational segmentation trajectory of CNC grinding of shoe uppers according to another embodiment of this application is shown; Figure 3 This illustration shows a schematic diagram of the shoe upper processing area segmentation provided in an embodiment of this application; Figure 4This paper presents a schematic diagram illustrating the principle of rotary cutting projection in the processing area provided in an embodiment of this application. Figure 5 This paper illustrates a schematic diagram of the cubic polynomial fitting effect provided in an embodiment of this application. Figure 6 This paper illustrates a schematic diagram of the approximation effect of multiple discrete line segments provided in an embodiment of this application. Figure 7 This illustration shows a schematic diagram of the machining key point path planning for converting discrete line segments into three-dimensional Cartesian coordinates according to an embodiment of this application. Figure 8 This paper illustrates a schematic diagram of a multi-layer closed-loop target machining trajectory provided in an embodiment of this application. Figure 9 A structural block diagram of a CNC grinding rotary segmentation trajectory planning device for shoe uppers, according to another embodiment of this application, is shown. Detailed Implementation

[0018] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0019] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0020] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0021] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0022] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.

[0023] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0024] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0025] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0026] This application provides a method for planning the rotational segmentation trajectory of CNC grinding of shoe uppers, such as... Figure 1 As shown, it includes: Step S101: Rotate and segment the point cloud data of the shoe upper processing area based on a predetermined rotation angle to obtain several point cloud sub-regions; In the specific implementation process, a three-dimensional Cartesian coordinate system is constructed with the geometric center of the shoe upper processing area as the origin. The y-axis of the three-dimensional Cartesian coordinate system points towards the shoe toe, the z-axis is perpendicular to the shoe upper processing area and points upward, and the x-axis points towards the side of the shoe upper using the right-hand rule. Several half-planes are generated based on the z-axis as the rotation axis and a predetermined rotation angle. The shoe upper processing area is divided based on each of the half-planes to obtain several point cloud sub-regions.

[0027] Step S102: Extract boundary points from the point cloud data in the point cloud sub-region to obtain boundary feature points corresponding to the point cloud sub-region; In the specific implementation process, boundary point projection optimization processing is performed based on the point cloud data in the point cloud sub-region to obtain candidate feature points; based on the candidate feature points, boundary point feature extraction is performed to obtain the boundary feature points corresponding to the point cloud sub-region.

[0028] Step S103: Perform curve fitting based on the boundary feature points to obtain the fitting curve of the point cloud sub-region; In the specific implementation process, a cubic polynomial is constructed to fit the fitted curve; the coefficients of the cubic polynomial are solved based on the boundary feature points to obtain the target cubic polynomial; and the bilinear interpolation algorithm is used to fit the curve based on the target cubic polynomial to obtain the fitted curve.

[0029] Step S104: Discretize the fitted curve based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve; In the specific implementation process, the preset discrete segmentation parameters include the target length that adapts to the grinding wheel processing range and the chord error threshold that ensures processing accuracy. Based on the preset discrete segmentation parameters, the fitted curve is discretized using a small step accumulation and linear interpolation method to generate discrete line segments that satisfy error constraints and length constraints.

[0030] Step S105: Based on the discrete line segments and the predetermined rotation angle, perform processing trajectory planning to obtain a multi-layer closed-loop target processing trajectory for rotational segmentation of the shoe upper processing area.

[0031] In the specific implementation process, the discrete line segments are converted into three-dimensional Cartesian coordinates; according to the rotation direction of the predetermined rotation angle, the target points corresponding to adjacent point cloud sub-regions are connected layer by layer according to the three-dimensional Cartesian coordinates to obtain a multi-layer closed-loop processing path for rotating segmentation of the shoe upper processing area; the tangential unit vector, normal unit vector, and radial unit vector of the multi-layer closed-loop processing path are calculated to obtain processing posture information; and a multi-layer closed-loop target processing trajectory is generated based on the processing posture information and the multi-layer closed-loop processing path.

[0032] This application achieves high segmentation accuracy by using rotational segmentation at a predetermined rotation angle and projection optimization to divide uniform sub-regions with unique boundary point assignments and a first-to-last connection deviation of ≤0.02mm, far exceeding the 0.5mm error level of existing technologies, thus meeting the millimeter-level precision requirements for shoe upper processing. It employs cubic polynomial fitting combined with chordal error constraints, resulting in no sharp corners after approximation of straight line segments, making it compatible with five-axis equipment processing. This avoids vibration and surface scratches, improving the flatness of the shoe upper boundary by 40% and enhancing trajectory smoothness. The generated trajectory inherently carries processing posture information, allowing direct adaptation to the coordinate systems of five-axis napping machines, grinding machines, and other equipment without secondary adjustments, thus enhancing compatibility. Through projection optimization, interval filtering, and other multiple denoising strategies, it exhibits strong resistance to scanning noise and point cloud redundancy, improving processing quality consistency. The processing error of products in the same batch is ≤0.05mm, demonstrating better stability.

[0033] Another embodiment of this application discloses a different method for planning the rotational segmentation trajectory of CNC grinding of shoe uppers, such as... Figure 2 As shown, it includes: Step S201: Rotate and segment the point cloud data of the shoe upper processing area based on a predetermined rotation angle to obtain several point cloud sub-regions; In this step, a three-dimensional Cartesian coordinate system is constructed with the geometric center of the shoe upper processing area as the origin. The y-axis of this three-dimensional Cartesian coordinate system points towards the toe of the shoe, the z-axis is perpendicular to the shoe upper processing area and points upwards, and the x-axis points towards the side of the shoe upper using the right-hand rule. Using the z-axis as the rotation axis, several half-planes are generated based on a predetermined rotation angle. The predetermined rotation angle can be 2°. When the predetermined rotation angle is 2°, rotating one revolution around the z-axis generates 180 half-planes at intervals within the shoe upper processing area. Each half-plane corresponds to an angle... Convert the predetermined rotation angle θ into radians. The shoe upper processing area is segmented based on each of the half-planes to obtain several point cloud sub-regions. For example... Figure 3 The diagram shown is a schematic representation of the shoe upper processing area division in this application.

[0034] Step S202: Perform boundary point projection optimization processing based on the point cloud data in the point cloud sub-region to obtain candidate feature points; In this step, the first position coordinates of the point cloud data are projected onto a first target plane to obtain the second position coordinates of the projected position corresponding to the point cloud data; for example... Figure 4 The diagram shown illustrates the principle of rotational cutting projection in the processing area of ​​this application. Specifically, the first target plane is a horizontal plane, i.e., the xy plane, constructed with the geometric center of the shoe upper processing area as the origin, forming a three-dimensional Cartesian coordinate system. The first position coordinates of any of the point cloud data are... Projecting the first target plane onto the xy plane yields the second position coordinates of the projected position corresponding to the point cloud data. The position vector of the projected position is calculated based on the second position coordinates and the origin coordinates corresponding to the geometric center point of the shoe upper processing area. The dot product value is obtained by calculating the dot product based on the position vector and the unit normal vector of the half-plane used to segment the point cloud sub-regions. The mathematical expression for the unit normal vector of the half-plane used to segment the point cloud sub-regions is as follows: ; The components are fixed as Ensure that the area is divided only in the horizontal direction, without affecting Shaft machining depth. The mathematical formula for calculating the dot product value is as follows:

[0035] in, It is a position vector; It is the unit normal vector; The x-coordinate of the second position; The ordinate of the second position; The dot product value is the radius of a predetermined rotation angle in radians. When the dot product value meets a preset condition, the point cloud data is determined as a candidate feature point; when the dot product value is less than 0, the dot product value meets the preset condition, and the point cloud data is determined as a candidate feature point.

[0036] Step S203: Based on the candidate feature points, perform boundary point feature extraction to obtain the boundary feature points corresponding to the point cloud sub-region; In this step, the candidate feature point is projected onto the half-plane along the direction of the unit normal vector to obtain the third position coordinates of the candidate feature point on the half-plane. Specifically, calculate the signed first distance from any point cloud data P to the half-plane. First distance The mathematical expression is as follows:

[0037] The first position coordinates based on the first distance and arbitrary point cloud data P The calculation process is performed to obtain the third position coordinates of the candidate feature point on the half-plane. ;in:

[0038] Ensure that each boundary point uniquely belongs to a sub-region, with no duplicates or omissions.

[0039] The radial distance corresponding to the candidate feature point is obtained by calculating based on the third position coordinates. Radial distance The mathematical formula for calculation is as follows:

[0040] The candidate feature points are filtered based on the radial distance and the predetermined Z-axis height interval to obtain the boundary feature points. Specifically, the three-dimensional coordinates of the third position coordinates are converted into two-dimensional polar coordinates. ; The height is defined as follows: the Z-axis height interval is divided into intervals of 0.1mm. Within each interval, the point with the largest r is selected as the boundary feature point. Redundant points and noise are removed, and the outermost contour point of the shoe upper is retained to obtain the boundary feature point corresponding to the point cloud sub-region.

[0041] Step S204: Perform curve fitting based on the boundary feature points to obtain the fitting curve of the point cloud sub-region; In this step, a cubic polynomial is constructed to fit the fitted curve; the mathematical expression is as follows:

[0042] in, , , , The coefficients of the cubic polynomial; The height is determined; the coefficients of the cubic polynomial are solved based on the boundary feature points to obtain the target cubic polynomial; specifically, the coordinates of different boundary feature points are substituted into the cubic polynomial to calculate the coefficient values ​​of the cubic polynomial, thus obtaining the target cubic polynomial. Based on the target cubic polynomial, a bilinear interpolation algorithm is used for curve fitting to obtain the fitted curve. Using the bilinear interpolation algorithm for curve fitting can obtain a smooth fitted curve. For example... Figure 5 The figure shown is a schematic diagram of the cubic polynomial fitting effect of this application.

[0043] Step S205: Discretize the fitted curve based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve; In this step, the preset discrete segmentation parameters include a target length suitable for the grinding wheel's machining range and a chord error threshold to ensure machining accuracy. Based on these preset discrete segmentation parameters, a small-step accumulation and linear interpolation method is used to discretize the fitted curve, generating discrete line segments that satisfy both error and length constraints. The chord error threshold can be 0.1 mm, and can be set according to actual needs. The target length can be 7 mm, and can be set according to actual needs. The arc length is calculated by accumulating in small steps of 0.01 mm, and the target point is located using linear interpolation to generate the discrete line segments. Figure 6 The diagram shown illustrates the approximate effect of multiple discrete line segments in this application.

[0044] Step S206: Based on the discrete line segments and the predetermined rotation angle, perform processing trajectory planning to obtain a multi-layer closed-loop target processing trajectory for rotational segmentation of the shoe upper processing area.

[0045] In this step, the discrete line segments are converted into three-dimensional Cartesian coordinates; x = r·cosθ, y = r·sinθ, and z remains unchanged; as shown... Figure 7The diagram shows the key point path planning for converting discrete line segments into three-dimensional Cartesian coordinates in this application. According to the rotation direction of the predetermined rotation angle, the target points corresponding to adjacent point cloud sub-regions are connected layer by layer according to the three-dimensional Cartesian coordinates to obtain a multi-layer closed-loop processing path for rotating segmentation of the shoe upper processing area. The tangential unit vector, normal unit vector, and radial unit vector of the multi-layer closed-loop processing path are calculated to obtain processing posture information. Specifically, adjacent sub-region trajectories are connected in angular order to construct a closed-loop processing path, while simultaneously calculating the tangential, normal, and radial unit vectors of the trajectory nodes. First, the line segment vector is extracted as the direction from the endpoint to the starting point of each discrete line segment, representing the tangential direction of the line segment; the connection vector is the line connecting two adjacent points of the layered trajectory, pointing from the former to the latter, representing the direction of the path; using the line segment vector as the reference vector, Schmitt orthogonalization is performed on the connection vector, assuming the line segment vector is... The connection vector is , The orthogonalization steps are as follows: Step 1: Calculate the projection coefficient: ( For vector dot product, (where the square of the magnitude of the reference vector is used). Step 2: Extract orthogonal components: ( and Strictly perpendicular, dot product ); These are the projection coefficients; Step 3: Obtain the "orthogonal connection vector" that is perpendicular to the line segment vector. (Used to determine the normal direction of machining), perform a cross product on the line segment vector and the orthogonal connection vector to obtain the "attitude vector" (representing the third direction of the machining equipment, the radial direction of the grinding wheel). Step 4: Normalize the line segment vector, orthogonal connection vector, and attitude vector respectively to obtain three sets of pairwise perpendicular unit vectors (forming the coordinate system of the processing attitude), which are the tangential, normal, and radial unit vectors of the trajectory node.

[0046] Step 5: Next, perform coordinate system translation, moving the origin from the geometric center to the workpiece coordinate system origin. Then, bind the machining posture information of each point to generate trajectory data that can be directly imported into a five-axis machine. This data includes the three-dimensional coordinates of the trajectory points and the machining tool posture information. Based on the machining posture information and the multi-layer closed-loop machining path, a multi-layer closed-loop target machining trajectory is generated. For example... Figure 8 The diagram shown is a schematic diagram of the multi-layer closed-loop target processing trajectory of this application.

[0047] This application achieves high segmentation accuracy by using rotational segmentation at a predetermined rotation angle and projection optimization to divide uniform sub-regions with unique boundary point assignments and a first-to-last connection deviation of ≤0.02mm, far exceeding the 0.5mm error level of existing technologies, thus meeting the millimeter-level precision requirements for shoe upper processing. It employs cubic polynomial fitting combined with chordal error constraints, resulting in no sharp corners after approximation of straight line segments, making it compatible with five-axis equipment processing. This avoids vibration and surface scratches, improving the flatness of the shoe upper boundary by 40% and enhancing trajectory smoothness. The generated trajectory inherently carries processing posture information, allowing direct adaptation to the coordinate systems of five-axis napping machines, grinding machines, and other equipment without secondary adjustments, thus enhancing compatibility. Through projection optimization, interval filtering, and other multiple denoising strategies, it exhibits strong resistance to scanning noise and point cloud redundancy, improving processing quality consistency. The processing error of products in the same batch is ≤0.05mm, demonstrating better stability.

[0048] Another embodiment of this application provides a CNC grinding rotary segmentation trajectory planning device 900 for shoe uppers, such as... Figure 9 As shown, it includes: The segmentation module 901 is used to perform rotational segmentation on the point cloud data of the shoe upper processing area based on a predetermined rotation angle to obtain several point cloud sub-regions. Boundary point extraction module 902 is used to extract boundary points from the point cloud data in the point cloud sub-region to obtain boundary feature points corresponding to the point cloud sub-region. The curve fitting module 903 is used to perform curve fitting based on the boundary feature points to obtain the fitted curve of the point cloud sub-region. Discrete processing module 904 is used to discretize the fitted curve based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve. The processing trajectory planning module 905 is used to plan the processing trajectory based on the discrete line segments and the predetermined rotation angle to obtain a multi-layer closed-loop processing path for the rotational segmentation of the shoe upper processing area.

[0049] In the specific implementation process, the segmentation module 901 is specifically used to construct a three-dimensional Cartesian coordinate system with the geometric center of the shoe upper processing area as the origin. The y-axis of the three-dimensional Cartesian coordinate system points towards the shoe toe, the z-axis is perpendicular to the shoe upper processing area and points upward, and the x-axis points towards the side of the shoe upper using the right-hand rule. Several half-planes are generated based on a predetermined rotation angle with the z-axis as the rotation axis. The shoe upper processing area is segmented based on each of the half-planes to obtain several point cloud sub-regions.

[0050] In the specific implementation process, the boundary point extraction module 902 is specifically used to perform boundary point projection optimization processing based on the point cloud data in the point cloud sub-region to obtain candidate feature points; and to perform boundary point feature extraction based on the candidate feature points to obtain boundary feature points corresponding to the point cloud sub-region.

[0051] In the specific implementation process, the boundary point extraction module 902 is further used to: project the first position coordinates of the point cloud data onto a first target plane to obtain the second position coordinates of the projected position corresponding to the point cloud data; calculate the position vector of the projected position based on the second position coordinates and the origin coordinates corresponding to the geometric center point of the shoe upper processing area; calculate the dot product value based on the position vector and the unit normal vector of the half-plane used to divide the point cloud sub-region; and determine the point cloud data as a candidate feature point when the dot product value meets the preset conditions.

[0052] In the specific implementation process, the boundary point extraction module 902 is further used to project the candidate feature point onto the half-plane along the direction of the unit normal vector to obtain the third position coordinates of the candidate feature point on the half-plane; perform calculation processing based on the third position coordinates to obtain the radial distance corresponding to the candidate feature point; and filter the candidate feature point based on the radial distance and the predetermined Z-axis height interval to obtain the boundary feature point.

[0053] In the specific implementation process, the curve fitting module 903 is specifically used to: construct a cubic polynomial for fitting the fitted curve; solve the coefficients of the cubic polynomial based on the boundary feature points to obtain a target cubic polynomial; and perform curve fitting using a bilinear interpolation algorithm based on the target cubic polynomial to obtain the fitted curve.

[0054] In the specific implementation process, the processing trajectory planning module 905 is specifically used to convert the discrete line segments into three-dimensional Cartesian coordinates; according to the rotation direction of the predetermined rotation angle, the target points corresponding to adjacent point cloud sub-regions are connected layer by layer according to the three-dimensional Cartesian coordinates to obtain a multi-layer closed-loop processing path for rotating segmentation of the shoe upper processing area; the tangential unit vector, normal unit vector and radial unit vector of the multi-layer closed-loop processing path are calculated to obtain processing posture information; and a multi-layer closed-loop target processing trajectory is generated based on the processing posture information and the multi-layer closed-loop processing path.

[0055] This application achieves high segmentation accuracy by using rotational segmentation at a predetermined rotation angle and projection optimization to divide uniform sub-regions with unique boundary point assignments and a first-to-last connection deviation of ≤0.02mm, far exceeding the 0.5mm error level of existing technologies, thus meeting the millimeter-level precision requirements for shoe upper processing. It employs cubic polynomial fitting combined with chordal error constraints, resulting in no sharp corners after approximation of straight line segments, making it compatible with five-axis equipment processing. This avoids vibration and surface scratches, improving the flatness of the shoe upper boundary by 40% and enhancing trajectory smoothness. The generated trajectory inherently carries processing posture information, allowing direct adaptation to the coordinate systems of five-axis napping machines, grinding machines, and other equipment without secondary adjustments, thus enhancing compatibility. Through projection optimization, interval filtering, and other multiple denoising strategies, it exhibits strong resistance to scanning noise and point cloud redundancy, improving processing quality consistency. The processing error of products in the same batch is ≤0.05mm, demonstrating better stability.

[0056] Another embodiment of this application provides a storage medium storing a computer program, which, when executed by a processor, implements the following method steps: Step 1: Rotate and segment the point cloud data of the shoe upper processing area based on a predetermined rotation angle to obtain several point cloud sub-regions; Step 2: Extract boundary points from the point cloud data in the point cloud sub-region to obtain the boundary feature points corresponding to the point cloud sub-region; Step 3: Perform curve fitting based on the boundary feature points to obtain the fitted curve of the point cloud sub-region; Step 4: Discretize the fitted curve based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve; Step 5: Based on the discrete line segments and the predetermined rotation angle, perform processing trajectory planning to obtain a multi-layer closed-loop target processing trajectory for rotational segmentation of the shoe upper processing area.

[0057] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0058] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0059] The specific implementation process of the above method steps can be found in the embodiment of the above-mentioned arbitrary shoe upper CNC grinding rotation segmentation trajectory planning method, which will not be repeated here.

[0060] This application achieves high segmentation accuracy by using rotational segmentation at a predetermined rotation angle and projection optimization to divide uniform sub-regions with unique boundary point assignments and a first-to-last connection deviation of ≤0.02mm, far exceeding the 0.5mm error level of existing technologies, thus meeting the millimeter-level precision requirements for shoe upper processing. It employs cubic polynomial fitting combined with chordal error constraints, resulting in no sharp corners after approximation of straight line segments, making it compatible with five-axis equipment processing. This avoids vibration and surface scratches, improving the flatness of the shoe upper boundary by 40% and enhancing trajectory smoothness. The generated trajectory inherently carries processing posture information, allowing direct adaptation to the coordinate systems of five-axis napping machines, grinding machines, and other equipment without secondary adjustments, thus enhancing compatibility. Through projection optimization, interval filtering, and other multiple denoising strategies, it exhibits strong resistance to scanning noise and point cloud redundancy, improving processing quality consistency. The processing error of products in the same batch is ≤0.05mm, demonstrating better stability.

[0061] Another embodiment of this application provides an electronic device, which can be a server. The electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the program is executed by the processor, it implements the functions or steps of a CNC grinding and rotary segmentation trajectory planning method for shoe uppers on the server side.

[0062] In one embodiment, an electronic device is provided, which can be a client. The electronic device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. When the program is executed by the processor, it implements the client-side functions or steps of a CNC grinding and rotary segmentation trajectory planning method for shoe uppers.

[0063] Another embodiment of this application provides an electronic device, including at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, performs the following method steps: Step 1: Rotate and segment the point cloud data of the shoe upper processing area based on a predetermined rotation angle to obtain several point cloud sub-regions; Step 2: Extract boundary points from the point cloud data in the point cloud sub-region to obtain the boundary feature points corresponding to the point cloud sub-region; Step 3: Perform curve fitting based on the boundary feature points to obtain the fitted curve of the point cloud sub-region; Step 4: Discretize the fitted curve based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve; Step 5: Based on the discrete line segments and the predetermined rotation angle, perform processing trajectory planning to obtain a multi-layer closed-loop target processing trajectory for rotational segmentation of the shoe upper processing area.

[0064] The specific implementation process of the above method steps can be found in the embodiment of the above-mentioned arbitrary shoe upper CNC grinding rotation segmentation trajectory planning method, which will not be repeated here.

[0065] This application achieves high segmentation accuracy by using rotational segmentation at a predetermined rotation angle and projection optimization to divide uniform sub-regions with unique boundary point assignments and a first-to-last connection deviation of ≤0.02mm, far exceeding the 0.5mm error level of existing technologies, thus meeting the millimeter-level precision requirements for shoe upper processing. It employs cubic polynomial fitting combined with chordal error constraints, resulting in no sharp corners after approximation of straight line segments, making it compatible with five-axis equipment processing. This avoids vibration and surface scratches, improving the flatness of the shoe upper boundary by 40% and enhancing trajectory smoothness. The generated trajectory inherently carries processing posture information, allowing direct adaptation to the coordinate systems of five-axis napping machines, grinding machines, and other equipment without secondary adjustments, thus enhancing compatibility. Through projection optimization, interval filtering, and other multiple denoising strategies, it exhibits strong resistance to scanning noise and point cloud redundancy, improving processing quality consistency. The processing error of products in the same batch is ≤0.05mm, demonstrating better stability.

[0066] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. Those skilled in the art can make various modifications or equivalent substitutions to this application within the scope and nature of this application, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A method for planning the rotational segmentation trajectory of CNC grinding of shoe uppers, characterized in that, include: Based on a predetermined rotation angle, the point cloud data of the shoe upper processing area is rotated and segmented to obtain several point cloud sub-regions; Boundary points are extracted from the point cloud data in the point cloud sub-region to obtain boundary feature points corresponding to the point cloud sub-region. Based on the boundary feature points, curve fitting is performed to obtain the fitted curve of the point cloud sub-region; The fitted curve is discretized based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve. Based on the discrete line segments and the predetermined rotation angle, a multi-layer closed-loop target processing trajectory for rotational segmentation of the shoe upper processing area is obtained.

2. The method as described in claim 1, characterized in that, The point cloud data of the shoe upper processing area is rotated and segmented based on a predetermined rotation angle to obtain several point cloud sub-regions, specifically including: A three-dimensional Cartesian coordinate system is constructed with the geometric center of the shoe upper processing area as the origin. The y-axis of the three-dimensional Cartesian coordinate system points towards the shoe toe, the z-axis is perpendicular to the shoe upper processing area and points upward, and the x-axis points towards the side of the shoe upper using the right-hand rule. Several half-planes are generated based on a predetermined rotation angle, with the z-axis as the rotation axis. The shoe upper processing area is segmented based on each of the half-planes to obtain several point cloud sub-regions.

3. The method as described in claim 1, characterized in that, The step of extracting boundary points from the point cloud data in the point cloud sub-region to obtain boundary feature points corresponding to the point cloud sub-region specifically includes: Based on the point cloud data in the point cloud sub-region, boundary point projection optimization processing is performed to obtain candidate feature points; Boundary point features are extracted based on the candidate feature points to obtain the boundary feature points corresponding to the point cloud sub-region.

4. The method as described in claim 3, characterized in that, The step of performing boundary point projection optimization processing based on the point cloud data in the point cloud sub-region to obtain candidate feature points specifically includes: The first position coordinates of the point cloud data are projected onto a first target plane to obtain the second position coordinates of the projected position corresponding to the point cloud data. The position vector of the projected position is obtained by calculating based on the second position coordinates and the origin coordinates corresponding to the geometric center point of the shoe upper processing area; The dot product value is obtained by calculating based on the position vector and the unit normal vector of the half-plane used to segment the point cloud sub-region; When the dot product value meets the preset conditions, the point cloud data is determined as a candidate feature point.

5. The method as described in claim 4, characterized in that, The step of extracting boundary point features based on the candidate feature points to obtain boundary feature points corresponding to the point cloud sub-region specifically includes: Projecting the candidate feature point along the direction of the unit normal vector onto the half-plane yields the third position coordinates of the candidate feature point on the half-plane. The radial distance corresponding to the candidate feature point is obtained by performing calculations based on the third position coordinates. The candidate feature points are filtered based on the radial distance and the predetermined Z-axis height interval to obtain the boundary feature points.

6. The method as described in claim 1, characterized in that, The step of performing curve fitting based on the boundary feature points to obtain the fitted curve of the point cloud sub-region specifically includes: Construct a cubic polynomial for fitting the fitted curve; The coefficients of the cubic polynomial are solved based on the boundary feature points to obtain the target cubic polynomial; Based on the target cubic polynomial, a bilinear interpolation algorithm is used to perform curve fitting to obtain the fitted curve.

7. The method as described in claim 1, characterized in that, The process trajectory planning based on the discrete line segments and the predetermined rotation angle to obtain a multi-layer closed-loop processing path for rotational segmentation of the shoe upper processing area specifically includes: Convert the discrete line segments into three-dimensional Cartesian coordinates; According to the rotation direction of the predetermined rotation angle, the target points corresponding to adjacent point cloud sub-regions are connected in layers according to the three-dimensional Cartesian coordinates to obtain a multi-layer closed-loop processing path for rotating segmentation of the shoe upper processing area. The tangential unit vector, normal unit vector, and radial unit vector of the multi-layer closed-loop machining path are calculated to obtain the machining posture information; A multi-layer closed-loop target machining trajectory is generated based on the machining posture information and the multi-layer closed-loop machining path.

8. A CNC grinding and rotary segmentation trajectory planning device for shoe uppers, characterized in that, include: The segmentation module is used to rotate and segment the point cloud data of the shoe upper processing area based on a predetermined rotation angle to obtain several point cloud sub-regions. The boundary point extraction module is used to extract boundary points from the point cloud data in the point cloud sub-region to obtain boundary feature points corresponding to the point cloud sub-region. The curve fitting module is used to perform curve fitting based on the boundary feature points to obtain the fitted curve of the point cloud sub-region. The discrete processing module is used to perform discrete processing on the fitted curve based on preset discrete segmentation parameters to obtain discrete line segments corresponding to the fitted curve. The processing trajectory planning module is used to plan the processing trajectory based on the discrete line segments and the predetermined rotation angle to obtain a multi-layer closed-loop processing path for the rotational segmentation of the shoe upper processing area.

9. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the CNC grinding and rotary segmentation trajectory planning method for shoe uppers as described in any one of claims 1-7.

10. An electronic device, characterized in that, It includes at least a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the shoe upper CNC grinding rotary segmentation trajectory planning method according to any one of claims 1-7.

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