Automatic typesetting and drawing method and system for lens laser cutting

Abstract

The invention relates to the technical field of typesetting and drawing, in particular to an automatic typesetting and drawing method and system for lens laser cutting. The method comprises the following steps: collecting an image of a to-be-cut lens, carrying out contour edge detection, generating a contour direction sequence, carrying out polygon fitting on the sequence, screening out effective contour data, constructing a three-dimensional lens model, carrying out classification clustering on the model to extract contour features, carrying out segmentation to obtain a segmented contour model, and carrying out image segmentation on the segmented contour model. The method comprises the steps of obtaining a model, carrying out range limitation and local typesetting processing on the model to form an accurate layout scheme, constructing a cutting path direction chain according to the scheme, carrying out path planning, generating cutting path data, carrying out sequential execution setting on cutting paths based on a preset blank drawing template, and finally obtaining a typesetting template for laser cutting. The automatic typesetting and drawing method for lens laser cutting is more efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of typesetting and drawing, and in particular to an automatic typesetting and drawing method and system for lens laser cutting. Background Art

[0002] Laser cutting technology for lenses has gradually been widely used as demand increases. Traditional manual cutting methods are not only inefficient, but also difficult to ensure cutting accuracy, resulting in increased production costs and time. As consumers' demands for lens quality and personalization increase, how to improve the automation level and accuracy of lens cutting has become an urgent problem to be solved. Existing laser cutting technology has certain limitations in image processing and cutting path planning. Traditional methods often rely on manual experience to design cutting paths, which can easily lead to unreasonable cutting paths and thus affect the quality of the final product. In addition, most existing technologies lack in-depth analysis of lens contour features and cannot fully explore the geometric features of lenses, resulting in low cutting efficiency and waste of materials. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for automatic layout and drawing for lens laser cutting to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for automatic layout and drawing for lens laser cutting is provided, comprising the following steps: Step S1: collecting an image of a lens to be cut; performing contour edge detection on the image of the lens to be cut to obtain an image edge curve; performing continuous contour discrete direction mapping on the image of the lens to be cut based on the image edge curve to generate a contour direction sequence; Step S2: polygon fitting is performed on the contour direction sequence to obtain screening contour data; three-dimensional contour modeling is performed based on the screening contour data to generate a reconstructed lens model; Step S3: performing contour classification and clustering processing on the reconstructed lens model to obtain lens contour feature data; performing contour segmentation on the reconstructed lens model according to the lens contour feature data to obtain a segmented lens contour model; Step S4: performing range-limited screening on the segmented lens contour model to obtain a compressed range contour model; performing local fine layout processing on the compressed range contour model to obtain an accurate layout solution; Step S5: constructing a direction chain according to the precise layout scheme to obtain a cutting path direction chain; planning a cutting path for the precise layout scheme based on the cutting path direction chain to generate cutting path data; Step S6: sequentially executing and setting the cutting path data based on a preset blank drawing template to obtain a cutting layout template to execute the automatic layout and drawing method for lens laser cutting.

[0005] The present invention can obtain accurate image information by collecting the image of the lens to be cut, and can extract clear edge curves by detecting the contour edge. The generated contour direction sequence provides a basis for subsequent processing. The polygon fitting of the contour direction sequence can optimize the contour data, and the obtained screened contour data provides a basis for three-dimensional modeling. The implementation of contour three-dimensional modeling based on the screened contour data can generate a high-precision reconstructed lens model. The implementation of classification and clustering processing on the reconstructed lens model can extract feature data. The generated lens contour feature data provides support for segmentation. The implementation of contour segmentation of the model according to the feature data can obtain a segmented contour model. The implementation of range-limited screening can ensure the applicability of the model. The generated compressed range contour model provides a basis for local typesetting. The implementation of local fine typesetting processing can optimize the layout plan. The obtained precise layout plan provides a clear direction for cutting path planning. The implementation of constructing a cutting path direction chain according to the layout plan can ensure the rationality of cutting. The generated cutting path data provides a basis for subsequent typesetting. The implementation of sequential execution setting based on a blank drawing template can realize automatic typesetting. The cutting typesetting template finally formed ensures the efficiency and accuracy of laser cutting.

[0006] Preferably, step S1 comprises the following steps: Step S11: collecting an image of the lens to be cut; performing brightness balance correction on the image of the lens to be cut to obtain a brightness balanced image; Step S12: performing bilateral filtering to reduce noise on the brightness-balanced image to generate a reduced-noise lens image; performing contour edge detection on the reduced-noise lens image to obtain an image edge curve; Step S13: Encode the edge curve of the image using eight-direction chain codes to obtain a direction code sequence; Step S14: performing continuous contour discrete direction mapping on the image of the lens to be cut according to the direction coding sequence to generate a contour direction sequence.

[0007] The present invention can ensure accurate data input by collecting the image of the lens to be cut, the implementation of brightness balance correction can improve the overall brightness and contrast of the image, and the generated brightness balanced image provides clearer visual information for subsequent processing. The implementation of bilateral filtering noise reduction can effectively remove the noise in the image, and the generated noise-reduced lens image provides a clean basis for contour detection. The implementation of contour edge detection can extract the edge features of the lens, and the obtained image edge curve provides important data for subsequent encoding. The implementation of eight-direction chain code encoding can convert edge information into a digital direction sequence, and the generated direction coding sequence is convenient for analyzing and processing the contour. The implementation of continuous contour discrete direction mapping according to the direction coding sequence can extract the contour direction information of the lens, and the generated contour direction sequence lays a foundation for subsequent three-dimensional modeling and cutting path planning, thereby improving the accuracy and efficiency of the lens laser cutting process as a whole.

[0008] Preferably, step S2 comprises the following steps: Step S21: performing density cluster analysis on the contour direction sequence to obtain key point distribution data; performing point distance calculation on the key point distribution data to generate a point spacing matrix; Step S22: performing threshold segmentation processing on the contour direction sequence according to the point spacing matrix to generate segment boundary data; performing polygon fitting on the contour direction sequence based on the segment boundary data to obtain filtered contour data; Step S23: extracting geometric features from the screened contour data to obtain a contour geometric feature description set; performing contour depth mapping based on the contour geometric feature description set to generate depth mapping data; Step S24: performing anisotropic diffusion filtering on the depth mapping data to obtain a smooth depth map; performing contour three-dimensional modeling on the screened contour data based on the smooth depth map to generate a reconstructed lens model.

[0009] The present invention can effectively identify key points in the contour by implementing density cluster analysis on the contour direction sequence, and the generated key point distribution data provides a basis for subsequent processing. The implementation of point distance calculation can reveal the spatial relationship between key points, and the generated point spacing matrix provides a basis for threshold segmentation processing of the contour. The implementation of threshold segmentation processing based on the point spacing matrix can divide the contour information into different segments, and the generated segment boundary data provides support for polygon fitting. The implementation of polygon fitting based on the segment boundary data can optimize the contour data, and the obtained screened contour data lays a foundation for further analysis. The implementation of contour geometric feature extraction can reveal the shape characteristics of the contour, and the generated contour geometric feature description set provides information for subsequent depth mapping. The implementation of contour depth mapping based on the contour geometric feature description set can generate richer depth information, and the obtained depth mapping data provides raw data for further filtering processing. The implementation of anisotropic diffusion filtering can smooth the depth map, and the generated smoothed depth map provides a clear basis for contour three-dimensional modeling. The implementation of three-dimensional modeling of the screened contour data based on the smoothed depth map can generate a high-quality reconstructed lens model, which improves the accuracy and efficiency of lens laser cutting as a whole.

[0010] Preferably, step S22 includes the following steps: Perform feature statistics processing on the point spacing matrix to obtain distance feature data; perform dynamic threshold calculation based on the distance feature data to generate a threshold parameter set; Perform sliding window analysis on the contour direction sequence to obtain local feature data; perform direction change rate analysis based on the local feature data to generate a direction change rate sequence; Perform peak detection on the direction change rate sequence to obtain a set of change rate key points; perform matching and screening on the set of change rate key points and the set of threshold parameters to generate candidate segmentation points; Perform threshold segmentation processing on the contour direction sequence according to the candidate segmentation points to generate segmentation boundary data; Perform feature geometry analysis on segmented boundary data to obtain feature geometry attributes; construct fitting constraints based on the feature geometry attributes to generate a constraint condition set; Perform polygon fitting on the contour direction sequence to obtain an initial fitting result; perform error calculation on the initial fitting result according to the constraint condition set to obtain error index data; Angle adjustment optimization is performed based on the error index data to obtain an optimized fitting result; and contour screening is performed on the optimized fitting result to obtain screened contour data.

[0011] The present invention can extract key distance features by implementing feature statistical processing on the point spacing matrix, and the generated distance feature data provides an important basis for dynamic threshold calculation. The implementation of dynamic threshold calculation based on distance feature data can generate a threshold parameter collection with strong adaptability. The implementation of sliding window analysis on the contour direction sequence can effectively capture local feature information, and the obtained local feature data provides support for the analysis of direction changes. The implementation of direction change rate analysis based on local feature data can reveal the dynamic changes of the contour. The generated direction change rate sequence provides a basis for subsequent peak detection. The implementation of peak detection can identify the key points of the change rate. The obtained change rate key point collection provides a basis for the screening of candidate segmentation points. The implementation of matching and screening the change rate key point collection and the threshold parameter collection can accurately generate candidate segmentation points. The implementation of threshold segmentation processing of contour direction sequence can optimize the contour segmentation effect, and the generated segmented boundary data provides important data for feature geometry analysis. The implementation of feature geometry analysis of segmented boundary data can extract the geometric properties of the contour. The obtained feature geometry properties provide a basis for fitting constraint construction. The implementation of fitting constraint construction based on feature geometry properties can generate a reasonable set of constraint conditions. The implementation of polygon fitting of contour direction sequence can obtain preliminary fitting results. The implementation of error calculation of initial fitting results can evaluate the accuracy of fitting. The generated error index data provides a basis for subsequent optimization. The implementation of angle adjustment optimization based on error index data can improve the accuracy of fitting. The final optimized fitting result can generate high-quality screening contour data after contour screening, which significantly improves the efficiency and accuracy of lens laser cutting.

[0012] Preferably, step S3 comprises the following steps: Step S31: locating edge points of the reconstructed lens model to obtain a model edge point set; calculating the curvature of the model edge point set to obtain a curvature feature vector; Step S32: performing principal component analysis on the reconstructed lens model according to the curvature feature vector to obtain dimension-reduced feature data; performing mean clustering processing on the dimension-reduced feature data to generate lens profile feature data; Step S33: positioning the segmentation key points of the lens profile feature data to obtain segmentation key point data; Step S34: Perform contour segmentation on the reconstructed lens model based on the segmentation key point data to obtain a segmented lens contour model.

[0013] The present invention can accurately identify the edge features of the model by implementing edge point positioning on the reconstructed lens model, and the generated model edge point set provides a data basis for subsequent curvature calculation. The implementation of curvature calculation can extract the curvature feature vector of the model, and the generated curvature feature vector provides key data for principal component analysis. The implementation of principal component analysis based on the curvature feature vector can achieve data dimensionality reduction processing, and the obtained reduced-dimensionality feature data provides effective information for subsequent clustering processing. The implementation of mean clustering processing can group the reduced-dimensionality feature data, and the generated lens contour feature data provides a basis for segmentation. The implementation of segmentation key point positioning on the lens contour feature data can accurately locate the segmentation point, and the obtained segmentation key point data provides support for contour segmentation. The implementation of contour segmentation of the reconstructed lens model based on the segmentation key point data can generate a segmented lens contour model, thereby improving the accuracy and efficiency of lens laser cutting and ensuring high-quality output in the production process.

[0014] Preferably, step S4 comprises the following steps: Step S41: limiting the scope of the segmented lens contour model by an envelope frame to obtain a collection of boundary frames; performing area optimization processing on the collection of boundary frames to generate compressed boundary data; Step S42: dividing the feasible area according to the compressed boundary data to obtain a limited feasible area; performing range-limited screening on the segmented lens contour model based on the limited feasible area to obtain a compressed range contour model; Step S43: meshing the compressed range contour model to obtain lens contour mesh data; performing layout cost calculation on the lens contour mesh data to obtain a layout cost matrix; Step S44: Perform local fine layout processing according to the layout cost matrix to obtain an accurate layout solution.

[0015] The present invention can effectively determine the boundary of the model by implementing the envelope frame range limitation on the segmented lens contour model, the generated boundary frame collection provides a basis for subsequent area optimization, the implementation of area optimization processing can reduce unnecessary calculation areas, the generated compressed boundary data provides a basis for feasible area division, the implementation of feasible area division based on the compressed boundary data can clarify the effective cutting area, the obtained limited feasible area provides support for the screening of the contour model, the implementation of range limitation screening on the segmented lens contour model based on the limited feasible area can generate a compressed range contour model, the implementation of grid division can convert the compressed range contour model into grid data, the obtained lens contour grid data provides necessary information for layout cost calculation, the implementation of layout cost calculation can evaluate the costs of different layout schemes, the generated layout cost matrix provides a basis for subsequent fine layout processing, the implementation of local fine layout processing based on the layout cost matrix can optimize the layout effect, and the final obtained precise layout scheme provides efficient and reasonable guidance for lens laser cutting, thereby improving cutting efficiency and finished product quality.

[0016] Preferably, step S42 includes the following steps: Gridding the compressed boundary data to obtain boundary grid data; marking obstacles on the boundary grid data to generate obstacle grid data; Based on the obstacle grid data, the connected domain of the boundary grid data is identified to obtain a regional connected graph; the boundary of the regional connected graph is extracted to generate a limited feasible area; Performing position mapping on the limited feasible area according to the segmented lens contour model to obtain position mapping data; performing boundary detection on the position mapping data to obtain boundary constraint data; Perform feasibility verification based on boundary constraint data to obtain verification result data; optimize the range of the limited feasible area based on the verification result data to obtain a compressed feasible area; The compression feasible area is regionally integrated to generate a compression range contour model.

[0017] The present invention can convert boundary information into structured data by implementing gridding processing on compressed boundary data, and the generated boundary grid data provides a basis for obstacle marking. The implementation of obstacle marking can clarify the position of obstacles, and the generated obstacle grid data provides important information for connected domain identification. The implementation of connected domain identification based on obstacle grid data can identify feasible areas and infeasible areas, and the obtained regional connectivity map provides a basis for subsequent boundary extraction. The implementation of boundary extraction can generate a limited feasible area and clarify the effective space for cutting. The implementation of position mapping of the limited feasible area according to the cut lens contour model can ensure the accurate positioning of the cutting path, and the generated position mapping data provides support for boundary detection. The implementation of boundary detection can extract effective boundary constraint data, and the implementation of feasibility verification based on boundary constraint data can evaluate the rationality of the cutting plan. The obtained verification result data provides a basis for optimization. The implementation of range optimization of the limited feasible area according to the verification result data can further improve the accuracy of the cutting area. The generated compressed feasible area provides an efficient space for subsequent processing. The implementation of regional integration can generate a compressed range contour model, thereby improving the accuracy and efficiency of lens laser cutting.

[0018] Preferably, step S44 includes the following steps: Optimize the objective function according to the layout cost matrix to obtain the objective optimization function; perform initial layout simulation according to the objective optimization function to obtain a simulated initial layout; Perform conflict detection on the simulated initial layout to obtain layout conflict points; perform local adjustment on the compressed range contour model based on the layout conflict points to obtain a local adjustment plan; Based on the preset layout scoring standard, the local adjustment plan is evaluated for layout to obtain a layout score; the layout score is searched locally for high scores to obtain an optimized adjustment plan; According to the optimization adjustment plan, the boundary is fine-tuned and optimized to obtain the boundary optimization layout; the data of the boundary optimization layout is integrated to generate an accurate layout plan.

[0019] The present invention can clarify the optimal target of the cutting layout by implementing the objective function optimization according to the layout cost matrix, the generated objective optimization function provides guidance for the initial layout simulation, the implementation of the initial layout simulation can generate a feasible layout plan, the obtained simulated initial layout provides a basis for subsequent conflict detection, the implementation of conflict detection can identify problems in the layout, generate layout conflict points, and the implementation of local adjustment of the compression range contour model based on the layout conflict points can optimize the layout in a targeted manner. The obtained local adjustment plan provides a basis for layout evaluation, the implementation of layout evaluation of the local adjustment plan based on the preset layout scoring standard can quantify the advantages and disadvantages of the layout, the generated layout score provides data support for high-score local search, the implementation of high-score local search can deeply explore better layout plans, the generation of optimized adjustment plans can further improve cutting efficiency, the implementation of boundary fine-tuning optimization according to the optimized adjustment plan can ensure the accuracy of the layout, the obtained boundary optimized layout provides a basis for data integration, the implementation of data integration can generate an accurate layout plan, comprehensively improve the quality and efficiency of lens laser cutting, and ensure the high-standard output of the final product.

[0020] Preferably, step S5 comprises the following steps: Step S51: performing contour vectorization on the precise layout scheme to obtain contour vector data; performing directional feature analysis based on the contour vector data to obtain directional feature data; Step S52: Analyze the continuity of the directional feature data to obtain a continuous segment set; perform direction chain encoding on the continuous segment set to generate a cutting path direction chain; Step S53: constructing a graph structure based on the cutting path direction chain to obtain an initial cutting path graph; searching for an optimal path on the initial cutting path graph to obtain initial optimal path data; Step S54: performing collision detection based on the initial preferred path data to obtain safe path data; integrating the safe path data and the initial preferred path data to generate cutting path data.

[0021] The present invention can convert layout information into processable vector data by implementing contour vectorization on the precise layout plan, and the generated contour vector data provides a basis for directional feature analysis. The implementation of directional feature analysis can extract key directional information, and the obtained directional feature data lays a foundation for subsequent continuity analysis. The implementation of continuity analysis on directional feature data can identify a continuous segment set, and the generated continuous segment set provides support for directional chain encoding. The implementation of directional chain encoding can convert contour information into a cutting path direction chain. The implementation of graph structure construction based on the cutting path direction chain can provide a framework for path planning. The obtained initial cutting path graph provides a data basis for subsequent path optimization. The implementation of optimal path search on the initial cutting path graph can identify the best cutting plan. The generated initial optimal path data provides a basis for collision detection. The implementation of collision detection based on the initial optimal path data can ensure cutting safety. The obtained safe path data provides support for final path integration. The implementation of data integration of safe path data and initial optimal path data can generate final cutting path data, which comprehensively improves the safety and effectiveness of lens laser cutting and ensures the efficiency and accuracy of the cutting process.

[0022] The present invention also provides an automatic layout and drawing system for lens laser cutting, which is used to execute the automatic layout and drawing method for lens laser cutting as described above. The automatic layout and drawing system for lens laser cutting includes: An image acquisition module is used to acquire an image of a lens to be cut; perform contour edge detection on the image of the lens to be cut to obtain an image edge curve; perform continuous contour discrete direction mapping on the image of the lens to be cut based on the image edge curve to generate a contour direction sequence; The model reconstruction module is used to perform polygon fitting on the contour direction sequence to obtain the screening contour data; perform contour three-dimensional modeling based on the screening contour data to generate a reconstructed lens model; The contour segmentation module is used to perform contour classification and clustering processing on the reconstructed lens model to obtain lens contour feature data; and to perform contour segmentation on the reconstructed lens model according to the lens contour feature data to obtain a segmented lens contour model; The layout design module is used to perform range-limited screening on the segmented lens contour model to obtain a compressed range contour model; perform local fine layout processing on the compressed range contour model to obtain an accurate layout solution; The path planning module is used to construct a direction chain according to the precise layout plan to obtain a cutting path direction chain; based on the cutting path direction chain, the cutting path is planned for the precise layout plan to generate cutting path data; The execution setting module is used to sequentially execute the setting of the cutting path data based on a preset blank drawing template to obtain a cutting layout template to execute the automatic layout drawing method for lens laser cutting.

[0023] The present invention can accurately obtain image information of the lens to be cut through the implementation of the image acquisition module, and can extract clear edge curves through the implementation of contour edge detection. The generated contour direction sequence provides a basis for subsequent modeling and cutting path planning. The implementation of polygon fitting of the contour direction sequence can optimize data processing, and the obtained screened contour data provides a basis for three-dimensional model reconstruction. The reconstructed lens model generated by the model reconstruction module can efficiently reflect the geometric characteristics of the lens. The implementation of the contour segmentation module processing the model through classification and clustering can accurately extract the feature data of the lens, and the obtained lens contour feature data provides support for segmentation. The range-limited screening of the layout design module can ensure the applicability of the model, and the generated compressed range contour model provides a basis for local fine typesetting. The implementation of local typesetting processing can optimize the layout plan, and the obtained precise layout plan provides a clear direction for subsequent path planning. The path planning module can ensure the rationality of the cutting path through the direction chain construction, and the generated cutting path data provides a basis for the final typesetting. The implementation of the execution setting module performing sequential execution settings through a blank drawing template can realize automatic typesetting, and the cutting typesetting template finally formed ensures the efficiency and accuracy of laser cutting, and improves the overall quality and efficiency of lens cutting. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a method for automatic layout and drawing for lens laser cutting; Figure 2 Detailed implementation flow chart of step S2; Figure 3 Detailed implementation flow chart of step S3.

[0025] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0028] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0029] To achieve this, please refer to Figures 1 to 3 , a method for automatic layout and drawing for lens laser cutting, comprising the following steps: Step S1: collecting an image of a lens to be cut; performing contour edge detection on the image of the lens to be cut to obtain an image edge curve; performing continuous contour discrete direction mapping on the image of the lens to be cut based on the image edge curve to generate a contour direction sequence; Step S2: polygon fitting is performed on the contour direction sequence to obtain screening contour data; three-dimensional contour modeling is performed based on the screening contour data to generate a reconstructed lens model; Step S3: performing contour classification and clustering processing on the reconstructed lens model to obtain lens contour feature data; performing contour segmentation on the reconstructed lens model according to the lens contour feature data to obtain a segmented lens contour model; Step S4: performing range-limited screening on the segmented lens contour model to obtain a compressed range contour model; performing local fine layout processing on the compressed range contour model to obtain an accurate layout solution; Step S5: constructing a direction chain according to the precise layout scheme to obtain a cutting path direction chain; planning a cutting path for the precise layout scheme based on the cutting path direction chain to generate cutting path data; Step S6: sequentially executing and setting the cutting path data based on a preset blank drawing template to obtain a cutting layout template to execute the automatic layout and drawing method for lens laser cutting.

[0030] The present invention can obtain accurate image information by collecting the image of the lens to be cut, and can extract clear edge curves by detecting the contour edge. The generated contour direction sequence provides a basis for subsequent processing. The polygon fitting of the contour direction sequence can optimize the contour data, and the obtained screened contour data provides a basis for three-dimensional modeling. The implementation of contour three-dimensional modeling based on the screened contour data can generate a high-precision reconstructed lens model. The implementation of classification and clustering processing on the reconstructed lens model can extract feature data. The generated lens contour feature data provides support for segmentation. The implementation of contour segmentation of the model according to the feature data can obtain a segmented contour model. The implementation of range-limited screening can ensure the applicability of the model. The generated compressed range contour model provides a basis for local typesetting. The implementation of local fine typesetting processing can optimize the layout plan. The obtained precise layout plan provides a clear direction for cutting path planning. The implementation of constructing a cutting path direction chain according to the layout plan can ensure the rationality of cutting. The generated cutting path data provides a basis for subsequent typesetting. The implementation of sequential execution setting based on a blank drawing template can realize automatic typesetting. The cutting typesetting template finally formed ensures the efficiency and accuracy of laser cutting.

[0031] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a method for automatic layout and drawing for laser cutting of lenses of the present invention. In this example, the method for automatic layout and drawing for laser cutting of lenses includes the following steps: Step S1: collecting an image of a lens to be cut; performing contour edge detection on the image of the lens to be cut to obtain an image edge curve; performing continuous contour discrete direction mapping on the image of the lens to be cut based on the image edge curve to generate a contour direction sequence; In this embodiment, an image of the lens to be cut is obtained by a high-resolution industrial camera or an image acquisition system, ensuring that the resolution of the image is at least 300 dpi to ensure accurate capture of details, and then the Canny edge detection algorithm is used to process the image to extract edge information of the lens image. After secondary Gaussian smoothing, a clear image edge curve is obtained, and then the image is divided into background and contour parts by an adaptive threshold segmentation method, and a precise contour curve is further extracted. Subsequently, the extracted contour edge curve is discretized, and the curve is fitted using the least squares method and the points are evenly distributed to ensure that the interval of each discrete point remains consistent. Then, according to the edge curve of the image, the direction of the contour is discretely mapped by the normal direction method to generate a contour direction sequence containing all discrete direction angles. The formed sequence is represented by angle information, which describes the direction change of each cutting point and provides basic data for subsequent path planning.

[0032] Step S2: polygon fitting is performed on the contour direction sequence to obtain screening contour data; three-dimensional contour modeling is performed based on the screening contour data to generate a reconstructed lens model; In this embodiment, polygon fitting is performed on each direction angle data in the contour direction sequence, and it is converted into a polygonal contour by a minimum error method. A polygon simplification algorithm is used to remove redundant points, and only key points that can best express the lens contour are retained. Based on the contour data screened out, a closed polygon is generated as a basis for subsequent three-dimensional modeling. Subsequently, the polygonal contour data is mapped to a three-dimensional coordinate system, and three-dimensional modeling software (such as a CAD tool) is used to convert these data into a three-dimensional model. According to parameters such as the thickness and curvature of the lens, the three-dimensional shape of the lens is accurately modeled, and its actual size and appearance are simulated to complete the generation of a three-dimensional reconstructed lens model. The entire modeling process is based on standardized parameters and fitting data. By optimizing the grid division, the accuracy and stability of the model are guaranteed. The final three-dimensional model meets the cutting requirements and accurately represents the shape and contour of the lens.

[0033] Step S3: performing contour classification and clustering processing on the reconstructed lens model to obtain lens contour feature data; performing contour segmentation on the reconstructed lens model according to the lens contour feature data to obtain a segmented lens contour model; In this embodiment, after the reconstructed lens model is generated, the various parts of the contour are first classified using the K-means clustering algorithm. The contour of the lens is divided into several categories based on the size, shape, edge curvature and other characteristics of the lens. The contour features of different categories are statistically analyzed to extract the shape features of the lens, including common contour shapes such as circle, ellipse, and rectangle. The size of each contour part is further standardized to ensure that all contour data have a consistent comparison benchmark. Then, according to the obtained contour feature data, the reconstructed lens model is segmented. Based on the contour feature value, the segmented cutting algorithm is used to divide the lens contour into multiple parts suitable for laser cutting. The cutting path of each part is appropriately simplified or refined according to the complexity of its contour to reduce the phenomenon of the cutting path being too long or too short. The segmented lens contour model finally obtained can effectively guide the subsequent cutting path generation.

[0034] Step S4: performing range-limited screening on the segmented lens contour model to obtain a compressed range contour model; performing local fine layout processing on the compressed range contour model to obtain an accurate layout solution; In this embodiment, the segmented lens contour model is subjected to range-limited screening. By setting a predetermined area range parameter, the portion of the lens contour that exceeds the range is screened out, and only the portion of the lens that meets the laser cutting process range is retained. A computational geometry algorithm is used to determine the effective cutting area of ​​the lens, and irrelevant portions outside the model are excluded through a screening algorithm, thereby obtaining a compressed range contour model. The model only retains the lens area suitable for the current cutting task. Next, the compressed range contour model is subjected to local fine layout processing. A heuristic layout algorithm is used to adjust the relative positions of each cutting portion through a local optimization method according to the shape features of each lens cutting area, maximize the use of cutting materials, ensure that each cutting path does not overlap and is appropriately spaced, and thereby obtain an accurate layout solution. The solution takes into account the material and thickness of the lens and the power distribution of the laser to ensure high efficiency and high precision of laser cutting.

[0035] Step S5: constructing a direction chain according to the precise layout scheme to obtain a cutting path direction chain; planning a cutting path for the precise layout scheme based on the cutting path direction chain to generate cutting path data; In this embodiment, a direction chain of the cutting path is constructed, and the cutting paths are connected in sequence to form a direction chain according to the relative position and direction of each cutting area. A path tracking algorithm is used to ensure the rationality of the cutting sequence and minimize the movement time of the laser cutting machine. A path chain of each lens cutting part is constructed through a shortest path algorithm (such as a Dijkstra algorithm). Subsequently, a cutting path is planned for each lens area according to the cutting path direction chain. The starting point, end point and turning point of each cutting path and the turning point on the path are determined through an optimization algorithm to ensure continuity and the optimal path during the cutting process, and to generate cutting path data. The path data includes information such as the coordinates of each cutting point, the path direction and laser parameters, to ensure that the cutting machine can accurately cut according to the planned path during execution.

[0036] Step S6: sequentially executing and setting the cutting path data based on a preset blank drawing template to obtain a cutting layout template to execute the automatic layout and drawing method for lens laser cutting.

[0037] In this embodiment, a pre-set blank drawing template is used, and the cutting path data is automatically matched with the blank template by calibrating the blank area in the template and the lens cutting area to ensure that the cutting path can meet the predetermined layout requirements. The cutting path data is sequentially executed and set using a path sorting algorithm to ensure that the path sequence of the cutting machine is consistent with the layout of the drawing template. Through the sequential execution setting, each cutting path is docked with the blank area in the preset template, and the path starting point and end point are adjusted according to the specific size and shape of the lens. Finally, a layout template that meets the cutting requirements is generated. The template can be directly imported into the laser cutting control system to ensure that the execution of the cutting path meets the cutting requirements of the lens. Each path in the layout template is finely optimized to ensure that the cutting machine can complete the task with the minimum time cost and the highest accuracy during the cutting process.

[0038] Preferably, step S1 comprises the following steps: Step S11: collecting an image of the lens to be cut; performing brightness balance correction on the image of the lens to be cut to obtain a brightness balanced image; Step S12: performing bilateral filtering to reduce noise on the brightness-balanced image to generate a reduced-noise lens image; performing contour edge detection on the reduced-noise lens image to obtain an image edge curve; Step S13: Encode the edge curve of the image using eight-direction chain codes to obtain a direction code sequence; Step S14: performing continuous contour discrete direction mapping on the image of the lens to be cut according to the direction coding sequence to generate a contour direction sequence.

[0039] In this embodiment, a high-resolution industrial camera (for example, a CCD camera with a resolution of 5000x5000) is used to shoot the lens to be cut to ensure that the lens image can cover its entire appearance. At the same time, a uniform light source system is used to avoid image quality problems caused by uneven lighting. When shooting, the camera shutter speed is 1 / 1000 second, and the ISO value is set to 100 to reduce image noise. After obtaining the lens image, adaptive histogram equalization (CLAHE, Contrast Limited AdaptiveHistogram Equalization) is used to perform brightness balance correction on the image. During the correction process, the size of each small area (tile) is selected as 32x32 pixels. By adjusting the local contrast, the brightness distribution of the image is ensured to be uniform. Bilateral filtering (Bilateral) is applied to the lens image after brightness balance. Filter) denoising, the filter parameters are set as a spatial neighborhood radius of 5 pixels, and an intensity value standard deviation of 50. Bilateral filtering effectively removes noise in the image by retaining edge information. The filtered image is smooth without losing details. Next, the Canny edge detection algorithm is used for edge detection. The threshold of the Canny algorithm is set to 100 and 200. First, the image is Gaussian filtered (Gaussian kernel size is 5x5, standard deviation is 1.4) Remove the residual noise, then calculate the gradient value of the image, perform non-maximum suppression, and finally obtain the edge curve of the lens image through thresholding. The obtained edge curve shows the precise shape of the lens contour. Based on the edge curve of the image, the eight-direction chain code (8-direction Chain Code) is used for encoding. First, the edge curve is discretized into a series of pixel points, the reference direction is set to "up", each edge point is connected to its adjacent edge point, and its direction is calculated and marked. Eight directions are used for encoding, namely: 0 degrees (right), 45 degrees (upper right), 90 degrees (upper), 135 degrees (upper left), 180 degrees (left), 225 degrees (lower left), 270 degrees (lower), 315 degrees (lower right). For each edge point, a coding value between 0 and 7 is assigned according to its positional relationship with the previous point. The generated direction coding sequence can accurately describe the direction and turning point of each edge point, forming a complete chain code sequence. Based on direction coding Sequence, by combining the chain code sequence with the coordinate information of the image, the contour discrete direction mapping is performed. First, the image area corresponding to each chain code is analyzed, and the corresponding direction vector is constructed on the coordinate plane according to the code value to determine the direction change of each point relative to its previous point. Combined with the spatial position of the image and the curvature of the contour curve, each direction chain code is mapped to a continuous contour direction in sequence. In this way, a continuous contour direction sequence is obtained. The generated direction sequence shows the change trend of the lens contour in different directions, which can effectively reflect the geometric shape and cutting path of the lens contour, ensuring that the subsequent laser cutting path planning can be efficiently optimized based on this data. .

[0040] Preferably, step S2 comprises the following steps: Step S21: performing density cluster analysis on the contour direction sequence to obtain key point distribution data; performing point distance calculation on the key point distribution data to generate a point spacing matrix; Step S22: performing threshold segmentation processing on the contour direction sequence according to the point spacing matrix to generate segment boundary data; performing polygon fitting on the contour direction sequence based on the segment boundary data to obtain filtered contour data; Step S23: extracting geometric features from the screened contour data to obtain a contour geometric feature description set; performing contour depth mapping based on the contour geometric feature description set to generate depth mapping data; Step S24: performing anisotropic diffusion filtering on the depth mapping data to obtain a smooth depth map; performing contour three-dimensional modeling on the screened contour data based on the smooth depth map to generate a reconstructed lens model.

[0041] In this embodiment, according to the characteristics of the contour direction sequence, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm for density clustering, setting the minimum sample number to 4 and the distance threshold to 0.1 pixels. Density clustering can classify adjacent direction points into the same category based on the distance relationship between the points, and obtain the key points of each cluster. The key point is the center point in the cluster and represents the significant feature of the contour change. Then, based on the position of the key points, the distance between the points is calculated, and the Euclidean distance between each pair of key points is calculated to generate a point spacing matrix, which reflects the relative position relationship between the key points. The matrix size is the square of the number of key points. Each element in the matrix represents the distance between the corresponding key point pairs. A distance threshold (for example, 0.3 pixels) is set to determine whether each element in the point spacing matrix exceeds the threshold. If the distance is greater than the threshold, it is regarded as a segmented boundary of the contour, and the start and end points of the segment are recorded to obtain the segmented boundary data. Based on these segmented boundaries, the least squares method is used to perform polygon fitting on each contour direction sequence, and the degree of the fitted polygon is selected as 3 (i.e., triangle). Each contour segment is Polygon fitting is performed to obtain a set of fitted points. Then, contour data that meets the requirements is screened out based on the fitting results. The screening criteria are that the fitting error is less than 0.05 pixels. Data that does not meet the contour characteristics and has a large fitting error are removed to obtain screened contour data. The boundary points of each contour are extracted using an edge detection algorithm. The perimeter, area, compactness, curvature and other geometric features of each contour are calculated. The perimeter is calculated as the total length of the line segments between the contour points, the area is calculated as the total number of pixels inside the contour, the compactness is expressed by the ratio of the perimeter to the area of ​​the contour, and the curvature is measured by calculating the curvature change of each contour point. A series of geometric feature data are obtained to form a contour geometric feature description set. Then, based on these geometric features, contour depth mapping is performed. The deep learning algorithm is used to perform spatial mapping of contour features. The input of the network is set as the geometric feature description set, and the output is the depth mapping data. The depth mapping data is the depth information of each contour point in three-dimensional space, which can accurately describe the spatial position and shape of each point. The diffusion coefficient is set to 0.5. Adjust the diffusion intensity of each point in different directions, and perform directional smoothing based on the gradient information of the pixel points. Diffusion filtering can effectively suppress noise based on edge information while retaining the details of the contour. By setting the number of iterations to 20, multiple diffusion processes are performed to finally obtain a smooth depth map. Next, based on the smooth depth map, a 3D reconstruction algorithm (such as the Marching Cubes algorithm) is used for 3D modeling. By combining the contour data with the depth information, a 3D lens model is generated. When 3D modeling is performed, the depth map data is first converted into a mesh model, and the mesh is subdivided into multiple small triangles with an accuracy of 1 pixel. On this basis, the 3D contour is reconstructed by solving the normal vector, and finally a high-precision 3D lens model is generated, providing accurate geometric data for subsequent laser cutting path planning. .

[0042] Preferably, step S22 includes the following steps: Perform feature statistics processing on the point spacing matrix to obtain distance feature data; perform dynamic threshold calculation based on the distance feature data to generate a threshold parameter set; Perform sliding window analysis on the contour direction sequence to obtain local feature data; perform direction change rate analysis based on the local feature data to generate a direction change rate sequence; Perform peak detection on the direction change rate sequence to obtain a set of change rate key points; perform matching and screening on the set of change rate key points and the set of threshold parameters to generate candidate segmentation points; Perform threshold segmentation processing on the contour direction sequence according to the candidate segmentation points to generate segmentation boundary data; Perform feature geometry analysis on segmented boundary data to obtain feature geometry attributes; construct fitting constraints based on the feature geometry attributes to generate a constraint condition set; Perform polygon fitting on the contour direction sequence to obtain an initial fitting result; perform error calculation on the initial fitting result according to the constraint condition set to obtain error index data; Angle adjustment optimization is performed based on the error index data to obtain an optimized fitting result; and contour screening is performed on the optimized fitting result to obtain screened contour data.

[0043] In this embodiment, each distance value in the point spacing matrix is ​​summed and the standard deviation is calculated, and the average distance and standard deviation between each point are statistically calculated to obtain distance feature data. The statistical average distance value reflects the typical spacing between contour points, and the standard deviation reflects the discrete degree of the distance between points. Based on these data, an adaptive algorithm is used to calculate the dynamic threshold value, and the threshold parameter calculation rule is set as follows: if the average distance is greater than twice the standard deviation, the distance value is considered to be a valid segmentation threshold. A series of threshold parameter sets are obtained through analysis, and the window size of the sliding window is set to 5 data points. The contour direction sequence is scanned step by step through the sliding window. At each sliding, the direction difference of the data points in the window is calculated, and its local feature data is extracted. The local feature The feature data includes the average direction value and standard deviation in the current window. Through these feature data, the local change information of the contour can be captured. Then, the direction change rate analysis is performed based on the local feature data. The direction change rate is defined as the ratio of the maximum direction change to the minimum direction change in the window. In this way, a direction change rate sequence is obtained, which reflects the degree of change of the contour direction in different intervals. The part with larger changes is marked as an important contour feature. The direction change rate sequence is peak detected, and the direction change rate sequence is processed using a peak detection algorithm (such as the second-order difference method). By detecting the peak points in the sequence, a collection of change rate key points is obtained. These key points represent areas with drastic direction changes and indicate important turning points in contour changes. Then , match and screen the set of change rate key points with the previously calculated threshold parameter set. During the matching process, check whether each change rate key point exceeds the corresponding threshold parameter. If it meets the conditions, it will be used as a candidate segmentation point. If it does not meet the conditions, it will be excluded to obtain the candidate segmentation point. According to the position of each candidate segmentation point, the contour direction sequence is cut, and the original sequence is divided into multiple subsequences according to the segmentation point position. In each subsequence, the local direction change is calculated. If the direction change in the subsequence exceeds the set threshold, the subsequence is considered to be a valid segmentation. Finally, the segmentation boundary data of the contour direction sequence is obtained. The segmentation boundary data includes the start and end positions of each subsequence, identifying different areas of the contour. Domain, local geometric features are extracted for each segmented area, and the perimeter, area, compactness, curvature and other features of each segment are calculated. The perimeter is the total length of the line connecting the boundary points of each segment, the area is the total number of pixels in the segment, the compactness is the ratio of the perimeter to the area, and the curvature is calculated by the angle change of the segment boundary. Through these geometric features, the characteristic geometric properties of each segment are obtained. Then, based on these geometric properties, fitting constraints are constructed and constraint condition sets are set. For example, the ratio of the perimeter to the area of ​​the segment is required not to exceed a certain threshold, and the curvature must not exceed a specific range. The least squares method is used for fitting, and the polygon degree of the fitting is set to 3 (i.e., triangle). Polygon fitting is performed on each segmented data to obtain the initial fitting result of each segment.The initial fitting result is the coordinates of the polygon vertices of each segment. Through this process, a rough contour shape is obtained. Then, according to the previously constructed constraint set, the initial fitting result is calculated for error. The error between the fitting result and the actual contour is calculated. By calculating the error index (such as mean square error, maximum error, etc.), the error index data is obtained. The optimization goal is set to minimize the fitting error. The vertex angle of the polygon is adjusted through the optimization algorithm (such as gradient descent method). During the optimization process, the angle parameters of the fitting polygon are gradually changed to minimize the error index and obtain the optimized fitting result. The optimized fitting result has higher accuracy and can express the contour shape more accurately. Then, the optimized fitting result is screened for contours. The screening standard is that the fitting error is less than 0.05 pixels. Through this screening step, the fitting results that do not meet the accuracy requirements are eliminated, and finally the screened contour data is obtained, which provides accurate contour information for subsequent depth mapping and three-dimensional modeling. ,

[0044] Preferably, step S3 comprises the following steps: Step S31: locating edge points of the reconstructed lens model to obtain a model edge point set; calculating the curvature of the model edge point set to obtain a curvature feature vector; Step S32: performing principal component analysis on the reconstructed lens model according to the curvature feature vector to obtain dimension-reduced feature data; performing mean clustering processing on the dimension-reduced feature data to generate lens profile feature data; Step S33: positioning the segmentation key points of the lens profile feature data to obtain segmentation key point data; Step S34: Perform contour segmentation on the reconstructed lens model based on the segmentation key point data to obtain a segmented lens contour model.

[0045] In this embodiment, edge point data is extracted from the reconstructed model, and the two-dimensional image of the lens model is processed using the Canny edge detection algorithm. The edge detection thresholds are set to 0.3 and 0.6 to ensure that accurate edge points are detected and a complete model edge point set is obtained. Then, the curvature of the model edge point set is calculated, and the discretized second-order derivative method is used to calculate the curvature value of each edge point. The curvature calculation formula is: ; in For the The curvature value of a point, For the The curvature feature vector calculated by this formula includes the curvature information of each edge point. The curvature feature vector reflects the curvature degree of the lens edge. The shape and structural characteristics of the lens model can be identified by analyzing this vector. The curvature feature vector is matrixed and the PCA algorithm is applied to reduce its dimension. The goal of PCA is to reduce high-dimensional curvature feature data to two dimensions, reduce feature dimensions, and improve data processing efficiency. In the PCA process, the first two principal components (the direction of maximizing variance) are selected as the feature data after dimension reduction. The obtained dimension reduction feature data contains the main geometric information of the lens model. Then, the dimension reduction feature data is mean clustered. The K-means clustering algorithm is used to perform cluster analysis on the dimension reduction data. The number of clusters is set to 3. The mean of the data points is iteratively updated to finally obtain the lens contour feature data. The feature data contains the contour information of each part of the lens. The feature data of the lens contour is scanned, and the dynamic programming algorithm is used to determine the position of the key cutting point according to the geometric characteristics of the contour. The algorithm determines each cutting point by minimizing the difference in contour curvature change and length. The threshold is set to 5 pixel units. When the curvature change of two adjacent points is greater than the set threshold, it is marked as a key point. The calculation process uses the following formula: ; in, Indicates the curvature change between the current point and the previous key point. When the change is greater than the threshold, The position is the segmentation point. After completing this step, the segmentation key point data of the lens contour is obtained. These key points represent the significant turning points of the lens contour and can effectively segment different areas of the lens contour. According to the position of the segmentation key point, a segmentation line is drawn on the edge point set of the lens model, and each segment of the contour is segmented using a polynomial fitting method. The specific steps are to take the contour points between every two adjacent key points and fit these points using quadratic or cubic spline interpolation to obtain an accurate contour segmentation line. Each segmented contour is a local area of ​​the lens. Then, the segmented local areas are arranged in order to obtain a segmented lens contour model. Each local area represents a part of the lens. The final generated segmented lens contour model contains the complete geometric shape and features of the lens, providing accurate contour data for subsequent laser cutting path planning.

[0046] Preferably, step S4 comprises the following steps: Step S41: limiting the scope of the segmented lens contour model by an envelope frame to obtain a collection of boundary frames; performing area optimization processing on the collection of boundary frames to generate compressed boundary data; Step S42: dividing the feasible area according to the compressed boundary data to obtain a limited feasible area; performing range-limited screening on the segmented lens contour model based on the limited feasible area to obtain a compressed range contour model; Step S43: meshing the compressed range contour model to obtain lens contour mesh data; performing layout cost calculation on the lens contour mesh data to obtain a layout cost matrix; Step S44: Perform local fine layout processing according to the layout cost matrix to obtain an accurate layout solution.

[0047] In this embodiment, when the envelope range of the segmented lens contour model is limited, the minimum circumscribed rectangular frame of the contour is first identified. The four sides of the rectangular frame are parallel to the farthest boundary point of the contour and tightly surround the contour. The size of the bounding box is determined by extracting the boundary point set of the contour. The generation of the bounding box depends on the envelope algorithm, such as using the convex hull algorithm to calculate the circumscribed rectangle. The calculation formula is: ; in, are the four boundary values ​​of the bounding box. The generated envelope box collection is the set of rectangular boxes containing all contours. Then the bounding box is optimized in area. The goal is to reduce redundant areas. The optimization process includes minimizing the area of ​​each envelope box and removing unnecessary blank areas. The adjustment is made by calculating the ratio of the area of ​​each box to the actual size of the lens. The obtained compressed boundary data reflects the minimum range after optimization. The area is divided using a two-dimensional space partitioning algorithm. A common method is grid-based space segmentation. The entire space is divided into multiple grid units, and the size of each unit is adjusted according to the compressed boundary data to ensure that the divided area meets the actual needs. The feasible area of ​​the division is determined by screening the overlapping parts between the bounding boxes, and finally a limited feasible area is obtained. Then, the segmented lens contour model is range-limited and screened according to the area. The valid contours within the limited area are selected and the parts that exceed or do not meet the size requirements are removed. The compressed range contour model is thus generated. The model only contains the contour parts that have been screened. According to the physical size of the lens and the required cutting accuracy, the model is divided into several uniform small grid units. By defining the side length of the grid as To achieve grid division, the number of grids The calculation is: ; in, is the total area of ​​the lens model, The side length of each grid unit is obtained after grid division to obtain the lens contour grid data. Next, the layout cost of the grid data is calculated. The cost calculation formula takes into account factors such as the distance between each grid, cutting efficiency, and material waste. Among them, the cost matrix The calculation method is: ; in, for Grid and The layout cost between grids, is the distance between the grids, For material waste, and The weight factor is used to optimize the layout according to the cost matrix to obtain the best layout solution. The heuristic optimization algorithm, such as the genetic algorithm or the simulated annealing algorithm, is used to locally adjust the grid. The optimized layout solution reduces the overall cost of the cost matrix by adjusting the grid position. The adjustment process takes into account the reasonable layout of the cutting area, the optimization of the edge and the minimization of the equipment cutting time. The parameters in the calculation process include the cutting time of each grid unit, the equipment accuracy and the floor space of each grid. The final generated accurate layout solution is obtained in the shortest time through a continuous optimization process. By calculating the final layout cost and efficiency, the feasibility of the accurate layout solution is confirmed to achieve the final laser cutting layout design.

[0048] Preferably, step S42 includes the following steps: Gridding the compressed boundary data to obtain boundary grid data; marking obstacles on the boundary grid data to generate obstacle grid data; Based on the obstacle grid data, the connected domain of the boundary grid data is identified to obtain a regional connected graph; the boundary of the regional connected graph is extracted to generate a limited feasible area; Performing position mapping on the limited feasible area according to the segmented lens contour model to obtain position mapping data; performing boundary detection on the position mapping data to obtain boundary constraint data; Perform feasibility verification based on boundary constraint data to obtain verification result data; optimize the range of the limited feasible area based on the verification result data to obtain a compressed feasible area; The compression feasible area is regionally integrated to generate a compression range contour model.

[0049] In this embodiment, the compressed boundary data is mapped to a two-dimensional grid space, and a suitable grid unit size is selected, for example, set to l×l. This size needs to be determined according to the actual situation. Usually, a size that can balance the calculation accuracy and calculation time is selected. Then, the boundary area is divided into multiple grid units, and which grid units contain the boundary are marked. Then, according to the characteristics of the actual boundary area, the image recognition technology is used to subdivide the boundary, and the obstacle position is gradually identified, such as irregular defects on the lens or parts that are discontinuous with the surrounding area. The grid units corresponding to these areas will be marked as obstacle grids. The accuracy of obstacle marking is optimized by image processing technology, and finally the obstacle grid data is obtained. After obtaining the obstacle grid data, the connected domain is identified. By using the adjacency rule (such as the 8-neighborhood rule), it is checked whether the neighborhood of each grid unit is connected, and then all connected areas are identified. In order to achieve this, a depth-first search (DFS) or breadth-first search (BFS) algorithm is used to traverse all grid units, merge all grid units belonging to the same area, and form a regional connectivity graph. Each node in the connectivity graph represents a connected area, and the edge represents the connection relationship between areas. When the connectivity graph is constructed, the boundary position, shape and area of ​​each area will be recorded in detail. Then, the boundary extraction of the connectivity graph is performed. With the help of image contour extraction technology, the actual boundary information is extracted from each area to obtain the limited feasible area. The contour data of the lens is converted into the grid coordinate system. The affine transformation method is used to perform position mapping according to the key points in the model (such as contour corners or center points). This mapping process ensures that the spatial position relationship between the lens contour and the grid is accurate and obtains the position mapping data. Then, the mapped data is subjected to boundary detection. The distance between the lens contour and the grid boundary is calculated using algorithms such as the Sobel operator or the Canny edge detection algorithm, and boundary constraint data is generated. By calculating the shortest distance of the cutting path and matching it with the gridded area, it is checked whether the cutting path will cross obstacles or exceed the boundary of the feasible area. The collision detection uses the ray-polygon intersection detection algorithm to determine the interaction between the path and obstacles or boundaries. If the path is valid, it means that the feasibility verification has passed; otherwise, the path is readjusted. According to the verification result data, the scope of the limited feasible area is optimized. The boundary of the feasible area is optimized through the particle swarm optimization algorithm (PSO), the shortest distance of the path is accurately adjusted, unnecessary blank areas are removed, and the cutting paths are gathered into the available area as much as possible to generate a compressed feasible area. The minimum spanning tree (MST) algorithm is used to merge adjacent compressed areas into an overall area to ensure the continuity and accessibility of the integrated area. At this time, multiple originally independent areas will be merged into a large area to remove redundant space.The resulting compressed range contour model provides accurate geometry for subsequent laser cutting path planning, ensuring that the cutting process can be carried out efficiently and accurately.

[0050] Preferably, step S44 includes the following steps: Optimize the objective function according to the layout cost matrix to obtain the objective optimization function; perform initial layout simulation according to the objective optimization function to obtain a simulated initial layout; Perform conflict detection on the simulated initial layout to obtain layout conflict points; perform local adjustment on the compressed range contour model based on the layout conflict points to obtain a local adjustment plan; Based on the preset layout scoring standard, the local adjustment plan is evaluated for layout to obtain a layout score; the layout score is searched locally for high scores to obtain an optimized adjustment plan; According to the optimization adjustment plan, the boundary is fine-tuned and optimized to obtain the boundary optimization layout; the data of the boundary optimization layout is integrated to generate an accurate layout plan.

[0051] In this embodiment, the objective function is optimized according to the layout cost matrix, and a mathematical programming method is used to set each element of the layout cost matrix to represent the cost of the cutting path or component position in the layout solution. An optimization algorithm, such as a genetic algorithm (GA) or a particle swarm optimization algorithm (PSO), is used to find the optimal layout solution. The objective optimization function minimizes the total value of the cost matrix or achieves optimization goals such as the shortest path and the minimum cutting loss. After multiple iterations, the objective optimization function is obtained. Then, an initial layout simulation is performed based on the objective optimization function. A preliminary layout is simulated by preliminarily arranging each lens contour. A simulated annealing algorithm is usually used to generate the initial layout. The initial temperature and the cooling rate are set. According to the objective optimization function, the position of each lens contour is gradually adjusted to finally generate a simulated initial layout. The step of simulating the initial layout includes preliminarily arranging the lens contour according to the generated objective optimization function according to the space restriction, and calculating the simulated initial layout result. The simulated initial layout is subjected to collision detection using a collision detection algorithm, such as a rectangular bounding box collision detection (AABB, Axis-Aligned Bounding Box), detect the lens contours in the simulated initial layout, find out which lens contours are overlapped or inappropriately contacted, and the specific steps include detecting the distance between each two lens contours, calculating their boundary intersections, identifying and recording all conflict points, and obtaining layout conflict point data. Then, the compression range contour model is locally adjusted according to the layout conflict points. The local adjustment scheme is generated by moving and rotating the lens contours around the conflict area, and using heuristic algorithms such as local search. The method of search) is used to adjust the layout around each conflict point to eliminate the conflict and optimize the space utilization, and a local adjustment plan is obtained. The specific adjustment method can be to exchange the position, rotate the angle or scale the ratio to ensure that the contours of each lens do not overlap and minimize the gap. The layout of the local adjustment plan is evaluated based on the preset layout scoring standard. The layout scoring standard can include space utilization, cutting path length, lens gap size, etc. These factors are combined using a weighted scoring algorithm to obtain a layout score. In specific implementation, the score of the current layout is calculated by combining the position and shape of each lens with the gap data. A layout with a high score indicates good space utilization, a shorter cutting path and no conflict. Then, a high-scoring local search is performed on the layout score, and a local search strategy (such as simulated annealing or local optimal search) is used to further optimize the plan with a higher layout score. During the search process, the local area is repeatedly adjusted and the details of the local adjustment are increased, so that the layout score is continuously improved, and finally an optimized adjustment plan is obtained. During the optimization process, multiple simulations and evaluations are performed to approach the optimal solution, and boundary fine-tuning optimization is performed according to the optimized adjustment plan.Boundary fine-tuning mainly involves fine-tuning the boundaries of each lens outline in the layout to ensure that the gap between each lens is minimal and the distance between the lenses and the cutting path is reasonable. Fine-tuning is minimized through mathematical models such as quadratic programming. The geometric characteristics of each lens outline and the distance constraints with the surrounding lenses are taken into account during the optimization process to ensure that the space utilization rate is further improved and the cutting path is optimized without introducing new conflicts. The fine-tuned boundary data is carefully adjusted to form a new layout model to ensure that it meets the cutting requirements and process standards. Finally, the boundary optimization layout is integrated, and all optimized layout information is uniformly encoded to form a complete data set, which is convenient for subsequent laser cutting control systems to read and execute, and finally generate an accurate layout plan that can be directly input into the laser cutting equipment. ,

[0052] Preferably, step S5 comprises the following steps: Step S51: performing contour vectorization on the precise layout scheme to obtain contour vector data; performing directional feature analysis based on the contour vector data to obtain directional feature data; Step S52: Analyze the continuity of the directional feature data to obtain a continuous segment set; perform direction chain encoding on the continuous segment set to generate a cutting path direction chain; Step S53: constructing a graph structure based on the cutting path direction chain to obtain an initial cutting path graph; searching for an optimal path on the initial cutting path graph to obtain initial optimal path data; Step S54: performing collision detection based on the initial preferred path data to obtain safe path data; integrating the safe path data and the initial preferred path data to generate cutting path data.

[0053] In this embodiment, the precise layout scheme is vectorized to extract the cutting contour of the lens, and the boundary of each contour is converted into a vector representation. Curve) or spline curve (SplineCurve) fits each part of the contour, and uses a vector point set to represent the boundary of the contour. Line segments are used to connect these points to ensure accuracy and smoothness, and complete contour vector data is obtained. Then, directional feature analysis is performed based on the contour vector data. The directional feature data is obtained by calculating the angle between the direction of each vector segment and the adjacent segment. Specifically, the vector calculation formula is used to analyze each pair of adjacent line segments, calculate the angle value, and determine its direction change to obtain the directional feature of each cutting path segment. The generation of directional feature data includes angle calibration for each line segment. By calculating the change trend of the angle between adjacent line segments, the directional information of the continuous cutting path is obtained. The directional feature data is analyzed for continuity to check whether there is a continuous change between each directional feature data. The sliding window technology is used to analyze the directional data. First, a threshold is set to determine the continuity of the direction. If the angle change between two adjacent directional features is less than the preset threshold, it is considered that the two directional features belong to the same continuous segment. Otherwise, it means that a direction change has occurred. A continuous segment set is further obtained. Then, the continuous segment is The segment set is directional chain encoded to generate a cutting path directional chain. The directional chain encoding forms a continuous direction identification by mapping each continuous segment to a unique coding value. In specific implementation, a hash algorithm is used to encode the direction change of each continuous segment. The path segments are numbered according to the direction chain according to the change of direction, so that all direction chains on each cutting path are connected in sequence to form a complete cutting path direction chain. Based on the cutting path direction chain, a graph structure is constructed. Each cutting path is regarded as a node, and the connection relationship of the direction chain is regarded as an edge. A directed graph is constructed. In this graph, each node represents a cutting path segment, and the edge represents the connection between the path segments. The node connection order of the graph is set according to the actual arrangement of the cutting path to obtain an initial cutting path graph. Then, the initial cutting path graph is searched for the preferred path, and the shortest path algorithm (such as Dijkstra algorithm) is used to optimize the path selection. By calculating the cost of each path (such as time, distance, cutting complexity, etc.), the path with the lowest cost is selected as the preferred path data. In specific implementation, the cost of each path in the graph is first calculated. The cost calculation formula is: ; in, represents the length of the path segment, represents the time consumption of the path segment, and is the weight coefficient. The total cost of the path is obtained through this formula. The path with the smallest cost is selected for further processing to obtain the initial preferred path data. Through geometric collision detection algorithms, such as AABB (Axis-Aligned Bounding Box) or spherical collision detection, check whether there is a collision with other cutting paths or lens contours in the path. The cutting path and lens contour are divided into multiple small units in a gridding manner. Check whether there is an intersection in each unit. By detecting the relative position relationship between the path segments, the safe path data is obtained. Then, the safe path data and the initial preferred path data are integrated, and the preferred path is combined with the safe path for the final optimization processing. In specific implementation, the path segments are combined in sequence to form complete cutting path data. At the same time, according to the actual cutting requirements, the path sequence is adjusted and the path bending angle is optimized to ensure that while ensuring the safety of the path, unnecessary movement is reduced. Finally, the cutting path data is generated and output in a format that can be recognized by the cutting machine, such as G code.

[0054] The present invention also provides an automatic layout and drawing system for lens laser cutting, which is used to execute the automatic layout and drawing method for lens laser cutting as described above. The automatic layout and drawing system for lens laser cutting includes: An image acquisition module is used to acquire an image of a lens to be cut; perform contour edge detection on the image of the lens to be cut to obtain an image edge curve; perform continuous contour discrete direction mapping on the image of the lens to be cut based on the image edge curve to generate a contour direction sequence; The model reconstruction module is used to perform polygon fitting on the contour direction sequence to obtain the screening contour data; perform contour three-dimensional modeling based on the screening contour data to generate a reconstructed lens model; The contour segmentation module is used to perform contour classification and clustering processing on the reconstructed lens model to obtain lens contour feature data; and to perform contour segmentation on the reconstructed lens model according to the lens contour feature data to obtain a segmented lens contour model; The layout design module is used to perform range-limited screening on the segmented lens contour model to obtain a compressed range contour model; perform local fine layout processing on the compressed range contour model to obtain an accurate layout solution; The path planning module is used to construct a direction chain according to the precise layout plan to obtain a cutting path direction chain; based on the cutting path direction chain, the cutting path is planned for the precise layout plan to generate cutting path data; The execution setting module is used to sequentially execute the setting of the cutting path data based on a preset blank drawing template to obtain a cutting layout template to execute the automatic layout drawing method for lens laser cutting.

[0055] The present invention can accurately obtain image information of the lens to be cut through the implementation of the image acquisition module, and can extract clear edge curves through the implementation of contour edge detection. The generated contour direction sequence provides a basis for subsequent modeling and cutting path planning. The implementation of polygon fitting of the contour direction sequence can optimize data processing, and the obtained screened contour data provides a basis for three-dimensional model reconstruction. The reconstructed lens model generated by the model reconstruction module can efficiently reflect the geometric characteristics of the lens. The implementation of the contour segmentation module processing the model through classification and clustering can accurately extract the feature data of the lens, and the obtained lens contour feature data provides support for segmentation. The range-limited screening of the layout design module can ensure the applicability of the model, and the generated compressed range contour model provides a basis for local fine typesetting. The implementation of local typesetting processing can optimize the layout plan, and the obtained precise layout plan provides a clear direction for subsequent path planning. The path planning module can ensure the rationality of the cutting path through the direction chain construction, and the generated cutting path data provides a basis for the final typesetting. The implementation of the execution setting module performing sequential execution settings through a blank drawing template can realize automatic typesetting, and the cutting typesetting template finally formed ensures the efficiency and accuracy of laser cutting, and improves the overall quality and efficiency of lens cutting.

[0056] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0057] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for automatic layout and drawing for lens laser cutting, characterized in that: The following steps are involved: Step S1: collecting an image of a lens to be cut; performing contour edge detection on the image of the lens to be cut to obtain an image edge curve; performing continuous contour discrete direction mapping on the image of the lens to be cut based on the image edge curve to generate a contour direction sequence; Step S2: polygon fitting is performed on the contour direction sequence to obtain screening contour data; three-dimensional contour modeling is performed based on the screening contour data to generate a reconstructed lens model; Step S3: performing contour classification and clustering processing on the reconstructed lens model to obtain lens contour feature data; performing contour segmentation on the reconstructed lens model according to the lens contour feature data to obtain a segmented lens contour model; Step S4: performing range-limited screening on the segmented lens contour model to obtain a compressed range contour model; performing local fine layout processing on the compressed range contour model to obtain an accurate layout solution; Step S5: constructing a direction chain according to the precise layout scheme to obtain a cutting path direction chain; planning a cutting path for the precise layout scheme based on the cutting path direction chain to generate cutting path data; Step S6: sequentially executing and setting the cutting path data based on a preset blank drawing template to obtain a cutting layout template to execute the automatic layout and drawing method for lens laser cutting.

2. The automatic layout and drawing method for lens laser cutting according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting an image of the lens to be cut; performing brightness balance correction on the image of the lens to be cut to obtain a brightness balanced image; Step S12: performing bilateral filtering to reduce noise on the brightness-balanced image to generate a reduced-noise lens image; performing contour edge detection on the reduced-noise lens image to obtain an image edge curve; Step S13: Encode the edge curve of the image using eight-direction chain codes to obtain a direction code sequence; Step S14: performing continuous contour discrete direction mapping on the image of the lens to be cut according to the direction coding sequence to generate a contour direction sequence.

3. The automatic layout and drawing method for lens laser cutting according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing density cluster analysis on the contour direction sequence to obtain key point distribution data; performing point distance calculation on the key point distribution data to generate a point spacing matrix; Step S22: performing threshold segmentation processing on the contour direction sequence according to the point spacing matrix to generate segment boundary data; performing polygon fitting on the contour direction sequence based on the segment boundary data to obtain filtered contour data; Step S23: extracting geometric features from the screened contour data to obtain a contour geometric feature description set; performing contour depth mapping based on the contour geometric feature description set to generate depth mapping data; Step S24: performing anisotropic diffusion filtering on the depth mapping data to obtain a smooth depth map; performing contour three-dimensional modeling on the screened contour data based on the smooth depth map to generate a reconstructed lens model.

4. The automatic layout and drawing method for lens laser cutting according to claim 3, characterized in that: Step S22 includes the following steps: Perform feature statistics processing on the point spacing matrix to obtain distance feature data; perform dynamic threshold calculation based on the distance feature data to generate a threshold parameter set; Perform sliding window analysis on the contour direction sequence to obtain local feature data; perform direction change rate analysis based on the local feature data to generate a direction change rate sequence; Perform peak detection on the direction change rate sequence to obtain a set of change rate key points; perform matching and screening on the set of change rate key points and the set of threshold parameters to generate candidate segmentation points; Perform threshold segmentation processing on the contour direction sequence according to the candidate segmentation points to generate segmentation boundary data; Perform feature geometry analysis on segmented boundary data to obtain feature geometry attributes; construct fitting constraints based on the feature geometry attributes to generate a constraint condition set; Perform polygon fitting on the contour direction sequence to obtain an initial fitting result; perform error calculation on the initial fitting result according to the constraint condition set to obtain error index data; Angle adjustment optimization is performed based on the error index data to obtain an optimized fitting result; and contour screening is performed on the optimized fitting result to obtain screened contour data.

5. The automatic layout and drawing method for lens laser cutting according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: locating edge points of the reconstructed lens model to obtain a model edge point set; calculating the curvature of the model edge point set to obtain a curvature feature vector; Step S32: performing principal component analysis on the reconstructed lens model according to the curvature feature vector to obtain dimension-reduced feature data; performing mean clustering processing on the dimension-reduced feature data to generate lens profile feature data; Step S33: positioning the segmentation key points of the lens profile feature data to obtain segmentation key point data; Step S34: Perform contour segmentation on the reconstructed lens model based on the segmentation key point data to obtain a segmented lens contour model.

6. The automatic layout and drawing method for lens laser cutting according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: limiting the scope of the segmented lens contour model by an envelope frame to obtain a collection of boundary frames; performing area optimization processing on the collection of boundary frames to generate compressed boundary data; Step S42: dividing the feasible area according to the compressed boundary data to obtain a limited feasible area; performing range-limited screening on the segmented lens contour model based on the limited feasible area to obtain a compressed range contour model; Step S43: meshing the compressed range contour model to obtain lens contour mesh data; performing layout cost calculation on the lens contour mesh data to obtain a layout cost matrix; Step S44: Perform local fine layout processing according to the layout cost matrix to obtain an accurate layout solution.

7. The automatic layout and drawing method for lens laser cutting according to claim 6, characterized in that: Step S42 includes the following steps: Gridding the compressed boundary data to obtain boundary grid data; marking obstacles on the boundary grid data to generate obstacle grid data; Based on the obstacle grid data, the connected domain of the boundary grid data is identified to obtain a regional connected graph; the boundary of the regional connected graph is extracted to generate a limited feasible area; Performing position mapping on the limited feasible area according to the segmented lens contour model to obtain position mapping data; performing boundary detection on the position mapping data to obtain boundary constraint data; Perform feasibility verification based on boundary constraint data to obtain verification result data; optimize the range of the limited feasible area based on the verification result data to obtain a compressed feasible area; The compression feasible area is regionally integrated to generate a compression range contour model.

8. The automatic layout and drawing method for lens laser cutting according to claim 6, characterized in that: Step S44 includes the following steps: Optimize the objective function according to the layout cost matrix to obtain the objective optimization function; perform initial layout simulation according to the objective optimization function to obtain a simulated initial layout; Perform conflict detection on the simulated initial layout to obtain layout conflict points; perform local adjustment on the compressed range contour model based on the layout conflict points to obtain a local adjustment plan; Based on the preset layout scoring standard, the local adjustment plan is evaluated for layout to obtain a layout score; the layout score is searched locally for high scores to obtain an optimized adjustment plan; According to the optimization adjustment plan, the boundary is fine-tuned and optimized to obtain the boundary optimization layout; the data of the boundary optimization layout is integrated to generate an accurate layout plan.

9. The automatic layout and drawing method for lens laser cutting according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing contour vectorization on the precise layout scheme to obtain contour vector data; performing directional feature analysis based on the contour vector data to obtain directional feature data; Step S52: Analyze the continuity of the directional feature data to obtain a continuous segment set; perform direction chain encoding on the continuous segment set to generate a cutting path direction chain; Step S53: constructing a graph structure based on the cutting path direction chain to obtain an initial cutting path graph; searching for an optimal path on the initial cutting path graph to obtain initial optimal path data; Step S54: performing collision detection based on the initial preferred path data to obtain safe path data; integrating the safe path data and the initial preferred path data to generate cutting path data.

10. An automatic layout and drawing system for lens laser cutting, characterized in that: Used to execute the automatic layout and drawing method for lens laser cutting as claimed in claim 1, the automatic layout and drawing system for lens laser cutting comprises: An image acquisition module is used to acquire an image of a lens to be cut; perform contour edge detection on the image of the lens to be cut to obtain an image edge curve; perform continuous contour discrete direction mapping on the image of the lens to be cut based on the image edge curve to generate a contour direction sequence; The model reconstruction module is used to perform polygon fitting on the contour direction sequence to obtain the screening contour data; perform contour three-dimensional modeling based on the screening contour data to generate a reconstructed lens model; The contour segmentation module is used to perform contour classification and clustering processing on the reconstructed lens model to obtain lens contour feature data; and to perform contour segmentation on the reconstructed lens model according to the lens contour feature data to obtain a segmented lens contour model; The layout design module is used to perform range-limited screening on the segmented lens contour model to obtain a compressed range contour model; perform local fine layout processing on the compressed range contour model to obtain an accurate layout solution; The path planning module is used to construct a direction chain according to the precise layout plan to obtain a cutting path direction chain; based on the cutting path direction chain, the cutting path is planned for the precise layout plan to generate cutting path data; The execution setting module is used to sequentially execute the setting of the cutting path data based on a preset blank drawing template to obtain a cutting layout template to execute the automatic layout drawing method for lens laser cutting.

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