Intelligent adjustment method and system for cutting spacing of die-cutting auxiliary materials based on machine vision

Abstract

The present invention relates to the field of automation control, and discloses a method and system for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision, including: first obtaining the original image before the die-cutting auxiliary materials are cut, graying, noise reduction filtering, and edge extraction to obtain a contour image, dividing the cutting area based on this, and analyzing the spacing deviation to mark the abnormal area, then associating the abnormal area parameters with historical operation data, calculating the degree of influence on the current operation parameters, planning the initial cutting path and simulating the cutting, generating an adjustment instruction to send to the second die-cutting device when the preset conditions are not met, adjusting to the cutting spacing to meet the standard, obtaining the image at this time and verifying it, and finally generating an intelligent adjustment plan based on the verification data. The present invention can improve the quality level of die-cutting processing.

Description

Technical Field

[0001] The invention relates to a method and system for intelligently adjusting cutting spacing of die-cutting auxiliary materials based on machine vision, and belongs to the field of automatic control. Background Art

[0002] In modern manufacturing, especially in the die-cutting industry, the cutting quality of die-cutting accessories has a vital impact on the performance, quality and production efficiency of the final product. Die-cutting accessories are widely used in many fields such as electronics, packaging, and automobiles. For example, insulating gaskets in electronic products, special-shaped cutting of packaging materials, etc. The cutting accuracy is directly related to whether the product can be assembled and operated normally and whether the appearance meets the standards.

[0003] At present, traditional die-cutting auxiliary material cutting mainly relies on pre-set fixed cutting parameters. This mode exposes many disadvantages in the face of increasingly diversified die-cutting product demands, high-precision quality requirements and complex and changeable material properties. On the one hand, fixed cutting spacing lacks flexibility when processing die-cutting auxiliary materials of different batches and materials. Due to differences in physical properties such as material thickness, hardness, and flexibility, the use of a unified cutting spacing can easily lead to quality problems such as burrs, tears, or dimensional deviations on the cutting edges, seriously affecting product yield. On the other hand, manual adjustment of the cutting spacing is inefficient and difficult to guarantee accuracy. Operators need to rely on experience and repeated trial cutting to fine-tune parameters, which not only consumes a lot of time and manpower costs, but also easily introduces new quality instability factors due to human judgment errors. Therefore, a machine vision-based intelligent adjustment method for the cutting spacing of die-cutting auxiliary materials is needed to improve the quality level of die-cutting processing. Summary of the invention

[0004] The present invention provides a method and system for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision, the main purpose of which is to improve the quality level of die-cutting processing.

[0005] To achieve the above object, the present invention provides a method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision, comprising:

[0006] Obtaining an original image of the die-cutting auxiliary material before cutting, graying the original image to obtain a grayscale image, performing noise reduction filtering on the grayscale image to obtain a filtered clear image, and extracting an edge contour corresponding to the filtered clear image to obtain a contour image;

[0007] Based on the contour image, the cutting area corresponding to the die-cutting auxiliary material is divided, the area spacing value between any two areas in the cutting area is calculated, the interval deviation value corresponding to the area spacing value and the preset standard spacing value is analyzed, and based on the interval deviation value, the abnormal cutting area in the contour image is marked;

[0008] Query the abnormal area parameters in the abnormal cutting area, associate the abnormal area parameters with the historical operation data corresponding to the preset first die-cutting device to obtain the operation association parameters, detect the current operation parameters of the second die-cutting device corresponding to the die-cutting auxiliary material in real time, and calculate the influence degree value of the operation association parameters on the current operation parameters;

[0009] Based on the impact degree value, an initial cutting path corresponding to the die-cutting auxiliary material is planned, key points of the path in the initial cutting path are extracted, and according to the key points of the path, the die-cutting auxiliary material is simulated cut to obtain a simulated cutting result, and when the simulated cutting result does not meet the preset condition, an adjustment operation instruction corresponding to the die-cutting device is generated according to the simulated cutting result;

[0010] After the adjustment operation instruction is sent to the second die-cutting device, the die-cutting device is adjusted to restore the corresponding cutting spacing of the die-cutting auxiliary material to within the standard range, and an image of the die-cutting auxiliary material when the corresponding cutting spacing of the die-cutting auxiliary material is restored to within the standard range is obtained, and the cutting spacing of the die-cutting auxiliary material image is verified to obtain verification data, and based on the verification data, an intelligent adjustment plan corresponding to the die-cutting auxiliary material is generated.

[0011] Optionally, extracting the edge contour corresponding to the filtered clear image to obtain a contour image includes:

[0012] Extracting initial edge information corresponding to the filtered clear image;

[0013] Based on the initial edge information, dividing the potential edge area in the filtered clear image;

[0014] Filling the potential edge area to obtain an edge filling area;

[0015] Identifying edge lines corresponding to the edge filling area;

[0016] Based on the edge lines, the edge contour corresponding to the filtered clear image is extracted to obtain a contour image.

[0017] Optionally, the calculating the area spacing value between any two areas in the cutting area includes:

[0018] The area spacing value between any two areas in the cutting area is calculated using the following formula:

[0019] ;

[0020] in, represents the area spacing value between any two areas in the cutting area, represents the total number of area pairs corresponding to the cutting area, Indicates the number index of region pairs, Indicates The design spacing value between the area pairs is Indicates The spacing sample values ​​of the area pairs in actual measurement, Indicates The variance of the distance measurement between regions is Indicates The tolerance allowed for the spacing between pairs of areas.

[0021] Optionally, marking the abnormal cutting area in the contour image based on the interval deviation value includes:

[0022] Based on the interval deviation value, determining the area deviation range corresponding to different cutting areas;

[0023] Based on the regional deviation range, filtering a set of deviation values ​​exceeding a preset deviation threshold;

[0024] According to the deviation value set, searching for cutting area indexes corresponding to different cutting areas;

[0025] Locating the abnormal area point corresponding to the cutting area index;

[0026] Based on the abnormal region points, an abnormal cutting region in the contour image is marked.

[0027] Optionally, querying the abnormal area parameters in the abnormal cutting area includes:

[0028] Extracting contour feature information in the abnormal cutting area;

[0029] Based on the contour feature information, identifying the shape category corresponding to the abnormal cutting area;

[0030] According to the shape category, construct a standard geometric framework corresponding to the abnormal cutting area;

[0031] Based on the standard geometric framework, determining standard size points in the abnormal cutting area;

[0032] Based on the standard size point, the abnormal area parameters in the abnormal cutting area are queried.

[0033] Optionally, the calculating the influence degree value of the operation-related parameter on the current operation parameter includes:

[0034] The influence degree of the operation-related parameter on the current operation parameter is calculated using the following formula:

[0035] ;

[0036] in, Indicates the influence degree of the operation-related parameter on the current operation parameter, Indicates the number of samples corresponding to the monitoring samples of the running associated parameters, represents the number index of the monitoring samples, represents the average reference value corresponding to the operation-related parameter, Indicates The associated parameter values ​​of the monitoring samples, Indicates The sample weight value of the monitoring sample, Indicates the number of features corresponding to the relevant features of the operation-related parameters, represents the number index of related features, Indicates The characteristic coefficients corresponding to the relevant features are Indicates The eigenvalues ​​corresponding to the relevant features.

[0037] Optionally, planning an initial cutting path corresponding to the die-cutting auxiliary material based on the impact degree value includes:

[0038] Querying the influencing factors corresponding to the influencing degree value;

[0039] Based on the influencing factors, analyzing the auxiliary material characteristics corresponding to the die-cutting auxiliary materials;

[0040] Based on the auxiliary material characteristics, determining the cutting priority corresponding to the die-cutting auxiliary material;

[0041] Based on the cutting priority, the die-cutting auxiliary material is grid-divided to obtain an auxiliary material cutting unit;

[0042] Based on the auxiliary material cutting unit, an initial cutting path corresponding to the die-cutting auxiliary material is planned.

[0043] Optionally, the simulating cutting of the die-cutting auxiliary material according to the key points of the path to obtain a simulated cutting result includes:

[0044] Sequentially number the key points of the path to obtain a key point sequence;

[0045] Based on the key point sequence, determining an initial cutting position corresponding to the die-cutting auxiliary material;

[0046] Based on the initial cutting position, analyzing the cutting motion trajectory corresponding to the die-cutting auxiliary material;

[0047] Based on the cutting motion trajectory, the die-cutting auxiliary material is simulated cut to obtain a simulated cutting result.

[0048] Optionally, generating an adjustment operation instruction corresponding to the die-cutting device according to the simulated cutting result includes:

[0049] Analyze the cutting deviation data in the simulated cutting results;

[0050] Based on the cutting deviation data, analyzing the tool movement amplitude corresponding to the tool in the die-cutting device;

[0051] Calculating the required position compensation amount of the tool in the die-cutting device based on the tool movement amplitude;

[0052] Based on the position compensation amount, planning a trajectory adjustment strategy for the tool in the die-cutting device to return to the preset cutting path;

[0053] Based on the trajectory adjustment strategy, an adjustment operation instruction corresponding to the die-cutting device is generated.

[0054] In order to solve the above problems, the present invention also provides a machine vision-based die-cutting auxiliary material cutting spacing intelligent adjustment system, the system comprising:

[0055] A contour extraction module is used to obtain an original image of the die-cutting auxiliary material before cutting, grayscale the original image to obtain a grayscale image, perform noise reduction filtering on the grayscale image to obtain a filtered clear image, and extract the edge contour corresponding to the filtered clear image to obtain a contour image;

[0056] A region marking module, for dividing the cutting region corresponding to the die-cutting auxiliary material based on the contour image, calculating the region spacing value between any two regions in the cutting region, analyzing the interval deviation value corresponding to the region spacing value and a preset standard spacing value, and marking the abnormal cutting region in the contour image based on the interval deviation value;

[0057] A degree value calculation module is used to query the abnormal area parameters in the abnormal cutting area, associate the abnormal area parameters with the historical operation data corresponding to the preset first die-cutting device, obtain the operation association parameters, detect the current operation parameters of the second die-cutting device corresponding to the die-cutting auxiliary material in real time, and calculate the influence degree value of the operation association parameters on the current operation parameters;

[0058] An instruction generation module is used to plan an initial cutting path corresponding to the die-cutting auxiliary material based on the influence degree value, extract path key points in the initial cutting path, simulate cutting the die-cutting auxiliary material according to the path key points, obtain a simulated cutting result, and generate an adjustment operation instruction corresponding to the die-cutting device according to the simulated cutting result when the simulated cutting result does not meet the preset conditions;

[0059] A scheme generating module is used for sending the adjustment operation instruction to the second die-cutting device, and then performing an adjustment operation on the die-cutting device so that the corresponding cutting spacing of the die-cutting auxiliary material is restored to within a standard range, and obtaining an image of the die-cutting auxiliary material when the corresponding cutting spacing of the die-cutting auxiliary material is restored to within the standard range, performing cutting spacing verification on the die-cutting auxiliary material image to obtain verification data, and generating an intelligent adjustment scheme corresponding to the die-cutting auxiliary material based on the verification data.

[0060] Compared with the problems described in the background technology, the present invention obtains the original image of the die-cutting auxiliary material before cutting to provide basic data for subsequent image processing. Operations such as grayscale, noise reduction filtering, and edge contour extraction all take this as the starting point to ensure the orderly advancement of the entire intelligent adjustment process and to intuitively present the initial state of the die-cutting auxiliary material. The present invention divides the cutting area corresponding to the die-cutting auxiliary material based on the contour image, and can accurately determine the cutting range according to the actual shape and contour of the die-cutting auxiliary material to avoid material waste. It can effectively guide the subsequent cutting process to ensure that the cutting path planning is reasonable and the cutting spacing is uniform, thereby improving the accuracy and quality of the die-cutting process. Furthermore, the present invention can accurately grasp the specific details of the cutting problem by querying the abnormal area parameters in the abnormal cutting area. For example, by analyzing the parameters, it is clear whether it is The root causes of problems such as cutting position offset, excessive size deviation or irregular shape can be identified, thereby improving production efficiency and product quality. Furthermore, the present invention plans the initial cutting path corresponding to the die-cutting auxiliary material based on the impact degree value, and can accurately design the cutting path according to the impact of each parameter on the operation, ensuring cutting accuracy, reducing cutting errors caused by equipment parameter fluctuations, optimizing the cutting process, and improving cutting efficiency. Finally, the present invention sends the adjustment operation instruction to the second die-cutting device, and then adjusts the die-cutting device to restore the corresponding cutting spacing of the die-cutting auxiliary material to the standard range, which can ensure stable product quality and help extend the service life of the equipment. Through precise adjustment, the equipment can operate in the best state, reducing additional wear and loss to equipment components caused by abnormal operation. Therefore, the machine vision-based intelligent adjustment method and system for cutting spacing of die-cutting auxiliary materials provided in the embodiment of the present invention can improve the quality level of die-cutting processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1A schematic diagram of a process flow of a method for intelligently adjusting cutting spacing of die-cutting auxiliary materials based on machine vision provided in one embodiment of the present invention;

[0062] Figure 2 A schematic diagram of a module for implementing the machine vision-based intelligent adjustment system for cutting spacing of die-cutting auxiliary materials provided in one embodiment of the present invention.

[0063] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0065] The embodiment of the present application provides a method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision. The execution subject of the method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0066] Embodiment 1:

[0067] Reference Figure 1 FIG. 1 is a flow chart of a method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to an embodiment of the present invention. In this embodiment, the method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision includes:

[0068] S1. Obtain an original image of the die-cutting auxiliary material before cutting, grayscale the original image to obtain a grayscale image, perform noise reduction filtering on the grayscale image to obtain a filtered clear image, and extract an edge contour corresponding to the filtered clear image to obtain a contour image.

[0069] The present invention obtains the original image of the die-cutting auxiliary material before cutting to provide basic data for subsequent image processing. Operations such as grayscale, noise reduction filtering, and edge contour extraction all take this as the starting point, ensuring the orderly advancement of the entire intelligent adjustment process and being able to intuitively present the initial state of the die-cutting auxiliary material.

[0070] Among them, the die-cutting auxiliary materials refer to the auxiliary materials used in the die-cutting process. These materials are widely used in many fields such as electronics, packaging, and automobiles. For example, the insulating gaskets in electronic products prevent short circuits and leakage between electronic components and ensure the safe and stable operation of electronic products. The special-shaped cutting parts of the packaging materials can make the packaging fit the product shape more closely and protect and beautify the products. In addition, they also include sound insulation and heat insulation materials in the production of automotive interior parts. The original image refers to the image data obtained by taking a picture of the die-cutting auxiliary materials through a specific image acquisition device (such as an industrial camera, etc.) before the die-cutting auxiliary materials are cut, and the image completely records the surface information of the die-cutting auxiliary materials in the initial state, including but not limited to the shape, texture, color distribution, and defects of the die-cutting auxiliary materials. Optionally, the original image of the die-cutting auxiliary materials before cutting can be obtained by an image acquisition device, such as an industrial CCD, an industrial CMOS, and other devices.

[0071] Furthermore, the present invention obtains a grayscale image by graying the original image, thereby simplifying the image data, converting the originally complex color image information into a grayscale value of a single channel, reducing the amount of data for subsequent processing, and improving processing efficiency.

[0072] Among them, the grayscale image refers to the image obtained after grayscale processing of the original image obtained before cutting of the die-cutting auxiliary material. It discards the color information in the original image and represents the color of each pixel with different degrees of grayscale values ​​through a specific algorithm. Usually, the grayscale value range is 0 (indicating black) to 255 (indicating white). The entire image presents different grayscale tones from black to white. Such an image focuses more on the shape, texture, edge and other features of the die-cutting auxiliary material. Optionally, the grayscale processing of the original image can be achieved through image processing algorithms, such as: average method, weighted average method and other algorithms.

[0073] Furthermore, the present invention performs noise reduction filtering on the grayscale image to obtain a filtered clear image, which can effectively remove the noise and restore the real details of the image, making the edges and textures of the die-cutting auxiliary materials clearer and more accurate. The clear image helps to extract the edge contour more accurately in the subsequent process, reduce the contour misjudgment caused by noise, and thus improve the accuracy of the cutting area division.

[0074] Among them, the filtered clear image refers to an image generated after the grayscale image obtained after grayscale processing is processed using the corresponding noise reduction filtering technology. Compared with the grayscale image, it reduces the messy information generated by various interference factors (such as the electronic noise of the image acquisition device itself, the noise caused by uneven or unstable external ambient light, etc.), making the overall image smoother and clearer, and the original shape, edge, texture and other key features of the die-cutting auxiliary materials can be more accurately presented. Optionally, the noise reduction filtering of the grayscale image can be achieved through a filtering algorithm, such as: mean filtering, median filtering and other algorithms.

[0075] Furthermore, the present invention obtains a contour image by extracting the edge contour corresponding to the filtered clear image, which clearly defines the shape of the die-cutting auxiliary materials and the boundaries of each part, providing a key basis for the subsequent precise division of the cutting area, making the determination of the cutting area more accurate and scientific.

[0076] Among them, the contour image refers to an image that is reconstructed on the basis of the filtered clear image by integrating all the identified edge lines according to their respective positions, directions and mutual connections, and can accurately present the complete edge contour of the die-cutting auxiliary materials. The image clearly outlines key information such as the shape of the die-cutting auxiliary materials and the boundaries of each component.

[0077] As an embodiment of the present invention, the step of extracting the edge contour corresponding to the filtered clear image to obtain a contour image includes: extracting initial edge information corresponding to the filtered clear image; dividing potential edge areas in the filtered clear image based on the initial edge information; filling the potential edge areas to obtain edge filling areas; identifying edge lines corresponding to the edge filling areas; and extracting the edge contour corresponding to the filtered clear image based on the edge lines to obtain a contour image.

[0078] Among them, the initial edge information refers to the basic information first obtained from the filtered clear image, which can reflect the approximate position and direction of the edge of the object in the image. It is formed by a specific edge detection method, such as using a gradient operator (such as Sobel operator, Prewitt operator, etc.) to calculate the grayscale changes of image pixels in the horizontal and vertical directions, and marking those pixels with more drastic grayscale changes. The potential edge area refers to the local image area that can contain a complete edge, which is delineated on the filtered clear image based on the initial edge information. By analyzing the scattered edge pixels in the initial edge information, according to the correlation between the pixels (such as the continuity and similarity of the grayscale change trend of adjacent pixels) and the distribution law, an area where pixels with the potential to become a complete edge part are located is delineated; the edge filling area refers to the area obtained after the potential edge area is filled. In the potential edge area, there can be In the case where the edge lines are discontinuous, have holes or gaps, a morphological filling algorithm (such as a seed-based filling method) is used to fill in the surrounding pixels that meet certain connectivity rules and have matching grayscale values, starting from the existing edge pixels in the area, so that the originally incomplete and discontinuous potential edge area becomes continuous and complete; the edge lines refer to the line information that can clearly and accurately reflect the shape contour of the die-cutting auxiliary materials in the edge filling area after the filling process. By applying an edge extraction algorithm (such as a curve fitting, line segment connection, etc.) to the edge filling area, those continuous lines that meet certain geometric shape characteristics and truly represent the boundaries of the die-cutting auxiliary materials are identified from the area. The lines can be straight line segments, curve segments, or a combination of the two, and are the basic components for constructing the final complete edge contour.

[0079] Furthermore, the extraction of initial edge information corresponding to the filtered clear image can be achieved through a gradient operator, such as: Sobel operator, Prewitt operator, etc.; the division of potential edge areas in the filtered clear image can be achieved through a watershed algorithm, such as: regarding the image as terrain, the grayscale value as the height of the terrain, and dividing the area by finding the "valley" (grayscale minimum point) and "ridge" (grayscale change watershed) in the image; the filling processing of the potential edge area can be achieved through a filling algorithm, such as: seed filling method, flood filling algorithm, etc.; the identification of edge lines corresponding to the edge filling area can be achieved through a Hough transform method, such as: Hough line transform, Hough circle transform, etc.; the extraction of the edge contour corresponding to the filtered clear image can be achieved through a contour extraction method based on topology, such as: using the topological structure characteristics of the image, analyzing the topological information such as connectivity and inclusion relationship between edge lines, combining edge lines with the same topological attributes, and determining a complete edge contour that conforms to the filtered clear image.

[0080] S2. Based on the contour image, divide the cutting area corresponding to the die-cutting auxiliary material, calculate the area spacing value between any two areas in the cutting area, analyze the interval deviation value corresponding to the area spacing value and the preset standard spacing value, and mark the abnormal cutting area in the contour image based on the interval deviation value.

[0081] The present invention divides the cutting area corresponding to the die-cutting auxiliary material based on the contour image, and can accurately determine the cutting range according to the actual shape and contour of the die-cutting auxiliary material to avoid material waste. It can effectively guide the subsequent cutting process, ensure that the cutting path planning is reasonable, and make the cutting spacing uniform and consistent, thereby improving the accuracy and quality of the die-cutting process.

[0082] Among them, the cutting area refers to a relatively independent local range delineated on the die-cutting auxiliary materials based on the contour image of the die-cutting auxiliary materials in accordance with product design requirements, processing technology standards and actual application scenarios. Each cutting area has its own boundary and shape characteristics. It is a key division part to ensure the precise implementation of the cutting action so that the die-cutting auxiliary materials can finally be cut into the desired shape and size, thereby meeting the requirements of different fields such as electronics, packaging, and automobiles for the finished specifications of die-cutting auxiliary materials. Optionally, the division of the cutting area corresponding to the die-cutting auxiliary materials based on the contour image can be achieved through a division method based on template matching. For example, in the production of electronic equipment, for the die-cutting of insulating gaskets with fixed shapes, there is a standard gasket shape template, and the cutting area can be determined by template matching.

[0083] Furthermore, the present invention can directly reflect the rationality of the current cutting layout by calculating the area spacing value between any two areas in the cutting area. By comparing with the preset standard spacing, potential cutting spacing problems can be quickly discovered to avoid die-cutting auxiliary material size deviations caused by improper spacing.

[0084] The region spacing value refers to the distance measurement between any two regions in the cutting area, which reflects the degree of separation between the two regions in space.

[0085] As an embodiment of the present invention, the calculating of the area spacing value between any two areas in the cutting area includes:

[0086] The area spacing value between any two areas in the cutting area is calculated using the following formula:

[0087] ;

[0088] in, represents the area spacing value between any two areas in the cutting area, represents the total number of area pairs corresponding to the cutting area, Indicates the number index of region pairs, Indicates The design spacing value between the area pairs is Indicates The spacing sample values ​​of the area pairs in actual measurement, Indicates The variance of the distance measurement between regions is Indicates The tolerance allowed for the spacing between two areas.

[0089] In detail, the area pair refers to a combination of any two areas selected from the cutting area. For example, on a material with multiple cutting areas, two different areas are selected each time as a pair for spacing analysis. Such a pair of areas is called an area pair. The design spacing value refers to the ideal distance that should be maintained between two areas specified in the product design stage. It is pre-set according to the function, structure and assembly requirements of the product and is the spacing standard expected to be achieved in the cutting operation. The spacing sample value refers to a specific measurement value obtained after multiple measurements of the distance between a certain area pair in the actual measurement process. It is data obtained by measuring the area spacing after actual cutting using a measuring tool (such as a caliper, a laser rangefinder, etc.). The variance value refers to a measure of the dispersion of the measurement value relative to its average value after multiple measurements of the spacing between a certain area pair. The variance value reflects the fluctuation of the measurement data. The larger the variance, the more dispersed the measurement data and the lower the measurement accuracy. The tolerance allowable value refers to the maximum range of the area spacing value allowed to deviate from the design spacing value in the production process. It is a limit value set according to product quality requirements and production process capabilities.

[0090] Furthermore, the present invention can quickly determine whether the cutting operation meets the quality requirements by analyzing the interval deviation value corresponding to the regional spacing value and the preset standard spacing value, which is convenient for timely discovering problems in the cutting process, helps to optimize the cutting process parameters, adjust the process according to the deviation value, reduce the scrap rate, and improve production efficiency.

[0091] Among them, the preset standard spacing value refers to the ideal spacing value between the two cutting areas that is preset before the die-cutting auxiliary material cutting operation is performed, based on product design requirements, production process specifications, and the functions and assembly requirements of the final product. This value is obtained through precise calculation and planning, and is the target spacing value expected to be achieved by the cutting operation; the interval deviation value refers to the difference between the actual spacing value between the two cutting areas measured after the actual cutting operation is completed and the preset standard spacing value. This difference can be positive (the actual spacing is greater than the standard spacing) or negative (the actual spacing is less than the standard spacing). By calculating the interval deviation value, the precision and accuracy of the cutting operation can be evaluated, and the product can be judged whether it meets the quality requirements based on the size of the deviation value. The smaller the deviation value, the better. Optionally, the analysis of the interval deviation value corresponding to the area spacing value and the preset standard spacing value can be implemented through a machine learning regression algorithm, such as linear regression, polynomial regression and other algorithms.

[0092] Furthermore, the present invention marks the abnormal cutting area in the contour image based on the interval deviation value, which can quickly and accurately locate the area that does not meet the quality standards, avoid defective products from entering subsequent processes, and help to timely trace and analyze problems in the production process, such as cutting tool wear, abnormal equipment parameters, etc., so as to carry out targeted equipment maintenance and process adjustments.

[0093] The abnormal cutting area refers to the entire area in the contour image, which is composed of or contains all abnormal area points and is judged to be abnormal because the cutting spacing does not meet the quality requirements. It is the non-compliant part that is finally presented visually.

[0094] As an embodiment of the present invention, marking the abnormal cutting area in the contour image based on the interval deviation value includes: determining the regional deviation range corresponding to different cutting areas based on the interval deviation value; screening the deviation value set that exceeds the preset deviation threshold based on the regional deviation range; searching for the cutting area index corresponding to different cutting areas based on the deviation value set; locating the abnormal area point corresponding to the cutting area index; and marking the abnormal cutting area in the contour image based on the abnormal area point.

[0095] Among them, the regional deviation range refers to the standard spacing value around each cutting area, which is an interval determined according to the allowable fluctuations in actual production. For example, if the standard spacing of the cutting area is 5mm, considering factors such as material expansion and contraction and cutting process error, its deviation is allowed to be within ±0.5mm, then the regional deviation range is 4.5mm to 5.5mm; the preset deviation threshold refers to a fixed value set before the start of the production process based on product design specifications, quality standards and a large number of similar production practice experiences in the past. When the actual interval deviation value of a cutting area exceeds this threshold, it indicates that the cutting spacing of the area is abnormal, which may lead to problems such as difficulty in product assembly and substandard performance; the deviation value set refers to the actual interval deviation value of each cutting area calculated by comparing it with the corresponding standard spacing value After the calculation and comparison, the set of deviation values ​​that exceed the preset deviation threshold are screened out. These deviation values ​​represent the part of the current production process where problems occur, which are important clues for subsequent search for abnormal cutting areas; the cutting area index refers to the unique identification number or symbol given to each cutting area according to a specific coding rule or sequence in order to clearly distinguish and accurately refer to each cutting area. In the die-cutting auxiliary materials with multiple cutting areas, different cutting areas have their own position, shape, size and other characteristics; the abnormal area point refers to the key points that can affect the product quality due to abnormal spacing deviation within the actual cutting area corresponding to the cutting area index. For example, when the spacing between two cutting areas is too small, material extrusion and deformation may occur at the boundary, and the pixel points at these boundaries are the abnormal area points.

[0096] Furthermore, the determination of the regional deviation range corresponding to different cutting areas can be achieved through statistical analysis methods, such as: collecting a large amount of historical data of the same type of cutting products, and analyzing the distribution of the spacing between cutting areas; the screening of the deviation value set that exceeds the preset deviation threshold can be achieved through a conditional screening function, such as: using list derivation or filter function in Python; the search for cutting area indexes corresponding to different cutting areas can be achieved through a hash table search method, such as: using a hash table (a dictionary in Python) to store the correspondence between cutting areas and indexes; the positioning of abnormal area points corresponding to the cutting area indexes can be achieved through a threshold segmentation method, such as: using threshold segmentation to separate the cutting area from the background. In the segmented area, points with sudden changes in pixel values ​​may be abnormal area points; marking the abnormal cutting areas in the contour image can be achieved through area marking methods, such as: color marking method, texture marking method and other methods.

[0097] S3. Query the abnormal area parameters in the abnormal cutting area, associate the abnormal area parameters with the historical operation data corresponding to the preset first die-cutting device to obtain operation association parameters, detect the current operation parameters of the second die-cutting device corresponding to the die-cutting auxiliary material in real time, and calculate the influence degree of the operation association parameters on the current operation parameters.

[0098] By querying the abnormal area parameters in the abnormal cutting area, the present invention can accurately grasp the specific details of the cutting problem. For example, by analyzing the parameters, it can be determined whether the root cause of the problem is cutting position offset, excessive size deviation or irregular shape, thereby improving production efficiency and product quality.

[0099] Among them, the abnormal area parameters refer to a set of related parameters that are obtained by comparing the abnormal cutting area with the corresponding standard geometric framework and taking the standard size point as a reference, which can reflect the degree to which the abnormal cutting area deviates from the standard state in terms of shape, size, etc., such as the difference between a certain side length and the standard side length, the angle deviation value, the difference in regional area, etc.

[0100] As an embodiment of the present invention, the querying of abnormal area parameters in the abnormal cutting area includes: extracting contour feature information in the abnormal cutting area; based on the contour feature information, identifying the shape category corresponding to the abnormal cutting area; according to the shape category, constructing a standard geometric framework corresponding to the abnormal cutting area; based on the standard geometric framework, determining the standard size points in the abnormal cutting area; based on the standard size points, querying the abnormal area parameters in the abnormal cutting area.

[0101] Among them, the contour feature information refers to a set of relevant information extracted from the abnormal cutting area that can describe the boundary morphological characteristics of the area, which covers aspects such as the length, curvature, and angle change of the boundary line, as well as the closedness and concavity of the area enclosed by the contour. For example, whether the contour line is a smooth curve or composed of multiple straight line segments, whether there are obvious depressions or convexities in the area enclosed by the boundary, etc.; the shape category refers to the common geometric shape types into which the abnormal cutting area is classified based on the extracted contour feature information. For example, if the contour feature information of the abnormal cutting area shows that its boundary is composed of four straight line segments that are roughly perpendicular to each other and of different lengths, then Its shape category can be determined as a rectangle; the standard geometric framework refers to an ideal reference framework that is constructed according to the determined shape category and conforms to the corresponding geometric shape rules. Taking the rectangular shape category as an example, its standard geometric framework is a closed rectangle surrounded by four straight line segments that are strictly perpendicular to each other and whose side lengths conform to a certain proportional relationship; the standard size points refer to key nodes selected on the constructed standard geometric framework for measuring the size characteristics of the geometric shape. For the rectangular standard geometric framework, its standard size points can be four vertices and the midpoints of each side, etc. The size data such as the side length and diagonal length determined by these points can fully describe the size specifications of the standard geometric shape.

[0102] Furthermore, the extraction of contour feature information in the abnormal cutting area can be achieved through an edge detection algorithm, such as the Canny edge detection algorithm; the identification of the shape category corresponding to the abnormal cutting area can be achieved through a classification algorithm, such as a support vector machine, a decision tree, and other algorithms; the construction of the corresponding standard geometric framework of the abnormal cutting area can be achieved through a framework construction tool, such as Webpack, Vite, and other tools; the determination of the standard size points in the abnormal cutting area can be achieved through a uniform sampling method, such as for a standard rectangular frame, points are selected as standard size points in the horizontal and vertical directions at a fixed pixel interval; the query of the abnormal area parameters in the abnormal cutting area can be achieved through a fitting analysis method, such as fitting the contour or internal feature points of the abnormal cutting area to a curve or straight line corresponding to the standard geometric framework, and finally obtaining the abnormal area parameters.

[0103] The present invention obtains operation correlation parameters by correlating the abnormal area parameters with the historical operation data corresponding to the preset first die-cutting device. The root cause of the problem can be quickly located with the help of historical experience. For example, by comparison, it can be found whether similar abnormalities have been caused by aging of a certain component of the equipment, improper parameter setting, etc., thereby providing a scientific basis for subsequent precise adjustment of the equipment, reducing trial and error costs, improving adjustment efficiency, and ensuring that the die-cutting equipment quickly recovers to the optimal operating state.

[0104] Among them, the preset first die-cutting equipment refers to a specific, pre-selected machine equipment for die-cutting processing operations in the current production scenario, which has a specific model, specification, performance parameters and mechanical structure characteristics, etc., and undertakes the key task of cutting raw materials into die-cutting auxiliary materials of specific shapes and sizes in the entire production process. It is the main equipment targeted by this analysis and processing of abnormal area parameters; the historical operation data refers to a collection of various types of data information accumulated by the preset first die-cutting equipment in the previous production operation process, including but not limited to the operating time of the equipment, the operating speed of each component, the pressure value of the cutting tool, the temperature change of the equipment, the specific parameters of each cutting operation (such as cutting depth, cutting speed, etc.) and the quality data of the die-cutting products produced (such as cutting dimension accuracy, edge flatness, etc.); the operation association parameters refer to a set of new data parameters obtained by organically combining and deeply mining the abnormal area parameters with the historical operation data of the preset first die-cutting device through a specific data association algorithm and analysis method. These parameters can reveal the intrinsic connection and correlation between the abnormal area parameters and the historical operation status of the equipment. For example, the dimensional deviation of a certain abnormal area can be closely related to the cutting speed fluctuation of the equipment in a certain period of history, the unstable tool pressure or the abnormal increase in the temperature of the equipment. Optionally, the data association of the abnormal area parameters with the historical operation data corresponding to the preset first die-cutting device can be achieved through an association rule mining algorithm, such as: Apriori, FP-Growth and other algorithms.

[0105] Furthermore, the present invention can timely detect subtle changes in the operating status of the equipment, such as changes in cutting force caused by tool wear, unstable motor speed, etc., by real-time detection of the current operating parameters of the second die-cutting equipment corresponding to the die-cutting auxiliary materials, so as to take measures before the problem worsens, which helps to ensure the processing accuracy and quality of the die-cutting auxiliary materials, and ensure that each cutting meets the standards through real-time control of the operating parameters.

[0106] The second die-cutting equipment refers to another special mechanical equipment used for cutting die-cutting auxiliary materials in a specific production environment or process flow, which is different from the previously mentioned "first die-cutting equipment". It has an independent mechanical structure, power system, control system, etc., and may differ from the first die-cutting equipment in terms of model, performance, technical parameters or application scenarios. In the entire production chain, it undertakes the key task of processing die-cutting auxiliary materials into product parts that meet specific specifications and quality requirements; the current operating parameters refer to a series of physical quantities or indicators that can reflect the operating status and performance of the equipment presented by the second die-cutting equipment during the die-cutting processing operation currently being carried out. These parameters include but are not limited to the operating speed of the equipment ( Such as the moving speed of the cutting tool, the operating speed of the feeding mechanism, etc.), the cutting force (the force applied to the die-cutting auxiliary materials for cutting), the temperature of each component of the equipment (such as the real-time temperature of key components such as motors and tools), the vibration amplitude of the equipment (reflecting the smoothness of the equipment operation), processing accuracy related parameters (such as real-time deviation of cutting size, flatness of cutting edge, etc.) and various electrical parameters (such as motor current, voltage, etc.). Optionally, the real-time detection of the current operating parameters of the second die-cutting device corresponding to the die-cutting auxiliary materials can be achieved through machine vision detection methods, such as: shooting the motion trajectory and position of the cutting tool, analyzing the relative position relationship between the tool and the die-cutting auxiliary materials to determine the cutting accuracy related parameters, and using them as the current operating parameters.

[0107] Furthermore, the present invention can accurately locate the effect of various factors on the current operating status of the equipment and identify key influencing factors by calculating the degree of influence of the operating-associated parameters on the current operating parameters, such as finding that specific historical equipment parameter fluctuations have a great influence on the current cutting accuracy. This provides a direct basis for optimizing equipment operation, quickly improves the current operating results, reduces defective product output, and improves production efficiency and product quality.

[0108] The impact degree value refers to the quantitative degree of influence of the operation-related parameters on the current operation parameters, which can help determine the effect of the operation-related parameters on the current operation parameters, and thus provide a basis for optimizing operation, fault diagnosis, etc.

[0109] As an embodiment of the present invention, the calculating the influence degree value of the operation-related parameter on the current operation parameter includes:

[0110] The influence degree of the operation-related parameter on the current operation parameter is calculated using the following formula:

[0111] ;

[0112] in, Indicates the influence degree of the operation-related parameter on the current operation parameter, Indicates the number of samples corresponding to the monitoring samples of the running associated parameters, represents the number index of the monitoring samples, represents the average reference value corresponding to the operation-related parameter, Indicates The associated parameter values ​​of the monitoring samples, Indicates The sample weight value of the monitoring sample, Indicates the number of features corresponding to the relevant features of the operation-related parameters, represents the number index of related features, Indicates The characteristic coefficients corresponding to the relevant features are Indicates The eigenvalues ​​corresponding to the relevant features.

[0113] In detail, the monitoring sample refers to the data sample collected from the operation process for analysis. For example, during the operation of a piece of production equipment, the equipment operation parameters (such as temperature, pressure, speed, etc.) recorded at regular intervals are monitoring samples; the average reference value refers to the average value of all monitoring samples corresponding to the operation-related parameters, which is used as a reference benchmark to measure the degree of deviation of each monitoring sample from the overall average level. For example, when calculating the impact of equipment temperature on production efficiency, the average temperature of the equipment over a period of time is the average reference value; the associated parameter value refers to the specific value of the operation-related parameters in each monitoring sample. For example, during the operation of the equipment, the parameter values ​​of the equipment temperature, pressure, etc. monitored each time are the associated parameter values; the sample weight value refers to the weight assigned to each monitoring sample, which can be calculated based on The relevant features are set according to the actual situation. For example, more recently collected samples or samples collected under critical conditions can be assigned higher weights. The relevant features refer to other features related to operation-related parameters. For example, when analyzing the impact of equipment temperature on production efficiency, the equipment's operating speed, ambient humidity, etc. can all be relevant features. The characteristic coefficient refers to the coefficient corresponding to each relevant feature. These coefficients are used to quantify the role of each relevant feature in affecting the current operating parameters. For example, in a multivariate linear regression model, the coefficient in front of each independent variable (relevant feature) is the characteristic coefficient. The characteristic value refers to the specific value of each relevant feature in the monitoring sample. These values ​​reflect the actual status of the relevant features at a specific monitoring moment. For example, during the operation of the equipment, the ambient humidity and equipment operating speed monitored each time are characteristic values.

[0114] S4. Based on the impact degree value, plan the initial cutting path corresponding to the die-cutting auxiliary material, extract the path key points in the initial cutting path, simulate the cutting of the die-cutting auxiliary material according to the path key points, and obtain the simulated cutting result. When the simulated cutting result does not meet the preset conditions, generate the adjustment operation instruction corresponding to the die-cutting equipment according to the simulated cutting result.

[0115] The present invention plans the initial cutting path corresponding to the die-cutting auxiliary material based on the influence degree value, can accurately design the cutting path according to the influence of each parameter on the operation, ensure the cutting accuracy, reduce the cutting error caused by the fluctuation of equipment parameters, optimize the cutting process and improve the cutting efficiency.

[0116] Among them, the initial cutting path refers to the motion trajectory of the cutting tool planned when starting to cut the die-cutting auxiliary materials. It is determined based on the auxiliary material cutting unit, taking into account factors such as auxiliary material characteristics and cutting priority. The initial cutting path is the first step in the cutting operation planning, which determines the starting point, end point and intermediate route of the cutting process.

[0117] As an embodiment of the present invention, planning the initial cutting path corresponding to the die-cutting auxiliary material based on the influence degree value includes: querying the influencing factors corresponding to the influence degree value; analyzing the auxiliary material characteristics corresponding to the die-cutting auxiliary material based on the influencing factors; determining the cutting priority corresponding to the die-cutting auxiliary material based on the auxiliary material characteristics; based on the cutting priority, gridding the die-cutting auxiliary material to obtain an auxiliary material cutting unit; and planning the initial cutting path corresponding to the die-cutting auxiliary material based on the auxiliary material cutting unit.

[0118] The influencing factors refer to those factors that affect the operation-related parameters and thus affect the cutting of die-cutting auxiliary materials. These factors may include equipment operation parameters (such as cutting tool speed, pressure, etc.), environmental factors (such as temperature, humidity, etc.) and physical properties of die-cutting auxiliary materials themselves (such as thickness, hardness, etc.); the auxiliary material properties refer to various properties of die-cutting auxiliary materials themselves, for example, material properties of die-cutting auxiliary materials (paper, plastic or metal, etc.), mechanical properties (such as tensile strength, shear strength, etc.), geometric properties (such as shape, size, etc.); the cutting priority is Refers to the order of cutting different areas or parts when cutting die-cutting auxiliary materials. For example, for auxiliary materials with irregular shapes and weak parts, the weak parts may need to be cut first to prevent damage to the parts during subsequent cutting. The cutting priority is a cutting order determined based on the auxiliary material characteristics and production goals; the auxiliary material cutting unit refers to the division of die-cutting auxiliary materials into several small units that are easy to cut according to certain rules. For example, on a large-sized die-cutting auxiliary material, it is divided into multiple small rectangular or other shaped cutting units according to the cutting priority and grid division method.

[0119] Furthermore, the query of the influencing factors corresponding to the influence degree value can be achieved through regression analysis, such as linear regression, logistic regression and other models; the analysis of the auxiliary material properties corresponding to the die-cutting auxiliary material can be achieved through material testing instruments, such as using a tensile testing machine to measure the tensile strength, elongation and other auxiliary material properties of the die-cutting auxiliary material; the determination of the cutting priority corresponding to the die-cutting auxiliary material can be achieved through a hierarchical analysis method, such as for an irregularly shaped die-cutting auxiliary material, its mechanical properties, geometric shape complexity and other factors are hierarchically analyzed to determine the cutting priority of different areas; the grid division of the die-cutting auxiliary material can be achieved through an image processing-based method, such as first binarizing the image of the die-cutting auxiliary material, and then using a Voronoi diagram algorithm or a Delaunay triangulation algorithm to divide the image area into a number of grid units; the planning of the initial cutting path corresponding to the die-cutting auxiliary material can be achieved through a genetic algorithm, such as defining parameters such as the starting point, end point and grid units passed by the cutting path as genes, and finding the optimal initial cutting path through iterative optimization of the genetic algorithm.

[0120] By extracting key points of the path in the initial cutting path, the present invention can simplify the path information, reduce the amount of data storage and the complexity of calculation, make the subsequent analysis and adjustment of the cutting path more efficient, and help to accurately control the steering, speed adjustment and other operations of the cutting equipment, improve the accuracy and stability of the cutting process, and ensure the processing quality of die-cutting auxiliary materials.

[0121] Among them, the path key points refer to specific points in the initial cutting path that are key representative and can reflect the important characteristics of the cutting path and key changes in direction, such as the endpoints of the starting and ending points of the cutting path, the turning points where the direction of the path changes significantly, the nodes where the cutting speed or tool state needs to be changed, or the position points corresponding to the need for special control operations on the equipment during the cutting process, etc. Optionally, the extraction of the path key points in the initial cutting path can be achieved through discrete point sampling, such as: discretizing the continuous cutting path to obtain a series of discrete points, and then analyzing the positions of these points and the relationship between adjacent points to extract key points.

[0122] Furthermore, the present invention simulates the cutting of the die-cutting auxiliary materials according to the key points of the path to obtain simulated cutting results, which can predict in advance the problems that may occur in the cutting process, such as whether there is a collision risk at a key turning point, so as to adjust the cutting path in advance to avoid material waste and equipment damage during actual cutting.

[0123] Among them, the simulated cutting results refer to the summary of various feedback information produced after the simulated cutting operation is carried out on the die-cutting auxiliary materials in a virtual simulation environment according to the established cutting motion trajectory, covering whether the molded appearance of the die-cutting auxiliary materials after cutting is completed meets the design expectations.

[0124] As an embodiment of the present invention, the die-cutting auxiliary material is simulated cut according to the path key points to obtain a simulated cutting result, including: serially numbering the path key points to obtain a key point sequence; based on the key point sequence, determining an initial cut-in position corresponding to the die-cutting auxiliary material; based on the initial cut-in position, analyzing a cutting motion trajectory corresponding to the die-cutting auxiliary material; based on the cutting motion trajectory, the die-cutting auxiliary material is simulated cut to obtain a simulated cutting result.

[0125] Among them, the key point sequence refers to the key points of the path extracted from the initial cutting path, which are numbered one by one according to the cutting order or specific logical rules, and thus form an ordered point set. These points carry the key direction information of the cutting path, such as the starting point, the direction mutation point, the end point, etc.; the initial entry position refers to the starting coordinate point where the cutting tool starts to cut into the die-cutting auxiliary material. With reference to the key point sequence, the die-cutting auxiliary material's own characteristics are comprehensively considered, such as material distribution, shape contour, internal structure layout, and production process constraints. The precise judgment is made; the cutting motion trajectory refers to the spatial route that the cutting tool traverses from the initial entry position according to a predetermined plan, and successively crosses each path key point until the entire cutting process is completed. This includes the direction of tool movement, speed change dynamics, and travel strategies in different sections, which fully demonstrates the dynamic changes of the cutting process.

[0126] Furthermore, the serial numbering of the path key points can be achieved through a method based on the path direction, such as: along the starting direction of the cutting path, the path key points encountered are numbered in sequence, and finally a key point sequence is obtained; the determination of the initial entry position corresponding to the die-cutting auxiliary material can be achieved through a method based on priority rules, such as: setting priority rules according to the material properties and processing requirements of the die-cutting auxiliary material to determine the initial entry position; the analysis of the cutting motion trajectory corresponding to the die-cutting auxiliary material can be achieved through an interpolation algorithm, such as: linear interpolation, spline interpolation and other algorithms; the simulated cutting of the die-cutting auxiliary material can be achieved through finite element analysis tools, such as: ANSYS, ABAQUS and other tools.

[0127] The present invention generates adjustment operation instructions corresponding to the die-cutting equipment according to the simulated cutting results when the simulated cutting results do not meet the preset conditions, thereby helping to discover and solve potential problems in advance, avoiding material waste, production stagnation and equipment damage caused by cutting errors in actual production, and reducing production costs and risks.

[0128] Among them, the preset conditions refer to a series of key indicators and limited ranges that are pre-set in the die-cutting production process according to product design requirements, quality standards, and production process specifications to measure whether the simulated cutting results are qualified and whether they can meet the actual production needs. These conditions cover multiple aspects, such as the shape accuracy of the die-cutting auxiliary materials after cutting, including dimensional tolerances, angle deviations, etc., which must be within the specified numerical range; the quality of the cutting edge, such as whether there are burrs and the burr height must be lower than a specific threshold; the time consumption and energy loss during the cutting process cannot exceed the predetermined maximum value; and the rationality of the cutting path to avoid tool collisions, path intersections, and other situations that do not meet safety and process requirements; the adjustment operation instructions refer to a series of instruction sets that are finally generated for directly controlling the die-cutting equipment to perform adjustment actions. These instructions are presented in the form of machine-recognizable codes or signals, covering the speed and steering control of the tool drive motor, and coordinated operation instructions for equipment auxiliary mechanisms (such as material clamping devices, cooling systems, etc.).

[0129] As an embodiment of the present invention, generating adjustment operation instructions corresponding to the die-cutting device based on the simulated cutting results includes: parsing the cutting deviation data in the simulated cutting results; analyzing the tool movement amplitude corresponding to the tool in the die-cutting device based on the cutting deviation data; calculating the position compensation required for the tool in the die-cutting device based on the tool movement amplitude; planning a trajectory adjustment strategy corresponding to the preset cutting path for the tool in the die-cutting device based on the position compensation; and generating adjustment operation instructions corresponding to the die-cutting device based on the trajectory adjustment strategy.

[0130] Among them, the cutting deviation data refers to the quantitative information extracted from the simulated cutting results, which reflects the difference between the actual cutting situation and the ideal preset cutting state. It covers many indicators, such as the deviation value of the size of each part of the die-cutting auxiliary material after cutting compared with the standard size, the difference between the spacing between adjacent cutting lines and the preset value, and the degree of deviation of the cutting edge from the ideal contour; the tool movement amplitude refers to the distance that the tool needs to move in each coordinate axis direction calculated based on the cutting deviation data. For example, if the length of one side of the cut die-cutting auxiliary material is shorter, the distance that the tool moves less in the corresponding direction can be determined by analyzing the cutting deviation data. This distance is the movement amplitude of the tool in this direction; the position compensation amount refers to the amount of compensation based on the tool movement amplitude, combined with the mechanical structure and motion of the die-cutting equipment itself. The preset cutting path refers to the ideal tool motion trajectory set according to the design requirements of die-cutting auxiliary materials, finished product specifications and production process planning before formal cutting. It specifies in detail the optimal route for the tool to cut in from the starting point, pass through various key nodes, and complete the cutting task, covering key information such as cutting direction and speed change nodes. The trajectory adjustment strategy refers to a detailed action plan formulated based on the position compensation to guide the tool from the current wrong position back to the preset cutting path, including determining the order of tool adjustment, the movement speed setting of each stage, and the steering strategy at the key nodes.

[0131] Furthermore, the analysis of the cutting deviation data in the simulated cutting results can be achieved through a clustering algorithm, such as K-Means clustering and other algorithms; the analysis of the tool movement amplitude corresponding to the tool in the die-cutting device can be achieved through a kinematic inverse solution algorithm, such as: the mechanical structure parameters of the die-cutting device are known, combined with the cutting end point position deviation reflected in the cutting deviation data, and the kinematic inverse solution algorithm is used to solve the angle or displacement that the tool needs to move in each joint axis direction, that is, the tool movement amplitude; the calculation of the position compensation required for the tool in the die-cutting device can be achieved through an adaptive control algorithm, such as MRAC, AGSC and other algorithms; the planning of the trajectory adjustment strategy corresponding to the tool in the die-cutting device returning to the preset cutting path can be achieved through a path planning algorithm, such as AI algorithm, Dijkstra algorithm, etc.; the generation of the adjustment operation instructions corresponding to the die-cutting device can be achieved through an instruction generation tool, such as Mastercam, UG NX and other tools.

[0132] S5. After sending the adjustment operation instruction to the second die-cutting device, the die-cutting device is adjusted to restore the corresponding cutting spacing of the die-cutting auxiliary material to within the standard range, and an image of the die-cutting auxiliary material when the corresponding cutting spacing of the die-cutting auxiliary material is restored to within the standard range is obtained, and the cutting spacing of the die-cutting auxiliary material image is verified to obtain verification data, and based on the verification data, an intelligent adjustment plan corresponding to the die-cutting auxiliary material is generated.

[0133] The present invention sends the adjustment operation instruction to the second die-cutting device, and then adjusts the die-cutting device so that the corresponding cutting spacing of the die-cutting auxiliary materials is restored to the standard range, thereby ensuring stable product quality and helping to extend the service life of the equipment. Through precise adjustment, the equipment can operate in the best condition and reduce additional wear and loss on equipment components caused by abnormal operation.

[0134] Among them, the cutting spacing refers to the distance between two adjacent cutting lines during the process of the die-cutting equipment cutting the die-cutting auxiliary materials, or the distance interval between the cutting tool and other related components (such as positioning devices, auxiliary cutting tools, etc.). For example, when a continuous label die-cutting material is cut into a single label, the distance between the edges of adjacent labels is the cutting spacing; the standard range refers to the allowable value range of the cutting spacing pre-set according to product design requirements, production process specifications or quality control standards. For example, for die-cutting auxiliary materials for high-precision electronic products, the standard range of the cutting spacing may be set to ±0.1 mm. Only when the cutting spacing is within this range can the products produced meet subsequent assembly and use requirements. Optionally, sending the adjustment operation instruction to the second die-cutting device can be implemented through an industrial Ethernet protocol, such as: EtherNet / IP, Profinet and other protocols; the adjustment operation of the die-cutting device can be implemented through a device control algorithm, such as: in the control system of the die-cutting device, a PID control algorithm can be used to achieve precise adjustment of the cutting spacing.

[0135] Furthermore, the present invention obtains an image of the die-cutting auxiliary material when the corresponding cutting spacing of the die-cutting auxiliary material is restored to the standard range, which can visually verify the cutting quality. The image clearly presents the cut integrity, dimensional accuracy and relative position relationship of each part of the die-cutting auxiliary material, so as to adjust the equipment parameters or process flow in a targeted manner and improve the overall production efficiency.

[0136] Among them, the die-cutting auxiliary material image refers to the visual image data obtained by shooting and collecting the die-cutting auxiliary materials that have been cut by the die-cutting equipment and the cutting spacing has been restored to the standard range. It can clearly present the appearance of the die-cutting auxiliary materials in the ideal cutting state, such as the flatness of the incision, the shape and size of each cutting part, the layout relationship between each area, etc. Optionally, the die-cutting auxiliary material image obtained when the corresponding cutting spacing of the die-cutting auxiliary material is restored to the standard range can be achieved by an industrial camera, such as: using an industrial camera to take a picture of the die-cutting auxiliary material in the standard cutting spacing state to obtain an image.

[0137] Furthermore, the present invention verifies the cutting spacing of the die-cutting auxiliary material image to obtain verification data, which can promptly discover potential problems in the production process. By accurately measuring the cutting spacing in the image and comparing it with the standard value, deviations can be quickly detected to avoid batch product failures due to inconsistent cutting spacing, thereby reducing production costs.

[0138] Among them, the verification data refers to a series of quantitative or qualitative result information obtained in the process of cutting spacing verification of the die-cutting auxiliary material image, which can reflect the actual cutting spacing and can be used to measure whether it meets the standard requirements, such as the specific value of the actual spacing between each cutting part obtained by image analysis (quantitative data), or the judgment result of whether the cutting spacing is within the standard range (qualitative data, such as qualified or unqualified), and can also include the uniformity of the cutting spacing, the deviation from the standard value and other related content. Optionally, the cutting spacing verification of the die-cutting auxiliary material image can be achieved through a spacing verification tool, such as: ImageJ, AutoCAD and other tools.

[0139] Furthermore, the present invention generates an intelligent adjustment plan corresponding to the die-cutting auxiliary materials based on the verification data, which can improve production efficiency. The die-cutting equipment can be quickly optimized through precise adjustment plans, reducing time waste due to repeated debugging, making the production process smoother and more efficient, and helping to improve product quality. According to problems such as cutting spacing deviation in the verification data, targeted adjustment strategies are formulated to ensure the cutting accuracy of the die-cutting auxiliary materials.

[0140] Among them, the intelligent adjustment scheme refers to a series of instruction sets and operation guidelines for adjusting the operating parameters, process steps and related operating conditions of the die-cutting equipment, which are generated by using intelligent algorithms, models or systems based on the verification data obtained after verifying the cutting spacing of the die-cutting auxiliary material image. It aims to accurately control key factors such as tool position, cutting speed, pressure, material conveying speed, etc. in the die-cutting process, so that the cutting spacing of the die-cutting auxiliary materials can accurately meet the preset standards, and can dynamically optimize the adjustment strategy according to real-time data feedback in the production process, so as to realize the automation, precision and efficiency of die-cutting production. Optionally, the generation of the intelligent adjustment scheme corresponding to the die-cutting auxiliary material can be achieved through a scheme generation tool, such as CLIPS and other tools. After the verification data of the die-cutting auxiliary material is input, the reasoning engine of CLIPS will infer according to these rules to generate specific intelligent adjustment schemes such as tool position adjustment amount and cutting speed adjustment ratio.

[0141] Compared with the problems described in the background technology, the present invention obtains the original image of the die-cutting auxiliary material before cutting to provide basic data for subsequent image processing. Operations such as grayscale, noise reduction filtering, and edge contour extraction all take this as the starting point to ensure the orderly advancement of the entire intelligent adjustment process and to intuitively present the initial state of the die-cutting auxiliary material. The present invention divides the cutting area corresponding to the die-cutting auxiliary material based on the contour image, and can accurately determine the cutting range according to the actual shape and contour of the die-cutting auxiliary material to avoid material waste. It can effectively guide the subsequent cutting process to ensure that the cutting path planning is reasonable and the cutting spacing is uniform, thereby improving the accuracy and quality of the die-cutting process. Furthermore, the present invention can accurately grasp the specific details of the cutting problem by querying the abnormal area parameters in the abnormal cutting area. For example, by analyzing the parameters, it is clear whether it is The root causes of problems such as cutting position offset, excessive size deviation or irregular shape can be identified, thereby improving production efficiency and product quality. Furthermore, the present invention plans the initial cutting path corresponding to the die-cutting auxiliary material based on the impact degree value, and can accurately design the cutting path according to the impact of each parameter on the operation, ensuring cutting accuracy, reducing cutting errors caused by equipment parameter fluctuations, optimizing the cutting process, and improving cutting efficiency. Finally, the present invention sends the adjustment operation instruction to the second die-cutting device, and then adjusts the die-cutting device to restore the corresponding cutting spacing of the die-cutting auxiliary material to the standard range, which can ensure stable product quality and help extend the service life of the equipment. Through precise adjustment, the equipment can operate in the best state, reducing additional wear and loss to equipment components caused by abnormal operation. Therefore, the machine vision-based intelligent adjustment method and system for cutting spacing of die-cutting auxiliary materials provided in the embodiment of the present invention can improve the quality level of die-cutting processing.

[0142] Embodiment 2:

[0143] like Figure 2 The figure shows a functional module diagram of a machine vision-based die-cutting auxiliary material cutting spacing intelligent adjustment system of the present invention.

[0144] The machine vision-based die-cutting auxiliary material cutting spacing intelligent adjustment system 200 of the present invention can be installed in an electronic device. According to the functions to be implemented, the machine vision-based die-cutting auxiliary material cutting spacing intelligent adjustment system can include a contour extraction module 201, an area marking module 202, a degree value calculation module 203, an instruction generation module 204 and a solution generation module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0145] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0146] The contour extraction module 201 is used to obtain the original image of the die-cutting auxiliary material before cutting, grayscale the original image to obtain a grayscale image, perform noise reduction filtering on the grayscale image to obtain a filtered clear image, and extract the edge contour corresponding to the filtered clear image to obtain a contour image;

[0147] The area marking module 202 is used to divide the cutting area corresponding to the die-cutting auxiliary material based on the contour image, calculate the area spacing value between any two areas in the cutting area, analyze the interval deviation value corresponding to the area spacing value and the preset standard spacing value, and mark the abnormal cutting area in the contour image based on the interval deviation value;

[0148] The degree value calculation module 203 is used to query the abnormal area parameters in the abnormal cutting area, associate the abnormal area parameters with the historical operation data corresponding to the preset first die-cutting device, obtain the operation association parameters, detect the current operation parameters of the second die-cutting device corresponding to the die-cutting auxiliary material in real time, and calculate the influence degree value of the operation association parameters on the current operation parameters;

[0149] The instruction generation module 204 is used to plan the initial cutting path corresponding to the die-cutting auxiliary material based on the influence degree value, extract the path key points in the initial cutting path, simulate cutting the die-cutting auxiliary material according to the path key points, obtain the simulated cutting result, and generate the adjustment operation instruction corresponding to the die-cutting device according to the simulated cutting result when the simulated cutting result does not meet the preset condition;

[0150] The solution generation module 205 is used to send the adjustment operation instruction to the second die-cutting device, and then perform an adjustment operation on the die-cutting device to restore the corresponding cutting spacing of the die-cutting auxiliary material to within the standard range, and obtain an image of the die-cutting auxiliary material when the corresponding cutting spacing of the die-cutting auxiliary material is restored to within the standard range, perform cutting spacing verification on the die-cutting auxiliary material image to obtain verification data, and generate an intelligent adjustment solution corresponding to the die-cutting auxiliary material based on the verification data.

[0151] In detail, the modules in the machine vision-based die-cutting auxiliary material cutting spacing intelligent adjustment system 200 in the embodiment of the present invention are used in the same manner as described above. Figure 1 The same technical means as the method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision described in the text and can produce the same technical effects will not be repeated here.

[0152] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision, characterized in that: The method comprises: Obtaining an original image of the die-cutting auxiliary material before cutting, graying the original image to obtain a grayscale image, performing noise reduction filtering on the grayscale image to obtain a filtered clear image, and extracting an edge contour corresponding to the filtered clear image to obtain a contour image; Based on the contour image, the cutting area corresponding to the die-cutting auxiliary material is divided, the area spacing value between any two areas in the cutting area is calculated, the interval deviation value corresponding to the area spacing value and the preset standard spacing value is analyzed, and based on the interval deviation value, the abnormal cutting area in the contour image is marked; Query the abnormal area parameters in the abnormal cutting area, associate the abnormal area parameters with the historical operation data corresponding to the preset first die-cutting device to obtain the operation association parameters, detect the current operation parameters of the second die-cutting device corresponding to the die-cutting auxiliary material in real time, and calculate the influence degree value of the operation association parameters on the current operation parameters; Based on the impact degree value, an initial cutting path corresponding to the die-cutting auxiliary material is planned, key points of the path in the initial cutting path are extracted, and according to the key points of the path, the die-cutting auxiliary material is simulated cut to obtain a simulated cutting result, and when the simulated cutting result does not meet the preset condition, an adjustment operation instruction corresponding to the die-cutting device is generated according to the simulated cutting result; After the adjustment operation instruction is sent to the second die-cutting device, the die-cutting device is adjusted to restore the corresponding cutting spacing of the die-cutting auxiliary material to within the standard range, and an image of the die-cutting auxiliary material when the corresponding cutting spacing of the die-cutting auxiliary material is restored to within the standard range is obtained, and the cutting spacing of the die-cutting auxiliary material image is verified to obtain verification data, and based on the verification data, an intelligent adjustment plan corresponding to the die-cutting auxiliary material is generated.

2. The method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to claim 1, characterized in that: The step of extracting the edge contour corresponding to the filtered clear image to obtain a contour image includes: Extracting initial edge information corresponding to the filtered clear image; Based on the initial edge information, dividing the potential edge area in the filtered clear image; Filling the potential edge area to obtain an edge filling area; Identifying edge lines corresponding to the edge filling area; Based on the edge lines, the edge contour corresponding to the filtered clear image is extracted to obtain a contour image.

3. The method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to claim 1, characterized in that: The calculating the area spacing value between any two areas in the cutting area includes: The area spacing value between any two areas in the cutting area is calculated using the following formula: ； in, represents the area spacing value between any two areas in the cutting area, represents the total number of area pairs corresponding to the cutting area, Indicates the number index of region pairs, Indicates The design spacing value between the area pairs is Indicates The spacing sample values ​​of the area pairs in actual measurement, Indicates The variance of the distance measurement between regions is Indicates The tolerance allowed for the spacing between pairs of areas.

4. The method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to claim 1, characterized in that: The step of marking the abnormal cutting area in the contour image based on the interval deviation value comprises: Based on the interval deviation value, determining the area deviation range corresponding to different cutting areas; Based on the regional deviation range, filtering a set of deviation values ​​exceeding a preset deviation threshold; According to the deviation value set, searching for cutting area indexes corresponding to different cutting areas; Locating the abnormal area point corresponding to the cutting area index; Based on the abnormal region points, an abnormal cutting region in the contour image is marked.

5. The method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to claim 1, characterized in that: The querying of abnormal area parameters in the abnormal cutting area includes: Extracting contour feature information in the abnormal cutting area; Based on the contour feature information, identifying the shape category corresponding to the abnormal cutting area; According to the shape category, construct a standard geometric framework corresponding to the abnormal cutting area; Based on the standard geometric framework, determining standard size points in the abnormal cutting area; Based on the standard size point, the abnormal area parameters in the abnormal cutting area are queried.

6. The method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to claim 1, characterized in that: The calculating the influence degree value of the operation-related parameter on the current operation parameter includes: The influence degree of the operation-related parameter on the current operation parameter is calculated using the following formula: ； in, Indicates the influence degree of the operation-related parameter on the current operation parameter, Indicates the number of samples corresponding to the monitoring samples of the running associated parameters, represents the number index of the monitoring samples, represents the average reference value corresponding to the operation-related parameter, Indicates The associated parameter values ​​of the monitoring samples, Indicates The sample weight value of the monitoring sample, Indicates the number of features corresponding to the relevant features of the operation-related parameters, represents the number index of related features, Indicates The characteristic coefficients corresponding to the relevant features are Indicates The eigenvalues ​​corresponding to the relevant features.

7. The method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to claim 1, characterized in that: The planning of the initial cutting path corresponding to the die-cutting auxiliary material based on the impact degree value includes: Querying the influencing factors corresponding to the influencing degree value; Based on the influencing factors, analyzing the auxiliary material characteristics corresponding to the die-cutting auxiliary materials; Based on the auxiliary material characteristics, determining the cutting priority corresponding to the die-cutting auxiliary material; Based on the cutting priority, the die-cutting auxiliary material is grid-divided to obtain an auxiliary material cutting unit; Based on the auxiliary material cutting unit, an initial cutting path corresponding to the die-cutting auxiliary material is planned.

8. The method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to claim 1, characterized in that: The step of simulating cutting the die-cutting auxiliary material according to the key points of the path to obtain a simulated cutting result includes: Sequentially number the key points of the path to obtain a key point sequence; Based on the key point sequence, determining an initial cutting position corresponding to the die-cutting auxiliary material; Based on the initial cutting position, analyzing the cutting motion trajectory corresponding to the die-cutting auxiliary material; Based on the cutting motion trajectory, the die-cutting auxiliary material is simulated cut to obtain a simulated cutting result.

9. The method for intelligently adjusting the cutting spacing of die-cutting auxiliary materials based on machine vision according to claim 1, characterized in that: Generating adjustment operation instructions corresponding to the die-cutting device according to the simulated cutting result includes: Analyze the cutting deviation data in the simulated cutting results; Based on the cutting deviation data, analyzing the tool movement amplitude corresponding to the tool in the die-cutting device; Calculating the required position compensation amount of the tool in the die-cutting device based on the tool movement amplitude; Based on the position compensation amount, planning a trajectory adjustment strategy for the tool in the die-cutting device to return to the preset cutting path; Based on the trajectory adjustment strategy, an adjustment operation instruction corresponding to the die-cutting device is generated.

10. A machine vision-based intelligent adjustment system for cutting spacing of die-cutting auxiliary materials, characterized in that: The system comprises: A contour extraction module is used to obtain an original image of the die-cutting auxiliary material before cutting, grayscale the original image to obtain a grayscale image, perform noise reduction filtering on the grayscale image to obtain a filtered clear image, and extract the edge contour corresponding to the filtered clear image to obtain a contour image; A region marking module, for dividing the cutting region corresponding to the die-cutting auxiliary material based on the contour image, calculating the region spacing value between any two regions in the cutting region, analyzing the interval deviation value corresponding to the region spacing value and a preset standard spacing value, and marking the abnormal cutting region in the contour image based on the interval deviation value; A degree value calculation module is used to query the abnormal area parameters in the abnormal cutting area, associate the abnormal area parameters with the historical operation data corresponding to the preset first die-cutting device, obtain the operation association parameters, detect the current operation parameters of the second die-cutting device corresponding to the die-cutting auxiliary material in real time, and calculate the influence degree value of the operation association parameters on the current operation parameters; An instruction generation module is used to plan an initial cutting path corresponding to the die-cutting auxiliary material based on the influence degree value, extract path key points in the initial cutting path, simulate cutting the die-cutting auxiliary material according to the path key points, obtain a simulated cutting result, and generate an adjustment operation instruction corresponding to the die-cutting device according to the simulated cutting result when the simulated cutting result does not meet the preset conditions; A scheme generating module is used for sending the adjustment operation instruction to the second die-cutting device, and then performing an adjustment operation on the die-cutting device so that the corresponding cutting spacing of the die-cutting auxiliary material is restored to within a standard range, and obtaining an image of the die-cutting auxiliary material when the corresponding cutting spacing of the die-cutting auxiliary material is restored to within the standard range, performing cutting spacing verification on the die-cutting auxiliary material image to obtain verification data, and generating an intelligent adjustment scheme corresponding to the die-cutting auxiliary material based on the verification data.

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