Control optimization method based on paperboard digital intelligent grooving machine

Abstract

The invention discloses a control optimization method based on a digital intelligent paperboard grooving machine, relates to the technical field of paperboard grooving, and solves the problems that the idle stroke distance of a cutter head is long and repeated motion is frequent due to the fact that a motion trail is not optimized in combination with the distribution condition of an actual grooving area. The invalid travel is reduced; and on the basis of a single-time or double-time confirmation process of the total number of grooving positions, path planning in each grooving area is further optimized, the finally formed optimal path remarkably shortens the movement distance of the grooving machine, the overall grooving speed is increased, the method is particularly suitable for a batch machining scene of multiple grooving areas, and the machining efficiency is improved. For the condition that irregular paperboards or multiple feature points exist, through a multi-round coincidence verification and path adjustment mechanism, the adaptability of equipment to complex processing scenes is enhanced, and the fault tolerance and stability of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of paperboard slotting, in particular to a control optimization method based on a paperboard digital intelligent slotting machine. BACKGROUND

[0002] In the field of paper box production and processing, the paperboard slotting process is a core link that determines the quality of paper box forming and the subsequent assembly efficiency, and its processing precision directly affects the folding flatness, sealing performance and structural strength of the paper box. With the rapid growth of demand for customized, small-batch and multi-specification paper boxes in the packaging industry, the control mode of traditional paperboard slotting machines gradually exposes many technical bottlenecks, making it difficult to adapt to modern production needs. Traditional slotting machines rely on manual experience for operation, and workers need to manually adjust the cutter position, feed speed and other parameters according to the size and material of the paperboard, and calibrate the slotting position by eye observation or simple measuring tools. Not only is the operation process tedious and labor costs high, but it is also easy to cause slotting position deviation and inconsistent groove depth due to human judgment deviation. At the same time, traditional equipment lacks precise recognition ability of paperboard surface features and cannot automatically adapt to the processing characteristics of different types of paperboard. In terms of path planning, traditional slotting machines mostly use fixed path mode and do not optimize the motion trajectory in combination with the distribution of the actual slotting area, resulting in long cutter idle distance and frequent repeated motion. Not only does this increase equipment energy consumption and mechanical wear, but it also prolongs the processing cycle of single paperboard, especially in batch processing scenarios with multiple slotting areas, the efficiency shortcoming is more prominent.

[0003] In addition, the control system of traditional equipment is mostly in independent operation mode, lacks linkage with digital systems, and cannot realize real-time storage, analysis and tracing of processing data. When slotting quality problems occur, it is difficult to quickly locate the root cause, and subsequent process optimization lacks data support. Therefore, developing a slotting machine control method that can realize accurate paperboard recognition, automatic parameter matching and intelligent path optimization has become a key requirement to solve current industry pain points and improve paper box processing automation level and product quality. SUMMARY

[0004] To overcome the shortcomings of the prior art, the present application provides a control optimization method based on a paperboard digital intelligent slotting machine, which solves the problem of long cutter idle distance and frequent repeated motion caused by not optimizing the motion trajectory in combination with the distribution of the actual slotting area.

[0005] To achieve the above purpose, the present application is implemented by the following technical scheme: a control optimization method based on a paperboard digital intelligent slotting machine, comprising the following steps: Step one, obtain the surface image of the need to be slotted paperboard, according to the edge profile of the surface image, confirm the midpoint of the slotted paperboard, and then according to the profile characteristics of the edge profile, confirm the calibration feature of the current slotted paperboard, the specific way is: When the slotted paperboard is placed in the processing area, the surface image of the slotted paperboard is obtained, and the surface image is processed by gray scale, the gray image associated with the surface image is confirmed, and the RGB value associated with different pixel points in the surface image is marked as R i , G i and B i , wherein i represents different pixel points, and HD i =0.299×R i +0.587×G i +0.114×B i is confirmed, and the gray value HD i associated with the corresponding pixel point is confirmed, and the gray image associated with the surface image is generated according to the different gray values associated with different pixel points; The comprehensive gradient associated with different gray points in the gray image is confirmed by using Sobel algorithm, the vertical gradient and horizontal gradient associated with the corresponding gray point are confirmed according to the different gray values of different gray points, and then: The comprehensive gradient associated with the corresponding gray point is confirmed, the gray points with comprehensive gradient satisfying comprehensive gradient≥Y1 are marked as gradient points, otherwise, no mark is made, wherein Y1 is a preset value; The adjacent gradient points of the gray image are confirmed in sequence, and the adjacent gradient points are connected to confirm the gradient contour line, whether the gradient contour line is closed is identified, if closed, it is recorded as the edge profile of the current slotted paperboard, otherwise, stop until the edge profile is determined; The edge profile is combined with the two-dimensional coordinate system to confirm the two-dimensional coordinates associated with different profile points on the edge profile, and the two-dimensional coordinates associated with a plurality of profile points are processed by mean value to confirm the mean value coordinates, and the point with the mean value coordinates is recorded as the midpoint of the slotted paperboard; According to the midpoint confirmed inside the slotted paperboard, the straight line distance between different profile points and the midpoint on the edge profile is confirmed, from the confirmed several groups of straight line distance, the maximum value is selected, and the profile point associated with the maximum value is recorded as the feature point; If there are multiple groups of maximum values, a group of profile points is randomly selected as the feature point, taking the midpoint as the starting point and the feature point as the ending point, a group of calibration inner lines is generated, and the generated calibration inner lines are taken as the calibration feature of the current slotted paperboard; Step two, according to the calibration feature confirmed by the current slotted paperboard, extract the preset standard template from the cloud database, compare the standard template with the current slotted paperboard, confirm the slotted position associated with the surface of the current slotted paperboard and record, the specific way is: Let the bus length associated with the calibration feature be denoted as L, and let the feature line length preset in the different preset standard templates be denoted as Q k where k represents different standard templates, and Q is determined from a plurality of Qk k =L standard templates, the standard template is denoted as a to-be-determined template, and if there is no Q k =L standard template, an error signal is directly generated to indicate; The gray-scale image associated with the slotted paperboard is compared with the standard template: the calibration feature of the gray-scale image is overlapped with the preset feature associated with the standard template, and after the overlapping process is completed, it is determined whether the gray-scale image is completely overlapped with the standard template. If it is overlapped, the current processing process is completed, and if it is not overlapped, the preset features in the gray-scale image are sequentially overlapped, and the process is stopped when the gray-scale image is completely overlapped with the standard template; According to the preset slotted position in the standard template, the gray-scale image of the current slotted paperboard is marked synchronously; Step three, according to the slotted position recorded on the surface of the current slotted paperboard, the slotted area is locked and path analysis is performed, the initial optimal path is selected, and based on the total number of slotted areas, the internal slotted path associated with each slotted area is analyzed, the optimal path is confirmed and executed. The specific way of selecting the initial optimal path is: and a plurality of slotted positions recorded by the slotted paperboard, the slotted area associated with each different slotted position is determined, and the center point associated with each slotted area is determined and denoted as a feature point. Then, a plurality of selection processes are performed from the plurality of marked feature points: a group of feature points are randomly selected as initial points, and other feature points are randomly selected in sequence. After all the feature points are selected, the selected feature points are connected in sequence according to the step-by-step selection process, and a driving path is generated. Let the total bus length of the driving path corresponding to the current selection process be denoted as ZS q where q represents different selection processes, and the different total bus lengths ZS q are associated with a plurality of selection processes, and the minimum value ZS q min is selected, and the selection process associated with the minimum value ZS q min is denoted as the determination process, and the driving path associated with the determination process is denoted as the initial optimal path; The specific way of confirming the optimal path is: According to the confirmed initial optimal path, the slotted positions associated with the driving process of the path are sorted in sequence, the slotted position sequence is confirmed, the specific total number ZZ of slotted positions in the slotted position sequence is identified, if ZZ is even, a single confirmation process is performed, and if ZZ is odd, a double confirmation process is performed; Single confirmation process: from the confirmed slotting position sequence, the two groups of slotting positions adjacent to each other are recorded as a unit set from the first slotting position, the region profiles of the two slotting regions in the unit set are confirmed, the two profile points closest in straight line distance on the two confirmed region profiles are confirmed, the two profile points are recorded as the slotting starting points corresponding to the slotting positions, and the slotting paths of the corresponding slotting regions are generated in the slotting regions according to the clockwise direction according to the slotting starting points, the slotting paths recorded in the determination process are sorted in turn according to the sorting mode of the slotting position sequence, and the optimal path is generated; Double confirmation process includes: generating slotting paths associated with different slotting positions in the slotting position sequence according to the slotting path confirmation mode of the single confirmation process, recording the straight line distance of the two nearest profile points in the unit set, and summing up the recorded several groups of straight line distances to confirm the total distance, which is recorded as the single feature; Then, from the second group of slotting positions, the two groups of slotting positions adjacent to each other are recorded as a unit set, and the slotting paths associated with different slotting regions are confirmed in the same way as the above slotting region slotting path confirmation, and the total distance associated with the current processing process is confirmed in the same way as the single feature total distance, which is recorded as the double feature; From the single feature or the double feature, the minimum value is selected, and the processing process associated with the minimum value is recorded as the determination process, and the slotting paths recorded in the determination process are sorted in turn according to the sorting mode of the slotting position sequence, and the optimal path is generated.

[0006] The application provides a control optimization method based on a paperboard digital intelligent slotting machine. The application realizes accurate positioning of different types and sizes of paperboards by surface image analysis and grayscale processing, combined with Sobel algorithm to extract edge profiles and determine midpoint and calibration features, laying a high-precision foundation for subsequent determination of slotting positions; the calibration feature and the standard template comparison mechanism can effectively adapt to the characteristics of various paperboards, ensuring the accuracy of the slotting position marking and greatly reducing the positioning deviation caused by the difference in paperboard specifications; In terms of efficiency improvement, the path optimization strategy shows obvious advantages; the initial optimal path selects the path with the shortest total distance of feature points, reducing the invalid travel; and the single or double confirmation process based on the total number of slotting positions further optimizes the path planning within each slotting region, and the final optimal path significantly shortens the movement distance of the slotting machine, improves the overall slotting rate, and is especially suitable for batch processing scenarios with multiple slotting regions; From the intelligence and adaptability, the method introduces a cloud database to store standard templates, combines image recognition and automatic comparison technology, reduces manual intervention, and realizes the automation of the slotting process. For irregular paperboards or multiple feature points, through multi-round coincidence verification and path adjustment mechanism, the adaptability of the equipment to complex processing scenes is enhanced, and the fault tolerance and stability of the system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 The figure is a schematic diagram of the method. DETAILED DESCRIPTION

[0008] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0009] Please refer to Figure 1 The present application provides a control optimization method based on a paperboard digital intelligent slotting machine, comprising the following steps: Step one, obtain the surface image of the paperboard to be slotted, confirm the midpoint of the slotted paperboard according to the edge profile of the surface image, and then confirm the calibration features of the current slotted paperboard according to the profile features of the edge profile. Specifically, for different types and sizes of paperboards, image acquisition is first needed, then feature analysis and verification are performed on the obtained image to identify some paperboard features of this type of paperboard, and then the specific slotting position of the corresponding paperboard can be effectively confirmed by combining the preset standard template, so as to quickly and effectively determine the slotting opening and complete the slotting process of the corresponding slotted paperboard. The specific way to confirm the midpoint of the slotted paperboard is as follows: When the slotted paperboard is placed in the processing area, the surface image of the slotted paperboard is obtained, and then the surface image is subjected to grayscale processing to confirm the grayscale image associated with the surface image. The RGB values associated with different pixel points in the surface image are sequentially marked as R i , G i and B i , where i represents different pixel points. Then, HD i = 0.299 * R i + 0.587 * G i + 0.114 * B i is used to confirm the grayscale value HD i associated with the corresponding pixel point, and different grayscale values associated with different pixel points are used to generate the grayscale image associated with the surface image. The Sobel algorithm is used to confirm the comprehensive gradient associated with different gray points in the gray image. According to different gray values of different gray points, the vertical gradient and the horizontal gradient associated with the corresponding gray points are confirmed, and then the comprehensive gradient is confirmed. The comprehensive gradient associated with the corresponding gray point is confirmed, and the gray points with comprehensive gradient satisfying comprehensive gradient >= Y1 are marked as gradient points, otherwise, no marking is performed, where Y1 is a preset value, and its specific value is determined by the operator according to experience; The adjacent gradient points of the gray image are confirmed in sequence, and the adjacent gradient points are connected to confirm the gradient contour line. Whether the gradient contour line is closed is identified. If it is closed, it is recorded as the edge contour of the current slotted paperboard, otherwise, it is stopped until the edge contour is determined; The edge contour is combined with the two-dimensional coordinate system to confirm the two-dimensional coordinates associated with different contour points on the edge contour. The two-dimensional coordinates associated with several contour points are processed by averaging to confirm the average coordinates, and the point with the average coordinates is recorded as the midpoint of the slotted paperboard; According to the midpoint confirmed inside the slotted paperboard, the straight line distance associated between different contour points and the midpoint on the edge contour is confirmed. From the confirmed several groups of straight line distances, the maximum value is selected, and the contour point associated with the maximum value is recorded as the feature point. If there are multiple groups of maximum values, a group of contour points is randomly selected as the feature point. With the midpoint as the starting point and the feature point as the ending point, a group of calibration inner lines is generated, and the generated calibration inner line is taken as the calibration feature of the current slotted paperboard. Specifically, in the specific slotted processing process, the slotted paperboard needs to confirm the gray feature associated with the paperboard according to the overall surface image of the corresponding paperboard, so as to identify the edge contour existing around the corresponding paperboard, and according to the edge contour, to confirm the slotted feature of the current paperboard, which is convenient for subsequent comparison and verification, and quickly realizes the slotted processing process of the slotted paperboard. Step two, according to the calibration feature confirmed by the current slotted paperboard, extract the preset standard template from the cloud database, compare the standard template with the current slotted paperboard, confirm the slotted position associated with the surface of the current slotted paperboard and record it. The specific way of confirming the slotted position in detail is: Record the total bus length associated with the calibration feature as L, and record the feature line length preset in different preset standard templates as Q k Where k represents different standard templates, Q is confirmed from several Qk k =L, and this standard template is recorded as the undetermined template. If Q k= L standard template, directly generate error signal display, in the actual operation process, the relevant management personnel need to preset the standard template in advance, according to the corresponding processing scene associated with the processing paperboard, the corresponding template associated with different processing paperboard is preset in advance, and the corresponding slotting position in the standard template is preset simultaneously, which is convenient for subsequent comparison process to confirm the corresponding slotting position on the corresponding paperboard; The gray image associated with the slotted paperboard is compared with the standard template: the calibration features of the gray image are overlapped with the preset features (that is, a group of feature lines associated with the corresponding feature line length) associated with the standard template. After completing the overlapping process, it is confirmed whether the gray image is completely overlapped with the standard template (in the overlapping process, scaling will be performed, and the size of the corresponding gray image contour will be adjusted to the preset size of the standard template). If it is overlapped, the current processing process is completed. If it is not overlapped, the other preset features in the gray image are overlapped in turn. Stop until the gray image and the standard template are completely overlapped. The reason why there are other preset features is that there are multiple maximum values of the corresponding contour points. For example, irregular paperboard may have multiple farthest points. After randomly selecting one farthest point, it cannot reach the overlapping state. Therefore, the farthest point selected and the farthest point on the corresponding template are not overlapped, and it needs to be processed again. Execute several overlapping processing processes to confirm the complete overlapping process. According to the preset slotting position in the standard template, the gray image of the current slotted paperboard is marked simultaneously. Specifically, in the actual processing process, through the corresponding overlapping processing process, the slotted paperboard of the corresponding standard template can be effectively marked in the corresponding gray image, which is convenient for subsequent execution of the corresponding slotting process. Step three, according to the slotting position recorded on the surface of the current slotted paperboard, lock the slotting area, and perform path analysis, select the initial optimal path, and then based on the total number of slotting areas, perform path analysis on the internal slotting path associated with each slotting area, confirm the optimal path and execute it. The specific way of selecting the initial optimal path is: And the several slotting positions recorded by the slotted paperboard, confirm the slotting area associated with each different slotting position (which can be directly extracted and confirmed from the standard template), determine the center point associated with each slotting area, and mark it as a feature point. Then, from the several marked feature points, execute several selection processes: randomly select a group of feature points as initial points, and then randomly select other feature points in turn. After all the feature points are selected, the selected feature points are connected according to the step-by-step selection process, and a driving path is generated. The total length of the driving path corresponding to the current selection process is recorded as ZS q Where q represents different selection processes, and the different total lengths ZSq In the specific implementation, the minimum value ZS q min, and the selection process associated with the minimum value ZS q min is recorded as a determination process, and the driving path associated with the determination process is recorded as a primary optimal path; Specifically, the primary optimal path is a path corresponding to the minimum distance traveled by the slotting machine when all the feature points are selected and confirmed in the path confirmation process. In the determination state of the primary optimal path, subsequent slotting paths are analyzed and processed again, so as to determine an optimal path and perform a slotting process on the corresponding slotting paperboard. The specific manner of confirming the optimal path is as follows: According to the confirmed primary optimal path, the slotting positions associated with the driving process are sequentially sorted according to the path, a slotting position sequence is confirmed, the specific total number ZZ of slotting positions in the slotting position sequence is identified, if ZZ is even, a single confirmation process is performed, and if ZZ is odd, a double confirmation process is performed. Single confirmation process: from the first slotting position in the confirmed slotting position sequence, two groups of slotting positions adjacent to each other are recorded as a unit set (each unit set includes two groups of slotting positions), the region contours of the two slotting regions in the unit set are confirmed, the two contour points closest in straight line distance on the two confirmed region contours (located on the two region contours, respectively) are confirmed as the slotting starting points of the corresponding slotting positions, and the slotting paths of the corresponding slotting regions are generated in the clockwise direction according to the slotting starting points. The slotting paths recorded in the determination process are sequentially sorted according to the sorting manner of the slotting position sequence, and the optimal path is generated, the slotting starting point of which is not only the starting point but also the ending point. Double confirmation process: according to the slotting path confirmation manner of the single confirmation process, the slotting paths associated with different slotting positions in the slotting position sequence are generated, the straight line distances of the two closest contour points in the corresponding unit set are recorded, and the total distance of the recorded several groups of straight line distances is summed up to confirm the total distance, which is recorded as a single feature. Then, from the second group of slotting positions, two groups of slotting positions adjacent to each other are recorded as a unit set, and the same manner of confirming the slotting path of the slotting region as described above is adopted to confirm the slotting paths associated with different slotting regions, and the same confirmation manner of the total distance of the single feature is adopted to confirm the total distance associated with the current processing process, which is recorded as a double feature. From the single feature or the double feature, the minimum value is selected, the processing process associated with the minimum value is recorded as a determination process, and the slotting path recorded in the determination process is sequentially sorted according to the sorting manner of the slotting position sequence to generate an optimal path. The optimal path is the shortest path associated with the corresponding slotting machine during the slotting process. When the slotting process is performed according to the shortest path, the slotting rate is the fastest, and the distance traveled by the corresponding slotting head is the shortest. This not only guarantees the normal slotting process of the paperboard, but also guarantees the specific efficiency of the slotting.

[0010] Some data in the above formula are dimensionless for numerical calculation, and the contents not described in detail in the specification all belong to the prior art known to those skilled in the art.

[0011] The above examples are only used to illustrate the technical method of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A control optimization method based on a digital intelligent cardboard slotting machine, characterized in that, Includes the following steps: Step 1: Obtain the surface image of the cardboard to be slotted. Based on the edge contour of the surface image, determine the midpoint of the cardboard to be slotted. Then, based on the contour features of the edge contour, determine the calibration features of the current cardboard to be slotted. Step 2: Based on the calibration characteristics confirmed by the current slotted cardboard, extract the preset standard template from the cloud database, compare the standard template with the current slotted cardboard, confirm the slotting position associated with the surface of the current slotted cardboard, and record it. Step 3: Based on the slotting position recorded on the surface of the slotted cardboard, locate the slotting area and perform path analysis to select the initial optimal path. Then, based on the total number of slotting areas, perform path analysis on the internal slotting path associated with each slotting area to confirm the optimal path and execute it.

2. The control optimization method based on a digital intelligent cardboard slotting machine according to claim 1, characterized in that, In step one, the specific method for confirming the calibration characteristics of the current slotted cardboard is as follows: When the slotted cardboard is placed in the processing area, a surface image of the cardboard is acquired, and then the surface image is converted to grayscale. The grayscale image associated with the surface image is identified, and the RGB values ​​associated with different pixels in the surface image are sequentially labeled as R. i G i And B i Where i represents different pixels, and then HD is used. i =0.299×R i +0.587×G i +0.114×B i Confirm the grayscale value HD associated with the corresponding pixel. i Based on the different gray values ​​associated with different pixels, a grayscale image associated with the surface image is generated. The Sobel algorithm is used to confirm the comprehensive gradient associated with different gray-level points within a grayscale image. Based on the different gray values ​​of different gray-level points, the vertical and horizontal gradients associated with the corresponding gray-level points are confirmed, and then: Confirm the comprehensive gradient associated with the corresponding grayscale point, and mark the grayscale point whose comprehensive gradient satisfies: comprehensive gradient ≥ Y1 as a gradient point, otherwise do not mark it, where Y1 is a preset value; The adjacent gradient points around the grayscale image are confirmed in sequence, and the adjacent gradient points are connected to confirm the gradient contour line. The closure of the gradient contour line is identified. If it is closed, it is recorded as the edge contour of the current slotted cardboard. Otherwise, the process continues until the edge contour is determined. Combine the edge contour with the two-dimensional coordinate system to confirm the two-dimensional coordinates associated with different contour points on the edge contour. Then, average the two-dimensional coordinates associated with several contour points to confirm the average coordinates, and record the point where the average coordinates are located as the midpoint of the slotted cardboard. Based on the midpoint identified inside the slotted cardboard, determine the straight-line distances between different profile points and the midpoint on the edge profile. From the confirmed sets of straight-line distances, select the maximum value and record the profile point associated with the maximum value as the feature point.

3. The control optimization method based on a digital intelligent cardboard slotting machine according to claim 2, characterized in that, If there are multiple sets of maximum values, a set of contour points is randomly selected and recorded as feature points. Starting from the midpoint and ending at the feature points, a set of calibration inner lines is generated, and the generated calibration inner lines are used as the calibration features of the current slotted cardboard.

4. The control optimization method based on a digital intelligent cardboard slotting machine according to claim 1, characterized in that, In step two, the specific method for confirming the slot location in detail is as follows: Let L be the bus length associated with the calibration feature, and let Q be the preset feature line length within different preset standard templates. k Where k represents different standard templates, and Q is determined from several Qk. k =L is the standard template, and this standard template is recorded as the template to be determined. The grayscale image associated with the slotted cardboard is compared with the standard template: the calibration features of the grayscale image are made to coincide with the preset features associated with the standard template. After the coincidence process is completed, it is confirmed whether the grayscale image is completely coincident with the standard template. If it coincides, the current processing process is completed. If it does not coincide, it is sequentially compared with other preset features in the grayscale image until the grayscale image is completely coincident with the standard template. Based on the preset slotting positions in the standard template, they are simultaneously marked in the grayscale image of the current slotted cardboard.

5. The control optimization method based on a digital intelligent cardboard slotting machine according to claim 4, characterized in that, If Q does not exist k If the standard template of =L is used, an error signal will be generated and displayed directly.

6. The control optimization method based on a digital intelligent cardboard slotting machine according to claim 1, characterized in that, In step three, the specific method for selecting the initial optimal path is as follows: And the slotted cardboard records several slotted positions, confirms the slotted area associated with each different slotted position, determines the center point associated with each slotted area and records it as a feature point, and then performs several selection processes from the marked feature points: randomly selects a set of feature points as the initial point, and then randomly selects other feature points in sequence until all feature points have been selected, and connects the sequentially selected feature points according to the step-by-step selection process to generate a driving path, and records the bus length of the driving path corresponding to the current selection process as ZS. q Where q represents different selection processes, and ZS represents different total threads associated with several selection processes. q In the middle, select the minimum value ZS q min, and set the minimum value ZS q The selection process associated with min is denoted as the determining process, and the driving path associated with the determining process is denoted as the initial optimal path.

7. The control optimization method based on a digital intelligent cardboard slotting machine according to claim 6, characterized in that, In step three, the specific method for confirming the optimal path is as follows: Based on the confirmed initial optimal path, the associated slotting positions are sorted according to the path's travel progress to confirm the slotting position sequence. The total number of slotting positions ZZ within the slotting position sequence is identified. If ZZ is even, a single confirmation process is executed; if ZZ is odd, a double confirmation process is executed. Single confirmation process: Starting from the first slotting position in the confirmed slotting position sequence, two consecutive adjacent slotting positions are recorded as a single position set. The regional contours of the two slotting regions within the single position set are confirmed. On the two confirmed regional contours, the two contour points with the closest straight-line distance are confirmed. The two confirmed contour points are recorded as the slotting start points of the corresponding slotting positions. Based on the slotting start points, the slotting paths of the corresponding slotting regions are generated in a clockwise direction within the slotting regions. The slotting paths recorded in the confirmation process are sorted according to the sorting method of the slotting position sequence to generate the optimal path.

8. The control optimization method based on a digital intelligent cardboard slotting machine according to claim 7, characterized in that, The double confirmation process includes: generating a slotting path associated with different slotting positions within the slotting position sequence according to the slotting path confirmation method of the single confirmation process, recording the straight-line distance between the two nearest contour points within the corresponding single position set, summing the recorded straight-line distances, confirming the total distance, and recording it as a single feature. Starting from the second set of slotting positions, two adjacent sets of slotting positions are recorded as a single position set. Using the same method as the above-mentioned slotting path confirmation, the slotting paths associated with different slotting areas are confirmed. Using the same method as the total distance of a single feature, the total distance associated with this processing process is confirmed and recorded as a double feature. From single or double features, select the minimum value, and record the processing process associated with the minimum value as the determination process. Sort the slotting paths recorded in the determination process according to the sorting method of the slotting position sequence to generate the optimal path.

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