Intelligent cutting piece layout optimization method considering quality partition constraint

Abstract

The invention discloses an intelligent cut-part layout optimization method considering a quality partition constraint. The method comprises the following steps: S1, data preprocessing; s2, grouping a vector, performing layout on a two-dimensional plane, and forming a grouping vector by two binary base vectors; the core solving thought of the two binary basis vectors is a minimum displacement vector for ensuring that the two cutting pieces are not overlapped at the joint; s3, generating an initial solution, solving a cut piece displacement vector based on the organized vector, and generating a temporary layout; calculating a convex hull area based on the temporary layout, and identifying a potential insertion space through the convex hull area; s4, layout of the remaining cutting pieces: after the initial solution is generated, if the remaining cutting pieces which are not placed still exist, layout based on the layout of the current initial solution is needed; and carrying out secondary optimization layout on the residual cutting pieces. According to the method, the problem of layout on irregular leather can be effectively solved, so that the material utilization rate and the production efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of two-dimensional material layout, and in particular to an intelligent piece layout optimization method considering quality zoning constraints. Background Art

[0002] Intelligent leather panel layout optimization methods that consider quality zoning constraints have important engineering value in modern manufacturing. For example, in the manufacturing of high-end leather goods, automotive seat coverings, luxury furniture, and other fields, this technology is needed to optimize panel layout and improve material utilization. The natural properties of raw materials and the functional differences of product components form a dual constraint: on the one hand, exposed decorative parts must strictly match flawless high-ductility areas to maintain aesthetic quality, while load-bearing structural parts must correspond to high-tear resistance areas to ensure physical performance. On the other hand, the growing trend of personalized customization forces companies to dynamically adapt to the production needs of multiple varieties and small batches on a limited raw material palette. Therefore, many industries urgently need to solve the problem of leather panel layout that considers quality zoning constraints.

[0003] In today's manufacturing industry, low material utilization and inefficient pattern layout remain widespread. For example, in the apparel industry, due to the irregularities and complexity of leather, traditional pattern layout methods often fail to fully utilize each leather sheet, resulting in significant material waste. This not only increases raw material costs but also places an unnecessary burden on the environment. In the furniture manufacturing industry, leather is widely used in the production of furniture such as sofas, chairs, and beds. Different products have varying quality requirements for leather, and the quality of each area of a leather sheet varies significantly. High-quality areas need to be used to produce high-value products to maximize profits. Therefore, these pattern layout issues are widespread across various industries and have become critical issues that need to be addressed. Although the application scenarios vary, the core issue is the same: how to efficiently arrange the required pieces according to rules using limited materials, maximize the use of each leather sheet, reduce waste, and improve material utilization, thereby achieving resource conservation and optimization. Summary of the Invention

[0004] In response to the above-mentioned defects, the purpose of the present invention is to propose a leather intelligent cutting pattern optimization method taking into account quality zoning constraints. This method can effectively solve the problem of pattern arrangement on irregular leather, thereby significantly improving material utilization and production efficiency.

[0005] To achieve this object, the present invention adopts the following technical solutions:

[0006] The intelligent cutting piece layout optimization method considering quality partition constraints includes the following steps:

[0007] S1. Data preprocessing, including obtaining the overall size information of the leather, the size and location distribution of each grade area, and the size, quantity, and grade information of irregular pieces, and sorting all irregular pieces based on the size and grade information;

[0008] S2. Grouping vectors. The pattern layout is performed on a two-dimensional plane. The grouping vectors are composed of two binary basis vectors. The core solution of these two binary basis vectors is to find the minimum displacement vector that ensures that the two pieces do not overlap at the junction.

[0009] S3, generating an initial solution, solving the piece displacement vector based on the grouped vector and generating a temporary layout; calculating the convex hull area based on the temporary layout, and identifying potential insertion spaces through the convex hull area; searching for the optimal placement point in the convex hull area for the initial remaining pieces in combination with the fitness function for insertion; if there are still initial remaining pieces that have not been placed after the insertion attempts are completed, jump to the first step to continue execution; the initial remaining pieces are inserted in this order, and the initial solution generation process ends when all the initial remaining pieces have completed the placement attempts or there is no placement area left;

[0010] S4. Layout of the remaining pieces. After the initial solution is generated, if there are still remaining pieces that have not been placed, it is necessary to select a single remaining piece from the final set of remaining pieces in order based on the layout of the current initial solution and select the placement position in combination with the fitness function; perform a secondary optimization layout of the remaining pieces.

[0011] Furthermore, step S2 includes the following steps during the solution process:

[0012] S21, first fix the first piece in order, and use the NFP geometry tool to calculate the placement position of the second piece;

[0013] S22, then find the minimum distance to move the second piece horizontally to the right from the position of the first piece to a non-overlapping position, and obtain vector v1 = (x1, 0);

[0014] S23, finally, the set of possible placement points of the third piece is calculated through NFP, and the points that do not overlap with the first and second pieces and have the smallest displacement are selected from them, so as to obtain the second vector v2 = (x2, y), that is, the grouped vector v = (v1, v2).

[0015] Furthermore, step S3 specifically includes the following steps:

[0016] S31, select the corresponding piece according to the layout order, and calculate the non-overlapping constraint space between the piece and the layout area through NFP to generate a set of geometrically feasible candidate placement points. In this process, a lower left placement strategy is adopted: the point with the smallest horizontal coordinate is prioritized. If there are multiple points, the point with the smallest vertical coordinate is further selected.

[0017] S32, combining the grouped vectors and calculating the subsequent placement positions of the pieces using formula (1) until the remaining number of pieces is zero or there is no placement area;

[0018] v=i*v1+j*v2 Formula (1);

[0019] Where v is the displacement vector of the piece, and i and j are both integers;

[0020] S33: The temporary nesting layout generated based on the grouped vectors may contain underutilized space. Therefore, it is necessary to calculate the convex hull area formed by all placed pieces to reflect the space occupancy of the current layout and provide a basis for optimizing space utilization. The convex hull area is constructed by first selecting the point with the smallest horizontal coordinate from the vertices of all placed pieces as the starting point. If there are multiple points with the same horizontal coordinate, the point with the smallest vertical coordinate is selected from the point set. Then, starting from the current point, all vertices are traversed to find the point with the smallest polar angle with the current point as the next convex hull point. The process is iterated until the starting point is returned, and the convex hull construction is finally completed. The convex hull area can be used to identify potential intervening spaces, laying a geometric foundation for subsequent piece placement and layout optimization, thereby improving space utilization and nesting efficiency.

[0021] S34, selecting the initial remaining pieces in the order of insertion, and finding the optimal placement point in the convex hull area in combination with the fitness function for placement; the introduction of the fitness function ensures that the placement of the initial remaining pieces can maximize the use of the remaining space while avoiding overlap with the already placed pieces; this process continues until all remaining pieces have completed the insertion attempt;

[0022] S35, if there are still unplaced initial remaining pieces after completing the above steps, jump to the first step to continue execution; when all initial remaining pieces have completed placement attempts or there is no placement area, the initial solution generation process ends.

[0023] Preferably, step S4 specifically includes the following steps:

[0024] S41. Select the remaining pieces from the last remaining piece set according to the arrangement order of the remaining pieces, calculate the candidate placement point set of the remaining pieces in the placement area, and select the placement point with the highest fitness according to formula (2) for placement; when placing, the remaining pieces may overlap with the area to be arranged;

[0025]

[0026] Among them A ij k represents the overlapping area of the minimum enveloping rectangle of pieces i and j; i ,k j k represents the area of the minimum enveloping rectangle of the piece divided by the actual area of the piece; ij A represents the distance between the centers of gravity of pieces i and j divided by the distance between the centroids of pieces i and j; im The area outside the fabric of the minimum enveloping rectangle of the expanded outline of piece i; γ constant coefficient (0–2.0). Adjusting the coefficient γ can produce different nesting results. γ represents the weight of the fit between the piece and the fabric boundary. The larger the γ value, the more preference will be given to the position close to the fabric boundary.

[0027] S42, calculating the overlap value between the remaining pieces and the positions to be placed, the overlap value calculation being achieved by the following steps: traversing all edges of the two overlapping polygons of the positions to be placed and the polygons of the remaining pieces, calculating the intersection points of the edges and storing them in a temporary array, identifying points within the two polygons to form a new intersecting polygon, and finally calculating the area of the intersecting polygon using the shoelace theorem formula. The area is the overlap value between the polygons of the positions to be placed and the polygons of the remaining pieces; by traversing all polygons of the positions to be placed and the polygons of the remaining pieces and repeating the above calculation, the overlap value of the current layout can be obtained;

[0028] S43, swap and move operations, in order to reduce the overlap value to 0 and thus obtain the optimal nesting layout, the swap and move operations are used for optimization; the swap operation randomly selects two pieces of the same level to swap positions; the nesting pieces that overlap with the remaining pieces in the initial solution are swapped, and other nesting pieces of the same level as the overlapping nesting pieces are selected in the current nesting to swap positions to reduce the overlap value or make the overlap value zero; after the swap, if the remaining selected pieces still overlap, the overlapping nesting pieces are moved to a placeable area other than the original position in the current nesting, thereby minimizing the overlap value of the nesting layout; the swap and move operations are repeated until the number of operations exceeds the limit or the overlap value of the current nesting layout is 0;

[0029] S44. If all remaining pieces have been attempted to be placed, the algorithm ends and outputs the final solution; otherwise, jump to S41 and continue execution until all remaining pieces have been attempted to be placed.

[0030] Preferably, in step S1, in terms of size information processing, it is first necessary to accurately record the input leather size information, the size and position information of each grade area on the leather, and the size, quantity and grade information of the irregular pieces. This size information is usually represented in the form of a point set stored in counterclockwise order; the area of these polygonal areas can be calculated using the shoelace theorem formula;

[0031] According to the corresponding relationship between the grade of the cutting piece and the grade of the leather area, the placement area of each cutting piece can be further solved;

[0032] The shoelace theorem formula is:

[0033] In terms of grouping and sorting, all pieces are first sorted from high to low according to their grades; if two pieces have the same grade, their areas are further compared, and the piece with the larger area is prioritized.

[0034] Preferably, the swap and move operations are combined to form a variable neighborhood search algorithm.

[0035] Preferably, the method further includes the step of generating a cutting path:

[0036] S5, extracts the independent contour of each part, including the outer contour and internal holes, calculates the geometric features of each contour, prepares for subsequent path planning, and initializes the list of unprocessed contours.

[0037] S6, taking the contour closest to the origin as the starting cutting contour, taking the lower left corner of the piece as the cutting point, and removing the contour from the unprocessed contour list.

[0038] S7, selecting a piece closest to the cutting point from the unprocessed contour list as a pre-cut piece, and removing the contour from the unprocessed contour list.

[0039] S8, if the unprocessed contour list is not empty, continue to execute the third step; if it is empty, the algorithm ends.

[0040] One of the above technical solutions includes the following beneficial effects: this solution solves the problem of irregular layout of leather in multiple regions, and by optimizing the multi-region division and layout strategy, improves the utilization rate of raw materials, and makes the layout plan more in line with actual production needs; in addition, this solution fully considers the requirements of cutting pieces for leather materials during the layout process, and improves leather utilization while ensuring product quality; the cutting layout optimization method proposed in this solution reduces dependence on manual layout, reduces manual intervention through intelligent algorithms and automation technology, improves layout efficiency and raw material utilization, and promotes the company's production automation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1Schematic diagram of the leather intelligent cutting pattern optimization method considering quality partition constraints of the present invention;

[0042] Figure 2 This is a flow chart of the leather intelligent cutting pattern optimization method considering quality partition constraints of the present invention;

[0043] Figure 3 is an initial solution generation flow chart of the present invention;

[0044] Figure 4 It is a flow chart of the remaining pieces arrangement process of the present invention;

[0045] Figure 5 This is the layout diagram of Case 1;

[0046] Figure 6 This is the layout diagram of Case 2. DETAILED DESCRIPTION

[0047] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0048] like Figure 1-2 As shown in FIG, the intelligent cutting piece layout optimization method considering quality partition constraints includes the following steps:

[0049] S1. Data preprocessing, including obtaining the overall size information of the leather, the size and location distribution of each grade area, and the size, quantity, and grade information of irregular pieces, and sorting all irregular pieces based on the size and grade information;

[0050] S2. Grouping vectors. The pattern layout is performed on a two-dimensional plane. The grouping vectors are composed of two binary basis vectors. The core solution of these two binary basis vectors is to find the minimum displacement vector that ensures that the two pieces do not overlap at the junction.

[0051] S3, generating an initial solution, solving the piece displacement vector based on the grouped vector and generating a temporary layout; calculating the convex hull area based on the temporary layout, and identifying potential insertion spaces through the convex hull area; searching for the optimal placement point in the convex hull area for the initial remaining pieces in combination with the fitness function for insertion; if there are still initial remaining pieces that have not been placed after the insertion attempts are completed, jump to the first step to continue execution; the initial remaining pieces are inserted in this order, and the initial solution generation process ends when all the initial remaining pieces have completed the placement attempts or there is no placement area left;

[0052] S4. Layout of the remaining pieces. After the initial solution is generated, if there are still remaining pieces that have not been placed, it is necessary to select a single remaining piece from the final set of remaining pieces in order based on the layout of the current initial solution and select the placement position in combination with the fitness function; perform a secondary optimization layout of the remaining pieces.

[0053] Beneficial effects: This solution solves the problem of irregular layout of leather in multiple regions. By optimizing the multi-region division and layout strategy, it improves the utilization rate of raw materials and makes the layout plan more in line with actual production needs. In addition, this solution fully considers the requirements of cutting pieces for leather materials during the layout process, improving leather utilization while ensuring product quality. The cutting layout optimization method proposed in this solution reduces dependence on manual layout, reduces manual intervention through intelligent algorithms and automation technology, improves layout efficiency and raw material utilization, and promotes the company's production automation process.

[0054] like Figure 2 As shown in , step S2 in the solution process includes the following steps:

[0055] S21, first fix the first piece in order, and use the NFP geometry tool to calculate the placement position of the second piece;

[0056] S22, then find the minimum distance to move the second piece horizontally to the right from the position of the first piece to a non-overlapping position, and obtain vector v1 = (x1, 0);

[0057] S23, finally, the set of possible placement points of the third piece is calculated through NFP, and the points that do not overlap with the first and second pieces and have the smallest displacement are selected from them, so as to obtain the second vector v2 = (x2, y), that is, the grouped vector v = (v1, v2).

[0058] Beneficial Effects: Through this process, the generation of grouping vectors not only ensures the geometric feasibility of the piece arrangement but also lays a solid foundation for the efficient optimization of the subsequent pattern layout. Grouping vectors are a key factor in achieving neat piece arrangement. By calculating the minimum non-overlapping displacement between pieces, grouping vectors can effectively guide the arrangement of pieces, thereby ensuring space utilization while avoiding overlap, providing important support for pattern layout optimization. This is achieved through the No-Fit Polygon (NFP).

[0059] like Figure 3 As shown, step S3 specifically includes the following steps:

[0060] S31, select the corresponding piece according to the layout order, and calculate the non-overlapping constraint space between the piece and the layout area through NFP to generate a set of geometrically feasible candidate placement points. In this process, a lower left placement strategy is adopted: the point with the smallest horizontal coordinate is prioritized. If there are multiple points, the point with the smallest vertical coordinate is further selected.

[0061] S32, combining the grouped vectors and calculating the subsequent placement positions of the pieces using formula (1) until the remaining number of pieces is zero or there is no placement area;

[0062] v=i*v1+j*v2 Formula (1);

[0063] Where v is the displacement vector of the piece, and i and j are both integers;

[0064] S33: The temporary nesting layout generated based on the grouped vectors may contain underutilized space. Therefore, it is necessary to calculate the convex hull area formed by all placed pieces to reflect the space occupancy of the current layout and provide a basis for optimizing space utilization. The convex hull area is constructed by first selecting the point with the smallest horizontal coordinate from the vertices of all placed pieces as the starting point. If there are multiple points with the same horizontal coordinate, the point with the smallest vertical coordinate is selected from the point set. Then, starting from the current point, all vertices are traversed to find the point with the smallest polar angle with the current point as the next convex hull point. The process is iterated until the starting point is returned, and the convex hull construction is finally completed. The convex hull area can be used to identify potential intervening spaces, laying a geometric foundation for subsequent piece placement and layout optimization, thereby improving space utilization and nesting efficiency.

[0065] S34, selecting the initial remaining pieces in the order of insertion, and finding the optimal placement point in the convex hull area in combination with the fitness function for placement; the introduction of the fitness function ensures that the placement of the initial remaining pieces can maximize the use of the remaining space while avoiding overlap with the already placed pieces; this process continues until all remaining pieces have completed the insertion attempt;

[0066] S35, if there are still unplaced initial remaining pieces after completing the above steps, jump to the first step to continue execution; when all initial remaining pieces have completed placement attempts or there is no placement area, the initial solution generation process ends.

[0067] Beneficial effects: Through the lower left placement strategy, the pieces can be accurately positioned at the local optimal position in the layout area to ensure maximum space utilization. At the same time, placing the pieces in a grouped vector-based manner will result in a more compact layout. For the gaps in the temporary layout obtained based on the grouped vector, the convex hull interpolation strategy is used to fill the gaps, which can further improve the overall layout. In addition, the interpolation strategy is strictly limited to the feasible domain defined by the convex hull boundary, thereby ensuring that it does not interfere with the subsequent grouped piece placement process. The initial solution obtained through the entire S3 is very compact as a whole and meets the leather quality constraints, providing a guarantee for subsequent iterative optimization.

[0068] like Figure 4 As shown, in step S4, the following steps are specifically included:

[0069] S41. Select the remaining pieces from the last remaining piece set according to the arrangement order of the remaining pieces, calculate the candidate placement point set of the remaining pieces in the placement area, and select the placement point with the highest fitness according to formula (2) for placement; when placing, the remaining pieces may overlap with the area to be arranged;

[0070]

[0071] Among them A ij k represents the overlapping area of the minimum enveloping rectangle of pieces i and j; i ,k j k represents the area of the minimum enveloping rectangle of the piece divided by the actual area of the piece; ij A represents the distance between the centers of gravity of pieces i and j divided by the distance between the centroids of pieces i and j; im The area outside the fabric of the minimum enveloping rectangle of the expanded outline of piece i; γ constant coefficient (0–2.0). Adjusting the coefficient γ can produce different nesting results. γ represents the weight of the fit between the piece and the fabric boundary. The larger the γ value, the more preference will be given to the position close to the fabric boundary.

[0072] S42, calculating the overlap value between the remaining pieces and the positions to be placed, the overlap value calculation being achieved by the following steps: traversing all edges of the two overlapping polygons of the positions to be placed and the polygons of the remaining pieces, calculating the intersection points of the edges and storing them in a temporary array, identifying points within the two polygons to form a new intersecting polygon, and finally calculating the area of the intersecting polygon using the shoelace theorem formula. The area is the overlap value between the polygons of the positions to be placed and the polygons of the remaining pieces; by traversing all polygons of the positions to be placed and the polygons of the remaining pieces and repeating the above calculation, the overlap value of the current layout can be obtained;

[0073] S43, swap and move operations, in order to reduce the overlap value to 0 and thus obtain the optimal nesting layout, the swap and move operations are used for optimization; the swap operation randomly selects two pieces of the same level to swap positions; the nesting pieces that overlap with the remaining pieces in the initial solution are swapped, and other nesting pieces of the same level as the overlapping nesting pieces are selected in the current nesting to swap positions to reduce the overlap value or make the overlap value zero; after the swap, if the remaining selected pieces still overlap, the overlapping nesting pieces are moved to a placeable area other than the original position in the current nesting, thereby minimizing the overlap value of the nesting layout; the swap and move operations are repeated until the number of operations exceeds the limit or the overlap value of the current nesting layout is 0;

[0074] S44. If all remaining pieces have been attempted to be placed, the algorithm ends and outputs the final solution; otherwise, jump to S41 and continue execution until all remaining pieces have been attempted to be placed.

[0075] By using the overlapping area between pieces to represent the overlap value, the overlap situation of the current layout can be more intuitively displayed. The coordinated application of swap and move operations can effectively adjust the current layout, significantly reducing the overlap value or even reducing it to zero, allowing the current illegal layout with overlap to be transformed into a legal layout without overlap. Through the entire S4 secondary nesting operation, more pieces can be placed based on the initial solution, further improving the utilization rate of the nesting layout.

[0076] As a supplement to the above technology, in step S1, in terms of size information processing, it is first necessary to accurately record the input leather size information, the size and position information of each grade area on the leather, and the size, quantity and grade information of the irregular pieces. This size information is usually represented as a point set stored in counterclockwise order; the area of these polygonal areas can be calculated using the shoelace theorem formula;

[0077] According to the corresponding relationship between the grade of the cutting piece and the grade of the leather area, the placement area of each cutting piece can be further solved;

[0078] The shoelace theorem formula is:

[0079] In terms of grouping and sorting, all pieces are first sorted from high to low according to their grades; if two pieces have the same grade, their areas are further compared, and the piece with the larger area is prioritized.

[0080] The core idea of the shoelace theorem formula is: no matter how the coordinate origin is selected, as long as the coordinates A of each vertex of the polygon are input in a clockwise or counterclockwise direction in order l, and cross-product the coordinates of adjacent vertices. Half the sum of the absolute values of these cross-product results is the area of the polygon. Based on this formula, the total area of the leather, the area of each grade, and the total area of the irregular pieces can be calculated separately.

[0081] Furthermore, based on the correspondence between piece grades and leather region grades, the placement area for each piece can be further determined. Specifically, a piece can only be placed in an area whose grade is no higher than the leather region grade, meaning the leather grade of the area must be higher than or equal to the piece's grade. This constraint ensures that high-grade pieces prioritize high-grade leather regions, maximizing the utilization of high-grade leather and providing clear regional restrictions for optimizing layout.

[0082] This sorting strategy ensures that high-grade pieces prioritize high-grade leather areas, maximizing the utilization of high-grade leather. During the initial solution generation process, the interleaving sequence employs a similar grouping and sorting approach: the remaining pieces are first grouped, and then the pieces within each group are sorted from smallest to largest in area. This sorting approach prioritizes smaller pieces during interleaving, allowing for more efficient use of remaining space and improving overall layout quality and space efficiency.

[0083] In addition, in step S43, the exchange and move operations are combined to form a variable neighborhood search algorithm.

[0084] It can effectively optimize the two-dimensional irregular layout and improve space utilization and layout effect.

[0085] In addition, the cutting path generation steps are included:

[0086] S5, extracts the independent contour of each part, including the outer contour and internal holes, calculates the geometric features of each contour, prepares for subsequent path planning, and initializes the list of unprocessed contours.

[0087] S6, taking the contour closest to the origin as the starting cutting contour, taking the lower left corner of the piece as the cutting point, and removing the contour from the unprocessed contour list.

[0088] S7, selecting a piece closest to the cutting point from the unprocessed contour list as a pre-cut piece, and removing the contour from the unprocessed contour list.

[0089] S8, if the unprocessed contour list is not empty, continue to execute the third step; if it is empty, the algorithm ends.

[0090] By adopting the above method, the cutting path of the cut pieces after layout can also be planned, and the leather cutting work head can efficiently complete the cutting work through the planned shortest cutting path.

[0091] Figure 5 and Figure 6 These are Case 1 and Case 2 in the following implementation case results.

[0092]

[0093] The technical principles of the present invention have been described above with reference to specific embodiments. These descriptions are intended solely to illustrate the principles of the present invention and are not to be construed in any way as limiting the scope of protection of the present invention. Based on the explanations herein, those skilled in the art will readily conceive of other specific embodiments of the present invention without inventive effort, and such embodiments will fall within the scope of protection of the present invention.

Claims

1. An intelligent cutting pattern optimization method considering quality partition constraints, characterized by: The steps include: S1. Data preprocessing, including obtaining the overall size information of the leather, the size and location distribution of each grade area, and the size, quantity, and grade information of irregular pieces, and sorting all irregular pieces based on the size and grade information; S2. Grouping vectors. The pattern layout is performed on a two-dimensional plane. The grouping vectors are composed of two binary basis vectors. The core solution of these two binary basis vectors is to find the minimum displacement vector that ensures that the two pieces do not overlap at the junction. S3, generating an initial solution, solving the piece displacement vector based on the grouped vector and generating a temporary layout; calculating the convex hull area based on the temporary layout, and identifying potential insertion spaces through the convex hull area; searching for the optimal placement point in the convex hull area for the initial remaining pieces in combination with the fitness function for insertion; if there are still initial remaining pieces that have not been placed after the insertion attempts are completed, jump to the first step to continue execution; the initial remaining pieces are inserted in this order, and the initial solution generation process ends when all the initial remaining pieces have completed the placement attempts or there is no placement area left; S4. Layout of the remaining pieces. After the initial solution is generated, if there are still remaining pieces that have not been placed, it is necessary to select a single remaining piece from the final set of remaining pieces in order based on the layout of the current initial solution and select the placement position in combination with the fitness function; perform a secondary optimization layout of the remaining pieces. Beneficial effects: This solution solves the problem of irregular layout of leather in multiple regions. By optimizing the multi-region division and layout strategy, it improves the utilization rate of raw materials and makes the layout plan more in line with actual production needs. In addition, this solution fully considers the requirements of cutting pieces for leather materials during the layout process, improving leather utilization while ensuring product quality. The cutting layout optimization method proposed in this solution reduces dependence on manual layout, reduces manual intervention through intelligent algorithms and automation technology, improves layout efficiency and raw material utilization, and promotes the company's production automation process.

2. The intelligent cutting piece layout optimization method considering quality zoning constraints according to claim 1 is characterized in that: Step S2 includes the following steps during the solution process: S21, first fix the first piece in order, and use the NFP geometry tool to calculate the placement position of the second piece; S22, then find the minimum distance to move the second piece horizontally to the right from the position of the first piece to a non-overlapping position, and obtain vector v1 = (x1, 0); S23, finally, the set of possible placement points of the third piece is calculated through NFP, and the points that do not overlap with the first and second pieces and have the smallest displacement are selected from them, so as to obtain the second vector v2 = (x2, y), that is, the grouped vector v = (v1, v2). Beneficial Effects: Through this process, the generation of grouping vectors not only ensures the geometric feasibility of the piece arrangement but also lays a solid foundation for the efficient optimization of the subsequent pattern layout. Grouping vectors are a key factor in achieving neat piece arrangement. By calculating the minimum non-overlapping displacement between pieces, grouping vectors can effectively guide the arrangement of pieces, thereby ensuring space utilization while avoiding overlap, providing important support for pattern layout optimization. This is achieved through the No-Fit Polygon (NFP).

3. The intelligent cutting piece layout optimization method considering quality zoning constraints according to claim 1 is characterized in that: Step S3 specifically includes the following steps: S31, select the corresponding piece according to the layout order, and calculate the non-overlapping constraint space between the piece and the layout area through NFP to generate a set of geometrically feasible candidate placement points. In this process, a lower left placement strategy is adopted: the point with the smallest horizontal coordinate is prioritized. If there are multiple points, the point with the smallest vertical coordinate is further selected. S32, combining the grouped vectors and calculating the subsequent placement positions of the pieces using formula (1) until the remaining number of pieces is zero or there is no placement area; v=i*v1+j*v2 Formula (1); Where v is the displacement vector of the piece, and i and j are both integers; S33: The temporary nesting layout generated based on the grouped vectors may contain underutilized space. Therefore, it is necessary to calculate the convex hull area formed by all placed pieces to reflect the space occupancy of the current layout and provide a basis for optimizing space utilization. The convex hull area is constructed by first selecting the point with the smallest horizontal coordinate from the vertices of all placed pieces as the starting point. If there are multiple points with the same horizontal coordinate, the point with the smallest vertical coordinate is selected from the point set. Then, starting from the current point, all vertices are traversed to find the point with the smallest polar angle with the current point as the next convex hull point. The process is iterated until the starting point is returned, and the convex hull construction is finally completed. The convex hull area can be used to identify potential intervening spaces, laying a geometric foundation for subsequent piece placement and layout optimization, thereby improving space utilization and nesting efficiency. S34, selecting the initial remaining pieces in the order of insertion, and finding the optimal placement point in the convex hull area in combination with the fitness function for placement; the introduction of the fitness function ensures that the placement of the initial remaining pieces can maximize the use of the remaining space while avoiding overlap with the already placed pieces; this process continues until all remaining pieces have completed the insertion attempt; S35, if there are still unplaced initial remaining pieces after completing the above steps, jump to the first step to continue execution; when all initial remaining pieces have completed placement attempts or there is no placement area, the initial solution generation process ends. Beneficial effects: Through the lower left placement strategy, the pieces can be accurately positioned at the local optimal position in the layout area to ensure maximum space utilization. At the same time, placing the pieces in a grouped vector-based manner will result in a more compact layout. For the gaps in the temporary layout obtained based on the grouped vector, the convex hull interpolation strategy is used to fill the gaps, which can further improve the overall layout. In addition, the interpolation strategy is strictly limited to the feasible domain defined by the convex hull boundary, thereby ensuring that it does not interfere with the subsequent grouped piece placement process. The initial solution obtained through the entire S3 is very compact as a whole and meets the leather quality constraints, providing a guarantee for subsequent iterative optimization.

4. The intelligent cutting piece layout optimization method considering quality zoning constraints according to claim 1 is characterized in that: In step S4, the following steps are specifically included: S41. Select the remaining pieces from the last remaining piece set according to the arrangement order of the remaining pieces, calculate the candidate placement point set of the remaining pieces in the placement area, and select the placement point with the highest fitness according to formula (2) for placement; when placing, the remaining pieces may overlap with the area to be arranged; Among them A ij k represents the overlapping area of the minimum enveloping rectangle of pieces i and j; i ,k j k represents the area of the minimum enveloping rectangle of the piece divided by the actual area of the piece; ij A represents the distance between the centers of gravity of pieces i and j divided by the distance between the centroids of pieces i and j; im The area outside the fabric of the minimum enveloping rectangle of the expanded outline of piece i; γ constant coefficient (0–2.0). Adjusting the coefficient γ can produce different nesting results. γ represents the weight of the fit between the piece and the fabric boundary. The larger the γ value, the more preference will be given to the position close to the fabric boundary. S42, calculating the overlap value between the remaining piece and the position to be placed, the overlap value calculation being achieved by traversing all edges of the two overlapping polygons of the position to be placed and the polygon of the remaining piece, calculating the intersection points of the edges and storing them in a temporary array, identifying points within the two polygons to form a new intersecting polygon, and finally calculating the area of the intersecting polygon using the shoelace theorem formula. The area is the overlap value between the polygon of the position to be placed and the polygon of the remaining piece; By traversing all the polygons of the positions to be placed and the remaining polygons of the pieces and repeating the above calculation, the overlap value of the current layout can be obtained; S43, swap and move operations, in order to reduce the overlap value to 0 and thus obtain the optimal nesting layout, the swap and move operations are used for optimization; the swap operation randomly selects two pieces of the same level to swap positions; the nesting pieces that overlap with the remaining pieces in the initial solution are swapped, and other nesting pieces of the same level as the overlapping nesting pieces are selected in the current nesting to swap positions to reduce the overlap value or make the overlap value zero; after the swap, if the remaining selected pieces still overlap, the overlapping nesting pieces are moved to a placeable area other than the original position in the current nesting, thereby minimizing the overlap value of the nesting layout; the swap and move operations are repeated until the number of operations exceeds the limit or the overlap value of the current nesting layout is 0; S44. If all remaining pieces have been attempted to be placed, the algorithm ends and outputs the final solution; otherwise, jump to S41 and continue execution until all remaining pieces have been attempted to be placed. By using the overlapping area between pieces to represent the overlap value, the overlap situation of the current layout can be more intuitively displayed. The coordinated application of swap and move operations can effectively adjust the current layout, significantly reducing the overlap value or even reducing it to zero, allowing the current illegal layout with overlap to be transformed into a legal layout without overlap. Through the entire S4 secondary nesting operation, more pieces can be placed based on the initial solution, further improving the utilization rate of the nesting layout.

5. The intelligent cutting piece layout optimization method considering quality zoning constraints according to claim 1 is characterized in that: In step S1, in terms of size information processing, it is first necessary to accurately record the input leather size information, the size and position information of each grade area on the leather, and the size, quantity and grade information of the irregular pieces. This size information is usually represented as a point set stored in counterclockwise order. The area of these polygonal areas can be calculated using the shoelace theorem formula; According to the corresponding relationship between the grade of the cutting piece and the grade of the leather area, the placement area of each cutting piece can be further solved; The shoelace theorem formula is: In terms of grouping and sorting, all pieces are first sorted from high to low according to their grades; if two pieces have the same grade, their areas are further compared, and the piece with the larger area is prioritized. The core idea of the shoelace theorem formula is: no matter how the coordinate origin is selected, as long as the coordinates A of each vertex of the polygon are input in a clockwise or counterclockwise direction in order l , and cross-product the coordinates of adjacent vertices. Half the sum of the absolute values of these cross-product results is the area of the polygon. Based on this formula, the total area of the leather, the area of each grade, and the total area of the irregular pieces can be calculated respectively. Furthermore, based on the correspondence between piece grades and leather region grades, the placement area for each piece can be further determined. Specifically, a piece can only be placed in an area whose grade is no higher than the leather region grade, meaning the leather grade of the area must be higher than or equal to the piece's grade. This constraint ensures that high-grade pieces prioritize high-grade leather regions, maximizing the utilization of high-grade leather and providing clear regional restrictions for optimizing layout. This sorting strategy ensures that high-grade pieces prioritize high-grade leather areas, maximizing the utilization of high-grade leather. During the initial solution generation process, the interleaving sequence employs a similar grouping and sorting approach: the remaining pieces are first grouped, and then the pieces within each group are sorted from smallest to largest in area. This sorting approach prioritizes smaller pieces during interleaving, allowing for more efficient use of remaining space and improving overall layout quality and space efficiency.

6. The intelligent cutting piece layout optimization method considering quality zoning constraints according to claim 4 is characterized in that: In step S43, the exchange and move operations are combined to form a variable neighborhood search algorithm. It can effectively optimize the two-dimensional irregular layout and improve space utilization and layout effect.

7. The intelligent cutting piece layout optimization method considering quality zoning constraints according to any one of claims 1 to 6, characterized in that: Also includes the cutting path generation steps: S5, extracts the independent contour of each part, including the outer contour and internal holes, calculates the geometric features of each contour, prepares for subsequent path planning, and initializes the list of unprocessed contours. S6, taking the contour closest to the origin as the starting cutting contour, taking the lower left corner of the piece as the cutting point, and removing the contour from the unprocessed contour list. S7, selecting a piece closest to the cutting point from the unprocessed contour list as a pre-cut piece, and removing the contour from the unprocessed contour list. S8, if the unprocessed contour list is not empty, continue to execute the third step; if it is empty, the algorithm ends. By adopting the above method, the cutting path of the cut pieces after layout can also be planned, and the leather cutting work head can efficiently complete the cutting work through the planned shortest cutting path.

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