Jacking method, jacking system, electronic equipment, medium and product

Abstract

The invention provides a nesting method, a nesting system, electronic equipment, a medium and a product, and relates to the field of intelligent manufacturing. The method comprises the following steps: obtaining target parameters of a plurality of target pieces and raw material parameters of a plurality of stock raw materials, and carrying out blanking solving of single target pieces on the plurality of target pieces one by one based on a preset sequence so as to obtain a plurality of candidate jacking schemes; the candidate nesting schemes are sets of blanking schemes for each target piece based on stock raw materials, and an optimal scheme is selected from the multiple candidate nesting schemes based on a loss score model to serve as a target nesting scheme. According to the trepanning method, the material utilization rate and the production efficiency are improved, compared with traditional dynamic distribution, the method has the advantages that the randomness is utilized, multiple better solutions can be selected, local optimum is avoided to a certain extent, the multiple better solutions are obtained, multi-dimensional scheme evaluation is achieved through the loss score model, and the optimal trepanning scheme is obtained through solving.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing, and in particular to a nesting method, a nesting system, an electronic device, a medium and a product. Background Art

[0002] Nesting refers to processing raw materials of various specifications, including raw materials of different lengths, numbers of roots, diameters, etc., including pipes, bars, profiles, wires and other raw materials of different materials, into the required multi-specification target parts through cutting, welding and other operations, with the number of welds, the number of residual pipes, and the length of waste as small as possible as the target conditions, and outputting the best raw material cutting plan and target material welding plan.

[0003] In the prior art, although the existing cutting and nesting methods have achieved automation and intelligence to a certain extent, they still have some defects, such as: the problem of local optimal solution, difficulty in handling multiple constraints, low algorithm efficiency, and difficulty in adjusting hyperparameters, etc. These defects limit the improvement of automation and intelligence in the field of material processing, and also limit the room for improvement in material utilization and production efficiency. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the problem of low material utilization rate caused by defects in the nesting method in the prior art, and to provide a nesting method, a nesting system, an electronic device, a medium and a product.

[0005] The present invention solves the above technical problems through the following technical solutions:

[0006] The present invention provides a nesting method, which comprises:

[0007] Obtain target parameters of multiple target parts and raw material parameters of multiple stock raw materials;

[0008] Based on a preset order, a plurality of target parts are solved for material cutting of a single target part one by one, thereby obtaining a plurality of candidate nesting schemes; the candidate nesting schemes are a set of material cutting schemes for each target part based on the inventory raw materials;

[0009] An optimal solution is selected from the multiple candidate nesting solutions as a target nesting solution based on the loss score model.

[0010] This solution improves material utilization and production efficiency. Compared with traditional dynamic allocation, this method uses randomness to select multiple better solutions, avoids falling into local optimality to a certain extent, and obtains multiple better solutions. Through the loss scoring model, multi-dimensional solution evaluation is realized to obtain the optimal nesting solution.

[0011] Preferably, the step of performing material cutting solutions for a single target part one by one for the plurality of target parts based on a preset order, and then obtaining a plurality of candidate nesting solutions, comprises:

[0012] Selecting a single target part based on the preset order;

[0013] Traversing the raw materials to be selected from the stock raw materials, and solving the material cutting for the raw materials to be selected based on the target parameters of the single target part and the raw material parameters of the raw materials to be selected, so as to obtain multiple first candidate solutions for the single target part;

[0014] Obtaining a second candidate solution from the plurality of first candidate solutions based on a greedy random algorithm;

[0015] Update the raw material to be selected based on the second candidate solution, and then select the next single target piece based on the preset sequence;

[0016] Repeat the above steps until all the target parts are traversed to obtain candidate nesting solutions; the candidate nesting solutions are a set of second candidate solutions for each target part based on the inventory raw materials;

[0017] Repeat the above steps, based on the step of obtaining the second candidate solution from the multiple first candidate solutions based on the greedy random algorithm, select different second candidate solutions to obtain the multiple candidate nesting solutions.

[0018] In this solution, through multi-constraint solving and optimization algorithms, the use of inventory raw materials can be maximized and waste can be reduced. The use of a greedy random algorithm can use randomness to find the optimal solution among multiple possible solutions while meeting basic needs, increasing the flexibility of the solution and helping to avoid falling into a local optimal solution during the solution process, making it possible to find a global optimal solution. Through the loss score model, multi-dimensional solution evaluation is achieved, and the optimal nesting solution is obtained. At the same time, this solution can improve the quality of decision-making, ensure that the selected solution is optimal in multiple dimensions, and improve material utilization and production efficiency.

[0019] Preferably, the step of traversing the raw materials to be selected from the stock raw materials, solving the raw materials to be selected based on the target parameters of the single target part and the raw material parameters of the raw materials to be selected, and then obtaining multiple first candidate solutions for the single target part includes:

[0020] Randomly select a raw material to be selected from the stock raw materials;

[0021] Based on the target parameters of the single target part and the raw material parameters of the raw material to be selected, a multi-constraint solving method is used to solve the raw material to be selected to obtain a first candidate solution;

[0022] Repeat the above steps until all the raw materials to be selected are traversed to obtain the multiple first candidate solutions.

[0023] In this scheme, by using the multi-constraint solving method, by repeating the steps until all the candidate raw materials are traversed, multiple first candidate schemes can be systematically generated, providing a basis for subsequent optimization and selection. This scheme can adapt to different production environments and raw material characteristics, and can flexibly respond to various production situations by adjusting the multi-constraint solving method.

[0024] Preferably, the step of performing a multi-constraint solving method to solve the raw material to be selected based on the target parameters of the single target part and the raw material parameters of the raw material to be selected to obtain the first candidate solution includes:

[0025] In response to the raw material parameters of the raw material to be selected satisfying preset constraints, the raw material to be selected is cut and solved based on the target parameters of the single target part and the raw material parameters of the raw material to be selected to obtain the first candidate solution.

[0026] In this scheme, by repeating the steps until all the raw materials to be selected are traversed, multiple first candidate schemes can be systematically generated, providing a basis for subsequent optimization and selection. This scheme can adapt to different production environments and raw material characteristics, and can flexibly respond to various production situations by adjusting the multi-constraint solving method.

[0027] Preferably, the preset constraint conditions include at least one of a loss constraint, a welding prohibition constraint, an elbow constraint, a weld number constraint and a loading constraint.

[0028] In this solution, loss constraints can be used to minimize material waste in the production process and improve material utilization; no-welding constraints help avoid welding in areas that are not suitable for welding, thereby ensuring welding quality and structural integrity; weld number constraints can control the number of welding points and reduce welding workload; elbow constraints allow special components such as elbows to be considered in the design, so that production plans can adapt to more complex product design requirements; loading constraints ensure that the produced parts can be efficiently loaded and transported, reducing logistics costs and time.

[0029] Preferably, the step of obtaining the second candidate solution from the plurality of first candidate solutions based on a greedy random algorithm comprises:

[0030] Selecting the first candidate solutions that meet the preset conditions from the multiple first candidate solutions to form a candidate solution set; the preset conditions are used to characterize the selection of the candidate solution set in accordance with the optimization principle of the greedy random algorithm;

[0031] One of the solutions is randomly selected from the candidate solution set as the second candidate solution.

[0032] In this scheme, the first candidate schemes that meet the preset conditions are selected from multiple first candidate schemes to form a candidate scheme set, which reflects the greediness of the greedy random algorithm; one of the schemes is randomly selected from the candidate scheme set as the second candidate scheme for the current single target item, which reflects the randomness of the greedy random algorithm.

[0033] The present invention also provides a nesting system, the nesting system comprising:

[0034] A parameter acquisition module, used to acquire target parameters of multiple target parts and raw material parameters of multiple stock raw materials;

[0035] A candidate nesting scheme determination module is used to solve the material cutting of a single target part for the multiple target parts one by one based on a preset order, thereby obtaining multiple candidate nesting schemes; the candidate nesting schemes are a set of cutting schemes for each target part based on the inventory raw materials;

[0036] The target nesting scheme determination module is used to select the best scheme from the multiple candidate nesting schemes as the target nesting scheme based on the loss score model.

[0037] Preferably, the candidate nesting scheme determination module includes a single target part selection unit, a first candidate scheme determination unit, a second candidate scheme determination unit, a next target part selection unit, a candidate nesting scheme determination unit and a multiple candidate scheme determination unit;

[0038] The single target part selection unit is used to select a single target part based on the preset order;

[0039] The first candidate solution determination unit is used to traverse the raw materials to be selected from the inventory raw materials, and based on the target parameters of the single target part and the raw material parameters of the raw materials to be selected, perform material cutting and solving for the raw materials to be selected, so as to obtain multiple first candidate solutions for the single target part;

[0040] The second candidate solution determining unit is configured to obtain a second candidate solution from the plurality of first candidate solutions based on a greedy random algorithm;

[0041] The next target part selection unit is used to update the raw material to be selected based on the second candidate solution, and then select the next single target part based on the preset sequence;

[0042] The candidate nesting scheme determining unit is used to repeat the above steps until all the target parts are traversed to obtain a candidate nesting scheme; the candidate nesting scheme is a set of second candidate schemes for each target part based on the inventory raw materials;

[0043] The multiple candidate solution determination unit is used to repeat the above steps, select different second candidate solutions based on the step of obtaining the second candidate solution from the multiple first candidate solutions based on the greedy random algorithm, and obtain the multiple candidate nesting solutions.

[0044] Preferably, the first candidate solution determination unit includes a raw material selection subunit, a first candidate solution solving subunit and a plurality of first candidate solution obtaining subunits;

[0045] The raw material selection subunit is used to select any raw material to be selected from the stock raw materials;

[0046] The first candidate solution solving subunit is used to perform a multi-constraint solving method on the raw material to obtain a first candidate solution based on the target parameters of the single target part and the raw material parameters of the raw material to be selected;

[0047] The multiple first candidate solution acquisition subunit is used to repeat the above steps until all the raw materials to be selected are traversed to obtain the multiple first candidate solutions.

[0048] Preferably, the first candidate solution solving subunit is specifically used to, in response to the raw material parameters of the raw material to be selected satisfying preset constraints, perform material cutting solution on the raw material to be selected based on the target parameters of a single target part and the raw material parameters of the raw material to be selected to obtain the first candidate solution.

[0049] Preferably, the second candidate solution determination unit includes a candidate solution set determination subunit and a second candidate solution selection subunit;

[0050] The candidate solution set determination subunit is used to select the first candidate solution that meets the preset condition from the multiple first candidate solutions to form a candidate solution set; the preset condition is used to characterize the selection of the candidate solution set in accordance with the optimization principle of the greedy random algorithm;

[0051] The second candidate solution selection subunit is used to randomly select one of the solutions from the candidate solution set as the second candidate solution.

[0052] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein the processor implements the nesting method as described above when executing the computer program.

[0053] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the nesting method as described above is implemented.

[0054] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the nesting method as described above is implemented.

[0055] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0056] The positive and progressive effect of the present invention is that through multi-constraint solving and optimization algorithms, the inventory of raw materials can be maximized and waste can be reduced. The use of a greedy random algorithm can take into account randomness while meeting basic needs, thereby finding the optimal solution among multiple possible solutions, increasing the flexibility of the solution, and helping to avoid falling into a local optimal solution during the solution process, thereby making it possible to find a global optimal solution. Through the loss score model, multi-dimensional solution evaluation is achieved, and the optimal nesting solution is obtained.

[0057] The solution disclosed by the present invention can improve the quality of decision-making, ensure that the selected solution is optimal in multiple dimensions, and improve material utilization and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic flow chart of a nesting method provided in Example 1 of the present invention;

[0059] Figure 2 A schematic diagram of a soldering prohibition update provided in Embodiment 1 of the present invention;

[0060] Figure 3 This is a schematic diagram of a single elbow pipe section provided in Example 1 of the present invention, with its long side close to the vehicle length for loading;

[0061] Figure 4 This is a schematic diagram of a single elbow pipe section provided in Example 1 of the present invention, where the virtual longest side is closely attached to the vehicle length and loaded onto the vehicle;

[0062] Figure 5 This is a schematic diagram of a concatenable interval that meets business constraints provided in Embodiment 1 of the present invention;

[0063] Figure 6 A schematic diagram of a process for solving the problem of cutting raw materials to be selected by using a multi-constraint solving method for a single target part provided in Embodiment 1 of the present invention;

[0064] Figure 7 A schematic diagram of a weight update flow chart combined with the AHP-CRITIC algorithm provided in Example 1 of the present invention;

[0065] Figure 8 A schematic flow chart of a nesting method based on a multi-constrained greedy random construction solution provided in Example 1 of the present invention;

[0066] Fig. 9 This is a module schematic diagram of a nesting system provided in Example 2 of the present invention;

[0067] Fig.10 This is a schematic diagram of the structure of an electronic device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0069] It should be noted that if this embodiment involves directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0070] In addition, if there are descriptions involving "first", "second", etc. in this embodiment, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0071] In the embodiments of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0072] Example 1

[0073] See also Figures 1 to 8 , this embodiment provides a nesting method, Figure 1This is a flow chart of a nesting method provided in Example 1 of the present invention. This specification provides method operation steps such as the embodiment or flow chart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or server product is executed, it can be executed in the order shown in the embodiment or the figure or in parallel (for example, in a parallel processor or multi-threaded processing environment). Specifically, Figure 1 As shown, the nesting method includes:

[0074] S101: Obtain target parameters of multiple target parts and raw material parameters of multiple stock raw materials.

[0075] In this embodiment, the raw materials may include pipes, bars, profiles, wires, etc. For the convenience of describing the nesting method below, the pipe is used as an example. The specification parameters of the raw materials include the length of the raw materials, the number of raw materials, and the diameter. Multi-specification raw materials are cut and welded to obtain multi-specification target parts. Cutting and welding need to meet a variety of constraints. Ultimately, the goal is to minimize the number of welds, the number of excess pipes, and the length of waste materials. The best raw material cutting plan and target part welding plan are output to realize intelligent nesting. Each of the multiple target parts obtained also includes target parameters, such as the length and diameter of the target part.

[0076] S102: solving the material cutting of a single target part for a plurality of target parts one by one based on a preset order, and then obtaining a plurality of candidate nesting schemes; the candidate nesting scheme is a collection of material cutting schemes for each target part based on the inventory raw materials.

[0077] In this embodiment, the step of performing material cutting solutions for a single target part one by one for a plurality of target parts based on a preset order, and then obtaining a plurality of candidate nesting solutions, includes:

[0078] S1021, selecting a single target part based on a preset order;

[0079] S1022, traversing the raw materials to be selected from the inventory raw materials, and solving the material cutting for the raw materials to be selected based on the target parameters of the single target part and the raw material parameters of the raw materials to be selected, thereby obtaining multiple first candidate solutions for the single target part;

[0080] S1023, obtaining a second candidate solution from multiple first candidate solutions based on a greedy random algorithm;

[0081] S1024, updating the raw material to be selected based on the second candidate solution, and then selecting the next single target part based on a preset order;

[0082] S1025, repeat the above steps until all target parts are traversed to obtain candidate nesting solutions; the candidate nesting solution is a set of second candidate solutions for each target part based on the inventory raw materials;

[0083] S1026, repeat the above steps, based on the step of obtaining the second candidate solution from multiple first candidate solutions based on the greedy random algorithm, select different second candidate solutions to obtain multiple candidate nesting solutions.

[0084] In this solution, through multi-constraint solving and optimization algorithms, the use of inventory raw materials can be maximized and waste can be reduced. The use of greedy random algorithms can take into account randomness while meeting basic needs, so as to find the optimal solution among multiple possible solutions, increase the flexibility of the solution, and help avoid falling into the local optimal solution during the solution process, so that it is possible to find the global optimal solution. Through the loss score model, multi-dimensional solution evaluation is realized, and the optimal nesting solution is obtained. This solution can also improve the quality of decision-making, ensure that the selected solution is optimal in multiple dimensions, and improve material utilization and production efficiency.

[0085] Specifically, the preset order is from long to short. First, multiple target parts are sorted from long to short, a single target part is selected, and a raw material to be selected is randomly selected from the inventory raw materials. Based on the target parameters of the single target part and the raw material parameters of the raw material to be selected, a multi-constraint solving method is used to solve the raw material to be selected to obtain the first candidate solution. In this solution, by repeating the steps until all the raw materials to be selected are traversed, multiple first candidate solutions can be systematically generated, providing a basis for subsequent optimization and selection. This solution can adapt to different production environments and raw material characteristics, and by adjusting the multi-constraint solving method, it can flexibly respond to various production situations.

[0086] The multi-constraint solution method is based on at least one business constraint in a complex scenario, constructs the calculation logic for each business constraint, and then calculates the raw material unloading solution logic that meets all business constraints. The business constraints may include at least one of loss constraints, welding prohibition constraints, elbow constraints, weld number constraints, or loading constraints. This multi-constraint solution method can find the splicable interval of the raw material to the target part under the business constraint conditions.

[0087] The calculation logic of each business constraint is as follows:

[0088] 1) Loss constraint: There is cutting loss when cutting raw materials cut , there is groove loss in the welding of the target part groove If the target part needs to be processed into an elbow, there will be elbow loss. elbow For example, the raw material length l org Much larger than the length of the spliced ​​pipe section l weld, and the pipe section contains only one elbow, then the length of the raw material to be cut is l cut = l weld +loss groove +loss elbow , the remaining length of the raw material is l remain = l org -l cut -loss cut , that is, losses must be considered.

[0089] 2) Welding prohibition restrictions: Figure 2 Schematic diagram of a welding prohibition update provided in Example 1 of the present invention. The target part has a welding prohibition area due to process requirements. At the same time, there is a concept of the shortest splicing length, that is, if the splicing pipe section is shorter than the shortest splicing length l mwl , then the pipe section cannot be used for splicing. Figure 2 As shown in FIG. 1 , the welding-forbidden area of ​​the target part is updated as the splicing segment is updated. Figure 2 The upper part represents the initial welding prohibition area of ​​the target part, including the process welding prohibition and the shortest splicing welding prohibition, that is, the beginning and end of the segment. Area A and area B are the allowed welding areas. Figure 2 The lower part shows that when the welding point of a spliced ​​pipe section 1 falls exactly within the weldable area A, the area A is divided into two parts: the left side is spliced ​​and the right side is not spliced. If the length of the unspliced ​​right side is less than l mwl , then the area is updated to the shortest splicing forbidden welding area.

[0090] 3) Elbow constraint: A target part has t elbows, which can only be obtuse angles. It is required that there must be welding points between the elbows. A target part is composed of at least t single elbow pipe sections.

[0091] 4) Constraint on the number of welds: The maximum number of welds allowed for single elbow pipe sections of different lengths is different. For example, the maximum number of welds allowed for a single elbow pipe section with a length greater than 2000mm and less than 5000mm is 1 weld; the maximum number of welds allowed for a single elbow pipe section with a length greater than 5000mm and less than 10000mm is 2 welds.

[0092] 5) Loading constraints: The target part is loaded with single elbow pipe segments during transportation, so the cutting position between the elbows needs to be adjusted so that each single elbow pipe segment can be loaded into a vehicle of specified size. Definition: The diameter of the pipe segment is d, the outer curvature radius of the elbow is r, the central angle of the obtuse elbow is α, and the short side length of the straight segment is l short , the length of the long side of the straight segment is l long , the length of the vehicle is Vehicle width Generally, the length of a transport vehicle is much greater than its width, so pipe section loading can be divided into two main situations, which are introduced below.

[0093] Case 1: Figure 3 This is a schematic diagram of a single elbow pipe section provided in Example 1 of the present invention, wherein the long side of the pipe section is closely attached to the vehicle length for loading. Figure 3 As shown, the length of the pipe section is: l x = l short *cosα+r*cosα+l long , the width occupied is l y = l short *sinα+r-(rd)*cosα, if If both conditions are met at the same time, the pipe section can be loaded.

[0094] Case 2: Figure 4 This is a schematic diagram of a single elbow pipe section provided in Example 1 of the present invention, where the virtual longest side is closely attached to the vehicle length for loading. Figure 4 As shown, the pipe section is tilted at an angle of θ so that the arc is tangent to the vehicle length. The width of the arc and the straight section on the left side of the tangent point is l y1 = l short *sin(α-θ)+r-(rd)*cos(α-θ), the width of the arc and the straight segment on the right side of the tangent point is l y2 = l long *sinθ+r-(rd)*cosθ, let l y1 = l y2 Solve to get θ. Continue to solve, the length of the pipe section is l x = l short *cos(α-θ)+r*(cos(α-θ)+Cosθ)+l long *cosθ, the width occupied is l y = l y1 ,like If both conditions are met at the same time, the pipe section can be loaded.

[0095] In this embodiment, the description of the above five business constraints is based on the known splicing segment length to determine whether the constraints are met. Reverse thinking is required to determine whether a certain raw material can be used to splice the unspliced ​​part of the current target part when the splicing segment length of the target part is known. If it can be spliced, what is the value range of the splicing length, that is, the splicing interval that meets all constraints. Figure 5 This is a schematic diagram of a concatenable interval that meets the service constraints provided in Embodiment 1 of the present invention, such as Figure 5As described above, according to the loss constraint, the maximum length that can be used for splicing is ai segment after deducting the loss from the length of the raw material; according to the welding prohibition constraint, it is calculated that the unspliced ​​segment aj of the target part has five weldable areas, namely bd, fg, hh, ii and jj segments; according to the elbow constraint, there must be a welding spot between the elbows, so the maximum splicable segment is ah segment; according to the weld joint constraint, the pipe length is required to be at least oc; according to the loading constraint, it is known that oa segment contains an elbow, and the longest pipe length that satisfies loading is oe. In summary, the intersection of the splicable intervals under each constraint is taken, and only cd segment satisfies the above constraints at the same time, then the length range for splicing after the raw material is unloaded is [ac, ad].

[0096] In this solution, loss constraints can be used to minimize material waste in the production process and improve material utilization; no-welding constraints help avoid welding in areas that are not suitable for welding, thereby ensuring welding quality and structural integrity; weld number constraints can control the number of welding points and reduce welding workload; elbow constraints allow special components such as elbows to be considered in the design, so that production plans can adapt to more complex product design requirements; loading constraints ensure that the produced parts can be efficiently loaded and transported, reducing logistics costs and time.

[0097] In this embodiment, the above method is used to perform multi-constraint solving to obtain the first candidate solution. Specifically, in response to the raw material parameters of the raw material to be selected satisfying the preset constraints, i.e., the business constraints, the raw material parameters of the raw material to be selected are used to perform material cutting and solving to obtain the first candidate solution based on the target parameters of the single target part and the raw material parameters of the raw material to be selected. Among them, the preset constraints, i.e., the business constraints, include loss constraints, welding prohibition constraints, elbow constraints, weld number constraints, and loading constraints. In this solution, material cutting and solving are performed only when the raw material parameters satisfy specific constraints, so as to avoid wasting time and resources on raw materials that do not meet the conditions and improve the practicality of the solution.

[0098] In other embodiments, the preset constraints when solving the cutting of the selected raw materials based on the target parameters of a single target part and the raw material parameters of the selected raw materials may be at least one of a loss constraint, a welding prohibition constraint, an elbow constraint, a weld number constraint and a loading constraint.

[0099] In this embodiment, after a candidate material is randomly selected from the inventory raw materials and the multi-constraint solving method is used to solve the material cutting of the candidate material to obtain the first candidate solution, the candidate material is then replaced and the above-mentioned solving steps are repeated until all the candidate materials are traversed to obtain multiple first candidate solutions, which are all possible cutting solutions for the same target part for all the candidate materials. A second candidate solution is obtained from the multiple first candidate solutions based on a greedy random algorithm; the candidate material is updated based on the second candidate solution, and then the next single target part is selected based on a preset order; the above-mentioned steps are repeated until all the target parts are traversed to obtain a candidate nesting solution; the candidate nesting solution is a set of second candidate solutions for each target part based on the inventory raw materials.

[0100] Specifically, Figure 6 This is a schematic diagram of a process for solving the problem of cutting raw materials to be selected using a multi-constraint solving method for a single target part provided in Example 1 of the present invention, such as Figure 6 As shown in the figure, for the construction of a single target part, first determine whether the target part has not completed all welding. If the target part has not completed all welding, proceed to the next step. If the target part has completed all welding, the process ends. Cutting solution: Cut the j-th raw material to meet the needs of the target part, enter the multi-constraint solution method, and determine whether the current raw material can meet the requirements of multi-constraint cutting. If the raw material can meet the requirements, proceed to the next step. If the raw material cannot meet the requirements, the process ends and the next raw material can be replaced for cutting again. Save the cutting plan of the current raw material that meets the requirements of multi-constraint cutting, that is, the first candidate plan. Determine whether the current raw material is the last raw material. If the current raw material is not the last one, repeat the above steps, and continue to solve the cutting of the next raw material until the current raw material is the last one, and obtain multiple first candidate plans. If the current raw material is the last one, proceed to the next step.

[0101] In this embodiment, the step of obtaining a second candidate solution from multiple first candidate solutions based on a greedy random algorithm includes: selecting first candidate solutions that meet preset conditions from multiple first candidate solutions to form a candidate solution set; the preset conditions are used to characterize the optimization principle of the greedy random algorithm for selecting the candidate solution set; and randomly selecting one of the solutions from the candidate solution set as the second candidate solution.

[0102] Specific, combined Figure 6To continue, a first candidate solution that meets the preset conditions is selected from multiple first candidate solutions to form a candidate solution set. The preset conditions here are the optimization principles of not cutting the whole material, cutting as much material as possible, and leaving as little excess material as possible. Based on the above principles, the better solutions are selected as the candidate solution set, which reflects the greediness of the greedy random algorithm. One of the solutions is randomly selected from the candidate solution set as the second candidate solution for the current single target part, which reflects the randomness of the greedy random algorithm. Based on the second candidate solution, the information of the raw material to be selected and the information of the target part are updated. Here, the remaining length of the raw material, the unjoined length of the target part, etc. can be updated. Then, the next single target part is selected in order from long to short, and the above steps are repeated until all target parts are traversed to obtain the candidate nesting solution; the candidate nesting solution is a set of second candidate solutions for each target part based on the inventory raw materials.

[0103] In this embodiment, the above steps are repeated, and different second candidate solutions are selected based on the step of obtaining the second candidate solution from multiple first candidate solutions based on the greedy random algorithm to obtain multiple candidate nesting solutions.

[0104] There is another preferred embodiment of the step of obtaining the second candidate solution from multiple first candidate solutions based on a greedy random algorithm. Figure 7 Schematic diagram of a weight update flow chart combined with the AHP-CRITIC algorithm provided in Example 1 of the present invention, such as Figure 7 As shown, in this embodiment, after the optimal nesting scheme is selected as the output of the algorithm, the weight of the selected preferred scheme can also be updated. The weight update method is combined with the AHP-CRITIC algorithm. The AHP algorithm takes the loss score of the known scheme and the frequency statistics of the m optimal principles in the scheme into consideration as experience to construct a judgment matrix

[0105] The empirical weight of selecting the optimal solution is w 1i , calculated as follows:

[0106]

[0107] CRITIC algorithm is based on the variability of evaluation indicators S i Conflict between indicators R i To comprehensively measure the objective weight of the indicator w 2i , variability and conflict depend on the values ​​of various indicators of various cutting schemes, and the objective weight w 2i The calculation is as follows:

[0108]

[0109] The weights assigned by the AHP-CRITIC algorithm are obtained by combining the empirical weights and the objective weights:

[0110] For example: a target traverses three candidate raw materials. If the raw material is 5m long, it can be cut into 3m and 5m long materials, and the above two cutting plans are obtained. If the raw material is 7m long, it can be cut into 3m, 5m, and 6m long materials, and three cutting plans can be obtained. If the raw material is 10m long, it can be cut into 3m, 5m, and 6m long materials, and the above three cutting plans can be obtained. A total of 8 cutting plans are obtained.

[0111] According to the principle of "no cutting of whole materials", the solution of "5m of raw materials and 5m of cutting" is included in the candidate set. According to the principle of "cutting as much materials as possible", the two solutions of "7m of raw materials and 6m of cutting, 10m of raw materials and 6m of cutting" are included in the candidate set. According to the principle of "leaving as little residual materials as possible", the solution of "7m of raw materials and 6m of cutting" is included in the candidate set. This reflects the "greediness";

[0112] The weights of the three optimization principles of "whole material without cutting, as much material as possible, and as little leftover material as possible" are 0.5, 0.3, and 0.2 respectively. The weights here are preset according to the actual situation, and the weight size is used as the probability distribution for sampling, that is, there is a 50% probability of selecting the "whole material without cutting" principle. If the sampled principle is "whole material without cutting", then a cutting plan is randomly selected from the corresponding candidate set "5m of raw material and 5m of cutting" as the final cutting plan. "Randomness" is reflected here.

[0113] The weight update example of the above three optimization principles is as follows: in the first n planning, when planning the complete nesting plan for all target tubes, the weights of the three principles are average, all of which are "0.33, 0.33, 0.33"; when planning the complete nesting plan for all target tubes starting from the n+1th time, the scores of the previous n planning plans will be compared, and the cutting information in the nesting plan with the best score will be selected to update the weight. For example, the optimal plan involves 100 cutting plans, of which 50 are cut according to principle 1, 30 are cut according to principle 2, and 20 are cut according to principle 3. Then the judgment matrix a 11 =1, a 22 =1, a 33 =1, then calculate and update the weight w according to the formula above i The cycle continues.

[0114] S103: Selecting the best solution from multiple candidate nesting solutions as the target nesting solution based on the loss score model.

[0115] In this embodiment, since the candidate nesting schemes obtained above are random, multiple candidate nesting schemes can be obtained by repeating multiple steps, and then the advantages and disadvantages of different candidate nesting schemes are evaluated by constructing a loss score model, and the optimal nesting scheme is selected as the algorithm output. Among them, the construction of the loss score model is measured from three dimensions, with the minimum number of welds, the minimum number of residual material tubes, and the minimum length of waste as the goals. The lower the loss score, the better the scheme.

[0116] Definition: The number of welds of target part i is The remaining length of raw material j is Waste refers to the residual material less than l waste (the shortest length of the remaining material) is waste. The calculation formula for the loss score is:

[0117]

[0118] where sgn(·) is the sign function, defined as:

[0119]

[0120] The above formula is used to calculate and evaluate the advantages and disadvantages of multiple candidate nesting schemes, and the optimal nesting scheme is selected as the algorithm output.

[0121] Figure 8 FIG. 1 is a flow chart of a nesting method based on a multi-constrained greedy random construction solution provided in Example 1 of the present invention, such as Figure 8 As shown, a number of cycles can be pre-set to obtain multiple candidate nesting solutions. First, check whether the number of cycles meets the requirements of the preset number of cycles. If not, the process returns to the beginning of the cycle and continues to iterate to check whether the feasible solutions of all target parts, i.e., target tubes, have been constructed. If the feasible solutions of all target parts have been completed, the process continues to the next step. If there are still feasible solutions for target tubes that have not been completed, the process enters the "multi-constraint solution module" to construct a feasible solution for a single target tube, i.e., a target part, until all target parts have been constructed. Finally, multiple candidate nesting solutions are obtained. If the preset number of cycles is met, that is, multiple candidate nesting solutions corresponding to the preset number of cycles have been obtained, the process continues to the next judgment node. Multi-dimensional evaluation selects the optimal solution, i.e., the loss score model selects the optimal solution from multiple candidate nesting solutions as the target nesting solution.

[0122] In simple terms, the whole process requires the construction of multiple target parts. The first target tube, i.e. the target part, is constructed one by one using all raw materials to obtain multiple solutions. A solution is selected based on the greedy random principle, and the raw material information is updated according to this solution, i.e., the length of the first target tube is subtracted, and the second target tube is constructed using all updated raw materials until all target tubes are planned and a complete nesting solution is obtained. Repeat the above steps multiple times to obtain multiple complete solutions, and then evaluate the advantages and disadvantages of different complete solutions through the loss score model, and finally obtain a target nesting solution.

[0123] The present invention improves material utilization and production efficiency. Compared with traditional dynamic allocation, this method uses randomness to select multiple optimal solutions, avoids falling into local optimality to a certain extent, obtains multiple optimal solutions, and realizes multi-dimensional solution evaluation through the loss score model to solve and obtain the optimal nesting solution. Compared with integer programming, this method can be applied to more complex business scenarios; compared with dynamic allocation, this method has the randomness to select multiple optimal solutions, avoids falling into local optimality to a certain extent, can obtain multiple optimal solutions, and prevents falling into local optimality; this method has more efficient solution logic, easier operation, and shorter solution time than genetic algorithms. At the same time, compared with manual calculations by engineers, this method can quickly solve better nesting solutions. Compared with manual calculations, it shortens the solution time by at least 90% and the number of welds by 10%.

[0124] The effect of the above nesting method will be demonstrated below through a specific embodiment:

[0125] Calculation category: Pipeline; ie target part

[0126] Target length: [14132.0, 38940.0, 14132.0, 38940.0] 4 pieces in total, total length 106144mm;

[0127] Raw material length: [7580.0, 8330.0, 8570.0, 9260.0, 9530.0, 9630.0, 9650.0, 9670.0, 9780.0, 9860.0, 9950.0, 10000.0, 10010.0, 10090.0] 14 pieces in total, total length 131910mm;

[0128] Cutting loss: 5mm; groove loss: 10mm; elbow loss: 200mm;

[0129] Forbidden welding points: [[2220.0, 400.0, 300.0, 300.0, 550.0, 150.0, 2800.0, 650.0, 200.0, 325.0], [825.0, 325.0, 200.0, 400.0, 400.0, 600.0, 550.0, 500.0, 300.0, 300.0, 6900.0, 2654.5, 450.0, 7900.0, 150. 0], [2220.0, 400.0, 300.0, 300.0, 550.0, 150.0, 2800.0, 650.0, 200.0, 325.0], [825.0, 325.0, 200.0, 400.0, 400.0, 600.0, 550.0, 500.0, 300.0, 300.0, 6900.0, 2654.5, 450.0, 7900.0, 150.0]];

[0130] Welding ban range: [[300.0, 300.0, 300.0, 300.0, 500.0, 2905.97, 500.0, 2905.97, 300.0, 500.0], [500.0, 300.0, 2905.97, 100.0, 2905.97, 2905.97, 500.0, 300.0, 300.0, 300.0, 500.0, 2905.97, 500.0, 500.0, 2905.9 7], [300.0, 300.0, 300.0, 300.0, 500.0, 2905.97, 500.0, 2905.97, 300.0, 500.0], [500.0, 300.0, 2905.97, 100.0, 2905.97, 2905.97, 500.0, 300.0, 300.0, 300.0, 500.0, 2905.97, 500.0, 500.0, 2905.97]];

[0131] Prohibited welding types: [["tube socket", "tube socket", "tube socket", "tube socket", "hanging point", "bending point", "hanging point", "bending point", "tube socket", "hanging point"], ["hanging point", "tube socket", "bending point", "hanging point", "bending point", "bending point", "hanging point", "tube socket", "tube socket", "hanging point", "bending point", "hanging point", "tube socket", "tube socket", "hanging point", "bending point", "hanging point", "hanging point", "bending point" "],["tube seat","tube seat","tube seat","tube seat","hanging point","bending point","hanging point","bending point","tube seat","hanging point"],["hanging point","tube seat","bending point","hanging point","bending point","bending point","hanging point","tube seat","tube seat","hanging point","bending point","hanging point","hanging point","bending point"]];

[0132] The number of welds corresponding to different pipe lengths L: L≤2000mm: 0; 2000mm<L≤5000mm: 1; 5000mm<L≤10000mm: 2; 10000mm<L: 3;

[0133] Loading constraints: vehicle length 13000mm, vehicle width 3000mm.

[0134] Under the premise of meeting the above constraints, the algorithm calculates for 4.4 seconds and outputs a solution with only 14 welds, while being able to save the cutting, welding plan and raw material properties.

[0135] Comparing the nesting time and number of welds between the manual method and this method, for the same nesting task, this method can output a complete and optimal nesting plan within 1 minute, which shortens the solution time by at least 90% compared with manual calculation, that is, the comprehensive input time and the number of welds by 10%.

[0136] Example 2

[0137] Corresponding to the aforementioned nesting method embodiment, the present invention further provides a nesting system, and the nesting system is implemented by the aforementioned nesting method. Fig. 9 Schematic diagram of a module of a nesting system provided in Example 2 of the present invention, such as Fig. 9 As shown, the nesting system includes:

[0138] A parameter acquisition module 1 is used to acquire target parameters of multiple target parts and raw material parameters of multiple stock raw materials;

[0139] The candidate nesting scheme determination module 2 is used to solve the material cutting of a single target part for multiple target parts one by one based on a preset order, and then obtain multiple candidate nesting schemes; the candidate nesting scheme is a set of cutting schemes for each target part based on the inventory raw materials;

[0140] The target nesting scheme determination module 3 is used to select the best scheme from multiple candidate nesting schemes as the target nesting scheme based on the loss score model.

[0141] Preferably, the candidate nesting scheme determination module includes a single target part selection unit, a first candidate scheme determination unit, a second candidate scheme determination unit, a next target part selection unit, a candidate nesting scheme determination unit and a multiple candidate scheme determination unit;

[0142] A single target part selection unit, used for selecting a single target part based on a preset order;

[0143] A first candidate solution determination unit is used to traverse the raw materials to be selected from the inventory raw materials, and based on the target parameters of the single target part and the raw material parameters of the raw materials to be selected, perform material cutting on the raw materials to be selected, and then obtain multiple first candidate solutions for the single target part;

[0144] A second candidate solution determining unit, configured to obtain a second candidate solution from a plurality of first candidate solutions based on a greedy random algorithm;

[0145] A next target part selection unit, used for updating the raw material to be selected based on the second candidate solution, and then selecting the next single target part based on a preset order;

[0146] A candidate nesting solution determination unit is used to repeat the above steps until all target parts are traversed to obtain a candidate nesting solution; the candidate nesting solution is a set of second candidate solutions for each target part based on the inventory raw materials;

[0147] The multiple candidate solution determination unit is used to repeat the above steps, select different second candidate solutions based on the step of obtaining the second candidate solution from multiple first candidate solutions based on the greedy random algorithm, and obtain multiple candidate nesting solutions.

[0148] Preferably, the first candidate solution determination unit includes a raw material selection subunit, a first candidate solution solving subunit and a plurality of first candidate solution obtaining subunits;

[0149] The raw material selection subunit is used to select any raw material to be selected from the stock raw materials;

[0150] A first candidate solution solving subunit is used to perform a multi-constraint solving method on the raw material to be selected based on the target parameters of a single target part and the raw material parameters of the raw material to be selected to obtain a first candidate solution;

[0151] The multiple first candidate solution acquisition subunits are used to repeat the above steps until all the raw materials to be selected are traversed to obtain multiple first candidate solutions.

[0152] Preferably, the first candidate solution solving subunit is specifically used to obtain the first candidate solution by performing material cutting solving on the raw material to be selected based on the target parameters of a single target part and the raw material parameters of the raw material to be selected in response to the raw material parameters of the raw material to be selected satisfying preset constraints.

[0153] Preferably, the second candidate solution determination unit includes a candidate solution set determination subunit and a second candidate solution selection subunit;

[0154] A candidate solution set determination subunit is used to select a first candidate solution that meets a preset condition from a plurality of first candidate solutions to form a candidate solution set; the preset condition is used to characterize the selection of the candidate solution set in accordance with the optimization principle of the greedy random algorithm;

[0155] The second candidate solution selection subunit is used to randomly select one of the solutions from the candidate solution set as the second candidate solution.

[0156] As for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the units described as separate components may or may not be physically separated, and the components as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present invention.

[0157] Example 3

[0158] Fig.10 This is a schematic diagram of the structure of an electronic device provided in Example 3 of the present invention, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor, and when the processor executes the computer program, the nesting method described in any of the above embodiments is implemented. Fig.10 The electronic device 100 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0159] like Fig.10 As shown, the electronic device 100 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 100 may include, but are not limited to: at least one processor 101, at least one memory 102, and a bus 103 connecting different system components (including the memory 102 and the processor 101).

[0160] The bus 103 includes a data bus, an address bus, and a control bus.

[0161] The memory 102 may include a volatile memory, such as a random access memory (RAM) 1021 and / or a cache memory 1022 , and may further include a read-only memory (ROM) 1023 .

[0162] The memory 102 may also include a program tool 1025 (or utility) having a set (at least one) of program modules 1024, such program modules 1024 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0163] The processor 101 executes various functional applications and data processing by running the computer program stored in the memory 102, such as the nesting method provided in any of the above embodiments.

[0164] The electronic device 100 may also communicate with one or more external devices 104 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via an input / output (I / O) interface 105. Furthermore, the electronic device 100 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 106. As shown, the network adapter 106 communicates with other modules of the electronic device 100 via a bus 103. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 100, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0165] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0166] Example 4

[0167] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the nesting method provided by any of the above embodiments is implemented.

[0168] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0169] Example 5

[0170] An embodiment of the present invention further provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the nesting method described in any one of the above items is implemented.

[0171] The program code for executing the computer program product of the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0172] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that this is only for illustration and the protection scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A nesting method, characterized in that: The nesting method comprises: Obtain target parameters of multiple target parts and raw material parameters of multiple stock raw materials; Based on a preset order, a plurality of target parts are solved for material cutting of a single target part one by one, thereby obtaining a plurality of candidate nesting schemes; the candidate nesting schemes are a set of material cutting schemes for each target part based on the inventory raw materials; An optimal solution is selected from the multiple candidate nesting solutions as a target nesting solution based on the loss score model.

2. The nesting method according to claim 1, characterized in that: The step of performing material cutting solutions for a single target part one by one for the multiple target parts based on a preset order, and then obtaining multiple candidate nesting solutions, comprises: Selecting a single target part based on the preset order; Traversing the raw materials to be selected from the stock raw materials, and solving the material cutting for the raw materials to be selected based on the target parameters of the single target part and the raw material parameters of the raw materials to be selected, so as to obtain multiple first candidate solutions for the single target part; Obtaining a second candidate solution from the plurality of first candidate solutions based on a greedy random algorithm; Update the raw material to be selected based on the second candidate solution, and then select the next single target piece based on the preset sequence; Repeat the above steps until all the target parts are traversed to obtain candidate nesting solutions; the candidate nesting solutions are a set of second candidate solutions for each target part based on the inventory raw materials; Repeat the above steps, based on the step of obtaining the second candidate solution from the multiple first candidate solutions based on the greedy random algorithm, select different second candidate solutions to obtain the multiple candidate nesting solutions.

3. The nesting method according to claim 2, characterized in that: The step of traversing the raw materials to be selected from the inventory raw materials, performing material cutting solutions for the raw materials to be selected based on the target parameters of the single target part and the raw material parameters of the raw materials to be selected, and then obtaining multiple first candidate solutions for the single target part includes: Randomly select a raw material to be selected from the stock raw materials; Based on the target parameters of the single target part and the raw material parameters of the raw material to be selected, a multi-constraint solving method is used to solve the raw material to be selected to obtain a first candidate solution; Repeat the above steps until all the raw materials to be selected are traversed to obtain the multiple first candidate solutions.

4. The nesting method according to claim 3, characterized in that: The step of performing a multi-constraint solving method to solve the raw material to be selected based on the target parameters of the single target part and the raw material parameters of the raw material to be selected to obtain a first candidate solution includes: In response to the raw material parameters of the raw material to be selected satisfying preset constraints, the raw material to be selected is cut and solved based on the target parameters of the single target part and the raw material parameters of the raw material to be selected to obtain the first candidate solution.

5. The nesting method according to claim 4, characterized in that: The preset constraint conditions include at least one of a loss constraint, a welding prohibition constraint, an elbow constraint, a weld number constraint and a loading constraint.

6. The nesting method according to claim 2, characterized in that: The step of obtaining a second candidate solution from the plurality of first candidate solutions based on a greedy random algorithm comprises: Selecting the first candidate solutions that meet the preset conditions from the multiple first candidate solutions to form a candidate solution set; the preset conditions are used to characterize the selection of the candidate solution set in accordance with the optimization principle of the greedy random algorithm; One of the solutions is randomly selected from the candidate solution set as the second candidate solution.

7. A nesting system, characterized in that: The nesting system comprises: A parameter acquisition module, used to acquire target parameters of multiple target parts and raw material parameters of multiple stock raw materials; A candidate nesting scheme determination module is used to solve the material cutting of a single target part for the multiple target parts one by one based on a preset order, thereby obtaining multiple candidate nesting schemes; the candidate nesting schemes are a set of cutting schemes for each target part based on the inventory raw materials; The target nesting scheme determination module is used to select the best scheme from the multiple candidate nesting schemes as the target nesting scheme based on the loss score model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, characterized in that: When the processor executes the computer program, the nesting method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the nesting method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the nesting method according to any one of claims 1 to 6 is implemented.

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