A Scheduling Method for Plate Processing

Abstract

The present invention relates to the field of data processing, and the present invention relates to a scheduling method for plate processing, which generates multiple layout schemes by obtaining the types of workpieces that can be processed by the factory and combining historical layout data and customer order requirements. Genetic algorithms are used to optimize the cutting sequence, and multiple strategies are used to deal with special situations such as uneven equipment load, dual-head cutting, and dual-gantry cutting. The production schedule is dynamically adjusted to improve equipment utilization, reduce material waste, and significantly improve production efficiency. The present invention dynamically optimizes plate processing scheduling by combining big data analysis, genetic algorithms, and real-time monitoring to achieve efficient production and maximize resource utilization.

Description

Technical Field

[0001] The invention relates to the field of data processing, and in particular to a scheduling method for plate processing. Background Art

[0002] In recent years, with the continuous listing of panel furniture manufacturers, the production scale of manufacturers has expanded rapidly, and the production method has gradually changed from single-machine to multi-device networking. In the past, a cutting device equipped with loading and unloading devices was called a production line in the factory. After a batch of panels were processed on the cutting production line, all workpieces were manually transferred to the edge banding production line for the next stage of processing. Nowadays, a production line can fully include the complete production links of cutting, edge banding, drilling and sorting, and in a production link, for example, the cutting link can include multiple cutting devices of the same or different types, and the drilling link can include multiple drilling devices of different types.

[0003] At present, due to the relative independence of each work link of cutting, edge banding, drilling, and sorting, their respective production scheduling is carried out independently. In terms of efficiency, each link is only responsible for itself, pursuing local optimality, and it is impossible to achieve the global optimality of each link. Before the incoming material layout is made, it is unknown which cutting equipment the current layout will be processed on. This is blind layout that ignores the processing link. After the layout is completed, the processing sequence will not be adjusted according to the status of the collaborative equipment in the same link on the production line and the working status of the equipment in the subsequent links, and the overall production line processing efficiency cannot be taken into account. When a bottleneck occurs in a link on the production line due to production efficiency or partial failure, the processing efficiency of the entire production line will be affected.

[0004] The existing technology has many deficiencies in solving the above technical problems. For example, the Chinese patent application document with publication number CN110147933A discloses a method for scheduling and scheduling CNC cutting workshops based on an improved gray wolf algorithm. It proposes a decimal gray wolf algorithm for solving by establishing a scheduling model and optimization target for the plate in the cutting workshop processing process, shortening the cutting production cycle of the cutting workshop and improving equipment utilization. However, this method has some technical defects: it lacks global optimization capabilities, is prone to fall into local optimality, and cannot maximize production efficiency and resource utilization; it lacks the support of real-time data, cannot be dynamically adjusted according to the actual situation of the production site, and the scheduling system responds laggingly, affecting production continuity. Summary of the invention

[0005] In view of the problem that the above scheduling results are difficult to balance the efficiency of each device, the present invention proposes a scheduling method for plate processing, including: obtaining the type of workpiece that can be processed by the factory according to equipment parameters and the type of workpiece ordered by the customer; obtaining multiple layout schemes for the main workpiece types of the current batch based on the workpiece type and the layout scheme of historical plate cutting, using a genetic algorithm to obtain an initial cutting order based on the multiple layout schemes, and sending all the plates of the current batch to the cutting equipment in turn for cutting according to the layout scheme set in the initial cutting order; each chromosome in the genetic algorithm represents a cutting order, and the information encoded by the gene in the chromosome includes: the serial number of the layout scheme , the number of times the layout scheme appears in the production line, and the serial number of the cutting equipment; the order of the gene code in the chromosome indicates the order of the plate processing layout scheme corresponding to the code in the entire processing flow; based on the first strategy, the second strategy and the third strategy, the crossover and mutation operations of the genetic algorithm are supplemented and limited. The first strategy refers to scheduling the plate to be diverted to the cutting equipment production line that is not blocked or faulty by monitoring the production quantity of the equipment; the second strategy adjusts the order of workpieces within the layout scheme to make it conform to the spacing between the dual-head cutting equipment; the third strategy refers to adjusting the cutting order so that the number of workpieces in the layout schemes of the two plates on the double gantry cutting equipment is the same or similar.

[0006] The present invention obtains the types of workpieces that can be processed by the factory based on equipment parameters and the types of workpieces ordered by customers, and then uses genetic algorithms to optimize the cutting order, and finally generates the layout plan for the current batch of plates and performs cutting operations. Compared with the prior art, the present invention significantly improves the flexibility and efficiency of factory production. Specifically, by optimizing a variety of layout plans with genetic algorithms, the present invention can dynamically adjust the layout order and cutting strategy according to actual working conditions, ensuring the best match between the processing technology and equipment capabilities of each batch, thereby reducing material waste, reducing equipment idle time, and improving the overall processing efficiency of the production line.

[0007] Furthermore, the first strategy also includes: adjusting the cutting sequence so that subsequent plates are diverted to a cutting equipment production line that is not blocked or malfunctioning, so that the layout plan of the subsequent plates has the highest processing efficiency on the cutting equipment that is not blocked or malfunctioning.

[0008] Furthermore, the fitness function of the genetic algorithm is:

[0009]

[0010] in Indicates chromosome The fitness function value of represents the total number of genes; It means the chromosome genes; Indicates The time required to cut the board corresponding to each gene.

[0011] The present invention directly links the optimization goal of the genetic algorithm with the cutting efficiency through the designed fitness function, so that the fitness value of each chromosome can accurately reflect the pros and cons of the current layout plan. Compared with the traditional optimization method, the use of this fitness function can more accurately evaluate the execution effect of each layout plan in actual production, thereby gradually improving the quality of the final layout plan during the genetic evolution process, ensuring the optimal production efficiency of the factory.

[0012] Furthermore, the types of workpieces that can be processed in the factory are obtained according to the equipment parameters and the types of workpieces in the customer orders, which also includes: obtaining the types of workpieces that need to be cut in all orders in the same batch and the quantity of each type of workpiece, and obtaining the total order workpiece database DB1; obtaining the standard parameters of the equipment involved in processing the plate on the production line, and obtaining the types of workpieces that can be cut in the processing plant according to the equipment parameters, and obtaining the processable workpiece database DB2; based on the total order workpiece database DB1 and the workpiece database DB2, obtaining the processable order workpiece database DB3, wherein the order workpiece database DB3 includes the types of workpieces processed in the total order and the quantity of each type of processable workpiece in the total order.

[0013] Furthermore, the method for obtaining the main workpiece types of the current batch also includes: using the DBSCAN clustering algorithm to obtain the main workpiece types in the order; the input of the algorithm is the feature matrix of the workpiece in the order workpiece database DB3, and the feature matrix includes the length, width and thickness of the workpiece; the algorithm setting parameters include the neighborhood radius And the minimum number of neighboring points minPts; the output of the algorithm includes the number of clusters and the index set of core points, and the Otsu threshold of the number of data points contained in all clusters is calculated; the clusters with the number of data points greater than the Otsu threshold are recorded as larger clusters, and the core point data of the larger clusters are compared with the workpiece feature matrix to obtain the workpiece type corresponding to the larger cluster; the workpiece types corresponding to all larger clusters constitute the main workpiece types.

[0014] The DBSCAN clustering algorithm is used to obtain the main workpiece types in the order, so that the present invention can automatically identify and classify the key workpiece types in the order. By reasonably setting the algorithm parameters, it can ensure that even in the case of large data volume, the algorithm can effectively distinguish the main workpiece types, avoiding the limitations of manual classification, and being more accurate in optimizing the typesetting plan, thereby improving the accuracy and efficiency of production.

[0015] Furthermore, the encoding method in the genetic algorithm is a combination of real number encoding and binary encoding.

[0016] By adopting a combination of real number coding and binary coding, the present invention achieves a balance between flexibility and accuracy in the chromosome representation of the genetic algorithm. The introduction of real number coding allows for precise control of the processing sequence of workpieces, while binary coding effectively ensures the operability of the scheme. Compared with the traditional single coding method, the coding method of the present invention can better adapt to the dynamic optimization requirements under complex production conditions.

[0017] Furthermore, the real number code includes an integer part and a decimal part, and the code of the decimal part represents the order in which the integer part of the current gene appears in the chromosome.

[0018] Furthermore, the selection algorithm in the genetic algorithm is a tournament selection algorithm.

[0019] Furthermore, the genetic algorithm adopts a uniform crossover method and a basic bit mutation method.

[0020] By adopting uniform crossover and basic position mutation in the genetic algorithm, the present invention takes into account both exploration and stability in genetic operations, avoids the problem of large fluctuations in fitness values ​​during crossover and mutation, and ensures the gradual optimization of the scheme. At the same time, compared with a single crossover and mutation method, the combination of uniform crossover and basic position mutation can more effectively balance the relationship between global search and local optimization, thereby improving the overall efficiency of the genetic algorithm.

[0021] The technical effects of the present invention are:

[0022] The present invention combines big data analysis, genetic algorithms and real-time monitoring to dynamically optimize plate processing scheduling, thereby achieving efficient production and maximizing resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0024] Figure 1 is a flow chart schematically showing a method for plate processing in an embodiment of the present invention;

[0025] Figure 2 is a schematic diagram schematically showing a layout scheme for flushing the edges of workpieces in an embodiment of the present invention;

[0026] Figure 3 It is a schematic diagram schematically showing a comparison of typesetting scheme optimization for single-head and double-head cutting equipment in an embodiment of the present invention;

[0027] Figure 4 The figure schematically shows a relatively complex layout scheme of a workpiece in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0029] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0030] Example of scheduling method for plate processing:

[0031] like Figure 1 As shown, the scheduling method for plate processing of the present invention includes:

[0032] S1. Obtain a database of processable order workpieces.

[0033] When the plate is processed on the production line, the stacked standard plates are first placed on the hydraulic loading platform for labeling. One of the functions of labeling is to ensure that the subsequent workpieces can be traced. The code also contains the cutting layout plan of the standard plate; then the labeled standard plate is sent to the cutting equipment, and the equipment scans the code and cuts it according to the set layout plan, and sends the cut workpiece to the edge banding line; then the edge banding line will perform edge banding operations on all workpieces one by one, and the workpieces after edge banding will enter the drilling equipment, and the equipment will perform drilling operations according to the set path; finally, the drilled workpieces enter the sorting workstation, and the workpieces are automatically sorted according to the information contained in the code, that is, to ensure that the workpieces of the same order are placed in one area, which is convenient for subsequent transportation.

[0034] In most processing plants, orders from multiple customers are processed as the same batch of work orders to improve the utilization rate and processing efficiency of the plates. The plates refer to the standard plates of the processing plant, which can be cut according to a certain layout plan to obtain multiple workpieces required by the customer. The plates mentioned in the subsequent content of this embodiment all refer to standard plates used for cutting workpieces, and the rest will not be repeated.

[0035] First, the types of workpieces that need to be cut in all orders in the same batch are obtained to obtain the total order workpiece database DB1, which includes the workpiece types and the quantity of each workpiece type; then the standard parameters of the equipment involved in processing the plate on the production line are obtained, and the types of workpieces that can be cut by the processing plant are obtained according to the equipment parameters to obtain the machinable workpiece database DB2, which only contains the types of workpieces that can be cut by the production line; further based on the workpiece databases DB1 and DB2, the machinable order workpiece database DB3 is obtained, which includes the types of workpieces that can be processed in the total order and the quantity of each type of workpiece that can be processed in the total order.

[0036] S2. Obtain the main workpiece types; obtain multiple layout schemes of the initial standard plate.

[0037] In step S1, the processable order workpiece database DB3 is obtained. Here, the DBSCAN clustering algorithm is used to obtain the main workpiece types in the order, that is, the workpiece types that require a large number of cuts. The input of the algorithm is the feature matrix of the workpieces in the workpiece database DB3. The features mainly include the length, width and thickness of the workpiece. The setting parameters include the neighborhood radius And the minimum number of neighboring points minPts, in this embodiment It can be set to 0.5, minPts can be set to 8, and implementers can also adjust the above parameters according to actual conditions to make the main workpiece types more in line with actual processing conditions.

[0038] The output of the algorithm includes the number of clusters and the index set of core points. The number of data points contained in each cluster is counted, and the segmentation threshold of the number of data points is obtained using the Otsu threshold. The cluster corresponding to the number of data points greater than the segmentation threshold is recorded as a larger cluster cluster. The workpiece type is identified by viewing the features of the core points in the cluster, so that the workpiece type corresponding to the larger cluster cluster is taken as the main workpiece type. The above-mentioned DBSCAN algorithm and Otsu threshold algorithm are well-known technologies, and the specific steps are not repeated here. For the workpiece types that cannot be processed on the production line in the total order workpiece database DB1 and the minor workpiece types other than the above-mentioned main workpiece types, they are generally some special-shaped workpieces customized by some customers, and their number is small. They can be handmade by professional masters outside the fully automatic production line.

[0039] The DBSCAN clustering algorithm is used to obtain the main workpiece types in the order, so that the present invention can automatically identify and classify the key workpiece types in the order. By reasonably setting the algorithm parameters, it can ensure that even in the case of large data volume, the algorithm can effectively distinguish the main workpiece types, avoiding the limitations of manual classification, and being more accurate in optimizing the typesetting plan, thereby improving the accuracy and efficiency of production.

[0040] All historical layout plans for plate cutting in the processing plant are obtained. Based on the main workpiece types and historical layout plans obtained above, a greedy algorithm is used to obtain multiple layout plans for the current batch of work order plate cutting. The input of the algorithm is a set of historical layout plans, the main workpiece types of the current batch, and the number of each workpiece type. In the processing plant, since all workpieces are cut based on standard plates, the default number of plates is infinite; the output of the algorithm is a variety of layout plans including the main workpiece types of the current batch. The above algorithm is a well-known technology and will not be described in detail in the present invention.

[0041] S3. Obtaining the initial cutting sequence based on genetic algorithm.

[0042] Genetic Algorithm is an optimization algorithm based on the principles of natural selection and genetics, and is widely used in global search and optimization of complex problems. It simulates biological evolution and gradually optimizes the solution to the problem through operations such as selection, crossover, and mutation. The genetic algorithm starts with a randomly generated initial population and gradually approaches the optimal solution after multiple generations of iterations. In each generation, the fitness of an individual determines its chance of survival and reproduction. Genetic algorithms are widely used in production scheduling, path planning, machine learning and other fields, and can effectively solve problems that are difficult to handle with traditional methods. Therefore, a genetic algorithm is used in the present invention to generate the initial cutting order for the current batch processing.

[0043] For the genetic algorithm, we first need to initialize a population. Each individual in the population is a "chromosome", which corresponds to a complete code. In this embodiment, it represents a possible plate cutting sequence scheme. Then the initial population needs to be encoded. The specific encoding method is as follows.

[0044] In this embodiment, real number coding is used to encode the layout scheme and cutting order of the plate, wherein the integer digit of the real number represents the layout scheme serial number of the current plate, and the decimal digit represents the order of the layout scheme represented by the integer digit in the processing equipment. Since the total number of each layout scheme is known before cutting, the decimal digit is used for counting; then binary coding is used to represent the cutting equipment serial number to which the current plate is preferentially assigned.

[0045] The following example illustrates the above encoding method: Assume that a total of 60 plates need to be processed on a production line, with 5 layout schemes and 4 cutting equipment. One of the plates is the first plate of the 4th layout scheme, and the plate is sent to the second cutting equipment for cutting first. Then the real number code of the plate is "4.1", and then the real number code is expressed as binary code, and the integer and decimal are converted separately during the expression, and there is a binary code "100.000001" to facilitate subsequent crossover and mutation operations. Assume that the serial numbers of the 4 cutting equipment on the production line are recorded as "00", "01", "10", and "11" in binary code, respectively, and the binary code "01" corresponding to the cutting equipment where the current plate is located is placed after the code "100.000001", and finally the cutting process code of this plate is "100.000001-01". The order of the code in a complete code indicates the order in which the plate is cut on the production line. That is, when the code is ordered in a complete code as follows: {xxx.xxxxxx-xx,100.000001-01,…,xxx.xxxxxx-xx}, it means that the plate in the above example is the second piece in the entire processing flow.

[0046] It should be noted that the number of bits after the integer part of the real number is converted to binary code depends on the total number of typesetting schemes. In the above example, since the total number of typesetting schemes is 5, which is less than , so the binary code corresponding to the integer is 3 digits; and the number of digits after the decimal part is converted into binary code depends on the total number of plates in the batch. In the above example, the total number of plates is 60, which is less than , so the number of binary digits corresponding to the decimal is 6.

[0047] The initialized population is generally generated completely randomly, but in this embodiment, since the decimal places of its code are used to assist in counting the typesetting scheme, there are certain rules in the random generation, that is, only the integer part of the real number code and the binary code corresponding to the serial number of the cutting equipment are randomly generated, and the decimal part of the real number code only represents the order in which the same integer part appears in the encoding process. For example: in the process of generating a chromosome, a real number coded integer of 5 is randomly generated. This integer appears in this chromosome for the third time. Then the real number code is "5.3", which is converted into binary code "101.000011", indicating the third plate of the fifth typesetting scheme, and it is randomly assigned to a cutting equipment "11" for cutting. Then the code of the plate in the chromosome is "101.000011-11".

[0048] By adopting a combination of real number coding and binary coding, the present invention achieves a balance between flexibility and accuracy in the chromosome representation of the genetic algorithm. The introduction of real number coding allows for precise control of the processing sequence of workpieces, while binary coding effectively ensures the operability of the scheme. Compared with the traditional single coding method, the coding method of the present invention can better adapt to the dynamic optimization requirements under complex production conditions.

[0049] Through the above encoding and a random generation method with certain rules, an initialized population is obtained, which includes multiple chromosome individuals. Since there are many plates to be processed on the production line, that is, the chromosomes contain more information, a large amount of computing resources may be required for subsequent real-time processing. Therefore, 50 chromosomes can be randomly generated in this embodiment. The number of chromosomes can also be 100, 150 or 200, etc., and the implementer can adjust it according to the actual situation.

[0050] In the genetic algorithm, after obtaining the initialized population, it is also necessary to set the fitness function to judge the quality of individual chromosomes, that is, to determine which chromosomes can be crossed and mutated. In this embodiment, the fitness function is mainly reflected by the processing efficiency, so it can be initially set as the inverse of the average cutting time, and the specific calculation is:

[0051]

[0052] in Indicates chromosome The fitness function value of Indicates the total number of "genes" contained in the chromosome, that is, the total number of boards that need to be cut on the production line; It means the Chromosome gene, i.e. A cut sheet of material; It means the The time required to cut a plate. The larger the fitness function value of a chromosome is, the better the gene it contains is, and the more likely the individual is to produce offspring chromosomes.

[0053] The present invention directly links the optimization goal of the genetic algorithm with the cutting efficiency through the designed fitness function, so that the fitness value of each chromosome can accurately reflect the pros and cons of the current layout plan. Compared with the traditional optimization method, the use of this fitness function can more accurately evaluate the execution effect of each layout plan in actual production, thereby gradually improving the quality of the final layout plan during the genetic evolution process, ensuring the optimal production efficiency of the factory.

[0054] Calculate the fitness function values ​​of all 50 chromosomes, and then use the tournament selection algorithm to select chromosomes. The input of the selection algorithm includes: the fitness function values ​​of the 50 chromosomes, the scale of the tournament can be set to the empirical value 3, the number of new populations can be set to 50% of the original population, then the output of the algorithm is 25 chromosomes with better fitness function values, and this population is recorded as the first population. The number setting ratio of the first population mentioned above can also be set to 55%, 60% or 65%, etc. The implementer can choose according to the actual situation.

[0055] After obtaining the above-mentioned first population containing 25 chromosome individuals, crossover and mutation operations are performed, in which the crossover operation adopts the uniform crossover method, and the mutation operation adopts the basic bit mutation method, and a population containing 25 chromosomes is obtained again, which is recorded as the second population here. The fitness function value of each chromosome in the second population is recalculated, and the same tournament selection algorithm as above is used to obtain the third population, and crossover and mutation are performed until only the last chromosome is left in the population. The opening order scheme corresponding to the chromosome encoding is the initial opening order.

[0056] By adopting uniform crossover and basic position mutation in the genetic algorithm, the present invention takes into account both exploration and stability in genetic operations, avoids the problem of large fluctuations in fitness values ​​during crossover and mutation, and ensures the gradual optimization of the scheme. At the same time, compared with a single crossover and mutation method, the combination of uniform crossover and basic position mutation can more effectively balance the relationship between global search and local optimization, thereby improving the overall efficiency of the genetic algorithm.

[0057] S4. Dynamically adjust the layout and cutting sequence of standard plates.

[0058] After obtaining the initial cutting sequence in step S3, the plates are cut according to the initial cutting sequence. However, due to the different processing efficiencies of the equipment, the processing efficiencies of different lines on the production line may differ greatly after a period of processing according to the initial cutting sequence, resulting in waste of resources or blockage of some processing links. Therefore, after processing according to the initial cutting sequence, the scheduling of the plates is dynamically adjusted according to the characteristics and efficiency of the main processing equipment on the production line to improve the efficiency of the entire production line. It should be noted that in the factory's production line, the edge banding equipment works on a single line, and the last sorting equipment is defaulted to have a stacking area in front, which does not affect the processing of the plates, so the edge banding and sorting operations of the plates are not analyzed too much.

[0059] In one embodiment, based on the Industrial Internet of Things IIOT, real-time status monitoring is performed on a total of 5 main equipment on the production line. The main equipment includes three cutting equipment and two drilling equipment, of which the cutting equipment includes two nesting cutting equipment and one electronic sawing cutting equipment; the monitoring content includes whether the equipment fails and the real-time production quantity of the equipment. The plates are placed in the cutting equipment in the initial cutting order for processing. There are some special situations during scheduling that require dynamic adjustment of the scheduling plan to ensure the maximum efficiency of the production line under different working conditions.

[0060] For a production line, if a certain device is blocked in the processing link due to too many production quantities, it has the highest priority level, which is recorded as the first strategy in this embodiment. The scheduling plan should be adjusted in time to ensure the load balance of each device so that the efficiency of the current production line is the highest. The situations that may be involved are: when the production quantity of a certain cutting device is larger than that of other cutting devices, the cutting order of the plates should be adjusted so that the layout plan of the subsequent plates is more suitable for the cutting device with a small production quantity and the subsequent plates are diverted to these devices until the current production quantities of multiple cutting devices remain similar; at the same time, if a drilling device is also blocked, the cutting order should be adjusted so that the layout plan of the subsequent plates is more suitable for the unblocked drilling equipment, so as to balance the workload between the drilling equipment and improve the overall production efficiency of the production line.

[0061] The following example illustrates this:

[0062] There is a chromosome , which represents the initial cutting order. After cutting for a period of time according to this cutting order, it is detected that the production quantity of the second cutting equipment is larger than that of the other cutting equipment. At this time, the layout plan of the subsequent plates should be changed to make them more suitable for the first cutting equipment or the third cutting equipment. Assume that the first cutting equipment is a single-faucet single-position nesting cutting equipment, and the binary code is "00", and the third cutting equipment is a single-faucet double-position nesting cutting equipment, and the binary code is "10", and there is: the first cutting equipment is more efficient in processing the plates of the 1st to 3rd layout plans, that is, when the gene encoding appears in the following three cases, the efficiency of the first cutting equipment is higher: "001.xxxxxx-00", "0 10.xxxxxx-00", "011.xxxxxx-00"; the third cutting equipment is more efficient when processing the plates with the 5th to 7th typesetting schemes, that is, when the gene encoding appears in the following three types, the third cutting equipment is more efficient: "101.xxxxxx-10", "110.xxxxxx-10", "111.xxxxxx-10"; then when mutating, first mutate the binary encoding "01" of the second cutting equipment to "00" or "10", that is, adjust the cutting order of subsequent plates to divert them to the first device or the third device, and then try to ensure that the gene maintains the above six types of encoding during mutation, so as to make the overall efficiency of the production line higher.

[0063] Example 2: The cutting equipment also includes an electronic sawing equipment, which has the feature of being able to saw all flush edges of workpieces at once. Unlike nesting equipment, the rotating saw blade of the electronic saw does not move during processing, and the saw blade cuts the workpiece by moving the plate. This equipment is suitable for processing plates with many flush edges in layout, such as Figure 2 As shown, during the crossover and mutation operations in step S3, this type of typesetting scheme is matched with the electronic sawing and cutting equipment as much as possible, so as to give full play to the characteristics of the electronic sawing and cutting equipment and improve the overall efficiency of the production line. Assuming that the binary code of this electronic sawing and cutting equipment is "01", and the typesetting scheme suitable for cutting is the fourth type, then when the gene code appears "100.xxxxxx-01", the cutting efficiency of this equipment is higher. Therefore, when the chromosomes in the population are crossed and mutated, try to ensure that the code "100.xxxxxx" of the fourth typesetting scheme is combined with the code "01" of the electronic sawing and cutting equipment to give full play to the characteristics of the equipment and improve the overall cutting efficiency.

[0064] At the same time, in addition to the above-mentioned coding combination strategy in the crossover and mutation process, when the cutting equipment with binary code "00" or "10" is blocked during the processing of the plate in the initial cutting order, after its binary code mutates to "01", it should be ensured as much as possible that the code of the subsequent gene appears "100.xxxxxx-01", so as to advance the order of the layout scheme suitable for the electronic sawing and cutting equipment ahead of other layout schemes, and divert it to the electronic sawing and cutting equipment.

[0065] It should be noted that if a cutting device fails, it can be considered that the device is blocked. The specific processing method is the same as the processing method for the above-mentioned equipment to be blocked due to excessive production quantity. The cutting sequence is adjusted to make the cutting sequence of subsequent plates more in line with the equipment that can work normally and divert them to these working equipment until the faulty equipment is repaired. The specific process will not be repeated here.

[0066] In another embodiment, the following may also occur: Figure 3 The layout scheme shown in the middle left figure, when cutting materials with a single-head nesting cutting equipment, generally cuts larger workpieces such as workpiece 1 and workpiece 2 before cutting smaller workpieces. However, if the layout scheme of the plate is processed by a nesting cutting equipment with two heads, the spacing between the heads needs to be considered. The heads may affect each other and reduce efficiency. At this time, the plate layout scheme needs to be adjusted internally to make it more in line with the working characteristics of the two heads, thereby improving the efficiency of the nesting cutting equipment with two heads. The following example illustrates: a plate layout scheme such as Figure 3 As shown in the left figure, it is the third type of layout scheme, so its coding type is "011.xxxxxx-xx". When the plate of this layout scheme is diverted to the nesting cutting equipment with a single head, the layout scheme does not need to be adjusted; but when the plate of this layout scheme is diverted to the nesting cutting equipment with a double head, the code of the equipment is set to "10", that is, the gene coding type is "011.xxxxxx-10", the layout scheme needs to be optimized internally. The optimized layout scheme is as follows Figure 3 As shown in the right figure, the internal optimization strategy of the above typesetting scheme is recorded as the second strategy.

[0067] When the double-gantry cutting equipment is processing the sheet material, it is possible that one sheet material has been cut while the other sheet material is still being cut, resulting in reduced equipment utilization and affecting the efficiency of the entire production line. The third strategy is set according to the above content, that is, when the above situation occurs during the real-time cutting process, the layout scheme of two workpieces with similar quantities is adjusted to be processed on the double-gantry cutting equipment first.

[0068] The following example illustrates: There are two consecutive gene codes in the chromosome as follows: "110.001001-10, 111.001101-10", which means that in a certain cutting process, the 9th plate of the 6th layout plan is diverted to the double gantry cutting equipment. Figure 2 As shown, the next plate is the 13th plate of the 7th layout plan, which is also diverted to the double gantry cutting equipment. The 7th layout plan is as follows Figure 4 As shown, since the double-gantry nesting cutting equipment can process two plates at the same time, when the first plate has been cut, the second plate is still being processed. At this time, the utilization rate of the double-gantry nesting cutting equipment is reduced, thereby affecting the efficiency of the entire production line. Therefore, in this case, the layout scheme similar to the sixth layout scheme can be processed together with the sixth layout scheme. Assuming that the number of workpieces in the first layout scheme is the same or close to the number of workpieces in the sixth layout scheme, then the part of the code "111.001101-10" corresponding to the layout scheme is mutated to "001", and the coding part corresponding to the decimal place is changed according to the order in which "001" appears in the chromosome code.

[0069] In this embodiment, the above three strategies may appear in one typesetting scheme at the same time. When the three strategies appear at the same time, the genetic algorithm crossover and genetic operation are adjusted according to the first, second and third priorities.

[0070] S5. Complete the workpiece processing of all orders in the same batch.

[0071] The plate is cut according to the initial cutting sequence obtained in step S3, and then the initial cutting sequence is dynamically adjusted according to the three strategies obtained in step S4, so that the efficiency of the entire production line is kept at the highest under different working conditions, thereby efficiently completing the processing operations of the workpieces in all orders of the batch.

[0072] The present invention obtains the types of workpieces that can be processed by the factory based on equipment parameters and the types of workpieces ordered by customers, and then uses genetic algorithms to optimize the cutting order, and finally generates the layout plan for the current batch of plates and performs cutting operations. Compared with the prior art, the present invention significantly improves the flexibility and efficiency of factory production. Specifically, by optimizing a variety of layout plans with genetic algorithms, the present invention can dynamically adjust the layout order and cutting strategy according to actual working conditions, ensuring the best match between the processing technology and equipment capabilities of each batch, thereby reducing material waste, reducing equipment idle time, and improving the overall processing efficiency of the production line.

[0073] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0074] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A scheduling method for plate processing, characterized in that: The method comprises: Obtain the workpiece types that can be processed in the factory based on equipment parameters and the workpiece types ordered by customers; Based on the workpiece type and the layout scheme of historical plate cutting, multiple layout schemes of the main workpiece types of the current batch are obtained, and based on the multiple layout schemes, a genetic algorithm is used to obtain an initial cutting sequence, and all the plates of the current batch are sequentially fed into the cutting equipment for cutting according to the layout scheme set in the initial cutting sequence; Each chromosome in the genetic algorithm represents a material cutting sequence. The information encoded by the gene in the chromosome includes: the serial number of the layout scheme, the number of times the layout scheme appears in the production line, and the serial number of the material cutting equipment; the order of the gene code in the chromosome represents the order of the plate processing layout scheme corresponding to the code in the entire processing flow; Based on the first strategy, the second strategy and the third strategy, the crossover and mutation operations of the genetic algorithm are supplemented and limited. The first strategy refers to scheduling the diversion of the plates to the cutting equipment production line that is not blocked or faulty by monitoring the production quantity of the equipment; the second strategy adjusts the order of workpieces within the layout plan to make it conform to the spacing between the dual-head cutting equipment; the third strategy refers to adjusting the cutting order so that the number of workpieces in the layout plan of the two plates on the double gantry cutting equipment is the same or similar.

2. A scheduling method for plate processing according to claim 1, characterized in that: The first strategy also includes: adjusting the cutting sequence so that subsequent plates are diverted to a cutting equipment production line that is not blocked or has not failed, so that the layout plan of the subsequent plates has the highest processing efficiency on the cutting equipment that is not blocked or has not failed.

3. A scheduling method for plate processing according to claim 1, characterized in that: The fitness function of the genetic algorithm is: ； in Indicates chromosome The fitness function value of represents the total number of genes; It means the chromosome genes; Indicates The time required to cut the board corresponding to each gene.

4. A scheduling method for plate processing according to claim 1, characterized in that: According to the equipment parameters and the workpiece type of the customer order, the types of workpieces that can be processed in the factory are obtained, including: Obtain the workpiece types that need to be cut and the quantity of each workpiece type in all orders of the same batch, and obtain a total order workpiece database DB1; Obtain standard parameters of equipment involved in processing plates on the production line, obtain the types of workpieces that can be cut in the processing plant based on the equipment parameters, and obtain a database of workpieces that can be processed DB2; A machinable order workpiece database DB3 is obtained based on the total order workpiece database DB1 and the workpiece database DB2. The order workpiece database DB3 includes the types of workpieces to be processed in the total order and the quantity of each type of machinable workpieces in the total order.

5. A scheduling method for plate processing according to claim 3, characterized in that: Methods for obtaining the main types of workpieces in the current batch include: The DBSCAN clustering algorithm is used to obtain the main workpiece types in the order; the input of the algorithm is the feature matrix of the workpiece in the order workpiece database DB3, which includes the length, width and thickness of the workpiece; the algorithm setting parameters include the neighborhood radius And the minimum number of neighboring points minPts; The output of the algorithm includes the number of clusters and the index set of core points. The Otsu threshold for the number of data points contained in all clusters is calculated; the clusters with the number of data points greater than the Otsu threshold are recorded as larger clusters, and the core point data of the larger clusters are compared with the workpiece feature matrix to obtain the workpiece type corresponding to the larger cluster; the workpiece types corresponding to all larger clusters constitute the main workpiece types.

6. A scheduling method for plate processing according to claim 1, characterized in that: The encoding method in the genetic algorithm is a combination of real number encoding and binary encoding.

7. A scheduling method for plate processing according to claim 6, characterized in that: The real number code includes an integer part and a decimal part, and the code of the decimal part represents the order in which the integer part of the current gene appears in the chromosome.

8. A scheduling method for plate processing according to claim 1, characterized in that: The selection algorithm in the genetic algorithm is a tournament selection algorithm.

9. A scheduling method for plate processing according to claim 1, characterized in that: The genetic algorithm adopts a uniform crossover method and a basic bit mutation method.

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