Parameter-based intelligent generation packaging box cutter plate design system

Abstract

The invention relates to the field of personnel computer aided design, in particular to a packaging box cutter plate design system based on parameter intelligent generation. The invention discloses a packaging box cutter plate design system based on parameter intelligent generation. The packaging box cutter plate design system comprises a packaging box cutter plate design requirement document acquisition module, a population set construction module, a fitness calculation module, a population iteration module and a packaging box cutter plate design strategy output module. According to the method, the design requirement document of the user for the packaging box is obtained, and the packaging box cutter plate design requirement item of the user for the packaging box is determined; the user provides guidance for recombination and variation of the packaging box knife plate design parameter simulation set according to the packaging box knife plate design requirement item of the packaging box, so that the convergence speed of simulation updating of the packaging box knife plate design parameters is increased, and then the packaging box knife plate design efficiency is improved.

Description

Technical Field

[0001] The invention relates to the field of computer-aided design, and in particular to a system for intelligently generating a packaging box cutter plate design based on parameters. Background Art

[0002] The design of packaging box die plate is a crucial link in the packaging industry. Its quality directly affects the production efficiency, material utilization rate and aesthetics of the finished product. However, the traditional packaging box die plate design mainly relies on the experience and manual operation of designers. Designers need to draw the die plate pattern step by step according to the size, shape, functional requirements and material properties of the packaging box, and ensure the feasibility of the design through multiple tests and adjustments. This design method is not only time-consuming and labor-intensive, but also easily restricted by human factors, resulting in low design efficiency. Especially when faced with complex or personalized packaging needs, designers often need to modify and verify repeatedly, resulting in a long design cycle and slow response speed. Summary of the invention

[0003] The present invention obtains the user's design requirement document for the packaging box, determines the user's packaging box cutter design requirement items for the packaging box, and in the subsequent process of simulating and updating the packaging box cutter design parameters through a genetic algorithm, provides guidance for the reorganization and mutation of the packaging box cutter design parameter simulation set based on the user's packaging box cutter design requirement items for the packaging box, thereby improving the convergence speed of the simulation update of the packaging box cutter design parameters, and further improving the efficiency of the packaging box cutter design.

[0004] A packaging box cutter design system based on parameter intelligent generation, comprising:

[0005] A packaging box cutter design requirement document acquisition module is used to acquire a user's packaging box cutter design requirement document for a packaging box. The packaging box cutter design requirement document includes H user's packaging box cutter design requirement items for a packaging box. Based on the packaging box cutter design requirement document, a packaging box cutter design requirement item weight distribution set is acquired. The packaging box cutter design requirement item weight distribution set includes the packaging box cutter design requirement item weights corresponding to the packaging box cutter design requirement items. The packaging box cutter design requirement item weight distribution set is used for fitness calculation in subsequent packaging box simulation design.

[0006] A population set construction module is used to construct N packaging box cutter design parameter simulation sets, wherein the packaging box cutter design parameter simulation sets include packaging box cutter design parameter values ​​corresponding to M packaging box cutter design parameters, and the packaging box cutter design parameter simulation sets are constructed in the following manner: a random value within a corresponding value range of the packaging box cutter design parameter value is set for each packaging box cutter design parameter value, and all packaging box cutter design parameter values ​​with set random values ​​are grouped into a packaging box cutter design parameter simulation set; and N packaging box cutter design parameter simulation sets are grouped into a population set;

[0007] A fitness calculation module is used to calculate the fitness corresponding to the packaging box cutter design parameter simulation set based on the weight distribution set of the packaging box cutter design requirement items;

[0008] The population iteration module is used to iteratively update the population set through a genetic algorithm, and the iterative update specifically includes the following steps: randomly selecting 0.5N packaging box blade design parameter simulation sets to form a parent set based on the fitness corresponding to the packaging box blade design parameter simulation set, and forming the unselected 0.5N packaging box blade design parameter simulation sets into a crossover candidate set; performing a recombination operation based on the parent set and the crossover candidate set to construct a temporary set, and in the recombination operation, adjusting the crossover probability of each packaging box blade design parameter based on the packaging box blade design requirement item weight distribution set; performing a mutation operation on all packaging box blade design parameter simulation sets in the temporary set to construct a new population set, and in the mutation operation, adjusting the mutation probability of each packaging box blade design parameter based on the packaging box blade design requirement item weight distribution set; and the population iteration module cooperates with the fitness calculation module to continuously iteratively update the population set;

[0009] The packaging box cutter design strategy output module is used to output the packaging box cutter design parameter simulation set with the highest fitness as the packaging box cutter design strategy after the number of iterations of the population iteration module cooperating with the fitness calculation module to iteratively update the population set reaches the maximum number of iterations.

[0010] As a preferred aspect, in the module for obtaining the packaging box cutting board design requirement document, obtaining the weight distribution set of the packaging box cutting board design requirement items based on the packaging box cutting board design requirement document specifically includes the following steps:

[0011] Preprocess the packaging box cutter design requirement document, construct a packaging box cutter design requirement word set, and send the packaging box cutter design requirement word set to the trained named entity extraction model for processing, and label each packaging box cutter design requirement word in the packaging box cutter design requirement word set with packaging box cutter design requirement item and priority word labeling;

[0012] For each packaging box cutting board design requirement item, all packaging box cutting board design requirement words marked as packaging box cutting board design requirement items are formed into a marked word set, and the marked word set is traversed. For each packaging box cutting board design requirement word in the marked word set, the priority word closest to the packaging box cutting board design requirement word is obtained, and the priority weight corresponding to the priority word closest to the packaging box cutting board design requirement word is obtained, and the priority average U of the priority weights corresponding to the priority words corresponding to all packaging box cutting board design requirement words in the marked word set is calculated, and the weight R of the pending packaging box cutting board design requirement item corresponding to the packaging box cutting board design requirement item is calculated by the following formula, R=α1(A / B)+α2U, where α1 and α2 are the word frequency weight and the priority comprehensive weight respectively, and satisfy α1+α2=1, A is the total number of packaging box cutting board design requirement words in the marked word set, and B is the total number of packaging box cutting board design requirement words in the packaging box cutting board design requirement word set;

[0013] The weights R of the pending package box cutter design requirement items corresponding to all package box cutter design requirement items are normalized to obtain the package box cutter design requirement item weights corresponding to the package box cutter design requirement items, and the package box cutter design requirement item weights corresponding to all package box cutter design requirement items are combined into a package box cutter design requirement item weight distribution set.

[0014] As a preferred aspect, in the fitness calculation module, the fitness corresponding to the packaging box cutter plate design parameter simulation set is calculated based on the weight distribution set of the packaging box cutter plate design requirement items, specifically including the following steps:

[0015] The packaging box blade design parameter simulation set is sent to the trained packaging box design demand prediction model for processing, and the packaging box design demand data set is output. The packaging box design demand data set includes the packaging box design demand data corresponding to the packaging box blade design demand items, and the packaging box design demand data set is traversed to perform fitness maximization processing, and the packaging box design demand data set to be calculated is constructed. The fitness maximization processing is specifically to perform inverse processing on the packaging box design demand data in the packaging box design demand data set that is opposite to the direction of improving fitness; the fitness δ corresponding to the packaging box blade design parameter simulation set is calculated by the following formula;

[0016] ;

[0017] where β i is the weight of the i-th packaging box cutter design requirement item in the packaging box cutter design requirement item weight distribution set, Q i is the i-th packaging box design requirement data to be calculated in the packaging box design requirement data set to be calculated, i=1, 2, 3, ..., I, I is the total number of packaging box knife design requirement items.

[0018] As a preferred aspect, in the population iteration module, a recombination operation is performed based on the parent set and the crossover candidate set to construct a temporary set, and in the recombination operation, the crossover probability of each packaging box blade design parameter is adjusted based on the weight distribution set of the packaging box blade design requirement items, specifically including the following steps:

[0019] Step T1: adjusting the crossover probability of each packaging box cutter design parameter based on the weight distribution set of packaging box cutter design requirement items;

[0020] Step T1.1: When performing the first iteration, traverse H packaging box cutter design requirement items, and perform the following operations for the h-th packaging box cutter design requirement item to obtain the packaging box cutter design parameter association set corresponding to the h-th packaging box cutter design requirement item, the packaging box cutter design parameter association set includes the packaging box cutter design parameters related to the h-th packaging box cutter design requirement item, and calculate the pending crossover probability P of the packaging box cutter design parameters in the packaging box cutter design parameter association set m =M -1 W h , where m=1, 2, 3, …, M, W h is the weight of the packaging box cutter design requirement item corresponding to the h-th packaging box cutter design requirement item, and enters step T1.2; in the subsequent iterations except the first iteration, traverse all packaging box cutter design parameters, and perform the following operations for the m-th packaging box cutter design parameter to calculate the first mean square error ζ of the packaging box cutter design parameter value corresponding to the m-th packaging box cutter design parameter of the simulation set of all packaging box cutter design parameters in the population set m , judge m >D is true, D is the convergence threshold, if ζ m >D holds true, then calculate the pending crossover probability of the mth packaging box blade design parameters , where e is a natural constant, go to step T1.2. If ζ m >D is not true, no operation;

[0021] Step T1.2: Set the pending crossover probability P of all packaging box blade design parameters m Perform normalization processing to construct the crossover probability of each packaging box blade design parameter;

[0022] Step T2: randomly select a packaging box cutter design parameter simulation set from the parent set and the crossover candidate set respectively, and the selected packaging box cutter design parameter simulation set will no longer be selected, for the two selected packaging box cutter design parameter simulation sets, based on the crossover probability of the packaging box cutter design parameters, select K packaging box cutter design parameters through the roulette algorithm, and exchange the packaging box cutter design parameter values ​​corresponding to the K packaging box cutter design parameters in the two selected packaging box cutter design parameter simulation sets to implement the reorganization operation until all the packaging box cutter design parameter simulation sets in the parent set are selected, and all the packaging box cutter design parameter simulation sets that have completed the reorganization operation and the parent set form a temporary storage set.

[0023] As a preferred aspect, in the population iteration module, a mutation operation is performed on all the simulation sets of packaging box blade design parameters in the temporary set to construct a new population set, and in the mutation operation, the mutation probability of each packaging box blade design parameter is adjusted based on the weight distribution set of the packaging box blade design requirement items, specifically including the following steps:

[0024] Step E1: adjusting the variation probability of each packaging box cutter design parameter based on the weight distribution set of packaging box cutter design requirement items;

[0025] Step E1.1: When performing the first iteration, traverse H packaging box cutter design requirement items, and perform the following operations for the h-th packaging box cutter design requirement item to obtain the packaging box cutter design parameter association set corresponding to the h-th packaging box cutter design requirement item, the packaging box cutter design parameter association set includes the packaging box cutter design parameters related to the h-th packaging box cutter design requirement item, and calculate the pending mutation probability F of the packaging box cutter design parameters in the packaging box cutter design parameter association set m =M -1 W h , where m=1, 2, 3, …, M, W h is the weight of the packaging box cutter design requirement item corresponding to the h-th packaging box cutter design requirement item, and enters step E1.2; in the subsequent iterations except the first iteration, traverse all packaging box cutter design parameters, and perform the following operations for the m-th packaging box cutter design parameter to obtain the mean square error η of the packaging box cutter design parameter value corresponding to the m-th packaging box cutter design parameter of the simulation set of all packaging box cutter design parameters in the temporary collection m , judge η m >D is true, if η m >D holds true, then calculate the probability of variation to be processed for the mth packaging box blade design parameters , go to step E1.2, if η m >D is not true, no operation;

[0026] Step E1.2: Traverse the packaging box cutter design parameter simulation sets in the temporary collection, and perform the following operations for each packaging box cutter design parameter simulation set in the temporary collection, generate a random value μ between 0 and 1 through a random function, and determine whether μ>Pc is established, Pc is the mutation threshold, if μ>Pc is not established, do not perform mutation operation on the selected packaging box cutter design parameter simulation set, if μ>Pc is established, select L packaging box cutter design parameters through a roulette algorithm based on the crossover probability of the packaging box cutter design parameters, and perform mutation operation on the packaging box cutter design parameter values ​​corresponding to the L packaging box cutter design parameters in the selected packaging box cutter design parameter simulation set; until all the packaging box cutter design parameter simulation sets in the temporary collection are traversed, and all the packaging box cutter design parameter simulation sets that have not performed mutation operation and those that have performed mutation operation are formed into a new population set.

[0027] As a preferred aspect, in the population iteration module, obtaining the packaging box cutter design parameter association set corresponding to the h-th packaging box cutter design requirement item specifically includes the following steps:

[0028] Match the h-th packaging box cutting board design requirement item with the packaging box cutting board design requirement item in the packaging box cutting board design parameter association library, output all the packaging box cutting board design parameters corresponding to the h-th packaging box cutting board design requirement item, and form all the packaging box cutting board design parameters corresponding to the h-th packaging box cutting board design requirement item into a packaging box cutting board design parameter association set, and the packaging box cutting board design parameter association library includes the packaging box cutting board design requirement item and several corresponding packaging box cutting board design parameters;

[0029] The method of constructing the packaging box cutter design parameter association library is as follows: obtain the trained packaging box design demand prediction model, and for each packaging box cutter design demand item, obtain the gradient value Y of the mth packaging box cutter design parameter in the packaging box design demand prediction model for the packaging box cutter design demand item. m , and judge Y m > Is X true? X is the gradient threshold. If Y m >X is established, the mth packaging box blade design parameters are corresponding to the packaging box blade design requirements. If Y m >X is not true, no operation; then all packaging box cutter design requirement items and their corresponding packaging box cutter design parameters are combined into a packaging box cutter design parameter association library.

[0030] As a preferred aspect, the named entity extraction model in the packaging box cutter design requirement document acquisition module is trained through the following steps:

[0031] Obtain several packaging box cutting design requirement training samples, which include a packaging box cutting design requirement word set, and annotate the packaging box cutting design requirement training samples with packaging box cutting design requirement items and priority words, and form all annotated packaging box cutting design requirement training samples into a packaging box cutting design requirement training set, and train a named entity extraction model with the packaging box cutting design requirement training set. During the training, the annotation of packaging box cutting design requirement items and priority words is used as the target, and the packaging box cutting design requirement training loss value is calculated to determine whether the packaging box cutting design requirement training loss value is within the packaging box cutting design requirement confidence range. If the packaging box cutting design requirement training loss value is within the packaging box cutting design requirement confidence range, output the trained named entity extraction model; otherwise, continue to train the named entity extraction model with the packaging box cutting design requirement training set.

[0032] As a preferred aspect, the packaging box design demand prediction model in the fitness calculation module is trained through the following steps:

[0033] Obtain packaging box design demand prediction training samples, which include packaging box cutter design parameter data sets, and the packaging box cutter design parameter data sets are stored in the same form as the packaging box cutter design parameter simulation set. Label the packaging box design demand prediction training samples through the packaging box design demand data sets, and form all labeled packaging box design demand prediction training samples into a packaging box design demand prediction training set. Train the packaging box design demand prediction model through the packaging box design demand prediction training set. During the training, the packaging box design demand data set is used as the target to calculate the packaging box design demand prediction loss value, and determine whether the packaging box design demand prediction loss value is within the packaging box design demand prediction confidence range. If the packaging box design demand prediction loss value is within the packaging box design demand prediction confidence range, output the trained packaging box design demand prediction model; otherwise, continue to train the packaging box design demand prediction model through the packaging box design demand prediction training set.

[0034] The present invention has the following advantages:

[0035] The present invention obtains the user's design requirement document for the packaging box, determines the user's packaging box cutter design requirement items for the packaging box, and in the subsequent process of simulating and updating the packaging box cutter design parameters through a genetic algorithm, provides guidance for the reorganization and mutation of the packaging box cutter design parameter simulation set based on the user's packaging box cutter design requirement items for the packaging box, thereby improving the convergence speed of the simulation update of the packaging box cutter design parameters, and further improving the efficiency of the packaging box cutter design. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the structure of the parameter-based intelligent generation of packaging box cutter plate design system used in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0038] Example 1, a system for intelligently generating a packaging box die design based on parameters, such as Figure 1 As shown, including:

[0039] The packaging box cutter design requirement document acquisition module is used to obtain the packaging box cutter design requirement document of the user for the packaging box. The packaging box cutter design requirement document includes H packaging box cutter design requirement items of the user for the packaging box, such as material utilization rate, material strength and production cost, etc. The packaging box cutter design requirement document is such as "the packaging box should save materials as much as possible, while ensuring sufficient strength, and the production cost should be controlled within a reasonable range". Based on the packaging box cutter design requirement document, the packaging box cutter design requirement item weight distribution set is obtained. The packaging box cutter design requirement item weight distribution set includes the packaging box cutter design requirement item weights corresponding to the packaging box cutter design requirement items, and the packaging box cutter design requirement item weight distribution set is used for fitness calculation in the subsequent packaging box simulation design. The packaging box cutter design requirement item weight describes the focus of the user on various requirements when designing the packaging box cutter, such as focusing on reducing production costs and focusing on higher material strength;

[0040] In the module for obtaining the packaging box cutter design requirement document, a weight distribution set of packaging box cutter design requirement items is obtained based on the packaging box cutter design requirement document, specifically including the following steps:

[0041] The packaging box cutting board design requirement document is preprocessed to construct a packaging box cutting board design requirement word set. The preprocessing operation includes word segmentation and deletion of stop words, etc. The packaging box cutting board design requirement word set is sent to the trained named entity extraction model for processing, and each packaging box cutting board design requirement word in the packaging box cutting board design requirement word set is labeled with packaging box cutting board design requirement items and priority words. It should be noted that the named entity extraction model is designed based on the existing technology, generally including the Bi-LSTM layer and the CRF layer. The packaging box cutting board design requirement item labeling refers to determining which packaging box cutting board design requirement item the packaging box cutting board design requirement word belongs to, and the priority word labeling refers to whether the packaging box cutting board design requirement word belongs to a priority word, such as "most important", "try", "reasonable" and "secondary";

[0042] For each packaging box cutting board design requirement item, all packaging box cutting board design requirement words marked as packaging box cutting board design requirement items are formed into a marked word set, and the marked word set is traversed. For each packaging box cutting board design requirement word in the marked word set, the priority word closest to the packaging box cutting board design requirement word is obtained. For example, in "the packaging box tries to save materials", the priority word corresponding to saving materials is "try your best", and the priority weight corresponding to the priority word closest to the packaging box cutting board design requirement word is obtained. It should be noted that these priority weights are set in advance by developers. For example, the priority weight corresponding to the most important one is 0.7, and the priority weight corresponding to "try your best" is 0.5. The standard is calculated. The priority average value U of the priority weights corresponding to the priority words corresponding to all the packaging box cutting board design demand words in the word set is recorded. The priority average value can reflect the user's emotional tendency for different goals of packaging box design demand. The weight R of the packaging box cutting board design demand item to be processed corresponding to the packaging box cutting board design demand item is calculated by the following formula: R=α1(A / B)+α2U, where α1 and α2 are the word frequency weight and the priority comprehensive weight, respectively, which are determined by the developer, generally 0.45 and 0.55, and satisfy α1+α2=1, A is the total number of packaging box cutting board design demand words in the marked word set, and B is the total number of packaging box cutting board design demand words in the packaging box cutting board design demand word set;

[0043] The weights R of the pending package box cutter design requirement items corresponding to all package box cutter design requirement items are normalized to obtain the package box cutter design requirement item weights corresponding to the package box cutter design requirement items, and the package box cutter design requirement item weights corresponding to all package box cutter design requirement items are combined into a package box cutter design requirement item weight distribution set;

[0044] The population set construction module is used to construct N packaging box cutter design parameter simulation sets, which include packaging box cutter design parameter values ​​corresponding to M packaging box cutter design parameters, such as material thickness, packaging box size (if the user needs to have packaging box size requirements, the packaging box size is not adjusted as a simulation parameter, but directly used as a fixed parameter), cutting line layout and material type, etc. It should be noted that the material thickness and packaging box size can be directly set with numerical values, while for example, the cutting line layout and material type cannot be directly described with numerical values, and the corresponding encoding can be set by word embedding; the packaging box cutter design parameter simulation set is constructed as follows: for each packaging box cutter design parameter value, a random value within the corresponding value range of the packaging box cutter design parameter value is set. It should be noted that setting a random value here also includes randomly selecting an encoding, and all packaging box cutter design parameter values ​​with random values ​​set are combined into a packaging box cutter design parameter simulation set; N packaging box cutter design parameter simulation sets are combined into a population set;

[0045] A fitness calculation module is used to calculate the fitness corresponding to the packaging box cutter design parameter simulation set based on the weight distribution set of the packaging box cutter design requirement items;

[0046] In the fitness calculation module, the fitness corresponding to the packaging box blade design parameter simulation set is calculated based on the weight distribution set of the packaging box blade design requirement items, which specifically includes the following steps: sending the packaging box blade design parameter simulation set to the trained packaging box design requirement prediction model for processing, and outputting the packaging box design requirement data set, which includes the packaging box design requirement data corresponding to the packaging box blade design requirement items, that is, the specific values ​​corresponding to the material utilization rate, material strength and production cost, and traversing the packaging box design requirement data set to perform fitness maximization processing, and construct the packaging box design requirement data set to be calculated. The fitness maximization processing is specifically to perform inverse processing on the packaging box design requirement data in the packaging box design requirement data set that is opposite to the direction of improving fitness. For example, the lower the production cost, the better the corresponding fitness, so the data corresponding to the production cost should be inversely processed; the fitness δ corresponding to the packaging box blade design parameter simulation set is calculated by the following formula: The packaging box design requirement prediction model is established based on the BP neural network, and with reference to the existing BP neural network, it also has an input layer, a hidden layer and an output layer;

[0047] ;

[0048] where β i is the weight of the i-th packaging box cutter design requirement item in the packaging box cutter design requirement item weight distribution set, Q i is the i-th packaging box design requirement data to be calculated in the packaging box design requirement data set to be calculated, i=1, 2, 3, ..., I, I is the total number of packaging box knife design requirement items;

[0049] A population iteration module is used to iteratively update a population set through a genetic algorithm. The iterative update specifically includes the following steps: randomly selecting 0.5N packaging box cutter plate design parameter simulation sets to form a parent set based on the fitness corresponding to the packaging box cutter plate design parameter simulation set, and forming a crossover candidate set with the unselected 0.5N packaging box cutter plate design parameter simulation sets. It should be noted that in the process of randomly selecting 0.5N packaging box cutter plate design parameter simulation sets to form a parent set, a roulette wheel selection algorithm can be used; a recombination operation is performed based on the parent set and the crossover candidate set to construct a temporary set, and in the recombination operation, the crossover probability of each packaging box cutter plate design parameter is adjusted based on the weight distribution set of the packaging box cutter plate design requirement items. It should be noted that the crossover probability here refers to the probability of exchanging the packaging box cutter plate design parameter values ​​corresponding to the packaging box cutter plate design parameters during the recombination operation. The crossover probability of each packaging box cutter design parameter is adjusted, so that in the iteration process, the simulation set of packaging box cutter design parameters is guided to search in the direction that meets the user's needs, thereby improving the convergence speed; a mutation operation is performed on all the simulation sets of packaging box cutter design parameters in the temporary set to construct a new population set, and in the mutation operation, the mutation probability of each packaging box cutter design parameter is adjusted based on the weight distribution set of the packaging box cutter design requirement items. It should be noted that the mutation probability refers to the probability of the packaging box cutter design parameter value corresponding to the packaging box cutter design parameter mutating during the recombination operation. The mutation probability of each packaging box cutter design parameter is adjusted based on the weight distribution set of the packaging box cutter design requirement items, so that in the iteration process, the simulation set of packaging box cutter design parameters is guided to search in the direction that meets the user's needs, thereby further improving the convergence speed; and the population iteration module cooperates with the fitness calculation module to continuously iteratively update the population set;

[0050] In the population iteration module, a recombination operation is performed based on the parent set and the crossover candidate set to construct a temporary set. In the recombination operation, the crossover probability of each packaging box cutter design parameter is adjusted based on the weight distribution set of the packaging box cutter design requirement items. Specifically, the steps include:

[0051] Step T1: adjusting the crossover probability of each packaging box cutter design parameter based on the weight distribution set of packaging box cutter design requirement items;

[0052] Step T1.1: When performing the first iteration, traverse H packaging box cutter design requirement items, and perform the following operations for the h-th packaging box cutter design requirement item to obtain the packaging box cutter design parameter association set corresponding to the h-th packaging box cutter design requirement item, the packaging box cutter design parameter association set includes the packaging box cutter design parameters related to the h-th packaging box cutter design requirement item, and calculate the pending crossover probability P of the packaging box cutter design parameters in the packaging box cutter design parameter association set m =M -1 W h , where m = 1, 2, 3, ..., M. It should be noted that the crossover probability P to be processed here m The m in the figure is the serial number of the design parameters of the packaging box blade, W h is the weight of the packaging box cutter design requirement item corresponding to the h-th packaging box cutter design requirement item, and enters step T1.2. Through the user's tendency towards design requirements, the crossover probability of the packaging box cutter design parameters corresponding to the design requirements tending to the user is improved, so that the packaging box cutter design parameter values ​​corresponding to these packaging box cutter design parameters corresponding to the design requirements tending to the user can be inherited from individuals with excellent performance, thereby improving the convergence speed; in the subsequent iterations except the first iteration, all packaging box cutter design parameters are traversed, and the following operations are performed for the m-th packaging box cutter design parameter to calculate the first mean square error ζ of the packaging box cutter design parameter value corresponding to the m-th packaging box cutter design parameter of all packaging box cutter design parameter simulation sets in the population set m , judge m >D is true, D is the convergence threshold, determined by the developer, if ζ m >D holds true, indicating that the packaging box blade design parameter value corresponding to the m-th packaging box blade design parameter is close to the optimal solution. At this time, it is necessary to reduce the crossover probability corresponding to the m-th packaging box blade design parameter and allocate more computing resources to the search of other packaging box blade design parameters. Then calculate the pending crossover probability of the m-th packaging box blade design parameter , where e is a natural constant, go to step T1.2. If ζ m >D does not hold, indicating that the packaging box blade design parameter value corresponding to the m-th packaging box blade design parameter is not close to the optimal solution, and no operation is performed;

[0053] Step T1.2: Set the pending crossover probability P of all packaging box blade design parameters m Perform normalization processing to construct the crossover probability of each packaging box blade design parameter;

[0054] Step T2: randomly select a packaging box cutter design parameter simulation set from the parent set and the crossover candidate set respectively, and the selected packaging box cutter design parameter simulation set will not be selected again. For the two selected packaging box cutter design parameter simulation sets, based on the crossover probability of the packaging box cutter design parameters, select K packaging box cutter design parameters through the roulette algorithm. K is determined by the developer. K is generally 1. When multi-point crossover is required, K can be selected as a value greater than 1. The packaging box cutter design parameter values ​​corresponding to the K packaging box cutter design parameters in the two selected packaging box cutter design parameter simulation sets are exchanged to implement the reorganization operation until all the packaging box cutter design parameter simulation sets in the parent set are selected. All the packaging box cutter design parameter simulation sets that have completed the reorganization operation and the parent set form a temporary storage set;

[0055] In the population iteration module, a mutation operation is performed on all the simulation sets of packaging box die design parameters in the temporary set to construct a new population set. In the mutation operation, the mutation probability of each packaging box die design parameter is adjusted based on the weight distribution set of the packaging box die design requirement items. Specifically, the following steps are included:

[0056] Step E1: adjusting the variation probability of each packaging box cutter design parameter based on the weight distribution set of packaging box cutter design requirement items;

[0057] Step E1.1: When performing the first iteration, traverse H packaging box cutter design requirement items, and perform the following operations for the h-th packaging box cutter design requirement item to obtain the packaging box cutter design parameter association set corresponding to the h-th packaging box cutter design requirement item, the packaging box cutter design parameter association set includes the packaging box cutter design parameters related to the h-th packaging box cutter design requirement item, and calculate the pending mutation probability F of the packaging box cutter design parameters in the packaging box cutter design parameter association set m =M -1 W h , where m=1, 2, 3, ..., M. It should be noted that the probability of mutation to be processed here is F m The m in the figure is the serial number of the design parameters of the packaging box blade, W his the weight of the packaging box cutter design requirement item corresponding to the h-th packaging box cutter design requirement item, and enters step E1.2. Through the user's tendency towards design requirements, the variation probability of the packaging box cutter design parameters corresponding to the design requirements tending to the user is improved, which can expand the search space corresponding to these packaging box cutter design parameters corresponding to the design requirements tending to the user, and avoid falling into the local optimal solution; in the subsequent iterations except the first iteration, all packaging box cutter design parameters are traversed, and the following operation is performed for the m-th packaging box cutter design parameter to obtain the m-th mean square error η of the packaging box cutter design parameter value corresponding to the m-th packaging box cutter design parameter of the simulation set of all packaging box cutter design parameters in the temporary set m , judge η m >D is true, if η m >D holds true, indicating that the packaging box blade design parameter value corresponding to the m-th packaging box blade design parameter is close to the optimal solution. At this time, it is necessary to reduce the mutation probability corresponding to the m-th packaging box blade design parameter and allocate more computing resources to the search of other packaging box blade design parameters. Then calculate the pending mutation probability of the m-th packaging box blade design parameter , go to step E1.2, if η m >D does not hold, indicating that the packaging box blade design parameter value corresponding to the m-th packaging box blade design parameter is not close to the optimal solution, and no operation is performed;

[0058] Step E1.2: traverse the packaging box blade design parameter simulation sets in the temporary collection, and perform the following operations for each packaging box blade design parameter simulation set in the temporary collection, generate a random value μ between 0 and 1 through a random function, and judge whether μ>Pc is established, Pc is the mutation threshold, generally 0.7, if μ>Pc is not established, no mutation operation is performed on the selected packaging box blade design parameter simulation set, if μ>Pc is established, based on the crossover probability of the packaging box blade design parameters, select L packaging box blade design parameters through the roulette algorithm, L is determined by the developer, L is generally 1, when multiple mutations are required, L can be selected as a value greater than 1, and the packaging box blade design parameter values ​​corresponding to the L packaging box blade design parameters in the selected packaging box blade design parameter simulation set are mutated, that is, the random value within the corresponding value range of the packaging box blade design parameter value is set; until all the packaging box blade design parameter simulation sets in the temporary collection are traversed, all the packaging box blade design parameter simulation sets that have not performed mutation operations and those that have performed mutation operations are formed into a new population set;

[0059] The packaging box cutter design strategy output module is used to output the packaging box cutter design parameter simulation set with the highest fitness as the packaging box cutter design strategy after the number of iterations of the population iteration module cooperating with the fitness calculation module to iteratively update the population set reaches the maximum number of iterations.

[0060] This application obtains the user's design requirement document for the packaging box, determines the user's packaging box cutter design requirement items for the packaging box, and in the subsequent process of simulating and updating the packaging box cutter design parameters through a genetic algorithm, provides guidance on the reorganization and mutation of the packaging box cutter design parameter simulation set based on the user's packaging box cutter design requirement items for the packaging box, thereby improving the convergence speed of the simulation update of the packaging box cutter design parameters, and further improving the efficiency of the packaging box cutter design.

[0061] In the population iteration module, the associated set of packaging box cutter design parameters corresponding to the h-th packaging box cutter design requirement item is obtained, which specifically includes the following steps:

[0062] Match the h-th packaging box cutting board design requirement item with the packaging box cutting board design requirement item in the packaging box cutting board design parameter association library, output all the packaging box cutting board design parameters corresponding to the h-th packaging box cutting board design requirement item, and form all the packaging box cutting board design parameters corresponding to the h-th packaging box cutting board design requirement item into a packaging box cutting board design parameter association set, and the packaging box cutting board design parameter association library includes the packaging box cutting board design requirement item and several corresponding packaging box cutting board design parameters;

[0063] The method of constructing the packaging box cutter design parameter association library is as follows: obtain the trained packaging box design demand prediction model, and for each packaging box cutter design demand item, obtain the gradient value Y of the mth packaging box cutter design parameter in the packaging box design demand prediction model for the packaging box cutter design demand item. m , and the gradient value Y m The larger the value is, the stronger the correlation between the mth packaging box blade design parameters and the packaging box blade design requirements is, and the Y m >X is true, X is the gradient threshold, determined by the developer, if Y m >X is established, indicating that the m-th packaging box blade design parameters are strongly correlated with the packaging box blade design requirements. The m-th packaging box blade design parameters are corresponding to the packaging box blade design requirements. If Y m >X does not hold, indicating that the correlation between the mth packaging box cutter design parameter and the packaging box cutter design requirement item is not strong, and no operation is performed; then all packaging box cutter design requirement items and their corresponding packaging box cutter design parameters are combined into a packaging box cutter design parameter association library.

[0064] The named entity extraction model in the packaging box cutter design requirement document acquisition module is trained through the following steps:

[0065] A number of packaging box cutting design requirement training samples are obtained, and the packaging box cutting design requirement training samples include a packaging box cutting design requirement word set. It should be noted that the packaging box cutting design requirement training samples here are constructed according to the actual packaging box cutting design requirement document, and the packaging box cutting design requirement training samples are annotated by packaging box cutting design requirement items and priority words. All annotated packaging box cutting design requirement training samples form a packaging box cutting design requirement training set, and the named entity extraction model is trained by the packaging box cutting design requirement training set. During the training, the annotation of packaging box cutting design requirement items and priority words is used as the target, and the packaging box cutting design requirement training loss value is calculated to determine whether the packaging box cutting design requirement training loss value is within the packaging box cutting design requirement confidence range. The packaging box cutting design requirement confidence range is used to characterize the accuracy of the named entity extraction model. If the packaging box cutting design requirement training loss value is within the packaging box cutting design requirement confidence range, the trained named entity extraction model is output; otherwise, the named entity extraction model continues to be trained by the packaging box cutting design requirement training set.

[0066] The packaging box design demand prediction model in the fitness calculation module is trained through the following steps:

[0067] Obtain packaging box design demand prediction training samples, which include packaging box cutter design parameter data sets, and the packaging box cutter design parameter data sets are stored in the same format as the packaging box cutter design parameter simulation set, and also include M packaging box cutter design parameter values ​​corresponding to the packaging box cutter design parameters. It should be noted that the packaging box cutter design parameter data sets are obtained by developers based on the packaging boxes that have been designed, and the packaging box design demand prediction training samples are annotated through the packaging box design demand data sets. The packaging box design demand data sets here are also obtained by developers based on the packaging boxes that have been designed, and the packaging box design demand prediction training samples and the packaging box design demand data sets maintain a one-to-one correspondence, and both come from the same For the designed packaging box, all the labeled packaging box design demand prediction training samples are combined into a packaging box design demand prediction training set, and the packaging box design demand prediction model is trained through the packaging box design demand prediction training set. During the training, the packaging box design demand data set is used as the target, the packaging box design demand prediction loss value is calculated, and it is determined whether the packaging box design demand prediction loss value is within the packaging box design demand prediction confidence range. The packaging box design demand prediction confidence range is used to characterize the accuracy of the packaging box design demand prediction model. If the packaging box design demand prediction loss value is within the packaging box design demand prediction confidence range, the trained packaging box design demand prediction model is output; otherwise, the packaging box design demand prediction model is continued to be trained through the packaging box design demand prediction training set.

[0068] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A system for intelligently generating packaging box die plate design based on parameters, characterized in that: include: A packaging box cutter design requirement document acquisition module is used to acquire a user's packaging box cutter design requirement document for a packaging box. The packaging box cutter design requirement document includes H user's packaging box cutter design requirement items for a packaging box. Based on the packaging box cutter design requirement document, a packaging box cutter design requirement item weight distribution set is acquired. The packaging box cutter design requirement item weight distribution set includes the packaging box cutter design requirement item weights corresponding to the packaging box cutter design requirement items. The packaging box cutter design requirement item weight distribution set is used for fitness calculation in subsequent packaging box simulation design. A population set construction module is used to construct N packaging box cutter design parameter simulation sets, wherein the packaging box cutter design parameter simulation sets include packaging box cutter design parameter values ​​corresponding to M packaging box cutter design parameters, and the packaging box cutter design parameter simulation sets are constructed in the following manner: a random value within a corresponding value range of the packaging box cutter design parameter value is set for each packaging box cutter design parameter value, and all packaging box cutter design parameter values ​​with set random values ​​are grouped into a packaging box cutter design parameter simulation set; and N packaging box cutter design parameter simulation sets are grouped into a population set; A fitness calculation module is used to calculate the fitness corresponding to the packaging box cutter design parameter simulation set based on the weight distribution set of the packaging box cutter design requirement items; The population iteration module is used to iteratively update the population set through a genetic algorithm, and the iterative update specifically includes the following steps: randomly selecting 0.5N packaging box blade design parameter simulation sets to form a parent set based on the fitness corresponding to the packaging box blade design parameter simulation set, and forming the unselected 0.5N packaging box blade design parameter simulation sets into a crossover candidate set; performing a recombination operation based on the parent set and the crossover candidate set to construct a temporary set, and in the recombination operation, adjusting the crossover probability of each packaging box blade design parameter based on the packaging box blade design requirement item weight distribution set; performing a mutation operation on all packaging box blade design parameter simulation sets in the temporary set to construct a new population set, and in the mutation operation, adjusting the mutation probability of each packaging box blade design parameter based on the packaging box blade design requirement item weight distribution set; and the population iteration module cooperates with the fitness calculation module to continuously iteratively update the population set; The packaging box cutter design strategy output module is used to output the packaging box cutter design parameter simulation set with the highest fitness as the packaging box cutter design strategy after the number of iterations of the population iteration module cooperating with the fitness calculation module to iteratively update the population set reaches the maximum number of iterations.

2. According to the parameter-based intelligent generation of packaging box die design system according to claim 1, it is characterized in that: In the module for obtaining the packaging box cutter design requirement document, a weight distribution set of packaging box cutter design requirement items is obtained based on the packaging box cutter design requirement document, specifically including the following steps: Preprocess the packaging box cutter design requirement document, construct a packaging box cutter design requirement word set, and send the packaging box cutter design requirement word set to the trained named entity extraction model for processing, and label each packaging box cutter design requirement word in the packaging box cutter design requirement word set with packaging box cutter design requirement item and priority word labeling; For each packaging box cutting board design requirement item, all packaging box cutting board design requirement words marked as packaging box cutting board design requirement items are formed into a marked word set, and the marked word set is traversed. For each packaging box cutting board design requirement word in the marked word set, the priority word closest to the packaging box cutting board design requirement word is obtained, and the priority weight corresponding to the priority word closest to the packaging box cutting board design requirement word is obtained, and the priority average U of the priority weights corresponding to the priority words corresponding to all packaging box cutting board design requirement words in the marked word set is calculated, and the weight R of the pending packaging box cutting board design requirement item corresponding to the packaging box cutting board design requirement item is calculated by the following formula, R=α1(A / B)+α2U, where α1 and α2 are the word frequency weight and the priority comprehensive weight respectively, and satisfy α1+α2=1, A is the total number of packaging box cutting board design requirement words in the marked word set, and B is the total number of packaging box cutting board design requirement words in the packaging box cutting board design requirement word set; The weights R of the pending package box cutter design requirement items corresponding to all package box cutter design requirement items are normalized to obtain the package box cutter design requirement item weights corresponding to the package box cutter design requirement items, and the package box cutter design requirement item weights corresponding to all package box cutter design requirement items are combined into a package box cutter design requirement item weight distribution set.

3. The system for intelligently generating packaging box die plate design based on parameters according to claim 2, characterized in that: In the fitness calculation module, the fitness corresponding to the packaging box cutter design parameter simulation set is calculated based on the weight distribution set of the packaging box cutter design requirement items, which specifically includes the following steps: The packaging box blade design parameter simulation set is sent to the trained packaging box design demand prediction model for processing, and the packaging box design demand data set is output. The packaging box design demand data set includes the packaging box design demand data corresponding to the packaging box blade design demand items, and the packaging box design demand data set is traversed to perform fitness maximization processing, and the packaging box design demand data set to be calculated is constructed. The fitness maximization processing is specifically to perform inverse processing on the packaging box design demand data in the packaging box design demand data set that is opposite to the direction of improving fitness; the fitness δ corresponding to the packaging box blade design parameter simulation set is calculated by the following formula; ； where β i is the weight of the i-th packaging box cutter design requirement item in the packaging box cutter design requirement item weight distribution set, Q i is the i-th packaging box design requirement data to be calculated in the packaging box design requirement data set to be calculated, i=1, 2, 3, ..., I, I is the total number of packaging box knife design requirement items.

4. The system for intelligently generating packaging box die plate design based on parameters according to claim 3 is characterized in that: In the population iteration module, a recombination operation is performed based on the parent set and the crossover candidate set to construct a temporary set. In the recombination operation, the crossover probability of each packaging box cutter design parameter is adjusted based on the weight distribution set of the packaging box cutter design requirement items. Specifically, the steps include: Step T1: adjusting the crossover probability of each packaging box cutter design parameter based on the weight distribution set of packaging box cutter design requirement items; Step T1.1: When performing the first iteration, traverse H packaging box cutter design requirement items, and perform the following operations for the h-th packaging box cutter design requirement item to obtain the packaging box cutter design parameter association set corresponding to the h-th packaging box cutter design requirement item, the packaging box cutter design parameter association set includes the packaging box cutter design parameters related to the h-th packaging box cutter design requirement item, and calculate the pending crossover probability P of the packaging box cutter design parameters in the packaging box cutter design parameter association set m =M -1 W h , where m=1, 2, 3, …, M, W h is the weight of the packaging box cutter design requirement item corresponding to the h-th packaging box cutter design requirement item, and enters step T1.2; in the subsequent iterations except the first iteration, traverse all packaging box cutter design parameters, and perform the following operations for the m-th packaging box cutter design parameter to calculate the first mean square error ζ of the packaging box cutter design parameter value corresponding to the m-th packaging box cutter design parameter of the simulation set of all packaging box cutter design parameters in the population set m , judge m >D is true, D is the convergence threshold, if ζ m >D holds true, then calculate the pending crossover probability of the mth packaging box blade design parameters , where e is a natural constant, go to step T1.

2. If ζ m >D is not true, no operation; Step T1.2: Set the pending crossover probability P of all packaging box blade design parameters m Perform normalization processing to construct the crossover probability of each packaging box blade design parameter; Step T2: randomly select a packaging box cutter design parameter simulation set from the parent set and the crossover candidate set respectively, and the selected packaging box cutter design parameter simulation set will no longer be selected, for the two selected packaging box cutter design parameter simulation sets, based on the crossover probability of the packaging box cutter design parameters, select K packaging box cutter design parameters through the roulette algorithm, and exchange the packaging box cutter design parameter values ​​corresponding to the K packaging box cutter design parameters in the two selected packaging box cutter design parameter simulation sets to implement the reorganization operation until all the packaging box cutter design parameter simulation sets in the parent set are selected, and all the packaging box cutter design parameter simulation sets that have completed the reorganization operation and the parent set form a temporary storage set.

5. The system for intelligently generating packaging box die plate design based on parameters according to claim 4, characterized in that: In the population iteration module, a mutation operation is performed on all the simulation sets of packaging box die design parameters in the temporary set to construct a new population set. In the mutation operation, the mutation probability of each packaging box die design parameter is adjusted based on the weight distribution set of the packaging box die design requirement items. Specifically, the following steps are included: Step E1: adjusting the variation probability of each packaging box cutter design parameter based on the weight distribution set of packaging box cutter design requirement items; Step E1.1: When performing the first iteration, traverse H packaging box cutter design requirement items, and perform the following operations for the h-th packaging box cutter design requirement item to obtain the packaging box cutter design parameter association set corresponding to the h-th packaging box cutter design requirement item, the packaging box cutter design parameter association set includes the packaging box cutter design parameters related to the h-th packaging box cutter design requirement item, and calculate the pending mutation probability F of the packaging box cutter design parameters in the packaging box cutter design parameter association set m =M -1 W h , where m=1, 2, 3, …, M, W h is the weight of the packaging box cutter design requirement item corresponding to the h-th packaging box cutter design requirement item, and enters step E1.2; in the subsequent iterations except the first iteration, traverse all packaging box cutter design parameters, and perform the following operations for the m-th packaging box cutter design parameter to obtain the mean square error η of the packaging box cutter design parameter value corresponding to the m-th packaging box cutter design parameter of the simulation set of all packaging box cutter design parameters in the temporary collection m , judge η m >D is true, if η m >D holds true, then calculate the probability of variation to be processed for the mth packaging box blade design parameters , go to step E1.2, if η m >D is not true, no operation; Step E1.2: traverse the packaging box cutter design parameter simulation sets in the temporary collection, and perform the following operations for each packaging box cutter design parameter simulation set in the temporary collection, generate a random value μ between 0 and 1 through a random function, and judge whether μ>Pc is established, Pc is the mutation threshold, if μ>Pc is not established, do not perform mutation operation on the selected packaging box cutter design parameter simulation set, if μ>Pc is established, select L packaging box cutter design parameters through a roulette algorithm based on the crossover probability of the packaging box cutter design parameters, and perform mutation operation on the packaging box cutter design parameter values ​​corresponding to the L packaging box cutter design parameters in the selected packaging box cutter design parameter simulation set; Until all the simulation sets of packaging box cutter design parameter in the temporary collection are traversed, all the simulation sets of packaging box cutter design parameter that have not performed mutation operation and those that have performed mutation operation are formed into a new population set.

6. The system for intelligently generating packaging box die plate design based on parameters according to claim 5, characterized in that: In the population iteration module, the associated set of packaging box cutter design parameters corresponding to the h-th packaging box cutter design requirement item is obtained, which specifically includes the following steps: Match the h-th packaging box cutting board design requirement item with the packaging box cutting board design requirement item in the packaging box cutting board design parameter association library, output all the packaging box cutting board design parameters corresponding to the h-th packaging box cutting board design requirement item, and form all the packaging box cutting board design parameters corresponding to the h-th packaging box cutting board design requirement item into a packaging box cutting board design parameter association set, and the packaging box cutting board design parameter association library includes the packaging box cutting board design requirement item and several corresponding packaging box cutting board design parameters; The method of constructing the packaging box cutter design parameter association library is as follows: obtain the trained packaging box design demand prediction model, and for each packaging box cutter design demand item, obtain the gradient value Y of the mth packaging box cutter design parameter in the packaging box design demand prediction model for the packaging box cutter design demand item. m , and judge Y m > Is X true? X is the gradient threshold. If Y m >X is established, the mth packaging box blade design parameters are corresponding to the packaging box blade design requirements. If Y m >X is not true, no operation; then all packaging box cutter design requirement items and their corresponding packaging box cutter design parameters are combined into a packaging box cutter design parameter association library.

7. The system for intelligently generating packaging box die plate design based on parameters according to claim 6, characterized in that: The named entity extraction model in the packaging box cutting design requirement document acquisition module is trained through the following steps: obtaining several packaging box cutting design requirement training samples, which include a packaging box cutting design requirement word set, and annotating the packaging box cutting design requirement training samples with packaging box cutting design requirement items and priority words, and forming all the annotated packaging box cutting design requirement training samples into a packaging box cutting design requirement training set, and training the named entity extraction model with the packaging box cutting design requirement training set. During the training, the annotation of packaging box cutting design requirement items and priority words is used as the target, and the packaging box cutting design requirement training loss value is calculated to determine whether the packaging box cutting design requirement training loss value is within the packaging box cutting design requirement confidence range. If the packaging box cutting design requirement training loss value is within the packaging box cutting design requirement confidence range, output the trained named entity extraction model; otherwise, continue to train the named entity extraction model with the packaging box cutting design requirement training set.

8. The system for intelligently generating packaging box die plate design based on parameters according to claim 7, characterized in that: The packaging box design demand prediction model in the fitness calculation module is trained through the following steps: obtaining packaging box design demand prediction training samples, the packaging box design demand prediction training samples include a packaging box blade design parameter data set, and the storage form of the packaging box blade design parameter data set is consistent with the storage form of the packaging box blade design parameter simulation set, labeling the packaging box design demand prediction training samples through the packaging box design demand data set, and forming all labeled packaging box design demand prediction training samples into a packaging box design demand prediction training set, and training the packaging box design demand prediction model through the packaging box design demand prediction training set. During the training, the packaging box design demand data set is used as the target, the packaging box design demand prediction loss value is calculated, and it is determined whether the packaging box design demand prediction loss value is within the packaging box design demand prediction confidence range. If the packaging box design demand prediction loss value is within the packaging box design demand prediction confidence range, the trained packaging box design demand prediction model is output; otherwise, the packaging box design demand prediction model continues to be trained through the packaging box design demand prediction training set.

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