CN113987919A - Corrugated carton material distribution generation method and device, storage medium and terminal - Google Patents
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Abstract
The invention discloses a corrugated carton material distribution generation method, a corrugated carton material distribution generation device, a storage medium and a terminal, wherein the method comprises the following steps: acquiring product parameters of a corrugated case to be manufactured; inputting product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputting a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence; the corrugated carton material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm; and determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured. Because the Apriori algorithm and the collaborative filtering algorithm are adopted to model the historical carton material matching data, the model can automatically generate an accurate material matching sequence according to the product parameters of the corrugated carton to be manufactured, the rationality and the accuracy of the material matching for generating the corrugated carton are improved, and the raw material cost and the labor cost are greatly reduced.
Description
Technical Field
The invention relates to the technical field of machine learning, in particular to a corrugated carton material distribution generation method and device, a storage medium and a terminal.
Background
Paper packaging is the largest sub-industry in the packaging industry. The paper packaging industry has wide development space and is in a high-speed development stage. With the implementation of environmental protection policies such as 'plastic restriction order', paper packaging as a renewable environment-friendly packaging material will inevitably replace other packaging modes such as plastic packaging and the like in the future, and occupy a larger share of the packaging market. Energy conservation and efficiency improvement are the goals pursued by enterprises, and the selection of materials for carton production is particularly important.
Currently, before the paper box is produced, sales and order auditors of paper packaging enterprises can manually select the materials of the paper box product to be produced according to the requirements of customers on parameters such as product corrugation, compressive strength, burst strength and the like and experience. The key point is that the quality and the cost of the carton must be considered, and the cost is reduced as far as possible under the condition of ensuring the quality. However, when the materials are selected by the traditional manual experience, the situation that the selected materials are unreasonable can occur, and the selected materials do not reach the standard, so that the strength of the carton is reduced, the bursting resistance is poor, the moisture resistance is poor, and the product quality is poor; or the selected material distribution performance is too high, the material distribution cost of the carton is greatly increased. In addition, once the selected material is not reasonable, the material is reworked and selected again, and the process is time-consuming and labor-consuming and influences the normal production of the order. The method for judging the distribution of the carton according to the human experience has the following defects: (1) a large amount of business and production experience is required; (2) a large number of tests are needed when a formula of the raw paper of the carton is selected, the formula needs to be changed for re-testing if the test result is unqualified, and a large amount of labor cost and time cost are consumed in the process; (3) real-time carton material distribution information can be missed by manpower inevitably.
Disclosure of Invention
The embodiment of the application provides a corrugated case material distribution generation method and device, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a corrugated carton material distribution generation method, including:
acquiring product parameters of a corrugated case to be manufactured;
inputting product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputting a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence; the corrugated carton material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm;
and determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured.
Optionally, determining a target material matching sequence from the multiple material matching sequences based on the confidence of each material matching sequence includes:
judging whether the confidence of each material distribution sequence is greater than or equal to a preset confidence threshold one by one to generate a plurality of judgment results;
determining at least one target material matching sequence from the plurality of material matching sequences according to the plurality of judgment results;
and receiving a selection instruction of at least one target material distribution sequence, and determining the target material distribution sequence from the at least one target material distribution sequence according to the selection instruction.
Optionally, determining at least one target material matching sequence from the plurality of material matching sequences according to the plurality of determination results, including:
obtaining a judgment result smaller than a preset confidence threshold value from the plurality of judgment results;
removing material matching sequences corresponding to judgment results smaller than a preset confidence coefficient threshold value from the multiple material matching sequences;
and generating at least one target material matching sequence.
Optionally, the generating a pre-trained corrugated carton material matching model according to the following steps includes:
acquiring and preprocessing historical orders and product data of historical corrugated cases to generate a case material distribution data set;
adopting an Apriori algorithm to create a corrugated carton material distribution model;
inputting the carton material distribution data set into a corrugated carton material distribution model to obtain an option set;
calculating the support degree of each data item in the option set;
comparing the support degree of each data item with a preset threshold value, and determining the data items with the support degree greater than the preset threshold value as a frequent item set;
when the number of the frequent items in the frequent item set is 1, generating an Apriori algorithm model;
training and generating a collaborative filtering algorithm model based on a preset number of historical corrugated case data;
and combining the Apriori algorithm model with the collaborative filtering algorithm model to generate a pre-trained corrugated carton material matching model.
Optionally, when the number of frequent items in the frequent item set is 1, generating an Apriori algorithm model includes:
when the number of the frequent items in the frequent item set is not 1, performing traversal splicing on the frequent items in the frequent item set to obtain a splicing option set;
calculating the support degree of each data item in the splicing option set;
and continuing to execute the step of comparing the support degree of each data item with a preset threshold until the number of the quantity items in the frequent item set is 1, and generating an Apriori algorithm model.
Optionally, training and generating a collaborative filtering algorithm model based on a preset number of historical corrugated case data includes:
collecting a preset amount of historical corrugated carton data;
constructing a corrugated case material distribution model by adopting a collaborative filtering algorithm, and inputting historical corrugated case data into the corrugated case material distribution model to obtain a plurality of similar product parameters of a first corrugated case;
obtaining the matched material of the product parameter of each similar corrugated case;
calculating a similarity matrix among a plurality of similar corrugated cases according to the product parameters of each similar corrugated case, and generating a similarity matrix;
when the similarity matrix is generated, a pre-trained corrugated carton material matching model is generated.
Optionally, inputting product parameters of the corrugated case to be manufactured into a pre-trained corrugated case matching model, and outputting a plurality of matching sequences corresponding to the corrugated case to be manufactured and a confidence of each matching sequence, including:
inputting product parameters of the corrugated case to be manufactured into an Apriori algorithm model, and generating a first number of material distribution sequences and a confidence coefficient of each first material distribution sequence;
outputting a first number of material matching sequences and the confidence of each first material matching sequence;
inputting product parameters of the corrugated case to be manufactured into the collaborative filtering algorithm model to obtain a plurality of similar product parameters of the second corrugated case; wherein the number of product parameters of the plurality of similar second corrugated boxes is less than the number of product parameters of the first corrugated box;
obtaining a plurality of matched materials of each second corrugated case from the product parameters of a plurality of similar second corrugated cases to generate a matched material set;
obtaining existing matched materials of product parameters of a corrugated case to be manufactured;
removing the existing material matching from the material matching set to generate a second number of material matching sequences;
calculating the confidence of each material matching sequence in the second number of material matching sequences;
and outputting the second quantity of material matching sequences and the confidence of each second material matching sequence.
In a second aspect, an embodiment of the present application provides a corrugated carton material distribution generating device, including:
the product parameter acquisition module is used for acquiring the product parameters of the corrugated case to be manufactured;
the parameter output module is used for inputting the product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model and outputting a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence;
the corrugated carton material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm;
and the material distribution sequence determining module is used for determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence of each material distribution sequence and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured.
In a third aspect, embodiments of the present application provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, a material matching generation device of a corrugated case firstly obtains product parameters of the corrugated case to be manufactured, then inputs the product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputs a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence, wherein the corrugated case material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm; and finally, determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence coefficient of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured. Because the Apriori algorithm and the collaborative filtering algorithm are adopted to model and fuse the historical carton material matching data, the model fused by the Apriori algorithm and the collaborative filtering algorithm based on the frequent item set searching technology not only solves the problem of sparse material matching data in collaborative filtering recommendation, but also solves the problem of large calculation amount of the association rule algorithm, so that the rationality and the accuracy of the material matching of the generated corrugated carton are improved, and the raw material cost and the labor cost are greatly reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart of a material distribution generation method for a corrugated carton according to an embodiment of the present application;
FIG. 2 is a schematic modeling diagram of an Apriori algorithm model provided in an embodiment of the present application;
fig. 3 is a schematic diagram illustrating a process of discovering a frequent item set by an Apriori algorithm according to an embodiment of the present application;
fig. 4 is a schematic view of an application scenario of a collaborative filtering algorithm according to an embodiment of the present application;
FIG. 5A is a schematic illustration of the relationship between carton product parameters and furnish as provided by an embodiment of the present application;
FIG. 5B is a graph of a similarity matrix of carton product parameters and furnish provided by an embodiment of the present application;
FIG. 5C is a graph of another similarity matrix of carton product parameters and furnish as provided by an embodiment of the present application;
fig. 6 is a schematic process block diagram of a corrugated carton material distribution generation process according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a corrugated carton distribution generating device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The application provides a corrugated carton material distribution generation method, a corrugated carton material distribution generation device, a storage medium and a terminal, which are used for solving the problems in the related technical problems. In the technical scheme provided by the application, because the Apriori algorithm and the collaborative filtering algorithm are adopted to model and fuse the historical carton matching material data, the problem of sparse matching material data in collaborative filtering recommendation is solved and the problem of large calculation amount of the association rule algorithm is solved based on a model fused by the Apriori algorithm and the collaborative filtering algorithm which are used for searching a frequent item set technology, so that the rationality and the accuracy of the matching material for generating the corrugated carton are improved, the raw material cost and the labor cost are greatly reduced, and the following exemplary embodiment is adopted for detailed description.
The method for producing a corrugated box material according to the embodiment of the present application will be described in detail below with reference to fig. 1 to 6. The method may be implemented by means of a computer program, which is executable on a furnish generating device for corrugated containers based on the von neumann system. The computer program may be integrated into the application or may run as a separate tool-like application.
Referring to fig. 1, a flow chart of a corrugated carton material distribution generating method is provided for an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the following steps:
s101, obtaining product parameters of a corrugated case to be manufactured;
the corrugated medium can be understood as a structural form of a building material, namely a corrugated medium. Corrugated boxes are made by die cutting, creasing, stapling or gluing. Corrugated containers are one of the most widely used packaging articles. The product parameters are flute shape, product category, compression strength and burst strength.
In an actual application scenario, other product parameters may be selected as inputs according to data and actual conditions.
Generally, the flute shapes are generally divided into a single flute, a double flute and a triple flute in the paper packaging industry, and the shrinkage rates of the three different flute shapes are different, so that the compression resistance and the impact strength are also different; the product category is the type or exact product of the product to be packaged; the compressive strength refers to the maximum load and deformation from the moment that a pressure tester uniformly applies dynamic pressure to the damage of the box body, and the unit is ibs; burst strength is the maximum pressure in kpa applied by a hydraulic system in a manner specified by the standard when the elastomeric film bursts through a round specimen.
In a possible implementation manner, when the material of the corrugated carton is generated, first, the product parameters of the corrugated carton to be manufactured need to be obtained, and the parameters may be provided by a customer or set by an expert according to the needs of the customer. For example, the product parameter of the corrugated case to be manufactured is { single-flute apples 70ibs1829kpa }, and it can be known that the corrugated case to be manufactured has a single-flute structure, is used for containing apples, and has a bearable compressive strength of 70ibs and a bearable burst strength of 1829 kpa.
S102, inputting product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputting a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence;
the corrugated carton material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm;
in a possible implementation manner, when an Apriori algorithm model is constructed based on an Apriori algorithm, firstly, historical orders and product data of a historical corrugated case are obtained and preprocessed, a case material distribution data set is generated, then, the Apriori algorithm is adopted to create the corrugated case material distribution model, then, the case material distribution data set is input into the corrugated case material distribution model to obtain an option set, then, the support degree of each data item in the option set is calculated, finally, the support degree of each data item is compared with a preset threshold value, the data item with the support degree larger than the preset threshold value is determined as a frequent item set, and when the number of the frequency items in the frequent item set is 1, the Apriori algorithm model is generated.
Specifically, when the number of the frequent items in the frequent item set is not 1, performing traversal splicing on each frequent item in the frequent item set to obtain a splicing option set; calculating the support degree of each data item in the splicing option set; and continuing to execute the step of comparing the support degree of each data item with a preset threshold until the number of the quantity items in the frequent item set is 1, and generating an Apriori algorithm model.
It should be noted that Apriori algorithm is a classical data mining algorithm that mines a frequent set of items and association rules. It uses an iterative approach to layer-by-layer search, where a set of c terms is used to explore a set of (c +1) terms. Firstly, scanning an order body paper data set, accumulating the count of each item, collecting the items meeting the minimum support degree, and finding out the set of frequent 1 item sets. This set is denoted as L1. Then, L1 is used to find the set of frequent 2-term sets, L2, L2 is used to find L3, and so on until no more frequent k-term sets can be found. A complete scan of the database is required each time an Lk is found.
Specifically, when a corrugated case material distribution model is constructed based on an Apriori algorithm, historical carton product data are firstly acquired, wherein the historical carton product data mainly comprise the flute shape, the product type, the material distribution, the compressive strength, the burst strength and the like of carton products, and for example, the historical carton product data can be acquired by the application, and the historical carton product data are sales order lists and product detail lists of certain leading enterprises in the paper packaging industry in the last 5 years. And then, carrying out data preprocessing on the historical carton product data to generate a carton material distribution data set, wherein the processing mode of the data preprocessing at least comprises column name renaming, duplicate value deletion, missing value processing, consistency processing, data sorting processing and abnormal value processing.
For example, as shown in fig. 2, after the data set is preprocessed, a option set c1 is generated, each option set has one data item, and then c1 passes through the support threshold to generate a frequent item set L1. The data items of L1 were spliced two by two into C2. Starting from candidate C2, L2 was generated by support filtering. L2 is spliced into candidate C3 according to Apriori's principle. Generating L3 through support filtering, when L3 can not be spliced, generating strong association rules from a frequent item set until Lk is a piece of data, and ending.
Further, when the number of the frequent items in the frequent item set is not 1, performing traversal splicing on the frequent items in the frequent item set to obtain a spliced item set, then calculating the support degree of each data item in the spliced item set, and finally continuing to perform the step of comparing the support degree of each data item with a preset threshold value until the number of the frequent item set is 1, and generating an Apriori algorithm model.
For example, the product parameters of the historical corrugated box order data and the example data of the corresponding materials are shown in table 1, wherein there are 4 historical corrugated boxes in table 1, and the product parameters of the 4 historical corrugated boxes are { B fruit 681829 }, { C fruit 732007 }, { a fruit 691805 }, and { E fruit 561416 }, respectively. Wherein
TABLE 1
| Serial number | Parameter(s) | Material distribution |
| 1 | { B fruit 681829 } | a、c、d |
| 2 | { C fruit 732007 } | b、c、e |
| 3 | { A fruit 691805 } | a、c、d |
| 4 | { E fruit 561416 } | b、e |
B. C, A, E, 68, 73, 69, and 1829, 2007, 1805, respectively, represent the reduction rate of the single-layer flute, the compressive strength is expressed in pounds (ibs), and the bursting strength is expressed in kilopascals (kpa). In the material distribution, a, b, c, d and e represent different material distributions.
For example, as shown in fig. 3, a cleaned carton single-product data set can be obtained after preprocessing according to the data in table 1, and a corrugated carton matching model created by combining Apriori algorithm can obtain, for example, an option set C1 in fig. 3, where a, b, C, d, e, a, b, C, d, e in the matching respectively correspond to a support degree, for example, a support degree threshold is 0.5, the support degrees corresponding to a, b, C, d, e are compared with the support degree threshold, and a matching with a support degree greater than 0.5 is determined to obtain frequent items L1, for example, a frequent item set L1 includes a, b, C, e, and it can be known that L1 has 4 items, and therefore, two items need to be combined by traversal to obtain C2, and L2 is generated by support degree filtering starting from a candidate set C2. L2 is spliced into candidate C3 according to Apriori principle, and it is known that the support greater than 0.5 in C3 has only one term { b, C, e }, so that L3{ b, C, e } is obtained as the final frequent term.
In another possible implementation manner, when the collaborative filtering algorithm model is constructed based on the collaborative filtering algorithm, firstly, a preset number of historical corrugated case data are collected, then the collaborative filtering algorithm is adopted to construct the corrugated case matching material model, the historical corrugated case data are input into the corrugated case matching material model to obtain a plurality of similar product parameters of the first corrugated case, then the matching material of the product parameters of each similar corrugated case is obtained, secondly, the similarity matrix among the similar corrugated cases is calculated according to the matching material of the product parameters of each similar corrugated case to generate the similarity matrix, and finally, when the similarity matrix is generated, the collaborative filtering algorithm model is generated.
It should be noted that, the collaborative filtering recommendation algorithm based on the user firstly uses a statistical technique to find neighbor users having the same preference as the target user, and then generates recommendation to the target user according to the preference of the neighbor users of the target user. The basic principle is to utilize the similarity of user access behaviors to recommend data which may be interesting to users to each other. For example, as shown in fig. 4, it is assumed that user a likes items a and C, user B likes item B, and user C likes items a, C, and D. From these user preferences, we can find that the preferences of user a and user C are similar, and prefer both item a and item C, and user C also prefers item D, and we can conclude that user a also prefers item D, so item D is recommended to user a. In the collaborative filtering algorithm, the similarity between users is calculated as follows: let N (u) be the favorite item set of user A, and N (v) be the favorite item set of user C, then the similarity between A and C adopts the formula:
the above formula can see that the higher the similarity between users, the more items that are commonly liked. In addition, the confidence of the user on the item is calculated as follows:
after the similarity between the users is obtained, the UserCF algorithm recommends the favorite items of the k users most similar to the user. The above right formula measures the confidence of user u for item i in the UserCF algorithm: wherein, S (u, K) includes K users with the closest interests to user u, n (i) is a set of users who have performed an action on item i, Wuv is a similarity between the interests of user u and user v, and Rvi represents the interest of user v on item i, and all Rvi are 1 because the hidden feedback data of a single action is used.
For example, the product parameter A, B, C, D of a plurality of similar first corrugated boxes has three flute 5 materials a, b, c, d and e respectively: the product A has single-edge matched materials, two types are a and b respectively, and one type is d on double edges; the product B has a single-edge matching material a and a double-edge matching material c; c, the product has a single-edge material b; three ridges have one kind of e; d, two types of double ridges of the product, namely c and D, and one type of three ridges, namely e, and the relationship between the carton product parameters and the material distribution scheme is shown in figure 5A. For each furnish, the product for which it is used, the same formulation between each two is identified as 1. For example, the products using formulation a have a and B, which are identified as 1 two by two in the matrix, as shown in fig. 5A. The matrix of FIG. 5C, for example, can be calculated by combining the matrix of FIG. 5A with the similarity calculation formula described above.
Further, after generating the Apriori algorithm model and the collaborative filtering algorithm model according to the above process, combining the Apriori algorithm model and the collaborative filtering algorithm model to generate a pre-trained corrugated carton material matching model.
In the embodiment of the application, when outputting a plurality of material distribution sequences corresponding to the corrugated case to be manufactured and the confidence of each material distribution sequence, first inputting the product parameters of the corrugated case to be manufactured into an Apriori algorithm model to generate a first number of material distribution sequences and the confidence of each first material distribution sequence, then outputting the first number of material distribution sequences and the confidence of each first material distribution sequence, and then inputting the product parameters of the corrugated case to be manufactured into a collaborative filtering algorithm model to obtain a plurality of similar product parameters of a second corrugated case; the method comprises the steps of obtaining a plurality of matching materials of each second corrugated case from the product parameters of the plurality of similar second corrugated cases, generating a matching material set, obtaining existing matching materials of the product parameters of the corrugated cases to be manufactured, removing the existing matching materials from the matching material set, generating a second number of matching material sequences, calculating the confidence coefficient of each matching material sequence in the second number of matching material sequences, and finally outputting the second number of matching material sequences and the confidence coefficient of each second matching material sequence.
Specifically, when the material matching is generated by using the collaborative filtering algorithm model, K products most similar to the target carton product parameter u need to be found out from the matrix of fig. 5C, the set S (u, K) is used to represent the K products, all the formulas used by the products in S are extracted, and the material matching used by u is removed. For each candidate furnish i, the degree to which the carton product u is interested in it is calculated using the following formula:
where rvi represents how much product v likes formula i.
Assuming we recommend to the carton product parameter a that K is 3 similar product parameters, which is B, C, D, then the formulas that they have used and a has not used are: c. e, then p (a, c) and p (a, e) are calculated, respectively (where p (a, c) and p (a, e) represent confidence between carton product parameter a and recipes c and e):
the similarity result indicates that the confidence of the product parameter a to the material matching scheme c and the material matching e may be the same and higher. In practical application, a threshold value, such as 0.5, is set for the confidence level, and when the confidence level is greater than 0.5, the material distribution scheme is recommended to the paper box product parameters.
And S103, determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured.
In a possible implementation manner, when a target material matching sequence is determined from a plurality of material matching sequences based on the confidence of each material matching sequence, firstly, whether the confidence of each material matching sequence is greater than or equal to a preset confidence threshold is judged one by one, a plurality of judgment results are generated, then, at least one target material matching sequence is determined from the plurality of material matching sequences according to the plurality of judgment results, finally, a selection instruction for the at least one target material matching sequence is received, and the target material matching sequence is determined from the at least one target material matching sequence according to the selection instruction.
Specifically, when at least one target material distribution sequence is determined from the plurality of material distribution sequences according to the plurality of judgment results, firstly, a judgment result smaller than a preset confidence threshold value is obtained from the plurality of judgment results, then, the material distribution sequence corresponding to the judgment result smaller than the preset confidence threshold value is removed from the plurality of material distribution sequences, and finally, at least one target material distribution sequence is generated.
For example, a pre-trained corrugated carton matching model generated based on Apriori algorithm and collaborative filtering algorithm may recommend the following carton matching schemes: ({ '200A Bow 170A 200A Bow B' }, { '130T Bow 120A Watt 160T Bow B' }, { '250A Bow 170A 200A Bow A' }); for example, when the product parameter of the corrugated box to be manufactured is input ({ double corrugated box 68ibs 1829kpa' }), the following result is recommended to be output:
({ '170A Bow 120A Tile 170A Bow 90A Tile 120A CB' })
({ '170H cow 110A tile 170H cow 150H cow 110A tile CB' })
({ '170A Bow 90A Watt 170H Bow 120A Watt CB' });
wherein '170A newtons' is an abbreviation for grade a kraft paper having a grammage of 170; when the flute type is 'B' and 'A', the flute type represents single-layer flutes with different reduction rates, the recommended formula sequence is surface paper, flutes and inner paper, when the flute type is 'CB', the flute type represents double-layer flutes, and the recommended formula sequence is surface paper, flutes 1, core paper, flutes 2 and inner paper.
For example, as shown in fig. 6, fig. 6 is a schematic block diagram of a process of a corrugated carton material distribution generation process provided by the present application, which is to first obtain historical corrugated carton order data, then perform data cleaning to obtain preprocessed data, input the preprocessed data into an Apriori algorithm model and a collaborative filtering algorithm model respectively, output a plurality of material distribution schemes respectively, and finally select an optimal material distribution scheme from the material distribution schemes by a user for output.
It should be noted that Apriori algorithm based on finding a frequent itemset technology outputs corresponding material matching sequence and confidence, and based on a collaborative filtering algorithm, outputs corresponding material matching sequence and confidence, and an experimental result surface shows that the recommendation results output by two algorithms are adopted independently, and obviously, the effect is not obvious when the results output by the two algorithms are simultaneously used. Therefore, the algorithm used in the experimental model is not only the superposition of the two algorithms, but also the fusion of the two algorithms can solve the problem of insufficient points corresponding to the respective algorithms, so that the effect of the model is more scientific and more accurate. Further, it is found that the generated corrugated carton material distribution sequence result is not good when the technical scheme of the invention is used, and other algorithms such as a model-based recommendation algorithm, a popularity-based recommendation algorithm, a utility-based recommendation algorithm and a content-based recommendation algorithm are continuously used in an overlapping manner.
In the embodiment of the application, a material matching generation device of a corrugated case firstly obtains product parameters of the corrugated case to be manufactured, then inputs the product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputs a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence, wherein the corrugated case material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm; and finally, determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence coefficient of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured. Because the Apriori algorithm and the collaborative filtering algorithm are adopted to model and fuse the historical carton material matching data, the model fused by the Apriori algorithm and the collaborative filtering algorithm based on the frequent item set searching technology not only solves the problem of sparse material matching data in collaborative filtering recommendation, but also solves the problem of large calculation amount of the association rule algorithm, so that the rationality and the accuracy of the material matching of the generated corrugated carton are improved, and the raw material cost and the labor cost are greatly reduced.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Referring to fig. 7, a schematic structural diagram of a corrugated carton distribution generating device according to an exemplary embodiment of the present invention is shown. The corrugated carton distribution material generation device can be realized by software, hardware or a combination of the software and the hardware to form all or part of the terminal. The device 1 comprises a product parameter acquisition module 10, a parameter output module 20 and a material distribution sequence determination module 30.
A product parameter obtaining module 10, configured to obtain product parameters of a corrugated carton to be manufactured;
a parameter output module 20, configured to input product parameters of the corrugated case to be manufactured into a pre-trained corrugated case matching model, and output a plurality of matching sequences corresponding to the corrugated case to be manufactured and a confidence of each matching sequence;
the corrugated carton material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm;
and a material distribution sequence determining module 30, configured to determine a target material distribution sequence from the plurality of material distribution sequences based on the confidence of each material distribution sequence, and determine the target material distribution sequence as the material distribution sequence of the corrugated carton to be manufactured.
It should be noted that, when the corrugated carton distribution generating device provided in the above embodiment executes the corrugated carton distribution generating method, only the division of the above functional modules is taken as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. In addition, the material distribution generating device for the corrugated case and the material distribution generating method for the corrugated case provided by the above embodiments belong to the same concept, and the detailed implementation process is shown in the method embodiments and will not be described herein.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, a material matching generation device of a corrugated case firstly obtains product parameters of the corrugated case to be manufactured, then inputs the product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputs a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence, wherein the corrugated case material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm; and finally, determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence coefficient of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured. Because the Apriori algorithm and the collaborative filtering algorithm are adopted to model and fuse the historical carton material matching data, the model fused by the Apriori algorithm and the collaborative filtering algorithm based on the frequent item set searching technology not only solves the problem of sparse material matching data in collaborative filtering recommendation, but also solves the problem of large calculation amount of the association rule algorithm, so that the rationality and the accuracy of the material matching of the generated corrugated carton are improved, and the raw material cost and the labor cost are greatly reduced.
The present invention also provides a computer readable medium, on which program instructions are stored, which when executed by a processor implement the corrugated box material distribution generation method provided by the above-mentioned method embodiments. The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to execute the furnish generating method for corrugated containers of the above-described respective method embodiments.
Please refer to fig. 8, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 8, terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 8, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a furnish generation application program of a corrugated box.
In the terminal 1000 shown in fig. 8, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to call the distribution generation application program of the corrugated box stored in the memory 1005, and specifically perform the following operations:
acquiring product parameters of a corrugated case to be manufactured;
inputting product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputting a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence; the corrugated carton material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm;
and determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured.
In one embodiment, when determining the target material matching sequence from the plurality of material matching sequences based on the confidence of each material matching sequence, the processor 1001 specifically performs the following operations:
judging whether the confidence of each material distribution sequence is greater than or equal to a preset confidence threshold one by one to generate a plurality of judgment results;
determining at least one target material matching sequence from the plurality of material matching sequences according to the plurality of judgment results;
and receiving a selection instruction of at least one target material distribution sequence, and determining the target material distribution sequence from the at least one target material distribution sequence according to the selection instruction.
In one embodiment, when the processor 1001 determines at least one target material matching sequence from the plurality of material matching sequences according to the plurality of determination results, the following operations are specifically performed:
obtaining a judgment result smaller than a preset confidence threshold value from the plurality of judgment results;
removing material matching sequences corresponding to judgment results smaller than a preset confidence coefficient threshold value from the multiple material matching sequences;
and generating at least one target material matching sequence.
In one embodiment, the processor 1001 generates the pre-trained corrugated box material distribution model according to the following steps, and specifically performs the following operations:
acquiring and preprocessing historical orders and product data of historical corrugated cases to generate a case material distribution data set;
adopting an Apriori algorithm to create a corrugated carton material distribution model;
inputting the carton material distribution data set into a corrugated carton material distribution model to obtain an option set;
calculating the support degree of each data item in the option set;
comparing the support degree of each data item with a preset threshold value, and determining the data items with the support degree greater than the preset threshold value as a frequent item set;
when the number of the frequent items in the frequent item set is 1, generating an Apriori algorithm model;
training and generating a collaborative filtering algorithm model based on a preset number of historical corrugated case data;
and combining the Apriori algorithm model with the collaborative filtering algorithm model to generate a pre-trained corrugated carton material matching model.
In one embodiment, the processor 1001 specifically performs the following operations when generating the Apriori algorithm model when the number of frequent terms in the frequent term set is 1:
when the number of the frequent items in the frequent item set is not 1, performing traversal splicing on the frequent items in the frequent item set to obtain a splicing option set;
calculating the support degree of each data item in the splicing option set;
and continuing to execute the step of comparing the support degree of each data item with a preset threshold until the number of the quantity items in the frequent item set is 1, and generating an Apriori algorithm model.
In one embodiment, the processor 1001 specifically performs the following operations when performing training to generate the collaborative filtering algorithm model based on a preset number of historical corrugated box data:
collecting a preset amount of historical corrugated carton data;
constructing a corrugated case material distribution model by adopting a collaborative filtering algorithm, and inputting historical corrugated case data into the corrugated case material distribution model to obtain a plurality of similar product parameters of a first corrugated case;
obtaining the matched material of the product parameter of each similar corrugated case;
calculating a similarity matrix among a plurality of similar corrugated cases according to the product parameters of each similar corrugated case, and generating a similarity matrix;
when the similarity matrix is generated, a pre-trained corrugated carton material matching model is generated.
In one embodiment, the processor 1001 specifically performs the following operations when inputting the product parameters of the corrugated box to be manufactured into the pre-trained corrugated box material matching model and outputting a plurality of material matching sequences corresponding to the corrugated box to be manufactured and the confidence of each material matching sequence:
inputting product parameters of the corrugated case to be manufactured into an Apriori algorithm model, and generating a first number of material distribution sequences and a confidence coefficient of each first material distribution sequence;
outputting a first number of material matching sequences and the confidence of each first material matching sequence;
inputting product parameters of the corrugated case to be manufactured into the collaborative filtering algorithm model to obtain a plurality of similar product parameters of the second corrugated case; wherein the number of product parameters of the plurality of similar second corrugated boxes is less than the number of product parameters of the first corrugated box;
obtaining a plurality of matched materials of each second corrugated case from the product parameters of a plurality of similar second corrugated cases to generate a matched material set;
obtaining existing matched materials of product parameters of a corrugated case to be manufactured;
removing the existing material matching from the material matching set to generate a second number of material matching sequences;
calculating the confidence of each material matching sequence in the second number of material matching sequences;
and outputting the second quantity of material matching sequences and the confidence of each second material matching sequence.
In the embodiment of the application, a material matching generation device of a corrugated case firstly obtains product parameters of the corrugated case to be manufactured, then inputs the product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputs a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence, wherein the corrugated case material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm; and finally, determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence coefficient of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured. Because the Apriori algorithm and the collaborative filtering algorithm are adopted to model and fuse the historical carton material matching data, the model fused by the Apriori algorithm and the collaborative filtering algorithm based on the frequent item set searching technology not only solves the problem of sparse material matching data in collaborative filtering recommendation, but also solves the problem of large calculation amount of the association rule algorithm, so that the rationality and the accuracy of the material matching of the generated corrugated carton are improved, and the raw material cost and the labor cost are greatly reduced.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments can be implemented by a computer program to instruct related hardware, and the program for generating the corrugated carton material can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A method for producing a material furnish for a corrugated cardboard box, the method comprising:
acquiring product parameters of a corrugated case to be manufactured;
inputting the product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model, and outputting a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence; the corrugated carton material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm;
determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence of each material distribution sequence, and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured.
2. The method according to claim 1, wherein determining a target material matching sequence from the plurality of material matching sequences based on the confidence of each material matching sequence comprises:
judging whether the confidence of each material distribution sequence is greater than or equal to a preset confidence threshold one by one to generate a plurality of judgment results;
determining at least one target material matching sequence from the plurality of material matching sequences according to the plurality of judgment results;
receiving a selection instruction of the at least one target material distribution sequence, and determining the target material distribution sequence from the at least one target material distribution sequence according to the selection instruction.
3. The method according to claim 2, wherein the determining at least one target material matching sequence from the plurality of material matching sequences according to the plurality of determination results comprises:
obtaining a judgment result smaller than the preset confidence threshold value from the plurality of judgment results;
removing material matching sequences corresponding to judgment results smaller than the preset confidence coefficient threshold value from the plurality of material matching sequences;
and generating at least one target material matching sequence.
4. The method of claim 1, wherein generating a pre-trained corrugated box furnish model comprises:
acquiring and preprocessing historical orders and product data of historical corrugated cases to generate a case material distribution data set;
adopting an Apriori algorithm to create a corrugated carton material distribution model;
inputting the carton material distribution data set into the corrugated carton material distribution model to obtain an option set;
calculating the support degree of each data item in the option set;
comparing the support degree of each data item with a preset threshold value, and determining the data items with the support degree greater than the preset threshold value as a frequent item set;
when the number of the frequent items in the frequent item set is 1, generating an Apriori algorithm model;
training and generating a collaborative filtering algorithm model based on a preset number of historical corrugated case data;
and combining the Apriori algorithm model and the collaborative filtering algorithm model to generate a pre-trained corrugated carton material matching model.
5. The method of claim 4, wherein generating an Apriori algorithm model when the number of frequent terms in the frequent term set is 1 comprises:
when the number of the frequent items in the frequent item set is not 1, performing traversal splicing on the frequent items in the frequent item set to obtain a splicing option set;
calculating the support degree of each data item in the splicing option set;
and continuing to execute the step of comparing the support degree of each data item with a preset threshold until the number of quantity items in the frequent item set is 1, and generating an Apriori algorithm model.
6. The method according to claim 4, wherein the training and generating a collaborative filtering algorithm model based on a preset number of historical corrugated box data comprises:
collecting a preset amount of historical corrugated carton data;
adopting a collaborative filtering algorithm to construct a corrugated case material matching model, and inputting the historical corrugated case data into the corrugated case material matching model to obtain a plurality of similar product parameters of a first corrugated case;
obtaining the matched material of the product parameter of each similar corrugated case;
calculating a similarity matrix among the plurality of similar corrugated cases according to the distribution of the product parameters of each similar corrugated case to generate a similarity matrix;
when the similarity matrix is generated, a pre-trained corrugated carton material matching model is generated.
7. The method according to claim 6, wherein the inputting the product parameters of the corrugated box to be manufactured into a pre-trained corrugated box material distribution model, and outputting a plurality of material distribution sequences corresponding to the corrugated box to be manufactured and the confidence of each material distribution sequence comprises:
inputting the product parameters of the corrugated case to be manufactured into an Apriori algorithm model, and generating a first number of material distribution sequences and a confidence coefficient of each first material distribution sequence;
outputting the first number of material matching sequences and the confidence of each first material matching sequence;
inputting the product parameters of the corrugated case to be manufactured into a collaborative filtering algorithm model to obtain a plurality of similar product parameters of a second corrugated case; wherein the number of product parameters of the plurality of similar second corrugated boxes is less than the number of product parameters of the first corrugated box;
obtaining a plurality of matched materials of each second corrugated case from the product parameters of the plurality of similar second corrugated cases to generate a matched material set;
obtaining existing matched materials of the product parameters of the corrugated case to be manufactured;
removing the existing material matching from the material matching set to generate a second number of material matching sequences;
calculating the confidence of each material matching sequence in the second number of material matching sequences;
and outputting the second quantity of material matching sequences and the confidence of each second material matching sequence.
8. A material distribution generating device for a corrugated box, the device comprising:
the product parameter acquisition module is used for acquiring the product parameters of the corrugated case to be manufactured;
the parameter output module is used for inputting the product parameters of the corrugated case to be manufactured into a pre-trained corrugated case material matching model and outputting a plurality of material matching sequences corresponding to the corrugated case to be manufactured and the confidence coefficient of each material matching sequence;
the corrugated carton material matching model is constructed based on an Apriori algorithm and a collaborative filtering algorithm;
and the material distribution sequence determining module is used for determining a target material distribution sequence from the plurality of material distribution sequences based on the confidence of each material distribution sequence and determining the target material distribution sequence as the material distribution sequence of the corrugated case to be manufactured.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1-7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-7.
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| CN117217028B (en) * | 2023-11-07 | 2024-02-02 | 国家超级计算天津中心 | Corrugated box design method, equipment and storage medium |
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