High-efficiency receiving system for clothing fast reverse supply chain packaging auxiliary materials

Abstract

The invention relates to the technical field of clothing supply management, and particularly discloses a clothing fast reverse supply chain packaging auxiliary material efficient receiving system, which is characterized in that auxiliary material receiving configuration schemes are preset for different commodities, the configuration schemes are bound with commodity archives, and after a production order is received, the auxiliary material receiving configuration schemes are stored in a database; therefore, the types and the number of required auxiliary materials can be quickly and accurately determined based on the commodity information contained in the production order, and the auxiliary material demand list is generated. Meanwhile, in order to ensure the suitability of the auxiliary material receiving configuration scheme and the bound commodity, a verification mechanism based on a deep learning technology is further introduced, and whether the auxiliary material receiving configuration scheme and the bound commodity file are matched or not is intelligently judged by performing semantic analysis and fine-grained semantic alignment interaction on the auxiliary material receiving configuration scheme and the bound commodity file. Through the mode, the auxiliary materials can be rapidly and accurately distributed, the reaction speed and efficiency of a clothing supply chain are remarkably improved, and production delay and cost increase caused by receiving errors or mismatching are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of clothing supply management, and more specifically, to an efficient collection system for packaging auxiliary materials in a clothing quick response supply chain. Background Art

[0002] In the apparel manufacturing industry, the efficiency and flexibility of supply chain management have a crucial impact on the competitiveness of enterprises. Since the apparel industry is a highly competitive and rapidly changing industry, the update and iteration speed of apparel products is relatively fast. The "small order and fast response" model requires the supply chain to respond quickly and adjust production quickly according to market response. In this model, the matching and supply of accessories must be very flexible and fast to adapt to market changes. With the rise of e-commerce and fast fashion brands, consumer demand has become increasingly diversified and fragmented, and there are huge differences in clothing styles, colors, sizes, and craftsmanship, resulting in a wide variety of clothing accessories, such as hang tags, hang tablets, size labels, genuine rings, washing instructions, etc. This diversity increases the complexity of accessory matching and makes the management of accessories extremely cumbersome.

[0003] Traditional methods of collecting auxiliary materials usually rely on manual verification and statistics, which is not only inefficient but also prone to errors. The limitations of this method are particularly obvious, especially when dealing with large-scale or urgent production orders. In recent years, some clothing companies have begun to try to introduce information technology to optimize auxiliary material management. For example, by establishing an auxiliary material database and order management system, digital management and tracking of auxiliary materials can be achieved. However, most of these systems focus on data recording and query, lack intelligent analysis and decision support, especially in the formulation and verification of auxiliary material collection configuration plans, which still rely on manual experience and judgment, making it difficult to ensure that the auxiliary material collection configuration plan is fully compatible with the bound products.

[0004] Therefore, an optimized and efficient system for collecting packaging accessories in the clothing quick response supply chain is desired. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a clothing quick response supply chain packaging auxiliary material efficient collection system, which pre-sets auxiliary material collection configuration schemes for different commodities, and binds the auxiliary material collection configuration schemes with commodity archives. After receiving the production order, it can quickly and accurately determine the required auxiliary material types and quantities based on the commodity information contained in the production order, and generate an auxiliary material demand list. At the same time, in order to ensure the compatibility of the auxiliary material collection configuration scheme with the bound commodity, a verification mechanism based on deep learning technology is further introduced, and semantic analysis and fine-grained semantic alignment interaction are performed on the auxiliary material collection configuration scheme and the bound commodity archives, so as to intelligently determine whether the two are compatible. In this way, the distribution of auxiliary materials can be carried out quickly and accurately, significantly improving the response speed and efficiency of the clothing supply chain, and reducing production delays and cost increases caused by collection errors or mismatches.

[0006] Accordingly, according to one aspect of the present application, a system for efficiently collecting packaging auxiliary materials for a clothing quick response supply chain is provided, which includes:

[0007] A configuration scheme setting module is used to set a configuration scheme for auxiliary material collection, wherein the configuration scheme for auxiliary material collection includes applicable brands, auxiliary material model numbers, and the collection ratio of each auxiliary material;

[0008] A file binding module is used to bind the auxiliary material collection configuration plan with the commodity file;

[0009] A matching analysis module, for determining whether the new production order matches the product file after receiving the new production order, so as to obtain a matching analysis result;

[0010] An auxiliary material demand calculation module, for calculating the types and quantities of various auxiliary materials required based on the order quantity in the production order and the auxiliary material requisition configuration plan in response to the matching analysis result that the production order matches the commodity file;

[0011] The auxiliary material requirement list generation module is used to generate an auxiliary material requirement list based on the types and quantities of the various auxiliary materials and send the auxiliary material requirement list to the corresponding warehouse management system.

[0012] In the above-mentioned clothing quick response supply chain packaging accessories efficient collection system, the file binding module includes: a semantic embedding coding unit, which is used to perform semantic embedding coding on the accessories collection configuration plan and the commodity file to obtain the accessories collection plan semantic embedding coding vector and the commodity file semantic embedding coding vector; a semantic alignment interaction unit, which is used to perform semantic alignment interaction on the accessories collection plan semantic embedding coding vector and the commodity file semantic embedding coding vector to obtain the commodity file-accessory material collection plan semantic alignment interaction coding vector; a binding verification unit, which is used to determine whether the accessories collection configuration plan and the commodity file are appropriately bound based on the commodity file-accessory material collection plan semantic alignment interaction coding vector.

[0013] Compared with the prior art, the clothing quick response supply chain packaging auxiliary materials efficient collection system provided by the present application pre-sets auxiliary materials collection configuration schemes for different commodities, and binds the auxiliary materials collection configuration schemes with commodity archives. After receiving the production order, it is able to quickly and accurately determine the required auxiliary materials types and quantities based on the commodity information contained in the production order, and generate an auxiliary materials demand list. At the same time, in order to ensure the compatibility of the auxiliary materials collection configuration scheme with the bound commodities, a verification mechanism based on deep learning technology is further introduced, and semantic analysis and fine-grained semantic alignment interaction are performed on the auxiliary materials collection configuration scheme and the bound commodity archives, so as to intelligently determine whether the two are compatible. In this way, the distribution of auxiliary materials can be carried out quickly and accurately, significantly improving the response speed and efficiency of the clothing supply chain, and reducing production delays and cost increases caused by collection errors or mismatches. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0015] Figure 1 It is a block diagram of a system for efficiently collecting packaging accessories for a clothing quick response supply chain according to an embodiment of the present application.

[0016] Figure 2 It is a block diagram of the file binding module in the efficient collection system of packaging accessories for the clothing quick response supply chain according to the embodiment of the present application.

[0017] Figure 3 This is a schematic diagram of the data flow of the file binding module in the efficient collection system of packaging accessories for the clothing quick response supply chain according to an embodiment of the present application.

[0018] Figure 4It is a block diagram of a semantic alignment interaction unit in an efficient collection system for packaging accessories in a clothing quick response supply chain according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] Below, the embodiments of the present application will be described in more detail in conjunction with the accompanying drawings, and the above and other purposes, features and advantages of the present application will become more apparent. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

[0020] In view of the defects of the traditional method of collecting clothing accessories, this application proposes a clothing quick response supply chain packaging accessories efficient collection system, which pre-sets the accessories collection configuration scheme for different commodities, and binds the accessories collection configuration scheme with the commodity archive. After receiving the production order, it can quickly and accurately determine the required accessories type and quantity based on the commodity information contained in the production order, and generate an accessories demand list. At the same time, in order to ensure the compatibility of the accessories collection configuration scheme with the bound commodities, a verification mechanism based on deep learning technology is further introduced, and the semantic analysis and fine-grained semantic alignment interaction of the accessories collection configuration scheme and the bound commodity archive are performed to intelligently determine whether the two are compatible. In this way, the distribution of accessories can be carried out quickly and accurately, significantly improving the response speed and efficiency of the clothing supply chain, and reducing production delays and cost increases caused by collection errors or mismatches.

[0021] Figure 1 FIG. 1 is a block diagram of a system for efficiently collecting packaging auxiliary materials for a clothing quick response supply chain according to an embodiment of the present application. Figure 1 As shown, the clothing quick response supply chain packaging auxiliary materials efficient collection system 100 includes: a configuration scheme setting module 110, which is used to set the auxiliary materials collection configuration scheme, and the auxiliary materials collection configuration scheme includes applicable brands, auxiliary materials model numbers, and collection multiples of each auxiliary material; a file binding module 120, which is used to bind the auxiliary materials collection configuration scheme with the commodity file; a matching analysis module 130, which is used to determine whether the production order matches the commodity file after receiving a new production order to obtain a matching analysis result; an auxiliary materials demand calculation module 140, which is used to respond to the matching analysis result that the production order matches the commodity file, and calculate the types and quantities of various auxiliary materials required based on the order quantity in the production order and the auxiliary materials collection configuration scheme; an auxiliary materials demand list generation module 150, which is used to generate an auxiliary materials demand list based on the types and quantities of the various auxiliary materials and send the auxiliary materials demand list to the corresponding warehouse management system.

[0022] In the above-mentioned clothing quick response supply chain packaging auxiliary materials efficient collection system 100, the configuration scheme setting module 110 is used to set the auxiliary materials collection configuration scheme, and the auxiliary materials collection configuration scheme includes applicable brands, auxiliary materials style numbers, and collection multiples of each auxiliary material. It should be understood that clothing production involves many brands and styles, and the packaging auxiliary materials required for each style are different in style number, specifications, and usage quantity. In order to achieve the normalization and standardization of auxiliary materials collection and avoid auxiliary materials waste or shortage due to human misjudgment or arbitrary collection, by pre-setting matching auxiliary materials collection configuration schemes for clothing products of different brands and styles, it is helpful to ensure the accurate grasp of auxiliary materials demand in each link in different production batches and orders, thereby ensuring the continuity and stability of the production process, while reducing production costs and inventory pressure.

[0023] When setting up auxiliary material allocation schemes, we can collect information on various types of packaging auxiliary materials required for different brands of clothing and their corresponding styles, including the model number of auxiliary materials, the specific specifications and characteristics of each auxiliary material, etc., by analyzing past production data, interpreting design drawings, and combining with market demand. At the same time, we can determine the reasonable allocation ratio of each auxiliary material based on production technology and experience, that is, the number of auxiliary materials required to produce a piece of clothing, so as to build a complete and detailed auxiliary material allocation configuration database as the basis for subsequent auxiliary material allocation decisions.

[0024] In the specific implementation, a visual configuration interface can be established in the clothing quick response supply chain packaging accessories efficient collection system, allowing managers with corresponding permissions to input and edit the accessories collection configuration information of various brands and styles of clothing. For newly launched brands or styles, the staff first conducts a detailed bill of materials (BOM) analysis to clarify the types and characteristics of the required packaging accessories. Then, combined with the trial and error data in actual production and industry standards, the collection rate of each auxiliary material is determined, and this information is entered into the system one by one. During the entry process, the system will conduct a preliminary check on the integrity and rationality of the data, such as checking whether the collection rate is within a reasonable numerical range to avoid entering errors or abnormal data.

[0025] In this way, the refined management of auxiliary materials can be achieved, the use of auxiliary materials can be controlled from the source, unnecessary waste can be reduced, and resource utilization can be improved. At the same time, the auxiliary materials allocation plan also provides accurate data support for subsequent production planning, procurement decisions and cost accounting, which helps to improve the collaborative efficiency and economic benefits of the entire supply chain.

[0026] In the above-mentioned clothing quick response supply chain packaging auxiliary materials efficient collection system 100, the file binding module 120 is used to bind the auxiliary materials collection configuration scheme with the commodity archive. It should be understood that in the clothing production process, the commodity archive is a detailed record of each clothing product, including the design, process, specifications, raw materials and other comprehensive information of the product. By binding the auxiliary materials collection configuration scheme with the commodity archive, it can be ensured that in the production process, for a specific commodity style, the corresponding auxiliary materials collection information can be obtained quickly and accurately, avoiding auxiliary materials collection errors caused by information confusion or inconsistency, thereby improving production efficiency and product quality stability. In a specific implementation, firstly, based on the unique identification of the commodity archive (such as product number, style code, etc.), a one-to-one association relationship is established with the pre-set auxiliary materials collection configuration scheme, and this association relationship is stored in the system database. Through this binding mechanism, when the system receives a production task or query request related to the commodity, it can quickly locate and extract accurate auxiliary materials collection information, and realize rapid matching and calling of information.

[0027] In particular, if the auxiliary material collection configuration plan fails to correctly match and bind with the commodity file, it may lead to excessive or insufficient collection of auxiliary materials. The former will cause inventory backlogs and capital occupation, and the latter may cause the production line to stop waiting for materials. Considering that the auxiliary material collection configuration plan is manually set, there may be a certain degree of subjectivity or input deviation. Therefore, in order to ensure the matching accuracy between the auxiliary material collection configuration plan and the commodity file, this application further matches and verifies the auxiliary material collection configuration plan and the commodity file bound to it to ensure that the correct auxiliary materials are used in the production process and avoid quality problems or rework caused by incorrect material selection.

[0028] Figure 2 It is a block diagram of the file binding module in the efficient collection system of packaging accessories for the clothing quick response supply chain according to the embodiment of the present application. Figure 3 This is a data flow diagram of the file binding module in the clothing quick response supply chain packaging auxiliary material efficient collection system according to the embodiment of the present application. Figure 2 and Figure 3As shown, the archive binding module 120 includes: a semantic embedding coding unit 121, which is used to perform semantic embedding coding on the auxiliary material collection configuration plan and the commodity archive to obtain an auxiliary material collection plan semantic embedding coding vector and a commodity archive semantic embedding coding vector; a semantic alignment interaction unit 122, which is used to perform semantic alignment interaction on the auxiliary material collection plan semantic embedding coding vector and the commodity archive semantic embedding coding vector to obtain a commodity archive-auxiliary material collection plan semantic alignment interaction coding vector; a binding verification unit 123, which is used to determine whether the auxiliary material collection configuration plan and the commodity archive are suitable for binding based on the commodity archive-auxiliary material collection plan semantic alignment interaction coding vector.

[0029] Specifically, the semantic embedding coding unit 121 is used to perform semantic embedding coding on the auxiliary material collection configuration plan and the commodity archive to obtain the auxiliary material collection scheme semantic embedding coding vector and the commodity archive semantic embedding coding vector. Specifically, considering that the traditional keyword or rule-based matching verification method may not be able to fully understand the true meaning of the auxiliary material collection configuration plan and the commodity archive when processing complex unstructured data such as product descriptions and specifications, resulting in inaccurate matching results. Therefore, the present application further performs semantic embedding coding on the auxiliary material collection configuration plan and the commodity archive to convert the unstructured text information into a vector representation with semantic information, so as to achieve a deep understanding and precise matching of the semantic content between the two. In a specific example of the present application, the auxiliary material collection configuration plan and the commodity archive are semantically embedded using a semantic embedding encoder based on the Bert model to obtain the auxiliary material collection scheme semantic embedding coding vector and the commodity archive semantic embedding coding vector. Those of ordinary skill in the art should know that the Bert model is a pre-trained language representation model that captures the contextual information of the text through a bidirectional Transformer architecture to generate a vector representation that can reflect the deep semantics of the text. In this application, by using the Bert model to perform the semantic embedding coding process, the deep semantic understanding ability of the Bert model can be effectively utilized to capture the key information and contextual relationships in the auxiliary material requisition configuration plan and the product archive, so as to achieve accurate matching of the complex semantic content between the two.

[0030] Specifically, the semantic alignment interaction unit 122 is used to perform semantic alignment interaction on the semantic embedding coding vector of the auxiliary material collection scheme and the semantic embedding coding vector of the commodity archive to obtain the commodity archive-auxiliary material collection scheme semantic alignment interaction coding vector. It should be understood that, considering that the auxiliary material collection configuration scheme and the commodity archive usually have certain differences in content and structure, directly performing semantic interaction on the semantic embedding coding vector of the auxiliary material collection scheme and the semantic embedding coding vector of the commodity archive will introduce unnecessary noise and errors, resulting in inaccurate verification results. In this regard, the present application proposes a semantic alignment interaction method, which realizes fine-grained semantic alignment and interaction between the two by constructing a semantic information field between the semantic embedding coding vector of the auxiliary material collection scheme and the semantic embedding coding vector of the commodity archive.

[0031] Figure 4 FIG. 1 is a block diagram of a semantic alignment interaction unit in a clothing quick response supply chain packaging auxiliary material efficient collection system according to an embodiment of the present application. Figure 4 As shown, the semantic alignment interaction unit 122 includes: a semantic offset analysis subunit 1221, which is used to perform semantic offset analysis on the semantic embedding coding vector of the auxiliary material requisition plan and the semantic embedding coding vector of the commodity archive to obtain a commodity archive-auxiliary material requisition plan fine-grained semantic information field; a semantic alignment coding subunit 1222, which is used to perform semantic mapping alignment interaction on the semantic embedding coding vector of the auxiliary material requisition plan and the semantic embedding coding vector of the commodity archive based on the commodity archive-auxiliary material requisition plan fine-grained semantic information field to obtain the commodity archive-auxiliary material requisition plan semantic alignment interaction coding vector.

[0032] Specifically, the semantic shift analysis subunit 1221 includes: a dimensional modulation secondary subunit, which is used to perform feature dimension modulation based on point convolution on the semantic embedding coding vector of the auxiliary material collection scheme and the semantic embedding coding vector of the commodity archive to obtain the modulated auxiliary material collection scheme semantic embedding coding vector and the modulated commodity archive semantic embedding coding vector having the same feature dimension, which is expressed by the formula:

[0033]

[0034]

[0035] in, represents the semantic embedding coding vector of the auxiliary material collection scheme, represents the semantic embedding encoding vector of the product profile, represents the point convolution operation, is the leaky linear rectification function, represents the semantic embedding coding vector of the auxiliary material collection scheme after modulation, Represents the modulated product profile semantic embedding encoding vector.

[0036] That is, firstly, the point convolution coding method is used to modulate the feature dimension of the semantic embedding coding vector of the auxiliary material collection plan and the semantic embedding coding vector of the commodity file to ensure that the two have the same feature dimension and achieve preliminary dimensional alignment.

[0037] Specifically, the semantic offset analysis subunit 1221 also includes: a semantic information field encoding secondary subunit, which is used to perform fine-grained association encoding on the semantic embedding encoding vector of the post-modulation auxiliary material collection plan and the semantic embedding encoding vector of the post-modulation commodity archive, and then input the convolution kernel-based semantic information field encoding network to obtain the commodity archive-auxiliary material collection plan fine-grained semantic information field, which is expressed by the formula:

[0038]

[0039] in, represents the transpose of a vector, represents the matrix multiplication operation, is the characteristic scale value of the semantic embedding coding vector of the post-modulation auxiliary material collection scheme and the semantic embedding coding vector of the post-modulation commodity archive, Represents the fine-grained semantic association encoding matrix of commodity archive-auxiliary material collection plan, represents a 3×3 convolution operation, Represents the commodity archive-auxiliary material requisition plan fine-grained semantic information field. That is, first, calculate the modulated auxiliary material requisition plan semantic embedding coding vector multiplied by the transposed vector of the modulated commodity archive semantic embedding coding vector, and then divide it by the square root of the characteristic scale value of the modulated commodity archive semantic embedding coding vector to obtain the commodity archive-auxiliary material requisition plan fine-grained association coding matrix; then, use the semantic information field coding network to perform multi-layer convolution processing based on 3×3 convolution kernels on the commodity archive-auxiliary material requisition plan fine-grained semantic information field.

[0040] Here, the semantic embedding coding vector of the auxiliary material collection scheme and the semantic embedding coding vector of the commodity archive after feature dimension modulation is fine-grainedly associated to capture the subtle semantic association between the two, and to construct the semantic information field accordingly. Specifically, the semantic information field encoding network stacks multiple convolutional layers to further extract the fine-grained semantic association features between the auxiliary material collection configuration scheme and the commodity archive, so as to mine the semantic offset information between the semantic embedding coding vector of the auxiliary material collection scheme and the semantic embedding coding vector of the commodity archive, and gradually learn the semantic alignment relationship between the two, so as to construct the commodity archive-auxiliary material collection scheme fine-grained semantic information field, providing precise guidance for subsequent feature mapping.

[0041] Specifically, the semantic alignment coding subunit 1222 is used to: map the semantic embedding coding vector of the post-modulation auxiliary material collection plan and the semantic embedding coding vector of the post-modulation commodity archive to the commodity archive-auxiliary material collection plan fine-grained semantic information field to obtain a fine-grained aligned auxiliary material collection plan semantic embedding coding vector and a fine-grained aligned commodity archive semantic embedding coding vector; calculate the position-weighted sum between the fine-grained aligned auxiliary material collection plan semantic embedding coding vector and the fine-grained aligned commodity archive semantic embedding coding vector to obtain the commodity archive-auxiliary material collection plan semantic alignment interaction coding vector, which is expressed as follows:

[0042]

[0043]

[0044]

[0045] in, and They represent the semantic embedding coding vector of the fine-grained aligned auxiliary material collection scheme and the semantic embedding coding vector of the fine-grained aligned commodity archive, and is the learnable weight parameter, Represents the semantically aligned interactive encoding vector of product profile and auxiliary material collection plan.

[0046] That is, by performing cross-domain feature conversion on the semantic embedding coding vector of the auxiliary material requisition plan and the semantic embedding coding vector of the commodity archive after the above-mentioned dimension modulation, the two are respectively mapped to the commodity archive-auxiliary material requisition plan fine-grained semantic information field, so as to achieve semantic alignment between the two. In the mapping process, the features of each position in the modulated semantic embedding coding vector of the auxiliary material requisition plan and the modulated commodity archive semantic embedding coding vector will be adjusted according to the semantic strength and correlation of the corresponding position in the commodity archive-auxiliary material requisition plan fine-grained semantic information field, so as to better match the semantic distribution in the semantic information field. In this way, not only the original semantic information of the auxiliary material requisition plan and the commodity archive is retained, but also through the guidance of the semantic information field, the two have the characteristics of fine-grained alignment of semantic information, which enhances the semantic consistency between each other.

[0047] Then, after completing the feature semantic alignment, the semantic interaction fusion between the semantic embedding coding vector of the auxiliary material collection plan and the semantic embedding coding vector of the commodity archive is further realized by calculating the weighted sum by position, so as to express the semantic association between the auxiliary material collection plan and the commodity archive. Through this semantic alignment interaction method, the correlation between the auxiliary material collection plan and the commodity archive can be effectively identified and strengthened, while the irrelevant information is weakened, so as to ensure that the generated commodity archive-auxiliary material collection plan semantic alignment interaction coding vector can accurately reflect the degree of adaptation between the auxiliary material collection plan and the commodity archive.

[0048] Specifically, the binding verification unit 123 is used to determine whether the auxiliary material collection configuration scheme and the commodity archive are suitable for binding based on the semantic alignment interactive coding vector of the commodity archive-auxiliary material collection scheme. In a specific example of the present application, the binding verification unit 123 is used to: pass the commodity archive-auxiliary material collection scheme semantic alignment interactive coding vector through a classifier-based binding verifier to obtain a verification result, and the verification result is used to indicate whether the binding is suitable or not suitable. Specifically, in the present application, the classifier is used to determine whether the auxiliary material collection configuration scheme matches the commodity archive according to the semantic association information contained in the commodity archive-auxiliary material collection scheme semantic alignment interactive coding vector. If it matches, the verification result of suitable binding is output; if it does not match, the verification result of inappropriate binding is output. It should be understood that the production order processing speed in the clothing quick response supply chain is extremely high, and manually checking the compatibility between each auxiliary material collection configuration scheme and the commodity archive is both time-consuming and error-prone. By using trained classifiers to automatically perform binding verification, incorrect auxiliary material collection configurations can be effectively identified and corrected, greatly improving production line efficiency and product quality, responding more quickly to changes in market demand, and ensuring consistency and accuracy in decision-making.

[0049] More specifically, the commodity profile-auxiliary material requisition plan semantic alignment interactive coding vector is passed through a classifier-based binding verifier to obtain a verification result, including: using the fully connected layer of the binding verifier to fully connect the commodity profile-auxiliary material requisition plan semantic alignment interactive coding vector to obtain the commodity profile-auxiliary material requisition plan semantic alignment interactive fully connected coding vector; inputting the commodity profile-auxiliary material requisition plan semantic alignment interactive fully connected coding vector into the Softmax classification function of the binding verifier to obtain the probability value of the commodity profile-auxiliary material requisition plan semantic alignment interactive coding vector belonging to each classification label, and the classification labels include binding suitable and binding unsuitable; the classification label corresponding to the largest of the probability values ​​is determined as the verification result.

[0050] Preferably, the process of passing the commodity file-auxiliary material collection plan semantic alignment interactive coding vector through a classifier-based binding verifier to obtain a verification result includes:

[0051] Determine the median eigenvalue, maximum eigenvalue and minimum eigenvalue in the semantic alignment interactive coding vector of the commodity archive-auxiliary material requisition scheme, and divide the median eigenvalue by the difference between the maximum eigenvalue and the minimum eigenvalue to obtain the commodity archive-auxiliary material requisition scheme semantic alignment interactive coding distribution probability value, that is, ,in, , and They respectively represent the median eigenvalue, maximum eigenvalue and minimum eigenvalue in the semantic alignment interactive coding vector of the commodity file-auxiliary material collection plan, Indicates the probability value of the semantic alignment interaction coding distribution of commodity file-auxiliary material collection plan;

[0052] The product file-auxiliary material collection plan semantic alignment interactive coding vector is normalized to the maximum value to obtain the product file-auxiliary material collection plan semantic alignment interactive coding probability feature vector ;

[0053] The power function of each eigenvalue of the semantic alignment interactive coding probability feature vector of the commodity archive-auxiliary material collection scheme is calculated by subtracting the distribution probability value of the semantic alignment interactive coding of the commodity archive-auxiliary material collection scheme as an exponent to obtain the first commodity archive-auxiliary material collection scheme semantic alignment interactive coding convergence feature vector ,in, It represents the power function of calculating each eigenvalue in the eigenvector by subtracting the probability value of the semantic alignment interaction coding distribution of the commodity file-auxiliary material collection plan from one;

[0054] The power function of each eigenvalue of the difference eigenvector between the unit eigenvector and the eigenvector of the semantic alignment interactive coding probability eigenvector of the commodity archive-auxiliary material collection scheme is calculated with the distribution probability value of the semantic alignment interactive coding of the commodity archive-auxiliary material collection scheme as an exponent to obtain the second commodity archive-auxiliary material collection scheme semantic alignment interactive coding convergence eigenvector ,in, is the unit eigenvector, Indicates the spread, It indicates that the power function of each eigenvalue in the eigenvector is calculated by taking the probability value of the semantic alignment interaction coding of the commodity file-auxiliary material collection plan as the exponent;

[0055] The product file-auxiliary material collection plan semantic alignment interactive coding probability feature vector is multiplied by the difference between one minus the product file-auxiliary material collection plan semantic alignment interactive coding distribution probability value to obtain the first product file-auxiliary material collection plan semantic alignment interactive coding limited feature vector ,in, represents dot product;

[0056] The point difference feature vector is multiplied by the commodity file-auxiliary material collection plan semantic alignment interactive coding distribution probability value to obtain the second commodity file-auxiliary material collection plan semantic alignment interactive coding limited feature vector ;

[0057] After multiplying the first commodity file-auxiliary material collection plan semantic alignment interactive coding convergence feature vector by the second commodity file-auxiliary material collection plan semantic alignment interactive coding convergence feature vector, the optimized commodity file-auxiliary material collection plan semantic alignment interactive coding limit feature vector is obtained by multiplying the first commodity file-auxiliary material collection plan semantic alignment interactive coding limit feature vector by the second commodity file-auxiliary material collection plan semantic alignment interactive coding limit feature vector. ,in, Indicates point addition;

[0058] The optimized commodity file-auxiliary material collection plan semantic alignment interactive coding vector is passed through a classifier-based binding verifier to obtain a verification result.

[0059] Here, considering that the semantic embedding coding vector of the auxiliary material requisition plan and the semantic embedding coding vector of the commodity archive respectively represent the textual semantic coding features of the auxiliary material requisition configuration plan and the textual semantic coding features of the commodity archive, when performing feature alignment interaction based on the semantic information field, the difference in source domain content between the auxiliary material requisition configuration plan and the commodity archive will make the fine-grained resolution of the semantic information field lower, and the commodity archive-auxiliary material requisition plan semantic alignment interaction coding vector will have probabilistic convergence and divergence based on different feature interactivity, thereby affecting the accuracy of the verification result obtained by the classifier-based binding verifier.

[0060] Based on this, by using the cross entropy form power series of the distribution analysis probability of the commodity archive-auxiliary material requisition scheme semantic alignment interactive coding vector as the probability distribution convergence limit, the regression probability convergence approximation is performed on the basis of the combination of the feature set distribution and the probability density distribution of the commodity archive-auxiliary material requisition scheme semantic alignment interactive coding vector, so as to avoid the fatal potential divergence of the commodity archive-auxiliary material requisition scheme semantic alignment interactive coding vector under the premise of conservative convergence control, further use the probability distribution cross entropy of the commodity archive-auxiliary material requisition scheme semantic alignment interactive coding vector as the objective function to guide the limited recovery strategy, so as to achieve the common agility of the convergence of the probability convergent and divergent feature set of the commodity archive-auxiliary material requisition scheme semantic alignment interactive coding vector to the probability density distribution space, and improve the accuracy of the verification result obtained by the classifier-based binding verifier of the commodity archive-auxiliary material requisition scheme semantic alignment interactive coding vector.

[0061] In the above-mentioned clothing quick response supply chain packaging auxiliary materials efficient collection system 100, the matching analysis module 130 is used to determine whether the production order matches the commodity file after receiving a new production order to obtain a matching analysis result. That is, when receiving a new order, by quickly judging whether the commodity involved in the order matches the existing commodity file, it is determined whether the existing auxiliary material collection configuration plan can be directly used for production, or whether the plan needs to be re-formulated or adjusted. This is of great significance for improving production response speed, reducing production costs, and ensuring consistency of product quality.

[0062] In the specific implementation, when the system receives a new production order, it automatically accesses the database storing a large number of commodity files, and compares and analyzes the key information (such as brand, style, specifications, etc.) in the new production order with the commodity file information stored in the system one by one. Then, according to the preset information matching algorithm and weight distribution rules, the order information and each commodity file are compared and calculated field by field to obtain the matching score of each file. Finally, according to the matching score, the commodity file with the highest matching degree with the new production order is automatically screened out, and its matching score is compared with the set threshold. When the similarity between the order information and a certain commodity file reaches or exceeds the threshold, it is determined that the two match; otherwise, it is determined to be mismatched. Finally, the matching analysis results are fed back to other modules of the system and relevant operators.

[0063] In this way, the matching relationship between new orders and existing product files can be quickly and accurately determined, providing an important decision-making basis for subsequent auxiliary material demand calculations and production arrangements. In addition, by timely reusing existing auxiliary material allocation plans, the time and cost of plan formulation caused by order differences are reduced, the overall response speed and operational efficiency of the supply chain are improved, and the consistency and standardization of auxiliary material use in different batches of products is also ensured, which helps maintain brand image and product quality.

[0064] In the above-mentioned clothing quick response supply chain packaging auxiliary materials efficient collection system 100, the auxiliary materials demand calculation module 140 is used to respond to the matching analysis result that the production order matches the commodity file, and calculate the types and quantities of various auxiliary materials required based on the order quantity in the production order and the auxiliary materials collection configuration plan. It should be understood that once it is determined that the production order matches the commodity file, it is necessary to accurately calculate the types and quantities of various auxiliary materials required based on the specific quantity of the order and the bound auxiliary materials collection configuration plan. By accurately grasping the demand for each auxiliary material, it is possible to avoid waste of funds or production delays caused by excessive or insufficient procurement, and ensure the continuity and stability of the production process.

[0065] In the specific implementation, after receiving the matching results fed back by the matching analysis module, the auxiliary material demand calculation module automatically obtains the corresponding production order information and auxiliary material collection configuration plan. First, the order quantity in the order is multiplied by the collection rate of each auxiliary material to obtain the preliminary demand for each auxiliary material. At the same time, considering the loss factors in the production process (such as cutting loss, processing loss, defective rate, etc.), the preliminary demand is further adjusted and calculated according to the loss rate data of various auxiliary materials in different production links pre-set in the system. For example, if the loss rate of a certain auxiliary material in the cutting link is 5%, then when calculating its actual demand, the preliminary demand will be multiplied by the coefficient of (1 + 5%) to ensure that the number of auxiliary materials purchased can meet the actual demand in the production process. Finally, the actual demand of various auxiliary materials calculated is summarized and sorted, and a detailed auxiliary material demand calculation result report is generated to provide accurate data support for subsequent procurement and inventory management.

[0066] In this way, the demand for auxiliary materials is accurately calculated, providing the purchasing department with a scientific and reasonable basis for purchasing, avoiding inventory backlogs or out-of-stock phenomena caused by blind purchasing. In addition, by reasonably considering the production loss factor, the sufficient supply of auxiliary materials in the production process is ensured, the production stagnation and delay caused by the shortage of auxiliary materials is reduced, the production efficiency and resource utilization are improved, and the production cost and operation risk are effectively reduced.

[0067] In the above-mentioned clothing quick response supply chain packaging accessories efficient collection system 100, the accessories demand list generation module 150 is used to generate an accessories demand list based on the types and quantities of the various accessories and send the accessories demand list to the corresponding warehouse management system. Considering that after calculating the types and quantity information of various accessories, it is necessary to present them to relevant departments (such as procurement departments, warehouse management departments, etc.) in a clear, intuitive and easy-to-operate form so that they can quickly and accurately perform the procurement, deployment and distribution of accessories. Therefore, this application further sorts and organizes the various accessories required according to certain classification standards (such as material, purpose, specification, etc.), and lists the name, model number, specification, quantity, unit and other relevant remarks (such as special requirements, supplier information, etc.) of each accessory in detail in the form of a list, and generates an accessories demand list, so as to provide accurate data support for subsequent procurement and inventory management. At the same time, in order to facilitate data interaction and information sharing with the warehouse management system, the accessories demand list will be generated and stored in a standardized data format (such as XML, CSV, etc.) to ensure the accuracy and compatibility of the data.

[0068] As a standardized information carrier, the auxiliary material demand list can effectively integrate and transmit auxiliary material demand information, ensure the smooth flow of information between various links in the supply chain, and improve the overall collaborative efficiency. In the specific implementation, a list generation template engine can be built into the system. According to the preset template format and data fields, the various data in the auxiliary material demand calculation results are automatically filled into the list template to generate a complete auxiliary material demand list. During the generation process, the system will re-check and verify the data in the list to ensure the integrity and accuracy of the data. Once the list is generated, it will be automatically sent to the corresponding warehouse management system, and a copy will be saved locally for subsequent query and traceability. In addition, the system also supports customized printing and output of the list according to user needs, which is convenient for relevant personnel to use in actual work.

[0069] In this way, clear and accurate auxiliary material demand information can be provided to each link of the supply chain, which improves the efficiency and accuracy of information transmission and reduces work errors and delays caused by poor information or misunderstandings. The purchasing department can quickly formulate a purchasing plan based on the list and communicate and coordinate with suppliers. The warehouse management department can prepare inventory and arrange shipments in advance, thereby ensuring that auxiliary materials can be supplied to the production line in a timely and accurate manner, ensuring the smooth progress of production activities, and improving the operational efficiency and response speed of the entire clothing quick response supply chain.

[0070] In summary, the clothing quick response supply chain packaging accessories efficient collection system according to the embodiment of the present application is explained, which pre-sets the accessories collection configuration scheme for different commodities, and binds the accessories collection configuration scheme with the commodity archive. After receiving the production order, it can quickly and accurately determine the required accessories and their quantity based on the commodity information contained in the production order, and generate an accessories demand list. At the same time, in order to ensure the compatibility of the accessories collection configuration scheme with the bound commodities, a verification mechanism based on deep learning technology is further introduced, and the semantic analysis and fine-grained semantic alignment interaction of the accessories collection configuration scheme and the bound commodity archive are performed to intelligently determine whether the two are compatible. In this way, the distribution of accessories can be carried out quickly and accurately, which significantly improves the response speed and efficiency of the clothing supply chain, and reduces production delays and cost increases caused by collection errors or mismatches.

[0071] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to be implemented by adopting the above specific details.

Claims

1. An efficient collection system for packaging auxiliary materials in a clothing quick response supply chain, characterized in that: include: A configuration scheme setting module is used to set a configuration scheme for auxiliary material collection, wherein the configuration scheme for auxiliary material collection includes applicable brands, auxiliary material model numbers, and the collection ratio of each auxiliary material; A file binding module is used to bind the auxiliary material collection configuration plan with the commodity file; A matching analysis module, for determining whether the new production order matches the product file after receiving the new production order, so as to obtain a matching analysis result; An auxiliary material demand calculation module, for calculating the types and quantities of various auxiliary materials required based on the order quantity in the production order and the auxiliary material requisition configuration plan in response to the matching analysis result that the production order matches the commodity file; The auxiliary material requirement list generation module is used to generate an auxiliary material requirement list based on the types and quantities of the various auxiliary materials and send the auxiliary material requirement list to the corresponding warehouse management system.

2. The clothing quick response supply chain packaging auxiliary materials efficient collection system according to claim 1 is characterized in that: The archive binding module includes: A semantic embedding coding unit, used for performing semantic embedding coding on the auxiliary material collection configuration plan and the commodity file to obtain a semantic embedding coding vector of the auxiliary material collection plan and a semantic embedding coding vector of the commodity file; A semantic alignment interaction unit, used for performing semantic alignment interaction on the auxiliary material collection scheme semantic embedding coding vector and the commodity archive semantic embedding coding vector to obtain a commodity archive-auxiliary material collection scheme semantic alignment interaction coding vector; The binding verification unit is used to determine whether the auxiliary material collection configuration plan and the commodity file are appropriately bound based on the semantic alignment interaction coding vector of the commodity file-auxiliary material collection plan.

3. The clothing quick response supply chain packaging auxiliary materials efficient collection system according to claim 2 is characterized in that: The semantic embedding encoding unit is used to: A semantic embedding encoder based on the Bert model is used to perform semantic embedding encoding on the auxiliary material collection configuration plan and the commodity file to obtain a semantic embedding encoding vector of the auxiliary material collection plan and a semantic embedding encoding vector of the commodity file.

4. The clothing quick response supply chain packaging auxiliary materials efficient collection system according to claim 3 is characterized in that: The semantic alignment interaction unit includes: A semantic offset analysis subunit, used for performing semantic offset analysis on the semantic embedding coding vector of the auxiliary material collection plan and the semantic embedding coding vector of the commodity archive to obtain a commodity archive-auxiliary material collection plan fine-grained semantic information field; The semantic alignment coding subunit is used to perform semantic mapping alignment interaction on the semantic embedding coding vector of the auxiliary material requisition plan and the semantic embedding coding vector of the commodity archive based on the fine-grained semantic information field of the commodity archive-auxiliary material requisition plan to obtain the semantic alignment interaction coding vector of the commodity archive-auxiliary material requisition plan.

5. The clothing quick response supply chain packaging auxiliary materials efficient collection system according to claim 4 is characterized in that: The semantic shift analysis subunit includes: A dimension modulation secondary subunit is used to perform feature dimension modulation based on point convolution on the semantic embedding coding vector of the auxiliary material collection scheme and the semantic embedding coding vector of the commodity archive to obtain the modulated semantic embedding coding vector of the auxiliary material collection scheme and the modulated semantic embedding coding vector of the commodity archive having the same feature dimension; The semantic information field encoding secondary sub-unit is used to perform fine-grained associative encoding on the semantic embedding encoding vector of the post-modulation auxiliary material collection plan and the semantic embedding encoding vector of the post-modulation commodity archive, and then input the vector into the convolution kernel-based semantic information field encoding network to obtain the commodity archive-auxiliary material collection plan fine-grained semantic information field.

6. The clothing quick response supply chain packaging auxiliary materials efficient collection system according to claim 5 is characterized in that: The semantic information field encoding secondary subunit is used for: Calculate the semantic embedding coding vector of the post-modulation auxiliary material collection plan and multiply it by the transposed vector of the semantic embedding coding vector of the post-modulation commodity archive, and then divide it by the square root of the characteristic scale value of the semantic embedding coding vector of the post-modulation commodity archive to obtain a commodity archive-auxiliary material collection plan fine-grained association coding matrix; The semantic information field encoding network is used to perform multi-layer convolution processing based on a 3×3 convolution kernel on the fine-grained association encoding matrix of the commodity file-auxiliary material requisition plan to obtain the fine-grained semantic information field of the commodity file-auxiliary material requisition plan.

7. The clothing quick response supply chain packaging auxiliary materials efficient collection system according to claim 6 is characterized in that: The semantic alignment encoding subunit is used to: Mapping the semantic embedding coding vector of the post-modulation auxiliary material collection plan and the semantic embedding coding vector of the post-modulation commodity archive to the commodity archive-auxiliary material collection plan fine-grained semantic information field respectively to obtain a fine-grained aligned auxiliary material collection plan semantic embedding coding vector and a fine-grained aligned commodity archive semantic embedding coding vector; The position-weighted sum between the fine-grained aligned auxiliary material requisition plan semantic embedding coding vector and the fine-grained aligned commodity profile semantic embedding coding vector is calculated to obtain the commodity profile-auxiliary material requisition plan semantic alignment interactive coding vector.

8. The clothing quick response supply chain packaging auxiliary materials efficient collection system according to claim 7 is characterized in that: The binding verification unit is used to: The commodity file-auxiliary material collection plan semantic alignment interactive coding vector is passed through a classifier-based binding verifier to obtain a verification result, and the verification result is used to indicate whether the binding is suitable or not.

9. The clothing quick response supply chain packaging auxiliary materials efficient collection system according to claim 8 is characterized in that: The binding verification unit is used to: Using the fully connected layer of the binding verifier to perform fully connected encoding on the commodity file-auxiliary material collection scheme semantic alignment interactive encoding vector to obtain the commodity file-auxiliary material collection scheme semantic alignment interactive fully connected encoding vector; Inputting the commodity file-auxiliary material collection plan semantic alignment interactive fully connected coding vector into the Softmax classification function of the binding verifier to obtain the probability value of the commodity file-auxiliary material collection plan semantic alignment interactive coding vector belonging to each classification label, wherein the classification labels include binding suitable and binding unsuitable; The classification label corresponding to the largest probability value among the probability values ​​is determined as the verification result.

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