Order and production factory matching method and device, equipment, medium and product

Abstract

The invention provides an order and production factory matching method and device, equipment, a medium and a product, and the method comprises the steps: carrying out the labeling of a shoe and clothes order when the shoe and clothes order issued by a client is received, and obtaining an order label of the shoe and clothes order; performing preliminary screening on the production factories in the factory side by using the order labels and the screening conditions to obtain candidate production factories; for a single candidate production factory, determining a label weight value of each factory label of the single candidate production factory by using a weight configuration strategy; calculating a matching score of a single candidate production factory by using the order label, each factory label and the label weight value of each factory label; and according to the matching score of each candidate production factory, determining a target production factory for processing the shoe and clothing order. According to the invention, comprehensive scoring is carried out according to the order labels, the factory labels and the label weights, so that the appropriate target production factory can be screened out, and the matching degree between the shoe and clothing order and the production factory can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, equipment, medium, and product for matching orders with production plants. Background Technology

[0002] In the footwear and apparel industry, the small-batch, rapid-response model is a new production mode that adapts to rapid market changes and personalized consumer demands. This model has several notable characteristics in the footwear and apparel industry: small-batch orders, rapid response, and high flexibility.

[0003] Currently, quickly matching suitable production factories for small-batch, diversified footwear and apparel orders typically requires manual evaluation of multiple factors, including production capacity, quality control, cooperation conditions, and others, before assigning the order to a specific factory. However, this method of matching footwear and apparel orders to production factories cannot accurately and rationally schedule factories, easily leading to unreasonable situations such as excessively high or low order saturation rates for factories. Therefore, a new method for matching orders and production factories is urgently needed to solve these problems. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method, apparatus, equipment, medium and product for matching orders with production plants that overcomes or at least partially solves the above problems.

[0005] To achieve the above and other related objectives, this invention provides a method for matching orders with production plants, applied to the platform side of a smart production collaboration system. The smart production collaboration system further includes a client and a factory side, wherein the client, the platform side, and the factory side are communicatively connected. The method includes:

[0006] Upon receiving a footwear and apparel order from the client, the order is tagged to obtain the order tag for the footwear and apparel order;

[0007] The order tags and preset filtering conditions are used to perform preliminary screening of each production plant in the factory to obtain candidate production plants;

[0008] For a single candidate production factory, a preset weight configuration strategy is used to determine the tag weight value of each factory tag of the candidate production factory, and the matching score of the single candidate production factory is calculated using the order tag, each factory tag and the tag weight value of each factory tag.

[0009] Based on the matching scores of each candidate production factory, a target production factory is determined for processing the footwear and apparel orders.

[0010] Optionally, before the step of affixing a label to the footwear and apparel order upon receiving the footwear and apparel order from the client to obtain the order label, the method further includes:

[0011] Each production plant within the factory is labeled to obtain a factory label for each production plant; the factory label is divided into static label and dynamic label, the static label reflects the fixed information of the production plant, and the dynamic label reflects the current status and changes of the production plant.

[0012] Optionally, the step of using the order tags and preset filtering conditions to perform preliminary screening of various production plants within the factory to obtain candidate production plants includes:

[0013] Based on the order tags, extract the factory tags of each production factory in the factory terminal, and compare the factory tags of each production factory with the preset necessary tags to detect whether each production factory is missing a necessary tag.

[0014] When it is detected that the production factory does not lack the necessary labels, the production factory that does not lack the necessary labels is identified as a candidate production factory for the footwear and apparel order.

[0015] Optionally, for a single candidate production plant, determining the tag weight value of each factory tag of the single candidate production plant using a preset weight configuration strategy includes:

[0016] Obtain the number of factory tags for a single candidate production plant, and calculate the tag weight value of each factory tag based on the number of tags and a preset average weight allocation method.

[0017] Optionally, for a single candidate production plant, determining the tag weight value of each factory tag of the candidate production plant using a preset weight configuration strategy includes:

[0018] Determine the priority of each factory tag for a single candidate production factory, and determine the tag weight value of each factory tag according to the priority and the preset priority weight allocation method.

[0019] Optionally, calculating the matching score of a single candidate production factory using the order tag, each factory tag, and the tag weight value of each factory tag includes:

[0020] Calculate the matching degree value between the order tag and each factory tag, and calculate the matching score of a single candidate production factory based on the matching degree value of each factory tag and the tag weight value of each factory tag.

[0021] Secondly, the present invention also provides an order-to-production-factory matching device, applied to the platform of a smart production collaboration system. The smart production collaboration system further includes a client and a factory, the client, the platform, and the factory being communicatively connected. The device includes:

[0022] The receiving module is used to affix a label to the footwear and apparel order when it receives the footwear and apparel order from the client, and obtain the order label of the footwear and apparel order;

[0023] The filtering module is used to perform preliminary filtering of each production plant in the factory using the order tags and preset filtering conditions to obtain candidate production plants.

[0024] The calculation module is used to determine the tag weight value of each factory tag of a single candidate production factory using a preset weight configuration strategy; and to calculate the matching score of the single candidate production factory using the order tag, each factory tag and the tag weight value of each factory tag.

[0025] The determination module is used to determine the target production factory for processing the footwear and apparel orders based on the matching scores of each candidate production factory.

[0026] Thirdly, the present invention provides an electronic device comprising: a memory and a processor; the memory for storing a computer program; and the processor for executing the computer program stored in the memory to cause the electronic device to perform the steps of the order and production plant matching method as described above.

[0027] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the steps of the order-to-manufacturing plant matching method as described above.

[0028] Fifthly, the present invention provides a computer program product, which includes computer program code that, when the computer program code is run on a computer, causes the computer to implement the steps of the order and production plant matching method as described above.

[0029] The above-described one or more technical solutions provided by this invention can have the following advantages or at least achieve the following technical effects:

[0030] This invention uses a comprehensive scoring system based on order tags, factory tags, and their weights to select suitable target production factories, thereby improving the matching rate between footwear and apparel orders and production factories. Attached Figure Description

[0031] Figure 1This is a flowchart illustrating a method for matching orders with production plants according to an embodiment of the present invention.

[0032] Figure 2 The diagram shown is a schematic representation of an order label according to an embodiment of the present invention;

[0033] Figure 3(a) shows a schematic diagram of the static label content in a factory label according to an embodiment of the present invention;

[0034] Figure 3(b) shows a schematic diagram of the dynamic label content in a factory label according to an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram illustrating the matching of order tags and production plants in one embodiment of the present invention;

[0036] Figure 5(a) shows a schematic diagram of static rating in a factory dynamic rating label in one embodiment of the present invention;

[0037] Figure 5(b) shows a schematic diagram of dynamic rating in the factory dynamic rating label in one embodiment of the present invention;

[0038] Figure 5(c) shows a schematic diagram of the grade classification in the factory dynamic rating label in one embodiment of the present invention;

[0039] Figure 6(a) shows a schematic diagram of an order label for a footwear and apparel order according to an embodiment of the present invention;

[0040] Figure 6(b) shows a schematic diagram of the matching scores between order tags and candidate production factory tags in one embodiment of the present invention;

[0041] Figure 7 This is a schematic diagram showing the matching scores of candidate production plants in one embodiment of the present invention;

[0042] Figure 8 This is a schematic diagram of the functional modules of the order and production plant matching device in one embodiment of the present invention;

[0043] Figure 9 The diagram shown is a schematic representation of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0044] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0045] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0046] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0047] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0048] Unless otherwise stated, the term "multiple" means two or more.

[0049] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0050] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0051] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0052] Please see Figure 1 An embodiment of the present invention provides a method for matching orders with production plants, applied to the platform of a smart production collaboration system. The smart production collaboration system further includes a client and a factory, and the platform is communicatively connected to both the client and the factory. The method may include the following steps S10 to S40:

[0053] Step S10: Upon receiving a footwear and apparel order from the client, a label is affixed to the footwear and apparel order to obtain the order label for the footwear and apparel order.

[0054] Among them, the Smart Production Collaboration System (also known as the SewSmart system) is an AI-driven smart production collaboration SaaS product designed specifically for the apparel industry, including client, platform, and factory components. It connects the client, platform, and factory components through digital means to achieve intelligent management of apparel manufacturing.

[0055] Client and Platform: The client, used by designers, brands, or retailers, places orders directly to the platform. The platform, as the central hub of the intelligent production collaboration system, receives and manages order information from clients, processes and allocates orders, and coordinates and manages the entire production process to ensure smooth order execution.

[0056] Platform and Factory: The platform sends the processed order information to the factory, and the factory starts organizing production after receiving this information.

[0057] Footwear and apparel orders can be production orders related to footwear and / or apparel issued by the client to the platform.

[0058] Order tags can be attribute descriptions and classifications of the characteristics of various footwear and apparel orders (such as order requirements); they can include, but are not limited to: category, region, price, payment terms, quality requirements, process requirements, production quantity, fast response requirements, third-party quality inspection, and delivery date tags.

[0059] Please see Figure 2 , Figure 2 This is a diagram showing an order label.

[0060] The category tag indicates the product category of the order style. For example, women's clothing - tops - suits.

[0061] Regional labels can indicate the requirements for the location of the production factory in footwear and apparel orders. For example, the production factory needs to be close to the origin of raw materials; or the production factory needs to be close to the warehouse where finished products are stored; if the product is exported overseas, the production factory is required to have export customs clearance qualifications.

[0062] The price tag indicates the unit price (including tax) of an order. For example, the unit price of the 2024 model is 66 yuan per piece, including tax (13%).

[0063] The payment terms label can indicate the terms of payment. For example, 30% deposit, 60% mid-term payment, 10% final payment; monthly settlement, etc.

[0064] Quality requirement labels can indicate the quality requirements of an order and the sales channel. For example, e-commerce - mid-to-high-end quality; shopping malls - high-end quality; markets - mid-range quality.

[0065] Process requirement labels can indicate the process requirements of an order, such as whether it is a knitted fabric or a woven fabric.

[0066] The production quantity label indicates the quantity required for an order; it can include the style number, color, and size. For example: 2024 style - red - size S - 4800 pieces.

[0067] The "fast response demand" tag indicates whether an order requires fast response. For example: the first order will arrive at the warehouse in 10 days, and subsequent orders will arrive in 7 days.

[0068] A third-party inspection label indicates whether an order requires third-party inspection. For example, it may indicate that third-party inspection is required, and a specific third-party inspection center (e.g., SGS Shanghai office) must be specified.

[0069] Delivery date labels indicate the order's delivery time. For example, arrival date at warehouse: August 8, 2024.

[0070] In practice, when the platform receives one or more footwear and apparel orders from the client, it can affix tags according to the order requirements of each footwear and apparel order to obtain the corresponding order tags for each footwear and apparel order.

[0071] Step S20: Use the order tags and preset filtering conditions to perform preliminary screening of each production factory in the factory to obtain candidate production factories.

[0072] The preset filtering criteria can be one or more factory (necessary) tags that each production factory within the factory terminal must possess to enter the order matching process. If any necessary tag is missing from a production factory, that production factory cannot enter the order matching process.

[0073] As an example, each production plant's factory label has four required labels: fabric type, customs clearance qualifications, whether it accepts third-party quality inspection, and whether it uses the system.

[0074] Fabric type: Must be filled in completely, such as knitted, woven, all-purpose, etc.

[0075] Customs declaration qualifications: Must be filled in as yes / no;

[0076] Do you accept third-party quality inspection? You must fill in Yes / No.

[0077] Is the system in use? The system must be in use.

[0078] It is important to note that if any of the four required tags for a production plant are missing, that production plant will not be included in the order matching process.

[0079] In the specific implementation, the order tags and preset filtering conditions are used to initially screen each production factory in the factory to detect whether each production factory meets the filtering conditions for entering the order matching process; then, when one or more production factories are detected to meet the filtering conditions, the one or more production factories can be identified as candidate production factories for footwear and apparel orders.

[0080] Step S30: For a single candidate production factory, determine the tag weight value of each factory tag of the single candidate production factory using a preset weight configuration strategy; and calculate the matching score of the single candidate production factory using the order tag, each factory tag and the tag weight value of each factory tag.

[0081] The preset weight configuration strategy may include, but is not limited to, average weight allocation, priority weight allocation, etc.

[0082] The average weighting method can distribute the total weight evenly among the factory labels of a single candidate production factory.

[0083] As an example, suppose a candidate production factory has 5 factory labels with a total weight of 1, then the weight of each factory label is 0.2.

[0084] The priority-weighting method can prioritize each factory label based on its actual importance, and then assign weights according to the priority of each factory label. It can adjust the importance and weight of each factory label according to different evaluation needs and situations to adapt to different order scenarios.

[0085] It should be noted that the weights are assigned based on the priority of each factory tag to a single candidate production factory. Factory tags with higher priority are given higher weights, while factory tags with lower priority are given lower weights.

[0086] Factory tags can summarize and identify the characteristics, capabilities, and advantages of each production factory; they help the platform quickly understand the situation of each production factory within the factory, so as to quickly filter out the production factories that meet the order requirements when allocating orders.

[0087] Factory labels can be divided into static labels and dynamic labels.

[0088] Static tags can represent fixed factory information for a single production plant.

[0089] For example, product categories, fabric categories, regions, quality, qualifications, etc.

[0090] Dynamic tags can represent dynamic information about a production plant, such as monthly remaining capacity, plant dynamic rating, and system usage.

[0091] Tag weight values ​​can be used to quantify the importance of each factory tag relative to a single candidate production factory.

[0092] The matching score reflects the degree of match between the order tags of footwear and apparel orders and the factory tags of each candidate production factory.

[0093] In the specific implementation, after determining one or more candidate production plants, for each candidate production plant, a weight value (i.e., a tag weight value) is assigned to each factory tag of the individual candidate production plant using a pre-set weight configuration strategy; then, the matching score of the individual candidate production plant is calculated using the degree of matching between the order tag and each factory tag, as well as the tag weight value of each factory tag.

[0094] Step S40: Determine the target production factory for processing the footwear and apparel orders based on the matching scores of each candidate production factory.

[0095] The target production plant can be one or more target production plants selected based on the matching score.

[0096] As an example, candidate production plants with matching scores exceeding a specified score can be selected as target production plants.

[0097] As another example, the matching scores can be sorted from high to low, and one or more candidate production plants with the highest matching scores can be selected as the target production plant.

[0098] In practice, after determining the matching score of a single candidate production plant, one or more target production plants can be identified based on the matching score to process footwear and apparel orders.

[0099] In this embodiment, upon receiving a footwear and apparel order from the client, the order is tagged to obtain an order tag. Preliminary screening of production factories within the factory sector is performed using preset filtering conditions to obtain candidate production factories. For each candidate production factory, the tag weight value of each factory tag is determined using the order tag and a preset weight configuration strategy. A matching score for each candidate production factory is calculated using the order tag, each factory tag, and the tag weight values. Based on the matching scores of each candidate production factory, a target production factory for processing the footwear and apparel order is determined. This invention uses a comprehensive scoring system based on order tags, factory tags, and their tag weights to select suitable target production factories, thereby improving the matching degree between footwear and apparel orders and production factories, and thus improving the processing efficiency and quality of footwear and apparel orders.

[0100] Based on the foregoing embodiments, a second embodiment of the method for matching orders with production factories of the present invention is proposed. In this embodiment, before step S10, the method for matching footwear and apparel orders with production factories may further include the following step S50:

[0101] Step S50: Label each production plant within the factory terminal to obtain a factory label for each production plant; wherein, the factory label is divided into static label and dynamic label, the static label reflects the fixed information of the production plant, and the dynamic label reflects the current status and changes of the production plant.

[0102] Please refer to Figures 3(a) and 3(b); Figure 3(a) shows static label content, and Figure 3(b) shows dynamic label content.

[0103] Static labels can be used to reflect fixed information about the production plant; these can include, but are not limited to: product category, region, price, payment terms, quality, fabric type, minimum order quantity, major customers, monthly production capacity, whether it is fast turnaround, customs clearance qualifications, and whether it accepts third-party quality inspection, etc.

[0104] Category labels can be used to label all the products produced by the factory; these category labels are categorized into three levels of styles, such as women's clothing - tops - shirts.

[0105] A geographic tag can represent the region where the factory's address is located. For example, Jiangsu-Changshu.

[0106] Price tags can represent the average price for a product category. For example, for women's shirts: the average price including materials and labor is 50 yuan; for women's shirts: the average price for processing with supplied materials is 30 yuan.

[0107] The payment terms label can indicate the factory's standard settlement requirements. For example, the standard requirement for RMB settlement is 50% down payment and 50% upon shipment; the standard requirement for USD settlement is 30% down payment and 70% upon shipment.

[0108] Quality labels can indicate the quality of goods produced by a factory in a regular manner; for example, high (premium goods), medium (general brand goods), and low (market goods).

[0109] Fabric type labels can indicate the type of product manufactured by the factory. Examples include knitted, woven, all-purpose, and PU.

[0110] The minimum order quantity label can indicate the minimum order quantity required by the factory, such as an order quantity of 1000 pieces or more.

[0111] Key customer tags can be represented by statistics on the customers served by the factory, which can be used to help demonstrate the factory's quality capabilities.

[0112] The monthly production capacity label represents the factory's monthly production capacity, the total SAM value produced each month, and the number of units produced based on product category. For example, the production capacity in October could be 1 trillion seconds, the average SAM value per product category could be 200,000 seconds, and the average number of units produced would be 880,000 (data from one factory).

[0113] The "fast response" label indicates whether a factory accepts a fast response production model.

[0114] A customs clearance qualification label can indicate whether a factory has the necessary customs clearance qualifications.

[0115] Whether or not a factory accepts third-party quality inspection labels indicates whether it is willing to accept third-party quality inspections specified by the client / platform.

[0116] Dynamic tags can be used to reflect the current status and changes of a production plant; they can include, but are not limited to, remaining capacity, system usage, and plant dynamic ratings.

[0117] The remaining capacity label indicates the factory's remaining capacity for the month; it can be calculated based on the current time and dynamically adjusted according to the factory's actual situation.

[0118] The system usage label indicates the usage status of the SewSmart system. It allows for periodic reviews of the factory's system usage and enables dynamic adjustments based on the factory's actual situation.

[0119] The factory dynamic rating label can represent the factory's score in a periodic review based on supplier assessment standards; it can be dynamically adjusted according to the actual situation of the factory.

[0120] It should be noted that the factory label (including static and dynamic labels) has four required labels, which may include: fabric type, customs declaration qualifications, whether third-party quality inspection is accepted, and whether the system is used.

[0121] In practical implementation, before receiving footwear and apparel orders from clients, the platform can tag each production factory based on its characteristics, advantages, and capabilities, thus obtaining factory tags for each factory. These factory tags can be divided into static tags and dynamic tags; static tags reflect fixed information about the production factory, while dynamic tags reflect its current status and changes.

[0122] In this embodiment, each production plant within the factory is tagged to obtain a factory tag. These factory tags are divided into static tags and dynamic tags. The static tags reflect fixed information about the production plant, while the dynamic tags reflect its current status and changes. By tagging each production plant within the factory, the platform can quickly understand the situation of each production plant, enabling it to quickly filter out target production plants that meet the order requirements when receiving orders from clients.

[0123] Based on the foregoing embodiments, a third embodiment of the order-to-production-plant matching method of the present invention is proposed. In this embodiment, step S20 may include the following sub-steps S201 to S202:

[0124] Sub-step S201: Based on the order tag, extract the factory tags of each production factory in the factory terminal, and compare the factory tags of each production factory with the preset necessary tags to detect whether each production factory is missing a necessary tag.

[0125] Among them, the preset necessary tags can be one or more factory tags that each production factory in the factory end must have to enter the order matching process.

[0126] As an example, each production plant's factory label has four required labels: fabric type, customs clearance qualifications, whether it accepts third-party quality inspection, and whether it uses the system.

[0127] It is important to note that if any one of the four required tags for a production plant is missing, that production plant will not be allowed to enter the order matching process.

[0128] In practical implementation, after determining the order tags for footwear and apparel orders, the platform can extract factory tags for each production factory within the factory network based on the order tags. These factory tags are then compared with one or more pre-defined essential tags to detect whether any production factory is missing a necessary tag. By appropriately setting one or more essential tags to filter production factories, the matching rate between orders and production factories can be improved.

[0129] In sub-step S202, when it is detected that the production factory does not lack the necessary labels, the production factory that does not lack the necessary labels is determined as the candidate production factory for the footwear and apparel order.

[0130] Among them, candidate production factories refer to production factories that meet the preliminary screening criteria and can receive footwear and apparel order allocations.

[0131] In practice, if a production factory is found to have all the necessary labels, it can be identified as a candidate production factory for the footwear and apparel order, and then the production factory can be allowed to enter the order matching process.

[0132] In this embodiment, factory tags for each production factory within the factory terminal are extracted, and these tags are compared with preset necessary tags to detect whether any production factory is missing a necessary tag. When a production factory is found to have all necessary tags, it is identified as a candidate production factory for the footwear and apparel order. Therefore, by reasonably setting one or more necessary tags to filter production factories, the matching degree between orders and production factories can be improved.

[0133] Based on the foregoing embodiments, a fourth embodiment of the order-to-production-plant matching method of the present invention is proposed. In this embodiment, step S30 may include the following sub-step S301:

[0134] Sub-step S301: Obtain the number of factory tags for a single candidate production factory, and calculate the tag weight value of each factory tag based on the number of tags and the preset average weight allocation method.

[0135] The number of tags can be the number of factory tags for a single candidate production plant.

[0136] As an example, suppose a candidate production factory has 10 factory labels and the total weight is set to 1. Then, the weight of each factory label can be calculated as 0.1 using the average weight method.

[0137] In practice, the number of factory tags for a single candidate production factory can be obtained first; then, based on the number of tags and the preset average weight allocation method, the tag weight value of each factory tag can be calculated.

[0138] In this embodiment, the number of factory tags for a single candidate production factory is obtained, and the tag weight value of each factory tag is calculated based on the number of tags and a preset average weight allocation method. Therefore, in the matching of order tags and factory tags, a matching score is calculated using the average weight, matching candidate production factories from multiple perspectives, thereby ensuring the stability of the matching results between footwear and apparel orders and production factories.

[0139] Based on the foregoing embodiments, a fifth embodiment of the order-to-production-plant matching method of the present invention is proposed. In this embodiment, step S30 may further include the following sub-step S302:

[0140] Sub-step S302: Determine the priority of each factory tag of a single candidate production factory, and determine the tag weight value of each factory tag according to the priority and the preset priority weight allocation method.

[0141] In practical implementation, for a single candidate production plant, the priority of each factory tag can be determined based on the importance of each factory tag of the single candidate production plant; then, the tag weight value of each factory tag can be determined based on the priority of each factory tag and the preset priority weight allocation method.

[0142] As an example, please refer to Figure 4 , Figure 4 This is a diagram showing the matching of order tags and production plants. From Figure 4 As can be seen, some factory labels of a candidate production factory (such as fabric type, customs declaration qualifications, whether to accept third-party quality inspection, and whether to use the system) need to be matched 100%, while the remaining factory labels can be matched with lower weight (such as region, quality, remaining capacity, etc.); therefore, the priority of these 4 factory labels (fabric type, customs declaration qualifications, whether to accept third-party quality inspection, and whether to use the system) can be set to be higher than the priority of the remaining factory labels.

[0143] It is important to note that if the order requires knitted fabric, the matching factory must be one that produces using knitted fabric. Any factory that produces non-knitted fabric will not be matched with this order.

[0144] If the order category is women's dresses, then you can match with factories that have produced women's dresses, or match with factories that have produced women's shirts but not dresses.

[0145] In this embodiment, the priority of each factory tag for a single candidate production factory is determined, and the tag weight value of each factory tag is determined according to the priority and a preset priority weight allocation method. Therefore, in the matching of order tags and factory tags, the matching score is calculated using the priority weight method, and the accuracy of matching footwear and apparel orders with production factories is improved based on the importance of each factory tag.

[0146] Based on the foregoing embodiments, a sixth embodiment of the order-to-production-plant matching method of the present invention is proposed. In this embodiment, step S30 may further include the following sub-step S303:

[0147] Sub-step S303: Calculate the matching degree value between the order tag and each factory tag, and calculate the matching score of a single candidate production factory based on the matching degree value of each factory tag and the tag weight value of each factory tag.

[0148] In practice, for a single candidate production factory, after determining the tag weight value of each factory tag, the matching degree value between the order tag and each factory tag can be calculated first; then, using the matching degree value of each factory tag and the tag weight value of each factory tag, the matching score of a single candidate production factory can be calculated.

[0149] As an example, please refer to Figures 5(a)-7 Figures 5(a), 5(b), and 5(c) illustrate the process of assigning factory labels (dynamic rating) to a candidate manufacturing plant. Referring to Figures 5(a) and 5(b), the candidate manufacturing plant's score is periodically reviewed based on the supplier evaluation criteria (the estimated score for each evaluation indicator is calculated based on its evaluation criteria and weight, and then the estimated scores for all evaluation indicators are comprehensively calculated) to determine the candidate manufacturing plant's dynamic rating. Referring to Figure 5(c), a score of 90-100 is designated as Grade A; a score of 80-89 is designated as Grade B; a score of 60-79 is designated as Grade C; and a score below 60 is designated as Grade D.

[0150] Please refer to Figure 6(a), which shows a schematic diagram of the order label for a footwear and apparel order; assuming the existing footwear and apparel order is a knitted women's dress, including materials and labor, 500 pieces, mid-range quality, third-party quality inspection, delivery at the end of the month, exported to Japan, customer's target price is 68 yuan, and the payment terms are 30% down payment and 70% upon shipment.

[0151] Please refer to Figure 6(b), which shows a schematic diagram of the matching scores between order tags and the factory tags of candidate production plants. Based on the matching of order tags with the factory tags of each candidate production plant (Plant A, Plant B, Plant C, Plant D, Plant E), the matching score for each candidate production plant is calculated. The specific calculation process is as follows:

[0152] Assuming that the average weighting method is used to calculate the matching score of a single candidate production factory (e.g., factory D), the scoring description of each factory tag can be determined first (i.e., the matching degree value between the order tag and each factory tag); then the matching degree value of each factory tag and the corresponding tag weight value are calculated, and the matching score of a single candidate production factory is obtained by adding the products together.

[0153] Please see Figure 7 , Figure 7 The diagram shows the matching scores of the candidate production plants; Plant A has a matching score of 82.67, Plant B has a matching score of 86.67, Plant D has a matching score of 90.00, Plant E has a matching score of 95.33, and Plant C has a matching score of 97.33.

[0154] In this embodiment, the matching degree value between the order tag and each factory tag is calculated, and the matching score of a single candidate production factory is calculated based on the matching degree value of each factory tag and the tag weight value of each factory tag. Thus, a comprehensive score is obtained for each candidate production factory based on the order tag, factory tag, and their tag weights; subsequently, suitable target production factories can be selected based on the matching score, thereby improving the matching degree between footwear and apparel orders and production factories.

[0155] Based on the same inventive concept, the seventh embodiment of the present invention also provides an order and production plant matching device corresponding to the order and production plant matching method of the foregoing embodiments. Since the principle by which the device in the seventh embodiment solves the problem is similar to the order and production plant matching method of the foregoing embodiments, the implementation of the device can refer to the implementation of the method, and repeated details will not be elaborated further. Please refer to... Figure 8 The order matching device of the present invention may include:

[0156] The receiving module 10 is used to affix a label to the footwear and apparel order when it receives the footwear and apparel order from the client, and obtain the order label of the footwear and apparel order;

[0157] The screening module 20 is used to perform preliminary screening of each production factory in the factory end using the order tags and preset screening conditions to obtain candidate production factories.

[0158] The calculation module 30 is used to determine the tag weight value of each factory tag of a single candidate production factory using a preset weight configuration strategy; and to calculate the matching score of the single candidate production factory using the order tag, each factory tag and the tag weight value of each factory tag.

[0159] The determination module 40 is used to determine the target production factory for processing the footwear and apparel orders based on the matching scores of each candidate production factory.

[0160] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for matching footwear and apparel orders with production factories.

[0161] This embodiment provides an electronic device, which includes at least a memory and a processor connected via a bus. The memory stores a computer program, and the processor executes the computer program stored in the memory to perform all or part of the steps in the aforementioned method embodiment.

[0162] Figure 9 This is a schematic block diagram of the electronic device provided in an embodiment of this application. Figure 9 As shown, the electronic device includes at least one processor 401, a memory 402, at least one network interface 403, and a user interface 405. The various components in the electronic device are coupled together via a bus system 404. It is understood that the bus system 404 is used to implement communication between these components. In addition to a data bus, the bus system 404 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 9 The general will label all buses as bus systems.

[0163] The user interface 405 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0164] It is understood that memory 402 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0165] In this embodiment of the invention, the memory 402 is used to store various types of data to support the operation of the electronic device 400. Examples of this data include: any executable program for operation on the electronic device 400, such as the operating system 4021 and application programs 4022; the operating system 4021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic business functions and handling hardware-based tasks. The application program 4022 may contain various applications, such as media players, browsers, etc., for implementing various application business functions. The order-to-production plant matching method provided in this embodiment of the invention can be included in the application program 4022.

[0166] The methods disclosed in the above embodiments of the present invention can be applied to processor 401, or implemented by processor 401. Processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 401 or by instructions in the form of software. The processor 401 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 401 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 401 may be a microprocessor or any conventional processor, etc. The steps of the order and manufacturing plant matching method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0167] In an exemplary embodiment, the electronic device 400 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to perform the aforementioned method.

[0168] In summary, this invention uses a comprehensive scoring system based on order tags, factory tags, and their weights to select suitable target production factories, thereby improving the matching rate between footwear and apparel orders and production factories.

[0169] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for matching orders with production plants, characterized in that, A platform terminal for an intelligent production collaboration system, the intelligent production collaboration system further including a client terminal and a factory terminal, the platform terminal being communicatively connected to the client terminal and the factory terminal respectively, the method comprising: Upon receiving a footwear and apparel order from the client, the order is tagged to obtain the order tag for the footwear and apparel order; The order tags and preset filtering conditions are used to perform preliminary screening of each production plant in the factory to obtain candidate production plants; For a single candidate production factory, a preset weight configuration strategy is used to determine the tag weight value of each factory tag of the single candidate production factory; and the matching score of the single candidate production factory is calculated using the order tag, each factory tag and the tag weight value of each factory tag. Based on the matching scores of each candidate production factory, a target production factory is determined for processing the footwear and apparel orders.

2. The method according to claim 1, characterized in that, Before the step of affixing a label to the footwear and apparel order upon receiving it from the client and obtaining the order label, the method further includes: Each production plant within the factory is labeled to obtain a factory label for each production plant; wherein, the factory label is divided into static label and dynamic label, the static label reflects the fixed information of the production plant, and the dynamic label reflects the current status and changes of the production plant.

3. The method according to claim 1, characterized in that, The process of using the order tags and preset filtering conditions to perform preliminary screening of various production plants within the factory sector to obtain candidate production plants includes: Based on the order tags, extract the factory tags of each production factory in the factory terminal, and compare the factory tags of each production factory with the preset necessary tags to detect whether each production factory is missing a necessary tag. When it is detected that the production factory does not lack the necessary labels, the production factory that does not lack the necessary labels is identified as a candidate production factory for the footwear and apparel order.

4. The method according to claim 1, characterized in that, For a single candidate production plant, the step of determining the tag weight value of each factory tag of the single candidate production plant using a preset weight configuration strategy includes: Obtain the number of factory tags for a single candidate production plant, and calculate the tag weight value of each factory tag based on the number of tags and a preset average weight allocation method.

5. The method according to claim 1, characterized in that, For a single candidate production factory, the step of determining the tag weight value of each factory tag of the candidate production factory using a preset weight configuration strategy includes: Determine the priority of each factory tag for a single candidate production factory, and determine the tag weight value of each factory tag according to the priority and the preset priority weight allocation method.

6. The method according to claim 1, 4, or 5, characterized in that, The step of calculating the matching score of a single candidate production factory using the order tag, each factory tag, and the tag weight value of each factory tag includes: Calculate the matching degree value between the order tag and each factory tag, and calculate the matching score of a single candidate production factory based on the matching degree value of each factory tag and the tag weight value of each factory tag.

7. An order matching device for production plants, characterized in that, A platform for use in a smart production collaboration system, the smart production collaboration system also includes a client and a factory, the client, the platform, and the factory are communicatively connected, and the device includes: The receiving module is used to affix a label to the footwear and apparel order when it receives the footwear and apparel order from the client, and obtain the order label of the footwear and apparel order; The filtering module is used to perform preliminary filtering of each production plant in the factory using the order tags and preset filtering conditions to obtain candidate production plants. The calculation module is used to determine the tag weight value of each factory tag of a single candidate production factory using a preset weight configuration strategy; and to calculate the matching score of the single candidate production factory using the order tag, each factory tag and the tag weight value of each factory tag. The determination module is used to determine the target production factory for processing the footwear and apparel orders based on the matching scores of each candidate production factory.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed, performs the steps of the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to perform the steps of the method as described in any one of claims 1 to 6.

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