Product order allocation methods, devices, equipment, and storage media

Abstract

This application discloses a product order allocation method, apparatus, device, and storage medium, belonging to the field of order management technology. This application obtains a bipartite graph model, where the bipartite graph model is a graph model representing the mapping relationship between an order set and a participant set. The order set is the set of product orders corresponding to users, and the participant set is the set of production plants. The mapping relationship is the correspondence between production plants and order tasks, and the mapping relationship meets task completion conditions, task quantity conditions, and / or pricing conditions. Based on the order tasks and production plants corresponding to the mapping relationship, product orders are allocated. That is, by aggregating the product orders corresponding to users and the production plants, and matching the two aggregated sets, a suitable mapping relationship can be determined in the order set and the participant set. When the mapping relationship meets the corresponding conditions, the corresponding order tasks are allocated, avoiding excessively high production plant costs.

Description

Technical Field

[0001] This application relates to the field of order management technology, and in particular to a product order allocation method, apparatus, device and storage medium. Background Technology

[0002] As people's demand for personalized or customized products continues to increase, traditional large-scale standard product production has gradually shifted to personalized product production. However, the number of customized products required by people is usually small, which means that the personalized products specified by users are small-scale products.

[0003] However, when a factory produces corresponding products, it will be based on the user's needs. However, the personalized products required by the user may be complex in type and small in quantity. As a result, the factory will develop multiple production processes for multiple types of products. However, due to the small output of each product, the factory's costs will be too high.

[0004] Therefore, how to reduce the production cost of personalized products remains an urgent problem to be solved. Summary of the Invention

[0005] The main purpose of this application is to provide a product order allocation method, apparatus, equipment and storage medium, which aims to solve the technical problem of high production costs for personalized products.

[0006] To achieve the above objectives, this application provides a product order allocation method applied to an industrial internet platform. The product order allocation method includes the following steps:

[0007] Obtain a bipartite graph model, wherein the bipartite graph model is a graph model representing the mapping relationship between the order set and the participant set, the order set is the set of product orders corresponding to users, the participant set is the set of production factories, the mapping relationship is the correspondence between the production factories and the order tasks, and the mapping relationship meets the task completion condition, the task quantity condition and / or the quotation condition;

[0008] The product orders are allocated based on the order tasks corresponding to the mapping relationship and the production plant.

[0009] Optionally, before the step of obtaining the bipartite graph model, the method further includes:

[0010] Get the order set and the set of participants;

[0011] Identify the production factory that matches the order task, where the order task belongs to the order set and the production factory belongs to the set of parties to be involved;

[0012] If each order task in the order set is matched with a corresponding production factory, a bipartite graph model is generated based on each order task and the corresponding production factory, so that each mapping relationship in the bipartite graph model meets the task completion condition. The bipartite graph model includes the order set, the set of participants, and edges, and the edges are the mapping relationships between the order tasks and the corresponding production factories.

[0013] Optionally, the step of determining the production plant matching the order task includes:

[0014] Determine the order quote for each order task in the order set, and determine the production quote for each order task corresponding to each factory in the participating factories;

[0015] If the order quote is greater than the production quote, then the order task and the production plant are matched, so that the mapping relationship between the order task and the production plant meets the quote conditions.

[0016] Optionally, the step of generating a bipartite graph model based on each order task and the corresponding production plant includes:

[0017] Based on each order task and the corresponding production factory, an initial bipartite graph model is generated.

[0018] If the number of orders matched by the production factory in the initial bipartite graph model is less than the preset number, then the production factory and the mapping relationship corresponding to the production factory are deleted, and the production factory corresponding to each order task in the deleted mapping relationship is rematched.

[0019] If a matching production factory cannot be found, then each order task in the mapping relationship is deleted.

[0020] Optionally, the step of determining the production plant matching the order task includes:

[0021] Determine the processing conditions for each order task and the production capacity of each production plant;

[0022] If the processing conditions and the production capacity match, then the production plant is determined to match the order task, so that the mapping relationship between the production plant and the order task meets the task quantity condition.

[0023] Optionally, before the step of determining the production plant matching the order task, the method further includes:

[0024] Determine the quantity of each product in the order set and arrange the product orders in descending order.

[0025] Determine the production demand of each production plant among the participating factories, and arrange the production plants in order from smallest to largest.

[0026] Product orders are allocated to the ordered order set and the factories to be involved in the order.

[0027] Optionally, the step of allocating the product order according to the order task corresponding to the mapping relationship and the production plant further includes:

[0028] Get a custom matching target;

[0029] Based on the custom matching target, adjust the mapping relationships in the bipartite graph model, and allocate the product orders according to the adjusted mapping relationships.

[0030] Furthermore, to achieve the above objectives, this application also provides a product order allocation device, the product order allocation device comprising:

[0031] The acquisition module is used to define the bipartite graph model as a graph model representing the mapping relationship between the order set and the participant set. The order set is the set of product orders corresponding to users, the participant set is the set of production factories, and the mapping relationship is the correspondence between the production factories and the order tasks. The mapping relationship meets the task completion condition, the task quantity condition, and / or the quotation condition.

[0032] The allocation module is used to allocate the product orders according to the order tasks corresponding to the mapping relationship and the production plant.

[0033] In addition, to achieve the above objectives, this application also provides a product order allocation device, which includes: a memory, a processor, and a product order allocation program stored on the memory and executable on the processor, wherein the product order allocation program is configured to implement the steps of the product order allocation method described above.

[0034] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a product order allocation program, which, when executed by a processor, implements the steps of the product order allocation method described above.

[0035] This application obtains a bipartite graph model, which is a graph model representing the mapping relationship between an order set and a participant set. The order set is a set of product orders corresponding to users, and the participant set is a set of production factories. The mapping relationship is the correspondence between the production factories and the order tasks. The mapping relationship meets the conditions of task completion, task quantity, and / or pricing. Based on the order tasks corresponding to the mapping relationship and the production factories, the product orders are allocated. That is, by aggregating the product orders corresponding to users and the production factories, and matching the two aggregated sets, a suitable mapping relationship can be determined in the order set and the participant set. When the mapping relationship meets the corresponding conditions, the corresponding order tasks are allocated, avoiding excessive costs for production factories. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating the first embodiment of the product order allocation method of this application;

[0037] Figure 2 This is a flowchart illustrating the second embodiment of the product order allocation method of this application;

[0038] Figure 3 This is a structural block diagram of an embodiment of the product order allocation device of this application;

[0039] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application.

[0040] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0041] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0042] Reference Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the product order allocation method of this application.

[0043] It's important to clarify that the Industrial Internet connects various factories to the internet and further leverages the internet to connect with users. In an automated production model based on the Industrial Internet, an internet platform enables automated matching between user orders and factories, with the factory then automating order production while users can track production progress in real time.

[0044] As people increasingly pursue personalization and production capacity continues to improve, traditional large-scale production of standardized products has gradually shifted to the production of personalized products. However, reducing the production cost of personalized products remains a challenge. The core of cost reduction lies in lowering the marginal costs of all parties involved in the production chain.

[0045] In relevant theories, mass customization proposes providing personalized products that meet specific user needs at a cost and time similar to standardization and mass production. The goal of mass customization is to resolve the contradiction between mass production and personalization, thereby achieving the mass production of personalized products—that is, increasing product variety and customization without correspondingly increasing costs.

[0046] The basic idea of ​​mass customization is to restructure the product structure and manufacturing process, and use a series of high technologies such as modern information technology, new materials technology, and flexible manufacturing technology to transform the customized production of personalized products into mass production, in whole or in part, at the cost and speed of mass production, so as to customize any number of products for a single user or a small-batch, multi-variety market.

[0047] However, while the concept of mass customization has been applied in some very large enterprises, there is still no good solution for how to aggregate numerous small and medium-sized enterprises and factories to produce personalized products and reduce costs.

[0048] In this embodiment, to address the aforementioned issues, a product order allocation method is proposed. Specifically, this method reduces costs by aggregating more orders for each participant in the production chain. This means enabling factories to participate in multiple personalized product production orders simultaneously, where the production work undertaken in these orders has a certain similarity (e.g., a factory producing the same component from different users, or producing components requiring the same raw materials). In essence, this is equivalent to aggregating multiple orders from multiple users and rationally allocating the aggregated orders to the corresponding factories according to the concept of mass customization, thereby reducing the high costs associated with factories producing personalized products.

[0049] In the first embodiment, the product order allocation method includes the following steps:

[0050] S10, Obtain a bipartite graph model, wherein the bipartite graph model is a graph model representing the mapping relationship between the order set and the participant set, the order set is the set of product orders corresponding to users, the participant set is the set of production factories, the mapping relationship is the correspondence between the production factories and the order tasks, and the mapping relationship meets the task completion condition, the task quantity condition and / or the quotation condition.

[0051] Understandably, the bipartite graph model is a graph model that maps the order set and the participant set. This mapping mainly refers to the matching relationship when product orders in the order set are allocated to production plants in the participant set. For example, some similar products are uniformly assigned to the same production plant to ensure the scale (i.e. the number of products produced) of the same batch in that plant and reduce its costs.

[0052] Here, the mapping relationship can refer to the matching relationship when the production factory can realize the production of the corresponding product. For example, the orders of user A and user B are both assigned to factory A (there is a mapping relationship among the three), and the orders of user C and user D are assigned to factory B (there is a mapping relationship among the three), etc. The mapping relationship can specify the corresponding matching rules, thereby limiting the product order allocation situation defined by the bipartite graph model. Specifically, in this embodiment, task completion condition, task quantity condition and / or quotation condition are set. The optimal allocation item is that all three conditions must be met. Secondly, it can be based on any combination of the three conditions.

[0053] The task completion condition refers to the requirement that all tasks in the order set be assigned to the corresponding production plants, that is, to ensure that all tasks in the order set are completed.

[0054] The task quantity condition refers to the fact that when a factory produces a certain product, its cost is the lowest. This task quantity condition is used to reduce the cost of producing the corresponding product.

[0055] The pricing condition refers to the requirement that the price quoted by each user in the order set must be greater than the price quoted by the production factory, thus guaranteeing the production factory's revenue.

[0056] It should be noted that on the industrial internet platform, users can post product orders for different personalized products.

[0057] The product order includes a restructured and optimized production process for personalized products, as well as the production scale.

[0058] In this process, the manufacturing plant can participate in order bidding by submitting a bid. On the one hand, the production scale of a single order may not be sufficient to meet the minimum quantity requirements for the plant to participate in production, or the marginal cost of production may be too high; on the other hand, the plant also hopes to participate in as many other orders as possible, thereby increasing its own production scale, reducing costs, and improving its competitiveness in order bidding.

[0059] It should be noted that under the above mechanism, due to the different bidding strategies of each user and the complexity of the bidding process, problems such as high cost for users to participate in bidding, chaotic bidding, and low order aggregation efficiency will occur, and it may even lead to malicious competition. Therefore, a purely distributed bidding mechanism is not feasible. In this embodiment, the industrial internet platform utilizes global information to achieve large-scale order matching, thereby aggregating more orders at the lowest possible cost while meeting the requirements of both users and factories (i.e., pre-obtaining the corresponding order set and participant set, and allocating product orders according to the two sets to obtain a bipartite graph model).

[0060] Specifically, suppose that within a certain time period, there are several orders on the industrial internet platform. Each order includes: a production process (each step of the process needs to complete a specific production task, such as the production of a certain component), and a price quote for each step (expressed as a unit price). For any production plant, it believes that it can complete the specific production tasks of certain steps in the orders, and therefore is suitable to participate in these orders.

[0061] In summary, the production process exhibits economies of scale. From the perspective of the production plant, the cumulative production scale of participating orders must be greater than a minimum quantity (otherwise, the cost of participation is considered too high, making it difficult to achieve profitability), and the price quoted for each product order for that production task must be greater than its minimum acceptable price (i.e., it is equivalent to constructing a bipartite graph model by limiting various conditions).

[0062] S20, allocate the product order according to the order task corresponding to the mapping relationship and the production factory.

[0063] Understandably, the above mapping relationship satisfies the relevant conditions for product order allocation. Therefore, product orders can be allocated based on the order tasks and production plants corresponding to this mapping relationship.

[0064] It should be noted that the process of allocating product orders mainly refers to classifying a small number of product orders according to the content of the corresponding products, and then allocating product orders of the same category to the same production plant or to several production plants. This ensures that the production plant receiving the product orders can achieve large-scale production and avoid excessive costs.

[0065] Furthermore, in this embodiment, the step of allocating the product order according to the order task and the production factory corresponding to the mapping relationship further includes: obtaining a custom matching target, adjusting each mapping relationship in the bipartite graph model according to the custom matching target, and allocating the product order according to the adjusted mapping relationship.

[0066] Understandably, when generating a bipartite graph model, a bipartite graph model can be constructed based on each mapping relationship, and the mapping relationship can be constrained to meet the corresponding conditions. Therefore, in this embodiment, the corresponding custom matching target (equivalent to the additional constraints of the mapping relationship) can be obtained, thereby further constraining the allocation effect of product orders when generating the corresponding bipartite graph model.

[0067] The custom matching objective can be to maximize the number of orders formed, maximize the production scale of orders, or other objectives.

[0068] This embodiment obtains a bipartite graph model, which is a graph model representing the mapping relationship between an order set and a participant set. The order set is the set of product orders corresponding to users, and the participant set is the set of production factories. The mapping relationship is the correspondence between the production factories and the order tasks. The mapping relationship meets the conditions of task completion, task quantity, and / or pricing. Based on the order tasks corresponding to the mapping relationship and the production factories, the product orders are allocated. That is, by aggregating the product orders corresponding to users and the production factories, and matching the two aggregated sets, a suitable mapping relationship can be determined in the order set and the participant set. When the mapping relationship meets the corresponding conditions, the corresponding order tasks are allocated, avoiding excessive costs for production factories.

[0069] like Figure 2 As shown, based on the first embodiment, a second embodiment of the product order allocation method of this application is proposed. In this embodiment, the method further includes:

[0070] S110, obtain the order set and the set of participants;

[0071] Understandably, the order collection includes multiple product orders for different or similar products initiated by different users, and each product order contains specific product details, process requirements, and product quotations.

[0072] Understandably, the set of participants refers to the group of companies that will participate in the production of the product.

[0073] S120, determine the production factory that matches the order task, wherein the order task belongs to the order set and the production factory belongs to the set of parties to be involved;

[0074] Understandably, when building a bipartite graph model, it is necessary to determine the matching relationship between order tasks and production plants in the two sets.

[0075] In this embodiment, the step of determining the production factory that matches the order task includes:

[0076] Determine the order price of the order task in the order set, and determine the production price of each order task corresponding to each factory in the factories to be participated in; if the order price is greater than the production price, then determine that the order task and the production factory are matched, so that the mapping relationship between the order task and the production factory meets the pricing conditions.

[0077] When the pricing conditions are met, the pricing conditions are expressed as condition (1): p(j(o,u))>p(u), where j(o,u) indicates that participant u can complete a specific task in order o, p(j(o,u)) indicates the price quoted by the order for that task, i.e. the order price, and p(u) indicates the lowest price that the participant can accept, i.e. the production price. In other words, if the order price is higher than the production price quoted by the production factory, the production factory is willing to do it.

[0078] In this embodiment, the step of determining the production factory that matches the order task includes: determining the processing conditions of each order task and determining the production capacity of each production factory; if the processing conditions and the production capacity match, then determining that the production factory matches the order task, so that the mapping relationship between the production factory and the order task meets the task quantity condition.

[0079] As can be understood from the above description, the cost of a production plant is low only when it reaches a certain production scale. Therefore, when allocating corresponding product orders, the quantity of product orders that can meet the production capacity requirements of the production plant can be taken into account.

[0080] The processing conditions refer to the processes required for the product, such as injection molding, film application, or printing. These processes need to be matched with factories that have the corresponding production capacity. After matching with the corresponding production factories, a mapping relationship is established between the two sets.

[0081] In this embodiment, before the step of determining the production factory matching the order task, the method further includes:

[0082] Determine the quantity of each product in the order set and arrange the product orders in descending order.

[0083] Determine the production demand of each production plant among the participating factories, and arrange the production plants in order from smallest to largest.

[0084] Product orders are allocated to the ordered order set and the factories to be involved in the order.

[0085] Specifically, O and U are sorted in descending order of q(o) and ascending order of q(u), respectively. This sorting increases the likelihood of order matching. It's important to note that q(u) represents a minimum cumulative production scale that u in the set of participating parties must meet, i.e., the production scale requirement. Ascending order, or sorting from smallest to largest, means that participants with lower production scale requirements have higher priority for order matching, making their conditions easier to meet, thus improving the success rate of matching. Similarly, q(o) represents the number of orders initiated by each user in the order set; its descending order also prioritizes matching large-scale product orders to production plants.

[0086] In this embodiment, the step of generating a bipartite graph model based on each order task and the corresponding production factory includes: generating an initial bipartite graph model based on each order task and the corresponding production factory; if the number of orders matched by the production factory in the initial bipartite graph model is less than a preset number, then the production factory and the mapping relationship corresponding to the production factory are deleted, and the production factory corresponding to each order task in the deleted mapping relationship is re-matched; if no corresponding production factory can be matched, then each order task in the mapping relationship is deleted.

[0087] Specifically, for each participant u in U, check whether its production scale condition is met according to condition (3). If not, remove u and the edge (o,u) connected to u from M. At this time, for the affected o, find a new u' in U starting from the current u and add the connection (o,u') to M. If no new u' exists, o cannot be completed, and o and the edge connected to o are removed from M.

[0088] S130, if each order task in the order set is matched with a corresponding production factory, then a bipartite graph model is generated based on each order task and the production factory corresponding to each order task, so that each mapping relationship in the bipartite graph model meets the task completion condition. The bipartite graph model includes the order set, the set of parties to participate, and edges, and the edges are the mapping relationships between the order tasks and the production factories corresponding to each order task.

[0089] Understandably, in a bipartite graph model, each of the two sets contains multiple content points. When a matching relationship is formed between the two sets, a mapping relationship can be established between the content points in the two sets. For example, a mapping relationship can be established between a product order submitted by user A and A's production factory. This relationship is represented by edges.

[0090] In summary, in this embodiment, a model can be built based on the above problems and the content involved, and a bipartite graph G(O,U,E,J(O),J(E)) can be used to represent the relationship between the order set O and the participant set U. E is the set of all edges in the bipartite graph, J(O) represents all tasks in the order set O, and J(E) represents the set of edges corresponding to all tasks in the order set.

[0091] In this bipartite graph model, any edge e(o,u) = (o,u) represents that participant u can complete one of the tasks J(o) of order o, j(o,u) = j(e(o,u)), and the price condition is satisfied, which is expressed as condition (1): p(j(o,u)) > p(u). Here, j(o,u) means that participant u can complete a specific task in order o, p(j(o,u)) means that the order quotes for that task, and p(u) means that the lowest quote that participant can accept. That is, if the order quote is higher than the production quote of the production factory, the production factory is willing to do it.

[0092] Specifically, by having the factory mark the orders it wants to participate in through the interactive interface of the industrial internet platform, E and J(E) can be obtained.

[0093] Furthermore, U_o represents all available participants in an order o, i.e., U_o = {u|e(o,u)∈E}, while O_u represents all orders that a participant can participate in, i.e., O_u = {o|e(o,u)∈E}. Given a matching result M (M is a subgraph of G), U_o(M) and O_u(M) represent the participants in the actual order o and the orders in which participant u participates, respectively.

[0094] For U_o(M), condition (2) must be satisfied: {j(o,u)|u∈U_o(M)}=J(o), that is, all tasks J(o) can be completed.

[0095] For O_u(M), condition (3) must be met: Σ[q(o),o∈O_u(M)]>q(u), that is, the total size of all orders must reach the minimum requirement.

[0096] Specifically, in this embodiment, the matching objective can be: 1) maximizing the number of orders formed |O(M)|; 2) maximizing the production scale of orders Σ[q(o),o∈O(M)]; etc. It should be noted that the above three conditions must be met in the optimal product order allocation scheme.

[0097] Furthermore, due to the complexity of the problem itself, we propose a heuristic method for solving it, as follows:

[0098] Step 0: Initialize and set the empty set M of the bipartite graph model;

[0099] Step 1: Sort the order set and the set of participants;

[0100] In this process, O and U are sorted in descending order of q(o) and ascending order of q(u), respectively. This sorting increases the likelihood of order matching. It's important to note that q(u) represents a minimum cumulative production scale that u in the set of participating parties must meet. Ascending order, or sorting from smallest to largest, means that participants with lower production scale requirements have higher priority for order matching, making their conditions easier to satisfy and thus improving the success rate. Similarly, q(o) represents the number of orders initiated by each user in the order set; its descending order ensures that large-scale product orders are prioritized for matching with production plants.

[0101] Step 2: Initial order matching;

[0102] For each order o in O, and for each task j in all tasks J(o) of o, the first u that can complete task j is selected from U, i.e., there exists j(o,u) = j. If all J(o) matches a corresponding u, then all edges (o,u) and their corresponding vertices are added to M; otherwise, M remains unchanged. After the initial matching, M contains the orders that can be completed and the participating parties in the matching.

[0103] Step 3: Check the matching results;

[0104] For each participant u in U, its production scale condition is checked according to condition (3). If not, u and the edge (o,u) connected to u are removed from M. At this time, for the affected o, a new u' is found in U starting from the current u, and the connection (o,u') is added to M. If no new u' exists, o cannot be completed, and o and the edge connected to o are removed from M.

[0105] Repeat the above process to check U until all u in M ​​meet the production scale conditions. At this point, M is the matching result that meets the requirements.

[0106] This embodiment obtains an order set and a set of potential participants; determines the production factory that matches the order task, where the order task belongs to the order set and the production factory belongs to the set of potential participants; if each order task in the order set is matched with a corresponding production factory, a bipartite graph model is generated based on each order task and its corresponding production factory, ensuring that the mapping relationships in the bipartite graph model meet the task completion condition. The bipartite graph model includes the order set, the set of potential participants, and edges, where each edge represents the mapping relationship between the order task and its corresponding production factory. In other words, a corresponding bipartite graph model is constructed using the corresponding order set and the set of potential participants. Furthermore, based on the construction conditions of the bipartite graph model, it is ensured that each product order can be mapped to a single production factory.

[0107] Furthermore, embodiments of this application also propose a product order allocation device, referring to... Figure 3 The product order allocation device includes:

[0108] The acquisition module 10 is used to make the bipartite graph model a graph model representing the mapping relationship between the order set and the participant set. The order set is the set of product orders corresponding to users, the participant set is the set of production factories, the mapping relationship is the correspondence between the production factories and the order tasks, and the mapping relationship meets the task completion condition, the task quantity condition and / or the quotation condition.

[0109] The allocation module 20 is used to allocate the product orders according to the order tasks corresponding to the mapping relationship and the production factory.

[0110] This embodiment obtains a bipartite graph model, which is a graph model representing the mapping relationship between an order set and a participant set. The order set is the set of product orders corresponding to users, and the participant set is the set of production factories. The mapping relationship is the correspondence between the production factories and the order tasks. The mapping relationship meets the conditions of task completion, task quantity, and / or pricing. Based on the order tasks corresponding to the mapping relationship and the production factories, the product orders are allocated. That is, by aggregating the product orders corresponding to users and the production factories, and matching the two aggregated sets, a suitable mapping relationship can be determined in the order set and the participant set. When the mapping relationship meets the corresponding conditions, the corresponding order tasks are allocated, avoiding excessive costs for production factories.

[0111] It should be noted that each module in the above-mentioned device can be used to implement each step in the above-mentioned method and achieve the corresponding technical effect. This embodiment will not elaborate further here.

[0112] Reference Figure 4 , Figure 4 This is a schematic diagram of the hardware operating environment of the device involved in the embodiments of this application.

[0113] like Figure 4 As shown, the device may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0114] Those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0115] like Figure 4 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a product order allocation program.

[0116] exist Figure 4 In the device shown, the network interface 1004 is mainly used for data communication with an external network; the user interface 1003 is mainly used for receiving user input commands; the device calls the product order allocation program stored in the memory 1005 through the processor 1001 and performs the following operations:

[0117] Obtain a bipartite graph model, wherein the bipartite graph model is a graph model representing the mapping relationship between the order set and the participant set, the order set is the set of product orders corresponding to users, the participant set is the set of production factories, the mapping relationship is the correspondence between the production factories and the order tasks, and the mapping relationship meets the task completion condition, the task quantity condition and / or the quotation condition;

[0118] The product orders are allocated based on the order tasks corresponding to the mapping relationship and the production plant.

[0119] Furthermore, the processor 1001 can call the product order allocation program stored in the memory 1005 and also perform the following operations:

[0120] Get the order set and the set of participants;

[0121] Identify the production factory that matches the order task, where the order task belongs to the order set and the production factory belongs to the set of parties to be involved;

[0122] If each order task in the order set is matched with a corresponding production factory, a bipartite graph model is generated based on each order task and the corresponding production factory, so that each mapping relationship in the bipartite graph model meets the task completion condition. The bipartite graph model includes the order set, the set of participants, and edges, and the edges are the mapping relationships between the order tasks and the corresponding production factories.

[0123] Furthermore, the processor 1001 can call the product order allocation program stored in the memory 1005 and also perform the following operations:

[0124] Determine the order quote for each order task in the order set, and determine the production quote for each order task corresponding to each factory in the participating factories;

[0125] If the order quote is greater than the production quote, then the order task and the production plant are matched, so that the mapping relationship between the order task and the production plant meets the quote conditions.

[0126] Furthermore, the processor 1001 can call the product order allocation program stored in the memory 1005 and also perform the following operations:

[0127] Based on each order task and the corresponding production factory, an initial bipartite graph model is generated.

[0128] If the number of orders matched by the production factory in the initial bipartite graph model is less than the preset number, then the production factory and the mapping relationship corresponding to the production factory are deleted, and the production factory corresponding to each order task in the deleted mapping relationship is rematched.

[0129] If a matching production factory cannot be found, then each order task in the mapping relationship is deleted.

[0130] Furthermore, the processor 1001 can call the product order allocation program stored in the memory 1005 and also perform the following operations:

[0131] Determine the processing conditions for each order task and the production capacity of each production plant;

[0132] If the processing conditions and the production capacity match, then the production plant is determined to match the order task, so that the mapping relationship between the production plant and the order task meets the task quantity condition.

[0133] Furthermore, the processor 1001 can call the product order allocation program stored in the memory 1005 and also perform the following operations:

[0134] Prior to the step of determining the production factory that matches the order task, the method further includes:

[0135] Determine the quantity of each product in the order set and arrange the product orders in descending order.

[0136] Determine the production demand of each production plant among the participating factories, and arrange the production plants in order from smallest to largest.

[0137] Product orders are allocated to the ordered order set and the factories to be involved in the order.

[0138] Furthermore, the processor 1001 can call the product order allocation program stored in the memory 1005 and also perform the following operations:

[0139] The step of allocating the product order according to the order task corresponding to the mapping relationship and the production plant further includes:

[0140] Get a custom matching target;

[0141] Based on the custom matching target, adjust the mapping relationships in the bipartite graph model, and allocate the product orders according to the adjusted mapping relationships.

[0142] This embodiment obtains a bipartite graph model, which is a graph model representing the mapping relationship between an order set and a participant set. The order set is the set of product orders corresponding to users, and the participant set is the set of production factories. The mapping relationship is the correspondence between the production factories and the order tasks. The mapping relationship meets the conditions of task completion, task quantity, and / or pricing. Based on the order tasks corresponding to the mapping relationship and the production factories, the product orders are allocated. That is, by aggregating the product orders corresponding to users and the production factories, and matching the two aggregated sets, a suitable mapping relationship can be determined in the order set and the participant set. When the mapping relationship meets the corresponding conditions, the corresponding order tasks are allocated, avoiding excessive costs for production factories.

[0143] Furthermore, embodiments of this application also propose a computer-readable storage medium storing a product order allocation program, which, when executed by a processor, performs the following operations:

[0144] Obtain a bipartite graph model, wherein the bipartite graph model is a graph model representing the mapping relationship between the order set and the participant set, the order set is the set of product orders corresponding to users, the participant set is the set of production factories, the mapping relationship is the correspondence between the production factories and the order tasks, and the mapping relationship meets the task completion condition, the task quantity condition and / or the quotation condition;

[0145] The product orders are allocated based on the order tasks corresponding to the mapping relationship and the production plant.

[0146] This embodiment obtains a bipartite graph model, which is a graph model representing the mapping relationship between an order set and a participant set. The order set is the set of product orders corresponding to users, and the participant set is the set of production factories. The mapping relationship is the correspondence between the production factories and the order tasks. The mapping relationship meets the conditions of task completion, task quantity, and / or pricing. Based on the order tasks corresponding to the mapping relationship and the production factories, the product orders are allocated. That is, by aggregating the product orders corresponding to users and the production factories, and matching the two aggregated sets, a suitable mapping relationship can be determined in the order set and the participant set. When the mapping relationship meets the corresponding conditions, the corresponding order tasks are allocated, avoiding excessive costs for production factories.

[0147] It should be noted that when the above-mentioned computer-readable storage medium is executed by the processor, it can also implement the various steps in the above method and achieve the corresponding technical effects. This embodiment will not be described in detail here.

[0148] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0149] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0150] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0151] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A product order allocation method, characterized in that, Applied to industrial internet platforms, the product order allocation method includes the following steps: Obtain a bipartite graph model, wherein the bipartite graph model is a graph model representing the mapping relationship between the order set and the participant set, the order set is the set of product orders corresponding to users, the participant set is the set of production plants, the mapping relationship is the correspondence between the production plants and the order tasks, and the mapping relationship meets the task completion condition, the task quantity condition and / or the quotation condition. The product orders are allocated according to the order tasks corresponding to the mapping relationship and the production plant; Prior to the step of obtaining the bipartite graph model, the method further includes: Get the order set and the set of participants; Identify the production factory that matches the order task, where the order task belongs to the order set and the production factory belongs to the set of parties to be involved; If each order task in the order set is matched with a corresponding production factory, then a bipartite graph model is generated based on each order task and the production factory corresponding to each order task, so that each mapping relationship in the bipartite graph model meets the task completion condition. The bipartite graph model includes the order set, the set of participants, and edges, and the edges are the mapping relationships between the order tasks and the production factories corresponding to each order task. The step of determining the production factory that matches the order task includes: Determine the order quote for each order task in the order set, and determine the production quote for each order task corresponding to each factory in the participating factories; If the order quote is greater than the production quote, then the order task and the production plant are matched, so that the mapping relationship between the order task and the production plant meets the quote conditions; The step of generating a bipartite graph model based on each order task and the corresponding production factory includes: Based on each order task and the corresponding production factory, generate an initial bipartite graph model; If the number of orders matched by the production factory in the initial bipartite graph model is less than the preset number, then the production factory and the mapping relationship corresponding to the production factory are deleted, and the production factory corresponding to each order task in the deleted mapping relationship is rematched. If a matching production factory cannot be found, then each order task in the mapping relationship is deleted.

2. The product order allocation method as described in claim 1, characterized in that, The step of determining the production factory that matches the order task includes: Determine the processing conditions for each order task and the production capacity of each production plant; If the processing conditions and the production capacity match, then the production plant is determined to match the order task, so that the mapping relationship between the production plant and the order task meets the task quantity condition.

3. The product order allocation method as described in claim 1, characterized in that, Prior to the step of determining the production factory that matches the order task, the method further includes: Determine the quantity of each product in the order set and arrange the product orders in descending order. Determine the production demand of each production plant among the participating factories, and arrange the production plants in order from smallest to largest. Product orders are allocated to the ordered order set and the factories to be involved in the order.

4. The product order allocation method as described in claim 1, characterized in that, The step of allocating the product order according to the order task corresponding to the mapping relationship and the production plant further includes: Get a custom matching target; Based on the custom matching target, adjust the mapping relationships in the bipartite graph model, and allocate the product orders according to the adjusted mapping relationships.

5. A product order allocation device, characterized in that, The product order allocation device includes: The acquisition module is used to acquire a bipartite graph model, wherein the bipartite graph model is a graph model representing the mapping relationship between the order set and the participant set, the order set is the set of product orders corresponding to users, the participant set is the set of production plants, the mapping relationship is the correspondence between the production plants and the order tasks, and the mapping relationship meets the task completion condition, the task quantity condition and / or the quotation condition. The allocation module is used to allocate the product orders according to the order tasks corresponding to the mapping relationship and the production plant; The product order allocation device is also used to achieve: Get the order set and the set of participants; Identify the production factory that matches the order task, where the order task belongs to the order set and the production factory belongs to the set of parties to be involved; If each order task in the order set is matched with a corresponding production factory, then a bipartite graph model is generated based on each order task and the production factory corresponding to each order task, so that each mapping relationship in the bipartite graph model meets the task completion condition. The bipartite graph model includes the order set, the set of participants, and edges, and the edges are the mapping relationships between the order tasks and the production factories corresponding to each order task. The product order allocation device is also used to achieve: Determine the order quote for each order task in the order set, and determine the production quote for each order task corresponding to each factory in the participating factories; If the order quote is greater than the production quote, then the order task and the production plant are matched, so that the mapping relationship between the order task and the production plant meets the quote conditions; The product order allocation device is also used to achieve: Based on each order task and the corresponding production factory, generate an initial bipartite graph model; If the number of orders matched by the production factory in the initial bipartite graph model is less than the preset number, then the production factory and the mapping relationship corresponding to the production factory are deleted, and the production factory corresponding to each order task in the deleted mapping relationship is rematched. If a matching production factory cannot be found, then each order task in the mapping relationship is deleted.

6. A product order allocation device, characterized in that, The product order allocation device includes: a memory, a processor, and a product order allocation program stored on the memory and executable on the processor, the product order allocation program being configured to implement the steps of the product order allocation method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a program that implements the product order allocation method, and the program that implements the product order allocation method is executed by a processor to implement the steps of the product order allocation method as described in any one of claims 1 to 4.

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