Data processing device, method and system and readable storage medium

Abstract

The invention provides a data processing device, method and system and a readable storage medium, and relates to the technical field of supply chains, and the data processing device comprises a data unit which is used for obtaining a data source which comprises supply data and demand data; constructing a supply network based on the data source; determining a demand side, a supplier, a supply-demand relationship and a constraint condition according to the supply network; the configuration unit is used for determining a supply-demand matching relationship according to the supply data, the demand data and the supply network; and the optimization unit is used for optimizing the supply-demand matching relationship through depth-first traversal and / or breadth-first traversal and / or a linear programming model, and determining the demand side and the supplier meeting the supply-demand relationship and the constraint conditions. According to the technical scheme, the supply-demand matching relation is optimized through the depth-first traversal and / or the breadth-first traversal and / or the linear programming model, more accurate supply-demand matching can be achieved, the phenomena of stock overstock and stockout are reduced, and the operation efficiency of the supply chain is improved.

Description

Technical Field

[0001] The present invention relates to the field of supply chain technology, and in particular to a data processing device, method, system and readable storage medium. Background Art

[0002] In the field of supply chain, it usually involves matching raw material supply with customer demand. The traditional method is to rely on manual experience and simple spreadsheets for analysis. This method can meet basic supply and demand matching needs, but it cannot achieve accurate supply and demand matching, and the supply chain operation efficiency is low. Summary of the invention

[0003] In order to solve or improve at least one of the above technical problems, an object of the present invention is to provide a data processing device.

[0004] Another object of the present invention is to provide a data processing method.

[0005] Another object of the present invention is to provide a data processing system.

[0006] Another object of the present invention is to provide a readable storage medium.

[0007] To achieve the above-mentioned purpose, the first aspect of the present invention provides a data processing device for optimizing the supply and demand matching relationship. The data processing device includes a data unit, a configuration unit and an optimization unit. The data unit is used to obtain a data source, which includes supply data and demand data; to build a supply network based on the data source; and to determine the demand side, the supply side, the supply and demand relationship and the constraints according to the supply network. The configuration unit is used to determine the supply and demand matching relationship based on the supply data, the demand data and the supply network. The optimization unit is used to optimize the supply and demand matching relationship through depth-first traversal and / or breadth-first traversal and / or a linear programming model, and to determine the demand side and the supply side that meet the supply and demand relationship and the constraints.

[0008] According to the technical solution of the data processing device provided by the present invention, the supply and demand matching relationship is optimized through depth-first traversal and / or breadth-first traversal and / or linear programming model, which can achieve more accurate supply and demand matching, thereby reducing inventory backlogs and out-of-stock phenomena and improving the operating efficiency of the supply chain.

[0009] In addition, the data processing device of the present invention adopts different data processing methods according to different supply and demand relationships (unlimited supply or limited supply), which is conducive to realizing rapid response to changes in supply and demand, timely adjustment of strategies, so that the supply chain can adapt to market fluctuations and emergencies more flexibly, and enhance the ability of enterprises to cope with risks. The data processing device of the present invention is different from the traditional practice of ignoring the impact of constraint rules (i.e., constraint conditions) on supply and demand matching. It fully incorporates multiple constraint conditions such as interaction deadlines, quality standards and cost requirements into the calculation, making supply and demand matching decisions more reasonable and feasible, which is conducive to improving the overall quality and efficiency of the supply chain. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to realize a cost-optimal supply and demand matching algorithm on a global network.

[0010] In addition, the above technical solution provided by the present invention may also have the following additional technical features: In some technical solutions, optionally, when the supply-demand relationship is an infinite supply, the optimization unit determines the supplier within the range allowed by the constraints by a depth-first traversal method.

[0011] In this technical solution, from the perspective of space occupation, depth-first traversal does not need to store all generated nodes, which is conducive to saving space occupation. With this data processing method, the optimization unit can optimize the supply and demand matching relationship when the supply and demand relationship is infinite.

[0012] Starting from the initial demand node, the path in the supply network is explored according to the depth-first principle (depth-first traversal method). For example, in the product recommendation scenario of an e-commerce platform, users have unlimited demand possibilities for a certain type of product. The data processing device quickly finds potential supply sources related to user needs through depth-first traversal, thereby achieving accurate matching recommendations.

[0013] In some technical solutions, optionally, when the supply-demand relationship is limited supply, the optimization unit solves the supply-demand matching through a linear programming model and determines the supplier within the range allowed by the constraints.

[0014] In this technical solution, when the supply and demand relationship is limited supply, a linear programming model is constructed using linear programming in operations research to solve the supply and demand matching. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to achieve a cost-optimal supply and demand matching algorithm on a global network.

[0015] The limited supply resources and demand conditions obtained from the supply network are converted into constraints and objective functions of linear programming. For example, in the manufacturing industry, the supply quantity of a certain component is limited, but there are multiple production links that have demand for it. By building a linear programming model, the optimal allocation plan is solved under the conditions of meeting the minimum demand of each production link and the total supply limit, so as to achieve the best match between supply and demand, maximize production efficiency and reduce costs.

[0016] In some technical solutions, optionally, when the supply and demand relationship is limited supply, the optimization unit determines the constraints and objective function of the linear programming based on the supply data, demand data and the supply network; constructs a linear programming model based on the constraints and the objective function; solves the supply and demand matching through the linear programming model, and determines the suppliers within the range allowed by the constraints.

[0017] In this technical solution, when the supply and demand relationship is limited supply, a linear programming model is constructed using linear programming in operations research to solve the supply and demand matching. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to achieve a cost-optimal supply and demand matching algorithm on a global network.

[0018] In some technical solutions, optionally, the constraint condition includes one or a combination of the following: delivery deadline, quality standard and cost requirement.

[0019] In this technical solution, the data processing device of the present invention, different from the traditional approach of ignoring the impact of constraint rules (i.e., constraint conditions) on supply and demand matching, fully incorporates multiple constraint conditions such as interaction deadlines, quality standards and cost requirements into the calculation, making supply and demand matching decisions more reasonable and feasible, which is conducive to improving the overall quality and efficiency of the supply chain.

[0020] In some technical solutions, optionally, the data unit is used to perform data conversion on the data source to obtain graph data, and determine the supply network based on the graph data.

[0021] In this technical solution, the data unit is used to determine a supply network based on a graph data structure according to supply data and demand data. The data processing device of the present invention is different from the traditional method of matching supply and demand by relying only on simple data statistics. It can build a supply network based on a graph data structure, which is conducive to fully and accurately capturing the complex supply and demand relationship in the supply chain, thereby optimizing the supply and demand matching relationship, reducing inventory backlogs and out-of-stock phenomena, and improving the operational efficiency of the supply chain.

[0022] In some technical solutions, optionally, the data processing device further includes a display unit. The display unit is used to display the optimized supply-demand matching relationship.

[0023] In this technical solution, by providing a display unit, the user can more directly grasp the demanders and suppliers that meet the requirements, that is, the demanders and suppliers that meet the supply-demand relationship and constraint conditions.

[0024] It should be noted that the supply and demand matching relationship here can be understood as the supply and demand matching result.

[0025] Optionally, in addition to optimizing the supply-demand matching relationship, the optimization unit also has the functions of matching gap calculation, supply-demand tracing, and supply-demand simulation. In addition, the display unit is also used to display the supply-demand tracing results and the simulation impact results.

[0026] The second aspect of the present invention provides a data processing method for optimizing the supply-demand matching relationship. The steps of the data processing method include: obtaining a data source, the data source includes supply data and demand data; building a supply network based on the data source; determining the demand side, the supply side, the supply-demand relationship and the constraints according to the supply network; determining the supply-demand matching relationship according to the supply data, the demand data and the supply network; optimizing the supply-demand matching relationship through depth-first traversal and / or breadth-first traversal and / or linear programming model, and determining the demand side and the supply side that meet the supply-demand relationship and the constraints.

[0027] According to the technical solution of the data processing method of the present invention, the supply and demand matching relationship is optimized through depth-first traversal and / or breadth-first traversal and / or linear programming model, which can achieve more accurate supply and demand matching, thereby reducing inventory backlogs and out-of-stock phenomena and improving the operational efficiency of the supply chain.

[0028] In addition, the data processing method of the present invention adopts different data processing methods according to different supply and demand relationships (unlimited supply or limited supply), which is conducive to achieving rapid response to changes in supply and demand, timely adjustment of strategies, so that the supply chain can more flexibly adapt to market fluctuations and emergencies, and enhance the ability of enterprises to cope with risks. The data processing method of the present invention is different from the traditional practice of ignoring the impact of constraint rules (i.e., constraint conditions) on supply and demand matching. It fully incorporates multiple constraint conditions such as interaction deadlines, quality standards, and cost requirements into the calculation, making supply and demand matching decisions more reasonable and feasible, which is conducive to improving the overall quality and efficiency of the supply chain. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to realize a cost-optimal supply and demand matching algorithm on a global network.

[0029] A third aspect of the present invention provides a data processing system, comprising a memory and a processor, wherein the memory stores programs or instructions that can be run on the processor, and when the processor executes the program or instruction, the steps of the data processing method in the above technical solution are implemented.

[0030] A fourth aspect of the present invention provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the data processing method in the above technical solution are implemented.

[0031] Additional aspects and advantages of the technical solutions of the present invention will become apparent in the following description or will be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A structural block diagram of a data processing device according to an embodiment of the present invention is shown; Figure 2 A structural block diagram of a data processing device according to another embodiment of the present invention is shown; Figure 3 A schematic diagram showing construction of a supply network based on data sources according to an embodiment of the present invention is shown; Figure 4 A flowchart of solving supply and demand matching by constructing a linear programming model according to one embodiment of the present invention is shown; Figure 5 A schematic diagram showing determining a result table based on a data source according to an embodiment of the present invention is shown; Figure 6 A flow chart showing a data processing method according to an embodiment of the present invention is shown; Figure 7 A structural block diagram of a data processing system according to an embodiment of the present invention is shown; Figure 8 A schematic diagram of a supply network according to an embodiment of the present invention is shown; Fig. 9 A schematic diagram showing a supply network according to another embodiment of the present invention; Fig.10 A flow chart showing a data processing method according to another embodiment of the present invention is shown; Fig.11 A flow chart showing a data processing method according to another embodiment of the present invention is shown; Fig.12 A flow chart showing a data processing method according to another embodiment of the present invention is shown; Fig.13 A flow chart of a data processing method according to another embodiment of the present invention is shown.

[0033] in, Figure 1 , Figure 2 and Figure 7 The corresponding relationship between the reference numerals and the component names is as follows: 100: data processing device; 110: data unit; 120: configuration unit; 130: optimization unit; 140: display unit; 300: data processing system; 310: memory; 320: processor. DETAILED DESCRIPTION

[0034] In order to more clearly understand the above-mentioned purposes, features and advantages of the embodiments of the present invention, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0035] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the embodiments of the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0036] Refer to the following Figures 1 to 13 The data processing device 100, data processing method, data processing system and readable storage medium provided according to some embodiments of the present invention are described.

[0037] In one embodiment of the present invention, the data processing device 100 is used to optimize the supply and demand matching relationship, so as to determine the demanders and suppliers that meet the requirements, that is, the demanders and suppliers that meet the supply and demand relationship and constraints in the case of complex multi-source supply and multi-purpose demand.

[0038] like Figure 1 and Figure 2 As shown, the data processing device 100 includes a data unit 110 , a configuration unit 120 and an optimization unit 130 .

[0039] The data unit 110 is used to obtain a data source, which includes supply data and demand data. The supply data includes but is not limited to warehouse inventory data and factory inventory data. By obtaining the supply data and demand data, it is convenient to determine the supply network according to the supply data and demand data in the subsequent steps.

[0040] The data unit 110 is also used to construct a supply network based on the data source (such as Figure 3 As shown), that is, a supply network based on a graph data structure is determined according to supply data and demand data. The data unit 110 is used to convert the data source to obtain graph data, and determine the supply network according to the graph data.

[0041] The supply network includes demander information, supplier information, supply-demand relationship information, and constraint information. The data unit 110 is also used to determine the demander, supplier, supply-demand relationship, and constraint conditions based on the supply network. Figure 8As shown in the figure, the supply network consists of multiple nodes and multiple edges. Two nodes are connected by at least one edge. The node represents the demand side or the supply side, and the edge represents the supply and demand relationship and constraints. Among them, store C is the demand side, and factory A, warehouse B, factory D and regional warehouse D are the suppliers. x1 and x2 indicate that factory A and warehouse B are on the same supply chain; y1 and y2 indicate that factory D and regional warehouse D are on the same supply chain. "Cost: 6", "Cost: 4" and "Cost: 3" represent constraints.

[0042] It should be noted that when the demander has a demand for a certain type of product and the supplier happens to be able to provide this type of product, it means that there is a supply-demand relationship between the demander and the supplier. Optionally, the constraints include but are not limited to interaction deadlines, quality standards, and cost requirements. The data processing device 100 of the present invention is different from the traditional practice of ignoring the impact of constraint rules (i.e., constraint conditions) on supply and demand matching. It fully incorporates multiple constraint conditions such as interaction deadlines, quality standards, and cost requirements into the calculation, making the supply and demand matching decision more reasonable and feasible, which is conducive to improving the overall quality and efficiency of the supply chain.

[0043] The configuration unit 120 is used to determine the supply-demand matching relationship based on the supply data, demand data, and supply network. The supply data includes suppliers and supply resources; the demand data includes demanders and demand resources. The demanders and suppliers that meet the supply-demand relationship are preliminarily determined based on the supply resources and demand resources. When the supply resources and demand resources are consistent, it means that there is a supply-demand relationship between the demander and the supplier.

[0044] The optimization unit 130 is used to optimize the supply-demand matching relationship through depth-first traversal and / or breadth-first traversal and / or linear programming model, and determine the demander and supplier that meet the supply-demand relationship and constraint conditions.

[0045] In the case of an infinite supply relationship, the optimization unit 130 is used to determine the suppliers within the permitted range of the constraints by depth-first traversal, thereby optimizing the supply-demand matching relationship. Fig. 9 As shown in the figure, starting from the initial demand node (store C), the paths in the supply network are explored according to the depth-first principle.

[0046] In the case of limited supply, the optimization unit 130 is used to solve the supply and demand matching through a linear programming model and determine the supplier within the permitted range of the constraint conditions, thereby optimizing the supply and demand matching relationship.

[0047] Optionally, the optimization unit 130 is further configured to determine suppliers within the permitted range of the constraints by means of breadth-first traversal, thereby optimizing the supply-demand matching relationship.

[0048] It should be noted that the main differences between depth-first traversal and breadth-first traversal are their search strategies, running speeds, and space occupied. ‌‌From the perspective of search strategy‌: Depth-first traversal is a strategy that first explores the branch of the tree in depth until the branch cannot go any deeper, then backtracks to the previous node and continues to explore the next branch. This strategy is suitable for situations where the deepest level of logic needs to be processed first; breadth-first traversal systematically expands and checks all nodes in the graph to find results, which is suitable for processing logic where nodes are expanded layer by layer. Breadth-first traversal starts from the root node and explores layer by layer until the target is found or the entire tree is explored. From the perspective of running speed: Depth-first traversal has backtracking operations during operation, which usually results in slower running speed; breadth-first traversal has no backtracking operations, so it runs faster. From the perspective of space occupied‌: Depth-first traversal does not need to store all generated nodes, so it occupies less space; breadth-first traversal needs to store all generated nodes, which causes it to occupy a larger space.

[0049] The present invention aims to provide a data processing device 100, which optimizes the supply and demand matching relationship through depth-first traversal and / or breadth-first traversal and / or linear programming model, so as to achieve more accurate supply and demand matching, thereby reducing inventory backlogs and out-of-stock phenomena and improving the operational efficiency of the supply chain.

[0050] In addition, the data processing device 100 of the present invention adopts different data processing methods according to different supply and demand relationships (unlimited supply or limited supply), which is conducive to achieving rapid response to changes in supply and demand, timely adjustment of strategies, so that the supply chain can more flexibly adapt to market fluctuations and emergencies, and enhance the ability of enterprises to cope with risks. The data processing device 100 of the present invention is different from the traditional practice of ignoring the impact of constraint rules (i.e., constraint conditions) on supply and demand matching. It fully incorporates multiple constraint conditions such as interaction deadlines, quality standards and cost requirements into the calculation, making supply and demand matching decisions more reasonable and feasible, which is conducive to improving the overall quality and efficiency of the supply chain. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to realize a cost-optimal supply and demand matching algorithm on a global network.

[0051] The data processing device 100 of the present invention is based on the supply network of the graph data structure and the reachability calculation function, which can comprehensively and accurately capture the supply and demand relationship, quickly respond to real-time changes, fully incorporate various constraint rules, accurately predict the supply and demand change trend, and achieve global network optimization based on cost optimization.

[0052] In some embodiments, optionally, when the supply-demand relationship is an infinite supply, the optimization unit 130 determines the suppliers within the permitted range of the constraints by depth-first traversal.

[0053] From the perspective of space occupation, depth-first traversal does not need to store all generated nodes, which is conducive to saving space occupation. With this data processing method, the optimization unit 130 can optimize the supply and demand matching relationship when the supply and demand relationship is infinite supply.

[0054] Starting from the initial demand node, the path in the supply network is explored according to the depth-first principle (depth-first traversal method). For example, in the commodity recommendation scenario of an e-commerce platform, users have unlimited demand possibilities for a certain type of product. The data processing device 100 quickly finds potential supply sources related to user demand through depth-first traversal, thereby achieving accurate matching recommendations.

[0055] In some embodiments, optionally, when the supply-demand relationship is limited supply, the optimization unit 130 solves the supply-demand matching through a linear programming model and determines the supplier within the permitted range of the constraints.

[0056] In the case of limited supply, a linear programming model is constructed using linear programming in operations research to solve the supply and demand matching. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to realize a cost-optimal supply and demand matching algorithm on a global network.

[0057] The limited supply resources and demand conditions obtained from the supply network are converted into constraints and objective functions of linear programming. For example, in the manufacturing industry, the supply quantity of a certain component is limited, but there are multiple production links that have demand for it. By building a linear programming model, the optimal allocation plan is solved under the conditions of meeting the minimum demand of each production link and the total supply limit, so as to achieve the best match between supply and demand, maximize production efficiency and reduce costs.

[0058] In some embodiments, optionally, when the supply and demand relationship is a limited supply, the optimization unit 130 determines the constraints and objective function of the linear programming based on the supply data, demand data and the supply network; constructs a linear programming model based on the constraints and objective function; solves the supply and demand matching through the linear programming model, and determines the supplier within the range allowed by the constraints.

[0059] In the case of limited supply, a linear programming model is constructed using linear programming in operations research to solve the supply and demand matching. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to realize a cost-optimal supply and demand matching algorithm on a global network.

[0060] In some embodiments, optionally, Figure 4 As shown in the figure, the specific steps of solving the supply and demand matching by constructing a linear programming model include: S302, obtaining network data.

[0061] The purpose of this step is to obtain data sources. Data sources include supply data and demand data. Supply data includes but is not limited to warehouse inventory data and factory inventory data.

[0062] S304, linear programming data processing.

[0063] Determine the constraints and objective function of linear programming based on supply data, demand data, and the supply network.

[0064] S306, calculating linear programming.

[0065] Construct a linear programming model based on constraints and objective functions.

[0066] S308, linear programming result conversion.

[0067] The result table is determined by the linear programming model.

[0068] S310, solving the supply and demand matching.

[0069] The purpose of this step is to determine the supply and demand match. Determine the supply and demand match based on the result table and determine the supplier within the constraints.

[0070] In some embodiments, optionally, Figure 5 As shown, the result table is determined based on the data source, specifically: determine the data source, which includes supply data and demand data. Supply data includes but is not limited to warehouse inventory data and factory inventory data. Starting from the starting demand node, explore the path in the supply network according to the depth-first principle (depth-first traversal method) and obtain network data. The network data here can be understood as data determined by the supply network, such as demanders, suppliers, supply and demand relationships, and constraints. A linear programming model is constructed by using linear programming in operations research to solve the supply and demand matching and determine the result table.

[0071] It should be noted that the linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to realize a cost-optimal supply and demand matching algorithm on the global network.

[0072] In some embodiments, optionally, the constraint condition includes one or a combination of the following: delivery deadline, quality standard, and cost requirement.

[0073] The data processing device 100 of the present invention is different from the traditional approach of ignoring the impact of constraint rules (i.e., constraint conditions) on supply and demand matching. It fully incorporates multiple constraint conditions such as interaction deadlines, quality standards and cost requirements into the calculation, making supply and demand matching decisions more reasonable and feasible, which is conducive to improving the overall quality and efficiency of the supply chain.

[0074] In some embodiments, optionally, the data unit 110 is used to perform data conversion on the data source to obtain graph data, and determine the supply network according to the graph data.

[0075] The data unit 110 is used to determine a supply network based on a graph data structure according to the supply data and the demand data. The data processing device 100 of the present invention is different from the traditional method of matching supply and demand by relying only on simple data statistics. It can build a supply network based on a graph data structure, which is conducive to fully and accurately capturing the complex supply and demand relationship in the supply chain, thereby optimizing the supply and demand matching relationship, reducing inventory backlogs and out-of-stock phenomena, and improving the operational efficiency of the supply chain.

[0076] In some embodiments, optionally, Figure 2 As shown, the data processing device 100 further includes a display unit 140. The display unit 140 is used to display the optimized supply-demand matching relationship.

[0077] By providing the display unit 140 , the user can more directly grasp the demanders and suppliers that meet the requirements, that is, the demanders and suppliers that meet the supply-demand relationship and constraint conditions.

[0078] It should be noted that the supply and demand matching relationship here can be understood as the supply and demand matching result.

[0079] Optionally, in addition to optimizing the supply-demand matching relationship, the optimization unit 130 also has the functions of matching gap calculation, supply-demand tracing, and supply-demand simulation. In addition, the display unit 140 is also used to display the supply-demand tracing results and the simulation impact results.

[0080] It should be emphasized that the data processing device 100 of the present invention relates to the field of supply chain technology, and adopts the technology of demand and supply matching and optimization. Through the supply network based on the graph data structure, the demand data and supply data are calculated based on the supply and demand relationship and constraint rules (constraint conditions) on the graph network (supply network) based on the cost-optimal reachability to achieve supply and demand matching and simulation. The data processing device 100 of the present invention is mainly used in the supply chain field supply and demand matching and optimization scenarios, and can be provided as an independent application component to multiple business fields of the supply chain for supply and demand matching and simulation optimization.

[0081] In one embodiment of the present invention, a data processing method is used to optimize the supply and demand matching relationship, so as to determine the demanders and suppliers that meet the requirements, that is, the demanders and suppliers that meet the supply and demand relationship and constraints, in the case of complex multi-source supply and multi-purpose demand.

[0082] like Figure 6 As shown, the steps of the data processing method include: S202, obtaining a data source, where the data source includes supply data and demand data.

[0083] Supply data includes but is not limited to warehouse inventory data and factory inventory data. By obtaining supply data and demand data, the supply network can be determined according to the supply data and demand data in the subsequent steps.

[0084] S204, building a supply network based on the data source.

[0085] A supply network based on a graph data structure is determined based on supply data and demand data. The data source is converted into graph data, and the supply network is determined based on the graph data.

[0086] S206, determining the demand side, the supply side, the supply-demand relationship and the constraints according to the supply network.

[0087] The supply network includes information about the demand side, the supply side, the supply-demand relationship, and the constraint conditions. The demand side, the supply side, the supply-demand relationship, and the constraint conditions are determined based on the supply network. The supply network consists of multiple nodes and multiple edges. Two nodes are connected by at least one edge. The node represents the demand side or the supply side, and the edge represents the supply-demand relationship and the constraint conditions.

[0088] It should be noted that when the demander has a demand for a certain type of product and the supplier happens to be able to provide this type of product, it means that there is a supply and demand relationship between the demander and the supplier. Optionally, the constraints include but are not limited to interaction deadlines, quality standards, and cost requirements. The data processing device of the present invention is different from the traditional approach of ignoring the impact of constraint rules (i.e., constraint conditions) on supply and demand matching. It fully incorporates multiple constraint conditions such as interaction deadlines, quality standards, and cost requirements into the calculation, making supply and demand matching decisions more reasonable and feasible, which is conducive to improving the overall quality and efficiency of the supply chain.

[0089] S208, determining a supply-demand matching relationship based on the supply data, demand data, and supply network.

[0090] Supply data includes suppliers and supply resources; demand data includes demanders and demand resources. Demanders and suppliers that meet the supply and demand relationship are preliminarily determined based on supply resources and demand resources. When supply resources and demand resources are consistent, it means that there is a supply and demand relationship between the demander and the supplier.

[0091] S210, optimizing the supply-demand matching relationship through depth-first traversal and / or breadth-first traversal and / or linear programming model, and determining the demander and supplier that satisfy the supply-demand relationship and constraint conditions.

[0092] When the supply and demand relationship is infinite, the optimization unit is used to determine the suppliers within the range allowed by the constraints through depth-first traversal, thereby optimizing the supply and demand matching relationship.

[0093] When the supply-demand relationship is limited supply, the optimization unit is used to solve the supply-demand matching through a linear programming model and determine the supplier within the range allowed by the constraints, thereby optimizing the supply-demand matching relationship.

[0094] Optionally, the optimization unit is further used to determine suppliers within the permitted range of the constraints by means of breadth-first traversal, thereby optimizing the supply-demand matching relationship.

[0095] It should be noted that the main differences between depth-first traversal and breadth-first traversal are their search strategies, running speeds, and space occupied. ‌‌From the perspective of search strategy‌: Depth-first traversal is a strategy that first explores the branch of the tree in depth until the branch cannot go any deeper, then backtracks to the previous node and continues to explore the next branch. This strategy is suitable for situations where the deepest level of logic needs to be processed first; breadth-first traversal systematically expands and checks all nodes in the graph to find results, which is suitable for processing logic where nodes are expanded layer by layer. Breadth-first traversal starts from the root node and explores layer by layer until the target is found or the entire tree is explored. From the perspective of running speed: Depth-first traversal has backtracking operations during operation, which usually results in slower running speed; breadth-first traversal has no backtracking operations, so it runs faster. From the perspective of space occupied‌: Depth-first traversal does not need to store all generated nodes, so it occupies less space; breadth-first traversal needs to store all generated nodes, which causes it to occupy a larger space.

[0096] The present invention aims to provide a data processing method, which optimizes the supply and demand matching relationship through depth-first traversal and / or breadth-first traversal and / or linear programming model, so as to achieve more accurate supply and demand matching, thereby reducing inventory backlogs and out-of-stock phenomena and improving the operational efficiency of the supply chain.

[0097] In addition, the data processing method of the present invention adopts different data processing methods according to different supply and demand relationships (unlimited supply or limited supply), which is conducive to achieving rapid response to changes in supply and demand, timely adjustment of strategies, so that the supply chain can more flexibly adapt to market fluctuations and emergencies, and enhance the ability of enterprises to cope with risks. The data processing method of the present invention is different from the traditional practice of ignoring the impact of constraint rules (i.e., constraint conditions) on supply and demand matching. It fully incorporates multiple constraint conditions such as interaction deadlines, quality standards, and cost requirements into the calculation, making supply and demand matching decisions more reasonable and feasible, which is conducive to improving the overall quality and efficiency of the supply chain. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to realize a cost-optimal supply and demand matching algorithm on a global network.

[0098] The data processing method of the present invention is based on the supply network of the graph data structure and the reachability calculation function. It can comprehensively and accurately capture the supply and demand relationship, quickly respond to real-time changes, fully incorporate various constraint rules, accurately predict the supply and demand change trend, and achieve global network optimization based on cost optimization.

[0099] In some embodiments, optionally, Fig.10 As shown, S210 (optimizing the supply-demand matching relationship through depth-first traversal and / or breadth-first traversal and / or linear programming model to determine the demand side and the supply side that meet the supply-demand relationship and constraint conditions) specifically includes the following steps: S212, when the supply-demand relationship is infinite supply, determine the suppliers within the scope allowed by the constraints through depth-first traversal.

[0100] From the perspective of space usage, depth-first traversal does not need to store all generated nodes, which helps save space. This data processing method can optimize the supply and demand matching relationship when the supply and demand relationship is infinite.

[0101] Starting from the initial demand node, the path in the supply network is explored according to the depth-first principle (depth-first traversal method). For example, in the product recommendation scenario of an e-commerce platform, users have unlimited demand possibilities for a certain type of product. The data processing method uses depth-first traversal to quickly find potential supply sources related to user needs, thereby achieving accurate matching recommendations.

[0102] S214, when the supply-demand relationship is limited supply, solve the supply-demand matching through a linear programming model, and determine the supplier within the scope allowed by the constraints.

[0103] In the case of limited supply, a linear programming model is constructed using linear programming in operations research to solve the supply and demand matching. The linear programming model can combine the advantages of depth-first traversal and breadth-first traversal to realize a cost-optimal supply and demand matching algorithm on a global network.

[0104] In some embodiments, optionally, Fig.11 As shown, S214 (when the supply and demand relationship is limited supply, solving the supply and demand matching through the linear programming model and determining the supplier within the scope allowed by the constraint conditions) specifically includes the following steps: S2142, when the supply-demand relationship is limited supply, determine the constraints and objective function of the linear programming based on the supply data, demand data and supply network.

[0105] S2144, construct a linear programming model based on constraints and objective functions.

[0106] S2146, solve the supply and demand matching through a linear programming model and determine the suppliers within the scope allowed by the constraints.

[0107] In some embodiments, optionally, Fig.12 As shown, S204 (building a supply network based on data sources) includes the following specific steps: S2042, convert the data source to obtain graph data, and determine the supply network based on the graph data.

[0108] Determine a supply network based on a graph data structure based on supply data and demand data. The data processing method of the present invention is different from the traditional method of matching supply and demand by relying only on simple data statistics. It can build a supply network based on a graph data structure, which is conducive to fully and accurately capturing the complex supply and demand relationship in the supply chain, thereby optimizing the supply and demand matching relationship, reducing inventory backlogs and out-of-stock phenomena, and improving the operational efficiency of the supply chain.

[0109] In some embodiments, optionally, Fig.13 As shown, after S210 (optimizing the supply-demand matching relationship through depth-first traversal and / or breadth-first traversal and / or linear programming model to determine the demand side and the supply side that meet the supply-demand relationship and constraint conditions), the specific steps of the data processing method further include: S216, displaying the optimized supply-demand matching relationship. This method enables the user to more directly grasp the demanders and suppliers that meet the requirements, that is, the demanders and suppliers that meet the supply-demand relationship and constraint conditions.

[0110] It should be noted that the supply and demand matching relationship here can be understood as the supply and demand matching result.

[0111] Optionally, while displaying the optimized supply-demand matching relationship, supply-demand tracing results and simulation impact results are also displayed.

[0112] It should be emphasized that the data processing method of the present invention relates to the field of supply chain technology, and adopts the technology of demand and supply matching and optimization. Through the supply network based on the graph data structure, the demand data and supply data are calculated based on the supply and demand relationship and constraint rules (constraint conditions) on the graph network (supply network) based on the cost-optimal reachability to achieve supply and demand matching and simulation. The data processing method of the present invention is mainly used in the supply and demand matching and optimization scenarios in the field of supply chain, and can be provided as an independent application component to multiple business fields of the supply chain for supply and demand matching and simulation optimization.

[0113] In one embodiment according to the present invention, Figure 7As shown, the data processing system 300 includes a memory 310 and a processor 320, wherein the memory 310 stores programs or instructions that can be run on the processor 320, and the processor 320 implements the steps of the data processing method in the above-mentioned embodiment when executing the program or instruction, so it has the technical effect of the above-mentioned embodiment, which will not be repeated here.

[0114] In one embodiment of the present invention, a readable storage medium stores a program or instruction. When the program or instruction is executed by a processor, the steps of the data processing method in the above embodiment are implemented, so it has the technical effect of the above embodiment and will not be repeated here.

[0115] In the present invention, the terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance; the term "plurality" refers to two or more, unless otherwise clearly defined. The terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0116] In the description of the present invention, it is necessary to understand that the directions or positional relationships indicated by terms such as “upper”, “lower”, “left”, “right”, “front” and “back” are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation, and therefore, cannot be understood as a limitation on the present invention.

[0117] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0118] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data processing device, characterized in that: Used to optimize the matching relationship between supply and demand, including: A data unit is used to obtain a data source, wherein the data source includes supply data and demand data; construct a supply network based on the data source; and determine a demander, a supplier, a supply-demand relationship, and constraints based on the supply network; A configuration unit, configured to determine a supply-demand matching relationship according to the supply data, the demand data and the supply network; An optimization unit is used to optimize the supply-demand matching relationship through depth-first traversal and / or breadth-first traversal and / or a linear programming model, and determine the demander and the supplier that satisfy the supply-demand relationship and the constraint conditions.

2. The data processing device according to claim 1, characterized in that: In the case where the supply-demand relationship is infinite supply, the optimization unit determines the supplier within the permitted range of the constraint conditions by means of the depth-first traversal method.

3. The data processing device according to claim 1, characterized in that: In the case where the supply-demand relationship is limited supply, the optimization unit solves the supply-demand matching through a linear programming model and determines the supplier within the permitted range of the constraint conditions.

4. The data processing device according to claim 3, characterized in that: In the case where the supply-demand relationship is limited supply, the optimization unit determines the restriction conditions and objective function of linear programming according to the supply data, the demand data and the supply network; The linear programming model is constructed based on the constraint conditions and the objective function; the supply and demand matching is solved by the linear programming model, and the supplier is determined within the permitted range of the constraint conditions.

5. The data processing device according to any one of claims 1 to 4, characterized in that: The constraints include one or a combination of the following: Delivery deadlines, quality standards and cost requirements.

6. The data processing device according to any one of claims 1 to 4, characterized in that: The data unit is used to perform data conversion on the data source to obtain graph data, and determine the supply network according to the graph data.

7. The data processing device according to any one of claims 1 to 4, characterized in that: Also includes: A display unit is used to display the optimized supply-demand matching relationship.

8. A data processing method, characterized in that: Used to optimize the matching relationship between supply and demand, including: Acquire a data source, wherein the data source includes supply data and demand data; Building a supply network based on the data source; Determine the demand side, the supply side, the supply-demand relationship and the constraints according to the supply network; Determining a supply-demand matching relationship according to the supply data, the demand data, and the supply network; The supply-demand matching relationship is optimized through depth-first traversal and / or breadth-first traversal and / or a linear programming model to determine the demander and the supplier that satisfy the supply-demand relationship and the constraint conditions.

9. A data processing system, characterized in that: include: A memory and a processor, wherein the memory stores a program or instruction that can be run on the processor, and the processor implements the steps of the data processing method as claimed in claim 8 when executing the program or the instruction.

10. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or the instruction is executed by a processor, the steps of the data processing method according to claim 8 are implemented.

Images