Data processing method and device, equipment and storage medium

[0036] In order to make those skilled in the art better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only The embodiments are part of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the scope of protection of the present application.

[0037] It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It is to be understood that data so used may be interchanged under appropriate circumstances such that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

[0038] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of the financial data or user data involved are all in compliance with relevant laws and regulations, and do not violate public order and good customs.

[0039] First, some technical terms involved in this application are explained:

[0040] Factory set: represented by F, any factory in the factory set is represented by f, among which,

[0041] Line body set: represented by L, any line body (ie production line) in the line body set is represented by l, among which,

[0042] Device set: represented by E, any device in the device set is represented by e, where,

[0043] The set of existing lines in factory f: use L f express;

[0044] A collection of existing equipment in factory f: with E f express;

[0045] Factory f can add a collection of line bodies: use L' f express;

[0046] A collection of new equipment that can be added to factory f: use E' f express;

[0047] Device type set: represented by H, any device type in the device type set is represented by h, among which,

[0048] Warehouse set: represented by W, any warehouse in the warehouse set is represented by w, among which,

[0049] Product set: represented by P, any product in the product set is represented by p, where,

[0050] Material collection: represented by S, any material in the material collection is represented by s, among which,

[0051] Price ladder for material s: use G s means that, given the premise of s,

[0052] Currently, capacity planning is usually done manually. Specifically, when planning the production capacity manually, the constraints that need to be comprehensively considered include at least: product model, materials involved in the product model (which can be understood as raw materials), execution price ladder corresponding to purchased materials, factories distributed in various places, distributed in various places resources such as warehouses, lines and equipment involved in the production process. Therefore, manual capacity planning is inefficient and imprecise, resulting in wasted resources.

[0053] Based on the above problems, the present application provides a data processing method, device, equipment and storage medium. Under the premise of considering the above constraints from a global perspective, the existing wires and equipment in the factory are reasonably used, and the equipment is allowed to be transferred between factories. And to deal with the shortage of line and equipment resources, it is allowed to increase the line and equipment, and the circulation of resources is converted into cost; the decision is based on the number of products that should be produced by each factory, the number of lines and equipment used for actual production, etc. Variables, the optimization objective function is established with comprehensive costs such as manufacturing cost, transportation cost, material procurement cost, resource circulation cost, etc., MIP is used to establish an optimization model, and the model is solved to obtain the value of the decision variable corresponding to the minimum value of the objective function. Therefore, the efficiency and accuracy of capacity planning can be greatly improved, and resources can be used rationally, thereby reducing supply chain costs and improving resource integration rates.

[0054] Hereinafter, the application scenarios of the solutions provided by the present application are firstly described with examples.

[0055] figure 1 This is a schematic diagram of an application scenario provided by an embodiment of the present application. like figure 1 As shown, in this application scenario, the server 102 receives the resource configuration constraint parameters from the client 101, the server 102 obtains the target resource configuration data according to the resource configuration constraint parameters, and sends the target resource configuration data to the client 101, and the client 101 Displays target resource configuration data. The specific implementation process of acquiring target resource configuration data by the server 102 according to the resource configuration constraint parameters may refer to the solutions of the following embodiments.

[0056] It should be noted, figure 1 It is only a schematic diagram of an application scenario provided by the embodiment of the present application, and the embodiment of the present application is not correct. figure 1 equipment included in the figure 1 The positional relationship between the devices is limited. For example, in figure 1 In the application scenario shown, a data storage device may also be included, and the data storage device may be an external memory relative to the client 101 or the server 102 , or may be an internal memory integrated in the client 101 or the server 102 .

[0057] Next, the data processing method is introduced through specific embodiments.

[0058] figure 2 This is a flowchart of a data processing method provided by an embodiment of the present application. The method of the embodiment of the present application may be applied to an electronic device, and the electronic device may be a server, for the server to obtain target resource configuration data according to the resource configuration constraint parameters from the client. like figure 2 As shown, the method of the embodiment of the present application includes:

[0059] S201. Obtain resource configuration constraint parameters.

[0060] In this embodiment of the present application, the resource configuration constraint parameter may be input by the user to the electronic device executing the method embodiment, or sent by other devices to the electronic device executing the method embodiment. Exemplarily, the resource configuration constraint parameter may include at least one of the following constraint parameters: the upper limit of the working time of the line body 1, which is set by T l L Represents; the beat of the line body l producing the product p, with Representation, that is, the time-consuming time to produce unit product p; the production capacity of equipment e, with Representation; the relationship between the line body and the product, with Indicates that the value includes 0 or 1, where 1 indicates that line l can produce product p, 0 indicates that line l cannot produce product p; the relationship between equipment type and product, use Indicates that the value includes 0 or 1, where 1 indicates that the equipment of type h needs to be used in the production process of product p, and 0 indicates that the equipment of type h does not need to be used in the production process of product p; the association between equipment types and specific equipment, use Indicates that the value includes 0 or 1, where 1 indicates that the device e is a device of type h, and 0 indicates that the device e is a device that does not belong to type h; the demand for product p by warehouse w, use D w,p Representation; the unit production cost of the product p produced by the factory f, with means; factory f increases the production cost of wire l, using means; factory f increases the production cost of equipment e, using Denote; the cost of transferring equipment e from one factory f to another (denoted by f'), with Represents; the unit cost of product p transported from factory f to warehouse w, denoted by C f,w,p Represents; the purchase price of factory f at the gth price ladder of material s, using Indicates; the lower purchase limit of material s corresponding to the g-th price ladder, using Indicates; the purchase limit of material s corresponding to the g-th price ladder, with Represents; the quantity of material s required by product p, with express.

[0061] S202: Determine resource allocation constraints and objective functions included in the MIP model according to resource allocation constraint parameters and corresponding decision variables under different production conditions.

[0062] Among them, the objective function is used to reflect the relationship between the total production cost and at least one single cost.

[0063] In this step, exemplarily, the decision variables corresponding to different production situations may include at least one of the following decision variables: the actual production situation of the wire body l in the factory f, use x f,l Indicates that the value includes 0 or 1. Among them, when the line body l in the factory f is actually produced, it is 1, otherwise it is 0; the actual production situation of the equipment e in the factory f, use y f,e Indicates that the value includes 0 or 1, among which, when the equipment e of factory f is actually produced, it is 1, otherwise it is 0; whether the factory f needs to add a new line body l, use Indicates that the value includes 0 or 1. Among them, when the factory f needs to add a new line l, it is 1, otherwise it is 0; whether the factory f needs to add a new device e, use Indicates that the value includes 0 or 1. Among them, when factory f needs to add new equipment e, it is 1, otherwise it is 0; Represents, the value includes 0 or 1, where it is 1 when the equipment e is transferred from the factory f to the factory f', otherwise it is 0; the transportation volume of the product p from the factory f to the warehouse w, with β f,w,p Represents; the production volume of product p in the line body l of factory f, denoted by γ f,l,p Represents; the purchase quantity of the material s of factory f at the gth price ladder, with η f,s,g Represents; the purchasing status of material s in the g-th price ladder of factory f, use Indicates that the value includes 0 or 1, where it is 1 when factory f purchases material s at the g-th price step, and 0 otherwise. understandably, x f,l , y f,e , are state variables.

[0064] In this step, after the resource configuration constraint parameters are obtained, the resource configuration constraints and objective functions included in the MIP model can be determined according to the resource configuration constraint parameters and corresponding decision variables under different production conditions. The resource allocation constraints contained in the MIP model include, for example, at least one of line constraints, equipment constraints, and material constraints; the objective function is used to reflect the relationship between the total production cost and at least one single cost. For details on how to determine the resource configuration constraints and objective functions included in the MIP model according to resource configuration constraint parameters and corresponding decision variables in different production situations, reference may be made to subsequent embodiments, which will not be repeated here.

[0065] S203. Use a preset optimization solver to analyze and process the MIP model, and obtain target resource configuration data corresponding to the minimum value of the objective function.

[0066] In this step, after the MIP model is obtained, a preset optimization solver can be used to analyze and process the MIP model to obtain target resource configuration data corresponding to the minimum value of the objective function.

[0067] Optionally, the preset optimization solver may include at least one of Gurobi, CPLEX, and SCIP.

[0068] Exemplarily, after the MIP model is obtained, a Gurobi solver may be used to analyze and process the MIP model, and when the objective function obtains the minimum value, the corresponding target resource configuration data is obtained. Specifically, the target resource configuration data is the value of the decision variable, and based on the decision variable in the example of step S202, at least one of the following information can be obtained from the value of the decision variable: According to γ f,l,p , the output of product p in line l of factory f can be obtained; according to β f,w,p , the transportation quantity of product p from factory f to warehouse w can be obtained (it can also be understood as the distribution quantity); according to x f,l , the actual production situation of line body l in factory f can be obtained; according to y f,e , the actual production situation of equipment e in factory f can be obtained; It can be obtained whether the factory f needs to add a new line; according to It can be obtained whether factory f needs new equipment; according to It can be obtained whether the equipment has been transferred between factories, including the specific factory from which it was transferred; according to η f,s,g , you can obtain the specific purchase quantity of materials in order to meet production; You can obtain the specific price of the material according to which price ladder is purchased.

[0069] For details on how to use the preset optimization solver to analyze and process the MIP model to obtain target resource configuration data corresponding to the minimum value of the objective function, reference may be made to subsequent embodiments, which will not be repeated here.

[0070] S204. Output target resource configuration data.

[0071] In this step, after obtaining the target resource configuration data, the target resource configuration data may be output. Exemplarily, for example, the target resource configuration data is sent to the client, and after receiving the target resource configuration data, the client can display the target resource configuration data to the user.

[0072] In the data processing method provided by the embodiment of the present application, by obtaining resource configuration constraint parameters, the resource configuration constraints and objective functions included in the MIP model are determined according to the resource configuration constraint parameters and corresponding decision variables under different production conditions; The device analyzes and processes the MIP model, obtains the target resource configuration data corresponding to the minimum value of the objective function, and outputs the target resource configuration data. Since the embodiment of the present application considers the resource configuration constraint parameters and the corresponding decision variables under different production conditions from a global perspective, and then determines the MIP model, and obtains the value of the decision variable corresponding to the minimum value of the objective function based on the MIP model, that is, the objective is obtained. Resource configuration data. Therefore, the efficiency and accuracy of capacity planning can be greatly improved, and resources can be used rationally, thereby reducing supply chain costs and improving resource integration rates.

[0073] image 3 This is a flowchart of a data processing method provided by another embodiment of the present application. On the basis of the foregoing embodiments, the embodiments of the present application further describe how to perform data processing. like image 3 As shown, the method of the embodiment of the present application may include:

[0074] S301. Obtain resource configuration constraint parameters.

[0075] A detailed description of this step can be found in figure 2 The relevant description of S201 in the illustrated embodiment will not be repeated here.

[0076] In the examples of this application, figure 2 The step S202 can be further refined into the following three steps S302 to S304:

[0077] S302. Determine that the resource configuration constraints contained in the MIP model include at least one of the following: when the resource configuration constraints are capacity constraints, determine the resource configuration according to the capacity constraints, the state variables in the decision variables, and the production volume of the product Line constraints and/or equipment constraints in the constraints; when the resource configuration constraints are warehouse demand constraints, the resource allocation is determined according to the warehouse demand constraints and the product production and product transportation in the decision variables. The warehouse demand constraints in the constraints; when the resource allocation constraints are material procurement constraints, determine the resource allocation constraints based on the material procurement constraints and the state variables in the decision variables, product production, and material procurement. Material constraints.

[0078] Exemplarily, based on the resource configuration constraint parameters of the example of step S201 and the decision variables of the example of step S202, the line body constraints include at least one of the following constraints: (1) Specify the actual production state of the line body 1 in each factory; and less than or equal to 1, the corresponding expression is: That is, any line body l can only belong to one factory f; (2) the relationship between the new line body l and the actual production state of the line body l in factory f, the corresponding expression is: That is, the new line body l in factory f is used for actual production; (3) the working time of the line body is restricted, and the corresponding expression is That is, the total time consumed by the production of products on the line body l cannot exceed the working time provided by the line body.

[0079] The equipment constraints include at least one of the following constraints: (1) The number of times of equipment allocation is limited, and the corresponding expression is: That is, device e can only be transferred at most once between factories; and the expression: That is to say, there is no allocation situation for equipment e in the same factory, and the number of allocations is set to 0; (2) The relationship between the new equipment, the allocation status and the actual factory status of the equipment, the corresponding expression is: That is, if the equipment e is transferred from the factory f to other factories f', the equipment e will not belong to the factory f; if the equipment e is not transferred from the factory f to other factories f', the equipment e still belongs to the factory f; and the expression : That is, if equipment e is transferred from other factory f' to factory f, then the equipment e will belong to factory f; if equipment e is not transferred from other factory f' to factory f, then the equipment e does not belong to factory f; and the expression That is, the device e in the factory f is directly added, and the newly added state is equal to the belonging state of the device e; and the expression: That is, there is no attribution relationship between the equipment e and the factory f that are constrained to belong to other factories f'; (3) the product output and equipment capacity constraints, the corresponding expression is: That is, for any type of equipment e in any factory f, the output of a certain product cannot exceed the capacity provided by this type of equipment.

[0080] The warehouse demand constraints include at least one of the following constraints: (1) The production volume of factory products is equal to the transportation volume, and the corresponding expression is: That is, the production volume of any product of any factory f must be equal to the sum of the transportation volume of the product from the factory to each factory; (2) the demand quantity constraint of the factory, the corresponding expression is: That is, for any warehouse w, the demand for any product is equal to the sum of the transportation volume of the product from each factory to the warehouse.

[0081] The material constraints include at least one of the following constraints: (1) The relationship between purchase quantity and purchase quantity status, and the corresponding expression is: That is, for any factory f, the purchase quantity of any material belongs to a certain price range; (2) The purchase quantity state constraint, the corresponding expression is: That is, for any factory f, the procurement status of any material is unique and belongs to one price ladder at most; (3) The relationship between the procurement quantity and the actual demand of the product, the corresponding expression is: That is, for any factory f, the purchase amount of any material should be equal to the sum of the actual demand for this material for all products in the factory.

[0082] S303. Determine at least one single cost according to resource configuration constraint parameters and decision variables.

[0083] In this step, after the resource configuration constraint parameters and decision variables are obtained, at least one single cost can be determined according to the resource configuration constraint parameters and decision variables. Exemplarily, the single cost includes transportation cost, production cost, material procurement cost, cost of adding wire body, cost of adding equipment, and cost of transferring equipment. Among them, (1) transportation cost (transport_cost), the corresponding expression is: That is, the transportation cost includes the sum of the transportation costs of all products shipped from all factories to each warehouse; (2) the production cost (product_cost), the corresponding expression is: That is, the production cost includes the sum of all product costs produced by each line of all factories; (3) material purchase cost (purchase_cost), the corresponding expression is: That is, the material procurement cost is equal to the sum of the costs of each material in all factories; (4) the line body add cost (line_add_cost), the corresponding expression is: That is, the cost of line addition is equal to the sum of the costs of all newly added lines; (5) the cost of equipment addition (equipment_add_cost), the corresponding expression is: That is, the cost of equipment addition is equal to the sum of the costs of all newly added equipment; (6) equipment transfer cost (equipment_transfer_cost), the corresponding expression is: That is, the sum of the transfer costs of all equipment.

[0084] S304. Determine the objective function included in the MIP model according to at least one single item cost and weight coefficient.

[0085] In this step, after the added cost of the equipment is obtained, the objective function can be determined according to at least one single cost and a weight coefficient, wherein the weight coefficient can be preset according to the actual situation.

[0086] Further, optionally, determining the objective function according to the at least one single item cost and the weight coefficient includes: determining the sum of the products of the at least one single item cost and the weight coefficient as the objective function.

[0087] Exemplarily, the objective function is the following formula:

[0088] total_cost_obj=a1*transport_cost+a2*product_cost+a3*purchase_cost+a4*line_add_cost+a5*equipment_add_cost+a6*equipment_transfer_cost

[0089] Among them, total_cost_obj represents the total production cost; transport_cost represents the transportation cost; product_cost represents the production cost; purchase_cost represents the material procurement cost; line_add_cost represents the line increase cost; equipment_add_cost represents the equipment increase cost; equipment_transfer_cost represents the equipment transfer cost; The value range is, for example, 0 to 10000. For example, the value of a1 is 0, which means that the MIP model does not consider the transportation cost.

[0090] In the examples of this application, figure 2 The step S203 can be further refined into the following three steps S305 to S307:

[0091] S305, according to the MIP model, obtain a file that meets the requirements of the preset optimization solver.

[0092] Exemplarily, the file satisfying the requirements of the preset optimization solver is, for example, a file in LP format. MIP models can be exported to files in LP format to obtain files that meet the requirements of preset optimization solvers.

[0093] S306. Input the file to a preset optimization solver for analysis and processing, and obtain corresponding processing results.

[0094] In this step, after obtaining a file that meets the requirements of the preset optimization solver, the file can be input to the preset optimization solver for analysis and processing, that is, the preset optimization solver is used to solve the problem to obtain corresponding processing results.

[0095] S307. According to the processing result, obtain target resource configuration data corresponding to the minimum value of the target function by traversing the decision variables.

[0096] In this step, after obtaining the processing result output by the preset optimization solver, the target resource configuration data corresponding to the minimum value of the objective function can be obtained by sequentially traversing the decision variables.

[0097] S308, output target resource configuration data.

[0098] A detailed description of this step can be found in figure 2 The relevant description of S204 in the illustrated embodiment is not repeated here.

[0099]In the data processing method provided by the embodiment of the present application, by acquiring resource configuration constraint parameters, when the resource configuration constraint parameter is a production capacity constraint parameter, the resource allocation constraint condition is determined according to the production capacity constraint parameter, the state variable in the decision variable, and the production volume of the product The line constraints and/or equipment constraints in ; when the resource allocation constraints are warehouse demand constraints, the resource allocation constraints are determined according to the warehouse demand constraints and the product production and product transportation in the decision variables. The warehouse demand constraints in Condition; according to the resource configuration constraint parameters and decision variables, determine at least one single cost, and determine the objective function according to at least one single cost and weight coefficient; according to the MIP model, obtain a file that meets the requirements of the preset optimization solver, and input the file into the preset The optimization solver is set to perform analysis and processing to obtain the corresponding processing results; according to the processing results, the target resource configuration data corresponding to the minimum value of the objective function is obtained by traversing the decision variables; and the target resource configuration data is output. Since the embodiment of the present application considers the resource configuration constraint parameters and the corresponding decision variables under different production conditions from a global perspective, and then determines the MIP model, and obtains the value of the decision variable corresponding to the minimum value of the objective function based on the MIP model, that is, the objective is obtained. Resource configuration data. Therefore, the efficiency and accuracy of capacity planning can be greatly improved, and resources can be used rationally, thereby reducing supply chain costs and improving resource integration rates.

[0100] Figure 4 A schematic structural diagram of a data processing apparatus provided in an embodiment of the present application is used for a server to obtain target resource configuration data according to a resource configuration constraint parameter from a client. like Figure 4 As shown, the data processing apparatus 400 in the embodiment of the present application includes: an acquisition module 401 , a determination module 402 , a processing module 403 , and an output module 404 . in:

[0101] The obtaining module 401 is configured to obtain resource configuration constraint parameters.

[0102] The determining module 402 is used to determine the resource configuration constraints and the objective function contained in the MIP model according to the resource configuration constraint parameters and the corresponding decision variables under different production conditions, and the objective function is used to reflect the total production cost and at least one single item cost correlation relationship .

[0103] The processing module 403 is configured to analyze and process the MIP model by using a preset optimization solver to obtain target resource configuration data corresponding to the minimum value of the objective function.

[0104] The output module 404 is used for outputting target resource configuration data.

[0105] In some embodiments, the determining module 402 may be specifically configured to: determine the resource configuration constraint condition including at least one of the following: when the resource configuration constraint parameter is the capacity constraint parameter, according to the capacity constraint parameter and the state variable in the decision variable and The production volume of products, determine the line body constraints and/or equipment constraints in the resource allocation constraints; when the resource allocation constraints are warehouse demand constraints, the production volume of products in the warehouse demand constraints and decision variables and The transportation volume of the product determines the warehouse demand constraint in the resource allocation constraint; when the resource allocation constraint is the material procurement constraint parameter, according to the material procurement constraint parameter and the state variable in the decision variable, the production volume of the product and the material procurement volume , to determine the material constraints in the resource allocation constraints.

[0106] In some embodiments, the determining module 402 may be specifically configured to: determine at least one single cost according to resource configuration constraint parameters and decision variables; and determine an objective function according to at least one single cost and a weight coefficient.

[0107] In some embodiments, when the determining module 402 is used to determine the objective function according to the at least one single item cost and the weight coefficient, it may be specifically configured to: determine the sum of the products of the at least one single item cost and the weight coefficient as the objective function.

[0108] In some embodiments, the processing module 403 may be specifically configured to: obtain a file that meets the requirements of the preset optimization solver according to the MIP model; input the file to the preset optimization solver for analysis and processing, and obtain corresponding processing results; As a result, the target resource configuration data corresponding to the minimum value of the target function is obtained by traversing the decision variables.

[0109] In some embodiments, the preset optimization solver includes at least one of Gurobi, CPLEX, SCIP.

[0110] The apparatus in this embodiment can be used to implement the technical solutions of any of the above-described method embodiments, and the implementation principles and technical effects thereof are similar, and details are not described herein again.

[0111] Figure 5 This is a schematic structural diagram of an electronic device according to an embodiment of the present application. Illustratively, the electronic device may be provided as a server or computer. refer to Figure 5 , electronic device 500 includes a processing component 501, which further includes one or more processors, and a memory resource, represented by memory 502, for storing instructions executable by processing component 501, such as an application program. An application program stored in memory 502 may include one or more modules, each corresponding to a set of instructions. Additionally, the processing component 601 is configured to execute instructions to perform any of the above-described method embodiments.

[0112] The electronic device 500 may also include a power supply assembly 503 configured to perform power management of the electronic device 500, a wired or wireless network interface 504 configured to connect the electronic device 500 to a network, and an input output (I/O) interface 505 . Electronic device 500 may operate based on an operating system stored in memory 502, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0113] The present application also provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the processor executes the computer-executable instructions, the solution of the above data processing method is implemented.

[0114] The present application also provides a computer program product, including a computer program, which implements the solution of the above data processing method when the computer program is executed by a processor.

[0115] The above-mentioned computer-readable storage medium, the above-mentioned readable storage medium can be realized by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.

[0116] An exemplary readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium may reside in Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium may also exist in the data processing apparatus as discrete components.

[0117] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments may be completed by program instructions related to hardware. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps including the above method embodiments are executed; and the foregoing storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0118] The above are only the preferred embodiments of the present application. It should be pointed out that for those skilled in the art, without departing from the principles of the present application, several improvements and modifications can also be made. It should be regarded as the protection scope of this application.