A data processing method, apparatus, electronic device, and storage medium

Abstract

The present application provides a data processing method, apparatus, electronic device, and storage medium. The method includes: obtaining order information of an order to be processed, device information of a device for processing the order, process information, and raw material information; inputting the order information, device information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan; wherein the scheduling plan is used to adjust various production decisions during the execution of the order to be processed. The data processing method of the present application analyzes the order information of the order to be processed, the device information of the device for processing the order, the process information, and the raw material information through the decision optimization model to obtain a scheduling plan, so that subsequent production decisions during the execution of the order to be processed can be adjusted according to the scheduling plan, thereby optimizing the production process, improving the product processing efficiency, and improving the execution efficiency of the order.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a data processing method, apparatus, electronic device, and storage medium. Background Art

[0002] With the improvement of intelligent production equipment and production levels, the degree of digitization and intelligence in the supply chain of traditional manufacturing enterprises has gradually increased. Currently, manufacturing enterprises in China have gradually upgraded from automated factories to digital information factories. While further improving the construction of digital supply chains, they have completed the development path of the integration of industrialization and informatization. Due to the large variety of equipment, numerous orders, and high mobility of operators in the workshops of manufacturing enterprises, the production department needs to formulate detailed production plans for coordinated operations in aspects such as machine selection, personnel allocation, and material supply. However, the existing production scheduling methods in the workshops of manufacturing enterprises have problems such as high labor costs and time costs, and low product processing efficiency. Therefore, in order to reduce labor costs and reduce processing and manufacturing time costs, and ensure the timely delivery of orders, it is necessary to optimize the existing production scheduling methods. Summary of the Invention

[0003] The present application provides a data processing method, apparatus, electronic device, and storage medium, which can adjust various production decisions during the execution of orders to be processed, thereby optimizing the production process, improving product processing efficiency, and enhancing the execution efficiency of orders.

[0004] The first aspect of the present application provides a data processing method, including:

[0005] Obtaining order information of an order to be processed, equipment information of equipment for processing the order, process information, and raw material information;

[0006] Inputting the order information, equipment information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan;

[0007] Wherein, the scheduling plan is used to adjust various production decisions during the execution of the order to be processed.

[0008] Optionally, inputting the order information, equipment information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan includes:

[0009] Inputting the order information, equipment information, process information, and raw material information into a first type of decision optimization model, where the first type of decision optimization model aims to minimize the sum of the number of orders with delays and the number of order replacements;

[0010] Obtaining the scheduling plan according to the output information of the first type of decision optimization model; or

[0011] Input the order information, equipment information, process information, and raw material information into the second - type decision - making optimization model, where the goal of the second - type decision - making optimization model is to minimize the sum of the number of delayed products and the number of order replacements;

[0012] Obtain the scheduling plan according to the output information of the second - type decision - making optimization model.

[0013] Optionally, input the order information, equipment information, process information, and raw material information into a pre - constructed decision - making optimization model to obtain a scheduling plan, including:

[0014] Input the order information, equipment information, process information, and raw material information into the first - type decision - making optimization model to obtain a first scheduling plan;

[0015] Input the order information, equipment information, process information, and raw material information into the second - type decision - making optimization model to obtain a second scheduling plan;

[0016] Determine the scheduling plan from the first scheduling plan and the second scheduling plan according to the user's selection operation.

[0017] The first - type decision - making optimization model is constructed through the following steps:

[0018] Obtain a first weight value set for the number of delayed orders and a second weight value set for the number of order replacements;

[0019] Construct the first - type decision - making optimization model according to the first weight value, the second weight value, the status of delayed orders to be processed, and the status of equipment order replacements.

[0020] Optionally, constructing the first - type decision - making optimization model according to the first weight value, the second weight value, the status of delayed orders to be processed, and the status of equipment order replacements includes:

[0021] Construct a first - type objective function according to the first weight value, the second weight value, the status of delayed orders to be processed, and the status of equipment order replacements;

[0022] Set constraint conditions for the first - type objective function, where the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, constraint conditions based on the status of delayed orders to be processed, and constraint conditions based on the variable value range.

[0023] Optionally, the second - type decision - making optimization model is constructed through the following steps:

[0024] Obtain a third weight value set for the quantity of the deferred products and a fourth weight value set for the number of replacement orders;

[0025] Construct the second - type decision optimization model according to the third weight value, the fourth weight value, the status of product deferral, and the status of equipment replacement orders.

[0026] Optionally, constructing the second - type decision optimization model according to the third weight value, the fourth weight value, the status of product deferral, and the status of equipment replacement orders includes:

[0027] Construct a second - type objective function according to the third weight value, the fourth weight value, the status of product deferral, and the status of equipment replacement orders;

[0028] Set constraint conditions for the second - type objective function, and the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, and constraint conditions based on the variable value range.

[0029] The second aspect of this application provides a data processing device, including:

[0030] A first obtaining module, configured to obtain order information of an order to be processed, equipment information of the equipment for processing the order, process information, and raw material information;

[0031] A second obtaining module, configured to input the order information, equipment information, process information, and raw material information into a pre - constructed decision optimization model to obtain a scheduling plan;

[0032] Wherein, the scheduling plan is used to adjust various production decisions during the execution of the order to be processed.

[0033] Optionally, the second obtaining module includes:

[0034] A first input sub - module, configured to input the order information, equipment information, process information, and raw material information into a first - type decision optimization model, and the first - type decision optimization model aims to minimize the sum value of the number of deferred orders and the number of replacement orders;

[0035] A first obtaining sub - module, configured to obtain the scheduling plan according to the output information of the first - type decision optimization model; or

[0036] A second input sub - module, configured to input the order information, equipment information, process information, and raw material information into a second - type decision optimization model, and the second - type decision optimization model aims to minimize the sum value of the quantity of deferred products and the number of replacement orders;

[0037] A second obtaining sub-module, configured to obtain the scheduling plan according to the output information of the second type of decision optimization model.

[0038] Optionally, the second obtaining module includes:

[0039] A third input sub-module, configured to input the order information, device information, process information, and raw material information into the first type of decision optimization model to obtain a first scheduling plan;

[0040] A fourth input sub-module, configured to input the order information, device information, process information, and raw material information into the second type of decision optimization model to obtain a second scheduling plan;

[0041] A first determination sub-module, configured to determine the scheduling plan from the first scheduling plan and the second scheduling plan according to the user's selection operation.

[0042] Optionally, the apparatus further includes a first construction module, configured to construct the first type of decision optimization model; the first construction module includes:

[0043] A third obtaining sub-module, configured to obtain a first weight value set for the number of delayed orders and a second weight value set for the number of order replacements;

[0044] A first construction sub-module, configured to construct the first type of decision optimization model according to the first weight value, the second weight value, the status of the pending order delay, and the status of the device order replacement.

[0045] Optionally, the first construction sub-module includes:

[0046] A second construction sub-module, configured to construct a first type of objective function according to the first weight value, the second weight value, the status of the pending order delay, and the status of the device order replacement;

[0047] A first setting sub-module, configured to set constraint conditions for the first type of objective function, where the constraint conditions at least include: constraint conditions based on device information, constraint conditions based on process information, constraint conditions based on time, constraint conditions based on the status of the pending order delay, and constraint conditions based on the variable value range.

[0048] Optionally, the apparatus further includes a second construction module, configured to construct the second type of decision optimization model; the second construction module includes:

[0049] A fourth obtaining sub-module, configured to obtain a third weight value set for the number of delayed products and a fourth weight value set for the number of order replacements;

[0050] The third construction sub-module is configured to construct the second type of decision optimization model according to the third weight value, the fourth weight value, the status of product delay, and the status of equipment replacement orders.

[0051] Optionally, the third construction sub-module includes:

[0052] The fourth construction sub-module is configured to construct a second type of objective function according to the third weight value, the fourth weight value, the status of product delay, and the status of equipment replacement orders.

[0053] The second setting sub-module is configured to set constraint conditions for the second type of objective function, and the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, and constraint conditions based on variable value ranges. Through the data processing method of the present application, first, order information of an order to be processed, equipment information of equipment for processing the order, process information, and raw material information are obtained; then the order information, equipment information, process information, and raw material information are input into a pre-constructed decision optimization model to obtain a scheduling plan; wherein the scheduling plan is used to adjust various production decisions during the execution of the order to be processed. The data processing method of the present application analyzes the order information of the order to be processed, the equipment information of the equipment for processing the order, the process information, and the raw material information through the decision optimization model to obtain a scheduling plan, so that subsequent production decisions during the execution of the order to be processed can be adjusted according to the scheduling plan, thereby optimizing the production process, improving the product processing efficiency, and improving the execution efficiency of the order. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0055] Figure 1 It is a schematic diagram of a production flow chart in the warp knitting field shown in an embodiment of the present application;

[0056] Figure 2 It is a flowchart of a data processing method shown in an embodiment of the present application;

[0057] Figure 3 It is a diagram of the output result of a model shown in an embodiment of the present application;

[0058] Figure 4 It is a schematic diagram of a set shown in an embodiment of the present application;

[0059] Figure 5 It is a parameter schematic diagram shown in an embodiment of the present application;

[0060] Figure 6 It is a schematic diagram of a decision variable shown in an embodiment of the present application;

[0061] Figure 7 It is a structural block diagram of a data processing device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] The technical solution of this application can be applied to any process production field and can effectively improve the product processing efficiency of the equipment. Since the technical solution of this application is particularly effective in improving the product processing efficiency when applied to the warp knitting field, the data processing method of this application is described in detail below using the production process in the warp knitting field as an example, and the implementation principle when applied to other process production fields is the same as that in the warp knitting field, and this application will not repeat it.

[0064] Figure 1 1 is a schematic diagram of a production flow chart in the warp knitting field shown in one embodiment of the present application. Figure 1 The equipment includes warping machines (with M 1* Indicates that Figure 1 M 11- M 1K ) and warp knitting machines (with M 2* Indicates that Figure 1 M 21- M 2N ), warping machines are used to produce warp heads (warp heads refer to the warp beams with gauze obtained after the gauze is rolled on the warp beam of the textile machine), and warp knitting machines are used to produce knitted fabrics based on the warp heads. Warping machines can be of various types, and the number of each type of warping machines can be multiple. One type of warping machine is used to process one type of raw material and obtain one type of warp heads. One order usually requires multiple different types of raw materials. Warp knitting machines can be of various types, and the number of each type of warp knitting machines can be multiple. Each order can be produced using multiple different types of warp knitting machines. Figure 1 In the figure, the shaded devices represent the devices currently in operation, and the solid line represents the execution path of order 1 (through warping machines M11, M21, M32, M43, M54, M65, M76, M80, M90, M100, M110, M120, M130, M140, M150, M160, M170, M180, M290, M210, M210 13 、M 14Obtain a warp beam, and then through the warp knitting machine M 21 、M 22 Process the warp beam to obtain the product of order 1), and the dotted line represents the execution path of order 2 (through the warping machine M 13 、M 14 、M 1k Obtain a warp beam, and then through the warp knitting machine M 25 、M 2N Process the warp beam to obtain the product of order 2).

[0065] Figure 2 is a flowchart of a data processing method shown in an embodiment of the present application. Refer to Figure 2 , the data processing method of the present application may include:

[0066] Step S21: Obtain the order information of the order to be processed, the equipment information of the equipment for processing the order, the process information, and the raw material information.

[0067] In this embodiment, J represents an order, and the set of orders to be scheduled is J = {1, 2,..., |J|}, with j as the index. The order information includes the order number, order type (regular order or urgent order), the delivery deadline DT j 、the actual completion time c j 、the product quantity of order j etc. This embodiment does not limit the specific content of the order information.

[0068] The equipment information includes: the set of types of available warping machines S = {1, 2,..., |S|} (indexed by s), the number Z of each type of warping machine s (numbered in sequence: 1, 2,...Z s , indexed by z s , the set of types of available warp knitting machines R = {1, 2,..., |R|} (indexed by r), the number B of each type of warp knitting machine r (numbered in sequence 1, 2,...B r , indexed by b r , the set of warping machine equipment the set of warp knitting machine equipment the unit processing time p of each type of raw material filament s 、the unit processing time of order j on the r-th type of warp knitting machine and the current status information of the equipment (including the order being processed, the processing progress, the unprocessed orders, etc.). This embodiment does not limit the specific content of the equipment information.

[0069] The process information includes: warping in the first process and warp knitting in the second process. Among them, the warping in the first process is executed by a warping machine, and the warp knitting in the second process is executed by a warp knitting machine.

[0070] The raw material information includes: the set of raw material silk types that still need to be processed for order j and the quantity of each type of raw material silk Raw material silk inventory information, etc. This embodiment does not limit the specific content of the raw material information.

[0071] In this embodiment, based on the above information, it can be deduced that the total duration of processing order j for the k-th task on the z-th machine of the s-th type of warping machine s where p is the unit processing time, that is, the time required to process one product, s is a decision variable, that is, the quantity of products processed for the k-th task on the z-th machine of the s-th type of raw material silk corresponding to order j, that is, is the processing quantity, that is, the total number of products processed. Similarly, the total duration of processing order j for the k-th task on the b-th machine of the r-th type of warp knitting machine s where is the unit processing time, r is a decision variable, that is, the quantity of products processed for the k-th task on the b-th machine of the r-th type of warp knitting machine for order j, that is, where is the unit processing time, is a decision variable, that is, the quantity of products processed for the k-th task on the b-th machine of the r-th type of warp knitting machine for order j, that is, r is the processing quantity, that is, the total number of products processed. is the processing quantity, that is, the total number of products processed.

[0072] In this embodiment, the order information of the order to be processed, the equipment information of the equipment used to process the order, the process information, and the raw material information can be obtained in any way, for example, obtained through the information input by the user, or obtained from the pre-stored information. This embodiment does not make specific limitations on this.

[0073] Step S22: Input the order information, equipment information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan.

[0074] Among them, the scheduling plan is used to adjust various production decisions during the execution of the order to be processed.

[0075] In this embodiment, the scheduling plan refers to the scheduling information of each device. The scheduling information is the information based on which each device actually executes an order and includes production decisions that need to be adjusted. For example, Table 1 below shows the production decisions of Device 1 adjusted according to the scheduling plan on a certain day, including: order information to be executed, order execution time, raw materials required for the order, equipment maintenance time, etc. This embodiment does not specifically limit the type of production decisions. It should be specifically noted here that Table 1 is only for illustration and does not represent the actual production situation.

[0076]

[0077] Table 1

[0078] Exemplarily, after inputting the order set J = {1, 2,..., |J|} to be scheduled, the type set S = {1, 2,..., |S|} of warping machines, the number of tasks K, the type set of raw silk The number Z of each type of warping machine s (numbered in sequence: 1, 2,... Z s ), the type set R = {1, 2,..., |R|} of warp knitting machines, the number B of each type of warp knitting machine r (numbered in sequence 1, 2,... B r ) etc. into the decision optimization model, the decision optimization model can output the scheduling information of each device (such as Table 1). After each device adjusts its production decision according to the scheduling information and produces according to the adjusted production decision, the product processing efficiency can be improved, and thus the order execution efficiency can be improved. The production decision can be specifically selected according to the actual needs of the user, and this embodiment does not limit this.

[0079] Figure 3 is an intention diagram of the output result of a model shown in an embodiment of the present application. In Figure 3Among them, the darker shaded part represents the warping process, and the lighter shaded part represents the warp knitting process. The vertical coordinate \(j_{s,z,k}^*\) represents the \(k\)-th task of order \(j\) on the \(z\)-th machine of the \(s\)-th type of warping machine, and the vertical coordinate \(j_{r,b,k}^*\) represents the \(k\)-th task of order \(j\) on the \(b\)-th machine of the \(r\)-th type of warp knitting machine. The horizontal coordinate represents the processing time from 0 to 5000 (unit: minute). \(j0_{s2,z2,k0}\) in the first row represents that order \(j0\) is processed as the \(k0\)-th task on the \(z2\)-th warping machine of model \(s2\), and \(j0_{r2,b1,k0}\) in the first row represents that order \(j0\) is processed as the \(k0\)-th task on the \(b1\)-th warp knitting machine of model \(r2\). Through the data processing method of this embodiment, first, obtain the order information of the order to be processed, the equipment information of the equipment used to process the order, the process information, and the raw material information; then input the order information, equipment information, process information, and raw material information into the pre-constructed decision optimization model to obtain a scheduling plan; wherein, the scheduling plan is used to adjust various production decisions during the execution of the order to be processed. The data processing method of this application analyzes the order information of the order to be processed, the equipment information of the equipment used to process the order, the process information, and the raw material information through the decision optimization model to obtain a scheduling plan, so that subsequent production decisions during the execution of the order to be processed can be adjusted according to the scheduling plan, thereby optimizing the production process, improving the product processing efficiency, and improving the order execution efficiency.

[0080] Combined with the above embodiments, in one implementation manner, the present application further provides a method for obtaining a scheduling plan according to a pre-constructed decision optimization model. Specifically, the above step S22 may include:

[0081] Input the order information, equipment information, process information, and raw material information into the first type of decision optimization model, and the first type of decision optimization model aims to minimize the sum value of the number of orders with delays and the number of order replacements;

[0082] Obtain the scheduling plan according to the output information of the first type of decision optimization model; or

[0083] Input the order information, equipment information, process information, and raw material information into the second type of decision optimization model, and the second type of decision optimization model aims to minimize the sum value of the number of products with delays and the number of order replacements;

[0084] Obtain the scheduling plan according to the output information of the second type of decision optimization model.

[0085] In this embodiment, the types of decision optimization models may include: the first type of decision optimization model and the second type of decision optimization model. Among them, the first type of decision optimization model aims to calculate the minimum sum value of the number of postponed orders and the number of order replacements, and the second type of decision optimization model aims to calculate the minimum sum value of the number of postponed products and the number of order replacements. Order types are divided into regular orders and urgent orders.

[0086] Exemplarily, regular orders include Order 1 and Order 2, and urgent orders include Order 3 and Order 4. If device A switches from processing Order 1 to processing Order 2, then the number of order replacements for device A is 1. If device A switches from processing Order 1 to processing Order 2 and then to Order 3, then the number of order replacements for device 1 is 2.

[0087] In specific implementation, order information, device information, process information, and raw material information can be input into the first type of decision optimization model. The first type of decision optimization model determines the minimum sum value of the number of postponed orders and the number of order replacements based on the correlation relationships among the various indicators involved in the order information, device information, and process information. Since at this minimum sum value, the scheduling information output by the first type of decision optimization model is most conducive to reducing the number of postponed orders and the number of order replacements, therefore, when the number of postponed orders and the number of order replacements are at the minimum sum value, the scheduling information output by the first type of decision optimization model can be used as the scheduling plan.

[0088] Similarly, order information, device information, process information, and raw material information can also be input into the second type of decision optimization model. The second type of decision optimization model determines the minimum sum value of the number of postponed products and the number of order replacements based on the correlation relationships among the various indicators involved in the order information, device information, and process information. Since at this minimum sum value, the scheduling information output by the second type of decision optimization model is most conducive to reducing the number of postponed products and the number of order replacements, therefore, when the number of postponed products and the number of order replacements are at the minimum sum value, the scheduling information output by the second type of decision optimization model can be used as the scheduling plan.

[0089] The user can arbitrarily select the first type of decision optimization model or the second type of decision optimization model according to actual production requirements, and this embodiment does not limit this.

[0090] In this embodiment, the scheduling plan can be obtained through the scheduling information output by the first type of decision optimization model or the second type of decision optimization model, so that when the device produces according to the production decision adjusted by the scheduling plan, the product processing efficiency can be effectively improved, and thus the order execution efficiency can be improved.

[0091] Combined with the above embodiments, in one implementation, the present application also provides another method for obtaining a scheduling plan according to a pre-constructed decision optimization model. Specifically, the above step S22 may include:

[0092] Input the order information, equipment information, process information, and raw material information into the first type of decision optimization model to obtain a first scheduling plan;

[0093] Input the order information, equipment information, process information, and raw material information into the second type of decision optimization model to obtain a second scheduling plan;

[0094] Determine the scheduling plan from the first scheduling plan and the second scheduling plan according to the user's selection operation.

[0095] In this embodiment, the first type of decision optimization model and the second type of decision optimization model can be used simultaneously, and then the scheduling plan is determined according to the user's selection operation. Among them, a first scheduling plan can be obtained by processing the order information, equipment information, process information, and raw material information through the first type of decision optimization model, and a second scheduling plan can be obtained by processing the order information, equipment information, process information, and raw material information through the second type of decision optimization model.

[0096] Since the calculation objectives of the first type of decision optimization model and the second type of decision optimization model are different, the first scheduling plan and the second scheduling plan may be different. At this time, the user can select one type from the first scheduling plan and the second scheduling plan as the scheduling plan according to actual needs. For example, select the first scheduling plan as the scheduling plan, or select the second scheduling plan as the scheduling plan.

[0097] This embodiment can simultaneously use the first type of decision optimization model and the second type of decision optimization model to obtain two sets of different ideal index values, and determine the final scheduling plan according to the user's selection operation, which can improve the flexibility of order execution while improving the order execution efficiency.

[0098] Combined with the above embodiments, in one implementation, the present application also provides a method for constructing the first type of decision optimization model. Specifically, the method may include:

[0099] Obtain a first weight value set for the number of overdue orders and a second weight value set for the number of order replacements;

[0100] Construct the first type of decision optimization model according to the first weight value, the second weight value, the overdue status of orders to be processed, and the status of equipment order replacements.

[0101] In one embodiment, constructing the first type of decision optimization model according to the first weight value, the second weight value, the status of the orders to be processed with delays, and the status of the equipment replacement orders may include:

[0102] Construct a first type of objective function according to the first weight value, the second weight value, the status of the orders to be processed with delays, and the status of the equipment replacement orders;

[0103] Set constraint conditions for the first type of objective function, where the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, constraint conditions based on the status of the orders to be processed with delays, and constraint conditions based on the variable value range. In this embodiment, the process of constructing the first type of decision optimization model may include the following steps:

[0104] Step1: Obtain the first weight value set for the number of orders with delays and the second weight value set for the number of replacement orders according to the user's decision preference, and then construct a first type of objective function according to the first weight value, the second weight value, the status of the orders to be processed with delays, and the status of the equipment replacement orders.

[0105] Step2: Construct equipment-related model constraints according to the equipment information, where the constraint conditions at least include: one device can only process one order at the same time, the number of devices used for each order cannot exceed the maximum number of the device, etc.

[0106] Step3: Construct process-related model constraints according to the process information, where the constraint conditions at least include: each order can only be processed on the device that can complete the order, the warp knitting process of each order can only start after the warping process, etc.

[0107] Step4: Construct time-related model constraints, where the constraint conditions at least include: the start time of the warp knitting process of each order needs to be after the completion time of the warping process, the start time of the next process of each device needs to be after the completion time of the previous process, etc.

[0108] Step5: Construct constraint conditions for determining whether an order is delayed.

[0109] Step6: Construct constraint conditions for the value range of decision variables.

[0110] Among them, the execution order of Step2 - Step6 can be adjusted arbitrarily according to actual needs, and this embodiment does not limit this.

[0111] Before elaborating on the process of constructing the decision optimization model of the present application in detail, the production process in the warp knitting field will be briefly introduced below.

[0112] In actual production, different types of raw material filaments use different types of warping machines. Therefore, warping machines can be divided into multiple subsets according to their types, and each subset processes one type of raw material filament. That is, there can be multiple warping machines for processing each type of raw material filament. In addition, an order generally requires multiple different types of raw material filaments.

[0113] A warp knitting machine can process multiple types of products, but it cannot produce all types of products. That is, for an order, the warp knitting process can be carried out by any warp knitting machine within a certain subset of warp knitting machines.

[0114] The unit processing time of the same order on different machines is not necessarily the same, and the unit processing time of different orders on the same machine is not necessarily different either.

[0115] Generally, warping machines and warp knitting machines do not actively change orders during product processing. However, in the case of an urgent order (order types are divided into regular orders and urgent orders), the warp knitting machine may be directly switched to process the urgent order when changing the raw material filament.

[0116] The beams produced by the warping machines are centrally stored in the inventory. If there is no urgent order, the beams are processed according to their respective required quantities. If there is an urgent order, it involves the situation where the urgent order preempts the beams generated by previous orders. That is, in the case of an urgent order, it involves allocating the beam inventory to the urgent order. For example: When scheduling production on a certain day, an urgent order appears and the beam inventory is known. Suppose this urgent order requires a total of a Class A beams, and the current available inventory is b (previously produced for previous orders). If the urgent order uses up the inventory, then the Class A beams of previous orders (including Order 1 and Order 2) need to be warped again. Let the additional warping quantity of Order 1 be b1 and that of Order 2 be b2, then b1 + b2 = b. b1, b2, and b are all parameters in the model and are allocated according to some artificial rules. For example: The beam quantity for 3 days can be fixed, and the remaining beams are all available for urgent orders. When using the beam inventory b, it can be carried out according to the size of the delivery date. The beam inventory of the order with the longest delivery date is preferentially used, and then the beam inventory of the order with the second-longest delivery date is used.

[0117] When optimizing production decisions using the decision optimization model, there are three goals to be concerned about: First, the number of delayed orders is minimized; second, the quantity of delayed products is minimized; third, the number of order changes is minimized as much as possible. The first type of decision optimization model mainly focuses on the first and third goals.

[0118] The objective function used when constructing the first type of decision optimization model based on the first and third goals in this embodiment is as follows:

[0119] minω1∑ j∈J Uj +ω2C (1)

[0120] In formula (1), the first weight value ω1 is the weight value pre-assigned for objective one (the least number of delayed products), and the second weight value ω2 is the weight value pre-assigned for objective three (minimize the number of order replacements). C is the sum of the introduced auxiliary variables obtained by summing If represents that the order starts processing at position k, and k represents that the corresponding order is the k-th to be processed on this machine. For example, if a warp knitting machine processes tasks A, B, and C successively, then the position of task A on this warp knitting machine is 1, the position of task B on this warp knitting machine is 2, and the position of task C on this warp knitting machine is 3. U j indicates whether order j is delayed.

[0121] In formula (1), U j corresponds to objective one and is used to reflect the situation of order delays to be processed. C corresponds to objective three and is used to reflect the situation of order replacements on the equipment. C is the sum of the number of orders processed on all equipment. Objective three is to make C as small as possible so that the same order is processed on one piece of equipment as much as possible. For example, for tasks A and B and two warp knitting machines, if the two warp knitting machines first complete task A together and then complete task B together, then C = 4, while if one warp knitting machine always does task A and the other warp knitting machine always does task B, then C = 2. Therefore, the latter is more conducive to achieving objective three.

[0122] The specific meanings of the letters involved in this objective function can be specifically referred to Figures 4 - 6 the shown table Figure 4 which is a set schematic diagram shown in an embodiment of the present application Figure 5 which is a parameter schematic diagram shown in an embodiment of the present application Figure 6 which is a decision variable schematic diagram shown in an embodiment of the present application

[0123] Figure 4 lists the sets required for the decision optimization model of the present application, including: J = {1, 2,..., |J|} (the set of orders to be scheduled, indexed by j), S = {1, 2,..., |S|} (the set of available types of warping machines, indexed by s), (the set of types of warping machines corresponding to the raw material yarn types that order j still needs to be processed), Z s = {1, 2,..., |Z s |} (the number of each available type of warping machine, indexed by z sindex), R = {1, 2, ..., |R|} (a set of types of available warp knitting machines, indexed by r), (the set of warp knitting machine types available for order j), B r ={1, 2, ..., |B r |}(The number of warp knitting machines of each type available, expressed as b r index), K = {1, 2, ..., |K|} (the kth task processed on a certain machine).

[0124] Figure 5 The parameters required for the decision optimization model of this application are listed in , including: DT j (delivery deadline for order j), (the amount of the sth type of raw silk that needs to be processed for order j), ps (the processing time of a single bobbin of the sth type of raw silk), (the number of products that need to be processed corresponding to order j), (unit processing time of order j on the warp knitting machine of type r), (A very large number).

[0125] Figure 6 The decision variables required for the decision optimization model of this application are listed in, including: (Whether the s-th type of raw silk in the first process of order j is selected as the k-th task of the zs-th machine (in the warping machine corresponding to the s-th type of raw silk) for processing, (Order j second process, whether to choose as the first b of the r type warp knitting machine r The kth task of machine No. is processed. (The sth type of raw silk corresponding to order j is in the zth s the number of tasks processed on the kth machine), (Order j is the bth order of the warp knitting machine of category r r The number of tasks processed on the kth machine), c j (the actual completion time of order j), U j (When delivering, is order j delayed, U j ∈{0,1}, if c j >U j , then U j =1, otherwise, U j =0).

[0126] In combination with the above embodiments, in one implementation, the present application further provides a method for constructing a second type of decision optimization model. Specifically, the method may include:

[0127] Obtain a third weight value set for the quantity of the deferred products and a fourth weight value set for the number of replacement orders;

[0128] Construct the second - type decision optimization model according to the third weight value, the fourth weight value, the status of product deferral, and the status of equipment replacement orders.

[0129] In one implementation manner, constructing the second - type decision optimization model according to the third weight value, the fourth weight value, the status of product deferral, and the status of equipment replacement orders may include:

[0130] Construct a second - type objective function according to the third weight value, the fourth weight value, the status of product deferral, and the status of equipment replacement orders;

[0131] Set constraint conditions for the second - type objective function, where the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, and constraint conditions based on the variable value range.

[0132] In this embodiment, the process of constructing the second - type decision optimization model may include the following steps:

[0133] Step1’: Obtain a third weight value set for the quantity of the deferred products and a fourth weight value set for the number of replacement orders according to the user's decision preference, and then construct a second - type objective function according to the third weight value, the fourth weight value, the status of the pending order deferral, and the status of equipment replacement orders.

[0134] Step2’: Construct equipment - related model constraints according to equipment information, where the constraint conditions at least include: one device can only process one order at the same time, the number of devices used for each order cannot exceed the maximum number of this device, etc.

[0135] Step3’: Construct process - related model constraints according to process information, where the constraint conditions at least include: each order can only be processed on the devices that can complete this order, the warp knitting process of each order can only start after the warping process, etc.

[0136] Step4’: Construct time - related model constraints, where the constraint conditions at least include: the start time of the warp knitting process of each order needs to be after the completion time of the warping process, the start time of the next process of each device needs to be after the completion time of the previous process, etc.

[0137] Step5’: Construct constraint conditions for the value range of decision variables.

[0138] Among them, the execution order of Step2’-Step5’ can be adjusted arbitrarily according to actual requirements, and this embodiment does not limit this. In this embodiment, the third weight value is the weight value set for Objective 2 (the least number of delayed products), and the fourth weight value is the weight value set for Objective 3 (the least number of order replacements).

[0139] Based on Objective 2 and Objective 3, a second type of decision optimization model is constructed. The specific objective function used is as follows:

[0140]

[0141] In formula (2), + represents: if the value in [ ] is negative, then + = 0; if the value in [ ] is positive, then + is equal to the value itself. represents the delay time caused by processing the assigned order j task on each warp knitting machine. represents the delay time multiplied by (rate), that is, the quantity of delayed products. represents only calculating the quantity of delayed products on the machines assigned tasks ( when).

[0142] In formula (2), corresponds to Objective 2 and is used to reflect the situation of product delay; C corresponds to Objective 3 and is used to reflect the situation of equipment order replacement. The specific meanings of each letter involved in formula (2) can be referred to the Figures 4 - 6 shown table.

[0143] In this application, the above formula (1) and formula (2) also need to satisfy the following constraint conditions (3)-(23):

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165] Constraint (3) is used to introduce auxiliary variables

[0166] Constraint (4) is a sum for indicating the number of times the warp knitting machine changes orders.

[0167] Constraint (5) means that for each order, the number of warping machines used for each type of raw material filament in the first process is at least 1 and does not exceed the maximum value Z s .

[0168] Constraint (6) represents that for each order, the number of warp knitting machines used in the second process is at least 1 and does not exceed the maximum value B r .

[0169] Constraints (7)-(8) mean avoiding arranging multiple orders simultaneously at the same position on the same machine of the same type of warping machine.

[0170] Constraints (9)-(10) mean that on the same machine, if the processing position with a lower code is idle, the subsequent processing positions are not used. Among them, {1} refers to the first position, K is all positions, that is, the positions can take 1, 2, 3,..., K\{1} means removing the first position, which are positions 2, 3,....

[0171] Constraint (11) represents the time of the s-type raw material yarn for the k-th task processing order j on the z-th s machine and the time of the k-th task processing order j on the b-th machine of the r-th warp knitting machine r machine and the time of the k-th task processing order j on the b-th

[0172] Constraint (12) means that the total amount of the s-type raw material yarn that needs to be warped for order j should be greater than or equal to

[0173] Constraint (13) means that the total amount of warp knitting for order j should be greater than or equal to

[0174] Constraints (14 - 16) represent the completion time of each order on the warping machine. t1 is the completion time of the first process, that is, the completion time of the warping process.

[0175] Constraints (17 - 19) represent the completion time of order j on the warp knitting machine. t2 is the completion time of the second process, that is, the completion time of the warp knitting process.

[0176] Constraint (20) represents the total completion time of order j.

[0177] Constraint (21) indicates whether order j is postponed.

[0178] Constraints (22 - 23) are used to limit the value range of variables.

[0179] Among them, the first type of decision optimization model needs to satisfy constraints (3) - (21), and the second type of decision optimization model needs to satisfy constraints (3) - (20). Specifically, the constraints based on equipment information correspond to formulas (5) - (6), the constraints based on process information correspond to formulas (5) - (6) and formula (17). The constraints based on time correspond to formulas (14) - (20). The constraints based on the postponed situation of the orders to be processed correspond to formula (21). The constraints based on the value range of variables correspond to formulas (22) and (23).

[0180] The data processing method of this application analyzes the order information of the orders to be processed, the equipment information of the equipment used to process the orders, the process information, and the raw material information through the decision optimization model, and obtains the optimized parameters, so that the subsequent production parameters during the execution of the orders to be processed can be adjusted according to the optimized parameters, thereby optimizing the production process, improving the product processing efficiency, and further improving the execution efficiency of the orders.

[0181] When implementing the data processing method of the present application, the warp knitting enterprise can input some actual conditions into the data processing device (the data processing device is the execution subject of the present application). For example, the actual conditions of the warp knitting enterprise are shown in Table 2 below. Under the actual conditions shown in Table 2, the warp knitting enterprise can still have many production scheduling plans (i.e., order execution plans) to choose from. How to choose the best production scheduling plan requires the help of the decision optimization model provided by the present application. The decision optimization model can optimize production decisions. There are multiple objective functions (the objective function corresponding to the first type of decision optimization model and the objective function corresponding to the second type of decision optimization model) set in the decision optimization model. The decision optimization model can solve the best production scheduling plan according to the objective function set by the user. The production scheduling plan gives the scheduling information of each device when the actual conditions in Table 2 are met (refer to Table 1 above), so that the warp knitting enterprise can produce according to the scheduling information through the data processing device, thereby realizing the optimization of the production scheduling method and improving the product processing efficiency. Among them, the information input in Table 2 can be set according to the needs of the user and is not unique. In Table 2, time refers to the time required for the decision optimization model to calculate and output the optimal production scheduling plan.

[0182]

[0183] Table 2

[0184] As shown in Table 2, when the actual situation is met (the number of orders is 5, the total number of warping machines is 12, and the total number of warp knitting machines is 15), the time required to obtain the optimal production scheduling plan through the objective function corresponding to the first type of decision optimization model is 1 second, and the time required to obtain the optimal production scheduling plan through the objective function corresponding to the second type of decision optimization model is 5 seconds. Users can refer to the time required to obtain the optimal production scheduling plan to choose whether to use the first type of decision optimization model to obtain the optimal production scheduling plan, or to use the second type of decision optimization model to obtain the optimal production scheduling plan.

[0185] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0186] Based on the same inventive concept, the present application also provides a data processing device 700. Figure 7 1 is a structural block diagram of a data processing device according to an embodiment of the present application. Figure 7 , the data processing device 700 of the present application may include:

[0187] The first acquisition module 701 is configured to acquire order information of an order to be processed, device information of a device for processing the order, process information, and raw material information;

[0188] The second acquisition module 702 is configured to input the order information, device information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan;

[0189] Wherein, the scheduling plan is used to adjust various production decisions during the execution of the order to be processed.

[0190] Optionally, the second acquisition module 702 includes:

[0191] The first input sub-module is configured to input the order information, device information, process information, and raw material information into a first type of decision optimization model, and the first type of decision optimization model aims to minimize the sum of the number of orders with delays and the number of order replacements;

[0192] The first acquisition sub-module is configured to obtain the scheduling plan according to the output information of the first type of decision optimization model; or

[0193] The second input sub-module is configured to input the order information, device information, process information, and raw material information into a second type of decision optimization model, and the second type of decision optimization model aims to minimize the sum of the number of products with delays and the number of order replacements;

[0194] The second acquisition sub-module is configured to obtain the scheduling plan according to the output information of the second type of decision optimization model.

[0195] Optionally, the second acquisition module 702 includes:

[0196] The third input sub-module is configured to input the order information, device information, process information, and raw material information into the first type of decision optimization model to obtain a first scheduling plan;

[0197] The fourth input sub-module is configured to input the order information, device information, process information, and raw material information into the second type of decision optimization model to obtain a second scheduling plan;

[0198] The first determination sub-module is configured to determine the scheduling plan from the first scheduling plan and the second scheduling plan according to the user's selection operation.

[0199] Optionally, the device 700 further includes a first construction module configured to construct the first type of decision optimization model; the first construction module includes:

[0200] A third acquisition sub-module, configured to acquire a first weight value set for the number of postponed orders and a second weight value set for the number of order replacements;

[0201] A first construction sub-module, configured to construct the first type of decision optimization model according to the first weight value, the second weight value, the status of postponed orders to be processed, and the status of equipment replacement orders.

[0202] Optionally, the first construction sub-module includes:

[0203] A second construction sub-module, configured to construct a first type of objective function according to the first weight value, the second weight value, the status of postponed orders to be processed, and the status of equipment replacement orders;

[0204] A first setting sub-module, configured to set constraint conditions for the first type of objective function, where the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, constraint conditions based on the status of postponed orders to be processed, and constraint conditions based on the variable value range.

[0205] Optionally, the apparatus 700 further includes a second construction module, configured to construct the second type of decision optimization model; the second construction module includes:

[0206] A fourth acquisition sub-module, configured to acquire a third weight value set for the number of postponed products and a fourth weight value set for the number of order replacements;

[0207] A third construction sub-module, configured to construct the second type of decision optimization model according to the third weight value, the fourth weight value, the status of product postponement, and the status of equipment replacement orders.

[0208] Optionally, the third construction sub-module includes:

[0209] A fourth construction sub-module, configured to construct a second type of objective function according to the third weight value, the fourth weight value, the status of product postponement, and the status of equipment replacement orders;

[0210] A second setting sub-module, configured to set constraint conditions for the second type of objective function, where the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, constraint conditions based on the status of product postponement, and constraint conditions based on the variable value range. Based on the same inventive concept, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes, the steps in the data processing method described in any one of the above embodiments of the present application are implemented.

[0211] Based on the same inventive concept, this application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the data processing method described in any of the above embodiments of this application are implemented.

[0212] For the apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments.

[0213] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, refer to each other.

[0214] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, an apparatus, or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0215] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate a device for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0216] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0217] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in one process or multiple processes and / or blocks. Figure 1 One process or multiple processes and / or blocks Figure 1 Steps for implementing the functions specified in one block or multiple blocks.

[0218] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

[0219] Finally, it should also be noted that in this application, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the element.

[0220] The above has introduced in detail a data processing method, device, electronic device and storage medium provided by the present invention. Specific examples are used in this application to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A data processing method, characterized in that, Applied to the production of knitted fabrics in the warp knitting field, the method includes: Obtaining order information of an order to be processed, equipment information of the equipment for processing the order, process information, and raw material information. The equipment for processing the order includes a warping machine and a warp knitting machine. The warping machine is used to produce beam. A beam refers to a warp beam with gauze obtained after winding the gauze on the warp beam of a textile machine. The warp knitting machine is used to produce knitted fabrics based on the beam. The process information includes: the first process of warping and the second process of warp knitting, where the first process of warping is executed by the warping machine, and the second process of warp knitting is executed by the warp knitting machine; one type of warping machine is used to process one type of raw material and obtain one type of beam, and an order requires multiple types of different raw materials; each order can be produced using multiple different types of warp knitting machines respectively; Inputting the order information, equipment information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan. The scheduling plan is the information based on which each device actually executes the order, and at least includes: order information to be executed, order execution time, raw materials required for the order, and equipment maintenance time; Among them, the scheduling plan is used to adjust various production decisions during the execution of the order to be processed; The construction process of the decision optimization model includes: Obtaining the weight value set for the number of order replacements; Constructing the decision optimization model according to the weight value set for the number of order replacements and the order replacement situation of the equipment. The goal of the decision optimization model includes minimizing the number of order replacements; The warping machine does not replace orders during product processing, and the beams produced by the warping machine are centrally placed in the inventory; The number of order replacements is: in the case of an urgent order, the number of times the warp knitting machine changes from producing a regular order using the beam inventory to producing an urgent order using the beam inventory.

2. The method according to claim 1, characterized in that, Inputting the order information, equipment information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan, including: Inputting the order information, equipment information, process information, and raw material information into the first type of decision optimization model, and the goal of the first type of decision optimization model is to minimize the sum of the number of postponed orders and the number of order replacements; Obtaining the scheduling plan according to the output information of the first type of decision optimization model; or Inputting the order information, equipment information, process information, and raw material information into the second type of decision optimization model, and the goal of the second type of decision optimization model is to minimize the sum of the number of postponed products and the number of order replacements; Obtaining the scheduling plan according to the output information of the second type of decision optimization model.

3. The method according to claim 2, wherein Inputting the order information, equipment information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan, including: Inputting the order information, equipment information, process information, and raw material information into the first type of decision optimization model to obtain a first scheduling plan; Inputting the order information, equipment information, process information, and raw material information into the second type of decision optimization model to obtain a second scheduling plan; Determine the scheduling plan from the first scheduling plan and the second scheduling plan according to the user's selected operation.

4. The method according to claim 2, characterized in that, The first type of decision optimization model is constructed through the following steps: Obtain a first weight value set for the number of postponed orders and a second weight value set for the number of order replacement times; Construct the first type of decision optimization model according to the first weight value, the second weight value, the status of postponed orders to be processed, and the status of equipment order replacement.

5. The method according to claim 4, wherein Construct the first type of decision optimization model according to the first weight value, the second weight value, the status of postponed orders to be processed, and the status of equipment order replacement, including: Construct a first type of objective function according to the first weight value, the second weight value, the status of postponed orders to be processed, and the status of equipment order replacement; Set constraint conditions for the first type of objective function, and the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, constraint conditions based on the status of postponed orders to be processed, and constraint conditions based on the variable value range.

6. The method according to claim 2, wherein The second type of decision optimization model is constructed through the following steps: Obtain a third weight value set for the number of postponed products and a fourth weight value set for the number of order replacement times; Construct the second type of decision optimization model according to the third weight value, the fourth weight value, the status of product postponement, and the status of equipment order replacement.

7. The method according to claim 6, wherein Construct the second type of decision optimization model according to the third weight value, the fourth weight value, the status of product postponement, and the status of equipment order replacement, including: Construct a second type of objective function according to the third weight value, the fourth weight value, the status of product postponement, and the status of equipment order replacement; Set constraint conditions for the second type of objective function, and the constraint conditions at least include: constraint conditions based on equipment information, constraint conditions based on process information, constraint conditions based on time, and constraint conditions based on the variable value range.

8. A data processing device, characterized in that, Applied to the production of knitted fabrics in the warp knitting field, the device includes: A first acquisition module for acquiring order information of orders to be processed, equipment information of equipment for processing the orders, process information, and raw material information. The equipment for processing the orders includes a warping machine and a warp knitting machine. The warping machine is used to produce a beam. A beam refers to a warp beam with gauze obtained after winding the gauze on the warp shaft of a textile machine. The warp knitting machine is used to produce knitted fabrics according to the beam. The process information includes: the first process of warping and the second process of warp knitting, where the first process of warping is executed by the warping machine and the second process of warp knitting is executed by the warp knitting machine; one type of warping machine is used to process one type of raw material and obtain one type of beam, and one order requires multiple types of different raw materials; each order can be produced by multiple different types of warp knitting machines respectively; A second acquisition module, configured to input the order information, equipment information, process information, and raw material information into a pre-constructed decision optimization model to obtain a scheduling plan, where the scheduling plan is the information based on which each device actually executes an order, and at least includes: order information to be executed, order execution time, raw materials required for the order, and equipment maintenance time; Wherein, the scheduling plan is used to adjust various production decisions during the execution of the order to be processed; The construction process of the decision optimization model includes: Obtaining a weight value set for the number of order replacements; Constructing the decision optimization model according to the weight value set for the number of order replacements and the order replacement situation of the equipment, and the goal of the decision optimization model includes minimizing the number of order replacements; The warping machine does not replace orders when processing products, and the beams produced by the warping machine are centrally placed in the inventory; The number of order replacements is: in the case of an urgent order, the number of times the warp knitting machine changes from producing a regular order using the beam inventory to producing an urgent order using the beam inventory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps in the data processing method according to any one of claims 1-7.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes, it implements the steps in the data processing method according to any one of claims 1-7.

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