Slab design methods, devices, equipment, media and products

Abstract

The present application provides a slab design method, apparatus, device, medium, and product, which obtain order information corresponding to multiple orders; obtain constraint rules, which include a first conversion rule between the slab and the mother roll, a second conversion rule between the mother roll and the daughter plate, and a slab processing rule; input the constraint rules and the order information corresponding to the multiple first orders into a first model to obtain an initial order combination scheme corresponding to multiple available slabs, and the initial order combination scheme is used to determine the initial design size corresponding to each available slab; for any first slab among the multiple available slabs, if the initial design size of the first slab does not meet the slab processing rule, based on a genetic algorithm and a knapsack problem, the initial order combination scheme is adjusted to obtain a target order combination scheme corresponding to the multiple available slabs. The embodiments of the present application can improve the effect of order combination and reduce production costs.

Description

Technical Field

[0001] The present application relates to the field of slab production technology, and in particular to a slab design method, device, equipment, medium and product. Background Art

[0002] The current ship plate production process faces the challenge of multi-variety, small-batch orders. In order to meet customer needs, multiple small-size orders need to be combined into one slab to fulfill them, thereby increasing the cross-cutting yield rate and improving production organization capabilities. This production process involves three types of products: slabs, mother coils, and daughter plates. Slabs are steel billets heated in a heating furnace, mother coils are materials formed one-to-one after the slabs are rolled by a rolling mill, and daughter plates are deliverable finished products after the mother coils are sheared. Order combination material design can be seen as an extension of the classic blanking problem. The classic blanking problem mainly studies how to cut small-size products required by users from a given large-size mother material.

[0003] However, in the current ship plate production, order combination material design mainly relies on manual work. When faced with multi-variety, small-batch orders, manual combination requires a lot of time and human resources, which makes it difficult to meet the requirements of production efficiency and cost optimization. Summary of the Invention

[0004] The present application provides a slab design method, device, equipment, medium and product, which can improve the effect of order combination and reduce production costs.

[0005] In a first aspect, an embodiment of the present application provides a slab design method, the method comprising:

[0006] Obtain order information corresponding to multiple orders. Each order information includes the width, length, quantity, steel grade, and thickness of the sub-plates. Sub-plates are processed from mother coils, which are processed from slabs. There is a one-to-one correspondence between slabs and mother coils.

[0007] Obtaining constraint rules, which include a first conversion rule between a slab and a mother roll, a second conversion rule between a mother roll and a daughter slab, and slab processing rules. The slab processing rules include a length range, a width range, and a weight range for slab processing;

[0008] Inputting the constraint rules and order information corresponding to the plurality of first orders into the first model, obtaining an initial order combination plan corresponding to the plurality of available slabs, wherein the initial order combination plan is used to determine an initial design size corresponding to each available slab, wherein one available slab is used to produce all sub-slabs of at least one first order, and the steel grade and thickness of the sub-slabs in each first order are the same;

[0009] For any first slab among multiple available slabs, when the initial design size of the first slab does not conform to the slab processing rules, the initial order combination plan is adjusted based on the genetic algorithm and the knapsack problem to obtain the target order combination plans corresponding to the multiple available slabs; wherein the target design size of the first slab determined according to the target order combination plan conforms to the slab processing rules.

[0010] In a second aspect, the present application provides a slab design device, the device comprising:

[0011] The first acquisition module is used to obtain order information corresponding to multiple orders. Each order information includes the width, length, quantity, steel grade and thickness of the sub-plates. The sub-plates are processed from the mother coils, which are processed from the slabs. The slabs and the mother coils have a one-to-one correspondence.

[0012] A second acquisition module is used to acquire constraint rules, which include a first conversion rule between a slab and a mother coil, a second conversion rule between a mother coil and a daughter slab, and slab processing rules, which include a length range, a width range, and a weight range for slab processing;

[0013] a determination module, configured to input the constraint rules and order information corresponding to the plurality of first orders into a first model, to obtain an initial order combination plan corresponding to the plurality of available slabs, wherein the initial order combination plan is configured to determine an initial design size corresponding to each available slab, wherein one available slab is used to produce all sub-slabs of at least one first order, and the steel grade and thickness of the sub-slabs in each first order are the same;

[0014] An adjustment module is used to adjust the initial order combination plan for any first slab among the multiple available slabs based on a genetic algorithm and a knapsack problem, when the initial design size of the first slab does not conform to the slab processing rules, so as to obtain a target order combination plan corresponding to the multiple available slabs; wherein the target design size of the first slab determined according to the target order combination plan conforms to the slab processing rules.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: a processor and a memory storing computer program instructions;

[0016] When the processor executes the computer program instructions, the slab design method as in any one of the embodiments of the first aspect is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, a slab design method as in any one of the embodiments in the first aspect is implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes a slab design method as in any one of the embodiments in the first aspect above.

[0019] In a slab design method, device, equipment, medium and product provided in an embodiment of the present application, the constraint rules and the order information corresponding to the multiple first orders are input into the first model to generate an initial order combination plan for multiple available slabs. Subsequently, when any available slab does not meet the slab processing rules, the genetic algorithm and the knapsack problem are used to adjust the initial order combination plan to obtain a target order combination plan, ensuring that each available slab meets the slab processing rules, thereby improving the efficiency and accuracy of order processing and greatly reducing the time and labor cost of manual order combination. The present application makes full use of the length, width and weight range of the slab, the first and conversion rules between the slab and the mother roll, and the second conversion rule between the mother roll and the daughter plate to ensure that each available slab can be used to the maximum extent and produce daughter plates that meet the order requirements. Through precise design and adjustment, the generation of waste materials can be minimized, the utilization efficiency of resources is optimized, and the production cost is reduced. In addition, the present application adopts an automated design method, which can accurately calculate according to the constraint rules and avoid the occurrence of human errors. Compared with traditional manual order combination, this application can ensure the accuracy and rationality of order combination, reduce the error rate caused by human factors, and improve production efficiency and quality stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 This is a flow chart of a slab design method provided by one embodiment of the present application;

[0022] Figure 2 This is a schematic diagram of a first slab and a second slab provided in one embodiment of the present application;

[0023] Figure 3 This is a schematic structural diagram of a slab design device provided in an embodiment of the present application;

[0024] Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0028] Currently, ship plate production is characterized by high-variety, small-batch orders. To meet customer needs, multiple small-size orders (same material, same thickness, different widths, and lengths) must be consolidated into a single coil. This improves cross-cutting yields and significantly enhances production organization. Heat-treated ship plate production involves heating the slab to a specified temperature in a furnace, rolling it through a rolling mill to the specified thickness, width, and length, and then shearing it to the specified width and length on a shearing line to create the ordered dimensions. This delivery method is known as sheet-by-sheet delivery.

[0029] The production process of heat-treated ship plates mainly involves three types of products, namely slabs (steel billets heated in a heating furnace), mother coils (materials formed one-to-one after the slabs are rolled by a rolling mill) and daughter plates (deliverable finished products after the mother coils are sheared).

[0030] The material, thickness, width, length and number of sub-plates required can be obtained from the sales order. Based on these requirements and some constraints or regulations in the production process, the size and number of slabs are designed, and multiple sub-plates are assembled on each mother roll to meet the delivery requirements of multiple sales orders at the same time. This is called one-roll-multiple-opening order combination material design. The sales order corresponding to the sub-plates on each mother roll is called the sub-order of the mother roll.

[0031] There are two important evaluation indicators for order combination material design: (1) coil weight, i.e., the weight of the parent coil; and (2) yield rate, i.e., order delivery quantity / coil weight × 100%. The order combination material design that maximizes both coil weight and yield rate is considered the optimal order combination material design.

[0032] The order combination material design problem can be regarded as an extension of the classic cutting stock problem (CSP), that is, the cutting stock problem that considers the uncertainty of the specifications of the parent coil and slab. Studying the optimization design method of this problem is of great significance to improving the operating efficiency of the system and reducing production costs.

[0033] The classic blanking problem primarily addresses the question of how to cut user-required small-size products from a given large-size parent material to optimize a specific objective. However, in the order combination material design problem, the design specifications are designed before production. Before design, the quantity and specifications of the slabs and parent coils are uncertain. Previous research has explored this uncertain blanking problem. For example, all slab cross-section specifications are numbered, the slab cross-section selection for each order is encoded, and the optimal solution for optimizing the production order combination is sought based on column generation techniques and tabu search. However, this algorithm is computationally expensive and does not effectively utilize the constraints between slab specifications and order sub-slabs.

[0034] In order to solve the problems of the prior art, the embodiments of the present application provide a slab design method, device, equipment, medium and product. The slab design method provided in the embodiments of the present application is first introduced below.

[0035] Figure 1 FIG. 1 shows a flow chart of a slab design method provided by an embodiment of the present application. Figure 1 As shown, the method may specifically include the following steps:

[0036] S100, obtain order information corresponding to multiple orders, each order information includes the width of the sub-plate, the length of the sub-plate, the number of sub-plates, the steel type of the sub-plate and the thickness of the sub-plate. The sub-plates are processed through the mother coil, and the mother coil is processed through the slab. The slab and the mother coil correspond one to one.

[0037] Optionally, in a feasible implementation of the present application, the sales department collects customer order requirements, including sub-plate width, length, quantity, steel type and thickness, etc. This order data can be input into the company's integrated business management system for management and processing.

[0038] The information of each order should include detailed information such as the width, length, quantity, steel grade and thickness of the sub-plate required by the order. This information is determined based on customer needs and is used to guide processing and production during the production process. Sub-plates are processed through mother rolls, and mother rolls are processed through slabs. In data management, the production process of an order can be ensured to proceed smoothly by matching mother rolls and slabs one by one. This association can be achieved through the data structure in the system or the associated fields in the database. In an embodiment of the present application, one order is used to guide the production of sub-plates of one specification.

[0039] The acquired order data can be in the form of a list or table containing all order information. This data can be used as input for optimization and combination to determine the best production plan for slabs.

[0040] S200, obtain constraint rules, which include a first conversion rule between slabs and mother rolls, a second conversion rule between mother rolls and sub-slabs, and slab processing rules. The slab processing rules include a length range of slab processing, a width range of slab processing, and a weight range of slab processing.

[0041] Optionally, a first conversion rule from slabs to mother rolls can be expressed as a feed factor. The feed factor is a parameter that measures the efficiency of converting slabs to mother rolls. It represents the conversion rate from slabs to mother rolls during the processing process. The feed factor can take into account factors such as the length, width, and thickness of the slabs to determine the length, width, and thickness of the mother roll. A second conversion rule from mother rolls to daughter boards can also be expressed as a feed factor, which represents the conversion efficiency from mother rolls to daughter boards.

[0042] Slab processing rules include the length range, width range and weight range of slab processing. These rules specify the restrictions on the slab during processing to ensure that the produced slabs meet the process requirements and can be smoothly converted into parent coils.

[0043] S300: Inputting the constraint rules and order information corresponding to the plurality of first orders into a first model to obtain an initial order combination plan corresponding to the plurality of available slabs, wherein the initial order combination plan is used to determine an initial design size corresponding to each available slab, wherein one available slab is used to produce all sub-slabs of at least one first order, and the steel grade and thickness of the sub-slabs in each first order are the same;

[0044] S400, for any first slab among the multiple available slabs, when the initial design size of the first slab does not comply with the slab processing rules, the initial order combination plan is adjusted based on the genetic algorithm and the knapsack problem to obtain the target order combination plan corresponding to the multiple available slabs; wherein the target design size of the first slab determined according to the target order combination plan complies with the slab processing rules.

[0045] In a slab design method, device, equipment, medium and product provided in an embodiment of the present application, the constraint rules and the order information corresponding to the multiple first orders are input into the first model to generate an initial order combination plan for multiple available slabs. Subsequently, when any available slab does not meet the slab processing rules, the genetic algorithm and the knapsack problem are used to adjust the initial order combination plan to obtain a target order combination plan, ensuring that each available slab meets the slab processing rules, thereby improving the efficiency and accuracy of order processing and greatly reducing the time and labor cost of manual order combination. The present application makes full use of the length, width and weight range of the slab, the first and conversion rules between the slab and the mother roll, and the second conversion rule between the mother roll and the daughter plate to ensure that each available slab can be used to the maximum extent and produce daughter plates that meet the order requirements. Through precise design and adjustment, the generation of waste materials can be minimized, the utilization efficiency of resources is optimized, and the production cost is reduced. In addition, the present application adopts an automated design method, which can accurately calculate according to the constraint rules and avoid the occurrence of human errors. Compared with traditional manual order combination, this application can ensure the accuracy and rationality of order combination, reduce the error rate caused by human factors, and improve production efficiency and quality stability.

[0046] In one embodiment, before step 200, the method may further perform the following steps:

[0047] S201, obtaining the objective function and the constraint condition function;

[0048] S202. Based on the objective function and the constraint function, construct the first model, which is used to combine some of the multiple orders on a single slab so that the yield rate of the combination of all orders is maximized, and the steel type and thickness of the sub-plate of each order in the partial orders are the same.

[0049] Optionally, in one feasible implementation of the present application, the objective function refers to the goal of the optimization problem, that is, the quantity to be maximized or minimized. In the present application, the objective function may be maximizing the yield rate, that is, minimizing the total weight of the parent roll to maximize the production efficiency.

[0050] Constraint functions are conditions that must be met during problem solving. In this application, these constraints include the conversion rules between motherboards and daughterboards, the length, width, and thickness ranges of motherboards, and the processing rules for slabs.

[0051] Subsequently, based on the objective function and the constraint function, a first model is constructed, which is used to combine some orders from the multiple orders on a single slab to maximize the yield rate.

[0052] Specifically, mathematical modeling methods can be used to convert the objective function and constraint function into mathematical expressions, and a mathematical model can be constructed based on these expressions. This mathematical model can be a linear programming model or a mixed integer programming model, where the optimized variables are the arrangement of orders on the slab and the size of the slab. When implementing S201 and S202, mathematical optimization software or a programming language (such as Python) can be used to construct and solve the mathematical model.

[0053] In these alternative embodiments, the objective function is set to prioritize yield, thereby maximizing raw material utilization and minimizing resource waste. The constraint function helps ensure that various production requirements are met, such as the size range, steel grade, and thickness of slabs and parent coils.

[0054] The first model was constructed to automatically combine parts of multiple orders onto a single slab. Through mathematical modeling and optimization algorithms, the optimal combination solution can be quickly and accurately generated, thereby improving production efficiency and reducing the need for manual intervention.

[0055] The objective function is to maximize the overall yield rate, that is, to minimize scrap and waste material and improve production efficiency. By optimizing order combinations to ensure that each order has the same sub-plate steel grade and thickness, the yield rate can be maximized. Optimizing production planning and improving the yield rate can reduce production costs. Reducing scrap and waste material not only saves on raw materials but also reduces subsequent processing and handling costs, thereby effectively controlling production costs. The automated solution of mathematical models and optimization algorithms reduces the possibility of human error. Pre-defined constraints ensure compliance and accuracy during the production process, reducing errors and deviations caused by human intervention.

[0056] In one embodiment, the objective function is:

[0057] min G coil , where G coil is the total weight of the parent roll;

[0058] Constraint functions include:

[0059]

[0060] T coil =T order

[0061]

[0062] λ1≥1,λ2≥1

[0063] and j = 1, 2, ..., p

[0064] Among them, the superscript slab represents the parameters of the slab, j is the slab number, and there are p slabs in total;

[0065] is the weight of the jth slab;

[0066] is the length of the jth slab;

[0067] is the width of the jth slab;

[0068] T slab is the thickness of the slab;

[0069] is the maximum weight of the slab;

[0070] is the minimum length of the slab;

[0071] is the maximum length of the slab;

[0072] The superscript "coil" represents the parameters of the mother coil;

[0073] is the weight of the parent coil corresponding to the j-th slab;

[0074] is the length of the mother roll corresponding to the j-th slab;

[0075] is the width of the mother roll corresponding to the j-th slab;

[0076] T coil is the thickness of the parent roll;

[0077] The superscript "order" represents the order parameter, i is the order number, and there are q orders in total;

[0078] is the length of the sub-board in the i-th order;

[0079] W iorder is the width of the sub-board in the i-th order;

[0080] T iorder is the thickness of the sub-board in the i-th order;

[0081] n i is the number of sub-boards in the i-th order;

[0082] ρ is the density of slab, mother coil and daughter board;

[0083] λ1 is the feeding coefficient from slab to mother coil;

[0084] λ2 is the feeding coefficient from mother roll to daughter board;

[0085] x ij It is a 0-1 variable, which is 1 if the parent roll corresponding to the j-th slab contains order i, otherwise it is 0.

[0086] The slab design method provided in this application is suitable for multiple multi-variety, small-batch order combinations. The design principle is to determine the combination of each sub-plate and its corresponding slab size specifications under the premise of complying with the constraints of the production process equipment. In order to improve production efficiency, the design that can fully utilize the maximum capacity of the slab should be selected as much as possible. In addition, after the sub-plate is delivered, there may be material left in the corresponding mother coil, which is called residual material. The generation of residual material should be minimized during design. According to the production process flow, the thickness of the slab is fixed. The slab is processed by the hot rolling mill and needs to be widened and compressed. Uneven deformation will cause irregular edges of the slab, which need to be cut neatly and cause material loss. In production, the designer will give the coefficient related to cutting loss, that is, the feeding coefficient, based on the above situation and the production statistical law. Therefore, it is necessary to consider the first conversion rule from slab to mother coil and the second conversion rule from mother coil to sub-plate.

[0087] Optionally, in a specific implementation of the present application, based on production requirements, the present application uses the first model to perform slab design, including the following default requirements:

[0088] 1) For orders delivered by the sheet, the minimum order weight can be as small as one steel plate (i.e., the weight of the slab is less than 1 ton), and the maximum weight is less than the minimum coil weight of the hot rolling mill. Therefore, it is impossible to schedule production for each order individually;

[0089] 2) Given the different types of requirements within an order, this application first groups the orders by steel grade and thickness, using each group of orders as input for the slab design method. In the following input order set, assuming that the orders have already been grouped and that they all have the same requirements for attributes such as steel grade and thickness (i.e., the sub-slab thickness and steel grade in the first order are the same), this application will not elaborate further.

[0090] 3) When combining, if the widths of the sub-orders are different, the weight of the mother roll and mother board will be derived based on the widest size of all the sub-orders.

[0091] Specifically, the order information is first received. The sales department issues the order through the integrated business management system, and the production system receives and saves the order information, which includes the thickness, width, length, steel type, and quantity of the ordered sub-plate.

[0092] Then obtain the design rules. The design rules entered by the on-site personnel are obtained from the specified page, including slab design rules and conversion rules between slabs, mother rolls and daughter plates. Slab design rules include: slab thickness, minimum and maximum slab length restrictions, and maximum slab weight constraints; slab mother roll daughter plate conversion rules include the feed coefficient, and the conversion method is in is the length of the jth slab, T slab is the thickness of the slab, is the length of the mother coil processed from the jth slab, T coil is the thickness of the parent coil, and λ1 is the feed coefficient from slab to parent coil (i.e., the first conversion rule), which is generally a real number not less than 1. It should be noted that the above data can be adjusted according to actual conditions.

[0093] Subsequently, a mathematical model of the combinatorial optimization design (ie, a first model) may be determined, where the first model includes an objective function and constraint conditions.

[0094] The objective function is:

[0095]

[0096] in

[0097]

[0098] The objective function (1) represents the maximization of the yield rate. According to the formula: yield rate = planned delivery quantity of sub-boards / weight of slab feed × 100%, it can be seen that the yield rate is inversely proportional to the slab feed quantity, while the slab quantity is linearly positively correlated with the mother roll quantity. The delivery quantity of sub-boards is the sum of the total quantity of each order. Therefore, the problem of maximizing the yield rate is converted into the problem of minimizing the total weight of the mother roll as shown in formula (4).

[0099] min G coil (4)

[0100] The constraints are:

[0101]

[0102] T coil =T order(10)

[0103]

[0104] λ1≥1,λ2≥1 (13)

[0105]

[0106] Constraints (5) to (7) describe the conversion rules between the weight and length of the slab and the length, width, and thickness of the parent coil. Constraints (8) to (10) describe the conversion relationship between the length, width, and thickness of the parent coil and the total length, width, and thickness of the combined orders on the parent coil. Taking into account factors such as material loss during rolling and trimming, the weight of the slab should be greater than the weight of the parent coil. Under the condition of the same density, the mass relationship between the two is shown in constraint (5). Similarly, considering the processing processes such as head and tail trimming, the length of the parent coil should be greater than the sum of the lengths of each sub-order, the width should be the maximum width of each sub-order, and the thickness should be the same as the thickness of the order.

[0107] Constraint (11) represents the maximum weight constraint of the slab. Since the load-bearing capacity of the crane is limited, the weight of the slab cannot be greater than the maximum load-bearing capacity of the crane.

[0108] Constraint (12) represents the minimum and maximum length constraints of the slab. On the one hand, if the slab is too short, it will reduce production efficiency and make it difficult to achieve economic benefits. On the other hand, if the slab is too long, it will increase the difficulty of production and increase the burden on the equipment. Therefore, the length of the slab must be controlled within an appropriate range. Combining constraints (5) and (11), it can be seen that for each slab with a certain width, its final maximum weight is:

[0109]

[0110] Constraints (13) to (14) represent the value ranges of several parameters.

[0111] In one embodiment, before step 400, the method may further perform the following steps:

[0112] S401, sort all available slabs in descending order according to their width. If there are slabs with the same width, sort them in descending order according to the minimum width of each slab order, and obtain the serial number corresponding to each available slab, with one available slab corresponding to one serial number.

[0113] Optionally, in an embodiment of the present application, each available slab may be used to satisfy multiple orders during the production process, and different orders may have different width requirements. Therefore, when sorting the available slabs, it is necessary to consider the maximum and minimum widths of the orders that each slab can satisfy.

[0114] Specifically, first, sort the slabs in descending order by the maximum width they can satisfy. This means that slabs with larger widths are ranked first, as they can satisfy orders with wider sub-slabs and are therefore more likely to be used. If multiple slabs have the same maximum order width, the minimum width of each slab is considered.

[0115] To better utilize the slabs, slabs with the same maximum order width are further sorted in descending order based on the smallest width of the order they can satisfy. This means that even if the slabs have the same maximum width, we still give priority to the slabs with the smallest width that can satisfy the widest order, as they can better utilize the plate and reduce waste.

[0116] In these alternative embodiments, this sorting method ensures that slabs that best meet order requirements are used first during production, minimizing waste and scrap. Furthermore, it simplifies subsequent optimization processes and provides better initial conditions for solving the knapsack problem using a genetic algorithm. This approach improves production efficiency, reduces costs, and achieves more optimized production organization and resource utilization.

[0117] In one embodiment, the above step 400 may specifically perform the following steps:

[0118] S410, for any first slab among the plurality of available slabs, if the initial design size of the first slab does not comply with the slab processing rules, obtaining a first type of the first slab and a second type of a second slab with an adjacent serial number corresponding to the first slab;

[0119] S420, determining the items in the knapsack problem based on the serial number corresponding to the first slab and the serial number corresponding to the second slab;

[0120] S430, determining a maximum capacity of a knapsack in the knapsack problem based on the first type, the second type, the weight of the first slab, and the weight of the second slab;

[0121] S440, solving the knapsack problem using a genetic algorithm based on the items in the knapsack problem and the maximum capacity of the knapsack in the knapsack problem, obtaining at least one solution result, each solution result corresponding to a slab yield rate;

[0122] S450, adjusting the initial order combination plan according to the target result to obtain the target order combination plan, wherein the target result is the solution result corresponding to the maximum slab yield rate in the at least one solution result.

[0123] Optionally, in a feasible implementation of the present application, the knapsack problem is solved by a genetic algorithm, which is specifically implemented as follows:

[0124] 1. Initialize the population: Randomly generate a set of individuals, each of which represents a possible solution, i.e., a slab order combination. Ensure that each individual complies with the slab processing rules and the knapsack problem constraints.

[0125] 2. Fitness evaluation: Calculate the fitness of each individual, which is the sum of the weights of the items in the backpack. Sort the individuals in descending order based on fitness.

[0126] 3. Selection: Use roulette wheel selection or other selection operators to select a portion of individuals from the population as parents for crossover and mutation.

[0127] 4. Crossover: Perform a crossover operation on the selected parent generation to generate new individuals. This can be done using single-point crossover or multi-point crossover.

[0128] 5. Mutation: Perform mutation operations on newly generated individuals to increase the diversity of the population. Slight random changes can be made to the size of individuals.

[0129] 6. Update the population: merge the newly generated individuals with the original population and sort the population again based on fitness.

[0130] 7. Repeat the above steps: Repeat the selection, crossover and mutation operations until a stopping condition is reached, such as reaching the maximum number of iterations or reaching a satisfactory solution.

[0131] 8. Evaluation results: Calculate the fitness of each individual and select the best solution as the final result. You can also save multiple optimal solutions for selection.

[0132] Through these steps, genetic algorithms can be used to effectively search for one or more solutions that meet the requirements to achieve the goal of maximizing the yield rate.

[0133] In these optional embodiments, by executing S410 to S450, the first slab that does not meet the slab processing rules can be adjusted to maximize its yield rate, thereby improving production efficiency and resource utilization. First, by obtaining the type of the first slab and the type of the second slab adjacent to it (S410), the slab that needs to be adjusted and the alternative slabs that can be used are determined. Then, based on the types of the first slab and the second slab, the items in the knapsack problem (S420) and the maximum capacity of the knapsack in the knapsack problem (S430) are determined. Then, the knapsack problem is solved by a genetic algorithm (S440) to obtain at least one solution result, each result corresponding to a slab yield rate. Finally, the initial order combination plan is adjusted according to the target result (S450) to maximize its yield rate. This can improve the efficiency of the production line, reduce material waste, reduce production costs, and obtain an optimized production plan in a short time to meet production needs and maximize resource utilization.

[0134] In one embodiment, the weight range of the slab processing includes a maximum weight of the slab processing and a minimum weight of the slab processing, and the length range of the slab processing includes a maximum length of the slab processing and a minimum length of the slab processing. The above step 410 may specifically perform the following steps:

[0135] S411, for any first slab among the plurality of available slabs, if the weight of the first slab is greater than the maximum weight of the slab to be processed, determine the first slab as an outgoing type and a second slab as an incoming type, where the outgoing type indicates the order weight corresponding to at least one outgoing order of the slab, and the incoming type indicates the order weight corresponding to at least one incoming order of the slab;

[0136] S412: When the length of the first slab is less than the minimum length for slab processing, the first slab is determined as an input type, and the second slab is determined as an output type.

[0137] Optionally, in a specific embodiment of the present application, first, it is necessary to obtain order information, including the width, length, quantity and weight of the order. Then, all orders are grouped according to width and sorted in descending order of width. The purpose of this step is to better combine on the slab in subsequent steps to improve the yield rate and optimize production efficiency. In the embodiment of the present application, it is assumed that there is no situation where a certain width alone does not exceed the maximum slab weight. If so, some orders can be grouped separately and then these orders can be removed from the grouping.

[0138] Specifically, width information is first extracted from the order data, and the orders are grouped by width. The orders in each group are of the same or similar width, which allows for better combination on the slab. Next, each group is sorted in descending order to ensure more efficient use of the slab when processing orders with larger widths in subsequent steps. When initializing the model and algorithm parameters, it is necessary to take into account the actual production conditions and constraints, such as the maximum weight of the slab and the maximum width of the slab. At the same time, it is necessary to adjust the algorithm parameters according to the specific situation to ensure that the subsequent steps can be executed smoothly and obtain the optimal production plan.

[0139] After sorting the orders by width, the number of slabs needs to be calculated. The minimum number of slabs is calculated based on the total weight of all orders in the group. Specifically, the total order weight is divided by the maximum slab weight: minimum slab quantity = [total weight of all orders / maximum slab weight], where [x] represents x rounded up. This calculation ensures that all orders can be produced and that the number of slabs meets the order requirements, while minimizing slab waste. If the remainder is greater than zero, an additional slab is required to meet production requirements.

[0140] Subsequently, based on the width groupings and minimum slab quantity obtained above, the first model was used to group the orders by width to obtain an initial solution, where the width of each slab was equal to the longest width of the sub-orders within that slab. According to the yield rate calculation formula, combining orders with smaller width spans on the same motherboard has a higher yield rate than combining orders with larger width spans on the same motherboard. Based on this characteristic, the present application strives to ensure that each motherboard does not exceed the maximum weight when assembling the slabs, or that the motherboard is complete when the maximum weight is exceeded.

[0141] Finally, the initial plan is adjusted based on the genetic algorithm. Mainly for the situation where some slabs are overweight or not long enough, part of the order can be allocated to the adjacent slabs, or some orders can be allocated from the adjacent slabs.

[0142] Specifically, in the embodiment of the present application, the order reallocation problem in the solution adjustment process is regarded as a 0-1 knapsack problem. First, it is necessary to know what items exist. The motherboard (i.e., slab) of the order is marked as out (i.e., the outgoing type), and the slab of the order is marked as in (i.e., the incoming type). Specifically, if the slab is overweight (i.e., the weight of the first slab is greater than the maximum weight of the slab processing), the slab is determined to be the outgoing type, and the slab adjacent to the slab sequence number is determined to be the incoming type; if the slab is not long enough (i.e., the length of the first slab is less than the minimum length of the slab processing), the slab is determined to be the incoming type, and the slab adjacent to the slab sequence number is determined to be the outgoing type.

[0143] In these optionally implemented embodiments, classifying the first slab according to different situations of the first slab can effectively process slabs that do not conform to the slab processing rules, and perform reasonable segmentation and adjustment, ensuring that each slab can be fully utilized, improving production efficiency and resource utilization rate, and thus reducing production costs.

[0144] In one embodiment, the first slab is used to produce multiple second orders. The first slab is of the separated type, and the second slab is of the incorporated type. The above step 420 can specifically be executed as follows:

[0145] S421, when the serial number corresponding to the first slab is greater than the serial number corresponding to the second slab, determine the second order with the largest sub-slab width among the multiple second orders as the item in the knapsack problem;

[0146] S422, when the serial number corresponding to the first slab is less than the serial number corresponding to the second slab, determine the second order with the smallest sub-slab width among the multiple second orders as the item in the knapsack problem.

[0147] In the embodiments of the present application, after sorting each slab, when analyzing the first slab each time, if the first slab does not conform to the slab processing rules, classify the first slab and the second slab adjacent to the first slab. Therefore, there is obviously no situation where the serial number of the out slab = the serial number of the in slab.

[0148] In one implementation manner of the present application, if the serial number of the out slab < the serial number of the in slab, then the "item" in the knapsack problem is the order with the smallest width in the out slab. If the serial number of the out slab > the serial number of the in slab, then the "item" in the knapsack problem is the order with the largest width in the out slab.

[0149] In these optionally implemented embodiments, by classifying the first slab and the adjacent second slab, it is possible to more flexibly handle situations that do not conform to the slab processing rules. This classification method allows the algorithm to consider the adjacent slab when processing slabs that do not conform to the rules, thereby better utilizing resources. It can also improve the utilization rate of slabs to a certain extent. By selecting the order with the smallest or largest width as the item, it is possible to more reasonably utilize the space of the slab, maximize the order weight on the slab, and thus improve the成材率 (成材率, which may need to be further defined in the context), reduce the余材率 (余材率, which may need to be further defined in the context), and thus reduce production costs. In summary, by considering the classification of adjacent slabs in the knapsack problem, it is possible to more flexibly handle situations that do not conform to the rules, improve resource utilization rate, and reduce production costs.

[0150] In one embodiment, the first slab is of the incorporated type, and the second slab is of the separated type. The above step 430 can specifically be executed as follows:

[0151] S431, at and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0152] S432, in and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0153] S433, in and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0154] S434, in and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0155] in, is the maximum weight of the second slab, is the weight of the first slab at least divided into, is the maximum weight of the first slab, is the weight of the second slab at least divided, The maximum remaining weight for the second slab, for is the upper limit of the weight of the second slab, is the excess weight of the second slab, G sum is the weight of the items in the knapsack problem.

[0156] In one embodiment, the first slab is of the outgoing type, and the second slab is of the incoming type; the above step 430 may further include the following steps:

[0157] S435, in and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0158] S436, in and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0159] S437, in and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0160] S438, in and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0161] in, is the maximum weight of the first slab, is the weight of the second slab at least divided into, is the maximum weight of the second slab, is the weight of the first slab at least divided, The maximum weight left for the first slab, for is the upper limit of the weight of the first slab, is the excess weight of the first slab, G sum is the weight of the items in the knapsack problem.

[0162] Optionally, in a specific implementation of the present application, another important issue in converting the above-mentioned initial design solution adjustment problem into a 0-1 knapsack problem is to calculate the maximum capacity of the "knapsack". In the implementation scheme, the capacity calculation rules are as follows:

[0163] (1) The length or weight of the slab in (i.e., the slab of the sorting type) is insufficient (i.e., when the first slab is determined to be the sorting type)

[0164] like and So And flag = 1;

[0165] like and So And flag = 1;

[0166] like and So And flag = 0;

[0167] like and So And flag=0.

[0168] (2) The length or weight of the slab out (i.e., the slab of the separated type) exceeds the standard (i.e., when the first slab is determined to be the separated type)

[0169] like and So And flag = 0;

[0170] like and So And flag = 0;

[0171] like and So And flag = 1;

[0172] like and So And flag=1.

[0173] Here, flag = 1 means that the orders placed in the "knapsack" in the solution to the 0-1 knapsack problem are orders assigned from the out slab to the in slab, and flag = 0 means that the orders not placed in the "knapsack" in the solution to the 0-1 knapsack problem are orders assigned from the out slab to the in slab. Assume that the total weight of the orders representing the "items" in the 0-1 knapsack problem is G sum , Represents the upper limit of the weight of the slab 0ut, and the meanings of the other variables are shown in Table 1. Among them, is the minimum length of the out slab, The minimum length of the slab is in.

[0174] Table 1 Meaning of variables

[0175]

[0176] The following is a case where the first slab is determined to be a separation type. and So And flag=0, explanation is given.

[0177] like Figure 2 As shown, ① is the weight of the out slab that exceeds the upper weight limit, ② is the difference between the weight of the in slab and the upper weight limit of the slab, ④ is the lower weight limit of the out slab, ⑤ is the weight of the in slab, ④+③ is the upper weight limit of the out slab, and ②+⑤ is the upper weight limit of the in slab.

[0178] If ①+③≤②, then the backpack capacity is ①+③. Since ② is large, we only need to consider the maximum weight of the orders that can be divided out of the out slab as the backpack capacity. The divided orders are the items placed in the backpack. Other different situations can refer to this example, and this application will not be repeated here.

[0179] In a specific implementation of this application, the heat treatment ship plate production of a certain enterprise is used as the experimental object. The density of the slab / coil / sub-plate of the experimental input data is ρ=7.85×10 -9 t / mm 3 , slab thickness Tslab = 240mm, slab maximum weight is The minimum and maximum lengths of the slab are The feeding coefficient λ1 = 1.013 (first conversion rule), λ2 = 1.013 (second conversion rule). The main information of the production order is shown in Table 2. The length range is 6000mm-14000mm, the width range is 1600mm-2000mm, and the quantity range is 1-5.

[0180] Table 2 Production Order

[0181] Order number Width (mm) Length (mm) quantity Weight (t) 1 1600 6100 1 0.46 2 1600 6200 1 0.467 3 1600 6600 1 0.497 … … … … … 98 2000 12000 2 2.26

[0182] Since the genetic algorithm itself has randomness, the implementation scheme of the present application was independently run 10 times, and the obtained yield rate is shown in Table 3, which is stable at about 95.80%.

[0183] Table 3 Yield rate

[0184] Serial number Yield rate Serial number Yield rate 1 95.79％ 6 95.79％ 2 95.80％ 7 95.80％ 3 95.80％ 8 95.80％ 4 95.80％ 9 95.79％ 5 95.80％ 10 95.80％

[0185] Taking the results of one of the experiments, the test paper information obtained is shown in Table 4:

[0186] Table 4 Test paper information

[0187] Roll number / slab number width length Maximum roll weight Roll weight Order weight 0 1800 10561 36.757 36.280 35.489 1 1900 10329 37.500 37.452 35.716 2 2000 9756 37.500 37.238 35.102

[0188] The order weight represents the sum of the weights of the sub-orders delivered by the parent volume.

[0189] The results are ultimately output to a designated data table, from which the production system reads the data, displays the results, and processes the slabs accordingly.

[0190] Optionally, in an embodiment of the present application, the main purpose of the present application is to design the size and number of slabs, as well as the delivery order of each mother roll combination based on some given rules through the delivery requirements of the sales order, so as to maximize the yield and minimize the amount of waste material.

[0191] In these optional embodiments, the slab design method of the present application establishes an optimization control model (i.e., the first model) with the goal of minimizing the number of slabs and maximizing the yield rate based on order characteristics, as well as constraints such as production processes and equipment, and makes decisions on the combination of sub-orders, the length and width of the slabs and the mother roll. After receiving the order information, a rough initial solution is first generated based on the order characteristics and the objective function, and then each slab is analyzed, and the slabs that do not meet the constraints are adjusted, which is converted into a 0-1 knapsack problem, and the "items" of the knapsack problem and the "maximum capacity" of the knapsack are determined. The solution is adjusted using a genetic algorithm-based solution. After all slabs are analyzed, the final solution is output. The present application can reduce motherboard waste and material loss, and can better meet production needs. Moreover, compared to the method of searching for the optimal solution globally, this local fine-tuning method can reduce a large amount of calculation and search while ensuring a better solution.

[0192] Figure 3 A structural schematic diagram of a slab design device provided in another embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0193] Reference Figure 3 , the slab design means may include:

[0194] The first acquisition module 301 is used to acquire order information corresponding to multiple orders. Each order information includes the width, length, quantity, steel grade, and thickness of the sub-plates. The sub-plates are processed from parent coils, which are processed from slabs. There is a one-to-one correspondence between slabs and parent coils.

[0195] A second acquisition module 302 is configured to acquire constraint rules, including a first conversion rule between a slab and a mother coil, a second conversion rule between a mother coil and a daughter slab, and slab processing rules. The slab processing rules include a length range, a width range, and a weight range for slab processing.

[0196] Determination module 303 is configured to input the constraint rules and order information corresponding to the plurality of first orders into a first model to obtain an initial order combination plan corresponding to the plurality of available slabs, wherein the initial order combination plan is used to determine an initial design size corresponding to each available slab, wherein one available slab is used to produce all sub-slabs of at least one first order, and the sub-slabs in each first order have the same steel grade and thickness.

[0197] The adjustment module 304 is used to adjust the initial order combination plan for any first slab among the multiple available slabs based on the genetic algorithm and the knapsack problem to obtain the target order combination plan corresponding to the multiple available slabs when the initial design size of the first slab does not conform to the slab processing rules; wherein the target design size of the first slab determined according to the target order combination plan conforms to the slab processing rules.

[0198] In one embodiment, the slab design device may further include:

[0199] The third acquisition module is used to obtain the objective function and the constraint condition function;

[0200] A construction module is used to construct the first model based on the objective function and the constraint condition function, wherein the first model is used to combine some of the multiple orders on a single slab so as to maximize the yield rate of the combination of all orders, and the steel type and thickness of the sub-plate of each order in the partial orders are the same.

[0201] In one embodiment, the objective function is:

[0202] min G coil , where G coil is the total weight of the parent roll;

[0203] Constraint functions include:

[0204]

[0205] T coil =T order

[0206]

[0207] λ1≥1,λ2≥1

[0208] and j = 1, 2, ..., p

[0209] Among them, the superscript slab represents the parameters of the slab, j is the slab number, and there are p slabs in total;

[0210] is the weight of the jth slab;

[0211] is the length of the jth slab;

[0212] is the width of the jth slab;

[0213] T slab is the thickness of the slab;

[0214] is the maximum weight of the slab;

[0215] is the minimum length of the slab;

[0216] is the maximum length of the slab;

[0217] The superscript "coil" represents the parameters of the mother coil;

[0218] is the weight of the parent coil corresponding to the j-th slab;

[0219] is the length of the mother roll corresponding to the j-th slab;

[0220] is the width of the mother roll corresponding to the j-th slab;

[0221] T coil is the thickness of the parent roll;

[0222] The superscript "order" represents the order parameter, i is the order number, and there are q orders in total;

[0223] is the length of the sub-board in the i-th order;

[0224] W i order is the width of the sub-board in the i-th order;

[0225] T i order is the thickness of the sub-board in the i-th order;

[0226] n i is the number of sub-boards in the i-th order;

[0227] ρ is the density of slab, mother coil and daughter board;

[0228] λ1 is the feeding coefficient from slab to mother coil;

[0229] λ2 is the feeding coefficient from mother roll to daughter board;

[0230] x ii It is a 0-1 variable, which is 1 if the parent roll corresponding to the j-th slab contains order i, otherwise it is 0.

[0231] In one embodiment, the slab design device may further include:

[0232] The sorting module is used to sort all available slabs in descending order according to their width. If there are slabs with the same width, they are sorted in descending order according to the minimum width of each slab order, and the serial number corresponding to each available slab is obtained, with one available slab corresponding to one serial number.

[0233] In one embodiment, the adjustment module 304 may include:

[0234] a first acquisition submodule configured to acquire, for any first slab among the plurality of available slabs, a first type of the first slab and a second type of a second slab having an adjacent serial number corresponding to the first slab when an initial design size of the first slab does not comply with slab processing rules;

[0235] A first determination submodule is used to determine the items in the knapsack problem according to the serial number corresponding to the first slab and the serial number corresponding to the second slab;

[0236] A second determining submodule is configured to determine a maximum capacity of a knapsack in the knapsack problem according to the first type, the second type, the weight of the first slab, and the weight of the second slab;

[0237] The third determination submodule is configured to solve the knapsack problem by a genetic algorithm according to the items in the knapsack problem and the maximum capacity of the knapsack in the knapsack problem, and obtain at least one solution result, wherein each solution result corresponds to a slab yield rate;

[0238] The adjustment submodule is used to adjust the initial order combination plan according to the target result to obtain the target order combination plan, and the target result is the solution result corresponding to the maximum slab yield rate in the at least one solution result.

[0239] In one embodiment, the weight range of the slab processing includes a maximum weight of the slab processing and a minimum weight of the slab processing, and the length range of the slab processing includes a maximum length of the slab processing and a minimum length of the slab processing; the first acquisition submodule may include:

[0240] a first determining unit configured to, for any first slab among the plurality of available slabs, determine the first slab as an outgoing type and a second slab as an incoming type when the weight of the first slab is greater than the maximum weight of the slab to be processed, wherein the outgoing type indicates an order weight corresponding to at least one outgoing order of the slab, and the incoming type indicates an order weight corresponding to at least one incoming order of the slab;

[0241] The second determining unit is configured to determine the first slab as an incoming type and the second slab as an outgoing type when the length of the first slab is less than a minimum length for slab processing.

[0242] In one embodiment, a first slab is used to produce a plurality of second orders, the first slab is of an outgoing type, and the second slab is of an incoming type; the first determining submodule may include:

[0243] a third determining unit, configured to determine, when the serial number corresponding to the first slab is greater than the serial number corresponding to the second slab, the second order with the largest sub-slab width among the plurality of second orders as the item in the knapsack problem;

[0244] The fourth determining unit is configured to determine, when the serial number corresponding to the first slab is smaller than the serial number corresponding to the second slab, the second order with the smallest sub-slab width among the plurality of second orders as the item in the knapsack problem.

[0245] In one embodiment, the first slab is of the incoming type and the second slab is of the outgoing type; the second determining submodule may include:

[0246] The fifth determining unit is configured to: and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0247] The sixth determining unit is configured to: and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0248] The seventh determining unit is configured to: and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0249] The eighth determining unit is configured to: and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0250] in, is the maximum weight of the second slab, is the weight of the first slab at least divided into, is the maximum weight of the first slab, is the weight of the second slab at least divided, The maximum remaining weight for the second slab, for is the upper limit of the weight of the second slab, is the excess weight of the second slab, G sum is the weight of the items in the knapsack problem.

[0251] In one embodiment, the first slab is of the outgoing type and the second slab is of the incoming type; the second determining submodule may further include:

[0252] The ninth determining unit is used to and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0253] The tenth determining unit is used to and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0254] The eleventh determining unit is used to and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0255] The twelfth determining unit is used to and In the case of , the maximum capacity of the backpack in the backpack problem is determined as

[0256] in, is the maximum weight of the first slab, is the weight of the second slab at least divided into, is the maximum weight of the second slab, is the weight of the first slab at least divided, The maximum weight left for the first slab, for is the upper limit of the weight of the first slab, is the excess weight of the first slab, G sum is the weight of the items in the knapsack problem.

[0257] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application, and are devices corresponding to the above-mentioned battery thermal runaway warning method. All implementation methods in the above-mentioned method embodiment are applicable to the embodiments of the device. Its specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0258] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0259] Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0260] The device may include a processor 401 and a memory 402 storing program instructions.

[0261] When the processor 401 executes the program, the steps in any of the above method embodiments are implemented.

[0262] For example, the program can be divided into one or more modules / units, one or more modules / units are stored in the memory 402 and executed by the processor 401 to complete the present application. One or more modules / units can be a series of program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the program in the device.

[0263] Specifically, the processor 401 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0264] Memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, memory 402 is a non-volatile solid-state memory.

[0265] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, generally, the memory includes one or more tangible (non-transitory) readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0266] The processor 401 implements any one of the methods in the above embodiments by reading and executing program instructions stored in the memory 402 .

[0267] In one example, the electronic device may further include a communication interface 403 and a bus 410. The processor 401, the memory 402, and the communication interface 403 are connected via the bus 410 and communicate with each other.

[0268] The communication interface 403 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0269] Bus 410 includes hardware, software or both, and the components of online data flow metering equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0270] In addition, in combination with the methods in the above embodiments, embodiments of the present application may provide a storage medium for implementation. The storage medium stores program instructions; when the program instructions are executed by a processor, any one of the methods in the above embodiments is implemented.

[0271] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0272] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0273] An embodiment of the present application provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0274] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0275] The functional modules shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), suitable firmware, a plug-in unit, a function card or the like. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. The example of a machine-readable medium includes an electronic circuit, a semiconductor memory device, a ROM, a flash memory, an erasable ROM (EROM), a floppy disk, a CD-ROM, an optical disk, a hard disk, an optical fiber medium, a radio frequency (RF) link, or the like. The code segment can be downloaded via a computer grid such as the Internet, an intranet, etc.

[0276] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0277] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed via the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. This processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or the flowchart and the combination of the boxes in the block diagram and / or the flowchart can also be implemented by the dedicated hardware that performs the specified function or action, or can be implemented by the combination of dedicated hardware and computer instructions.

[0278] The above is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application.

Claims

1. A slab design method, characterized in that: The method comprises: Obtaining order information corresponding to multiple orders, where each order information includes the width, length, quantity, steel grade, and thickness of a sub-plate. The sub-plates are processed from a mother coil, which is processed from a slab. The slabs and the mother coil have a one-to-one correspondence. Obtaining constraint rules, where the constraint rules include a first conversion rule between a slab and a mother roll, a second conversion rule between a mother roll and a daughter slab, and a slab processing rule, where the slab processing rule includes a length range, a width range, and a weight range for slab processing; Inputting the constraint rules and order information corresponding to the plurality of first orders into a first model, obtaining an initial order combination plan corresponding to the plurality of available slabs, wherein the initial order combination plan is used to determine an initial design size corresponding to each available slab, wherein one available slab is used to produce all sub-slabs of at least one first order, and the steel grade and thickness of the sub-slabs in each first order are the same; For any first slab among the plurality of available slabs, if an initial design size of the first slab does not conform to the slab processing rule, adjusting the initial order combination plan based on a genetic algorithm and a knapsack problem to obtain a target order combination plan corresponding to the plurality of available slabs; wherein the target design size of the first slab determined according to the target order combination plan conforms to the slab processing rule; Before obtaining the constraint rules, the method further includes: Get the objective function and constraint function; Based on the objective function and the constraint function, constructing a first model, wherein the first model is used to combine some of the multiple orders on a single slab so as to maximize the yield rate of the combination of all orders, wherein the steel grade and thickness of the sub-slab of each order in the partial orders are the same; The objective function is: min G coil , where G coil is the total weight of the parent roll; The constraint function includes: T coil =T order ; λ1≥1,λ2≥1; and j = 1, 2, …, p; Among them, the superscript slab represents the parameters of the slab, j is the slab number, and there are p slabs in total; is the weight of the jth slab; is the length of the jth slab; is the width of the jth slab; T slab is the thickness of the slab; is the maximum weight of the slab; is the minimum length of the slab; is the maximum length of the slab; The superscript "coil" represents the parameters of the mother coil; is the weight of the parent coil corresponding to the j-th slab; is the length of the mother roll corresponding to the j-th slab; is the width of the mother roll corresponding to the j-th slab; T coil is the thickness of the parent roll; The superscript "order" represents the order parameter, i is the order number, and there are q orders in total; is the length of the sub-board in the i-th order; W i order is the width of the sub-board in the i-th order; T i order is the thickness of the sub-board in the i-th order; n i is the number of sub-boards in the i-th order; ρ is the density of slab, mother coil and daughter board; λ1 is the feeding coefficient from slab to mother coil; λ2 is the feeding coefficient from mother roll to daughter board; x ij It is a 0-1 variable, which is 1 if the parent roll corresponding to the j-th slab contains order i, otherwise it is 0.

2. The method according to claim 1, characterized in that For any first slab among the plurality of available slabs, if the initial design size of the first slab does not conform to the slab processing rule, before obtaining a target order combination plan corresponding to the plurality of available slabs by adjusting the initial order combination plan based on a genetic algorithm and a knapsack problem, the method further includes: All available slabs are sorted in descending order according to their width. If there are slabs with the same width, they are sorted in descending order according to the minimum width of each slab order. The serial number corresponding to each available slab is obtained, and each available slab corresponds to one serial number. For any first slab among the plurality of available slabs, when the initial design size of the first slab does not conform to the slab processing rule, adjusting the initial order combination plan based on the genetic algorithm and the knapsack problem to obtain a target order combination plan corresponding to the plurality of available slabs includes: For any first slab among the plurality of available slabs, if an initial design size of the first slab does not conform to the slab processing rule, obtaining a first type of the first slab and a second type of a second slab with an adjacent serial number corresponding to the first slab; Determining items in the knapsack problem according to the serial number corresponding to the first slab and the serial number corresponding to the second slab; determining a maximum capacity of a knapsack in the knapsack problem according to the first type, the second type, the weight of the first slab, and the weight of the second slab; Solving the knapsack problem by a genetic algorithm according to the items in the knapsack problem and the maximum capacity of the knapsack in the knapsack problem to obtain at least one solution result, wherein each solution result corresponds to a slab yield rate; The initial order combination plan is adjusted according to the target result to obtain the target order combination plan, and the target result is the solution result corresponding to the maximum value of the slab yield rate in the at least one solution result.

3. The method according to claim 2, characterized in that The weight range of the slab processing includes the maximum weight of the slab processing and the minimum weight of the slab processing; the length range of the slab processing includes the maximum length of the slab processing and the minimum length of the slab processing; The method of acquiring, for any first slab among the plurality of available slabs, a first type of the first slab and a second type of a second slab having an adjacent sequence number corresponding to the first slab when an initial design size of the first slab does not conform to the slab processing rule, includes: For any first slab among the plurality of available slabs, if the weight of the first slab is greater than the maximum weight of the slab to be processed, the first slab is determined as an outgoing type, and the second slab is determined as an incoming type, wherein the outgoing type is used to indicate the order weight corresponding to at least one outgoing order of the slab, and the incoming type is used to indicate the order weight corresponding to at least one incoming order of the slab; In a case where the length of the first slab is less than the minimum length of the slab processing, the first slab is determined as an input type, and the second slab is determined as an output type.

4. The method according to claim 2, characterized in that The first slab is used to produce a plurality of second orders, the first slab is of an outgoing type, and the second slab is of an incoming type; The step of determining the items in the knapsack problem according to the serial number corresponding to the first slab and the serial number corresponding to the second slab includes: When the serial number corresponding to the first slab is greater than the serial number corresponding to the second slab, determining the second order with the largest sub-slab width among the multiple second orders as the item in the knapsack problem; When the serial number corresponding to the first slab is smaller than the serial number corresponding to the second slab, the second order with the smallest sub-slab width among the multiple second orders is determined as the item in the knapsack problem.

5. The method according to claim 2, characterized in that The first slab is of the input type, and the second slab is of the output type; Determining the maximum capacity of the knapsack in the knapsack problem according to the first type, the second type, the weight of the first slab, and the weight of the second slab includes: exist and In the case of, the maximum capacity of the backpack in the backpack problem is determined as exist and In the case of, the maximum capacity of the backpack in the backpack problem is determined as exist and In the case of, the maximum capacity of the backpack in the backpack problem is determined as exist and In the case of, the maximum capacity of the backpack in the backpack problem is determined as in, The maximum weight of the second slab is is the weight of the first slab at least divided into, is the maximum weight of the first slab, is the weight of at least the second slab, The maximum weight left for the second slab is for is the upper limit of the weight of the second slab, The excess weight of the second slab, G sum is the weight of the items in the knapsack problem.

6. The method according to claim 2, characterized in that The first slab is of the outgoing type, and the second slab is of the incoming type; Determining the maximum capacity of the knapsack in the knapsack problem according to the first type, the second type, the weight of the first slab, and the weight of the second slab includes: exist and In the case of, the maximum capacity of the backpack in the backpack problem is determined as exist and In the case of, the maximum capacity of the backpack in the backpack problem is determined as exist and In the case of, the maximum capacity of the backpack in the backpack problem is determined as exist and In the case of, the maximum capacity of the backpack in the backpack problem is determined as in, is the maximum weight of the first slab, is the weight of the second slab at least divided into, is the maximum weight of the second slab, is the weight of at least the first slab, The maximum remaining weight for the first slab is for is the upper limit of the weight of the first slab, is the excess weight of the first slab, G sum is the weight of the items in the knapsack problem.

7. A slab design device, characterized in that: The device comprises: A first acquisition module is configured to acquire order information corresponding to a plurality of orders, wherein each order information includes the width, length, quantity, steel grade, and thickness of a sub-plate. The sub-plates are processed from parent coils, which are processed from slabs. The slabs and parent coils have a one-to-one correspondence. a second acquisition module, configured to acquire constraint rules, wherein the constraint rules include a first conversion rule between a slab and a mother coil, a second conversion rule between a mother coil and a daughter slab, and slab processing rules, wherein the slab processing rules include a length range, a width range, and a weight range for slab processing; a determination module, configured to input the constraint rules and order information corresponding to the plurality of first orders into a first model, to obtain an initial order combination plan corresponding to the plurality of available slabs, wherein the initial order combination plan is configured to determine an initial design size corresponding to each available slab, wherein one available slab is used to produce all sub-slabs of at least one first order, and the steel grade and thickness of the sub-slabs in each first order are the same; an adjustment module configured to adjust, for any first slab among the plurality of available slabs, the initial order combination plan based on a genetic algorithm and a knapsack problem if the initial design size of the first slab does not conform to the slab processing rule, to obtain a target order combination plan corresponding to the plurality of available slabs; wherein the target design size of the first slab determined according to the target order combination plan conforms to the slab processing rule; The second acquisition module is further used to: Obtaining an objective function and a constraint function; constructing a first model based on the objective function and the constraint function, wherein the first model is used to combine some of the multiple orders on a single slab so as to maximize the yield rate of the combination of all orders, wherein the steel grade and thickness of the sub-slab of each order in the partial orders are the same; The objective function is: min G coil , where G coil is the total weight of the parent roll; The constraint function includes: T coil =T order ; λ1≥1,λ2≥1; and j = 1, 2, …, p; Among them, the superscript slab represents the parameters of the slab, j is the slab number, and there are p slabs in total; is the weight of the jth slab; is the length of the jth slab; is the width of the jth slab; T slab is the thickness of the slab; is the maximum weight of the slab; is the minimum length of the slab; is the maximum length of the slab; The superscript "coil" represents the parameters of the mother coil; is the weight of the parent coil corresponding to the j-th slab; is the length of the mother roll corresponding to the j-th slab; is the width of the mother roll corresponding to the j-th slab; T coil is the thickness of the parent roll; The superscript "order" represents the order parameter, i is the order number, and there are q orders in total; is the length of the sub-board in the i-th order; W i order is the width of the sub-board in the i-th order; T i order is the thickness of the sub-board in the i-th order; n i is the number of sub-boards in the i-th order; ρ is the density of slab, mother coil and daughter board; λ1 is the feeding coefficient from slab to mother coil; λ2 is the feeding coefficient from mother roll to daughter board; x ij It is a 0-1 variable, which is 1 if the parent roll corresponding to the j-th slab contains order i, otherwise it is 0.

8. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the slab design method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the slab design method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the slab design method according to any one of claims 1 to 6.

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