Optimal blank weight calculation method and system for improving crosscutting correction rate

Abstract

The optimal blank weight calculation method and system for improving the crosscutting correction rate provided by the invention are suitable for a plurality of orders with the same material, the same thickness and the similar width, the orders can be cashed through crosscutting by using steel coils rolled by slabs with the same specification, and the design principle is that under the premise of conforming to the constraint of production process equipment, the optimal blank weight is calculated. And calculating the weight of the slab which enables the overall correction rate to be maximum. According to the production process flow, the thickness of a plate blank is fixed, a mother plate is processed by a hot mill and needs to be subjected to broadening and compression rolling, a quality design calculation model after steel optimization is carried out, and the non-scale rate of transverse cutting is reduced, so that enterprises can better balance the production efficiency and the positive scale rate to cope with the diversification of market requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of hot processing cross-cutting, and particularly provides an optimal billet weight calculation method and system for improving cross-cutting accuracy. BACKGROUND

[0002] The production of hot processing cross-cutting is a process of heating steel billets to a specified temperature in a heating furnace, then rolling to a specified thickness, width and length after leaving the furnace, and then cutting the width and length on a cutting line to form the order delivery size.

[0003] The production process of hot processing cross-cutting mainly involves three types of products, namely slab (steel billet heated in the heating furnace), mother roll (material formed one-to-one after slab rolling by the rolling mill) and sub-plate (finished product after cutting of the mother roll).

[0004] Cross-cutting accuracy refers to the proportion or percentage of cross-cutting sub-plate size meeting the contract requirements, which is an evaluation index for the accuracy and quality of cross-cutting size control in the cross-cutting production process of steel enterprises. Higher cross-cutting accuracy means that the enterprise has better size control capability and can produce cross-cutting that meets the design requirements, reducing production cost and resource waste. Therefore, in order to improve cross-cutting accuracy, methods such as improving production process, strengthening quality management and optimizing material design are used to achieve this goal. In the steel MMS system, the method of optimizing material design is generally used to improve cross-cutting accuracy.

[0005] Under normal circumstances, the steelmaking, billet drawing and other production processes of steel enterprises will determine the specifications of the slab according to the maximum steelmaking capacity of the production line. This is because the maximum steelmaking capacity of the production line determines the maximum size and weight of the slab, which in turn affects the production capacity and efficiency of the downstream process. Therefore, when planning production, steel enterprises will determine the specifications of the slab according to the technical parameters and capacity of the production line to ensure smooth production and maximize production efficiency. However, this method also has some potential drawbacks:

[0006] (1) Reduced flexibility: Relying on the maximum capacity of the production line to set the specifications of the slab may limit the enterprise's ability to respond to market changes. When market demand fluctuates, especially when there is demand for different specifications or small batch orders, the enterprise may not be able to adjust quickly, as changing the specifications of the slab requires significant technical and operational adjustments.

[0007] (2) Resource waste: When producing according to the maximum steelmaking capacity, there may be some leftover materials that are not suitable in size, which cannot be directly used to fulfill other orders and are treated as waste, resulting in resource waste.

[0008] (3) Over-reliance on a single production strategy: Long-term reliance on the maximum capacity of the production line as the determining factor for slab size can lead to insufficient innovation in production technology and management strategies, affecting long-term competitiveness.

[0009] To address these issues, it is often necessary for enterprises to increase the flexibility of the production process while maintaining production efficiency and maximizing capacity. To improve the positive rate, theoretically, the most effective solution is to produce according to orders, that is, to calculate the slab size based on the order size combined with the maximum production capacity of the production line. However, this order-based production mode will actually lead to frequent changes in slab length or width on the slab continuous casting production line, which may in turn cause a series of problems:

[0010] (1) Decreased production efficiency: Frequent changes in slab width or length require downtime for adjustments, which reduces production efficiency and increases production costs.

[0011] (2) Increased equipment wear: Frequent adjustments to continuous casting equipment can exacerbate wear on equipment components, as the equipment needs to frequently adapt to different operating conditions.

[0012] (3) Quality fluctuations: Frequent changes in slab length or width can affect the stability of the production process, leading to fluctuations in slab quality and even substandard products.

[0013] (4) Increased energy consumption: Frequent changes in slab length or width may require adjustments to heating furnaces and other equipment parameters, increasing energy consumption. SUMMARY

[0014] To address one or more of the problems in the prior art, especially to enhance the flexibility of the production process while also maintaining the continuity and stability of the production process as much as possible.

[0015] The present invention takes into account the above two problems and proposes a batch order production mode. Taking hot-rolled slab as the research object, an effective algorithm for maximizing slab weight and coexisting with fixed-size and fixed-weight quality design is studied, the quality design calculation model of the steel is optimized, and the non-size rate of the cross-cutting is reduced. In this way, enterprises can better balance production efficiency and positive rate to cope with the diversification of market demand.

[0016] The main purpose of the present invention is to design an optimal slab weight for a group of sales orders with the same material, thickness, and similar width based on the simulated annealing algorithm, under the premise of meeting the equipment constraints, so that the overall positive rate of the group of orders is maximized when the designed slab is used to fulfill the orders.

[0017] The evaluation index of the optimal billet weight design is the positive meter rate, that is, the positive meter amount / steel coil feeding weight*100%, but the range of the billet weight also needs to be considered. Too low billet weight may affect the production efficiency. Therefore, in actual operation, the contradiction between the two goals needs to be considered to ensure that the positive meter rate is improved and the production efficiency is maintained.

[0018] To achieve the above-mentioned purposes, the present application provides a method for calculating the optimal billet weight for heat treatment cross-cutting, comprising the following steps:

[0019] S1: access order information, issue orders from ERP, and receive and save order information by the production system. The order information includes the thickness, width, length and quantity of the order sub-plate;

[0020] S2: order grouping, classify the orders according to the steel grade and thickness, and then group them according to the width, and determine the width and thickness of the plate blank;

[0021] S3: obtain the design rules, including the plate blank design rules and the plate blank coil sub-plate conversion rules. The plate blank design rules include the plate blank thickness, the minimum and maximum length limit, the maximum weight constraint, and the equipment production capacity constraint;

[0022] S4: determine the mathematical model of the optimal billet weight design, including the objective function and the constraint condition;

[0023] S5: solve the optimal billet weight according to the heuristic search algorithm and the objective function and the constraint condition.

[0024] Further, all sub-plates of order i in n (n≥1) orders are made by q i (q i ≥1,1≤i≤n,q i ∈N + ,i∈N + ) plate blanks with a weight of G slab .

[0025] Further, the plate blank design rules are:

[0026] S31: calculate the weight G slab of a single plate blank:

[0027] G slab =ρL slab W slab T slab (1)

[0028] Wherein, the plate blank weight G slab is t, the width W slab is mm, the thickness T slab is mm, and the length L slabUnit: mm, slab density ρ, unit: t / mm 3 ;

[0029] S32: The density of the steel coil and the density of the sub-plate are consistent with the slab density, and the feeding coefficients of the slab to the steel coil and the steel coil to the cut plate are λ1, λ2, respectively, and λ1, λ2> 1; then,

[0030] The weight G of the steel coil coil is:

[0031]

[0032] The weight G of the cross-cut plate crosscut is:

[0033]

[0034] S33: The length, width and thickness of the order i sub-plate are Unit: mm,

[0035] The weight of the order i sub-plate is:

[0036]

[0037] For order i, the number of cut plates N that a slab can deliver i is:

[0038]

[0039] The order quantity can be calculated is:

[0040]

[0041] The overall positive size can be calculated is:

[0042]

[0043] Further, the determination of the constraint condition is:

[0044] Based on the crane load bearing and the hot rolling production line inlet material weight limit, wherein is the minimum value of the crane load bearing and the hot rolling production line inlet material weight limit, is the maximum value of the crane load bearing and the hot rolling production line inlet material weight limit, then the slab weight needs to meet the condition:

[0045]

[0046] Based on the heat treatment cross-cut inlet material weight limit, wherein the minimum value of the weight limit of the cross-sectional entry material for heat treatment, the maximum value of the weight limit of the cross-sectional entry material for heat treatment, then the coil weight needs to meet the condition:

[0047]

[0048] based on the length limit of the slab, wherein the minimum value of the length limit of the slab, the maximum value of the length limit of the slab, then the length of the slab needs to meet the condition:

[0049]

[0050] the weight of the slab G slab is calculated under the following conditions:

[0051]

[0052] in combination with formula (2), and formula (8)-(10), the constraint condition is calculated as:

[0053]

[0054] further, the objective function is:

[0055]

[0056] further, the heuristic search algorithm is a simulated annealing algorithm.

[0057] further, the simulated annealing algorithm is a random optimization algorithm based on Monte-Carlo iterative solution strategy; the random optimization algorithm is divided into three parts: solution space, objective function and initial solution, and the optimization steps are:

[0058] Step 1: select an initial solution from the solution space calculate its objective function value and select the initial control temperature T, the end temperature T end , the length of the Markov chain L k and the annealing coefficient α (0 < α < 1);

[0059] Step 2: generate a random disturbance in the solution space to generate a new solution calculate its objective function value

[0060] Step 3: judge whether to accept according to the new solution acceptance condition: if then accept the new solution as the current solution, otherwise decide whether to accept according to the Metropolis criterion if accepted Let the current solution equal If not accepted, let the current solution equal The Metropolis criterion is

[0061] Step 4: According to the convergence criterion, it is judged whether the sampling process is terminated, yes, go to step 5, otherwise go to step 2;

[0062] Step 5: Reduce the control temperature T = αT;

[0063] Step 6: According to whether T is greater than T end , it is judged whether the annealing process is terminated, yes, go to step 7, otherwise go to step 2;

[0064] Step 7: The current solution is output as the optimal solution.

[0065] In addition, the application also provides a kind of optimal blank weight calculation system for improving cross positive size ratio, comprising:

[0066] Access module, for accessing order information, order is issued from ERP, and production system receives order information and saves, and order information includes the thickness, width, length and quantity of order sub-plate;

[0067] Grouping module, for order grouping, order is classified according to steel grade and thickness, and then grouped according to width, while the width and thickness of slab are determined;

[0068] Obtaining module, for obtaining design rules, including slab design rules and slab coil sub-plate conversion rules, slab design rules include slab thickness, minimum and maximum length limit, maximum weight constraint, equipment production capacity constraint;

[0069] Determination module, for determining the mathematical model of optimal blank weight design, including objective function and constraint condition;

[0070] Solving module, for solving optimal blank weight according to heuristic search algorithm and objective function and constraint condition.

[0071] In addition, the application also provides a kind of computer equipment, including memory, processor and computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the computer program, the method as described above is realized.

[0072] In addition, the application also provides a kind of computer readable storage medium, characterized in that, the computer readable storage medium stores computer program, when the computer program is executed by processor, the method as described above is realized

[0073] Compared with prior art, the application has the advantages that:

[0074] 1. The main purpose of the present application is to design an optimal slab weight for heat treatment cross-cutting based on simulated annealing algorithm for a group of sales orders with the same material, the same thickness and similar width, so that the overall positive size ratio is maximized after the group of orders is realized with the designed slab.

[0075] 2. The evaluation index of optimal slab weight design is the positive size ratio, i.e. the positive size amount / steel coil feeding weight x 100%, but the slab weight range should also be considered. Too low slab weight may affect production efficiency. Therefore, in actual operation, the contradiction between the two goals should be considered to ensure that the positive size ratio is improved and the production efficiency is maintained.

[0076] 3. The heat treatment cross-cutting optimal slab weight calculation method provided by the present application is suitable for a plurality of orders with the same material, the same thickness and similar width. These orders can be realized by cross-cutting the steel coil rolled by the same grouping slab. The design principle is to calculate the slab weight that maximizes the overall positive size ratio under the premise of meeting the production process equipment constraints. According to the production process flow, the thickness of the slab is fixed. The mother plate needs to be expanded and compressed after being processed by the hot rolling mill. Non-uniform deformation will cause irregularity at the edge of the mother plate, which needs to be cut to size, resulting in a slight decrease in width. In production, the designer will give the cutting loss related coefficient, i.e. the feeding coefficient, according to the above situation and the production statistical law, and at the same time, the slab width will be determined according to the width range of the order.

[0077] 4. The present patent considers the problems of low efficiency of single production and low positive size ratio of maximum capacity steelmaking, and proposes a batch order production mode. Taking heat treatment cross-cutting plate as the research object, an effective algorithm for the coexistence of maximum heat rolling slab weight and sizing weight quality design is studied. For a batch of orders with the same material, the same thickness and similar width, an optimal slab weight is designed based on simulated annealing algorithm, so that the positive size ratio is maximized after the batch of orders is realized by the designed slab, thereby improving the positive size ratio of cross-cutting, optimizing the quality design calculation model of the steel, and promoting the steel enterprise to better respond to the diversification of market demand. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1 is the algorithm step diagram for solving the optimal slab weight based on simulated annealing;

[0079] Figure 2 is the graph of the change of the positive size ratio with the slab weight;

[0080] Figure 3 is the relationship between the optimal solution and the initial solution;

[0081] Figure 4 is the optimal solution and the positive size ratio of the program running;

[0082] Figure 5 is the optimal solution under different Markov chain lengths;

[0083] Figure 6 is a structural schematic diagram of a computer device of the present application;

[0084] Figure 7 is a structural schematic diagram of another computer device of the present application. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0086] The present application is further described in detail below in combination with the drawings and specific embodiments of the present application.

[0087] The present application is a method for calculating optimal billet weight for improving cross-cutting yield, which is designed for the optimal billet weight problem of cross-cutting production order batch, and limits:

[0088] 1) One or more slabs can be used for each order;

[0089] 2) There are different types of requirements in the order. Before applying the method, the order is first classified according to steel grade and thickness, and then the production personnel further group according to width and determine the width of the slab. Each group of orders is input respectively, and then the method is applied for solving. In the following input order set, it is assumed that the groups have been divided, and each order has the same requirements in terms of steel grade, thickness, etc. The change of the width of each order is controlled within the range that can be realized by using the same specification slab, and the following will not be described in detail.

[0090] In some embodiments, the method for calculating optimal billet weight for improving cross-cutting yield in batch order production includes the following steps:

[0091] S1: Access order information, receive order information from ERP and save it, and the order information includes the thickness, width, length and quantity of the order sub-plate;

[0092] S2: Order grouping, classify the order according to steel grade and thickness, and then group according to width, and determine the width and thickness of the slab;

[0093] S3: Obtain design rules, including slab design rules and slab coil sub-plate conversion rules, the slab design rules including slab thickness, minimum and maximum length limit, maximum weight constraint, and equipment production capacity constraint;

[0094] S4: Determine the mathematical model of the optimal blank weight design, including the objective function and the constraint condition;

[0095] S5: Solve the optimal blank weight according to the heuristic search algorithm and the objective function and the constraint condition.

[0096] In some embodiments, all the sub-plates of order i in n (n≥1) orders are made of q i (q i ≥1,1≤i≤n,q i ∈N + ,i∈N + ) plates with a weight of G slab .

[0097] In some embodiments, the plate blank design rules are:

[0098] S31: Calculate the weight of a single plate blank G slab :

[0099] G slab = ρL slab W slab T slab (1)

[0100] Wherein, the weight of the plate blank G slab is t, the width W slab is mm, the thickness T slab is mm, the length L slab is mm, and the density of the plate blank is ρ, which is t / mm 3 .

[0101] S32: The density of the coil and the density of the sub-plate are consistent with the density of the plate blank, and the feeding coefficients of the plate blank to the coil and the coil to the cutting plate are λ1, λ2, respectively, and λ1, λ2>1; then,

[0102] The weight of the coil G coil is:

[0103]

[0104] The weight of the cross-cut plate G crosscut is:

[0105]

[0106] S33: The length, width and thickness of the order i sub-plate are , all in mm,

[0107] The weight of the order i sub-plate is :

[0108]

[0109] The number of slabs N that one slab can deliver for order i i is:

[0110]

[0111] The order quantity can be calculated is:

[0112]

[0113] The total gauge can be calculated is:

[0114]

[0115] In some embodiments, the determination of the constraint condition is:

[0116] Based on the crane load capacity and the hot rolling line entry material weight limit, where is the minimum of the crane load capacity and the hot rolling line entry material weight limit, is the maximum of the crane load capacity and the hot rolling line entry material weight limit, then the slab weight needs to satisfy the condition:

[0117]

[0118] Based on the heat treatment cross cut entry material weight limit, where is the minimum of the heat treatment cross cut entry material weight limit, is the maximum of the heat treatment cross cut entry material weight limit, then the coil weight needs to satisfy the condition:

[0119]

[0120] Based on the slab length limit, where is the minimum of the slab length limit, is the maximum of the slab length limit, then the slab length needs to satisfy the condition:

[0121]

[0122] The slab weight G is calculated slab with the limit condition:

[0123]

[0124] In combination with equation (2), and equations (8)-(10), the determination of the constraint condition is:

[0125]

[0126] In some embodiments, the objective function is:

[0127]

[0128] In some embodiments, the heuristic search algorithm is a simulated annealing algorithm.

[0129] In some embodiments, the simulated annealing algorithm is a random optimization algorithm based on Monte-Carlo iterative solution strategy; the random optimization algorithm is divided into three parts: solution space, objective function and initial solution, and the optimization steps are:

[0130] Step 1: select an initial solution from the solution space Calculate its objective function value And select the initial control temperature T, the end temperature T end , the length L of the Markov chain k And annealing coefficient α (0 < α < 1);

[0131] Step 2: generate a random disturbance in the solution space to generate a new solution Calculate its objective function value

[0132] Step 3: judge whether to accept according to the new solution acceptance condition: if The new solution is accepted as the current solution, otherwise the Metropolis criterion is used to judge whether to accept If accepted Let the current solution equal to If not accepted, let the current solution equal to The Metropolis criterion is

[0133] Step 4: judge whether the sampling process is terminated according to the convergence criterion, yes to step 5, otherwise to step 2;

[0134] Step 5: reduce the control temperature T = αT;

[0135] Step 6: judge whether the annealing process is terminated according to whether T is greater than T end , yes to step 7, otherwise to step 2;

[0136] Step 7: the current solution is output as the optimal solution.

[0137] In some embodiments, the present application also provides a system for calculating the optimal billet weight for improving the transverse normality, comprising:

[0138] An access module is configured to access order information, and issue orders from an ERP, and a production system receives and stores the order information, and the order information includes thickness, width, length and quantity of order sub-panels;

[0139] A grouping module is configured to group orders, and classify orders according to steel grades and thicknesses, and further group the orders according to widths, and determine widths and thicknesses of slabs;

[0140] An acquisition module is configured to acquire design rules, including slab design rules and slab-to-sub-panel conversion rules, and the slab design rules include slab thicknesses, minimum and maximum length limits, maximum weight constraints and equipment production capacity constraints;

[0141] A determination module is configured to determine a mathematical model of optimal slab weight design, including an objective function and constraint conditions;

[0142] A solution module is configured to solve the optimal slab weight according to a heuristic search algorithm and the objective function and constraint conditions.

[0143] For the above optimization problem, the simplest and most direct method is to use the exhaustive method, that is, to compare all possible solutions, but this will cause waste of resources. Therefore, the present application adopts a heuristic search algorithm. Since the solution space of the optimization problem is continuous, there is no scheduling problem, and therefore a simulated annealing algorithm is used to solve it.

[0144] The simulated annealing algorithm is a random optimization algorithm based on the Monte-Carlo iterative solution strategy, and its starting point is based on the similarity between the annealing process of solid materials in physics and general combinatorial optimization problems. The simulated annealing algorithm starts from a relatively high initial temperature, and with the continuous decrease of the temperature parameter, it randomly searches for the global optimal solution of the objective function in the solution space, that is, it can probabilistically jump out of the local optimal solution and eventually tend to the global optimum. The simulated annealing algorithm is a general optimization algorithm, and theoretically the algorithm has a global optimization performance with probability, and has been widely used in engineering, such as VLSI, production scheduling, control engineering, machine learning, neural networks, signal processing and other fields.

[0145] Specifically, in the implementation process of the scheme of the present application, the heat treatment cross-cut production of a domestic enterprise is taken as an experimental object. The following four orders are taken as examples:

[0146] Table 1 Order Information Table

[0147] Number Length Width Thickness Cutting plate weight Slab quantity 0 10100 mm 1600 mm 8 mm 1.015t 1 1 9000 mm 1610 mm 8 mm 0.910t 2 2 12000 mm 1620 mm 8 mm 1.221t 3 3 14000 mm 1630 mm 8 mm 1.433t 1

[0148] A slab weight is sought, so that the above several orders are realized by the slab weight, and the gauge ratio is maximum. The slab width is 1650 mm, the thickness is 230 mm, the minimum length is 4000 mm, the maximum length is 10980 mm, the feeding coefficients of slab to coil and coil to cutting plate are 1.013 and 1.055 respectively, and it is stipulated that the designed coil weight cannot be less than 25t. The algorithm uses Python programming and runs on a computer with Windows 10 operating system.

[0149] Firstly, we calculate the value range of the slab by the formula, which is [25.325t, 32.710t]. In order to better compare the performance of the algorithm, the optimal slab weight is first calculated, which makes the above orders realized and the gauge ratio maximum. This patent first uses the exhaustive method to observe the change of the gauge ratio with the solution (as shown in Figure 2 The maximum gauge ratio is 94.303% when the slab weight is 26.258t.

[0150] Change the width and thickness of the slab, and solve the optimal slab weight by the same method. The results are shown in Table 2:

[0151] Table 2 Optimal slab weight and optimal gauge ratio under different slab width and thickness settings

[0152] Experiment Slab width Slab thickness Minimum value Maximum value Optimal slab weight Optimal gauge 1 1650 mm 230 mm 25.325t 32.710t 26.258t 94.303％ 2 1650 mm 240 mm 25.325t 34.132t 34.038t 94.347％ 3 1640 mm 230 mm 25.325t 32.512t 26.258t 94.303％ 4 1640 mm 240 mm 25.325t 33.926t 26.258t 94.303％ 5 1630 mm 230 mm 25.325t 32.314t 26.258t 94.303％ 6 1630 mm 240 mm 25.325t 33.719t 26.258t 94.303％

[0153] From the above experiments, it can be seen that except for the optimal slab weight of the second experiment, the change of width and thickness has no effect on the results of the other experiments, because the change of slab width and thickness only affects the value range of slab or coil. If the optimal result is not generated in the maximum minimum value field, the thickness and width limit of the slab has little effect on the result.

[0154] The initial solution in the algorithm is randomly selected. In order to verify the influence of the initial solution on the optimal solution, different initial solutions are set in the feasible solution interval, and other parameters are set as in Table 3. The optimal solution is solved by simulated annealing algorithm.

[0155] Table 3 Parameter settings

[0156]

[0157]

[0158] The results are shown in Figure 3As shown in the figure, the blue dots representing the "SA algorithm" are the optimal solutions obtained by the simulated annealing algorithm. For comparison, the actual optimal solution, represented by the red solid line, is also plotted in the figure. Based on previous experiments, this solution is 26.258t. The figure shows that regardless of the initial solution selection, the optimal solution obtained by the simulated annealing algorithm differs from the actual optimal solution by approximately 0.02t. Most solutions are distributed around 26.26t, meaning that most optimal solutions are located near the actual optimal solution. Furthermore, in actual production, the precision of billet pulling is generally within ±200 kg, making it difficult to control precisely at the 10 kg level. Therefore, for operators in actual production, the difference between the optimal solution obtained by the simulated annealing algorithm and the actual optimal solution is almost negligible.

[0159] The optimal solution and the positive scale result obtained by running the program are as follows: Figure 4 As shown:

[0160] Figure 5 It is about choosing different Markov chain lengths L k The optimal solution obtained shows that the choice of length does not have a significant impact on the optimal solution.

[0161] When the slab thickness is 230mm and 240mm, and the slab width is 1630mm, 1640mm, and 1650mm, the algorithm yields the results shown in Table 4. The true value is the optimal solution shown in Table 2, and the error is the difference between the optimal slab weight obtained by the algorithm and the true value.

[0162] Table 4 Optimal solutions under different slab width and thickness settings

[0163] Experiment Slab width Slab thickness Optimal slab weight Optimal gauge Real value Error 1 1650 mm 230 mm 26.259t 94.298％ 26.258t 0.001t 2 1650 mm 240 mm 34.038t 94.347％ 34.038t 0t 3 1640 mm 230 mm 26.261t 94.291％ 26.258t 0.003t 4 1640 mm 240 mm 26.258t 94.301％ 26.258t 0t 5 1630 mm 230 mm 26.263t 94.284％ 26.258t 0.005t 6 1630 mm 240 mm 26.270t 94.258％ 26.258t 0.012t

[0164] As can be seen from Table 4, the error between the optimal solution obtained by the simulated annealing algorithm and the actual optimal solution is basically at the level of ten kilograms, which can be ignored.

[0165] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface (I / O for short), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement an optimal billet weight calculation method for improving cross-section gauge rate.

[0166] In one embodiment, a computer device which can be a terminal is provided, and an internal structure diagram thereof can be as shown in the figure. Figure 7 As shown in the figure. The computer device includes a processor, a memory, an input / output interface (I / O for short), a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be realized through WIFI, mobile cellular network, NFC (near field communication), or other technologies. The computer program is executed by the processor to implement an optimal billet weight calculation method for improving cross-section gauge rate. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball, or touchpad arranged on the shell of the computer device, or can be an external keyboard, touchpad, or mouse, etc.

[0167] Those skilled in the art can understand that, Figure 6 and Figure 7The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0168] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0169] In one embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implements the steps in the above method embodiments when executed by a processor.

[0170] In one embodiment, a computer program product is provided, including a computer program, and the computer program implements the steps in the above method embodiments when executed by a processor.

[0171] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant national and regional laws, regulations and standards.

[0172] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in any type of volatile or non-volatile storage device or a combination thereof. The non-volatile storage device can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD ROM). The magnetic surface memory can be a disk memory or a tape memory. The volatile storage device can be a random access memory (RAM) used as an external cache.By way of example and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Sync Link Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM). The storage media described in the embodiments of the present application is intended to include, without being limited to, these and any other suitable types of memory.

[0173] Any combination of the technical features in the above embodiments can be made, and for the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist in contradiction, it should be considered as within the scope of the present disclosure.

[0174] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those skilled in the art, some modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for calculating the optimal billet weight to improve the cross-cutting accuracy rate, characterized in that, Includes the following steps: S1: Access order information, issue orders from ERP, and the production system receives and saves the order information, which includes the thickness, width, length, and quantity of the order sub-boards; S2: Order grouping. Classify orders according to steel type and thickness, and then group them according to width. At the same time, determine the width and thickness of the slab. S3: Obtain design rules, including slab design rules and slab-steel coil conversion rules. Slab design rules include slab thickness, minimum and maximum length limits, maximum weight constraints, and equipment production capacity constraints. S4: Determine the mathematical model for the optimal billet weight design, including the objective function and constraints; S5: Solve for the optimal billet weight based on the heuristic search algorithm, objective function, and constraints.

2. The optimal billet weight calculation method for improving the cross-cutting accuracy rate according to claim 1, characterized in that: In n (n≥1) orders, all sub-boards of order i are made by q i (q i ≥1, 1≤i≤n, q i ∈N + ,i∈N + ) each weighing G slab The slab is made from the slab.

3. The optimal billet weight calculation method for improving the cross-cutting accuracy rate according to claim 2, characterized in that, The design rules for the slab blank are as follows: S31: Calculate the weight G of a single slab slab : G slab =ρL slab IN slab T slab (1) Among them, the weight of the slab G slab The unit is t, and the width is W. slab Units are mm and thickness T. slab The unit is mm, and the length is L. slab The unit is mm, the density of the slab is ρ, and the unit is t / mm. 3 ; S32: The density of the steel coil and the density of the sheet are both consistent with the density of the slab. The feeding coefficients from slab to steel coil and from steel coil to cut plate are λ1 and λ2, respectively, and λ1, λ2 > 1; then, Weight of steel coil (G) coil for: Weight G of the cross-section crosscut for: S33: The length, width, and thickness of the sub-board in order i are respectively... All units are in mm. Weight of order i-board for: For order i, the number of cut sheets N that can be delivered from one slab. i for: Calculate order volume for: Calculate the overall standard measurement. for:

4. The optimal billet weight calculation method for improving the cross-cutting accuracy rate according to claim 3, characterized in that, The constraints are determined as follows: Due to the load-bearing capacity of the crane and the weight restrictions on the materials entering the hot rolling production line, among which This is the minimum value for the load-bearing capacity of the crane and the weight limit of the material entering the hot rolling production line. To meet the maximum limits for crane load capacity and the weight of materials entering the hot rolling production line, the slab weight must satisfy the following conditions: Due to the weight limitations of the heat-treated transverse entry material, among which This is the minimum weight limit for the material at the heat treatment inlet. To meet the maximum weight limit for the heat-treated cross-section entry material, the steel coil weight must meet the following conditions: Due to the length limitation of the slab, This is the minimum value that limits the length of the slab. If the maximum length limit for the slab is given, then the slab length must meet the following conditions: Calculate the weight of the slab G slab Restrictions: Combining formula (2) and formulas (8)-(10), the constraint conditions are calculated as follows:

5. The optimal billet weight calculation method for improving the cross-cutting accuracy rate according to claim 4, characterized in that, The objective function is:

6. The optimal billet weight calculation method for improving the cross-cutting accuracy rate according to claim 1, characterized in that, The heuristic search algorithm is the simulated annealing algorithm.

7. The optimal billet weight calculation method for improving the cross-cutting accuracy rate according to claim 6, characterized in that, The simulated annealing algorithm is a stochastic optimization algorithm based on the Monte Carlo iterative solution strategy. The stochastic optimization algorithm is decomposed into three parts: solution space, objective function, and initial solution. Its optimization steps are as follows: Step 1: Select any initial solution from the solution space. Calculate its objective function value Select the initial control temperature T and the final temperature T. end The length L of the Markov chain k and the annealing coefficient α (0 < α < 1); Step 2: Generate a random perturbation in the solution space to generate a new solution. Calculate its objective function value Step 3: Determine whether to accept the new solution based on its acceptance criteria: If If the new solution is accepted, it is adopted as the current solution; otherwise, the Metropolis criterion is used to determine whether to accept it. If accepted Let the current solution equal to If not accepted, then set the current solution equal to The Metropolis criteria are as follows: Step 4: Based on the convergence criterion, determine whether the sampling process has terminated. If yes, proceed to step 5; otherwise, proceed to step 2. Step 5: Lower the control temperature T = αT; Step 6: Check if T is greater than T end Determine whether the annealing process has terminated. If yes, proceed to step 7; otherwise, proceed to step 2. Step 7: Output the current solution as the optimal solution.

8. An optimal billet weight calculation system for improving the cross-cutting accuracy rate, characterized in that, include: The access module is used to access order information, issue orders from the ERP system, and receive and save the order information, which includes the thickness, width, length, and quantity of the order sub-boards. The grouping module is used for order grouping. Orders are classified according to steel type and thickness, and then further grouped according to width. At the same time, the width and thickness of the slab are determined. The acquisition module is used to acquire design rules, including slab design rules and slab-steel coil conversion rules. Slab design rules include slab thickness, minimum and maximum length limits, maximum weight constraints, and equipment production capacity constraints. The determination module is used to determine the mathematical model for the optimal billet weight design, including the objective function and constraints; The solution module is used to solve for the optimal billet weight based on a heuristic search algorithm, objective function, and constraints.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 8.

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